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Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
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they are not comprehensive nor are they the most current set.
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1

World Oil Refining Logistics Demand Model  

Reports and Publications (EIA)

This manual is intended primarily for use as a reference by analysts applying the WORLD model to regional studies. It also provides overview information on WORLD features of potential interest to managers and analysts.

Information Center

1994-03-01T23:59:59.000Z

2

World Oil Refining Logistics Demand Model "World" Reference Manual  

Reports and Publications (EIA)

This manual is intended primarily for use as a reference by analysts applying the WORLD model to regional studies. It also provides overview information on WORLD features of potential interest to managers and analysts.

Information Center

1994-03-01T23:59:59.000Z

3

EIA model documentation: World oil refining logistics demand model,``WORLD`` reference manual. Version 1.1  

SciTech Connect

This manual is intended primarily for use as a reference by analysts applying the WORLD model to regional studies. It also provides overview information on WORLD features of potential interest to managers and analysts. Broadly, the manual covers WORLD model features in progressively increasing detail. Section 2 provides an overview of the WORLD model, how it has evolved, what its design goals are, what it produces, and where it can be taken with further enhancements. Section 3 reviews model management covering data sources, managing over-optimization, calibration and seasonality, check-points for case construction and common errors. Section 4 describes in detail the WORLD system, including: data and program systems in overview; details of mainframe and PC program control and files;model generation, size management, debugging and error analysis; use with different optimizers; and reporting and results analysis. Section 5 provides a detailed description of every WORLD model data table, covering model controls, case and technology data. Section 6 goes into the details of WORLD matrix structure. It provides an overview, describes how regional definitions are controlled and defines the naming conventions for-all model rows, columns, right-hand sides, and bounds. It also includes a discussion of the formulation of product blending and specifications in WORLD. Several Appendices supplement the main sections.

Not Available

1994-04-11T23:59:59.000Z

4

WORLD OIL REFINING LOGISTICS DEMAND MODEL  

U.S. Energy Information Administration (EIA)

Energy Information Administration 1000 Independence Avenue, S.W., Washington, DC 20585. ... OB1 Optimization with Barriers 1 OSL Optimization Subroutine Library

5

Travel Demand Modeling  

SciTech Connect

This chapter describes the principal types of both passenger and freight demand models in use today, providing a brief history of model development supported by references to a number of popular texts on the subject, and directing the reader to papers covering some of the more recent technical developments in the area. Over the past half century a variety of methods have been used to estimate and forecast travel demands, drawing concepts from economic/utility maximization theory, transportation system optimization and spatial interaction theory, using and often combining solution techniques as varied as Box-Jenkins methods, non-linear multivariate regression, non-linear mathematical programming, and agent-based microsimulation.

Southworth, Frank [ORNL; Garrow, Dr. Laurie [Georgia Institute of Technology

2011-01-01T23:59:59.000Z

6

Reducing Logistics Footprints and Replenishment Demands: Nano-engineered Silica Aerogels a Proven Method for Water Treatment  

SciTech Connect

Rapid deployment and the use of objective force aggressively reduce logistic footprints and replenishment demands. Maneuver Sustainment requires that Future Combat Systems be equipped with water systems that are lightweight, have small footprints, and are highly adaptable to a variety of environments. Technologies employed in these settings must be able to meet these demands. Lawrence Livermore National Laboratory has designed and previously field tested nano-engineered materials for the treatment of water. These materials have been either based on silica aerogel materials or consist of composites of these aerogels with granular activated carbon (GAC). Recent tests have proven successful for the removal of contaminants including uranium, hexavalent chromium, and arsenic. Silica aerogels were evaluated for their ability to purify water that had been spiked with the nerve agent VX (O-ethyl S-(2-diisopropylaminoethyl) methylphosphonothiolate). These results demonstrated that silica aerogels were able to remove the VX from the supply water and were nearly 30 times more adsorbent than GAC. This performance could result in REDUCING CHANGEOUT FREQUENCY BY A FACTOR OF 30 or DECREASING the VOLUME of adsorbent BY A FACTOR OF 30; thereby significantly reducing logistic footprints and replenishment demands. The use of the nano-engineered Silica Aerogel/GAC composites would provide a water purification technology that meets the needs of Future Combat Systems.

Daily, W; Coleman, S; Love, A; Reynolds, J; O'Brien, K; Gammon, S

2004-09-22T23:59:59.000Z

7

Space Logistics Modeling and Simulation Analysis using SpaceNet: Four Application Cases  

E-Print Network (OSTI)

The future of space exploration will not be limited to sortie-style missions to single destinations. Even in present exploration taking place at the International Space Station in low-Earth orbit, logistics is complicated by flights arriving from five launch sites on Earth. The future challenges of space logistics given complex campaigns of interconnected missions in deep space will require innovative tools to aid planning and conceptual design. This paper presents a modeling framework to evaluate the propulsive and logistics feasibility of space exploration from the macro-logistics perspective, which covers the delivery of elements and resources to support demands generated during exploration. The modeling framework is implemented in a versatile and unifying software tool, SpaceNet, for general space exploration scenario analysis. Four space exploration scenarios are presented as application cases to highlight the applicability of the framework across vastly different scenarios. The first case investigates the resupply of the International Space Station between 2010 and 2015 using 77 missions combining NASA, European Space Agency, Japanese Space Agency, Russian Space Agency, and commercial space transportation. The second case models a lunar outpost build-up consisting of 17 flights to achieve continuous human presence over eight years. The third case models and evaluates a conceptual sortie-style mission to a near-Earth object, 1999 AO10. Finally, the fourth case models a flexible path type human exploration in the vicinity of Mars using a combination of human and tele-operated exploration. Taken together these cases demonstrate the challenges and logistical requirements of future human space exploration campaigns during the period from 2010-2050 and illustrate the ability of SpaceNet to model and simulate the feasibility of meeting these requirements. I.

Paul T. Grogan; Howard K. Yue; Olivier L. De Weck

2011-01-01T23:59:59.000Z

8

Residential Sector Demand Module 2009, Model Documentation  

Reports and Publications (EIA)

Model Documentation - Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2009-05-01T23:59:59.000Z

9

Note: The Newsvendor Model with Endogenous Demand  

Science Conference Proceedings (OSTI)

This paper considers a firm's price and inventory policy when it faces uncertain demand that depends on both price and inventory level. The authors extend the classic newsvendor model by assuming that expected utility maximizing consumers choose between ... Keywords: Demand Uncertainty, Fill Rate Competition, Inventory, Newsvendor Model, Pricing, Service Levels, Service Rate Competition

James D. Dana; Nicholas C. Petruzzi

2001-11-01T23:59:59.000Z

10

A possibilistic approach to the modeling and resolution of uncertain closed-loop logistics  

Science Conference Proceedings (OSTI)

Closed-loop logistics planning is an important tactic for the achievement of sustainable development. However, the correlation among the demand, recovery, and landfilling makes the estimation of their rates uncertain and difficult. Although the fuzzy ... Keywords: Closed-loop logistics, Fuzzy number, Genetic algorithms, Possibilistic mean, Shortage and surplus

Hsiao-Fan Wang; Hsin-Wei Hsu

2012-06-01T23:59:59.000Z

11

Residential Sector Demand Module 2000, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

1999-12-01T23:59:59.000Z

12

Residential Sector Demand Module 2004, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2004-02-01T23:59:59.000Z

13

Residential Sector Demand Module 2001, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2000-12-01T23:59:59.000Z

14

Residential Sector Demand Module 2002, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2001-12-01T23:59:59.000Z

15

Residential Sector Demand Module 2005, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2005-04-01T23:59:59.000Z

16

Residential Sector Demand Module 2003, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2003-01-01T23:59:59.000Z

17

Residential Sector Demand Module 2008, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2008-10-10T23:59:59.000Z

18

Residential Sector Demand Module 2006, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2006-03-01T23:59:59.000Z

19

Residential Sector Demand Module 1999, Model Documentation  

Reports and Publications (EIA)

This is the fifth edition of the Model Documentation Report: Residential Sector DemandModule of the National Energy Modeling System (NEMS). It reflects changes made to themodule over the past year for the Annual Energy Outlook 1999.

John H. Cymbalsky

1998-12-01T23:59:59.000Z

20

Residential Sector Demand Module 2007, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2007-04-26T23:59:59.000Z

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


21

A flexible, modular approach to integrated space exploration campaign logistics modeling, simulation, and analysis  

E-Print Network (OSTI)

A space logistics modeling framework to support space exploration to remote environments is the target of research within the MIT Space Logistics Project. This thesis presents a revised and expanded framework providing ...

Grogan, Paul Thomas

2010-01-01T23:59:59.000Z

22

Forecasting Electricity Demand by Time Series Models  

Science Conference Proceedings (OSTI)

Electricity demand is one of the most important variables required for estimating the amount of additional capacity required to ensure a sufficient supply of energy. Demand and technological losses forecasts can be used to control the generation and distribution of electricity more efficiently. The aim of this paper is to utilize time series model

E. Stoimenova; K. Prodanova; R. Prodanova

2007-01-01T23:59:59.000Z

23

China End-Use Energy Demand Modeling  

NLE Websites -- All DOE Office Websites (Extended Search)

China End-Use Energy Demand Modeling China End-Use Energy Demand Modeling Speaker(s): Nan Zhou Date: October 8, 2009 (All day) Location: 90-3122 As a consequence of soaring energy demand due to the staggering pace of its economic growth, China overtook the United States in 2007 to become the world's biggest contributor to CO2 emissions (IEA, 2007). Since China is still in an early stage of industrialization and urbanization, economic development promises to keep China's energy demand growing strongly. Furthermore, China's reliance on fossil fuel is unlikely to change in the long term, and increased needs will only heighten concerns about energy security and climate change. In response, the Chinese government has developed a series of policies and targets aimed at improving energy efficiency, including both short-term targets and long-term strategic

24

Integrated modeling and simulation of lunar exploration campaign logistics  

E-Print Network (OSTI)

As NASA prepares to establish a manned outpost on the lunar surface, it is essential to consider the logistics of both the construction and operation of this outpost. This thesis presents an interplanetary supply chain ...

Shull, Sarah A. (Sarah Anderson)

2007-01-01T23:59:59.000Z

25

Modeling the residential demand for energy  

Science Conference Proceedings (OSTI)

Demand for energy is derived from the demand for services that appliances and energy together provide. This raises a number of serious econometric issues when estimating energy-demand functions: delineation of short-run and long-run household responses, specification of the price variable and in particular, the assumption that the model is recursive, or in other words, that the appliance choice equation and the energy consumption equation are uncorrelated. The dissertation utilizes a structural model of energy use whose theoretical underpinnings derive from the conditional logit model and an extension of that model to the joint-discrete/continuous case by Dubin and McFadden (1980). It uses the 1978 to 1979 National Interim Energy Comsumption Survey. Three appliance portfolio choices are analyzed; choice of water and space heating and central air-conditioning; choice of room air conditioners; and choice of clothes dryers, either as multinomial logit or binary probit choices. Results varied widely across the appliance choice considered; use of Hausman's test led to acceptance of the null hypothesis of orthogonality in some cases but not in others. Demand for electricity and natural gas tended to be price inelastic; however, estimated own-price effects differed considerably when disaggregated by appliance categories and across methods of estimation.

Kirby, S.N.

1983-01-01T23:59:59.000Z

26

Residential Sector Demand Module 1998, Model Documentation  

Reports and Publications (EIA)

This is the fourth edition of the Model Documentation Report: Residential Sector DemandModule of the National Energy Modeling System (NEMS). It reflects changes made to themodule over the past year for the Annual Energy Outlook 1998. Since last year, severalnew end-use services were added to the module, including: Clothes washers,dishwashers, furnace fans, color televisions, and personal computers. Also, as with allNEMS modules, the forecast horizon has been extended to the year 2020.

John H. Cymbalsky

1998-01-01T23:59:59.000Z

27

A Structural Model of Demand for Apprentices ?  

E-Print Network (OSTI)

It is a widely held opinion that apprenticeship training represents a net investment for training firms, and that therefore firms only train if they have the possibility to recoup these investments after the training period. A recent study using a new firm-level dataset for Switzerland showed, however, that for 60 percent of the firms, the apprenticeship training itself does not result in net cost. In this context it seems important to examine the question whether the potential net cost of training (during the training period) are a major determinant for the demand for apprentices. Different count data models, in particular hurdle models, are used to estimate the effect of net cost on the demand for apprentices. The results show that the net cost have a significant impact on the training decision but no significant influence on the demand for apprentices, once the firm has decided to train. For policy purposes, these results indicate that subsidies for firms that already train apprentices would not boost the demand for apprentices. JEL Classification: J24, C25

Samuel Mühlemann; Jürg Schweri; Rainer Winkelmann; Stefan C. Wolter

2005-01-01T23:59:59.000Z

28

The Ramsey model with logistic population growth and Benthamite felicity function  

Science Conference Proceedings (OSTI)

This paper evaluates the effects of a Benthamite formulation for the utility function into the Ramsey model with logistic population growth, introduced by Brida and Accinelli (2007). Within this framework, we demonstrate the economy to be described by ... Keywords: Benthamite, Ramsey, logistic population

Massimiliano Ferrara; Luca Guerrini

2009-03-01T23:59:59.000Z

29

Residential Sector Demand Module 1997, Model Documentation  

Reports and Publications (EIA)

This is the third edition of the Model Documentation Report: Residential Sector DemandModule of the National Energy Modeling System. It reflects changes made to the moduleover the past year for the Annual Energy Outlook 1997. Since last year, a subroutinewas added to the model which allows technology and fuel switching when space heaters,heat pump air conditioners, water heaters, stoves, and clothes dryers are retired in bothpre-1994 and post-1993 single-family homes. Also, a time-dependant function forcomputing the installed capital cost of equipment in new construction and the retail costof replacement equipment in existing housing was added.

John H. Cymbalsky

1997-01-01T23:59:59.000Z

30

Residential Demand Module of the National Energy Modeling ...  

U.S. Energy Information Administration (EIA)

Residential Demand Module of the National Energy Modeling System: Model Documentation 2013 November 2013 Independent Statistics & Analysis ...

31

A Note on the Estimation of the Multinomial Logistic Model with Correlated Responses  

E-Print Network (OSTI)

We show how multinomial logistic models with correlated responses can be estimated within SAS software. To achieve this, random effects and marginal models are introduced and the respective SAS code is given. An example data set on physicians ’ recommendations and preferences in traumatic brain injury rehabilitation is used for illustration. The main motivation for this work are two recent papers that recommend estimating multinomial logistic models with correlated responses by using a Poisson likelihood which is statistically correct but computationally inefficient.

Oliver Kuss; Dale Mclerran

2007-01-01T23:59:59.000Z

32

Computational intelligence methods: joint use in discrete event simulation model of logistics processes  

Science Conference Proceedings (OSTI)

The objective of the paper is to present the concept of using selected computational intelligence methods in conjunction with discrete event simulation (DES) models of chosen logistics processes. A review of the recent literature in the scope of applications ...

Marek Karkula; Lech Bukowski

2012-12-01T23:59:59.000Z

33

A Small Aircraft Transportation System (SATS) Demand Model  

Science Conference Proceedings (OSTI)

The Small Aircraft Transportation System (SATS) demand modeling is a tool that will be useful for decision makers to analyze SATS demands in both airport and airspace. We constructed a series of models following the general top- down, modular principles ...

Long Dou; Lee David; Johnson Jesse; Kostiuk Peter

2001-06-01T23:59:59.000Z

34

Industrial Demand Module (IDM) - 2002 EIA Models Directory  

U.S. Energy Information Administration (EIA)

The Industrial Demand Module incorporates three components: buildings; process and assembly; and boiler, steam, and cogeneration. Last Model Update:

35

Commercial Demand Module of the National Energy Modeling System ...  

U.S. Energy Information Administration (EIA)

Commercial Demand Module of the National Energy Modeling System: Model Documentation 2012 November 2012 . Independent Statistics & Analysis . www.eia.gov

36

Septic shock : providing early warnings through multivariate logistic regression models  

E-Print Network (OSTI)

(cont.) The EWS models were then tested in a forward, casual manner on a random cohort of 500 ICU patients to mimic the patients' stay in the unit. The model with the highest performance achieved a sensitivity of 0.85 and ...

Shavdia, Dewang

2007-01-01T23:59:59.000Z

37

Two market models for demand response in power networks  

E-Print Network (OSTI)

Abstract — In this paper, we consider two abstract market models for designing demand response to match power supply and shape power demand, respectively. We characterize the resulting equilibria in competitive as well as oligopolistic markets, and propose distributed demand response algorithms to achieve the equilibria. The models serve as a starting point to include the appliance-level details and constraints for designing practical demand response schemes for smart power grids. I.

Lijun Chen; Na Li; Steven H. Low; John C. Doyle

2010-01-01T23:59:59.000Z

38

Industrial Demand Module 1999, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. Crawford Honeycutt

1999-01-01T23:59:59.000Z

39

Industrial Demand Module 2005, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. C. Honeycutt

2005-05-01T23:59:59.000Z

40

Industrial Demand Module 2006, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. C. Honeycutt

2006-07-01T23:59:59.000Z

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


41

Industrial Demand Module 2009, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. C. Honeycutt

2009-05-20T23:59:59.000Z

42

Industrial Demand Module 2003, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. Crawford Honeycutt

2003-12-01T23:59:59.000Z

43

Industrial Demand Module 2007, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. C. Honeycutt

2007-03-21T23:59:59.000Z

44

Industrial Demand Module 2002, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. Crawford Honeycutt

2001-12-01T23:59:59.000Z

45

Industrial Demand Module 2001, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. Crawford Honeycutt

2000-12-01T23:59:59.000Z

46

Industrial Demand Module 2008, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. C. Honeycutt

2008-06-01T23:59:59.000Z

47

Industrial Demand Module 2000, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. Crawford Honeycutt

2000-01-01T23:59:59.000Z

48

Industrial Demand Module 2004, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. Crawford Honeycutt

2004-02-01T23:59:59.000Z

49

Development of the Integrated Biomass Supply Analysis and Logistics Model (IBSAL)  

DOE Green Energy (OSTI)

The Integrated Biomass Supply & Logistics (IBSAL) model is a dynamic (time dependent) model of operations that involve collection, harvest, storage, preprocessing, and transportation of feedstock for use at a biorefinery. The model uses mathematical equations to represent individual unit operations. These unit operations can be assembled by the user to represent the working rate of equipment and queues to represent storage at facilities. The model calculates itemized costs, energy input, and carbon emissions. It estimates resource requirements and operational characteristics of the entire supply infrastructure. Weather plays an important role in biomass management and thus in IBSAL, dictating the moisture content of biomass and whether or not it can be harvested on a given day. The model calculates net biomass yield based on a soil conservation allowance (for crop residue) and dry matter losses during harvest and storage. This publication outlines the development of the model and provides examples of corn stover harvest and logistics.

Sokhansanj, Shahabaddine [ORNL; Webb, Erin [ORNL; Turhollow Jr, Anthony F [ORNL

2008-06-01T23:59:59.000Z

50

The application of brute force logistic regression to corporate credit scoring models: Evidence from Serbian financial statements  

Science Conference Proceedings (OSTI)

In this paper a brute force logistic regression (LR) modeling approach is proposed and used to develop predictive credit scoring model for corporate entities. The modeling is based on 5years of data from end-of-year financial statements of Serbian corporate ... Keywords: Corporate entities, Credit scoring, Logistic regression, Probability of default, Weight of evidence approach

Nebojsa Nikolic, Nevenka Zarkic-Joksimovic, Djordje Stojanovski, Iva Joksimovic

2013-11-01T23:59:59.000Z

51

Spatial pattern formation in the Keller-Segel model with a logistic source  

Science Conference Proceedings (OSTI)

This paper deals with a Neumann boundary value problem in a d-dimensional box T^d=(0,@p)^d(d=1,2,3) for the chemotaxis-diffusion-growth model (@? ){U"t=@?(D"u@?U-@gU@?V)+rU(1-U/K),V"t=D"v@?^2V+@aU-@bV, which describes the movement of cells in response ... Keywords: Keller-Segel model, Logistic source, Nonlinear dynamics, Pattern formation

Shengmao Fu, Ji Liu

2013-09-01T23:59:59.000Z

52

Forecasting electricity demand by hybrid machine learning model  

Science Conference Proceedings (OSTI)

This paper proposes a hybrid machine learning model for electricity demand forecasting, based on Bayesian Clustering by Dynamics (BCD) and Support Vector Machine (SVM). In the proposed model, a BCD classifier is firstly applied to cluster the input data ...

Shu Fan; Chengxiong Mao; Jiadong Zhang; Luonan Chen

2006-10-01T23:59:59.000Z

53

Control theoretic model of automobile demand and gasoline consumption  

SciTech Connect

The purpose of this research is to examine the controllability of gasoline consumption and automobile demand using gasoline price as a policy instrument. The author examines the problem of replacing the standby motor-fuel rationing plan with use of the federal excise tax on gasoline. It is demonstrated that the standby targets are attainable with the tax. The problem of multiple control of automobile demand and gasoline consumption is also addressed. When the federal gasoline excise tax is used to control gasoline consumption, the policy maker can also use the tax to direct automobile demand. There exists a trade-off between various automobile demand targets and the target implied for gasoline consumption. We seek to measure this trade-off and use the results for planning. This research employs a time series of cross section data base with a disaggregated model of automobile demand, and an aggregate model of gasoline consumption. Automobile demand is divided into five mutually exclusive classes of cars. Gasoline demand is model as the sum of regular, premium, and unleaded gasoline. The pooled data base is comprised of a quarterly time series running from 1963 quarter one through 1979 quarter four, for each of the 48 continuous states. The demand equations are modelled using dynamic theories of demand. Estimates of the respective equations are made with error components and covariance techniques. Optimal control is applied to examine the gasoline-control problem.

Panerali, R.B.

1982-01-01T23:59:59.000Z

54

Modeling, Analysis, and Control of Demand Response Resources  

NLE Websites -- All DOE Office Websites (Extended Search)

Modeling, Analysis, and Control of Demand Response Resources Modeling, Analysis, and Control of Demand Response Resources Speaker(s): Johanna Mathieu Date: April 27, 2012 - 12:00pm Location: 90-3122 Seminar Host/Point of Contact: Sila Kiliccote While the traditional goal of an electric power system has been to control supply to fulfill demand, the demand-side can play an active role in power systems via Demand Response (DR). Recent DR programs have focused on peak load reduction in commercial buildings and industrial facilities (C&I facilities). We present a regression-based baseline model, which allows us to quantify DR performance. We use this baseline model to understand the performance of C&I facilities participating in an automated dynamic pricing DR program in California. In this program, facilities are

55

MODELING THE DEMAND FOR E85 IN THE UNITED STATES  

SciTech Connect

How demand for E85 might evolve in the future in response to changing economics and policies is an important subject to include in the National Energy Modeling System (NEMS). This report summarizes a study to develop an E85 choice model for NEMS. Using the most recent data from the states of Minnesota, North Dakota, and Iowa, this study estimates a logit model that represents E85 choice as a function of prices of E10 and E85, as well as fuel availability of E85 relative to gasoline. Using more recent data than previous studies allows a better estimation of non-fleet demand and indicates that the price elasticity of E85 choice appears to be higher than previously estimated. Based on the results of the econometric analysis, a model for projecting E85 demand at the regional level is specified. In testing, the model produced plausible predictions of US E85 demand to 2040.

Liu, Changzheng [ORNL] [ORNL; Greene, David L [ORNL] [ORNL

2013-10-01T23:59:59.000Z

56

Building Energy Software Tools Directory: Energy Demand Modeling  

NLE Websites -- All DOE Office Websites (Extended Search)

Energy Demand Modeling Energy Demand Modeling The software is intended to be used for Energy Demand Modeling. This can be utilized from regional to national level. A Graphical User Interface of the software takes the input from the user in a quite logical and sequential manner. These input leads to output in two distinct form, first, it develops a Reference Energy System, which depicts the flow of energy from the source to sink with all the losses incorporated and second, it gives a MATLAB script file for advance post processing like graphs, visualization and optimizations to develop and evaluate the right energy mix policy frame work for a intended region. Keywords Reference Energy System, Software, GUI, Planning, Energy Demand Model EDM, Energy Policy Planning Validation/Testing

57

Modeling, Analysis, and Control of Demand Response Resources  

NLE Websites -- All DOE Office Websites (Extended Search)

Modeling, Analysis, and Control of Demand Response Resources Speaker(s): Johanna Mathieu Date: April 27, 2012 - 12:00pm Location: 90-3122 Seminar HostPoint of Contact: Sila...

58

A dynamic model of industrial energy demand in Kenya  

Science Conference Proceedings (OSTI)

This paper analyses the effects of input price movements, technology changes, capacity utilization and dynamic mechanisms on energy demand structures in the Kenyan industry. This is done with the help of a variant of the second generation dynamic factor demand (econometric) model. This interrelated disequilibrium dynamic input demand econometric model is based on a long-term cost function representing production function possibilities and takes into account the asymmetry between variable inputs (electricity, other-fuels and Tabour) and quasi-fixed input (capital) by imposing restrictions on the adjustment process. Variations in capacity utilization and slow substitution process invoked by the relative input price movement justifies the nature of input demand disequilibrium. The model is estimated on two ISIS digit Kenyan industry time series data (1961 - 1988) using the Iterative Zellner generalized least square method. 31 refs., 8 tabs.

Haji, S.H.H. [Gothenburg Univ. (Sweden)

1994-12-31T23:59:59.000Z

59

Cogeneration System Size Optimization Constant Capacity and Constant Demand Models  

E-Print Network (OSTI)

This paper presents the development of a quasi-linear optimization model for a cogeneration system subject to constant heat and power demands or loads. The linear model is next modified to a non-linear one to account for economies of scale. The models define the necessary and sufficient conditions for system size optimality. Thus, the underlying methodology constitutes the foundation for a subsequent series of more sophisticated cogeneration design models. Several examples are presented to illustrate the models.

Wong-Kcomt, J. B.; Turner, W. C.

1993-03-01T23:59:59.000Z

60

The National Energy Modeling System: An Overview 1998 - Commercial Demand  

Gasoline and Diesel Fuel Update (EIA)

COMMERCIAL DEMAND MODULE COMMERCIAL DEMAND MODULE blueball.gif (205 bytes) Floorspace Submodule blueball.gif (205 bytes) Energy Service Demand Submodule blueball.gif (205 bytes) Equipment Choice Submodule blueball.gif (205 bytes) Energy Consumption Submodule The commercial demand module (CDM) forecasts energy consumption by Census division for eight marketed energy sources plus solar thermal energy. For the three major commercial sector fuels, electricity, natural gas and distillate oil, the CDM is a "structural" model and its forecasts are built up from projections of the commercial floorspace stock and of the energy-consuming equipment contained therein. For the remaining five marketed "minor fuels," simple econometric projections are made. The commercial sector encompasses business establishments that are not

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


61

An efficient load model for analyzing demand side management impacts  

SciTech Connect

The main objective of implementing Demand Side Management (DSM) in power systems is to change the utility's load shape--i.e. changes in the time pattern and magnitude of utility's load. Changing the load shape as a result of demand side activities could change the peak load, base load and/or energy demand. Those three variables have to be explicitly modeled into the load curve for properly representing the effects of demand side management. The impact of DSM will be manifested as higher or lower reliability levels. This paper presents an efficient technique to model the system load such that the impact of demand side management on the power system can be easily and accurately evaluated. The proposed technique to model the load duration curve will facilitate the representation of DSM impacts on loss-of-load probability, energy not served and energy consumption. This will provide an analytical method to study the impact of DSM on capacity requirements. So far iterative methods have been applied to study these impacts. The proposed analytical method results in a faster solution with higher accuracy. It takes only 18 seconds on an 80486 PC to solve each case study involving different peak and base loads, and energy use.

Rahman, S.; Rinaldy (Virginia Polytechnic Inst. and State Univ., Blacksburg, VA (United States))

1993-08-01T23:59:59.000Z

62

Energy Demand Modelling Introduction to the PhD project  

E-Print Network (OSTI)

Energy Demand Modelling Introduction to the PhD project Erika Zvingilaite Risø DTU System Analysis for optimization of energy systems Environmental effects Global externalities cost of CO2 Future scenarios for the Nordic energy systems 2010, 2020, 2030, 2040, 2050 (energy-production, consumption, emissions, net costs

63

A Model of Household Demand for Activity Participation and Mobility  

E-Print Network (OSTI)

household car ownership, car usage, and travel by differentownership demand, and car usage demand. Modal travel demand,mode), car ownership, and car usage for spatial aggregations

Golob, Thomas F.

1996-01-01T23:59:59.000Z

64

The National Energy Modeling System: An Overview 1998 - Residential Demand  

Gasoline and Diesel Fuel Update (EIA)

RESIDENTIAL DEMAND MODULE RESIDENTIAL DEMAND MODULE blueball.gif (205 bytes) Housing Stock Submodule blueball.gif (205 bytes) Appliance Stock Submodule blueball.gif (205 bytes) Technology Choice Submodule blueball.gif (205 bytes) Shell Integrity Submodule blueball.gif (205 bytes) Fuel Consumption Submodule The residential demand module (RDM) forecasts energy consumption by Census division for seven marketed energy sources plus solar thermal and geothermal energy. The RDM is a structural model and its forecasts are built up from projections of the residential housing stock and of the energy-consuming equipment contained therein. The components of the RDM and its interactions with the NEMS system are shown in Figure 5. NEMS provides forecasts of residential energy prices, population, and housing starts,

65

Common Information Model On Demand Meter Read Interoperability Test Procedure  

Science Conference Proceedings (OSTI)

The Common Information Model (CIM) On Demand Meter Read Interoperability Test Procedure is one in a series of EPRI Interoperability Test Procedures (ETIPs) created by EPRI whose purpose is to thoroughly document the actors, interfaces, and test steps for the interoperability testing of specific parts of the International Electrotechnical Commission (IEC) Common Information Model (CIM) standard. The Test Procedures are initially being used for EPRI demonstration tests and are intended, over time, to form ...

2011-12-14T23:59:59.000Z

66

Industrial Demand Module 1998, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code. This document serves three purposes. First, it is a reference document providing a detailed description ofthe NEMS Industrial Model for model analysts, users, and the public. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in supportof its models (Public Law 94-385, section 57.b2). Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements as future projects.

T. Crawford Honeycutt

1998-01-01T23:59:59.000Z

67

Modelling Recreation Demand using Choice Experiments: Climbing in Scotland  

E-Print Network (OSTI)

This paper is concerned with the use of the choice experiment method for modelling the demand for recreation, using the example of rock-climbing in Scotland. We begin by outlining the method itself, including its theoretical and econometric underpinnings. Data collection procedures are then outlined. We present results from both nested and non-nested models, and report some tests for the implications of choice complexity and rationality. Finally, we compare our results with a revealed preference data model based on the same sample of climbers.

Nick Hanley; Robert E. Wright; Gary Koop

2000-01-01T23:59:59.000Z

68

Electric Water Heater Modeling and Control Strategies for Demand Response  

Science Conference Proceedings (OSTI)

Abstract— Demand response (DR) has a great potential to provide balancing services at normal operating conditions and emergency support when a power system is subject to disturbances. Effective control strategies can significantly relieve the balancing burden of conventional generators and reduce investment on generation and transmission expansion. This paper is aimed at modeling electric water heaters (EWH) in households and tests their response to control strategies to implement DR. The open-loop response of EWH to a centralized signal is studied by adjusting temperature settings to provide regulation services; and two types of decentralized controllers are tested to provide frequency support following generator trips. EWH models are included in a simulation platform in DIgSILENT to perform electromechanical simulation, which contains 147 households in a distribution feeder. Simulation results show the dependence of EWH response on water heater usage . These results provide insight suggestions on the need of control strategies to achieve better performance for demand response implementation. Index Terms— Centralized control, decentralized control, demand response, electrical water heater, smart grid

Diao, Ruisheng; Lu, Shuai; Elizondo, Marcelo A.; Mayhorn, Ebony T.; Zhang, Yu; Samaan, Nader A.

2012-07-22T23:59:59.000Z

69

Anisotropic mesoscopic traffic simulation approach to support large-scale traffic and logistic modeling and analysis  

Science Conference Proceedings (OSTI)

Large-scale traffic and transportation logistics analysis requires a realistic depiction of network traffic condition in a dynamic manner. In the past decades, vehicular traffic simulation approaches have been increasingly developed and applied to describe ...

Ye Tian; Yi-Chang Chiu

2011-12-01T23:59:59.000Z

70

Univariate Modeling and Forecasting of Monthly Energy Demand Time Series  

E-Print Network (OSTI)

in this report. #12;i ABSTRACT These electricity demand forms and instructions ask load-serving entities and Instructions for Electricity Demand Forecasts. California Energy Commission, Electricity Supply Analysis.................................................................................................................................7 Form 1 Historic and Forecast Electricity Demand

Abdel-Aal, Radwan E.

71

Modeling Structural Changes in Market Demand and Supply  

E-Print Network (OSTI)

Economic events may cause structural changes in markets. To know the effect of the economic event we should analyze the structural changes in the market demand and supply. The purpose of this dissertation is to analyze the effect of selected economic events on market demand and supply using econometric models. Structural changes can be modeled according to the types of changes. For an abrupt and instantaneous break, a dummy variable model can be used. For a smooth and gradual movement, proxy variables which represent the event can be applied, if we know the variables. If we don?t know the appropriate proxy variables, a smooth transition regression model can be employed. The BSE (Bovine Spongiform Encephalopathy) outbreak in the U.S. in 2003 is assumed to make abrupt and instantaneous changes in Korean meat consumption. To analyze the effect on Korean meat consumption, the Korean demands of beef, pork, chicken, and U.S. beef are estimated using an LA/AIDS (Linear Approximate Almost Ideal Demand System) model with the dummy variable specifying the time before and after the BSE. From the results we can confirm that food safety concerns caused by the BSE case changed Korean meat consumption structure. Korean beef and U.S. beef became less elastic, and pork and chicken got more elastic to budget. Korean beef became less price elastic, but pork and U.S. beef got more price elastic. The changes of U.S. natural gas supply caused by technology development and depletion in reserves are analyzed using a smooth transition regression model. From the results, we can confirm that the productivity improvement by technology development is greater than the labor cost increase by depletion, but not greater than the capital cost increase by depletion in mid-2000s. The effects of posting the winning bid in a repeated Vickrey auction are examined using a proxy variable. By applying an unobserved effect Tobit model to the experimental auction done by Corrigan and Rousu (2006) for a candy bar, we can confirm that the changes of bidding behavior are significant, especially when the winning bid is high. By extracting the bid affiliation effects, we showed that true willingness to pay can be estimated.

Park, Beom Su

2010-08-01T23:59:59.000Z

72

Communicating Logistics  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Logistics Logistics * Delayed or nonexistent communication * Lack of consistency-follow protocols * No overarching coordination * DUF6 communication is poor * Lesson learned: need enhanced, on-going, open communication prior to planning Routing * Re-examine avoiding population centers * Analyze delta between mixed vs. dedicated trains * Flexibility is key component of planning * Selection needs to include state infrastructure Inspections * Cooperate with federal, state, local parties for an integrated role * Schedule during planning process * Coordination to improve use of limited resources * Integrate new technologies TRANSCOM * Needs full funding * Continuous improvement of technology * Great when working * Protocols for assuring data accuracy * Back-up procedure when system is down Incidents & Accidents

73

The National Energy Modeling System: An Overview 2000 - Industrial Demand  

Gasoline and Diesel Fuel Update (EIA)

industrial demand module (IDM) forecasts energy consumption for fuels and feedstocks for nine manufacturing industries and six nonmanufactur- ing industries, subject to delivered prices of energy and macroeconomic variables representing the value of output for each industry. The module includes industrial cogeneration of electricity that is either used in the industrial sector or sold to the electricity grid. The IDM structure is shown in Figure 7. industrial demand module (IDM) forecasts energy consumption for fuels and feedstocks for nine manufacturing industries and six nonmanufactur- ing industries, subject to delivered prices of energy and macroeconomic variables representing the value of output for each industry. The module includes industrial cogeneration of electricity that is either used in the industrial sector or sold to the electricity grid. The IDM structure is shown in Figure 7. Figure 7. Industrial Demand Module Structure Industrial energy demand is projected as a combination of “bottom up” characterizations of the energy-using technology and “top down” econometric estimates of behavior. The influence of energy prices on industrial energy consumption is modeled in terms of the efficiency of use of existing capital, the efficiency of new capital acquisitions, and the mix of fuels utilized, given existing capital stocks. Energy conservation from technological change is represented over time by trend-based “technology possibility curves.” These curves represent the aggregate efficiency of all new technologies that are likely to penetrate the future markets as well as the aggregate improvement in efficiency of 1994 technology.

74

Model documentation report: Short-term Integrated Forecasting System demand model 1985. [(STIFS)  

DOE Green Energy (OSTI)

The Short-Term Integrated Forecasting System (STIFS) Demand Model consists of a set of energy demand and price models that are used to forecast monthly demand and prices of various energy products up to eight quarters in the future. The STIFS demand model is based on monthly data (unless otherwise noted), but the forecast is published on a quarterly basis. All of the forecasts are presented at the national level, and no regional detail is available. The model discussed in this report is the April 1985 version of the STIFS demand model. The relationships described by this model include: the specification of retail energy prices as a function of input prices, seasonal factors, and other significant variables; and the specification of energy demand by product as a function of price, a measure of economic activity, and other appropriate variables. The STIFS demand model is actually a collection of 18 individual models representing the demand for each type of fuel. The individual fuel models are listed below: motor gasoline; nonutility distillate fuel oil, (a) diesel, (b) nondiesel; nonutility residual fuel oil; jet fuel, kerosene-type and naphtha-type; liquefied petroleum gases; petrochemical feedstocks and ethane; kerosene; road oil and asphalt; still gas; petroleum coke; miscellaneous products; coking coal; electric utility coal; retail and general industry coal; electricity generation; nonutility natural gas; and utility petroleum. The demand estimates produced by these models are used in the STIFS integrating model to produce a full energy balance of energy supply, demand, and stock change. These forecasts are published quarterly in the Outlook. Details of the major changes in the forecasting methodology and an evaluation of previous forecast errors are presented once a year in Volume 2 of the Outlook, the Methodology publication.

Not Available

1985-07-01T23:59:59.000Z

75

The National Energy Modeling System: An Overview 2000 - Residential Demand  

Gasoline and Diesel Fuel Update (EIA)

residential demand module (RDM) forecasts energy consumption by Census division for seven marketed energy sources plus solar and geothermal energy. RDM is a structural model and its forecasts are built up from projections of the residential housing stock and of the energy-consuming equipment contained therein. The components of RDM and its interactions with the NEMS system are shown in Figure 5. NEMS provides forecasts of residential energy prices, population, and housing starts, which are used by RDM to develop forecasts of energy consumption by fuel and Census division. residential demand module (RDM) forecasts energy consumption by Census division for seven marketed energy sources plus solar and geothermal energy. RDM is a structural model and its forecasts are built up from projections of the residential housing stock and of the energy-consuming equipment contained therein. The components of RDM and its interactions with the NEMS system are shown in Figure 5. NEMS provides forecasts of residential energy prices, population, and housing starts, which are used by RDM to develop forecasts of energy consumption by fuel and Census division. Figure 5. Residential Demand Module Structure RDM incorporates the effects of four broadly-defined determinants of energy consumption: economic and demographic effects, structural effects, technology turnover and advancement effects, and energy market effects. Economic and demographic effects include the number, dwelling type (single-family, multi-family or mobile homes), occupants per household, and location of housing units. Structural effects include increasing average dwelling size and changes in the mix of desired end-use services provided by energy (new end uses and/or increasing penetration of current end uses, such as the increasing popularity of electronic equipment and computers). Technology effects include changes in the stock of installed equipment caused by normal turnover of old, worn out equipment with newer versions which tend to be more energy efficient, the integrated effects of equipment and building shell (insulation level) in new construction, and in the projected availability of even more energy-efficient equipment in the future. Energy market effects include the short-run effects of energy prices on energy demands, the longer-run effects of energy prices on the efficiency of purchased equipment and the efficiency of building shells, and limitations on minimum levels of efficiency imposed by legislated efficiency standards.

76

2002 EIA Models Directory  

U.S. Energy Information Administration (EIA)

DRI Model of the U.S. Economy. Ron Earley (202) 586-1398. World Oil Refining, Logistics, and Demand Model. Dan Butler (202) 586-9503 ...

77

Modeling, Analysis, and Control of Demand Response Resources  

E-Print Network (OSTI)

comes to demand response is FERC is own worst enemy? Tech.9.1-2 (1986), pp. 5–18. [46] FERC. A national assessment of09-demand-response.pdf. [47] FERC. National action plan on

Mathieu, Johanna L.

2013-01-01T23:59:59.000Z

78

Model Documentation Report: Residential Demand Module of the ...  

U.S. Energy Information Administration (EIA)

rebates used in demand-side management programs), can be modified at the equipment level. Housing ... Residential retired equipment efficiencies of 2005 stock

79

Identification of time series model of heat demand using mathematica environment  

Science Conference Proceedings (OSTI)

The paper presents possibility of model design of time series of heat demand course. The course of heat demand and heat consumption can be demonstrated by means of heat demand diagrams. The most important one is the Daily Diagram of Heat Supply (DDHS) ... Keywords: box-jenkins, control algorithms, district heating control, modelling, prediction, time series analysis

Bronislav Chramcov

2011-05-01T23:59:59.000Z

80

A Dynamic Supply-Demand Model for Electricity Prices Manuela Buzoianu  

E-Print Network (OSTI)

played a role during the crisis period. 1 Introduction The energy industry provides electrical powerA Dynamic Supply-Demand Model for Electricity Prices Manuela Buzoianu , Anthony E. Brockwell of supply and demand equilibrium. The model includes latent supply and demand curves, which may vary over

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


81

A critical review of single fuel and interfuel substitution residential energy demand models  

E-Print Network (OSTI)

The overall purpose of this paper is to formulate a model of residential energy demand that adequately analyzes all aspects of residential consumer energy demand behavior and properly treats the penetration of new technologies, ...

Hartman, Raymond Steve

1978-01-01T23:59:59.000Z

82

Model documentation report: Commercial Sector Demand Module of the National Energy Modeling System  

Science Conference Proceedings (OSTI)

This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components. The NEMS Commercial Sector Demand Module is a simulation tool based upon economic and engineering relationships that models commercial sector energy demands at the nine Census Division level of detail for eleven distinct categories of commercial buildings. Commercial equipment selections are performed for the major fuels of electricity, natural gas, and distillate fuel, for the major services of space heating, space cooling, water heating, ventilation, cooking, refrigeration, and lighting. The algorithm also models demand for the minor fuels of residual oil, liquefied petroleum gas, steam coal, motor gasoline, and kerosene, the renewable fuel sources of wood and municipal solid waste, and the minor services of office equipment. Section 2 of this report discusses the purpose of the model, detailing its objectives, primary input and output quantities, and the relationship of the Commercial Module to the other modules of the NEMS system. Section 3 of the report describes the rationale behind the model design, providing insights into further assumptions utilized in the model development process to this point. Section 3 also reviews alternative commercial sector modeling methodologies drawn from existing literature, providing a comparison to the chosen approach. Section 4 details the model structure, using graphics and text to illustrate model flows and key computations.

NONE

1998-01-01T23:59:59.000Z

83

Density-based logistic regression  

Science Conference Proceedings (OSTI)

This paper introduces a nonlinear logistic regression model for classification. The main idea is to map the data to a feature space based on kernel density estimation. A discriminative model is then learned to optimize the feature weights as well as ... Keywords: density estimation, logistic regression, medical prediction, nonlinear classification

Wenlin Chen, Yixin Chen, Yi Mao, Baolong Guo

2013-08-01T23:59:59.000Z

84

Market-based airport demand management : theory, model and applications  

E-Print Network (OSTI)

The ever-increasing demand for access to the world's major commercial airports combined with capacity constraints at many of these airports have led to increasing air traffic congestion. In particular, the scarcity of ...

Fan, Terence P

2004-01-01T23:59:59.000Z

85

Modeling, Analysis, and Control of Demand Response Resources  

E-Print Network (OSTI)

9.1-2 (1986), pp. 5–18. [46] FERC. A national assessment ofmeet/2008/101608/E-1.pdf. [49] FERC. Order No. 745, Demand17-000.pdf. BIBLIOGRAPHY [50] FERC. Order No. 755, Frequency

Mathieu, Johanna L.

2012-01-01T23:59:59.000Z

86

Electrical ship demand modeling for future generation warships  

E-Print Network (OSTI)

The design of future warships will require increased reliance on accurate prediction of electrical demand as the shipboard consumption continues to rise. Current US Navy policy, codified in design standards, dictates methods ...

Sievenpiper, Bartholomew J. (Bartholomew Jay)

2013-01-01T23:59:59.000Z

87

A Single-Product Inventory Model for Multiple Demand Classes  

E-Print Network (OSTI)

We consider a single-product inventory system that serves multiple demand classes, which differ in their shortage costs or service level requirements. We assume a critical-level control policy, and show the equivalence ...

Arslan, Hasan

2005-05-27T23:59:59.000Z

88

Model documentation report: Industrial sector demand module of the national energy modeling system  

SciTech Connect

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code. This document serves three purposes. First, it is a reference document providing a detailed description of the NEMS Industrial Model for model analysts, users, and the public. Second, this report meets the legal requirements of the Energy Information Administration (EIA) to provide adequate documentation in support of its model. Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements as future projects.

NONE

1998-01-01T23:59:59.000Z

89

Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks  

Science Conference Proceedings (OSTI)

Neural networks have been widely used for short-term, and to a lesser degree medium and long-term, demand forecasting. In the majority of cases for the latter two applications, multivariate modeling was adopted, where the demand time series is related ... Keywords: Abductive networks, Energy demand, Medium-term load forecasting, Neural networks, Time series forecasting, Univariate time series analysis

R. E. Abdel-Aal

2008-05-01T23:59:59.000Z

90

Model documentation report: Residential sector demand module of the national energy modeling system  

SciTech Connect

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code. This reference document provides a detailed description for energy analysts, other users, and the public. The NEMS Residential Sector Demand Module is currently used for mid-term forecasting purposes and energy policy analysis over the forecast horizon of 1993 through 2020. The model generates forecasts of energy demand for the residential sector by service, fuel, and Census Division. Policy impacts resulting from new technologies, market incentives, and regulatory changes can be estimated using the module. 26 refs., 6 figs., 5 tabs.

NONE

1998-01-01T23:59:59.000Z

91

A Probabilistic Deformation Demand Model and Fragility Estimates for Asymmetric Offshore Jacket Platforms  

E-Print Network (OSTI)

Interest in evaluating the performance and safety of offshore oil and gas platforms has been expanding due to the growing world energy supply and recent offshore catastrophes. In order to accurately assess the reliability of an offshore platform, all relevant uncertainties must be properly accounted for. This necessitates the development of a probabilistic demand model that accounts for the relevant uncertainties and model errors. In this study, a probabilistic demand model is developed to assess the deformation demand on asymmetric offshore jacket platforms subject to wave and current loadings. The probabilistic model is constructed by adding correction terms and a model error to an existing deterministic deformation demand model. The correction terms are developed to capture the bias inherent in the deterministic model. The model error is developed to capture the accuracy of the model. The correction terms and model errors are estimated through a Bayesian approach using simulation data obtained from detailed dynamic analyses of a set of representative asymmetric offshore platform configurations. The proposed demand model provides accurate and unbiased estimates of the deformation demand on offshore jacket platforms. The developed probabilistic demand model is then used to assess the reliability of a typical offshore platform considering serviceability and ultimate performance levels. In addition, a sensitivity analysis is conducted to assess the effect of key parameters on the results of the analyses. The proposed demand model can be used to assess the reliability of different design options and for the reliability-based optimal design of offshore jacket platforms.

Fallon, Michael

2012-12-01T23:59:59.000Z

92

Modelling the Energy Demand of Households in a Combined  

E-Print Network (OSTI)

. Emissions from passenger transport, households'electricity and heat consumption are growing rapidly despite demand analysis for electricity (e.g. Larsen and Nesbakken, 2004; Holtedahl and Joutz, 2004; Hondroyiannis, 2004) and passenger cars (Meyer et al., 2007). Some recent studies cover the whole residential

Steininger, Karl W.

93

Decline curve analysis in unconventional resource plays using logistic growth models.  

E-Print Network (OSTI)

??Current models used to forecast production in unconventional oil and gas formations are often not producing valid results. When traditional decline curve analysis models are… (more)

Clark, Aaron James

2011-01-01T23:59:59.000Z

94

Perspectives for logistics clusters development in Russia  

E-Print Network (OSTI)

This thesis is a normative work aimed at identifying locations in Russia with high, medium and unclear potentials for logistics cluster development. As a framework this work uses four different models of logistics clusters: ...

Tantsuyev, Andriy

2012-01-01T23:59:59.000Z

95

Estimating Demand Response Load Impacts: Evaluation of Baseline Load Models for Non-  

E-Print Network (OSTI)

, and the Office of Electricity Delivery and Energy Reliability, Permitting, Siting and Analysis of the ULBNL-63728 Estimating Demand Response Load Impacts: Evaluation of Baseline Load Models for Non .............................................................................................................. 9 4. Baseline Profile (BLP) Models

96

AN INTRODUCTION TO SEMANTIC MODELING FOR LOGISTICAL SYSTEMS David L. Brock  

E-Print Network (OSTI)

methods such as bar codes, all have combined to drastically improve temporal and spatial utility (Coyle capabilities and infrastructure. Although there is a strong history of applying models to help managers make. Beginning in the 1980's, software companies started to embed models into software pack- ages installed

Brock, David

97

A new model for allocating resources to scheduled lightpath demands  

Science Conference Proceedings (OSTI)

Recent research has clearly established that holding-time-aware routing-and-wavelength-assignment (RWA) schemes lead to significant improvements in resource utilization for scheduled traffic. Two different models have been proposed for scheduled traffic ... Keywords: Routing and wavelength assignment, Scheduled traffic model, Segmented sliding window model, Wavelength division multiplexing

Ying Chen; Arunita Jaekel; Ataul Bari

2011-09-01T23:59:59.000Z

98

Pensacola Smart Grid RFI Addressing Policy and Logistical Challenges...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Logistical Challenges. Providing comment on: Consumer facing programs such as feedback, demand response, energy efficiency, and automation strategies. Pensacola Smart Grid RFI...

99

Regression Models for Demand Reduction based on Cluster Analysis of Load Profiles  

Science Conference Proceedings (OSTI)

This paper provides new regression models for demand reduction of Demand Response programs for the purpose of ex ante evaluation of the programs and screening for recruiting customer enrollment into the programs. The proposed regression models employ load sensitivity to outside air temperature and representative load pattern derived from cluster analysis of customer baseline load as explanatory variables. The proposed models examined their performances from the viewpoint of validity of explanatory variables and fitness of regressions, using actual load profile data of Pacific Gas and Electric Company's commercial and industrial customers who participated in the 2008 Critical Peak Pricing program including Manual and Automated Demand Response.

Yamaguchi, Nobuyuki; Han, Junqiao; Ghatikar, Girish; Piette, Mary Ann; Asano, Hiroshi; Kiliccote, Sila

2009-06-28T23:59:59.000Z

100

National micro-data based model of residential electricity demand: new evidence on seasonal variation  

SciTech Connect

Building on earlier estimates of electricity demand, the author estimates elasticities by month to determine differences between heating and cooling seasons. He develops a three equation model of residential electricity demand that includes all the main components of economic theory. The model generates seasonal elasticity estimates that generally support economic theory. Based on the model using a national current household data set (monthly division), the evidence indicates there is a seasonal pattern for price elasticity of demand. While less pronounced, there also appears to be seasonal patterns for cross-price elasticity of alternative fuels, for the elasticity of appliance stock index, and for an intensity of use variable.

Garbacz, C.

1984-07-01T23:59:59.000Z

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


101

Model documentation report: Commercial Sector Demand Module of the National Energy Modeling System  

SciTech Connect

This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components. This report serves three purposes. First, it is a reference document providing a detailed description for model analysts, users, and the public. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its statistical and forecast reports (Public Law 93-275, section 57(b)(1)). Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements as future projects.

NONE

1995-02-01T23:59:59.000Z

102

Modeling, Analysis, and Control of Demand Response Resources  

E-Print Network (OSTI)

4.2.1 Individual TCL model . . . . . . . . . . . . . .4.2.2 Plant: The TCL population . . . . . . . .5 TCL Resource, Revenues & Costs 5.1 Chapter

Mathieu, Johanna L.

2012-01-01T23:59:59.000Z

103

Propane demand modeling for residential sectors- A regression analysis.  

E-Print Network (OSTI)

??This thesis presents a forecasting model for the propane consumption within the residential sector. In this research we explore the dynamic behavior of different variables… (more)

Shenoy, Nitin K.

2011-01-01T23:59:59.000Z

104

Modeling, Analysis, and Control of Demand Response Resources  

E-Print Network (OSTI)

buildings”. In: Journal of Solar Energy Engineering 120 (I-II”. In: Journal of Solar Energy Engineering 120 (1998),modeling”. In: Journal of Solar Energy Engineering 120 (

Mathieu, Johanna L.

2013-01-01T23:59:59.000Z

105

Modeling, Analysis, and Control of Demand Response Resources  

E-Print Network (OSTI)

buildings”. In: Journal of Solar Energy Engineering 120 (I-II”. In: Journal of Solar Energy Engineering 120 (1998),modeling”. In: Journal of Solar Energy Engineering 120 (

Mathieu, Johanna L.

2012-01-01T23:59:59.000Z

106

Model Documentation Report: Residential Demand Module of the ...  

U.S. Energy Information Administration (EIA)

New home heating technology choice model log-linear parameter ?. ... Percent of homes meeting ENERGY STAR Home criteria or better by heating technology

107

Regression Models for Demand Reduction based on Cluster Analysis of Load  

NLE Websites -- All DOE Office Websites (Extended Search)

Regression Models for Demand Reduction based on Cluster Analysis of Load Regression Models for Demand Reduction based on Cluster Analysis of Load Profiles Speaker(s): Nobuyuki Yamaguchi Date: March 26, 2009 - 12:00pm Location: 90-3122 This seminar provides new regression models for demand reduction of Demand Response programs for the purpose of ex ante evaluation of the programs and screening for recruiting customer enrollment into the programs. The proposed regression models employ load sensitivity to outside air temperature and representative load pattern derived from cluster analysis of customer baseline load as explanatory variables. We examined the performance of the proposed models with respect to the validity of explanatory variables and fitness of regressions, using actual load profile data of Pacific Gas and Electric Company's commercial and industrial

108

Lazy meta-learning: creating customized model ensembles on demand  

Science Conference Proceedings (OSTI)

In the not so distant future, we expect analytic models to become a commodity. We envision having access to a large number of data-driven models, obtained by a combination of crowdsourcing, crowdservicing, cloud-based evolutionary algorithms, outsourcing, ... Keywords: Pareto set, coal-fired power plant management, computational intelligence, ensemble, entropy, fusion, lazy learning, machine learning, meta-learning, neural networks

Piero P. Bonissone

2012-06-01T23:59:59.000Z

109

Comparison of multimarker logistic regression models, with application to a genomewide scan of schizophrenia.  

E-Print Network (OSTI)

with asymptotics do not explain the pattern of ?^ seen above, since the multimarker model with fewest parameters has the higher inflation in the med- ian. Other quality control factors could be responsible, but the pre-study QC step should have gone some way... , for example the window on chromosome 16 starting at rs17618203 using the haplotype test, actually become more significant after correction; others are cor- rected by an order of magnitude or more. QC of top ranked windows We examined quality control statistics...

Wason, James M S; Dudbridge, Frank

2010-09-09T23:59:59.000Z

110

Residential energy demand modeling and the NIECS data base : an evaluation  

E-Print Network (OSTI)

The purpose of this report is to evaluate the 1978-79 National Interim Energy Consumption Survey (NIECS) data base in terms of its usefulness for estimating residential energy demand models based on household appliance ...

Cowing, Thomas G.

1982-01-01T23:59:59.000Z

111

Smart finite state devices: A modeling framework for demand response technologies  

E-Print Network (OSTI)

We introduce and analyze Markov Decision Process (MDP) machines to model individual devices which are expected to participate in future demand-response markets on distribution grids. We differentiate devices into the ...

Turitsyn, Konstantin

112

Model for Analysis of Energy Demand (MAED-2) | Open Energy Information  

Open Energy Info (EERE)

Website http:www-pub.iaea.orgMTCDp References MAED 21 "MAED model evaluates future energy demand based on medium- to long-term scenarios of socio-economic,...

113

Model documentation report: Residential sector demand module of the National Energy Modeling System  

SciTech Connect

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code. This document serves three purposes. First, it is a reference document providing a detailed description for energy analysts, other users, and the public. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its statistical and forecast reports according to Public Law 93-275, section 57(b)(1). Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements.

NONE

1995-03-01T23:59:59.000Z

114

Model documentation report: Residential sector demand module of the National Energy Modeling System  

Science Conference Proceedings (OSTI)

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code. This document serves three purposes. First, it is a reference document that provides a detailed description for energy analysts, other users, and the public. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its statistical and forecast reports according to Public Law 93-275, section 57(b)(1). Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements.

NONE

1997-01-01T23:59:59.000Z

115

Abstract--An optimization model that incorporates demand in the paradigm of smart grids and distributed generation is  

E-Print Network (OSTI)

, Maximum expected demand in the optimization period Cost associated to energy generated by demand from1 Abstract--An optimization model that incorporates demand in the paradigm of smart grids and distributed generation is formulated. The objective is to transform the demand into an active agent that helps

Catholic University of Chile (Universidad Católica de Chile)

116

Simple models of district heating systems for load and demand side management  

E-Print Network (OSTI)

Simple models of district heating systems for load and demand side management and operational Energiforskningsprogrammet EFP ENS J.nr. 1373/01-0041 December 2004 #12;Simple models of district heating systems for load 87-7475-323-1 #12;Preface The research project "Simple models of district heating systems for load

117

Reduced-Order Modeling of Aggregated Thermostatic Loads With Demand Response  

Science Conference Proceedings (OSTI)

Demand Response is playing an increasingly important role in smart grid control strategies. Modeling the behavior of populations of appliances under demand response is especially important to evaluate the effectiveness of these demand response programs. In this paper, an aggregated model is proposed for a class of Thermostatically Controlled Loads (TCLs). The model efficiently includes statistical information of the population, systematically deals with heterogeneity, and accounts for a second-order effect necessary to accurately capture the transient dynamics in the collective response. However, an accurate characterization of the collective dynamics however requires the aggregate model to have a high state space dimension. Most of the existing model reduction techniques require the stability of the underlying system which does not hold for the proposed aggregated model. In this work, a novel model reduction approach is developed for the proposed aggregated model, which can significantly reduce its complexity with small performance loss. The original and the reducedorder aggregated models are validated against simulations of thousands of detailed building models using GridLAB-D, which is a realistic open source distribution simulation software. Index Terms – demand response, aggregated model, ancillary

Zhang, Wei; Lian, Jianming; Chang, Chin-Yao; Kalsi, Karanjit; Sun, Yannan

2012-12-12T23:59:59.000Z

118

Logistics & Supply Chain Management  

E-Print Network (OSTI)

and the logistics industry as the largest growing sector of the transportation industry. Start your own home-based

Asaithambi, Asai

119

Research on Fuzzy Evaluation in Coal Enterprises Logistic System  

Science Conference Proceedings (OSTI)

According to the logistic system current situation of coal enterprises, a practical evaluation index system for its logistic system was established, constructing fuzzy comprehensive evaluation model, with this model, exiting logistic system of one coal ... Keywords: coal enterprises, logistic system, fuzzy evaluation

Xu Jun

2009-10-01T23:59:59.000Z

120

Forest biomass supply logistics for a power plant using the discrete-event simulation approach  

Science Conference Proceedings (OSTI)

This study investigates the logistics of supplying forest biomass to a potential power plant. Due to the complexities in such a supply logistics system, a simulation model based on the framework of Integrated Biomass Supply Analysis and Logistics (IBSAL) is developed in this study to evaluate the cost of delivered forest biomass, the equilibrium moisture content, and carbon emissions from the logistics operations. The model is applied to a proposed case of 300 MW power plant in Quesnel, BC, Canada. The results show that the biomass demand of the power plant would not be met every year. The weighted average cost of delivered biomass to the gate of the power plant is about C$ 90 per dry tonne. Estimates of equilibrium moisture content of delivered biomass and CO2 emissions resulted from the processes are also provided.

Mobini, Mahdi [University of British Columbia, Vancouver; Sowlati, T. [University of British Columbia, Vancouver; Sokhansanj, Shahabaddine [ORNL

2011-04-01T23:59:59.000Z

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


121

ORNL Residential Reference House Energy Demand model (ORNL-RRHED). Volume 4. Case studies  

Science Conference Proceedings (OSTI)

This report describes the use and structure of the ORNL Residential Reference House Energy Demand Model (RRHED). RRHED is a computer-based engineering-economic end-use simulation model which forecasts energy demand based on a detailed evaluation of how households use energy for particular appliances. The report is organized into four volumes. The first volume provides an overview of the modeling approach and gives a short summary of the material presented in the other three volumes. The second volume is a user reference guide which provides the details necessary for users of the model to run the code and make changes to fit their particular application. Volume 3 presents the basic theoretical rationale for the RRHED model structure. The last volume reports on the application of the model to the analysis of two different kinds of issues: one is the examination of conservation policy impacts and the other is the forecasting of electricity demand in a need for power assessment. The report has two major objectives. The first is to provide a reader with little background in end-use modeling with an introduction to how the RRHED model works. The second is to provide the details needed by a user of the model to understand not only the theory behind the model specification, but also the structure of the code. This information will allow for the modification of subroutines to fit particular applications.

Hamblin, D.M.; Thomas, B. Jr.; Maddigan, R.J.; Forman, C.W. Jr.; Bibo, L.J.; McKeehan, K.M.

1986-02-01T23:59:59.000Z

122

Modeling the Capacity and Emissions Impacts of Reduced Electricity Demand. Part 1. Methodology and Preliminary Results.  

E-Print Network (OSTI)

Impacts of Reduced Electricity Demand. Part 1. MethodologyImpacts of Reduced Electricity Demand. Part 1. MethodologyFigure 3: Commercial electricity demand with and without the

Coughlin, Katie

2013-01-01T23:59:59.000Z

123

Regression Models for Demand Reduction based on Cluster Analysis of Load Profiles  

E-Print Network (OSTI)

Methods for Customer and Demand Response Policies SelectionC. McParland,“Open Automated Demand Response Communicationset al, “Estimating Demand Response Load Impacts: Evaluation

Kiliccote, Sila

2010-01-01T23:59:59.000Z

124

Modeling of GE Appliances in GridLAB-D: Peak Demand Reduction  

Science Conference Proceedings (OSTI)

The widespread adoption of demand response enabled appliances and thermostats can result in significant reduction to peak electrical demand and provide potential grid stabilization benefits. GE has developed a line of appliances that will have the capability of offering several levels of demand reduction actions based on information from the utility grid, often in the form of price. However due to a number of factors, including the number of demand response enabled appliances available at any given time, the reduction of diversity factor due to the synchronizing control signal, and the percentage of consumers who may override the utility signal, it can be difficult to predict the aggregate response of a large number of residences. The effects of these behaviors can be modeled and simulated in open-source software, GridLAB-D, including evaluation of appliance controls, improvement to current algorithms, and development of aggregate control methodologies. This report is the first in a series of three reports describing the potential of GE's demand response enabled appliances to provide benefits to the utility grid. The first report will describe the modeling methodology used to represent the GE appliances in the GridLAB-D simulation environment and the estimated potential for peak demand reduction at various deployment levels. The second and third reports will explore the potential of aggregated group actions to positively impact grid stability, including frequency and voltage regulation and spinning reserves, and the impacts on distribution feeder voltage regulation, including mitigation of fluctuations caused by high penetration of photovoltaic distributed generation and the effects on volt-var control schemes.

Fuller, Jason C.; Vyakaranam, Bharat GNVSR; Prakash Kumar, Nirupama; Leistritz, Sean M.; Parker, Graham B.

2012-04-29T23:59:59.000Z

125

CONSULTANT REPORT DEMAND FORECAST EXPERT  

E-Print Network (OSTI)

CONSULTANT REPORT DEMAND FORECAST EXPERT PANEL INITIAL forecast, end-use demand modeling, econometric modeling, hybrid demand modeling, energyMahon, Carl Linvill 2012. Demand Forecast Expert Panel Initial Assessment. California Energy

126

Worldwide transportation/energy demand, 1975-2000. Revised Variflex model projections  

SciTech Connect

The salient features of the transportation-energy relationships that characterize the world of 1975 are reviewed, and worldwide (34 countries) long-range transportation demand by mode to the year 2000 is reviewed. A worldwide model is used to estimate future energy demand for transportation. Projections made by the forecasting model indicate that in the year 2000, every region will be more dependent on petroleum for the transportation sector than it was in 1975. This report is intended to highlight certain trends and to suggest areas for further investigation. Forecast methodology and model output are described in detail in the appendices. The report is one of a series addressing transportation energy consumption; it supplants and replaces an earlier version published in October 1978 (ORNL/Sub-78/13536/1).

Ayres, R.U.; Ayres, L.W.

1980-03-01T23:59:59.000Z

127

16th Annual Freight and Logistics Symposium  

E-Print Network (OSTI)

performance and results, prices and demand for oil, our ability to make acquisitions on economically/7/2012 2 #12;Shale Development ­ "Boom" Shale Development Wind Energy Ethanol 2003 · Logistics and related infrastructure of greater importance in shale development, and therefore a major

Minnesota, University of

128

Models for estimating saturation flow and maximum demand at closely spaced intersections  

E-Print Network (OSTI)

This thesis describes models for saturation flow and maximum demand at closely spaced intersections. The effects of queue interaction between these two intersections are taken into account in both models. The saturation flow model is based on the Prosser-Dunne model. The presence of queues in the inter-signal link causes a reduction in saturation flow and capacity. The analytical model on which the methodology is based assumes that upstream movements discharge at their normal saturation flow rate or arrival flow rate until the downstream queue extends back to the upstream intersection and blocking occurs. The model calculates the capacities of movements at the upstream intersection as a reduced effective green period. The model can be used to estimate capacities at paired intersections with multiple upstream and downstream green periods. The results from the model are compared with TRAF-NETSIM simulation results. The results of this comparison show that the model predicts throughput better when movements at the upstream intersection (for which throughput are being calculated) are oversaturated. This thesis recommends that the capacity of movements be calculated using the reduced effective green period rather than the reduced saturation flow. The second model developed as a part of this research predicts the maximum demand at the downstream intersection. The through movement at the upstream intersection is assumed to be oversaturated and cross street movements are not considered. The analysis shows that either the upstream capacity, downstream capacity or storage capacity becomes critical and influences the maximum demand depending on the different combinations of upstream and downstream green and storage spacing considered. The demand from the models is used as input to the 1994 Highway Capacity Manual (HCM) delay equation and the delay compared with that simulated by TRAF-NETSIM for various cases. The comparison shows that the models developed predict values that compare favorably with results from TRAF NETSIM. It is recommended that the models be used to compute the upper bound for the HCM delay equation for the cases analyzed.

Nanduri, Sreelata

1995-01-01T23:59:59.000Z

129

Modeling, Estimation, and Control in Energy Systems: Batteries & Demand Response  

NLE Websites -- All DOE Office Websites (Extended Search)

Modeling, Modeling, Estimation, and Control in Energy Systems: Batteries & Demand Response Scott Moura Assistant Professor Civl & Environmental Engineering University of California, Berkeley EETD | LBNL Scott Moura | UC Berkeley Control, Batts, DR December 4, 2013 | Slide 1 Source: Vaclav Smil Estimates from Energy Transitions Scott Moura | UC Berkeley Control, Batts, DR December 4, 2013 | Slide 2 Energy Initiatives Denmark 50% wind penetration by 2025 Brazil uses 86% renewables China's aggressive energy/carbon intensity reduction EV Everywhere SunShot Green Button Zero emissions vehicle (ZEV) 33% renewables by 2020 Go Solar California Scott Moura | UC Berkeley Control, Batts, DR December 4, 2013 | Slide 3 Energy Systems of Interest Energy storage Smart Grids (e.g., batteries) (e.g., demand response) Scott Moura | UC Berkeley Control, Batts, DR December 4, 2013 | Slide 4 Energy

130

Continuous review inventory models with a mixture of backorders and lost sales under fuzzy demand and different decision situations  

Science Conference Proceedings (OSTI)

In this paper, continuous review inventory models in which a fraction of demand is backordered and the remaining fraction is lost during the stock out period are considered under fuzzy demands. In order to find the optimal decision under different situations, ... Keywords: Continuous review inventory model, Defuzzification, Differential evolution algorithm, Fuzzy simulation, Possibilistic mean value

Lin Wang; Qing-Liang Fu; Yu-Rong Zeng

2012-03-01T23:59:59.000Z

131

Validation Test Report For The CRWMS Analysis and Logistics Visually Interactive Model Calvin Version 3.0, 10074-Vtr-3.0-00  

SciTech Connect

This report describes the tests performed to validate the CRWMS ''Analysis and Logistics Visually Interactive'' Model (CALVIN) Version 3.0 (V3.0) computer code (STN: 10074-3.0-00). To validate the code, a series of test cases was developed in the CALVIN V3.0 Validation Test Plan (CRWMS M&O 1999a) that exercises the principal calculation models and options of CALVIN V3.0. Twenty-five test cases were developed: 18 logistics test cases and 7 cost test cases. These cases test the features of CALVIN in a sequential manner, so that the validation of each test case is used to demonstrate the accuracy of the input to subsequent calculations. Where necessary, the test cases utilize reduced-size data tables to make the hand calculations used to verify the results more tractable, while still adequately testing the code's capabilities. Acceptance criteria, were established for the logistics and cost test cases in the Validation Test Plan (CRWMS M&O 1999a). The Logistics test cases were developed to test the following CALVIN calculation models: Spent nuclear fuel (SNF) and reactivity calculations; Options for altering reactor life; Adjustment of commercial SNF (CSNF) acceptance rates for fiscal year calculations and mid-year acceptance start; Fuel selection, transportation cask loading, and shipping to the Monitored Geologic Repository (MGR); Transportation cask shipping to and storage at an Interim Storage Facility (ISF); Reactor pool allocation options; and Disposal options at the MGR. Two types of cost test cases were developed: cases to validate the detailed transportation costs, and cases to validate the costs associated with the Civilian Radioactive Waste Management System (CRWMS) Management and Operating Contractor (M&O) and Regional Servicing Contractors (RSCs). For each test case, values calculated using Microsoft Excel 97 worksheets were compared to CALVIN V3.0 scenarios with the same input data and assumptions. All of the test case results compare with the CALVIN V3.0 results within the bounds of the acceptance criteria. Therefore, it is concluded that the CALVIN V3.0 calculation models and options tested in this report are validated.

S. Gillespie

2000-07-27T23:59:59.000Z

132

Examining Uncertainty in Demand Response Baseline Models and Variability in Automated Response to Dynamic Pricing  

Science Conference Proceedings (OSTI)

Controlling electric loads to deliver power system services presents a number of interesting challenges. For example, changes in electricity consumption of Commercial and Industrial (C&I) facilities are usually estimated using counterfactual baseline models, and model uncertainty makes it difficult to precisely quantify control responsiveness. Moreover, C&I facilities exhibit variability in their response. This paper seeks to understand baseline model error and demand-side variability in responses to open-loop control signals (i.e. dynamic prices). Using a regression-based baseline model, we define several Demand Response (DR) parameters, which characterize changes in electricity use on DR days, and then present a method for computing the error associated with DR parameter estimates. In addition to analyzing the magnitude of DR parameter error, we develop a metric to determine how much observed DR parameter variability is attributable to real event-to-event variability versus simply baseline model error. Using data from 38 C&I facilities that participated in an automated DR program in California, we find that DR parameter errors are large. For most facilities, observed DR parameter variability is likely explained by baseline model error, not real DR parameter variability; however, a number of facilities exhibit real DR parameter variability. In some cases, the aggregate population of C&I facilities exhibits real DR parameter variability, resulting in implications for the system operator with respect to both resource planning and system stability.

Mathieu, Johanna L.; Callaway, Duncan S.; Kiliccote, Sila

2011-08-15T23:59:59.000Z

133

Sluggish Responses of Prices and Inflation to Monetary Shocks in an Inventory Model of Money Demand  

E-Print Network (OSTI)

We examine the responses of prices and inflation to monetary shocks in an inventory-theoretic model of money demand. We show that the price level responds sluggishly to an exogenous increase in the money stock because the dynamics of households ’ money inventories leads to a partially offsetting endogenous reduction in velocity. We also show that inflation responds sluggishly to an exogenous increase in the nominal interest rate because changes in monetary policy affect the real interest rate. In a quantitative example, we show that this nominal sluggishness is substantial and persistent if inventories in the model are calibrated to match U.S. households ’ holdings of M2. I.

Fernando Alvarez; Andrew Atkeson; Chris Edmond

2008-01-01T23:59:59.000Z

134

Regression Models for Demand Reduction based on Cluster Analysis of Load Profiles  

E-Print Network (OSTI)

ESCRIPTIVE S TATISTICS Maximum Demand (kW) Num. of Obs. Meanrate and customer’s maximum demand. C’ i, t : a constant, Arate and customer’s maximum demand. The load sensitivity to

Kiliccote, Sila

2010-01-01T23:59:59.000Z

135

A supply-demand model for OPEC oil-pricing policies  

SciTech Connect

OPEC and its pricing policies have been subjected to constant international attention as well as criticism since 1973. Consumers find OPEC behavior irrational, while OPEC tries to justify its policies as rational and in accordance with the realities of the international oil market. The focus of this study is to contribute toward an analytical and empirical work on OPEC pricing behavior, and highlight the various factors believed to affect the future oil policies of OPEC member countries. After a survey of literature on the theoretical framework of world oil models in general, and OPEC models in particular, a linear econometric model for pricing OPEC oil is formulated which is a supply-demand equilibrium model comprising of supply, demand, and inflation-rate functions. Estimation of the behavioral equations are carried out by Ordinary and Two-Stage Least Square estimators. Econometric results from the estimation and simulation of the model seem to indicate that OPEC's pricing behavior is market-responsive and may best be explained by employing the theoretical framework of market-equilibrium condition.

Heiat, N.

1988-01-01T23:59:59.000Z

136

A new model and a computational study for Demand-wise Shared ...  

E-Print Network (OSTI)

pacity is dedicated to a particular demand, but shared within a demand. It combines .... Two cases have been considered: (i) exploration of the maximum.

137

GenCLOn: An ontology for city logistics  

Science Conference Proceedings (OSTI)

City logistics is a discipline specialized to cope with the sustainability problems encountered in urban freight transport. A key characteristic of it is the heterogeneity of the stakeholders involved. Besides the traditional logistics actors such as ... Keywords: Agent-based modeling, City logistics, Ontology

Nilesh Anand; Mengchang Yang; J. H. R. Van Duin; Lori Tavasszy

2012-11-01T23:59:59.000Z

138

Outsourcing Logistics in the Oil and Gas Industry  

E-Print Network (OSTI)

The supply chain challenges that the Oil and Gas industry faces in material logistics have enlarged in the last few decades owing to an increased hydro-carbon demand. Many reasons justify the challenges, such as exploration activities which have moved to remote locations, not only increasing distances from supply houses and refineries but also escalating logistics costs. Mammoth costs of material unavailability drive the inefficiencies largely. The objectives of the study is to discover the logistics needs of oil and gas companies, the motivation, benefits and the requirements of outsourcing logistics. The study aims to identify the material supply chain inefficiencies in the industry and proposes solutions to solve them. In this study, Oil and Gas industry’s outsourcing practices in logistics are analyzed along with the trends of the third party logistics companies serving the industry. The participants of this study are from different companies in the Oil and Gas industry dealing with supply chain operations.

Herrera, Cristina 1988-

2012-05-01T23:59:59.000Z

139

Smart Finite State Devices: A Modeling Framework for Demand Response Technologies  

E-Print Network (OSTI)

We introduce and analyze Markov Decision Process (MDP) machines to model individual devices which are expected to participate in future demand-response markets on distribution grids. We differentiate devices into the following four types: (a) optional loads that can be shed, e.g. light dimming; (b) deferrable loads that can be delayed, e.g. dishwashers; (c) controllable loads with inertia, e.g. thermostatically-controlled loads, whose task is to maintain an auxiliary characteristic (temperature) within pre-defined margins; and (d) storage devices that can alternate between charging and generating. Our analysis of the devices seeks to find their optimal price-taking control strategy under a given stochastic model of the distribution market.

Turitsyn, Konstantin; Ananyev, Maxim; Chertkov, Michael

2011-01-01T23:59:59.000Z

140

What belongs where? Variable selection for zero-inflated count models with an application to the demand for health care  

Science Conference Proceedings (OSTI)

This paper develops a Bayesian spike and slab model for zero-inflated count models which are commonly used in health economics. We account for model uncertainty and allow for model averaging in situations with many potential regressors. The proposed ... Keywords: Bayesian, C11, C25, Count data, Demand for health care, I11, Model averaging, Model uncertainty, Spike and slab model, Zero-inflation

Markus Jochmann

2013-10-01T23:59:59.000Z

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


141

Regression Models for Demand Reduction based on Cluster Analysis of Load Profiles  

E-Print Network (OSTI)

Demand Response Research Center, Lawrence Berkeley National Laboratory, One Cyclotron Road, MS: 90- 3111, Berkeley, CA 94720 USA.

Kiliccote, Sila

2010-01-01T23:59:59.000Z

142

Statistical analysis of what drives industrial energy demand: Volume III of the PURHAPS model documentation  

Science Conference Proceedings (OSTI)

The overall price of energy has far less direct effect on industrial demand than conventional models, such as the Jorgenson translog model, have indicated. Much of what appears to be conservation in recent years can be explained as the result of structural changes (e.g., less steel production), electrification, and a slowdown in the long-term trend towards more use of energy relative to other factors of production. This report documents these findings and the other findings from the statistical analysis used in developing the PURchased Heat And Power System, as used in producing the 1982 Annual Energy Outlook forecasts. This report is intended partly to convey these findings to substantive energy experts and energy policy analysts; it is also intended to fulfill EIA requirements for model documentation. Volume I of this series documents the full mathematical specification of the model, including accounting identites and benchmarks; Volume II documents the data used both in the estimation and in the model. Appendix B of this report provides a purely historical breakdown of actual changes in oil and electricity use from 1974 to 1981, showing what changes are due to general economic growth, improved general productivity, etc. preliminary work for the 1983 Annual Energy Outlook is discussed in general terms.

Werbos, P.J.

1983-12-01T23:59:59.000Z

143

ANN-based residential water end-use demand forecasting model  

Science Conference Proceedings (OSTI)

Bottom-up urban water demand forecasting based on empirical data for individual water end uses or micro-components (e.g., toilet, shower, etc.) for different households of varying characteristics is undoubtedly superior to top-down estimates originating ... Keywords: Artificial neural network, Residential water demand forecasting, Water demand management, Water end use, Water micro-component

Christopher Bennett; Rodney A. Stewart; Cara D. Beal

2013-03-01T23:59:59.000Z

144

A Comparison between Raw Ensemble Output, (Modified) Bayesian Model Averaging, and Extended Logistic Regression Using ECMWF Ensemble Precipitation Reforecasts  

Science Conference Proceedings (OSTI)

Using a 20-yr ECMWF ensemble reforecast dataset of total precipitation and a 20-yr dataset of a dense precipitation observation network in the Netherlands, a comparison is made between the raw ensemble output, Bayesian model averaging (BMA), and ...

Maurice J. Schmeits; Kees J. Kok

2010-11-01T23:59:59.000Z

145

Demand Management Demonstration Project, Stage 5: development of industrial load simulation model. Executive summary. Final report  

SciTech Connect

The purpose of this project was to design, develop, test and document a computer simulation model of electric utility generating costs required to meet industrial power demands and the effects of utility load management on these generating costs. The results showed that the model developed is a well conceived load management testing, marginal costing tool. What if situations can be readily tested to determine their impact on system profile and short run marginal costs. The terms unshaped and shaped refer to customers or system use patterns before and after some load management technique was tested. The total flexibility of the model is only apparent after the user has studied test runs in detail. Hourly marginal costs reveal many unexpected changes as a result of shaping loads. Other unexpected changes due to varying economic dispatch schedules while shaping, illustrate the unprecedental latitude for the user to explore optimum generation and load management combinations. The general concept of the model is depicted in the flow chart on the next page.

1977-04-01T23:59:59.000Z

146

A unified architectural model for on-demand user-centric communications  

E-Print Network (OSTI)

The rapid growth of networking technologies has drastically changed the way we communicate and enabled a wide range of communication applications. However, these applications have been conceived, designed, and developed separately with little or no connection to each other, resulting in a fragmented and incompatible set of technologies and products. Building new communication applications requires a lengthy and costly development cycle, which severely limits the pace of innovation. Current applications are also typically incapable of responding to changes in user communication needs as well as changing network infrastructure and device technology. In this article, we address these issues and present the Unified Communication Model (UCM), a new and user-centric approach for conceiving, generating, and delivering communication applications on-demand. We also introduce a prototype design and implementation of UCM and discuss future research directions toward realizing next generation communication applications. 1.

Yi Deng; S. Masoud Sadjadi; Peter Clarke; Chi Zhang; Vagelis Hristidis; Raju Rangaswami; Nagarajan Prabakar

2005-01-01T23:59:59.000Z

147

Ownership transfer for non-federate object and time management in developing an hla compliant logistics model.  

Science Conference Proceedings (OSTI)

A seaport simulation model, PORTSIM, has been developed for the Department of Defense (DOD) at Argonne National Laboratory. PORTSIM simulates the detailed processes of cargo loading and unloading in a seaport and provides throughput capability, resource utilization, and other important information on the bottlenecks in a seaport operation, which are crucial data in determining troop and equipment deployment capability. There are two key problems to solve in developing the HLA-compliant PORTSIM model. The first is the cargo object ownership transfer problem. In PORTSIM, cargo items, e.g. vehicles, containers, and pallets, are objects having asset attributes. Cargo comes to a seaport for loading or unloading. The ownership of a cargo object transfers from its carrier to the port and then from the port to a new carrier. Each owner of the cargo object is responsible for publishing and updating the attributes of the cargo object when it has the ownership. This creates a unique situation in developing the PORTSIM federate object model, that is, the ownership of the object instead of the attributes needs to be changed in handling the cargo object in the PORTSIM federate. The ownership management service provided by the current RTI does not directly address this issue. The second is the time management issue. PORTSIM is an event-driven simulation that models seaport operations over time. To make PORTSIM HLA compliant, time management must be addressed to allow for synchronization with other simulation models. This paper attempts to address these two issues and methodologies developed for solving these two problems.

Li, Z.

1998-01-12T23:59:59.000Z

148

Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

page intentionally left blank page intentionally left blank 69 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2011 Transportation Demand Module The NEMS Transportation Demand Module estimates transportation energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), buses, freight and passenger aircraft, freight and passenger rail, freight shipping, and miscellaneous

149

Integrated framework for reverse logistics  

Science Conference Proceedings (OSTI)

Although reverse logistics has been disregarded for many years, pressures from both environmental awareness and business sustainability have risen. Reverse logistical activities include return, repair and recycle products. Traditionally, since the information ... Keywords: gent-based system, information transparency, reverse logistics

Heng-Li Yang; Chen-Shu Wang

2007-06-01T23:59:59.000Z

150

Demand Response  

NLE Websites -- All DOE Office Websites (Extended Search)

Peak load diagram Demand Response Demand Response (DR) is a set of time-dependent activities that reduce or shift electricity use to improve electric grid reliability, manage...

151

Demand Response  

NLE Websites -- All DOE Office Websites (Extended Search)

Peak load diagram Demand Response Demand response (DR) is a set of time-dependent activities that reduce or shift electricity use to improve electric grid reliability, manage...

152

The dynamics of electricity demand and supply in the low voltage distribution grid: a model study:.  

E-Print Network (OSTI)

??In this thesis a simulation study is executed that analyses how new developments of household electricity demand and decentralised electricity generation affect the low voltage… (more)

Van Zoest, P.L.A.

2013-01-01T23:59:59.000Z

153

A Dynamic household Alternative-fuel Vehicle Demand Model Using Stated and Revealed Transaction Information  

E-Print Network (OSTI)

Potential Demand for Electric Cars”, Journal of Economrtricsand one large car) and one mini electric car. The two modelsscenarios: (i) a subcompact electric car is introduced to

Sheng, Hongyan

1999-01-01T23:59:59.000Z

154

A Multi-Objective Production Inventory Model with Backorder for Fuzzy Random Demand Under Flexibility and Reliability  

Science Conference Proceedings (OSTI)

In this paper, an Economic Production Quantity (EPQ) model is developed with flexibility and reliability consideration of production process in an imprecise and uncertain mixed environment. The model has incorporated fuzzy random demand, an imprecise ... Keywords: Flexibility, Fuzzy random variable, Imprecise preparation time, Interval arithmetic, Reliability

Nita H. Shah; Hardik Soni

2011-12-01T23:59:59.000Z

155

A Unit Commitment Model with Demand Response for the Integration of Renewable Energies  

E-Print Network (OSTI)

The output of renewable energy fluctuates significantly depending on weather conditions. We develop a unit commitment model to analyze requirements of the forecast output and its error for renewable energies. Our model obtains the time series for the operational state of thermal power plants that would maximize the profits of an electric power utility by taking into account both the forecast of output its error for renewable energies and the demand response of consumers. We consider a power system consisting of thermal power plants, photovoltaic systems (PV), and wind farms and analyze the effect of the forecast error on the operation cost and reserves. We confirm that the operation cost was increases with the forecast error. The effect of a sudden decrease in wind power is also analyzed. More thermal power plants need to be operated to generate power to absorb this sudden decrease in wind power. The increase in the number of operating thermal power plants within a short period does not affect the total opera...

Ikeda, Yuichi; Kataoka, Kazuto; Ogimoto, Kazuhiko

2011-01-01T23:59:59.000Z

156

A Model for Estimating Demand for Irrigation Water on the Texas High Plains  

E-Print Network (OSTI)

With rapidly changing conditions in production agriculture, the need for highly flexible and quickly applicable methods of analysis is emphasized. The purpose of this study was to develop such a model for a homogeneous production region in the Texas High Plains. A linear programming model was constructed whereby crop or input prices are readily adjustable. In addition, limitations on quantities of inputs available can easily be evaluated. The model contains cotton, grain sorghum, corn, wheat and soybeans. Inputs that can be evaluated include irrigation water, natural gas, diesel, nitrogen fertilizer and herbicides. The primary focus of this work was to estimate the demand for irrigation water in the study area. The model was applied using alternative crop prices and input prices. Assuming average crop prices, current input prices and only variable costs of production, as the price of water was increased wheat shifted from irrigated to dryland production, then grain sorghum, cotton, corn and soybeans, in that order. The price of water was $71.75 per acre foot plus current pumping cost when all land shifted to dryland production. The same analysis, except variable and fixed costs both included, gave similar results relative to the sequence of crops that shift to dryland production as the price of water was increased. However, the shifts occurred at much lower water prices; i.e., at $24.47 per acre foot plus current pumping costs, all land had shifted to dryland production. This suggests that over the long run, irrigation in the Texas High Plains is quite sensitive to the price of energy used in pumping water. Further, there are strong implications relative to farmer's "ability to pay" for water imported to the High Plains from other regions. In this report, several scenarios including low, high and average crop prices and average and high input prices were evaluated.

Condra, G. D.; Lacewell, R. D.; Sprott, J. M.; Adams, B. M.

1975-05-01T23:59:59.000Z

157

A multi-sensor based occupancy estimation model for supporting demand driven HVAC operations  

Science Conference Proceedings (OSTI)

Heating, ventilation, and air conditioning (HVAC) is a major energy consumer in buildings, and implementing demand driven HVAC operations is a way to reduce HVAC related energy consumption. This relies on the availability of occupancy information, which ... Keywords: HVAC, building energy consumption, demand driven, non-intrusive sensor, occupancy estimation

Zheng Yang; Nan Li; Burcin Becerik-Gerber; Michael Orosz

2012-03-01T23:59:59.000Z

158

Import Demand of Crude Oil and Economic Growth in China: Evidence from the ARDL Model  

Science Conference Proceedings (OSTI)

In order to quantify the demand elasticity of China's imported crude oil, a long-run stable relationship is estimated among the crude oil import, income and crude oil prices by the autoregressive distributed lag (ARDL) bound testing approach over the ... Keywords: ARDL bound test, price elasticity, income elasticity, crude oil demand

Wei Sun; Zhongying Qi; Niannian Jia

2010-08-01T23:59:59.000Z

159

The addition of a US Rare Earth Element (REE) supply-demand model improves the characterization and scope of the United States Department of Energy's effort to forecast US REE Supply and Demand  

E-Print Network (OSTI)

This paper presents the development of a new US Rare Earth Element (REE) Supply-Demand Model for the explicit forecast of US REE supply and demand in the 2010 to 2025 time period. In the 2010 Department of Energy (DOE) ...

Mancco, Richard

2012-01-01T23:59:59.000Z

160

Fuzzy Analytical Hierarchy Process Applied to Port Logistics Efficiency Evaluation  

Science Conference Proceedings (OSTI)

This paper aims to construct analysis model of port logistics arrangement using Delphi and AHP, furthermore, establishment of fuzzy theory and analytical hierarchy process model and factor set. And calculate every index weight with the weighting method—G1 ... Keywords: Mathematical model, Fuzzy Analytical Hierarchy Process, Port Logistics, Efficiency Evaluation

Xuelian Liu

2010-05-01T23:59:59.000Z

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


161

Modeling the Capacity and Emissions Impacts of Reduced Electricity Demand. Part 1. Methodology and Preliminary Results.  

E-Print Network (OSTI)

demand changes impact the electric power sector. Figure 2:for electricity on the electric power sector as a whole. Thedemand changes impact the electric power sector. We refer to

Coughlin, Katie

2013-01-01T23:59:59.000Z

162

Incorporating endogenous demand dynamics into long-term capacity expansion power system models for Developing countries  

E-Print Network (OSTI)

This research develops a novel approach to long-term power system capacity expansion planning for developing countries by incorporating endogenous demand dynamics resulting from social processes of technology adoption. ...

Jordan, Rhonda LeNai

2013-01-01T23:59:59.000Z

163

Addressing Energy Demand through Demand Response: International...  

NLE Websites -- All DOE Office Websites (Extended Search)

Energy Demand through Demand Response: International Experiences and Practices Title Addressing Energy Demand through Demand Response: International Experiences and Practices...

164

Addressing Energy Demand through Demand Response: International...  

NLE Websites -- All DOE Office Websites (Extended Search)

Addressing Energy Demand through Demand Response: International Experiences and Practices Title Addressing Energy Demand through Demand Response: International Experiences and...

165

Oxygenate Supply/Demand Balances in the Short-Term Integrated Forecasting Model (Released in the STEO March 1998)  

Reports and Publications (EIA)

The blending of oxygenates, such as fuel ethanol and methyl tertiary butyl ether (MTBE), into motor gasoline has increased dramatically in the last few years because of the oxygenated and reformulated gasoline programs. Because of the significant role oxygenates now have in petroleum product markets, the Short-Term Integrated Forecasting System (STIFS) was revised to include supply and demand balances for fuel ethanol and MTBE. The STIFS model is used for producing forecasts in the Short-Term Energy Outlook. A review of the historical data sources and forecasting methodology for oxygenate production, imports, inventories, and demand is presented in this report.

Information Center

1998-03-01T23:59:59.000Z

166

Estimating Demand Response Load Impacts: Evaluation of Baseline Load Models for Non-Residential Buildings in California  

E-Print Network (OSTI)

Protocols  for  Demand  Response  Load  Impacts  Estimates, Potter  2006.     The  Demand  Response Baseline, v.1.75.   Assessment  of  Demand  Response  and  Advanced  Metering, 

Coughlin, Katie; Piette, Mary Ann; Goldman, Charles; Kiliccote, Sila

2008-01-01T23:59:59.000Z

167

Residential Sector Demand Module  

Reports and Publications (EIA)

Model Documentation - Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

Owen Comstock

2012-12-19T23:59:59.000Z

168

Industrial Demand Module  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Module. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

Kelly Perl

2013-05-14T23:59:59.000Z

169

Industrial Demand Module  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Module. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

Kelly Perl

2013-09-30T23:59:59.000Z

170

Residential Sector Demand Module  

Reports and Publications (EIA)

Model Documentation - Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

Owen Comstock

2013-11-05T23:59:59.000Z

171

Structuring energy supply and demand networks in a general equilibrium model to simulate global warming control strategies  

Science Conference Proceedings (OSTI)

Global warming control strategies which mandate stringent caps on emissions of greenhouse forcing gases can substantially alter a country's demand, production, and imports of energy products. Although there is a large degree of uncertainty when attempting to estimate the potential impact of these strategies, insights into the problem can be acquired through computer model simulations. This paper presents one method of structuring a general equilibrium model, the ENergy and Power Evaluation Program/Global Climate Change (ENPEP/GCC), to simulate changes in a country's energy supply and demand balance in response to global warming control strategies. The equilibrium model presented in this study is based on the principle of decomposition, whereby a large complex problem is divided into a number of smaller submodules. Submodules simulate energy activities and conversion processes such as electricity production. These submodules are linked together to form an energy supply and demand network. Linkages identify energy and fuel flows among various activities. Since global warming control strategies can have wide reaching effects, a complex network was constructed. The network represents all energy production, conversion, transportation, distribution, and utilization activities. The structure of the network depicts interdependencies within and across economic sectors and was constructed such that energy prices and demand responses can be simulated. Global warming control alternatives represented in the network include: (1) conservation measures through increased efficiency; and (2) substitution of fuels that have high greenhouse gas emission rates with fuels that have lower emission rates. 6 refs., 4 figs., 4 tabs.

Hamilton, S.; Veselka, T.D.; Cirillo, R.R.

1991-01-01T23:59:59.000Z

172

Structural changes between models of fossil-fuel demand by steam-electric power plants  

SciTech Connect

A consumption function for multi-fuel steam-electric power plants is used to investigate fossil-fuel demand behavior. The input consumption equations for a plant's primary and alternate fossil fuels are derived by Shepard's lemma from a generalized Cobb-Douglas cost function reflecting average variable cost minimization constrained by technology and the demand for electricity. These equations are estimated by primary and alternate fuel subsets with ordinary least squares and seemingly unrelated regression techniques for 1974, 1977, and 1980. The results of the regression analysis show the importance of consumer demand in the fossil fuel consumption decision; it has the only significant parameter in all of the estimated equations. The estimated own- and cross-price elasticities are small, when they are statistically significant. The results for the primary fuel equations are better than those for the alternate fuel equations in all of the fuel pair subsets.

Gerring, L.F.

1984-01-01T23:59:59.000Z

173

Economic Modeling of Mid-Term Gas Demand and Electric Generation Capacity Trends  

Science Conference Proceedings (OSTI)

The U.S. power sector natural gas use over the next 10 to 20 years is a topic of significant uncertainty and debate. The industry expects the power sector to be the principal source of growth in national gas demand in the short run; and the manner in which it drives demand and affects the market over the "mid term," to 2020-2030, is an important consideration for planners in both the electric and gas industries. With abundant, relatively low-priced supplies, gas-fired generation can be a strong competito...

2009-12-22T23:59:59.000Z

174

Variable-response model of electricity demand by time of day: Results of a Wisconsin pricing experiment: Final report  

Science Conference Proceedings (OSTI)

Observationally alike households may differ in demand parameters and thus in economic quantities that are functions of those parameters. We have proposed a methodology for dealing with this variation. Estimation of both translog and CES versions of the model with data from the Wisconsin Electricity Pricing Experiment revealed considerable variation among households in time-of-day electricity consumption demand parameters for both summer and winter seasons and for several different definitions of the peak period. Observed household characteristics explained only a small share of total household differences, but permanent household differences dominated month-to-month variation in either expenditure shares or log consumption ratios in most cases. Permanent differences among households are important relative to total variation, including transitory month-to-month variation. We calculated various economic variables from the demand parameters, including the partial elasticity of substitution, compensated and uncompensated elasticities, and a measure of electricity expenditure under peak load pricing required to maintain the utility level under flat rate pricing relative to the flat rate expenditure. Because these are nonlinear functions of the household demand parameters, the mean parameter value over households with different demand parameters may be substantially different from the value of the function at mean values, under the representative household paradigm. For time-of-day electricity demand, variation among households is significant but small relative to mean parameter values. Therefore, controlling for the effect of household variation makes little difference in these mean calculations, but it does imply substantial variation among households in the welfare implications (and elasticities of response) of the introduction of time-of-day pricing. 25 refs., 12 tabs.

Lillard, L.

1987-06-01T23:59:59.000Z

175

Information Management for Reverse Logistics  

E-Print Network (OSTI)

Introduction In this chapter, we examine how Information and Communication Technologies (ICT) are being used to support reverse logistics. In this respet, this chapter does not follow a quantitative approach as the rest of the book. Nonetheless, the topics covered in this section outline how ICT systems enable and support the quantitative approaches presented in other chapters of this book. Furthermore, this chapter provides a roadmap to the reader about what aspects of reverse logistics are implemented and what remains to be addressed in the future. Most ICT systems for reverse logistics have been developed to address needs in a specific sector (i.e. decision making on di#erent recovery options of returns, designing a product for optimal end of use recovery, etc.) or to cover the reverse logistics requirements of a particular company. Thus, in our attempt to present this area systematically we need to develop a framework of reference first. For that reason, we go back to the essent

Angelika Kokkinaki; Rob Zuidwijk

2002-01-01T23:59:59.000Z

176

Modeling the Capacity and Emissions Impacts of Reduced Electricity Demand. Part 1. Methodology and Preliminary Results.  

E-Print Network (OSTI)

Modeling the Capacity and Emissions Impacts of Reducedpurposes. Modeling the Capacity and Emissions Impacts ofFigure 2: Comparison of capacity projections from AEO2011

Coughlin, Katie

2013-01-01T23:59:59.000Z

177

Efficient approximate leave-one-out cross-validation for kernel logistic regression  

Science Conference Proceedings (OSTI)

Kernel logistic regression (KLR) is the kernel learning method best suited to binary pattern recognition problems where estimates of a-posteriori probability of class membership are required. Such problems occur frequently ... Keywords: Kernel logistic regression, Model selection

Gavin C. Cawley; Nicola L. Talbot

2008-06-01T23:59:59.000Z

178

Development of a commercial-sector data base and forecasting model for electricity usage and demand. Volume I. Preliminary model specification. [Description of subprograms BEHAV, DEMAND, ECON, ENER, and INGEN  

SciTech Connect

This is the first of twelve major technical reports under the Commission's contract with Hittman Associates. The contract will lead to the development of a data base on commercial space, and the development of a model to forecast electricity usage and demand. This report presents a preliminary specification of the model to be developed. The model being developed combines econometric and engineering approaches, and consists of five subprograms and an overall executing program. The first subprogram forecasts the stock of commercial space, based on employment data and other economic inputs. It also distinguishes among various types of commercial space, and breaks the commercial space into segments according to fuels for various end uses, such as heating, cooling, etc. The second subprogram uses detailed building-survey data to specify a typical, or characteristic building for each unique type of floorspace considered in the study. The third subprogram calculates monthly electricity usage for the typical buildings specified, using standard engineering techniques, and then scales up the electricity use for each building type according to the amount of space, of that type, in the entire building stock. The fourth subprogram performs a similar function, but produces hourly electricity demands, rather than monthly electricity usage. The fifth, and final subprogram adjusts the energy usage and demand values calculated to simulate the impact of certain economic conditions or policy measures. The report presents a flow chart for each subprogram, and a table of inputs and outputs required for each. The logic, structure, flow, and information transfer of each is described.

1980-02-01T23:59:59.000Z

179

NERSC/DOE HEP Requirements Workshop Logistics  

NLE Websites -- All DOE Office Websites (Extended Search)

at NERSC HPC Requirements Reviews Requirements for Science: Target 2014 High Energy Physics (HEP) Logistics Workshop Logistics Workshop Location Hilton Washington...

180

New generation of software? Modeling of energy demands for residential ventilation with HTML interface  

SciTech Connect

The paper presents an interactive on-line package for calculation of energy and cost demands for residential infiltration and ventilation, with input and output data entry through a web browser. This is a unique tool. It represents a new kind of approach to developing software employing user (client) and server (package provider) computers. The main program, servicing {open_quotes}intelligent{close_quotes} CGI (Common Gateway Interface) calls, resides on the server and dynamically handles the whole package performance and the procedure of calculations. The {open_quotes}computing engine{close_quotes} consists of two parts: RESVENT - the previously existing program for ventilation calculations and ECONOMICS - for heating and cooling system energy and cost calculations. The user interface is designed in such a way, that it allows simultaneous access by many users from all over the world.

Forowicz, T.

1997-06-01T23:59:59.000Z

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


181

Model of medical supply demand and astronaut health for long-duration human space flight  

E-Print Network (OSTI)

The medical care of space crews is the primary limiting factor in the achievement of long-duration space missions. (Nicogossian 2003) The goal of this thesis was to develop a model of long-duration human space flight ...

Assad, Albert

2009-01-01T23:59:59.000Z

182

Ergodic distribution for a fuzzy inventory model of type (s,S) with gamma distributed demands  

Science Conference Proceedings (OSTI)

In this study, a stochastic process (X(t)), which describes a fuzzy inventory model of type (s,S) is considered. Under some weak assumptions, the ergodic distribution of the process X(t) is expressed by a fuzzy renewal function U(x). Then, membership ... Keywords: Ergodic distribution, Fuzzy inventory model of type (s,S), Fuzzy renewal function, Gamma distribution with fuzzy parameter

Tahir Khaniyev; I. Burhan Turksen; Fikri Gokpinar; Basak Gever

2013-02-01T23:59:59.000Z

183

Acquiring logistics process intelligence: Methodology and an application for a Chinese bulk port  

Science Conference Proceedings (OSTI)

The processes of logistics service providers are considered as highly human-centric, flexible and complex. Deviations from the standard operating procedures as described in the designed process models, are not uncommon and may result in significant uncertainties. ... Keywords: Knowledge discovery, Logistics process, Logistics process intelligence, Process mining

Ying Wang, Filip Caron, Jan Vanthienen, Lei Huang, Yi Guo

2014-01-01T23:59:59.000Z

184

Commercial Sector Demand Module  

Reports and Publications (EIA)

Documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components.

Kevin Jarzomski

2012-11-15T23:59:59.000Z

185

Commercial Sector Demand Module  

Reports and Publications (EIA)

Documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components.

Kevin Jarzomski

2013-10-10T23:59:59.000Z

186

Logistics  

NLE Websites -- All DOE Office Websites (Extended Search)

Location and Schedule Large Scale Production Computing and Storage Requirements for Basic Energy Sciences: Target 2017 BES ASCR NERSC Requirements Review The day-and-a-half...

187

Transportation Energy Futures Series: Freight Transportation Demand: Energy-Efficient Scenarios for a Low-Carbon Future  

SciTech Connect

Freight transportation demand is projected to grow to 27.5 billion tons in 2040, and to nearly 30.2 billion tons in 2050. This report describes the current and future demand for freight transportation in terms of tons and ton-miles of commodities moved by truck, rail, water, pipeline, and air freight carriers. It outlines the economic, logistics, transportation, and policy and regulatory factors that shape freight demand, the trends and 2050 outlook for these factors, and their anticipated effect on freight demand. After describing federal policy actions that could influence future freight demand, the report then summarizes the capabilities of available analytical models for forecasting freight demand. This is one in a series of reports produced as a result of the Transportation Energy Futures project, a Department of Energy-sponsored multi-agency effort to pinpoint underexplored strategies for reducing GHGs and petroleum dependence related to transportation.

Grenzeback, L. R.; Brown, A.; Fischer, M. J.; Hutson, N.; Lamm, C. R.; Pei, Y. L.; Vimmerstedt, L.; Vyas, A. D.; Winebrake, J. J.

2013-03-01T23:59:59.000Z

188

Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

2 2 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 non-manufacturing industries. The manufacturing industries are further subdivided into the energy- intensive manufacturing industries and non-energy-intensive manufacturing industries (Table 6.1). The manufacturing industries are modeled through the use of a detailed process-flow or end-use accounting procedure, whereas the non- manufacturing industries are modeled with substantially less detail. The petroleum refining industry is not included in the Industrial Demand Module, as it is simulated separately in the Petroleum Market Module of NEMS. The Industrial Demand Module calculates energy consumption for the four Census Regions (see Figure 5) and disaggregates the energy consumption

189

demand | OpenEI  

Open Energy Info (EERE)

demand demand Dataset Summary Description This dataset contains hourly load profile data for 16 commercial building types (based off the DOE commercial reference building models) and residential buildings (based off the Building America House Simulation Protocols). This dataset also includes the Residential Energy Consumption Survey (RECS) for statistical references of building types by location. Source Commercial and Residential Reference Building Models Date Released April 18th, 2013 (9 months ago) Date Updated July 02nd, 2013 (7 months ago) Keywords building building demand building load Commercial data demand Energy Consumption energy data hourly kWh load profiles Residential Data Quality Metrics Level of Review Some Review Comment Temporal and Spatial Coverage Frequency Annually

190

Comparison of Demand Response Performance with an EnergyPlus Model in a Low Energy Campus Building  

NLE Websites -- All DOE Office Websites (Extended Search)

4E 4E Comparison of Demand Response Performance with an EnergyPlus Model in a Low Energy Campus Building J.H. Dudley, D. Black, M. Apte, M.A. Piette Lawrence Berkeley National Laboratory P. Berkeley University of California, Berkeley May 2010 Presented at the 2010 ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, August 15-20, 2010, and published in the Proceedings DISCLAIMER This document was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor The Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information,

191

Reserved or On-Demand Instances? A Revenue Maximization Model for Cloud Providers  

E-Print Network (OSTI)

We examine the problem of managing a server farm in a way that attempts to maximize the net revenue earned by a cloud provider by renting servers to customers according to a typical Platform-as-a-Service model. The Cloud provider offers its resources to two classes of customers: `premium' and `basic'. Premium customers pay upfront fees to reserve servers for a specified period of time (e.g. a year). Premium customers can submit jobs for their reserved servers at any time and pay a fee for the server-hours they use. The provider is liable to pay a penalty every time a `premium' job can not be executed due to lack of resources. On the other hand, `basic' customers are served on a best-effort basis, and pay a server-hour fee that may be higher than the one paid by premium customers. The provider incurs energy costs when running servers. Hence, it has an incentive to turn off idle servers. The question of how to choose the number of servers to allocate to each pool (basic and premium) is answered by analyzing a s...

Mazzucco, Michele

2011-01-01T23:59:59.000Z

192

CliCrop: a Crop Water-Stress and Irrigation Demand Model for an Integrated Global Assessment Model Approach  

E-Print Network (OSTI)

This paper describes the use of the CliCrop model in the context of climate change general assessment

Fant, C.A.

193

Transportation Demand This  

U.S. Energy Information Administration (EIA) Indexed Site

Transportation Demand Transportation Demand This page inTenTionally lefT blank 75 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Transportation Demand Module The NEMS Transportation Demand Module estimates transportation energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific and associated technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), buses, freight and passenger aircraft, freight

194

Fusion Energy Sciences Review Meeting Logistics  

NLE Websites -- All DOE Office Websites (Extended Search)

Logistics Logistics Location and Schedule The day-and-a-half workshop will be held all day Tuesday, March 19 and on the morning of Wednesday, March 20, 2013. Hotel The hotel will...

195

Commoditization of the third party logistics industry  

E-Print Network (OSTI)

Third party logistics companies in the US emerged in the 1980s and have been providing valuable service for companies willing to outsource logistics. Since then the industry has been growing substantially both in terms ...

Manatayev, Yerlan Yergalievich, 1980-

2004-01-01T23:59:59.000Z

196

An Academic development model for university and technikon students : meeting the demands of the 21st century.  

E-Print Network (OSTI)

??The demands of a rapidly changing future on learners of Higher Education Institutions who need to be effectively employed, necessitate that these institutions become responsive… (more)

Celliers, Mariana

2007-01-01T23:59:59.000Z

197

Demand Response  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Assessment for Eastern Interconnection Youngsun Baek, Stanton W. Hadley, Rocio Martinez, Gbadebo Oladosu, Alexander M. Smith, Fran Li, Paul Leiby and Russell Lee Prepared for FY12 DOE-CERTS Transmission Reliability R&D Internal Program Review September 20, 2012 2 Managed by UT-Battelle for the U.S. Department of Energy DOE National Laboratory Studies Funded to Support FOA 63 * DOE set aside $20 million from transmission funding for national laboratory studies. * DOE identified four areas of interest: 1. Transmission Reliability 2. Demand Side Issues 3. Water and Energy 4. Other Topics * Argonne, NREL, and ORNL support for EIPC/SSC/EISPC and the EISPC Energy Zone is funded through Area 4. * Area 2 covers LBNL and NREL work in WECC and

198

Robust Dynamic Traffic Assignment under Demand and Capacity Uncertainty  

E-Print Network (OSTI)

Assignment under Demand and Capacity Uncertainty ? Giuseppeworst-case sce- nario of demand and capacity con?gurations.uncertain demands and capacities are modeled as unknown-but-

Calafiore, Giuseppe; El Ghaoui, Laurent

2008-01-01T23:59:59.000Z

199

Estimating Demand Response Load Impacts: Evaluation of BaselineLoad Models for Non-Residential Buildings in California  

SciTech Connect

Both Federal and California state policymakers areincreasingly interested in developing more standardized and consistentapproaches to estimate and verify the load impacts of demand responseprograms and dynamic pricing tariffs. This study describes a statisticalanalysis of the performance of different models used to calculate thebaseline electric load for commercial buildings participating in ademand-response (DR) program, with emphasis onthe importance of weathereffects. During a DR event, a variety of adjustments may be made tobuilding operation, with the goal of reducing the building peak electricload. In order to determine the actual peak load reduction, an estimateof what the load would have been on the day of the event without any DRactions is needed. This baseline load profile (BLP) is key to accuratelyassessing the load impacts from event-based DR programs and may alsoimpact payment settlements for certain types of DR programs. We testedseven baseline models on a sample of 33 buildings located in California.These models can be loosely categorized into two groups: (1) averagingmethods, which use some linear combination of hourly load values fromprevious days to predict the load on the event, and (2) explicit weathermodels, which use a formula based on local hourly temperature to predictthe load. The models were tested both with and without morningadjustments, which use data from the day of the event to adjust theestimated BLP up or down.Key findings from this study are: - The accuracyof the BLP model currently used by California utilities to estimate loadreductions in several DR programs (i.e., hourly usage in highest 3 out of10 previous days) could be improved substantially if a morning adjustmentfactor were applied for weather-sensitive commercial and institutionalbuildings. - Applying a morning adjustment factor significantly reducesthe bias and improves the accuracy of all BLP models examined in oursample of buildings. - For buildings with low load variability, all BLPmodels perform reasonably well in accuracy. - For customer accounts withhighly variable loads, we found that no BLP model produced satisfactoryresults, although averaging methods perform best in accuracy (but notbias). These types of customers are difficult to characterize withstandard BLP models that rely on historic loads and weather data.Implications of these results for DR program administrators andpolicymakersare: - Most DR programs apply similar DR BLP methods tocommercial and industrial sector customers. The results of our study whencombined with other recent studies (Quantum 2004 and 2006, Buege et al.,2006) suggests that DR program administrators should have flexibility andmultiple options for suggesting the most appropriate BLP method forspecific types of customers.

Coughlin, Katie; Piette, Mary Ann; Goldman, Charles; Kiliccote,Sila

2008-01-01T23:59:59.000Z

200

Research on operation and management of railway transport of dangerous goods in third-party logistics enterprises  

Science Conference Proceedings (OSTI)

With China's rapid economic development, the demand for railway transportation of dangerous chemicals is getting stronger and stronger. Consequently, the construction of chemical logistics parks has become hotter than ever. This paper is aimed at describing ... Keywords: qualification management, safety management, the third-party logistics, transport of dangerous goods, vehicles management

Xin Li; Yue-fang Yang

2012-09-01T23:59:59.000Z

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


201

Logistical and transportation infrastructure in Asia : potential for growth and development to support increasing trade with Europe  

E-Print Network (OSTI)

This thesis examines the implications of the rapid growth in demand for trade between Europe and Asia for the existing transportation network and logistical infrastructure. In general terms, technologies need to improve ...

Deonás, Nikolaos, 1978-

2004-01-01T23:59:59.000Z

202

Testing linearity in a cointegrating STR model for the money demand function: International evidence from G-7 countries  

Science Conference Proceedings (OSTI)

The motivation behind this paper is to re-investigate the stability of the long-run money demand function (MDF) in a non-linear cointegrating framework for G-7 countries. Previous studies on non-linearity in the MDF are only related to the short-run ... Keywords: G-7 countries, Money demand function, Non-linear cointegration, Smooth transition regression

Chien-Chiang Lee; Pei-Fen Chen; Chun-Ping Chang

2007-12-01T23:59:59.000Z

203

Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 12 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module forecasts energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region forecast using the SEDS 27 data.

204

Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling  

Science Conference Proceedings (OSTI)

Data collection for landslide susceptibility modeling is often an inhibitive activity. This is one reason why for quite some time landslides have been described and modelled on the basis of spatially distributed values of landslide-related attributes. ... Keywords: Artificial neural network, GIS, Klang Valley, Landslide, Malaysia, Susceptibility

Biswajeet Pradhan; Saro Lee

2010-06-01T23:59:59.000Z

205

The application of discrete event simulation and system dynamics in the logistics and supply chain context  

Science Conference Proceedings (OSTI)

Discrete event simulation (DES) and system dynamics (SD) are two modelling approaches widely used as decision support tools in logistics and supply chain management (LSCM). A widely held belief exists that SD is mostly used to model problems at a strategic ... Keywords: Comparison of methods, Discrete-event simulation, Logistics and supply chain management, Simulation modelling, System dynamics

Antuela A. Tako; Stewart Robinson

2012-03-01T23:59:59.000Z

206

Oxygenate Supply/Demand Balances  

Gasoline and Diesel Fuel Update (EIA)

Oxygenate Supply/Demand Oxygenate Supply/Demand Balances in the Short-Term Integrated Forecasting Model By Tancred C.M. Lidderdale This article first appeared in the Short-Term Energy Outlook Annual Supplement 1995, Energy Information Administration, DOE/EIA-0202(95) (Washington, DC, July 1995), pp. 33-42, 83-85. The regression results and historical data for production, inventories, and imports have been updated in this presentation. Contents * Introduction o Table 1. Oxygenate production capacity and demand * Oxygenate demand o Table 2. Estimated RFG demand share - mandated RFG areas, January 1998 * Fuel ethanol supply and demand balance o Table 3. Fuel ethanol annual statistics * MTBE supply and demand balance o Table 4. EIA MTBE annual statistics * Refinery balances

207

High Temperatures & Electricity Demand  

E-Print Network (OSTI)

High Temperatures & Electricity Demand An Assessment of Supply Adequacy in California Trends.......................................................................................................1 HIGH TEMPERATURES AND ELECTRICITY DEMAND.....................................................................................................................7 SECTION I: HIGH TEMPERATURES AND ELECTRICITY DEMAND ..........................9 BACKGROUND

208

Sixth Northwest Conservation and Electric Power Plan Appendix C: Demand Forecast  

E-Print Network (OSTI)

Sixth Northwest Conservation and Electric Power Plan Appendix C: Demand Forecast Energy Demand................................................................................................................................. 1 Demand Forecast Methodology.................................................................................................. 3 New Demand Forecasting Model for the Sixth Plan

209

NERSC/DOE HEP 2012 Review Logistics  

NLE Websites -- All DOE Office Websites (Extended Search)

NERSC HPC Achievement Awards Home Science at NERSC HPC Requirements Reviews High Energy Physics (HEP) Logistics Hotel Information Location The review will be held at...

210

NERSC/DOE ASCR Requirements Workshop Logistics  

NLE Websites -- All DOE Office Websites (Extended Search)

Workshop Logistics Large Scale Computing and Storage Requirements for Advanced Scientific Computing Research January 5-6, 2011 Location The workshop will be held at NERSC's...

211

Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

This page intentionally left blank This page intentionally left blank 51 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2011 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 non-manufacturing industries. The manufacturing industries are further subdivided into the energy- intensive manufacturing industries and nonenergy-intensive manufacturing industries (Table 6.1). The manufacturing industries are modeled through the use of a detailed process-flow or end-use accounting procedure, whereas the non- manufacturing industries are modeled with substantially less detail. The petroleum refining industry is not included in the Industrial Module, as it is simulated separately in the Petroleum Market Module of NEMS. The Industrial Module calculates

212

Regional load-curve models: QUERI's model long-run forecasts and sensitivity analysis. Volume 4. Final report. [Hourly demand in 32 US regions  

SciTech Connect

This report presents detailed forecasts of the hourly demand for electricity in 32 regions of the US through the year 2000. The forecasts are generated by a load curve model estimated by QUERI and described in Volume II of this report. Two primary sets of input assumptions for this model are utilized: one based on DRI's macro, regional and sectoral models is called the Baseline Scenario while the other, which is a projection of historical trends, is the Extrapolation Scenario. Under both assumptions, the growth rates of electricity are forecast to slow from historical levels. Load factors are generally projected to continue to decline; most regions are forecast to remain Summer peaking but this is rather sensitive to the choice of scenario. By considering other scenarios which are small perturbations of the Baseline assumptions, elasticities of average, peak and hourly loads are calculated. Different weather assumptions are also examined for the sensitivity of the load shapes to changes in the weather.

Engle, R.F.; Granger, C.W.J.; Ramanathan, R.

1981-09-01T23:59:59.000Z

213

Demand and Price Uncertainty: Rational Habits in International Gasoline Demand  

E-Print Network (OSTI)

Model of the Global Crude Oil Market and the U.S. RetailNoureddine. 2002. World crude oil and natural gas: a demandanalysis of the demand for oil in the Middle East. Energy

Scott, K. Rebecca

2013-01-01T23:59:59.000Z

214

Demand and Price Volatility: Rational Habits in International Gasoline Demand  

E-Print Network (OSTI)

A Joint Model of the Global Crude Oil Market and the U.S.Noureddine. 2002. World crude oil and natural gas: a demandelasticity of demand for crude oil, not gasoline. Results

Scott, K. Rebecca

2011-01-01T23:59:59.000Z

215

Demand and Price Uncertainty: Rational Habits in International Gasoline Demand  

E-Print Network (OSTI)

A Joint Model of the Global Crude Oil Market and the U.S.Noureddine. 2002. World crude oil and natural gas: a demandelasticity of demand for crude oil, not gasoline. Results

Scott, K. Rebecca

2013-01-01T23:59:59.000Z

216

DRSG Comments to DOE Smart Grid RFI: Addressing Policy and Logistical Challenges  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Comments to DOE Smart Grid RFI: Addressing Policy and Logistical Challenges Comments to DOE Smart Grid RFI: Addressing Policy and Logistical Challenges DRSG- 1 DOE Smart Grid RFI Titled "Addressing Policy and Logistical Challenges to Smart Grid Implementation" Submitted by the Demand Response and Smart Grid Coalition (DRSG) November 1, 2010 DRSG Comments to DOE Smart Grid RFI: Addressing Policy and Logistical Challenges DRSG- 2 I. Definition and Scope 1. What significant policy challenges are likely to remain unaddressed if we employ Title XIII's definition? In light of the fact that smart grid deployments are moving forward with pace and at scale, DRSG advises the DOE against seeking to redefine the term "smart grid" as a semantic exercise, as such an effort would introduce delay, generate uncertainty, and likely prove

217

Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

4 4 The commercial module forecasts consumption by fuel 15 at the Census division level using prices from the NEMS energy supply modules, and macroeconomic variables from the NEMS Macroeconomic Activity Module (MAM), as well as external data sources (technology characterizations, for example). Energy demands are forecast for ten end-use services 16 for eleven building categories 17 in each of the nine Census divisions (see Figure 5). The model begins by developing forecasts of floorspace for the 99 building category and Census division combinations. Next, the ten end-use service demands required for the projected floorspace are developed. The electricity generation and water and space heating supplied by distributed generation and combined heat and power technologies are projected. Technologies are then

218

Visualizing the logistic map with a microcontroller  

E-Print Network (OSTI)

The logistic map is one of the simplest nonlinear dynamical systems that clearly exhibit the route to chaos. In this paper, we explored the evolution of the logistic map using an open-source microcontroller connected to an array of light emitting diodes (LEDs). We divided the one-dimensional interval $[0,1]$ into ten equal parts, and associated and LED to each segment. Every time an iteration took place a corresponding LED turned on indicating the value returned by the logistic map. By changing some initial conditions of the system, we observed the transition from order to chaos exhibited by the map.

Juan D. Serna; Amitabh Joshi

2011-12-25T23:59:59.000Z

219

Visualizing the logistic map with a microcontroller  

E-Print Network (OSTI)

The logistic map is one of the simplest nonlinear dynamical systems that clearly exhibit the route to chaos. In this paper, we explored the evolution of the logistic map using an open-source microcontroller connected to an array of light emitting diodes (LEDs). We divided the one-dimensional interval $[0,1]$ into ten equal parts, and associated and LED to each segment. Every time an iteration took place a corresponding LED turned on indicating the value returned by the logistic map. By changing some initial conditions of the system, we observed the transition from order to chaos exhibited by the map.

Serna, Juan D

2011-01-01T23:59:59.000Z

220

Design of a knowledge-based logistics strategy system  

Science Conference Proceedings (OSTI)

Traditionally, the formulation of logistics strategies to execute various logistics services is done by human experts. In this paper, a knowledge-based logistics strategy system (KLSS) is designed in helping them to support the logistics strategy development ... Keywords: Case-based reasoning, Data warehouse, Knowledge-based system, Logistics strategy, On-line analytical processing

Harry K. H. Chow; K. L. Choy; W. B. Lee; Felix T. S. Chan

2005-08-01T23:59:59.000Z

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


221

Advanced Demand Responsive Lighting  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Demand Responsive Lighting Host: Francis Rubinstein Demand Response Research Center Technical Advisory Group Meeting August 31, 2007 10:30 AM - Noon Meeting Agenda * Introductions (10 minutes) * Main Presentation (~ 1 hour) * Questions, comments from panel (15 minutes) Project History * Lighting Scoping Study (completed January 2007) - Identified potential for energy and demand savings using demand responsive lighting systems - Importance of dimming - New wireless controls technologies * Advanced Demand Responsive Lighting (commenced March 2007) Objectives * Provide up-to-date information on the reliability, predictability of dimmable lighting as a demand resource under realistic operating load conditions * Identify potential negative impacts of DR lighting on lighting quality Potential of Demand Responsive Lighting Control

222

Transportation Demand This  

Annual Energy Outlook 2012 (EIA)

69 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2012 Transportation Demand Module The NEMS Transportation Demand Module estimates...

223

Demand Response Spinning Reserve  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response Spinning Reserve Title Demand Response Spinning Reserve Publication Type Report Year of Publication 2007 Authors Eto, Joseph H., Janine Nelson-Hoffman, Carlos...

224

Addressing Energy Demand  

NLE Websites -- All DOE Office Websites (Extended Search)

Addressing Energy Demand through Demand Response: International Experiences and Practices Bo Shen, Girish Ghatikar, Chun Chun Ni, and Junqiao Dudley Environmental Energy...

225

Propane Sector Demand Shares  

U.S. Energy Information Administration (EIA)

... agricultural demand does not impact regional propane markets except when unusually high and late demand for propane for crop drying combines with early cold ...

226

Felton Bay Logistics, LLC | Open Energy Information  

Open Energy Info (EERE)

Felton Bay Logistics, LLC Felton Bay Logistics, LLC Jump to: navigation, search Logo: Felton Bay Logistics, LLC Name Felton Bay Logistics, LLC Place San Diego Zip 92115 Sector Services Product Strategies for Sustainability Year founded 2010 Number of employees 1-10 Website http://www.feltonbay.com Coordinates 32.7612759°, -117.0735241° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":32.7612759,"lon":-117.0735241,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

227

Logistical Multicast for Data Distribution linkbordercolor  

NLE Websites -- All DOE Office Websites (Extended Search)

Logistical Logistical Multicast for Data Distribution Jason Zurawski, Martin Swany Micah Beck, Ying Ding Department of Computer and Information Sciences Department of Computer Science University of Delaware, Newark, DE 19716 University of Tennessee, Knoxville, TN 37996 {zurawski, swany}@cis.udel.edu {mbeck, ying}@cs.utk.edu Abstract This paper describes a simple scheduling procedure for use in multicast data distribution within a logistical networking infrastructure. The goal of our scheduler is to generate a distribution schedule that will exploit the best network paths by using historic network perfor- mance information. A "spanning tree" is constructed between available logistical depots to help reduce the overall time of data movement. Our hypothesis is that we can generate appropri- ate schedules from historical network measurements. In order to evaluate

228

Cargo revenue management for space logistics  

E-Print Network (OSTI)

This thesis covers the development of a framework for the application of revenue management, specifically capacity control, to space logistics for use in the optimization of mission cargo allocations, which in turn affect ...

Armar, Nii A

2009-01-01T23:59:59.000Z

229

Demand Response for Ancillary Services  

Science Conference Proceedings (OSTI)

Many demand response resources are technically capable of providing ancillary services. In some cases, they can provide superior response to generators, as the curtailment of load is typically much faster than ramping thermal and hydropower plants. Analysis and quantification of demand response resources providing ancillary services is necessary to understand the resources economic value and impact on the power system. Methodologies used to study grid integration of variable generation can be adapted to the study of demand response. In the present work, we describe and illustrate a methodology to construct detailed temporal and spatial representations of the demand response resource and to examine how to incorporate those resources into power system models. In addition, the paper outlines ways to evaluate barriers to implementation. We demonstrate how the combination of these three analyses can be used to translate the technical potential for demand response providing ancillary services into a realizable potential.

Alkadi, Nasr E [ORNL; Starke, Michael R [ORNL

2013-01-01T23:59:59.000Z

230

Price and Non-Price Influences on Water Conservation: An Econometric Model of Aggregate Demand under Nonlinear Budget Constraint  

E-Print Network (OSTI)

PRICE INFLUENCES ON WATER CONSERVATION: ECONOMETRIC AN MODELPrice Influences on Water Conservation: An Econometric ModelPrice Influences on Water Conservation: An Econometric Model

Corral, Leonardo; Fisher, Anthony C.; Hatch, Nile W.

1999-01-01T23:59:59.000Z

231

building demand | OpenEI  

Open Energy Info (EERE)

demand demand Dataset Summary Description This dataset contains hourly load profile data for 16 commercial building types (based off the DOE commercial reference building models) and residential buildings (based off the Building America House Simulation Protocols). This dataset also includes the Residential Energy Consumption Survey (RECS) for statistical references of building types by location. Source Commercial and Residential Reference Building Models Date Released April 18th, 2013 (9 months ago) Date Updated July 02nd, 2013 (7 months ago) Keywords building building demand building load Commercial data demand Energy Consumption energy data hourly kWh load profiles Residential Data Quality Metrics Level of Review Some Review Comment Temporal and Spatial Coverage Frequency Annually

232

Demand Response and Open Automated Demand Response Opportunities...  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response and Open Automated Demand Response Opportunities for Data Centers Title Demand Response and Open Automated Demand Response Opportunities for Data Centers...

233

Addressing Energy Demand through Demand Response: International Experiences and Practices  

E-Print Network (OSTI)

of integrating demand response and energy efficiencyand D. Kathan (2009), Demand Response in U.S. ElectricityFRAMEWORKS THAT PROMOTE DEMAND RESPONSE 3.1. Demand Response

Shen, Bo

2013-01-01T23:59:59.000Z

234

Reducing the demand forecast error due to the bullwhip effect in the computer processor industry  

E-Print Network (OSTI)

Intel's current demand-forecasting processes rely on customers' demand forecasts. Customers do not revise demand forecasts as demand decreases until the last minute. Intel's current demand models provide little guidance ...

Smith, Emily (Emily C.)

2010-01-01T23:59:59.000Z

235

Addressing Policy and Logistical Challenges to Smart Grid Implementati...  

NLE Websites -- All DOE Office Websites (Extended Search)

Addressing Policy and Logistical Challenges to Smart Grid Implementation: Federal Register Notice Volume 75, No. 180 - Sep. 17, 2010 Addressing Policy and Logistical Challenges to...

236

Southern Company: DOE Smart Grid RFI Addressing Policy and Logistical...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Southern Company: DOE Smart Grid RFI Addressing Policy and Logistical Challenges Southern Company: DOE Smart Grid RFI Addressing Policy and Logistical Challenges Southern Company:...

237

Steffes Corporation Smart Grid RFI: Addressing Policy and Logistical...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Steffes Corporation Smart Grid RFI: Addressing Policy and Logistical Challenges Steffes Corporation Smart Grid RFI: Addressing Policy and Logistical Challenges Steffes Corporation...

238

Demand Trading: Building Liquidity  

Science Conference Proceedings (OSTI)

Demand trading holds substantial promise as a mechanism for efficiently integrating demand-response resources into regional power markets. However, regulatory uncertainty, the lack of proper price signals, limited progress toward standardization, problems in supply-side markets, and other factors have produced illiquidity in demand-trading markets and stalled the expansion of demand-response resources. This report shows how key obstacles to demand trading can be overcome, including how to remove the unce...

2002-11-27T23:59:59.000Z

239

Demand and Price Outlook for Phase 2 Reformulated Gasoline, 2000  

Gasoline and Diesel Fuel Update (EIA)

Demand and Price Outlook for Demand and Price Outlook for Phase 2 Reformulated Gasoline, 2000 Tancred Lidderdale and Aileen Bohn (1) Contents * Summary * Introduction * Reformulated Gasoline Demand * Oxygenate Demand * Logistics o Interstate Movements and Storage o Local Distribution o Phase 2 RFG Logistics o Possible Opt-Ins to the RFG Program o State Low Sulfur, Low RVP Gasoline Initiatives o NAAQS o Tier 2 Gasoline * RFG Production Options o Toxic Air Pollutants (TAP) Reduction o Nitrogen Oxides (NOx) Reduction o Volatile Organic Compounds (VOC) Reduction o Summary of RFG Production Options * Costs of Reformulated Gasoline o Phase 1 RFG Price Premium o California Clean Gasoline Price Premium o Phase 2 RFG Price Premium o Reduced Fuel Economy

240

Real lessons for venture capitalists in multimodal logistics systems : where does profitability come from?  

E-Print Network (OSTI)

In this thesis we review three case studies in multimodal logistics and transportation systems and analyze the reasons that lead to failure or success. We present the business idea and model of each case study and study ...

Veniamis, Nikolas Th

2006-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


241

Demand Response-Enabled Model Predictive HVAC Load Control in Buildings using Real-Time Electricity Pricing.  

E-Print Network (OSTI)

??A practical cost and energy efficient model predictive control (MPC) strategy is proposed for HVAC load control under dynamic real-time electricity pricing. The MPC strategy… (more)

Avci, Mesut

2013-01-01T23:59:59.000Z

242

Freedom Energy Logistics | Open Energy Information  

Open Energy Info (EERE)

Logistics Logistics Jump to: navigation, search Name Freedom Energy Logistics Place Manchester, New Hampshire Product Manchester-based energy management company. Coordinates 53.479605°, -2.248818° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":53.479605,"lon":-2.248818,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

243

ASSET LOGISTIC AG | Open Energy Information  

Open Energy Info (EERE)

ASSET LOGISTIC AG ASSET LOGISTIC AG Jump to: navigation, search Name ASSET@LOGISTIC AG Place Hamburg, Hamburg, Germany Zip 20148 Sector Wind energy Product Developer of 3 wind farms in Almeria, Spain Coordinates 53.553345°, 9.992455° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":53.553345,"lon":9.992455,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

244

Mass Market Demand Response  

NLE Websites -- All DOE Office Websites (Extended Search)

Mass Market Demand Response Mass Market Demand Response Speaker(s): Karen Herter Date: July 24, 2002 - 12:00pm Location: Bldg. 90 Demand response programs are often quickly and poorly crafted in reaction to an energy crisis and disappear once the crisis subsides, ensuring that the electricity system will be unprepared when the next crisis hits. In this paper, we propose to eliminate the event-driven nature of demand response programs by considering demand responsiveness a component of the utility obligation to serve. As such, demand response can be required as a condition of service, and the offering of demand response rates becomes a requirement of utilities as an element of customer service. Using this foundation, we explore the costs and benefits of a smart thermostat-based demand response system capable of two types of programs: (1) a mandatory,

245

Demand Impacted by Weather  

U.S. Energy Information Administration (EIA)

When you look at demand, it’s also interesting to note the weather. The weather has a big impact on the demand of heating fuels, if it’s cold, consumers will use ...

246

Demand Trading Toolkit  

Science Conference Proceedings (OSTI)

Download report 1006017 for FREE. The global movement toward competitive markets is paving the way for a variety of market mechanisms that promise to increase market efficiency and expand customer choice options. Demand trading offers customers, energy service providers, and other participants in power markets the opportunity to buy and sell demand-response resources, just as they now buy and sell blocks of power. EPRI's Demand Trading Toolkit (DTT) describes the principles and practice of demand trading...

2001-12-10T23:59:59.000Z

247

Alternative Fuels Data Center: Reynolds Logistics Reduces Fuel Costs With  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Reynolds Logistics Reynolds Logistics Reduces Fuel Costs With EVs to someone by E-mail Share Alternative Fuels Data Center: Reynolds Logistics Reduces Fuel Costs With EVs on Facebook Tweet about Alternative Fuels Data Center: Reynolds Logistics Reduces Fuel Costs With EVs on Twitter Bookmark Alternative Fuels Data Center: Reynolds Logistics Reduces Fuel Costs With EVs on Google Bookmark Alternative Fuels Data Center: Reynolds Logistics Reduces Fuel Costs With EVs on Delicious Rank Alternative Fuels Data Center: Reynolds Logistics Reduces Fuel Costs With EVs on Digg Find More places to share Alternative Fuels Data Center: Reynolds Logistics Reduces Fuel Costs With EVs on AddThis.com... July 23, 2011 Reynolds Logistics Reduces Fuel Costs With EVs F ind out how Reynolds Logistics uses electric vehicles to offset petroleum

248

A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings  

Science Conference Proceedings (OSTI)

This paper addresses the problem of predicting demand for natural gas for the purpose of realizing energy cost savings. Daily monitoring of a rooftop unit wireless sensor system provided feedback for a decision support system that supplied the demand ... Keywords: Artificial neural networks, Decision support system, Energy forecasting, Natural gas demand, Nearest neighbor method, Wireless sensor networks

James A. Rodger

2014-03-01T23:59:59.000Z

249

Impact of improved building thermal efficiency on residential energy demand  

SciTech Connect

The impact of improved building shell thermal efficiency on residential energy demand is explored in a theoretical framework. The important economic literature on estimating the price elasticity of residential energy demand is reviewed. The specification of the residential energy demand model is presented. The data used are described. The empirical estimation of the residential energy demand model is described. (MHR)

Adams, R.C.; Rockwood, A.D.

1983-04-01T23:59:59.000Z

250

Floating offshore wind farms : demand planning & logistical challenges of electricity generation  

E-Print Network (OSTI)

Floating offshore wind farms are likely to become the next paradigm in electricity generation from wind energy mainly because of the near constant high wind speeds in an offshore environment as opposed to the erratic wind ...

Nnadili, Christopher Dozie, 1978-

2009-01-01T23:59:59.000Z

251

Demand Response and Open Automated Demand Response Opportunities...  

NLE Websites -- All DOE Office Websites (Extended Search)

Response and Open Automated Demand Response Opportunities for Data Centers Title Demand Response and Open Automated Demand Response Opportunities for Data Centers Publication Type...

252

Electrical Demand Management  

E-Print Network (OSTI)

The Demand Management Plan set forth in this paper has proven to be a viable action to reduce a 3 million per year electric bill at the Columbus Works location of Western Electric. Measures are outlined which have reduced the peak demand 5% below the previous year's level and yielded $150,000 annual savings. These measures include rescheduling of selected operations and demand limiting techniques such as fuel switching to alternate power sources during periods of high peak demand. For example, by rescheduling the startup of five heat treat annealing ovens to second shift, 950 kW of load was shifted off peak. Also, retired, non-productive steam turbine chillers and a diesel air compressor have been effectively operated to displaced 1330 kW during peak periods each day. Installed metering devices have enabled the recognition of critical demand periods. The paper concludes with a brief look at future plans and long range objectives of the Demand Management Plan.

Fetters, J. L.; Teets, S. J.

1983-01-01T23:59:59.000Z

253

Demand Dispatch-Intelligent  

NLE Websites -- All DOE Office Websites (Extended Search)

and energy efficiency throughout the value chain resulting in the most economical price for electricity. Having adequate quantities and capacities of demand resources is a...

254

C*-algebras associated with reversible extensions of logistic maps  

Science Conference Proceedings (OSTI)

The construction of reversible extensions of dynamical systems presented in a previous paper by the author and A.V. Lebedev is enhanced, so that it applies to arbitrary mappings (not necessarily with open range). It is based on calculating the maximal ideal space of C*-algebras that extends endomorphisms to partial automorphisms via partial isometric representations, and involves a new set of 'parameters' (the role of parameters is played by chosen sets or ideals). As model examples, we give a thorough description of reversible extensions of logistic maps and a classification of systems associated with compression of unitaries generating homeomorphisms of the circle. Bibliography: 34 titles.

Kwasniewski, Bartosz K [Institute of Mathematics, University of Bialystok (Poland)

2012-10-31T23:59:59.000Z

255

Demand Response Valuation Frameworks Paper  

E-Print Network (OSTI)

xxxv Option Value of Electricity Demand Response, Osmanelasticity in aggregate electricity demand. With these newii) reduction in electricity demand during peak periods (

Heffner, Grayson

2010-01-01T23:59:59.000Z

256

U.S. Propane Demand  

U.S. Energy Information Administration (EIA)

Demand is higher in 1999 due to higher petrochemical demand and a strong economy. We are also seeing strong demand in the first quarter of 2000; however, ...

257

Automated Demand Response and Commissioning  

E-Print Network (OSTI)

Fully-Automated Demand Response Test in Large Facilities14in DR systems. Demand Response using HVAC in Commercialof Fully Automated Demand Response in Large Facilities”

Piette, Mary Ann; Watson, David S.; Motegi, Naoya; Bourassa, Norman

2005-01-01T23:59:59.000Z

258

Demand Response Spinning Reserve Demonstration  

E-Print Network (OSTI)

F) Enhanced ACP Date RAA ACP Demand Response – SpinningReserve Demonstration Demand Response – Spinning Reservesupply spinning reserve. Demand Response – Spinning Reserve

2007-01-01T23:59:59.000Z

259

Electricity Demand and Energy Consumption Management System  

E-Print Network (OSTI)

This project describes the electricity demand and energy consumption management system and its application to the Smelter Plant of Southern Peru. It is composted of an hourly demand-forecasting module and of a simulation component for a plant electrical system. The first module was done using dynamic neural networks, with backpropagation training algorithm; it is used to predict the electric power demanded every hour, with an error percentage below of 1%. This information allows management the peak demand before this happen, distributing the raise of electric load to other hours or improving those equipments that increase the demand. The simulation module is based in advanced estimation techniques, such as: parametric estimation, neural network modeling, statistic regression and previously developed models, which simulates the electric behavior of the smelter plant. These modules allow the proper planning because it allows knowing the behavior of the hourly demand and the consumption patterns of the plant, in...

Sarmiento, Juan Ojeda

2008-01-01T23:59:59.000Z

260

Comparative Usability Study of Two Space Logistics Analysis Tools  

E-Print Network (OSTI)

Future space exploration missions and campaigns will require sophisticated tools to help plan and analyze logistics. To encourage their use, space logistics tools must be usable: a design concept encompassing terms such ...

Lee, Chairwoo

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


261

Demand-side management in office buildings in Kuwait through an ice-storage assisted HVAC system with model predictive control.  

E-Print Network (OSTI)

??Examining methods for controlling the electricity demand in Kuwait was the main objective and motivation of this researchp roject. The extensiveu se of air-conditioning for… (more)

Al-Hadban, Yehya

2005-01-01T23:59:59.000Z

262

Comparison of Demand Response Performance with an EnergyPlus...  

NLE Websites -- All DOE Office Websites (Extended Search)

of Demand Response Performance with an EnergyPlus Model in a Low Energy Campus Building Title Comparison of Demand Response Performance with an EnergyPlus Model in a Low Energy...

263

Automated Demand Response and Commissioning  

E-Print Network (OSTI)

internal conditions. Maximum Demand Saving Intensity [W/ft2]automated electric demand sheds. The maximum electric shed

Piette, Mary Ann; Watson, David S.; Motegi, Naoya; Bourassa, Norman

2005-01-01T23:59:59.000Z

264

Biological Clustering Method for Logistic Place Decision Making  

Science Conference Proceedings (OSTI)

One of the main tasks in supply chain network is to identify the determination of logistic location. The main factors could influence the selections are costs and profits for the company itself. Most appropriate place is urgently essentials in today ... Keywords: Biologically inspired computing, DNA computing, Determination, Logistic location, Logistic problem, cluster-based

Rohani Binti Abu Bakar; Junzo Watada

2008-09-01T23:59:59.000Z

265

Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

2 2 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2035. The definition of the commercial sector is consistent with EIA's State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA

266

Demand Response Database & Demo  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response Database & Demo Speaker(s): Mike Graveley William M. Smith Date: June 7, 2005 - 12:00pm Location: Bldg. 90 Seminar HostPoint of Contact: Mary Ann Piette Infotility...

267

Tankless Demand Water Heaters  

Energy.gov (U.S. Department of Energy (DOE))

Demand (tankless or instantaneous) water heaters have heating devices that are activated by the flow of water, so they provide hot water only as needed and without the use of a storage tank. They...

268

Automated Demand Response Tests  

Science Conference Proceedings (OSTI)

This report includes assessments and test results of four end-use technologies, representing products in the residential, commercial, and industrial sectors, each configured to automatically receive real-time pricing information and critical peak pricing (CPP) demand response (DR) event notifications. Four different vendors were asked to follow the interface requirements set forth in the Open Automated Demand Response (OpenADR) standard that was introduced to the public in 2008 and currently used in two ...

2008-12-22T23:59:59.000Z

269

Automated Demand Response Tests  

Science Conference Proceedings (OSTI)

This report, which is an update to EPRI Report 1016082, includes assessments and test results of four end-use vendor technologies. These technologies represent products in the residential, commercial, and industrial sectors, each configured to automatically receive real-time pricing information and critical peak pricing (CPP) demand response (DR) event notifications. Four different vendors were asked to follow the interface requirements set forth in the Open Automated Demand Response (OpenADR) Communicat...

2009-03-30T23:59:59.000Z

270

Ensemble Kalman Filter Configurations and Their Performance with the Logistic Map  

Science Conference Proceedings (OSTI)

This paper examines ensemble Kalman filter (EnKF) performance for a number of different EnKF configurations. The study is performed in a perfect-model context using the logistic map as forecast model. The focus is on EnKF performance when the ...

Herschel L. Mitchell; P. L. Houtekamer

2009-12-01T23:59:59.000Z

271

Financial Market Risk and U.S. Money Demand  

E-Print Network (OSTI)

This paper empirically examines U.S. broad money demand, emphasizing the role of financial market risk. Broad money demand displays long-run stability after controlling for financial market factors. We show that money demand rises with the liquidity risk of stock markets or the credit risk of corporate bond markets. The financial risk model for money demand surpasses the traditional model in explaining the persistent fluctuations observed in broad money demand in the last 15 years. Also, the models estimated in an error-correction specification suggest that financial market risk affects substantially the short-term fluctuations of broad money demand since the early 1990s.

Woon Gyu Choi; David Cook

2008-01-01T23:59:59.000Z

272

Addressing Energy Demand through Demand Response: International Experiences and Practices  

E-Print Network (OSTI)

time. 4 Reducing this peak demand through DR programs meansthat a 5% reduction in peak demand would have resulted insame 5% reduction in the peak demand of the US as a whole.

Shen, Bo

2013-01-01T23:59:59.000Z

273

Demand and Price Volatility: Rational Habits in International Gasoline Demand  

E-Print Network (OSTI)

shift in the short-run price elasticity of gasoline demand.A meta-analysis of the price elasticity of gasoline demand.2007. Consumer demand un- der price uncertainty: Empirical

Scott, K. Rebecca

2011-01-01T23:59:59.000Z

274

California Independent System Operator demand response & proxy demand resources  

Science Conference Proceedings (OSTI)

Demand response programs are designed to allow end use customers to contribute to energy load reduction individually or through a demand response provider. One form of demand response can occur when an end use customer reduces their electrical usage ...

John Goodin

2012-01-01T23:59:59.000Z

275

Coupling Renewable Energy Supply with Deferrable Demand  

E-Print Network (OSTI)

function of real-time electricity prices (left) and windinflexible) demand and real-time prices. The case study inas a special case. The real-time price process is modeled as

Papavasiliou, Anthony

2011-01-01T23:59:59.000Z

276

Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

This page intentionally left blank This page intentionally left blank 39 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2011 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2035. The definition of the commercial sector is consistent with EIA's State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial.

277

Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

2 2 Residential Demand Module The NEMS Residential Demand Module projects future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the "unit energy consumption" (UEC) by appliance (in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type

278

Demand Response In California  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Energy Efficiency & Energy Efficiency & Demand Response Programs Dian M. Grueneich, Commissioner Dian M. Grueneich, Commissioner California Public Utilities Commission California Public Utilities Commission FUPWG 2006 Fall Meeting November 2, 2006 Commissioner Dian M. Grueneich November 2, 2006 1 Highest Priority Resource Energy Efficiency is California's highest priority resource to: Meet energy needs in a low cost manner Aggressively reduce GHG emissions November 2, 2006 2 Commissioner Dian M. Grueneich November 2, 2006 3 http://www.cpuc.ca.gov/PUBLISHED/REPORT/51604.htm Commissioner Dian M. Grueneich November 2, 2006 4 Energy Action Plan II Loading order continued "Pursue all cost-effective energy efficiency, first." Strong demand response and advanced metering

279

Automated Demand Response Today  

Science Conference Proceedings (OSTI)

Demand response (DR) has progressed over recent years beyond manual and semi-automated DR to include growing implementation and experience with fully automated demand response (AutoDR). AutoDR has been shown to be of great value over manual and semi-automated DR because it reduces the need for human interactions and decisions, and it increases the speed and reliability of the response. AutoDR, in turn, has evolved into the specification known as OpenADR v1.0 (California Energy Commission, PIER Program, C...

2012-03-29T23:59:59.000Z

280

United States lubricant demand  

Science Conference Proceedings (OSTI)

This paper examines United States Lubricant Demand for Automotive and Industrial Lubricants by year from 1978 to 1992 and 1997. Projected total United States Lubricant Demand for 1988 is 2,725 million (or MM) gallons. Automotive oils are expected to account for 1,469MM gallons or (53.9%), greases 59MM gallons (or 2.2%), and Industrial oils will account for the remaining 1,197MM gallons (or 43.9%) in 1988. This proportional relationship between Automotive and Industrial is projected to remain relatively constant until 1992 and out to 1997. Projections for individual years between 1978 to 1992 and 1997 are summarized.

Solomon, L.K.; Pruitt, P.R.

1988-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


281

Demand Response Valuation Frameworks Paper  

E-Print Network (OSTI)

No. ER06-615-000 CAISO Demand Response Resource User Guide -8 2.1. Demand Response Provides a Range of Benefits to8 2.2. Demand Response Benefits can be Quantified in Several

Heffner, Grayson

2010-01-01T23:59:59.000Z

282

Pepco Holdings, Inc. Smart Grid RFI: Addressing Policy and Logistical  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Holdings, Inc. Smart Grid RFI: Addressing Policy and Holdings, Inc. Smart Grid RFI: Addressing Policy and Logistical Challenges Pepco Holdings, Inc. Smart Grid RFI: Addressing Policy and Logistical Challenges Pepco Holdings, Inc. Smart Grid RFI: Addressing Policy and Logistical Challenges. Pepco Holdings, Inc. (PHI) is pleased to respond to the US Department of Energy (DOE) request for information regarding addressing policy and logistical challenges to smart grid implementation. This follows on the heels of PHI's responses to two other DOE RFls on data access and communications requirements. Pepco Holdings, Inc. Smart Grid RFI: Addressing Policy and Logistical Challenges More Documents & Publications DC OPC Comments. September 17, 2010 Addressing Policy and Logistical Challenges to smart grid Implementation:

283

On Demand Guarantees in Iran.  

E-Print Network (OSTI)

??On Demand Guarantees in Iran This thesis examines on demand guarantees in Iran concentrating on bid bonds and performance guarantees. The main guarantee types and… (more)

Ahvenainen, Laura

2009-01-01T23:59:59.000Z

284

A Review of Green Logistics Schemes Used in Cities Around the World  

E-Print Network (OSTI)

Green Logistics (The Paradoxes of), Handbook of Logistics and Supply Chain Management, edited by Brewer, A. , Button,

Geroliminis, Nikolaos; Daganzo, Carlos F.

2005-01-01T23:59:59.000Z

285

Transportation Demand Management Plan  

E-Print Network (OSTI)

Transportation Demand Management Plan FALL 2009 #12;T r a n s p o r t a t i o n D e m a n d M a n the transportation impacts the expanded enrollment will have. Purpose and Goal The primary goal of the TDM plan is to ensure that adequate measures are undertaken and maintained to minimize the transportation impacts

286

Logistics | U.S. DOE Office of Science (SC)  

Office of Science (SC) Website

Logistics Logistics Basic Energy Sciences Advisory Committee (BESAC) BESAC Home Meetings Meeting Presentations History Logistics Members Charges/Reports Charter .pdf file (41KB) BES Committees of Visitors BES Home Meetings Logistics Print Text Size: A A A RSS Feeds FeedbackShare Page Transportation Airport Directions and Services Ronald Reagan (National) Airport External link Baltimore-Washington International Airport (BWI) External link Dulles International Airport (Dulles) External link Ground Transportation Ronald Reagan (National) Airport External link Baltimore-Washington International Airport (BWI) External link Dulles International Airport (Dulles) External link Hotel North Bethesda Marriott and Conference Center External link 5701 Marinelli Road North Bethesda, MD 20852 Phone: 1-301-822-9200

287

Logistics and Supply Chain Management - Center for Transportation...  

NLE Websites -- All DOE Office Websites (Extended Search)

analysis. Simulation study of logistics support requirements for nuclear non-proliferation support. Evaluation of practical capabilities of electronic (RF) tags in...

288

Okaloosa Gas District Smart Grid RFI: Addressing Policy and Logistical...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Okaloosa Gas District Smart Grid RFI: Addressing Policy and Logistical Challenges to Smart Grid Implementation Okaloosa Gas District Smart Grid RFI: Addressing Policy and...

289

Smart Grid RFI: Addressing Policy and Logistical Challenges....  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Challenges. Comments of the Alliance to Save Energy. Smart Grid RFI: Addressing Policy and Logistical Challenges. Comments of the Alliance to Save Energy. The Alliance to Save...

290

Smart Grid RFI: Addressing Policy and Logistical Challenges,...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Challenges, Comments from the Edison Electric Institute Smart Grid RFI: Addressing Policy and Logistical Challenges, Comments from the Edison Electric Institute The Edison Electric...

291

Techno-economic analysis of using corn stover to supply heat and power to a corn ethanol plant - Part 1: Cost of feedstock supply logistics  

Science Conference Proceedings (OSTI)

Supply of corn stover to produce heat and power for a typical 170 dam3 dry mill ethanol plant is proposed. The corn ethanol plant requires 5.6 MW of electricity and 52.3 MW of process heat, which creates the annual stover demand of as much as 140 Gg. The corn stover supply system consists of collection, preprocessing, transportation and on-site fuel storage and preparation to produce heat and power for the ethanol plant. Economics of the entire supply system was conducted using the Integrated Biomass Supply Analysis and Logistics (IBSAL) simulation model. Corn stover was delivered in three formats (square bales, dry chops and pellets) to the combined heat and power plant. Delivered cost of biomass ready to be burned was calculated at 73 $ Mg-1 for bales, 86 $ Mg-1 for pellets and 84 $ Mg-1 for field chopped biomass. Among the three formats of stover supply systems, delivered cost of pelleted biomass was the highest due to high pelleting cost. Bulk transport of biomass in the form of chops and pellets can provide a promising future biomass supply logistic system in the US, if the costs of pelleting and transport are minimized.

Sokhansanj, Shahabaddine [ORNL; Mani, Sudhagar [University of Georgia; Togore, Sam [U.S. Department of Energy; Turhollow Jr, Anthony F [ORNL

2010-01-01T23:59:59.000Z

292

Sustainable Logistics Website | Open Energy Information  

Open Energy Info (EERE)

Sustainable Logistics Website Sustainable Logistics Website Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Sustainable Logistics Website Focus Area: Clean Transportation Topics: Best Practices Website: www.duurzamelogistiek.nl/ Equivalent URI: cleanenergysolutions.org/content/sustainable-logistics-website Language: "English,Dutch" is not in the list of possible values (Abkhazian, Achinese, Acoli, Adangme, Adyghe; Adygei, Afar, Afrihili, Afrikaans, Afro-Asiatic languages, Ainu, Akan, Akkadian, Albanian, Aleut, Algonquian languages, Altaic languages, Amharic, Angika, Apache languages, Arabic, Aragonese, Arapaho, Arawak, Armenian, Aromanian; Arumanian; Macedo-Romanian, Artificial languages, Assamese, Asturian; Bable; Leonese; Asturleonese, Athapascan languages, Australian languages, Austronesian languages, Avaric, Avestan, Awadhi, Aymara, Azerbaijani, Balinese, Baltic languages, Baluchi, Bambara, Bamileke languages, Banda languages, Bantu (Other), Basa, Bashkir, Basque, Batak languages, Beja; Bedawiyet, Belarusian, Bemba, Bengali, Berber languages, Bhojpuri, Bihari languages, Bikol, Bini; Edo, Bislama, Blin; Bilin, Blissymbols; Blissymbolics; Bliss, Bosnian, Braj, Breton, Buginese, Bulgarian, Buriat, Burmese, Caddo, Catalan; Valencian, Caucasian languages, Cebuano, Celtic languages, Central American Indian languages, Central Khmer, Chagatai, Chamic languages, Chamorro, Chechen, Cherokee, Cheyenne, Chibcha, Chichewa; Chewa; Nyanja, Chinese, Chinook jargon, Chipewyan; Dene Suline, Choctaw, Chuukese, Chuvash, Classical Newari; Old Newari; Classical Nepal Bhasa, Classical Syriac, Coptic, Cornish, Corsican, Cree, Creek, Creoles and pidgins , Crimean Tatar; Crimean Turkish, Croatian, Cushitic languages, Czech, Dakota, Danish, Dargwa, Delaware, Dinka, Divehi; Dhivehi; Maldivian, Dogri, Dogrib, Dravidian languages, Duala, Dutch; Flemish, Dyula, Dzongkha, Eastern Frisian, Efik, Egyptian (Ancient), Ekajuk, Elamite, English, Erzya, Esperanto, Estonian, Ewe, Ewondo, Fang, Fanti, Faroese, Fijian, Filipino; 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Kyrgyz, Klingon; tlhIngan-Hol, Komi, Kongo, Konkani, Korean, Kosraean, Kpelle, Kru languages, Kuanyama; Kwanyama, Kumyk, Kurdish, Kurukh, Kutenai, Ladino, Lahnda, Lamba, Land Dayak languages, Lao, Latin, Latvian, Lezghian, Limburgan; Limburger; Limburgish, Lingala, Lithuanian, Lojban, Lower Sorbian, Lozi, Luba-Katanga, Luba-Lulua, Luiseno, Lule Sami, Lunda, Luo (Kenya and Tanzania), Lushai, Luxembourgish; Letzeburgesch, Macedonian, Madurese, Magahi, Maithili, Makasar, Malagasy, Malay, Malayalam, Maltese, Manchu, Mandar, Mandingo, Manipuri, Manobo languages, Manx, Maori, Mapudungun; Mapuche, Marathi, Mari, Marshallese, Marwari, Masai, Mayan languages, Mende, Mi'kmaq; Micmac, Minangkabau, Mirandese, Mohawk, Moksha, Mon-Khmer languages, Mongo, Mongolian, Mossi, Multiple languages, Munda languages, N'Ko, Nahuatl languages, Nauru, Navajo; Navaho, Ndebele, North; North Ndebele, Ndebele, South; South Ndebele, Ndonga, Neapolitan, Nepal Bhasa; Newari, Nepali, Nias, Niger-Kordofanian languages, Nilo-Saharan languages, Niuean, North American Indian languages, Northern Frisian, Northern Sami, Norwegian, Nubian languages, Nyamwezi, Nyankole, Nyoro, Nzima, Occitan (post 1500); 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Castilian, Sranan Tongo, Sukuma, Sumerian, Sundanese, Susu, Swahili, Swati, Swedish, Swiss German; Alemannic; Alsatian, Syriac, Tagalog, Tahitian, Tai languages, Tajik, Tamashek, Tamil, Tatar, Telugu, Tereno, Tetum, Thai, Tibetan, Tigre, Tigrinya, Timne, Tiv, Tlingit, Tok Pisin, Tokelau, Tonga (Nyasa), Tonga (Tonga Islands), Tsimshian, Tsonga, Tswana, Tumbuka, Tupi languages, Turkish, Turkmen, Tuvalu, Tuvinian, Twi, Udmurt, Ugaritic, Uighur; Uyghur, Ukrainian, Umbundu, Uncoded languages, Undetermined, Upper Sorbian, Urdu, Uzbek, Vai, Venda, Vietnamese, Volapük, Votic, Wakashan languages, Walamo, Walloon, Waray, Washo, Welsh, Western Frisian, Wolof, Xhosa, Yakut, Yao, Yapese, Yiddish, Yoruba, Yupik languages, Zande languages, Zapotec, Zaza; Dimili; Dimli; Kirdki; Kirmanjki; Zazaki, Zenaga, Zhuang; Chuang, Zulu, Zuni) for this property.

293

Source Recertification, Refurbishment, and Transfer Logistics  

SciTech Connect

The 2012 Gap Analysis of Department of Energy Radiological Sealed Sources, Standards, and Materials for Safeguards Technology Development [1] report, and the subsequent Reconciliation of Source Needs and Surpluses across the U.S. Department of Energy National Laboratory Complex [2] report, resulted in the identification of 33 requests for nuclear or radiological sealed sources for which there was potentially available, suitable material from within the U.S. Department of Energy (DOE) complex to fill the source need. Available, suitable material was defined by DOE laboratories as material slated for excess, or that required recertification or refurbishment before being used for safeguards technology development. This report begins by outlining the logistical considerations required for the shipment of nuclear and radiological materials between DOE laboratories. Then, because of the limited need for transfer of matching sources, the report also offers considerations for an alternative approach – the shipment of safeguards equipment between DOE laboratories or technology testing centers. Finally, this report addresses repackaging needs for the two source requests for which there was available, suitable material within the DOE complex.

Gastelum, Zoe N.; Duckworth, Leesa L.; Greenfield, Bryce A.; Doll, Stephanie R.

2013-09-01T23:59:59.000Z

294

On Demand Paging Using  

E-Print Network (OSTI)

The power consumption of the network interface plays a major role in determining the total operating lifetime of wireless handheld devices. On demand paging has been proposed earlier to reduce power consumption in cellular networks. In this scheme, a low power secondary radio is used to wake up the higher power radio, allowing the latter to sleep or remain off for longer periods of time. In this paper we present use of Bluetooth radios to serve as a paging channel for the 802.11 wireless LAN. We have implemented an on-demand paging scheme on a WLAN consisting of iPAQ PDAs equipped with Bluetooth radios and Cisco Aironet wireless networking cards. Our results show power saving ranging from 19% to 46% over the present 802.11b standard operating modes with negligible impact on performance.

Bluetooth Radios On; Yuvraj Agarwal; Rajesh K. Gupta

2003-01-01T23:59:59.000Z

295

ENERGY DEMAND FORECAST METHODS REPORT  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION ENERGY DEMAND FORECAST METHODS REPORT Companion Report to the California Energy Demand 2006-2016 Staff Energy Demand Forecast Report STAFFREPORT June 2005 CEC-400 .......................................................................................................................................1-1 ENERGY DEMAND FORECASTING AT THE CALIFORNIA ENERGY COMMISSION: AN OVERVIEW

296

Demand Forecast INTRODUCTION AND SUMMARY  

E-Print Network (OSTI)

Demand Forecast INTRODUCTION AND SUMMARY A 20-year forecast of electricity demand is a required of any forecast of electricity demand and developing ways to reduce the risk of planning errors that could arise from this and other uncertainties in the planning process. Electricity demand is forecast

297

Net Demand3 Production  

E-Print Network (OSTI)

Contract Number: DE-FE0004002 (Subcontract: S013-JTH-PPM4002 MOD 00) Summary The US DOE has identified a number of materials that are both used by clean energy technologies and are at risk of supply disruptions in the short term. Several of these materials, especially the rare earth elements (REEs) yttrium, cerium, and lanthanum were identified by DOE as critical (USDOE 2010) and are crucial to the function and performance of solid oxide fuel cells (SOFCs) 1. In addition, US DOE has issued a second Request For Information regarding uses of and markets for these critical materials (RFI;(USDOE 2011)). This report examines how critical materials demand for SOFC applications could impact markets for these materials and vice versa, addressing categories 1,2,5, and 6 in the RFI. Category 1 – REE Content of SOFC Yttria (yttrium oxide) is the only critical material (as defined for the timeframe of interest for SOFC) used in SOFC 2. Yttrium is used as a dopant in the SOFC’s core ceramic cells.. In addition, continuing developments in SOFC technology will likely further reduce REE demand for SOFC, providing credible scope for at least an additional 50 % reduction in REE use if desirable. Category 2 – Supply Chain and Market Demand SOFC developers expect to purchase

J. Thijssen Llc

2011-01-01T23:59:59.000Z

298

Interfirm strategic information flows in logistics supply chain relationships  

Science Conference Proceedings (OSTI)

This paper focuses on strategic information flows between buyers and suppliers within logistics supply chain relationships and on subsequent relationship-specific performance outcomes. Our analysis of dyadic data collected from 91 buyer-supplier logistics ... Keywords: IT customization, dependence, dyads, interfirm relationships, organization size, relational view, relationship longevity, strategic information flows, trust

Richard Klein; Arun Rai

2009-12-01T23:59:59.000Z

299

Building Energy Software Tools Directory: Demand Response Quick Assessment  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response Quick Assessment Tool Demand Response Quick Assessment Tool Demand response quick assessment tool image The opportunities for demand reduction and cost savings with building demand responsive controls vary tremendously with building type and location. This assessment tool will predict the energy and demand savings, the economic savings, and the thermal comfort impact for various demand responsive strategies. Users of the tool will be asked to enter the basic building information such as types, square footage, building envelope, orientation, utility schedule, etc. The assessment tool will then use the prototypical simulation models to calculate the energy and demand reduction potential under certain demand responsive strategies, such as precooling, zonal temperature set up, and chilled water loop and air loop set points

300

Smart Grid RFI: Addressing Policy and Logistical Challenges. Comments of  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Smart Grid RFI: Addressing Policy and Logistical Challenges. Smart Grid RFI: Addressing Policy and Logistical Challenges. Comments of the Alliance to Save Energy. Smart Grid RFI: Addressing Policy and Logistical Challenges. Comments of the Alliance to Save Energy. The Alliance to Save Energy is a coalition of prominent business, government, environmental, and consumer leaders who promote the efficient use of energy worldwide to benefit consumers, the environment, economy, and national security. The Alliance to Save Energy (the Alliance) thanks the Department of Energy for the opportunity to comment on broad issues of policy and logistical challenges faced in smart grid implementation. Smart Grid RFI: Addressing Policy and Logistical Challenges. Comments of the Alliance to Save Energy. More Documents & Publications

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


301

Southern Company: DOE Smart Grid RFI Addressing Policy and Logistical  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Southern Company: DOE Smart Grid RFI Addressing Policy and Southern Company: DOE Smart Grid RFI Addressing Policy and Logistical Challenges Southern Company: DOE Smart Grid RFI Addressing Policy and Logistical Challenges Southern Company: DOE Smart Grid RFI Addressing Policy and Logistical Challenges. Southern recognizes that many policy and logistical concerns must be addressed for the promises of smart grid technologies and applications to be fully realized in ways that are beneficial, secure, and cost-effective lor utility customers. Southern Company: DOE Smart Grid RFI Addressing Policy and Logistical Challenges More Documents & Publications Re: DOE Request for Information - Implementing the National Broadband Plan by Studying the Communications Requirements of Electric Utilities to Inform Federal Smart Grid Policy

302

California Energy Demand Scenario Projections to 2050  

E-Print Network (OSTI)

Natural Gas Demands..xi Annual natural gas demand for each alternativeused in natural gas demand projections. 34

McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

2008-01-01T23:59:59.000Z

303

FROM PLANT AND LOGISTICS CONTROL TO MULTI-ENTERPRISE COLLABORATION: Milestone report of the Manufacturing & Logistics Systems Coordinating Committee  

E-Print Network (OSTI)

, product life cycles shrink, and profit margins decrease. In addition, the capital costs of manufacturing of the Manufacturing & Logistics Systems Coordinating Committee S.Y. Nofa* , G. Morelb , L. Monostoric , A. Molinad , F-765-494-1299 Abstract: Current and emerging manufacturing and logistics systems are posing new challenges

Paris-Sud XI, Université de

304

California Energy Demand Scenario Projections to 2050  

E-Print Network (OSTI)

Minimum demand and Maximum demand incorporate assumptionslevels, or very minor Maximum demand household size, growthvehicles in Increasing Maximum demand 23 mpg truck share

McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

2008-01-01T23:59:59.000Z

305

Dividends with Demand Response  

SciTech Connect

To assist facility managers in assessing whether and to what extent they should participate in demand response programs offered by ISOs, we introduce a systematic process by which a curtailment supply curve can be developed that integrates costs and other program provisions and features. This curtailment supply curve functions as bid curve, which allows the facility manager to incrementally offer load to the market under terms and conditions acceptable to the customer. We applied this load curtailment assessment process to a stylized example of an office building, using programs offered by NYISO to provide detail and realism.

Kintner-Meyer, Michael CW; Goldman, Charles; Sezgen, O.; Pratt, D.

2003-10-31T23:59:59.000Z

306

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat-Turkey)  

Science Conference Proceedings (OSTI)

The purpose of this study is to compare the landslide susceptibility mapping methods of frequency ratio (FR), logistic regression and artificial neural networks (ANN) applied in the Kat County (Tokat-Turkey). Digital elevation model (DEM) was first constructed ... Keywords: Artificial neural networks, Frequency ratio, GIS, Kat (Tokat-Turkey), Landslide, Logistic regression, Susceptibility map

I??k Yilmaz

2009-06-01T23:59:59.000Z

307

Chinese demand drives global deforestation Chinese demand drives global deforestation  

E-Print Network (OSTI)

Chinese demand drives global deforestation Chinese demand drives global deforestation By Tansa Musa zones and do not respect size limits in their quest for maximum financial returns. "I lack words economy. China's demand for hardwood drives illegal logging says "Both illegal and authorized

308

Estimating a Demand System with Nonnegativity Constraints: Mexican Meat Demand  

E-Print Network (OSTI)

: Properties of the AIDS Generalized Maximum Entropy Estimator 24 #12;Estimating a Demand SystemEstimating a Demand System with Nonnegativity Constraints: Mexican Meat Demand Amos Golan* Jeffrey with nonnegativity constraints is presented. This approach, called generalized maximum entropy (GME), is more

Perloff, Jeffrey M.

309

CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST Demand Forecast report is the product of the efforts of many current and former California Energy Commission staff. Staff contributors to the current forecast are: Project Management and Technical Direction

310

Demand Controlled Ventilation and Classroom Ventilation  

NLE Websites -- All DOE Office Websites (Extended Search)

3 3 Authors Fisk, William J., Mark J. Mendell, Molly Davies, Ekaterina Eliseeva, David Faulkner, Tienzen Hong, and Douglas P. Sullivan Publisher Lawrence Berkeley National Laboratory City Berkeley Keywords absence, building s, carbon dioxide, demand - controlled ventilation, energy, indoor air quality, schools, ventilation Abstract This document summarizes a research effort on demand controlled ventilation and classroom ventilation. The research on demand controlled ventilation included field studies and building energy modeling. Major findings included: ï‚· The single-location carbon dioxide sensors widely used for demand controlled ventilation frequently have large errors and will fail to effectively control ventilation rates (VRs). ï‚· Multi-location carbon dioxide measurement systems with more expensive sensors connected to multi-location sampling systems may measure carbon dioxide more accurately.

311

Software demonstration: Demand Response Quick Assessment Tool  

NLE Websites -- All DOE Office Websites (Extended Search)

Software demonstration: Demand Response Quick Assessment Tool Software demonstration: Demand Response Quick Assessment Tool Speaker(s): Peng Xu Date: February 4, 2008 - 12:00pm Location: 90-3122 The potential for utilizing building thermal mass for load shifting and peak demand reduction has been demonstrated in a number of simulation, laboratory, and field studies. The Demand Response Quick Assessment Tools developed at LBNL will be demonstrated. The tool is built on EnergyPlus simulation and is able to evaluate and compare different DR strategies, such as global temperature reset, chiller cycling, supply air temperature reset, etc. A separate EnergyPlus plotting tool will also be demonstrated during this seminar. Users can use the tool to test EnergyPlus models, conduct parametric analysis, or compare multiple EnergyPlus simulation

312

NALDA (Naval Aviation Logistics Data Analysis) CAI (computer aided instruction)  

SciTech Connect

Data Systems Engineering Organization (DSEO) personnel developed a prototype computer aided instruction CAI system for the Naval Aviation Logistics Data Analysis (NALDA) system. The objective of this project was to provide a CAI prototype that could be used as an enhancement to existing NALDA training. The CAI prototype project was performed in phases. The task undertaken in Phase I was to analyze the problem and the alternative solutions and to develop a set of recommendations on how best to proceed. The findings from Phase I are documented in Recommended CAI Approach for the NALDA System (Duncan et al., 1987). In Phase II, a structured design and specifications were developed, and a prototype CAI system was created. A report, NALDA CAI Prototype: Phase II Final Report, was written to record the findings and results of Phase II. NALDA CAI: Recommendations for an Advanced Instructional Model, is comprised of related papers encompassing research on computer aided instruction CAI, newly developing training technologies, instructional systems development, and an Advanced Instructional Model. These topics were selected because of their relevancy to the CAI needs of NALDA. These papers provide general background information on various aspects of CAI and give a broad overview of new technologies and their impact on the future design and development of training programs. The paper within have been index separately elsewhere.

Handler, B.H. (Oak Ridge K-25 Site, TN (USA)); France, P.A.; Frey, S.C.; Gaubas, N.F.; Hyland, K.J.; Lindsey, A.M.; Manley, D.O. (Oak Ridge Associated Universities, Inc., TN (USA)); Hunnum, W.H. (North Carolina Univ., Chapel Hill, NC (USA)); Smith, D.L. (Memphis State Univ., TN (USA))

1990-07-01T23:59:59.000Z

313

Addressing Policy and Logistical Challenges to Smart Grid Implementation:  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Smart Grid Smart Grid Implementation: Comments by the Office of the Ohio Consumers' Counsel Addressing Policy and Logistical Challenges to Smart Grid Implementation: Comments by the Office of the Ohio Consumers' Counsel The Office of the Ohio Consumers' Counsel ("OCC") hereby submits the following comments in response to the United States Department of Energy ("DOE") Request for Information ("RFI") entitled "Addressing Policy and Logistical Challenges to Smart Grid Implementation" See 75 Fed. Reg. 57006 (September 17, 201 0). The RFI requests comments and information from interested parties to assist DOE in understanding "policy and logistical challenges that confront smart grid implementation, as well as recommendations on how to best overcome those challenges."

314

Smart Grid RFI: Addressing Policy and Logistical Challenges, Comments from  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

RFI: Addressing Policy and Logistical Challenges, RFI: Addressing Policy and Logistical Challenges, Comments from the Edison Electric Institute Smart Grid RFI: Addressing Policy and Logistical Challenges, Comments from the Edison Electric Institute The Edison Electric Institute ("EEI"), on behalf of its member companies, hereby submits the following comments in response to the request by the Department of Energy ("DOE" or "Department") for information on a wide range of issues dealing with Smart Grid technology, applications, consumer interaction, policy initiatives and economic impacts, including the definition of Smart Grid; interactions with and implications for residential, commercial and industrial customers; Smart Grid costs and benefits; collaboration between utilities, device manufacturers and energy

315

Valid Inequalities Based on Demand Propagation for Chemical ...  

E-Print Network (OSTI)

Oct 26, 2012 ... Valid Inequalities Based on Demand Propagation for Chemical Production Scheduling MIP Models. Sara Velez(szenner ***at*** wisc.edu)

316

Demand Response | Department of Energy  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response Demand Response Demand Response Demand Response Demand response provides an opportunity for consumers to play a significant role in the operation of the electric grid by reducing or shifting their electricity usage during peak periods in response to time-based rates or other forms of financial incentives. Demand response programs are being used by electric system planners and operators as resource options for balancing supply and demand. Such programs can lower the cost of electricity in wholesale markets, and in turn, lead to lower retail rates. Methods of engaging customers in demand response efforts include offering time-based rates such as time-of-use pricing, critical peak pricing, variable peak pricing, real time pricing, and critical peak rebates. It also includes direct load control programs which provide the

317

ELECTRICITY DEMAND FORECAST COMPARISON REPORT  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION ELECTRICITY DEMAND FORECAST COMPARISON REPORT STAFFREPORT June 2005 ..............................................................................3 Residential Forecast Comparison ..............................................................................................5 Nonresidential Forecast Comparisons

318

Volvo Logistics Corporation Returnable Packaging System.  

E-Print Network (OSTI)

?? This thesis is a study for analysing costs affected by packaging in a producing industry. The purpose is to develop a model that will… (more)

Beselin Hallberg, Jacob

2008-01-01T23:59:59.000Z

319

Overview of Demand Response  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

08 PJM 08 PJM www.pjm.com ©2003 PJM Overview of Demand Response PJM ©2008 PJM www.pjm.com ©2003 PJM Growth, Statistics, and Current Footprint AEP, Dayton, ComEd, & DUQ Dominion Generating Units 1,200 + Generation Capacity 165,000 MW Peak Load 144,644 MW Transmission Miles 56,070 Area (Square Miles) 164,250 Members 500 + Population Served 51 Million Area Served 13 States and DC Generating Units 1,200 + Generation Capacity 165,000 MW Peak Load 144,644 MW Transmission Miles 56,070 Area (Square Miles) 164,250 Members 500 + Population Served 51 Million Area Served 13 States and DC Current PJM RTO Statistics Current PJM RTO Statistics PJM Mid-Atlantic Integrations completed as of May 1 st , 2005 ©2008 PJM

320

Climate policy implications for agricultural water demand  

SciTech Connect

Energy, water and land are scarce resources, critical to humans. Developments in each affect the availability and cost of the others, and consequently human prosperity. Measures to limit greenhouse gas concentrations will inevitably exact dramatic changes on energy and land systems and in turn alter the character, magnitude and geographic distribution of human claims on water resources. We employ the Global Change Assessment Model (GCAM), an integrated assessment model to explore the interactions of energy, land and water systems in the context of alternative policies to limit climate change to three alternative levels: 2.5 Wm-2 (445 ppm CO2-e), 3.5 Wm-2 (535 ppm CO2-e) and 4.5 Wm-2 (645 ppm CO2-e). We explore the effects of two alternative land-use emissions mitigation policy options—one which taxes terrestrial carbon emissions equally with fossil fuel and industrial emissions, and an alternative which only taxes fossil fuel and industrial emissions but places no penalty on land-use change emissions. We find that increasing populations and economic growth could be anticipated to almost triple demand for water for agricultural systems across the century even in the absence of climate policy. In general policies to mitigate climate change increase agricultural demands for water still further, though the largest changes occur in the second half of the century, under both policy regimes. The two policies examined profoundly affected both the sources and magnitudes of the increase in irrigation water demands. The largest increases in agricultural irrigation water demand occurred in scenarios where only fossil fuel emissions were priced (but not land-use change emission) and were primarily driven by rapid expansion in bioenergy production. In these scenarios water demands were large relative to present-day total available water, calling into question whether it would be physically possible to produce the associated biomass energy. We explored the potential of improved water delivery and irrigation system efficiencies. These could potentially reduce demands substantially. However, overall demands remained high under our fossil-fuel-only tax policy. In contrast, when all carbon was priced, increases in agricultural water demands were smaller than under the fossil-fuel-only policy and were driven primarily by increased demands for water by non-biomass crops such as rice. Finally we estimate the geospatial pattern of water demands and find that regions such as China, India and other countries in south and east Asia might be expected to experience greatest increases in water demands.?

Chaturvedi, Vaibhav; Hejazi, Mohamad I.; Edmonds, James A.; Clarke, Leon E.; Kyle, G. Page; Davies, Evan; Wise, Marshall A.; Calvin, Katherine V.

2013-03-28T23:59:59.000Z

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


321

U.S. Marine Corp Logistics Base | Open Energy Information  

Open Energy Info (EERE)

Marine Corp Logistics Base Marine Corp Logistics Base Jump to: navigation, search Name U.S. Marine Corp Logistics Base Facility U.S. Marine Corp Logistics Base Sector Wind energy Facility Type Community Wind Facility Status In Service Location Barstow CA Coordinates 34.85832705°, -116.9559002° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":34.85832705,"lon":-116.9559002,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

322

NAP Coalition Response to DOE RFI: Addressing Policy and Logistical  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

NAP Coalition Response to DOE RFI: Addressing Policy and Logistical NAP Coalition Response to DOE RFI: Addressing Policy and Logistical Challenges to Smart Grid Implementation NAP Coalition Response to DOE RFI: Addressing Policy and Logistical Challenges to Smart Grid Implementation The NAP Coalition is a "Coalition of Coalitions" that has been formed for the purpose of implementing the National Action Plan released by FERC in cooperation with DOE in June of 2010. Organizations working together on NAP implementation in include EEI, APPA, NRECA, ASE, ACEEE, NASUCA, NARUC, NASEO, DRSG, DRCC and EDF. The NAP Coalition submits a response in this RFI only to question #14 in Section II of the RFI. NAP Coalition Response to DOE RFI: Addressing Policy and Logistical Challenges to Smart Grid Implementation More Documents & Publications

323

NAP Coalition Response to DOE RFI: Addressing Policy and Logistical  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

NAP Coalition Response to DOE RFI: Addressing Policy and Logistical NAP Coalition Response to DOE RFI: Addressing Policy and Logistical Challenges to Smart Grid Implementation NAP Coalition Response to DOE RFI: Addressing Policy and Logistical Challenges to Smart Grid Implementation The NAP Coalition is a "Coalition of Coalitions" that has been formed for the purpose of implementing the National Action Plan released by FERC in cooperation with DOE in June of 2010. Organizations working together on NAP implementation in include EEI, APPA, NRECA, ASE, ACEEE, NASUCA, NARUC, NASEO, DRSG, DRCC and EDF. The NAP Coalition submits a response in this RFI only to question #14 in Section II of the RFI. NAP Coalition Response to DOE RFI: Addressing Policy and Logistical Challenges to Smart Grid Implementation More Documents & Publications

324

MIT- Center for Transportation and Logistics | Open Energy Information  

Open Energy Info (EERE)

MIT- Center for Transportation and Logistics MIT- Center for Transportation and Logistics Jump to: navigation, search Logo: MIT- Center for Transportation and Logistics Name MIT- Center for Transportation and Logistics Address 77 Massachusetts Avenue Place Cambridge, Massachusetts Zip 02139 Region Greater Boston Area Coordinates 42.359089°, -71.093412° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":42.359089,"lon":-71.093412,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

325

Okaloosa Gas District Smart Grid RFI: Addressing Policy and Logistical  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Okaloosa Gas District Smart Grid RFI: Addressing Policy and Okaloosa Gas District Smart Grid RFI: Addressing Policy and Logistical Challenges to Smart Grid Implementation Okaloosa Gas District Smart Grid RFI: Addressing Policy and Logistical Challenges to Smart Grid Implementation Okaloosa Gas District (The District) an Independent Special District of the State of Florida is appreciative of the opportunity to submit for your consideration the following comments in response to the U.S. Department of Energy, Office of Electricity Delivery and Energy Reliability's Request for Information Addressing Policy and Logistical Challenges to Smart Grid Implementation, 75 Fed. Reg. 57,006 (Sep. 17, 2010). Okaloosa Gas District Smart Grid RFI: Addressing Policy and Logistical Challenges to Smart Grid Implementation More Documents & Publications

326

The use of a logistic map for key generation  

E-Print Network (OSTI)

A key generation scheme is proposed and its performance analyzed. The method, the logistic map scheme (LMS), is applicable for use on wireless networks because it does not require devices to engage in computationally ...

Ando, Megumi, M. Eng. Massachusetts Institute of Technology

2010-01-01T23:59:59.000Z

327

Forecasting Uncertain Hotel Room Demand  

E-Print Network (OSTI)

Economic systems are characterized by increasing uncertainty in their dynamics. This increasing uncertainty is likely to incur bad decisions that can be costly in financial terms. This makes forecasting of uncertain economic variables an instrumental activity in any organization. This paper takes the hotel industry as a practical application of forecasting using the Holt-Winters method. The problem here is to forecast the uncertain demand for rooms at a hotel for each arrival day. Forecasting is part of hotel revenue management system whose objective is to maximize the revenue by making decisions regarding when to make rooms available for customers and at what price. The forecast approach discussed in this paper is based on quantitative models and does not incorporate management expertise. Even though, forecast results are found to be satisfactory for certain days, this is not the case for other arrival days. It is believed that human judgment is important when dealing with ...

Mihir Rajopadhye Mounir; Mounir Ben Ghaliay; Paul P. Wang; Timothy Baker; Craig V. Eister

2001-01-01T23:59:59.000Z

328

Ethanol Demand in United States Gasoline Production  

SciTech Connect

The Oak Ridge National Laboratory (OWL) Refinery Yield Model (RYM) has been used to estimate the demand for ethanol in U.S. gasoline production in year 2010. Study cases examine ethanol demand with variations in world oil price, cost of competing oxygenate, ethanol value, and gasoline specifications. For combined-regions outside California summer ethanol demand is dominated by conventional gasoline (CG) because the premised share of reformulated gasoline (RFG) production is relatively low and because CG offers greater flexibility for blending high vapor pressure components like ethanol. Vapor pressure advantages disappear for winter CG, but total ethanol used in winter RFG remains low because of the low RFG production share. In California, relatively less ethanol is used in CG because the RFG production share is very high. During the winter in California, there is a significant increase in use of ethanol in RFG, as ethanol displaces lower-vapor-pressure ethers. Estimated U.S. ethanol demand is a function of the refiner value of ethanol. For example, ethanol demand for reference conditions in year 2010 is 2 billion gallons per year (BGY) at a refiner value of $1.00 per gallon (1996 dollars), and 9 BGY at a refiner value of $0.60 per gallon. Ethanol demand could be increased with higher oil prices, or by changes in gasoline specifications for oxygen content, sulfur content, emissions of volatile organic compounds (VOCS), and octane numbers.

Hadder, G.R.

1998-11-24T23:59:59.000Z

329

Carbon management in assembly manufacturing logistics  

Science Conference Proceedings (OSTI)

In this paper, we present the IBM Carbon Analyzer Tool, a software solution that models and quantifies carbon emissions and explores ways to reduce emissions through advanced analytics. The tool is designed to manage carbon emissions associated with ...

K. Sourirajan; P. Centonze; M. E. Helander; K. Katircioglu; M. Ben-Hamida; C. Boucher

2009-05-01T23:59:59.000Z

330

Assumptions to the Annual Energy Outlook 2002 - Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 9 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The distinction between the two sets of manufacturing industries pertains to the level of modeling. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 19). The Industrial Demand Module forecasts energy consumption at the four Census region levels; energy consumption at the Census Division level is allocated

331

Lessons for China from a comparison of logistics in the U.S. and China  

E-Print Network (OSTI)

Logistics efficiency is low in China. In 2008, total logistics costs accounted for 18.1% of gross domestic product (GDP) in China, which was almost twice that of the United States. Increasing logistics efficiency can save ...

Xiong, Ming, S.M. Massachusetts Institute of Technology

2010-01-01T23:59:59.000Z

332

Satisfiability of Elastic Demand in the Smart Grid  

E-Print Network (OSTI)

We study a stochastic model of electricity production and consumption where appliances are adaptive and adjust their consumption to the available production, by delaying their demand and possibly using batteries. The model incorporates production volatility due to renewables, ramp-up time, uncertainty about actual demand versus planned production, delayed and evaporated demand due to adaptation to insufficient supply. We study whether threshold policies stabilize the system. The proofs use Markov chain theory on general state space.

Tomozei, Dan-Cristian

2010-01-01T23:59:59.000Z

333

Demand Response Programs, 6. edition  

Science Conference Proceedings (OSTI)

The report provides a look at the past, present, and future state of the market for demand/load response based upon market price signals. It is intended to provide significant value to individuals and companies who are considering participating in demand response programs, energy providers and ISOs interested in offering demand response programs, and consultants and analysts looking for detailed information on demand response technology, applications, and participants. The report offers a look at the current Demand Response environment in the energy industry by: defining what demand response programs are; detailing the evolution of program types over the last 30 years; discussing the key drivers of current initiatives; identifying barriers and keys to success for the programs; discussing the argument against subsidization of demand response; describing the different types of programs that exist including:direct load control, interruptible load, curtailable load, time-of-use, real time pricing, and demand bidding/buyback; providing examples of the different types of programs; examining the enablers of demand response programs; and, providing a look at major demand response programs.

NONE

2007-10-15T23:59:59.000Z

334

Addressing Energy Demand through Demand Response: International Experiences and Practices  

E-Print Network (OSTI)

2007 EMCS EPACT ERCOT FCM FERC FRCC demand side managementEnergy Regulatory Commission (FERC). EPAct began the processin wholesale markets, which FERC Order 888 furthered by

Shen, Bo

2013-01-01T23:59:59.000Z

335

The optimal approach for parameter settings based on adjustable contracting capacity for the hospital supply chain logistics system  

Science Conference Proceedings (OSTI)

This paper establishes a simulation model for the supply chain of the hospital logistic system (SCHLS) based on the dynamic Taguchi method. The model derives optimal factor level combinations in the SCHLS setting when establishing adjustable contracting ... Keywords: Genetic algorithm (GA), Neural network (NN), Supply chain (SC), Taguchi method

Hung-Chang Liao; Hsu-Hwa Chang

2011-05-01T23:59:59.000Z

336

file://P:\\Smart Grid\\Smart Grid RFI Policy and Logistical Comme  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

24 Policy and Logistical Challenges to Smart Grid Implementation Comment On: DOE-HQ-2010-0024-0001 Policy and Logistical Challenges to Smart Grid Implementation Document:...

337

electricity demand | OpenEI  

Open Energy Info (EERE)

demand demand Dataset Summary Description The New Zealand Ministry of Economic Development publishes energy data including many datasets related to electricity. Included here are three electricity consumption and demand datasets, specifically: annual observed electricity consumption by sector (1974 to 2009); observed percentage of consumers by sector (2002 - 2009); and regional electricity demand, as a percentage of total demand (2009). Source New Zealand Ministry of Economic Development Date Released Unknown Date Updated July 03rd, 2009 (5 years ago) Keywords Electricity Consumption electricity demand energy use by sector New Zealand Data application/vnd.ms-excel icon Electricity Consumption by Sector (1974 - 2009) (xls, 46.1 KiB) application/vnd.ms-excel icon Percentage of Consumers by Sector (2002 - 2009) (xls, 43.5 KiB)

338

Annual World Oil Demand Growth  

Gasoline and Diesel Fuel Update (EIA)

6 6 Notes: Following relatively small increases of 1.3 million barrels per day in 1999 and 0.9 million barrels per day in 2000, EIA is estimating world demand may grow by 1.6 million barrels per day in 2001. Of this increase, about 3/5 comes from non-OECD countries, while U.S. oil demand growth represents more than half of the growth projected in OECD countries. Demand in Asia grew steadily during most of the 1990s, with 1991-1997 average growth per year at just above 0.8 million barrels per day. However, in 1998, demand dropped by 0.3 million barrels per day as a result of the Asian economic crisis that year. Since 1998, annual growth in oil demand has rebounded, but has not yet reached the average growth seen during 1991-1997. In the Former Soviet Union, oil demand plummeted during most of the

339

Demand Response Valuation Frameworks Paper  

E-Print Network (OSTI)

lvi Southern California Edison filed its SmartConnectinfrastructure (e.g. , Edison Electric Institute, DemandSouthern California Edison Standard Practice Manual

Heffner, Grayson

2010-01-01T23:59:59.000Z

340

Automated Demand Response and Commissioning  

NLE Websites -- All DOE Office Websites (Extended Search)

and Commissioning Title Automated Demand Response and Commissioning Publication Type Conference Paper LBNL Report Number LBNL-57384 Year of Publication 2005 Authors Piette, Mary...

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


341

Demand Uncertainty and Price Dispersion.  

E-Print Network (OSTI)

??Demand uncertainty has been recognized as one factor that may cause price dispersion in perfectly competitive markets with costly and perishable capacity. With the persistence… (more)

Li, Suxi

2007-01-01T23:59:59.000Z

342

1995 Demand-Side Managment  

U.S. Energy Information Administration (EIA)

U.S. Electric Utility Demand-Side Management 1995 January 1997 Energy Information Administration Office of Coal, Nuclear, Electric and Alternate Fuels

343

System Demand-Side Management: Regional results  

DOE Green Energy (OSTI)

To improve the Bonneville Power Administration's (Bonneville's) ability to analyze the value and impacts of demand-side programs, Pacific Northwest Laboratory (PNL) developed and implemented the System Demand-Side Management (SDSM) model, a microcomputer-based model of the Pacific Northwest Public Power system. This document outlines the development and application of the SDSM model, which is an hourly model. Hourly analysis makes it possible to examine the change in marginal revenues and marginal costs that accrue from the movement of energy consumption from daytime to nighttime. It also allows a more insightful analysis of programs such as water heater control in the context of hydroelectric-based generation system. 7 refs., 10 figs., 10 tabs.

Englin, J.E.; Sands, R.D.; De Steese, J.G.; Marsh, S.J.

1990-05-01T23:59:59.000Z

344

Demand Responsive Lighting: A Scoping Study  

E-Print Network (OSTI)

8 Figure 7: Maximum Demands Savings Intensity due toaddressed in this report. Maximum Demand Savings Intensity (Echelon Figure 7: Maximum Demands Savings Intensity due to

Rubinstein, Francis; Kiliccote, Sila

2007-01-01T23:59:59.000Z

345

Coordination of Energy Efficiency and Demand Response  

E-Print Network (OSTI)

energy efficiency and demand response programs and tariffs.energy efficiency and demand response program and tariffenergy efficiency and demand response programs and tariffs.

Goldman, Charles

2010-01-01T23:59:59.000Z

346

Wireless Demand Response Controls for HVAC Systems  

E-Print Network (OSTI)

Strategies Linking Demand Response and Energy Efficiency,”Fully Automated Demand Response Tests in Large Facilities,technical support from the Demand Response Research Center (

Federspiel, Clifford

2010-01-01T23:59:59.000Z

347

Demand Response Quick Assessment Tool (DRQAT)  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response Quick Assessment Tool (DRQAT) The opportunities for demand reduction and cost saving with building demand responsive control vary tremendously with building type...

348

Demand Responsive Lighting: A Scoping Study  

E-Print Network (OSTI)

2 2.0 Demand ResponseFully Automated Demand Response Tests in Large Facilities,was coordinated by the Demand Response Research Center and

Rubinstein, Francis; Kiliccote, Sila

2007-01-01T23:59:59.000Z

349

Coupling Renewable Energy Supply with Deferrable Demand  

E-Print Network (OSTI)

8.4 Demand Response Integration . . . . . . . . . . .for each day type for the demand response study - moderatefor each day type for the demand response study - moderate

Papavasiliou, Anthony

2011-01-01T23:59:59.000Z

350

Installation and Commissioning Automated Demand Response Systems  

E-Print Network (OSTI)

their partnership in demand response automation research andand Techniques for Demand Response. LBNL Report 59975. Mayof Fully Automated Demand Response in Large Facilities.

Kiliccote, Sila; Global Energy Partners; Pacific Gas and Electric Company

2008-01-01T23:59:59.000Z

351

Coordination of Energy Efficiency and Demand Response  

E-Print Network (OSTI)

and D. Kathan (2009). Demand Response in U.S. ElectricityEnergy Financial Group. Demand Response Research Center [2008). Assessment of Demand Response and Advanced Metering.

Goldman, Charles

2010-01-01T23:59:59.000Z

352

Strategies for Demand Response in Commercial Buildings  

E-Print Network (OSTI)

Fully Automated Demand Response Tests in Large Facilities”of Fully Automated Demand Response in Large Facilities”,was coordinated by the Demand Response Research Center and

Watson, David S.; Kiliccote, Sila; Motegi, Naoya; Piette, Mary Ann

2006-01-01T23:59:59.000Z

353

Retail Demand Response in Southwest Power Pool  

E-Print Network (OSTI)

23 ii Retail Demand Response in SPP List of Figures and10 Figure 3. Demand Response Resources by11 Figure 4. Existing Demand Response Resources by Type of

Bharvirkar, Ranjit

2009-01-01T23:59:59.000Z

354

Option Value of Electricity Demand Response  

E-Print Network (OSTI)

Table 1. “Economic” demand response and real time pricing (Implications of Demand Response Programs in CompetitiveAdvanced Metering, and Demand Response in Electricity

Sezgen, Osman; Goldman, Charles; Krishnarao, P.

2005-01-01T23:59:59.000Z

355

A spatial agent-based model for assessing strategies of adaptation to climate and tourism demand changes in an alpine tourism destination  

Science Conference Proceedings (OSTI)

A vast body of literature suggests that the European Alpine Region is amongst the most sensitive socio-ecosystems to climate change impacts. Our model represents the winter tourism socio-ecosystem of Auronzo di Cadore, located in the Dolomites (Italy), ... Keywords: Adaptation strategies, Alpine tourism, Climate change, Social simulation, Spatial agent-based model

Stefano Balbi, Carlo Giupponi, Pascal Perez, Marco Alberti

2013-07-01T23:59:59.000Z

356

Wireless Demand Response Controls for HVAC Systems  

Science Conference Proceedings (OSTI)

The objectives of this scoping study were to develop and test control software and wireless hardware that could enable closed-loop, zone-temperature-based demand response in buildings that have either pneumatic controls or legacy digital controls that cannot be used as part of a demand response automation system. We designed a SOAP client that is compatible with the Demand Response Automation Server (DRAS) being used by the IOUs in California for their CPP program, design the DR control software, investigated the use of cellular routers for connecting to the DRAS, and tested the wireless DR system with an emulator running a calibrated model of a working building. The results show that the wireless DR system can shed approximately 1.5 Watts per design CFM on the design day in a hot, inland climate in California while keeping temperatures within the limits of ASHRAE Standard 55: Thermal Environmental Conditions for Human Occupancy.

Federspiel, Clifford

2009-06-30T23:59:59.000Z

357

Centralized and Decentralized Control for Demand Response  

Science Conference Proceedings (OSTI)

Demand response has been recognized as an essential element of the smart grid. Frequency response, regulation and contingency reserve functions performed traditionally by generation resources are now starting to involve demand side resources. Additional benefits from demand response include peak reduction and load shifting, which will defer new infrastructure investment and improve generator operation efficiency. Technical approaches designed to realize these functionalities can be categorized into centralized control and decentralized control, depending on where the response decision is made. This paper discusses these two control philosophies and compares their relative advantages and disadvantages in terms of delay time, predictability, complexity, and reliability. A distribution system model with detailed household loads and controls is built to demonstrate the characteristics of the two approaches. The conclusion is that the promptness and reliability of decentralized control should be combined with the predictability and simplicity of centralized control to achieve the best performance of the smart grid.

Lu, Shuai; Samaan, Nader A.; Diao, Ruisheng; Elizondo, Marcelo A.; Jin, Chunlian; Mayhorn, Ebony T.; Zhang, Yu; Kirkham, Harold

2011-04-29T23:59:59.000Z

358

Scenario Analysis of Peak Demand Savings for Commercial Buildings with  

NLE Websites -- All DOE Office Websites (Extended Search)

Scenario Analysis of Peak Demand Savings for Commercial Buildings with Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California Title Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California Publication Type Conference Paper LBNL Report Number LBNL-3636e Year of Publication 2010 Authors Yin, Rongxin, Sila Kiliccote, Mary Ann Piette, and Kristen Parrish Conference Name 2010 ACEEE Summer Study on Energy Efficiency in Buildings Conference Location Pacific Grove, CA Keywords demand response and distributed energy resources center, demand response research center, demand shifting (pre-cooling), DRQAT Abstract This paper reports on the potential impact of demand response (DR) strategies in commercial buildings in California based on the Demand Response Quick Assessment Tool (DRQAT), which uses EnergyPlus simulation prototypes for office and retail buildings. The study describes the potential impact of building size, thermal mass, climate, and DR strategies on demand savings in commercial buildings. Sensitivity analyses are performed to evaluate how these factors influence the demand shift and shed during the peak period. The whole-building peak demand of a commercial building with high thermal mass in a hot climate zone can be reduced by 30% using an optimized demand response strategy. Results are summarized for various simulation scenarios designed to help owners and managers understand the potential savings for demand response deployment. Simulated demand savings under various scenarios were compared to field-measured data in numerous climate zones, allowing calibration of the prototype models. The simulation results are compared to the peak demand data from the Commercial End-Use Survey for commercial buildings in California. On the economic side, a set of electricity rates are used to evaluate the impact of the DR strategies on economic savings for different thermal mass and climate conditions. Our comparison of recent simulation to field test results provides an understanding of the DR potential in commercial buildings.

359

Logistic Management Forms (4000-4999) | Department of Energy  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Logistic Management Forms (4000-4999) Logistic Management Forms (4000-4999) Logistic Management Forms (4000-4999) DOE F 4200.33 (fillable pdf) Procurement Request-Authorization DOE F 4200.34 (fillable pdf) Procurement Request-Authorization Funding Data (Continuation Sheet) DOE F 4200.40 (pdf) Individual Procurement Action Report (IPAR) DOE F 4200.40A (pdf) Individual Procurement Action Report (IPAR) DOE F 4200.41 (pdf) Individual Procurement Action Report Supplement for Procurement and Financial Assistance DOE F 4220.2 (fillable pdf) Small Business Review DOE F 4220.10 (fillable pdf) Congressional and Intergovernmental Affairs (CI) Notification DOE F 4220.23 (pdf) Weighted Guidelines Profit/Fee Objective DOE F 4250.2 (fillable pdf) Requisition for Supplies, Equipment, or Service DOE F 4250.3

360

Smart Grid RFI: Addressing Policy and Logistical Challenges. Comments of  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Challenges. Challenges. Comments of the Alliance to Save Energy. Smart Grid RFI: Addressing Policy and Logistical Challenges. Comments of the Alliance to Save Energy. The Alliance to Save Energy is a coalition of prominent business, government, environmental, and consumer leaders who promote the efficient use of energy worldwide to benefit consumers, the environment, economy, and national security. The Alliance to Save Energy (the Alliance) thanks the Department of Energy for the opportunity to comment on broad issues of policy and logistical challenges faced in smart grid implementation. Smart Grid RFI: Addressing Policy and Logistical Challenges. Comments of the Alliance to Save Energy. More Documents & Publications DC OPC Comments. September 17, 2010 Comments of the National Rural Electric Cooperative Association, Request

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


361

Enhancing Transportation Education through On-line Simulation Using an Agent-based Demand and Assignment Model (07-0533) presented at the 86th Annual Meeting of the Transportation Research Board in  

E-Print Network (OSTI)

This research explores the effectiveness of using simulation as a tool for enhancing classroom learning in the Civil Engineering Department of the University of Minnesota at Twin Cities. The authors developed a modern transportation planning software package, Agent-based Demand and Assignment Model (ADAM), that is consistent with our present understanding of travel behavior, that is platform independent, and that is easy to learn and is thus usable by students. An in-class project incorporated ADAM and the performance of this education strategy was evaluated through pre-class survey, post-class survey, scores in the quiz focusing on travel demand modeling and final scores. Results showed that ADAM effectively enhanced students ’ self-reported understanding of transportation planning and their skills of forming opinions, evaluating projects and making judgments. Students of some learning styles were found to benefit more than others through simulation-based teaching strategy. Findings in this research could have significant implications for future practice of simulation-based teaching strategy.

Shanjiang Zhu; Feng Xie; David Levinson

2007-01-01T23:59:59.000Z

362

title Automated Price and Demand Response Demonstration for Large Customers  

NLE Websites -- All DOE Office Websites (Extended Search)

Automated Price and Demand Response Demonstration for Large Customers Automated Price and Demand Response Demonstration for Large Customers in New York City using OpenADR booktitle International Conference for Enhanced Building Operations ICEBO year month address Montreal Quebec abstract p class p1 Open Automated Demand Response OpenADR an XML based information exchange model is used to facilitate continuous price responsive operation and demand response participation for large commercial buildings in New York who are subject to the default day ahead hourly pricing We summarize the existing demand response programs in New York and discuss OpenADR communication prioritization of demand response signals and control methods Building energy simulation models are developed and field tests are conducted to evaluate continuous energy management

363

Defense waste transportation: cost and logistics studies  

SciTech Connect

Transportation of nuclear wastes from defense programs is expected to significantly increase in the 1980s and 1990s as permanent waste disposal facilities come into operation. This report uses models of the defense waste transportation system to quantify potential transportation requirements for treated and untreated contact-handled transuranic (CH-TRU) wastes and high-level defense wastes (HLDW). Alternative waste management strategies in repository siting, waste retrieval and treatment, treatment facility siting, waste packaging and transportation system configurations were examined to determine their effect on transportation cost and hardware requirements. All cost estimates used 1980 costs. No adjustments were made for future changes in these costs relative to inflation. All costs are reported in 1980 dollars. If a single repository is used for defense wastes, transportation costs for CH-TRU waste currently in surface storage and similar wastes expected to be generated by the year 2000 were estimated to be 109 million dollars. Recovery and transport of the larger buried volumes of CH-TRU waste will increase CH-TRU waste transportation costs by a factor of 70. Emphasis of truck transportation and siting of multiple repositories would reduce CH-TRU transportation costs. Transportation of HLDW to repositories for 25 years beginning in 1997 is estimated to cost $229 M in 1980 costs and dollars. HLDW transportation costs could either increase or decrease with the selection of a final canister configuration. HLDW transportation costs are reduced when multiple repositories exist and emphasis is placed on truck transport.

Andrews, W.B.; Cole, B.M.; Engel, R.L.; Oylear, J.M.

1982-08-01T23:59:59.000Z

364

China, India demand cushions prices  

SciTech Connect

Despite the hopes of coal consumers, coal prices did not plummet in 2006 as demand stayed firm. China and India's growing economies, coupled with solid supply-demand fundamentals in North America and Europe, and highly volatile prices for alternatives are likely to keep physical coal prices from wide swings in the coming year.

Boyle, M.

2006-11-15T23:59:59.000Z

365

Harnessing the power of demand  

Science Conference Proceedings (OSTI)

Demand response can provide a series of economic services to the market and also provide ''insurance value'' under low-likelihood, but high-impact circumstances in which grid reliablity is enhanced. Here is how ISOs and RTOs are fostering demand response within wholesale electricity markets. (author)

Sheffrin, Anjali; Yoshimura, Henry; LaPlante, David; Neenan, Bernard

2008-03-15T23:59:59.000Z

366

Application of cycle-based simulation to estimate loss of logistics productivity on construction sites  

Science Conference Proceedings (OSTI)

Logistics management is a critical factor that determines the successful delivery of a construction project. The logistics activities have close connection with other logistics/construction activities, often producing hazards on site. Moreover, the policies ... Keywords: cycle-based simulation, hazard prevention, hazardous interaction, logistics productivity loss, safety

Feng Xu; Yuanbin Song; Hao Hu

2012-09-01T23:59:59.000Z

367

Qualitative choice modeling of energy-conservation decisions: a micro-economic analysis of the determinants of residential space-heating energy demand  

Science Conference Proceedings (OSTI)

This study develops an economic model of household decisions to install major conservation measures such as storm windows, attic insulation, and wall insulation. The structural core of the model is the neoclassical economic paradigm of constrained discounted expected utility maximization. Household choices are modeled as being determined by household preferences across space-heating comfort levels and a composite of all other goods and services. These preferences interact with alternative household budget constraints which are determined by the household's conservation decisions. Nested Logit estimation techniques, using the observed discrete choices of a representative sample of households (in owner-occupied, single-family dwellings), are shown to be superior to simple Multinomial Logit estimation. This superiority arises from the importance of correlation among the error terms associated with indirect utility derived from certain subsets of available conservation alternatives.

Cameron, T.A.

1982-01-01T23:59:59.000Z

368

Strategies for Demand Response in Commercial Buildings  

E-Print Network (OSTI)

the average and maximum peak demand savings. The electricity1: Average and Maximum Peak Electric Demand Savings during

Watson, David S.; Kiliccote, Sila; Motegi, Naoya; Piette, Mary Ann

2006-01-01T23:59:59.000Z

369

Demand Response Opportunities in Industrial Refrigerated Warehouses...  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response Opportunities in Industrial Refrigerated Warehouses in California Title Demand Response Opportunities in Industrial Refrigerated Warehouses in California...

370

Comparison of energy modeling and laboratory tests on green roof potential to decrease the cooling demand for North European office buildings  

Science Conference Proceedings (OSTI)

Greenroofs have been shown to reduce the rooftop heat transfer, offering enhancement to a building's thermal resistance or R-value in warm climate zones. However a comprehensive study of neither the magnitude of that effect, nor the impact of green roof ... Keywords: cooling load, energy efficiency, energy modeling, greenroofs

Hendrik Voll; Teet-Andrus Kõiv

2011-05-01T23:59:59.000Z

371

Demand and Price Uncertainty: Rational Habits in International Gasoline Demand  

E-Print Network (OSTI)

global gasoline and diesel price and income elasticities.shift in the short-run price elasticity of gasoline demand.Habits and Uncertain Relative Prices: Simulating Petrol Con-

Scott, K. Rebecca

2013-01-01T23:59:59.000Z

372

Simulation as a support tool for training logistic operators  

Science Conference Proceedings (OSTI)

Ports play a very important role in the economy of a nation, as well as other crucial infrastructures like railways, motorways and airports, because, if managed properly, can significantly increase the competitiveness of a particular area. For this reason ... Keywords: CBT (computer based training) logistics, VV&A (validation, verification and accreditation), simulation, virtual reality

Enrico Briano; Claudia Caballini

2012-02-01T23:59:59.000Z

373

Planning and control of logistics for offshore wind farms  

Science Conference Proceedings (OSTI)

Construction and utilization of offshore wind farms will increase within the next years. So far the first German offshore wind farm was constructed and put into operation by "Alpha Ventus". Experiences illustrate that bad weather conditions are the main ... Keywords: MILP, installation scheduling, maritime logistics, offshore wind farm, supply chain

Bernd Scholz-Reiter; Michael Lütjen; Jens Heger; Anne Schweizer

2010-11-01T23:59:59.000Z

374

Effects of the drought on California electricity supply and demand  

E-Print Network (OSTI)

Acknowledgments SUMMARY Electricity Demand ElectricityAdverse Impacts ELECTRICITY DEMAND . . . .Demand forElectricity Sales Electricity Demand by Major Utility

Benenson, P.

2010-01-01T23:59:59.000Z

375

Annual Review of Demand-Side Planning Research: 1985 Proceedings  

Science Conference Proceedings (OSTI)

EPRI's demand-side planning research spans a wide range of utility activities: planning and evaluating demand-side management programs, investigating end-use forecasting techniques, and analyzing the effect of innovative rates. Reflecting efforts to develop utility applications of EPRI research products in 1985, this report focuses on computer models such as REEPS, COMMEND, HELM, and INDEPTH.

None

1987-01-01T23:59:59.000Z

376

Environmental Considerations for Backup Generation Applications to Demand Response  

Science Conference Proceedings (OSTI)

This report investigates the pros and cons of customer backup generation (BUG) for offsetting electric demand through demand response programs. The report examines the environmental issues related to this technology and contrasts this information with air quality and environmental agency regulations prevalent in California, Texas, and the model emissions standards being developed in the United States.

2002-11-22T23:59:59.000Z

377

Demand Response Research in Spain  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response Research in Spain Demand Response Research in Spain Speaker(s): Iñigo Cobelo Date: August 22, 2007 - 12:00pm Location: 90-3122 Seminar Host/Point of Contact: Mary Ann Piette The Spanish power system is becoming increasingly difficult to operate. The peak load grows every year, and the permission to build new transmission and distribution infrastructures is difficult to obtain. In this scenario Demand Response can play an important role, and become a resource that could help network operators. The present deployment of demand response measures is small, but this situation however may change in the short term. The two main Spanish utilities and the transmission network operator are designing research projects in this field. All customer segments are targeted, and the research will lead to pilot installations and tests.

378

EIA - AEO2010 - Electricity Demand  

Gasoline and Diesel Fuel Update (EIA)

Electricity Demand Electricity Demand Annual Energy Outlook 2010 with Projections to 2035 Electricity Demand Figure 69. U.S. electricity demand growth 1950-2035 Click to enlarge » Figure source and data excel logo Figure 60. Average annual U.S. retail electricity prices in three cases, 1970-2035 Click to enlarge » Figure source and data excel logo Figure 61. Electricity generation by fuel in three cases, 2008 and 2035 Click to enlarge » Figure source and data excel logo Figure 62. Electricity generation capacity additions by fuel type, 2008-2035 Click to enlarge » Figure source and data excel logo Figure 63. Levelized electricity costs for new power plants, 2020 and 2035 Click to enlarge » Figure source and data excel logo Figure 64. Electricity generating capacity at U.S. nuclear power plants in three cases, 2008, 2020, and 2035

379

Winter Demand Impacted by Weather  

Gasoline and Diesel Fuel Update (EIA)

8 Notes: Heating oil demand is strongly influenced by weather. The "normal" numbers are the expected values for winter 2000-2001 used in EIA's Short-Term Energy Outlook. The chart...

380

Demand for money in China .  

E-Print Network (OSTI)

??This research investigates the long-run equilibrium relationship between money demand and its determinants in China over the period 1952-2004 for three definitions of money –… (more)

Zhang, Qing

2006-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


381

Thermal Mass and Demand Response  

NLE Websites -- All DOE Office Websites (Extended Search)

Thermal Mass and Demand Response Speaker(s): Gregor Henze Phil C. Bomrad Date: November 2, 2011 - 12:00pm Location: 90-4133 Seminar HostPoint of Contact: Janie Page The topic of...

382

Automated Demand Response and Commissioning  

E-Print Network (OSTI)

Conference on Building Commissioning: May 4-6, 2005 Motegi,National Conference on Building Commissioning: May 4-6, 2005Demand Response and Commissioning Mary Ann Piette, David S.

Piette, Mary Ann; Watson, David S.; Motegi, Naoya; Bourassa, Norman

2005-01-01T23:59:59.000Z

383

Distillate Demand Strong Last Winter  

Gasoline and Diesel Fuel Update (EIA)

4 Notes: Well, distillate fuel demand wasn't the reason that stocks increased in January 2001 and kept prices from going higher. As you will hear shortly, natural gas prices spiked...

384

Leslie Mancebo (7234) Transportation Demand &  

E-Print Network (OSTI)

Leslie Mancebo (7234) Transportation Demand & Marketing Coordinator 1 FTE, 1 HC Administrative Vice Chancellor Transportation and Parking Services Clifford A. Contreras (0245) Director 30.10 FTE Alternative Transportation & Marketing Reconciliation Lourdes Lupercio (4723) Michelle McArdle (7512) Parking

Hammock, Bruce D.

385

STEO December 2012 - coal demand  

U.S. Energy Information Administration (EIA) Indexed Site

coal demand seen below 1 billion tons in 2012 for fourth year in a row Coal consumption by U.S. power plants to generate electricity is expected to fall below 1 billion tons in...

386

Demand Response Spinning Reserve Demonstration  

Science Conference Proceedings (OSTI)

The Demand Response Spinning Reserve project is a pioneeringdemonstration of how existing utility load-management assets can providean important electricity system reliability resource known as spinningreserve. Using aggregated demand-side resources to provide spinningreserve will give grid operators at the California Independent SystemOperator (CAISO) and Southern California Edison (SCE) a powerful, newtool to improve system reliability, prevent rolling blackouts, and lowersystem operating costs.

Eto, Joseph H.; Nelson-Hoffman, Janine; Torres, Carlos; Hirth,Scott; Yinger, Bob; Kueck, John; Kirby, Brendan; Bernier, Clark; Wright,Roger; Barat, A.; Watson, David S.

2007-05-01T23:59:59.000Z

387

Development of a data base and forecasting model for commercial-sector electricity usage and demand. Volume VII. Detailed survey, sampling methodology  

Science Conference Proceedings (OSTI)

This report describes the work performed toward obtaining two sets of primary data, from which econometric and engineering parameters for the model were to be derived. The first type will be collected in a mail survey of utility-company customers determined by an analysis of customer-account data. These data have been collected from Pacific Gas and Electric, Los Angeles Div. of Water and Power, San Diego Gas and Electric, and Sacramento Municipal Utility District (SMUD) and have been analyzed and the survey customers selected. The second type will consist of detailed technical data on buildings in the SMSA's of Los Angeles, San Diego, San Francisco, and Sacramento. This report presents the final methodology for the selection of building samples, by type and location, for the detailed building data collection. Eleven tables present the results of the analysis. Within service areas and/or SMSA's, significant establishment classifications are illustrated with their energy characteristics. The allocation of the detailed survey-sample members is illustrated, according to establishment classifications and the 24 different building types. This specification is further detailed as to allocations within the SMUD service area and those to be taken from other areas. The methodology presented in this final report is being used to select sample members for the detailed survey.

Not Available

1980-02-01T23:59:59.000Z

388

EIA - Assumptions to the Annual Energy Outlook 2008 - Industrial Demand  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2008 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 21 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module projects energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region projection using the SEDS1 data.

389

National Action Plan on Demand Response  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Action Plan on Demand National Action Plan on Demand Action Plan on Demand National Action Plan on Demand Response Response Federal Utilities Partnership Working Group Federal Utilities Partnership Working Group November 18, 2008 November 18, 2008 Daniel Gore Daniel Gore Office of Energy Market Regulation Office of Energy Market Regulation Federal Energy Regulatory Commission Federal Energy Regulatory Commission The author's views do not necessarily represent the views of the Federal Energy Regulatory Commission Presentation Contents Presentation Contents Statutory Requirements Statutory Requirements National Assessment [Study] of Demand Response National Assessment [Study] of Demand Response National Action Plan on Demand Response National Action Plan on Demand Response General Discussion on Demand Response and Energy Outlook

390

Addressing Policy and Logistical Challenges to smart grid Implementation:  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

smart grid smart grid Implementation: eMeter Response to Department of Energy RFI Addressing Policy and Logistical Challenges to smart grid Implementation: eMeter Response to Department of Energy RFI eMeter is a smart grid software company that provides smart network application platform (SNAP) software to integrate smart meters and smart grid communications networks and devices with utility IT systems. eMeter also provides smart grid application software such as meter data management (MDM) and consumer engagement software. Being vendor-neutral toward all meter, hardware, and legacy utility software systems (e.g. CIS and Billing), eMeter has a unique, unbiased and global perspective on smart grid IT issues. Addressing Policy and Logistical Challenges to smart grid Implementation:

391

Overview of AREVA Logistics Business Unit Capabilities and Expertise  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Outline Outline Presentation Outline Overview of AREVA Logistics Business Unit capabilities and E ti Expertise Overview of Transnuclear Inc Transportation Capabilities in the United States Questions Quick Reminder of Fuel Cycle - p.2 AREVA Logistics Business Unit - p.3 Around 4 000 transports each year Around 4,000 transports each year More than 200 transports of used fuel (France and Europe), of vitrified and compacted waste (Europe and Japan) of vitrified and compacted waste (Europe and Japan) More than 150 MOX fuel transports More than 300 transports of low level waste More than 2,700 front-end transports More than 400 transports of heavy industrial equipment Around 150 transports for research reactors and laboratories - p.4 Around 150 transports for research reactors and laboratories Design, Testing and Licensing:

392

Addressing Policy and Logistical Challenges to Smart Grid Implementation  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Before the Before the Department of Energy Washington, D.C. 20585 In the Matter of Addressing Policy and Logistical Challenges to Smart Grid Implementation Smart Grid RFI: Addressing Policy and Logistical Challenges COMMENTS OF BALTIMORE GAS & ELECTRIC COMPANY I. Introduction BGE is the nation's oldest utility company. It has met the energy needs of Central Maryland for nearly 200 years. Today, it serves more than 1.2 million business and residential electric customers and approximately 650,000 gas customers in an economically diverse, 2,300- square-mile area encompassing Baltimore City and all or part of 10 central Maryland counties. BGE already has many systems that it considers to be "smart." For example:

393

Smart Grid RFI: Addressing Policy and Logistical Challenges  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Electricity Delivery and Energy Reliability Electricity Delivery and Energy Reliability 1000 Independence Avenue, SW Room 8H033 Washington, DC 20585 Submitted electronically via smartgridpolicy@hq.doe.gov Smart Grid Request for Information: Addressing Policy and Logistical Challenges Comments of the Alliance to Save Energy The Alliance to Save Energy (the Alliance) thanks the Department of Energy for the opportunity to comment on broad issues of policy and logistical challenges faced in smart grid implementation. The Alliance to Save Energy is a coalition of prominent business, government, environmental, and consumer leaders who promote the efficient use of energy worldwide to benefit consumers, the environment, economy, and national security. The Alliance is a nonprofit 501 (c) (3) organization.

394

Price Responsive Demand in New York Wholesale Electricity Market using  

NLE Websites -- All DOE Office Websites (Extended Search)

Price Responsive Demand in New York Wholesale Electricity Market using Price Responsive Demand in New York Wholesale Electricity Market using OpenADR Title Price Responsive Demand in New York Wholesale Electricity Market using OpenADR Publication Type Report LBNL Report Number LBNL-5557E Year of Publication 2012 Authors Kim, Joyce Jihyun, and Sila Kiliccote Date Published 06/2012 Publisher LBNL/NYSERDA Keywords commercial, demand response, dynamic pricing, mandatory hourly pricing, open automated demand response, openadr, pilot studies & implementation, price responsive demand Abstract In New York State, the default electricity pricing for large customers is Mandatory Hourly Pricing (MHP), which is charged based on zonal day-ahead market price for energy. With MHP, retail customers can adjust their building load to an economically optimal level according to hourly electricity prices. Yet, many customers seek alternative pricing options such as fixed rates through retail access for their electricity supply. Open Automated Demand Response (OpenADR) is an XML (eXtensible Markup Language) based information exchange model that communicates price and reliability information. It allows customers to evaluate hourly prices and provide demand response in an automated fashion to minimize electricity costs. This document shows how OpenADR can support MHP and facilitate price responsive demand for large commercial customers in New York City.

395

Demand Response and Open Automated Demand Response Opportunities for Data Centers  

E-Print Network (OSTI)

Standardized Automated Demand Response Signals. Presented atand Automated Demand Response in Industrial RefrigeratedActions for Industrial Demand Response in California. LBNL-

Mares, K.C.

2010-01-01T23:59:59.000Z

396

Open Automated Demand Response Communications in Demand Response for Wholesale Ancillary Services  

E-Print Network (OSTI)

A. Barat, D. Watson. 2006 Demand Response Spinning ReserveKueck, and B. Kirby 2008. Demand Response Spinning ReserveReport 2009. Open Automated Demand Response Communications

Kiliccote, Sila

2010-01-01T23:59:59.000Z

397

Definition: Demand | Open Energy Information  

Open Energy Info (EERE)

form form View source History View New Pages Recent Changes All Special Pages Semantic Search/Querying Get Involved Help Apps Datasets Community Login | Sign Up Search Definition Edit with form History Facebook icon Twitter icon » Definition: Demand Jump to: navigation, search Dictionary.png Demand The rate at which electric energy is delivered to or by a system or part of a system, generally expressed in kilowatts or megawatts, at a given instant or averaged over any designated interval of time., The rate at which energy is being used by the customer.[1] Related Terms energy, electricity generation References ↑ Glossary of Terms Used in Reliability Standards An i Like Like You like this.Sign Up to see what your friends like. nline Glossary Definition Retrieved from "http://en.openei.org/w/index.php?title=Definition:Demand&oldid=480555"

398

Successful demand-side management  

Science Conference Proceedings (OSTI)

This article is a brief summary of a series of case studies of five publicly-owned utilities that are noted for their success with demand-side management. These utilities are: (1) city of Austin, Texas, (2) Burlington Electric Department in Vermont, (3) Sacramento Municipal Utility District in California, (4) Seattle City Light, and (5) Waverly Light and Power in Iowa. From these case studies, the authors identified a number of traits associated with a successful demand-side management program. These traits are: (1) high rates, (2) economic factors, (3) environmental awareness, (4) state emphasis on integrated resource planning/demand side management, (5) local political support, (6) large-sized utilities, and (7) presence of a champion.

Hadley, S. [Oak Ridge National Laboratory, TN (United States); Flanigan, T. [Results Center, Aspen, CO (United States)

1995-05-01T23:59:59.000Z

399

Winter Demand Impacted by Weather  

Gasoline and Diesel Fuel Update (EIA)

8 8 Notes: Heating oil demand is strongly influenced by weather. The "normal" numbers are the expected values for winter 2000-2001 used in EIA's Short-Term Energy Outlook. The chart indicates the extent to which the last winter exhibited below-normal heating degree-days (and thus below-normal heating demand). Temperatures were consistently warmer than normal throughout the 1999-2000 heating season. This was particularly true in November 1999, February 2001 and March 2001. For the heating season as a whole (October through March), the 1999-2000 winter yielded total HDDs 10.7% below normal. Normal temperatures this coming winter would, then, be expected to bring about 11% higher heating demand than we saw last year. Relative to normal, the 1999-2000 heating season was the warmest in

400

Turkey's energy demand and supply  

SciTech Connect

The aim of the present article is to investigate Turkey's energy demand and the contribution of domestic energy sources to energy consumption. Turkey, the 17th largest economy in the world, is an emerging country with a buoyant economy challenged by a growing demand for energy. Turkey's energy consumption has grown and will continue to grow along with its economy. Turkey's energy consumption is high, but its domestic primary energy sources are oil and natural gas reserves and their production is low. Total primary energy production met about 27% of the total primary energy demand in 2005. Oil has the biggest share in total primary energy consumption. Lignite has the biggest share in Turkey's primary energy production at 45%. Domestic production should be to be nearly doubled by 2010, mainly in coal (lignite), which, at present, accounts for almost half of the total energy production. The hydropower should also increase two-fold over the same period.

Balat, M. [Sila Science, Trabzon (Turkey)

2009-07-01T23:59:59.000Z

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


401

Assessment of Industrial Load for Demand Response across Western Interconnect  

SciTech Connect

Demand response (DR) has the ability to both increase power grid reliability and potentially reduce operating system costs. Understanding the role of demand response in grid modeling has been difficult due to complex nature of the load characteristics compared to the modeled generation and the variation in load types. This is particularly true of industrial loads, where hundreds of different industries exist with varying availability for demand response. We present a framework considering industrial loads for the development of availability profiles that can provide more regional understanding and can be inserted into analysis software for further study. The developed framework utilizes a number of different informational resources, algorithms, and real-world measurements to perform a bottom-up approach in the development of a new database with representation of the potential demand response resource in the industrial sector across the U.S. This tool houses statistical values of energy and demand response (DR) potential by industrial plant and geospatially locates the information for aggregation for different territories without proprietary information. This report will discuss this framework and the analyzed quantities of demand response for Western Interconnect (WI) in support of evaluation of the cost production modeling with power grid modeling efforts of demand response.

Alkadi, Nasr E [ORNL] [ORNL; Starke, Michael R [ORNL] [ORNL; Ma, Ookie [United States Department of Energy (DOE), Office of Efficiency and Renewable Energy (EERE)] [United States Department of Energy (DOE), Office of Efficiency and Renewable Energy (EERE)

2013-11-01T23:59:59.000Z

402

Forecasting Electricity Demand on Short, Medium and Long Time Scales Using Neural Networks  

Science Conference Proceedings (OSTI)

This paper examines the application of artificial neural networks (ANNs) to the modelling and forecasting of electricity demand experienced by an electricity supplier. The data used in the application examples relates to the national electricity demand ... Keywords: Box–Jenkins model, artificial neural networks, electrical load, electricity demand, load forecasting

J. V. Ringwood; D. Bofelli; F. T. Murray

2001-05-01T23:59:59.000Z

403

California Energy Demand Scenario Projections to 2050  

E-Print Network (OSTI)

residential electricity consumption, the flattening of the demand curves (except Maximum demand) reflects decreasing population growth ratesresidential electricity demand are described in Table 11. For simplicity, end use-specific UEC and saturation rates

McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

2008-01-01T23:59:59.000Z

404

EIA projections of coal supply and demand  

SciTech Connect

Contents of this report include: EIA projections of coal supply and demand which covers forecasted coal supply and transportation, forecasted coal demand by consuming sector, and forecasted coal demand by the electric utility sector; and policy discussion.

Klein, D.E.

1989-10-23T23:59:59.000Z

405

Coordination of Energy Efficiency and Demand Response  

E-Print Network (OSTI)

percent of 2008 summer peak demand (FERC, 2008). Moreover,138,000 MW (14 percent of peak demand) by 2019 (FERC, 2009).non-coincident summer peak demand by 157 GW” by 2030, or 14–

Goldman, Charles

2010-01-01T23:59:59.000Z

406

Retail Demand Response in Southwest Power Pool  

E-Print Network (OSTI)

pricing tariffs have a peak demand reduction potential ofneed to reduce summer peak demand that is used to set demandcustomers and a system peak demand of over 43,000 MW. SPP’s

Bharvirkar, Ranjit

2009-01-01T23:59:59.000Z

407

Demand Responsive Lighting: A Scoping Study  

E-Print Network (OSTI)

with total Statewide peak demand and on peak days isto examine the electric peak demand related to lighting inDaily) - TOU Savings - Peak Demand Charges - Grid Peak -Low

Rubinstein, Francis; Kiliccote, Sila

2007-01-01T23:59:59.000Z

408

Tankless Demand Water Heaters | Department of Energy  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Demand Water Heaters Tankless Demand Water Heaters August 19, 2013 - 2:57pm Addthis Illustration of an electric demand water heater. At the top of the image, the heating unit is...

409

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 2: Electricity Demand.Oglesby Executive Director #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product to the contributing authors listed previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad

410

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST Volume 2: Electricity Demand The demand forecast is the combined product of the hard work and expertise of numerous California Energy previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad Soltani Nia helped prepare

411

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST Volume 2: Electricity Demand Robert P. Oglesby Executive Director #12;i ACKNOWLEDGEMENTS The demand forecast is the combined prepared the commercial sector forecast. Mehrzad Soltani Nia helped prepare the industrial forecast

412

On-demand computation of policy based routes for large-scale network simulation  

Science Conference Proceedings (OSTI)

Routing table storage demands pose a significant obstacle for large-scale network simulation. On-demand computation of routes can alleviate those problems for models that do not require representation of routing dynamics. However, policy based routes, ...

Michael Liljenstam; David M. Nicol

2004-12-01T23:59:59.000Z

413

Driving change : evaluating strategies to control automotive energy demand growth in China  

E-Print Network (OSTI)

As the number of vehicles in China has relentlessly grown in the past decade, the energy demand, fuel demand and greenhouse gas emissions associated with these vehicles have kept pace. This thesis presents a model to project ...

Bonde Åkerlind, Ingrid Gudrun

2013-01-01T23:59:59.000Z

414

Optimal Control of Distributed Energy Resources and Demand Response under Uncertainty  

E-Print Network (OSTI)

and Demand Response under Uncertainty • F P t : wholesale natural gasdemand response and DER under uncertain electricity and natural gasand Demand Response under Uncertainty Energy Price Models We assume that the logarithms of the deseasonalized electricity and natural gas

Siddiqui, Afzal

2010-01-01T23:59:59.000Z

415

Calibrated Short-Range Ensemble Precipitation Forecasts Using Extended Logistic Regression with Interaction Terms  

Science Conference Proceedings (OSTI)

Extended logistic regression has been shown to be a method well suited to calibrating precipitation forecasts from medium-range ensemble prediction systems. The extension of the logistic regression unifies the separate predictive equations for ...

Zied Ben Bouallègue

2013-04-01T23:59:59.000Z

416

Generalized multi-commodity network flows : case studies in space logistics and complex infrastructure systems  

E-Print Network (OSTI)

In transition to a new era of human space exploration, the question is what the next-generation space logistics paradigm should be. The past studies on space logistics have been mainly focused on a "vehicle" perspective ...

Ishimatsu, Takuto

2013-01-01T23:59:59.000Z

417

GridWise Alliance: Smart Grid RFI: Addressing Policy and Logistical...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

GridWise Alliance: Smart Grid RFI: Addressing Policy and Logistical Challenges GridWise Alliance: Smart Grid RFI: Addressing Policy and Logistical Challenges The GridWise Alliance...

418

Beyond Rationality in Travel Demand Models  

E-Print Network (OSTI)

Subjects were more likely to choose a hybrid car if we toldemissions), purchase a hybrid car (with the same set ofmore likely to choose a hybrid car if we told them that a

Walker, Joan L.

2011-01-01T23:59:59.000Z

419

Residential Sector Demand Module 1995, Model Documentation  

Reports and Publications (EIA)

This updated version of the NEMS Residential Module Documentation includes changesmade to the residential module for the production of the Annual Energy Outlook 1995.

John H. Cymbalsky

1995-03-01T23:59:59.000Z

420

Covering models with time-dependent demand  

E-Print Network (OSTI)

The different solutions in the search space are given in terms of the variables yk .... symbolize the new facilities to enter the market, and the dark-grey circles ...

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


421

Electric Utility Demand-Side Management 1997  

U.S. Energy Information Administration (EIA)

Electric Utility Demand-Side Management 1997 Executive Summary Background Demand-side management (DSM) programs consist of the planning, implementing, and monitoring ...

422

Equity Capital Flows and Demand for REITs  

Science Conference Proceedings (OSTI)

This paper examines the shape of the market demand curve for ... Our results do not support a downward demand curve for ... Charleston, IL 61920, USA e-mail: ...

423

Home Network Technologies and Automating Demand Response  

NLE Websites -- All DOE Office Websites (Extended Search)

electricity generation capacity to meet unrestrained future demand. To address peak electricity use Demand Response (DR) systems are being proposed to motivate reductions in...

424

Coordination of Energy Efficiency and Demand Response  

E-Print Network (OSTI)

California Long-term Energy Efficiency Strategic Plan. B-2 Coordination of Energy Efficiency and Demand Response> B-4 Coordination of Energy Efficiency and Demand Response

Goldman, Charles

2010-01-01T23:59:59.000Z

425

Installation and Commissioning Automated Demand Response Systems  

E-Print Network (OSTI)

al: Installation and Commissioning Automated Demand ResponseConference on Building Commissioning: April 22 – 24, 2008al: Installation and Commissioning Automated Demand Response

Kiliccote, Sila; Global Energy Partners; Pacific Gas and Electric Company

2008-01-01T23:59:59.000Z

426

EIA - Annual Energy Outlook 2009 - Electricity Demand  

Gasoline and Diesel Fuel Update (EIA)

data Rate of Electricity Demand Growth Slows, Following the Historical Trend Electricity demand fluctuates in the short term in response to business cycles, weather conditions,...

427

Demand Response as a System Reliability Resource  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response as a System Reliability Resource Title Demand Response as a System Reliability Resource Publication Type Report Year of Publication 2012 Authors Eto, Joseph H.,...

428

Option Value of Electricity Demand Response  

E-Print Network (OSTI)

Oakland CA, December. PJM Demand Side Response WorkingPrice Response Program a PJM Economic Load Response ProgramLoad Response Statistics PJM Demand Response Working Group

Sezgen, Osman; Goldman, Charles; Krishnarao, P.

2005-01-01T23:59:59.000Z

429

Retail Demand Response in Southwest Power Pool  

E-Print Network (OSTI)

Regulatory Commission (FERC) 2006. “Assessment of DemandRegulatory Commission (FERC) 2007. “Assessment of DemandRegulatory Commission (FERC) 2008a. “Wholesale Competition

Bharvirkar, Ranjit

2009-01-01T23:59:59.000Z

430

California Energy Demand Scenario Projections to 2050  

E-Print Network (OSTI)

29 5.6. Peak and hourly demand43 6.6. Peak and seasonal demandthe average percent of peak demand) significantly impact the

McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

2008-01-01T23:59:59.000Z

431

Water demand management in Kuwait  

E-Print Network (OSTI)

Kuwait is an arid country located in the Middle East, with limited access to water resources. Yet water demand per capita is much higher than in other countries in the world, estimated to be around 450 L/capita/day. There ...

Milutinovic, Milan, M. Eng. Massachusetts Institute of Technology

2006-01-01T23:59:59.000Z

432

Demand-Side Management Glossary  

Science Conference Proceedings (OSTI)

In recent years, demand-side management (DSM) programs have grown in significance within the U.S. electric power industry. Such rapid growth has resulted in new terms, standards, and vocabulary used by DSM professionals. This report is a first attempt to provide a consistent set of definitions for the expanding DSM terminology.

1992-11-01T23:59:59.000Z

433

Hydrogen Demand and Resource Assessment Tool | Open Energy Information  

Open Energy Info (EERE)

Hydrogen Demand and Resource Assessment Tool Hydrogen Demand and Resource Assessment Tool Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Hydrogen Demand and Resource Assessment Tool Agency/Company /Organization: National Renewable Energy Laboratory Sector: Energy Focus Area: Hydrogen, Transportation Topics: Technology characterizations Resource Type: Dataset, Software/modeling tools User Interface: Website Website: maps.nrel.gov/ Web Application Link: maps.nrel.gov/hydra Cost: Free Language: English References: http://maps.nrel.gov/hydra Logo: Hydrogen Demand and Resource Assessment Tool Use HyDRA to view, download, and analyze hydrogen data spatially and dynamically. HyDRA provides access to hydrogen demand, resource, infrastructure, cost, production, and distribution data. A user account is

434

Assisting Mexico in Developing Energy Supply and Demand Projections | Open  

Open Energy Info (EERE)

Assisting Mexico in Developing Energy Supply and Demand Projections Assisting Mexico in Developing Energy Supply and Demand Projections Jump to: navigation, search Name Assisting Mexico in Developing Energy Supply and Demand Projections Agency/Company /Organization Argonne National Laboratory Sector Energy Topics GHG inventory, Background analysis Resource Type Software/modeling tools Website http://www.dis.anl.gov/news/Me Country Mexico UN Region Latin America and the Caribbean References Assisting Mexico in Developing Energy Supply and Demand Projections[1] "CEEESA and the team of experts from Mexico analyzed the country's entire energy supply and demand system using CEEESA's latest version of the popular ENPEP-BALANCE software. The team developed a system representation, a so-called energy network, using ENPEP's powerful graphical user

435

Demand Dispatch — Intelligent Demand for a More Efficient Grid  

E-Print Network (OSTI)

This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference therein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed therein do not necessarily state or reflect those of the United States Government or any agency thereof. Demand Dispatch: Intelligent Demand for a More Efficient Grid

Keith Dodrill

2011-01-01T23:59:59.000Z

436

Elasticities of Electricity Demand in Urban Indian Households  

E-Print Network (OSTI)

Energy demand, and in particular electricity demand in India has been growing at a very rapid rate over the last decade. Given, current trends in population growth, industrialisation, urbanisation, modernisation and income growth, electricity consumption is expected to increase substantially in the coming decades as well. Tariff reforms could play a potentially important role as a demand side management tool in India. However, the effects of any price revisions on consumption will depend on the price elasticity of demand for electricity. In the past, electricity demand studies for India published in international journals have been based on aggregate macro data at the country or sub-national / state level. In this paper, price and income elasticities of electricity demand in the residential sector of all urban areas of India are estimated for the first time using disaggregate level survey data for over thirty thousand households. Three electricity demand functions have been estimated using monthly data for the following seasons: winter, monsoon and summer. The results show electricity demand is income and price inelastic in all three seasons, and that household, demographic and geographical variables are important in determining electricity demand, something that is not possible to determine using aggregate macro models alone. Key Words Residential electricity demand, price elasticity, income elasticity Short Title Electricity demand in Indian households Acknowledgements: The authors would like to gratefully acknowledge the National Sample Survey Organisation, Department of Statistics of the Government of India, for making available to us the unit level, household survey data. We would also like to thank Prof. Daniel Spreng for his support of our research. 2 1.

Shonali Pachauri

2002-01-01T23:59:59.000Z

437

The alchemy of demand response: turning demand into supply  

Science Conference Proceedings (OSTI)

Paying customers to refrain from purchasing products they want seems to run counter to the normal operation of markets. Demand response should be interpreted not as a supply-side resource but as a secondary market that attempts to correct the misallocation of electricity among electric users caused by regulated average rate tariffs. In a world with costless metering, the DR solution results in inefficiency as measured by deadweight losses. (author)

Rochlin, Cliff

2009-11-15T23:59:59.000Z

438

Structural fatigue assessment and management of large-scale port logistics equipments  

Science Conference Proceedings (OSTI)

With the advances of port enterprises, much intensive research has been gradually involved in the structural fatigue assessment and management of port logistics equipments. However, relevant work on large-scale port logistics equipments is still ... Keywords: S-N curve, crack formation, crack propagation life, fatigue assessment, fracture mechanics, gantry cranes, large-scale port logistics equipment, structural safety assessment

Yuan Liu; Weijian Mi; Huiqiang Zheng

2008-11-01T23:59:59.000Z

439

Projecting market demand for residential heat pumps  

SciTech Connect

Primarily because of technological improvements and sharp increases in energy prices after the 1970s energy crises, the sale of residential electric heat pumps rose ninefold from 1970 to 1983. This report describes current and future market demand for heat pumps used for space heating and cooling. A three-step approach was followed. In the first step, the historical growth of residential electric heat pumps was analyzed, and factors that may have affected market growth were examined. Also examined were installation trends of heat pumps in new single-family and multifamily homes. A market segmentation analysis was used to estimate market size by categories. In the second step, several methods for forecasting future market demand were reviewed and evaluated to select the most suitable one for this study. The discrete-choice approach was chosen. In the third step, a market penetration model based on selected discrete-choice methods was developed to project heat pump demand in key market segments such as home type (single-family or multifamily), new or existing construction, and race-ethnic origin of household (black, Hispanic, or white).

Teotia, A.P.S.; Raju, P.S.; Karvelas, D.; Anderson, J.

1987-04-01T23:59:59.000Z

440

US Residential Energy Demand and Energy Efficiency: A Stochastic Demand Frontier  

E-Print Network (OSTI)

US Residential Energy Demand and Energy Efficiency: A Stochastic Demand Frontier Approach Massimo www.cepe.ethz.ch #12;US Residential Energy Demand and Energy Efficiency: A Stochastic Demand Frontier Approach Page 1 of 25 US Residential Energy Demand and Energy Efficiency: A Stochastic Demand Frontier

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


441

Topics in ordinal logistic regression and its applications  

E-Print Network (OSTI)

Sample size calculation methods for ordinal logistic regression are proposed to test statistical hypotheses. The author was motivated to do this work by the need for statistical analysis of the red imported ?re ants data. The proposed methods use the concept of approximation by the moment-generating function. Some correction methods are also suggested. When a prior data set is available, an empirical method is explored. Application of the proposed methodology to the ?re ant mating ?ight data is demonstrated. The proposed sample size and power calculation methods are applied in the hypothesis testing problems. Simulation studies are also conducted to illustrate their performance and to compare them with existing methods.

Kim, Hyun Sun

2004-08-01T23:59:59.000Z

442

USCG Energy Program Resource Management, Fuel Logistics, and Facility Energy  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Energy Program Energy Program Resource Management, Fuel Logistics, and Facility Energy Presented by Daniel Gore USCG Energy Program Manager Office of Resource Management 1 1 2 Presentation Contents * Overview CG Energy Program * Highlights * Interesting Projects for Utilities * Alternatively Financed Projects Discussion 2 3 Overview 3 USCG Energy Program Growth * CG represents 80% of DHS energy consumption * Obligations up 210% from FY 2000 * Energy = 25% of O&M budget 4 4 Energy Program Dynamics Increasing Expenditures Increasing Politics & Mandates Increasing Scrutiny & Reporting Procurement & Credit Card Transformations Accounting System Improvements Organizational Strategic Transformations 5 5 What is CG Energy Management? * Policies impacting $306M annual obligations

443

Open Automated Demand Response Communications Specification (Version 1.0)  

Science Conference Proceedings (OSTI)

The development of the Open Automated Demand Response Communications Specification, also known as OpenADR or Open Auto-DR, began in 2002 following the California electricity crisis. The work has been carried out by the Demand Response Research Center (DRRC), which is managed by Lawrence Berkeley National Laboratory. This specification describes an open standards-based communications data model designed to facilitate sending and receiving demand response price and reliability signals from a utility or Independent System Operator to electric customers. OpenADR is one element of the Smart Grid information and communications technologies that are being developed to improve optimization between electric supply and demand. The intention of the open automated demand response communications data model is to provide interoperable signals to building and industrial control systems that are preprogrammed to take action based on a demand response signal, enabling a demand response event to be fully automated, with no manual intervention. The OpenADR specification is a flexible infrastructure to facilitate common information exchange between the utility or Independent System Operator and end-use participants. The concept of an open specification is intended to allow anyone to implement the signaling systems, the automation server or the automation clients.

Piette, Mary Ann; Ghatikar, Girish; Kiliccote, Sila; Koch, Ed; Hennage, Dan; Palensky, Peter; McParland, Charles

2009-02-28T23:59:59.000Z

444

Energy Demand | Open Energy Information  

Open Energy Info (EERE)

Energy Demand Energy Demand Jump to: navigation, search Click to return to AEO2011 page AEO2011 Data Figure 55 From AEO2011 report . Market Trends Growth in energy use is linked to population growth through increases in housing, commercial floorspace, transportation, and goods and services. These changes affect not only the level of energy use, but also the mix of fuels used. Energy consumption per capita declined from 337 million Btu in 2007 to 308 million Btu in 2009, the lowest level since 1967. In the AEO2011 Reference case, energy use per capita increases slightly through 2013, as the economy recovers from the 2008-2009 economic downturn. After 2013, energy use per capita declines by 0.3 percent per year on average, to 293 million Btu in 2035, as higher efficiency standards for vehicles and

445

Q:\asufinal_0107_demand.vp  

Gasoline and Diesel Fuel Update (EIA)

00 00 (AEO2000) Assumptions to the January 2000 With Projections to 2020 DOE/EIA-0554(2000) Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Household Expenditures Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Commercial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Natural Gas Transmission and Distribution

446

Demand Response and Risk Management  

Science Conference Proceedings (OSTI)

For several decades, power companies have deployed various types of demand response (DR), such as interruptible contracts, and there is substantial ongoing research and development on sophisticated mechanisms for triggering DR. In this white paper, EPRI discusses the increasing use of electricity DR in the power industry and how this will affect the practice of energy risk management. This paper outlines 1) characteristics of a common approach to energy risk management, 2) the variety of types of DR impl...

2008-12-18T23:59:59.000Z

447

Building Technologies Office: Integrated Predictive Demand Response  

NLE Websites -- All DOE Office Websites (Extended Search)

Integrated Predictive Integrated Predictive Demand Response Controller Research Project to someone by E-mail Share Building Technologies Office: Integrated Predictive Demand Response Controller Research Project on Facebook Tweet about Building Technologies Office: Integrated Predictive Demand Response Controller Research Project on Twitter Bookmark Building Technologies Office: Integrated Predictive Demand Response Controller Research Project on Google Bookmark Building Technologies Office: Integrated Predictive Demand Response Controller Research Project on Delicious Rank Building Technologies Office: Integrated Predictive Demand Response Controller Research Project on Digg Find More places to share Building Technologies Office: Integrated Predictive Demand Response Controller Research Project on AddThis.com...

448

Global irrigation demand - A holistic approach  

Science Conference Proceedings (OSTI)

To develop a research track on global irrigation demand and the use of future water resources to help feed the world, we need to adopt a holistic approach to understand inter-dependencies and the main drivers of the global water system and unravel positive (reinforcing) and negative (balancing) feedback loops that can lead to cascading consequences. Thus, there needs to be more research dedicated to 1) the modeling of the agricultural and water systems as components within an integrated assessment human-Earth modeling framework, 2) the understanding of the linkages between the physical processes and the human system, and to integrate them in an economic framework to capture the dynamics of market price, and institutional regulations. This editorial discusses the importance of tackling the global irrigation problem in an integrated assessment modeling framework.

Hejazi, Mohamad I.; Edmonds, James A.; Chaturvedi, Vaibhav

2012-09-30T23:59:59.000Z

449

Demand Trading: Measurement, Verification, and Settlement (MVS)  

Science Conference Proceedings (OSTI)

With this report, EPRI's trilogy of publications on demand trading is complete. The first report (1006015), the "Demand Trading Toolkit," documented how to conduct demand trading based on price. The second report (1001635), "Demand Trading: Building Liquidity," focused on the problem of liquidity in the energy industry and developed the Demand Response Resource Bank concept for governing electricity markets based on reliability. The present report focuses on the emerging price/risk partnerships in electr...

2004-03-18T23:59:59.000Z

450

Density Forecasting for Long-Term Peak Electricity Demand  

E-Print Network (OSTI)

Long-term electricity demand forecasting plays an important role in planning for future generation facilities and transmission augmentation. In a long-term context, planners must adopt a probabilistic view of potential peak demand levels. Therefore density forecasts (providing estimates of the full probability distributions of the possible future values of the demand) are more helpful than point forecasts, and are necessary for utilities to evaluate and hedge the financial risk accrued by demand variability and forecasting uncertainty. This paper proposes a new methodology to forecast the density of long-term peak electricity demand. Peak electricity demand in a given season is subject to a range of uncertainties, including underlying population growth, changing technology, economic conditions, prevailing weather conditions (and the timing of those conditions), as well as the general randomness inherent in individual usage. It is also subject to some known calendar effects due to the time of day, day of week, time of year, and public holidays. A comprehensive forecasting solution is described in this paper. First, semi-parametric additive models are used to estimate the relationships between demand and the driver variables, including temperatures, calendar effects and some demographic and economic variables. Then the demand distributions are forecasted by using a mixture of temperature simulation, assumed future economic scenarios, and residual bootstrapping. The temperature simulation is implemented through a new seasonal bootstrapping method with variable blocks. The proposed methodology has been used to forecast the probability distribution of annual and weekly peak electricity demand for South Australia since 2007. The performance of the methodology is evaluated by comparing the forecast results with the actual demand of the summer 2007–2008.

Rob J. Hyndman; Shu Fan

2009-01-01T23:59:59.000Z

451

Automated Demand Response Opportunities in Wastewater Treatment Facilities  

E-Print Network (OSTI)

Interoperable Automated Demand Response Infrastructure,study of automated demand response in wastewater treatmentopportunities for demand response control strategies in

Thompson, Lisa

2008-01-01T23:59:59.000Z

452

Opportunities, Barriers and Actions for Industrial Demand Response in California  

E-Print Network (OSTI)

and Techniques for Demand Response, report for theand Reliability Demand Response Programs: Final Report.Demand Response

McKane, Aimee T.

2009-01-01T23:59:59.000Z

453

Northwest Open Automated Demand Response Technology Demonstration Project  

E-Print Network (OSTI)

Report 2009. Open Automated Demand Response Communicationsand Techniques for Demand Response. California Energyand S. Kiliccote. Estimating Demand Response Load Impacts:

Kiliccote, Sila

2010-01-01T23:59:59.000Z

454

Effects of the drought on California electricity supply and demand  

E-Print Network (OSTI)

DEMAND . . . .Demand for Electricity and Power PeakDemand . . • . . ELECTRICITY REQUIREMENTS FOR AGRICULTUREResults . . Coriclusions ELECTRICITY SUPPLY Hydroelectric

Benenson, P.

2010-01-01T23:59:59.000Z

455

Northwest Open Automated Demand Response Technology Demonstration Project  

E-Print Network (OSTI)

14 Peak Demand Baselinewinter morning electric peak demand in commercial buildings.California to reduce peak demand during summer afternoons,

Kiliccote, Sila

2010-01-01T23:59:59.000Z

456

Price-elastic demand in deregulated electricity markets  

E-Print Network (OSTI)

by the amount of electricity demand that is settled forward.unresponsive demand side, electricity demand has to be metxed percentage of overall electricity demand. The ISO, thus,

Siddiqui, Afzal S.

2003-01-01T23:59:59.000Z

457

Building Energy Software Tools Directory : Demand Response Quick...  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response Quick Assessment Tool Back to Tool Demand response quick assessment tool screenshot Demand response quick assessment tool screenshot Demand response quick...

458

Automated Demand Response Strategies and Commissioning Commercial Building Controls  

E-Print Network (OSTI)

Braun (Purdue). 2004. Peak demand reduction from pre-coolingthe average and maximum peak demand savings. The electricityuse charges, demand ratchets, peak demand charges, and other

Piette, Mary Ann; Watson, David; Motegi, Naoya; Kiliccote, Sila; Linkugel, Eric

2006-01-01T23:59:59.000Z

459

Assessing the Control Systems Capacity for Demand Response in California  

NLE Websites -- All DOE Office Websites (Extended Search)

the Control Systems Capacity for Demand Response in California the Control Systems Capacity for Demand Response in California Industries Title Assessing the Control Systems Capacity for Demand Response in California Industries Publication Type Report LBNL Report Number LBNL-5319E Year of Publication 2012 Authors Ghatikar, Girish, Aimee T. McKane, Sasank Goli, Peter L. Therkelsen, and Daniel Olsen Date Published 01/2012 Publisher CEC/LBNL Keywords automated dr, controls and automation, demand response, dynamic pricing, industrial controls, market sectors, openadr Abstract California's electricity markets are moving toward dynamic pricing models, such as real-time pricing, within the next few years, which could have a significant impact on an industrial facility's cost of energy use during the times of peak use. Adequate controls and automated systems that provide industrial facility managers real-time energy use and cost information are necessary for successful implementation of a comprehensive electricity strategy; however, little is known about the current control capacity of California industries. To address this gap, Lawrence Berkeley National Laboratory, in close collaboration with California industrial trade associations, conducted a survey to determine the current state of controls technologies in California industries. This study identifies sectors that have the technical capability to implement Demand Response (DR) and Automated Demand Response (Auto-DR). In an effort to assist policy makers and industry in meeting the challenges of real-time pricing, facility operational and organizational factors were taken into consideration to generate recommendations on which sectors Demand Response efforts should be focused. Analysis of the survey responses showed that while the vast majority of industrial facilities have semi- or fully automated control systems, participation in Demand Response programs is still low due to perceived barriers. The results also showed that the facilities that use continuous processes are good Demand Response candidates. When comparing facilities participating in Demand Response to those not participating, several similarities and differences emerged. Demand Response-participating facilities and non-participating facilities had similar timings of peak energy use, production processes, and participation in energy audits. Though the survey sample was smaller than anticipated, the results seemed to support our preliminary assumptions. Demonstrations of Auto-Demand Response in industrial facilities with good control capabilities are needed to dispel perceived barriers to participation and to investigate industrial subsectors suggested of having inherent Demand Response potential.

460

Swarm intelligence approaches to estimate electricity energy demand in Turkey  

Science Conference Proceedings (OSTI)

This paper proposes two new models based on artificial bee colony (ABC) and particle swarm optimization (PSO) techniques to estimate electricity energy demand in Turkey. ABC and PSO electricity energy estimation models (ABCEE and PSOEE) are developed ... Keywords: Ant colony optimization, Artificial bee colony, Electricity energy estimation, Particle swarm optimization, Swarm intelligence

Mustafa Servet K?Ran; Eren ÖZceylan; Mesut GüNdüZ; Turan Paksoy

2012-12-01T23:59:59.000Z

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


461

Demand Response Valuation Frameworks Paper  

Science Conference Proceedings (OSTI)

While there is general agreement that demand response (DR) is a valued component in a utility resource plan, there is a lack of consensus regarding how to value DR. Establishing the value of DR is a prerequisite to determining how much and what types of DR should be implemented, to which customers DR should be targeted, and a key determinant that drives the development of economically viable DR consumer technology. Most approaches for quantifying the value of DR focus on changes in utility system revenue requirements based on resource plans with and without DR. This ''utility centric'' approach does not assign any value to DR impacts that lower energy and capacity prices, improve reliability, lower system and network operating costs, produce better air quality, and provide improved customer choice and control. Proper valuation of these benefits requires a different basis for monetization. The review concludes that no single methodology today adequately captures the wide range of benefits and value potentially attributed to DR. To provide a more comprehensive valuation approach, current methods such as the Standard Practice Method (SPM) will most likely have to be supplemented with one or more alternative benefit-valuation approaches. This report provides an updated perspective on the DR valuation framework. It includes an introduction and four chapters that address the key elements of demand response valuation, a comprehensive literature review, and specific research recommendations.

Heffner, Grayson

2009-02-01T23:59:59.000Z

462

Demand Side Bidding. Final Report  

SciTech Connect

This document sets forth the final report for a financial assistance award for the National Association of Regulatory Utility Commissioners (NARUC) to enhance coordination between the building operators and power system operators in terms of demand-side responses to Location Based Marginal Pricing (LBMP). Potential benefits of this project include improved power system reliability, enhanced environmental quality, mitigation of high locational prices within congested areas, and the reduction of market barriers for demand-side market participants. NARUC, led by its Committee on Energy Resources and the Environment (ERE), actively works to promote the development and use of energy efficiency and clean distributive energy policies within the framework of a dynamic regulatory environment. Electric industry restructuring, energy shortages in California, and energy market transformation intensifies the need for reliable information and strategies regarding electric reliability policy and practice. NARUC promotes clean distributive generation and increased energy efficiency in the context of the energy sector restructuring process. NARUC, through ERE's Subcommittee on Energy Efficiency, strives to improve energy efficiency by creating working markets. Market transformation seeks opportunities where small amounts of investment can create sustainable markets for more efficient products, services, and design practices.

Spahn, Andrew

2003-12-31T23:59:59.000Z

463

Modelling the natural gas consumption in a changing environment  

Science Conference Proceedings (OSTI)

A composite function was used successfully for modelling the Natural Gas (NG) consumption in 16 European energy markets. Background of the model is a logistic function where the upper limit is also a logistic function of time, with secondary parameters ...

F. A. Batzias; N. P. Nikolaou; A. S. Kakos; I. Michailides

2003-09-01T23:59:59.000Z

464

Grid Integration of Aggregated Demand Response, Part 1: Load Availability  

NLE Websites -- All DOE Office Websites (Extended Search)

Grid Integration of Aggregated Demand Response, Part 1: Load Availability Grid Integration of Aggregated Demand Response, Part 1: Load Availability Profiles and Constraints for the Western Interconnection Title Grid Integration of Aggregated Demand Response, Part 1: Load Availability Profiles and Constraints for the Western Interconnection Publication Type Report LBNL Report Number LBNL-6417E Year of Publication 2013 Authors Olsen, Daniel, Nance Matson, Michael D. Sohn, Cody Rose, Junqiao Han Dudley, Sasank Goli, Sila Kiliccote, Marissa Hummon, David Palchak, Paul Denholm, Jennie Jorgenson, and Ookie Ma Date Published 09/2013 Abstract Demand response (DR) has the potential to improve electric grid reliability and reduce system operation costs. However, including DR in grid modeling can be difficult due to its variable and non-traditional response characteristics, compared to traditional generation. Therefore, efforts to value the participation of DR in procurement of grid services have been limited. In this report, we present methods and tools for predicting demand response availability profiles, representing their capability to participate in capacity, energy, and ancillary services. With the addition of response characteristics mimicking those of generation, the resulting profiles will help in the valuation of the participation of demand response through production cost modeling, which informs infrastructure and investment planning.

465

Assumptions to the Annual Energy Outlook - Transportation Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumption to the Annual Energy Outlook Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, sport utility vehicles and vans), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger airplanes, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

466

Tankless or Demand-Type Water Heaters | Department of Energy  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Tankless or Demand-Type Water Heaters Tankless or Demand-Type Water Heaters Tankless or Demand-Type Water Heaters May 2, 2012 - 6:47pm Addthis Diagram of a tankless water heater. Diagram of a tankless water heater. How does it work? Tankless water heaters deliver hot water as it is needed, eliminating the need for storage tanks. Tankless water heaters, also known as demand-type or instantaneous water heaters, provide hot water only as it is needed. They don't produce the standby energy losses associated with storage water heaters, which can save you money. Here you'll find basic information about how they work, whether a tankless water heater might be right for your home, and what criteria to use when selecting the right model. Check out the Energy Saver 101: Water Heating infographic to learn if a tankless water heater is right for you.

467

EIA - Assumptions to the Annual Energy Outlook 2008 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2008 Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

468

EIA - Assumptions to the Annual Energy Outlook 2009 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2009 Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight, rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

469

EIA - Assumptions to the Annual Energy Outlook 2009 - Industrial Demand  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2009 Industrial Demand Module Table 6.1. Industry Categories. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 6.2.Retirement Rates. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries (Table 6.1). The manufacturing industries are modeled through the use of a detailed process flow or end use accounting

470

Unlocking the potential for efficiency and demand response throughadvanced metering  

Science Conference Proceedings (OSTI)

Reliance on the standard cumulative kilowatt-hour metersubstantially compromises energy efficiency and demand response programs.Without advanced metering, utilities cannot support time-differentiatedrates or collect the detailed customer usage information necessary to (1)educate the customer to the economic value of efficiency and demandresponse options, or (2) distribute load management incentivesproportional to customer contribution. These deficiencies prevent thecustomer feedback mechanisms that would otherwise encourage economicallysound demand-side investments and behaviors. Thus, the inability tocollect or properly price electricity usage handicaps the success ofalmost all efficiency and demand response options. Historically,implementation of the advanced metering infrastructure (AMI) necessaryfor the successful efficiency and demand response programs has beenprevented by inadequate cost-benefit analyses. A recent California efforthas produced an expanded cost-effectiveness methodology for AMI thatintroduces previously excluded benefits. In addition to utility-centriccosts and benefits, the new model includes qualitative and quantitativecosts and benefits that accrue to both customers and society.

Levy, Roger; Herter, Karen; Wilson, John

2004-06-30T23:59:59.000Z

471

A distributed renewable energy system meeting 100% of electricity demand in Humboldt County: a feasibility study.  

E-Print Network (OSTI)

??A model of electricity supply and demand in Humboldt County, California over the course of one year is presented. Wind, ocean–wave, solar, and biomass electricity… (more)

Ross, Darrell Adam

2009-01-01T23:59:59.000Z

472

Optimal Control of Distributed Energy Resources and Demand Response under Uncertainty  

E-Print Network (OSTI)

Solution Procedure for SDP Energy Prices We use electricityLondon for assistance with energy price modeling. Siddiquiof DER under uncertain energy prices with demand response

Siddiqui, Afzal

2010-01-01T23:59:59.000Z

473

Implementing Innovation in Planning Practice: The Case of Travel Demand Forecasting  

E-Print Network (OSTI)

Urban Travel Demand Forecasting Project. Institute ofTRB. Metropolitan Travel Forecasting: Current Practice andPurvis. Regional Travel Forecasting Model System for the San

Newmark, Gregory Louis

2011-01-01T23:59:59.000Z

474

Definition: Peak Demand | Open Energy Information  

Open Energy Info (EERE)

Peak Demand Peak Demand Jump to: navigation, search Dictionary.png Peak Demand The highest hourly integrated Net Energy For Load within a Balancing Authority Area occurring within a given period (e.g., day, month, season, or year)., The highest instantaneous demand within the Balancing Authority Area.[1] View on Wikipedia Wikipedia Definition Peak demand is used to refer to a historically high point in the sales record of a particular product. In terms of energy use, peak demand describes a period of strong consumer demand. Related Terms Balancing Authority Area, energy, demand, balancing authority, smart grid References ↑ Glossary of Terms Used in Reliability Standards An inli LikeLike UnlikeLike You like this.Sign Up to see what your friends like. ne Glossary Definition Retrieved from

475

Distillate Demand Strong in December 1999  

Gasoline and Diesel Fuel Update (EIA)

5% higher than in the prior year, due mainly to diesel demand growth, since warm weather kept heating oil demand from growing much. Last December, when stocks dropped below...

476

Solar in Demand | Department of Energy  

NLE Websites -- All DOE Office Websites (Extended Search)

in Demand Solar in Demand June 15, 2012 - 10:23am Addthis Kyle Travis, left and Jon Jackson, with Lighthouse Solar, install microcrystalline PV modules on top of Kevin Donovan's...

477

Demand Response - Policy | Department of Energy  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

over the last 11 years when interest in demand response increased. Demand response is an electricity tariff or program established to motivate changes in electric use by end-use...

478

Energy Basics: Tankless Demand Water Heaters  

Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

only as needed and without the use of a storage tank. They don't produce the standby energy losses associated with storage water heaters. How Demand Water Heaters Work Demand...

479

Propane Demand by Sector - Energy Information Administration  

U.S. Energy Information Administration (EIA)

In order to understand markets you also have to look at supply and demand. First, demand or who uses propane. For the most part, the major components of propane ...

480

Travel Behavior and Demand Analysis and Prediction  

E-Print Network (OSTI)

and Demand Analysis and Prediction Konstadinos G. Goulias University of California Santa Barbara, Santa Barbara, CA, USA

Goulias, Konstadinos G

2007-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "logistics demand model" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


481

Forecasting the demand for commercial telecommunications satellites  

Science Conference Proceedings (OSTI)

This paper summarizes the key elements of a forecast methodology for predicting demand for commercial satellite services and the resulting demand for satellite hardware and launches. The paper discusses the characterization of satellite services into more than a dozen applications (including emerging satellite Internet applications) used by Futron Corporation in its forecasts. The paper discusses the relationship between demand for satellite services and demand for satellite hardware

Carissa Bryce Christensen; Carie A. Mullins; Linda A. Williams

2001-01-01T23:59:59.000Z

482

Logistics tool selection with two-phase fuzzy multi criteria decision making: A case study for personal digital assistant selection  

Science Conference Proceedings (OSTI)

Efficient logistics and supply chain management are enabled through the use of efficient information technologies (IT). The mobile logistics tools represent the IT interface in the supply chain. This paper aims to aid decision makers to identify the ... Keywords: Fuzzy AHP, Fuzzy TOPSIS, Fuzzy axiomatic design, Group decision-making, Logistics industry, Logistics tool selection

Gülçin Büyüközkan; Jbid Arsenyan; Da Ruan

2012-01-01T23:59:59.000Z

483

Forecasting demand of commodities after natural disasters  

Science Conference Proceedings (OSTI)

Demand forecasting after natural disasters is especially important in emergency management. However, since the time series of commodities demand after natural disasters usually has a great deal of nonlinearity and irregularity, it has poor prediction ... Keywords: ARIMA, Demand forecasting, EMD, Emergency management, Natural disaster

Xiaoyan Xu; Yuqing Qi; Zhongsheng Hua

2010-06-01T23:59:59.000Z

484

Leveraging gamification in demand dispatch systems  

Science Conference Proceedings (OSTI)

Modern demand-side management techniques are an integral part of the envisioned smart grid paradigm. They require an active involvement of the consumer for an optimization of the grid's efficiency and a better utilization of renewable energy sources. ... Keywords: demand response, demand side management, direct load control, gamification, smart grid, sustainability

Benjamin Gnauk; Lars Dannecker; Martin Hahmann

2012-03-01T23:59:59.000Z

485

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 2014­2024 FINAL FORECAST Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product of the hard work to the contributing authors listed previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad

486

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 2014­2024 REVISED FORECAST Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product of the hard work listed previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad Soltani Nia helped

487

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022  

E-Print Network (OSTI)

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 2: Electricity Demand by Utility ACKNOWLEDGEMENTS The staff demand forecast is the combined product of the hard work and expertise of numerous, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad Soltani Nia helped prepare

488

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022  

E-Print Network (OSTI)

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The staff demand forecast is the combined product of the hard work listed previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad Soltani Nia helped

489

FINAL STAFF FORECAST OF 2008 PEAK DEMAND  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION FINAL STAFF FORECAST OF 2008 PEAK DEMAND STAFFREPORT June 2007 CEC-200 of the information in this paper. #12;Abstract This document describes staff's final forecast of 2008 peak demand demand forecasts for the respective territories of the state's three investor-owned utilities (IOUs

490

Ups and downs of demand limiting  

SciTech Connect

Electric power load management by limiting power demand can be used for energy conservation. Methods for affecting demand limiting, reducing peak usage in buildings, particularly usage for heating and ventilating systems, and power pricing to encourage demand limiting are discussed. (LCL)

Pannkoke, T.

1976-12-01T23:59:59.000Z

491

Feedstock Logistics Datasets from DOE's Bioenergy Knowledge Discovery Framework (KDF)  

DOE Data Explorer (OSTI)

The Bioenergy Knowledge Discovery Framework invites users to discover the power of bioenergy through an interface that provides extensive access to research data and literature, GIS mapping tools, and collaborative networks. The Bioenergy KDF supports efforts to develop a robust and sustainable bioenergy industry. The KDF facilitates informed decision making by providing a means to synthesize, analyze, and visualize vast amounts of information in a relevant and succinct manner. It harnesses Web 2.0 and social networking technologies to build a collective knowledge system that can better examine the economic and environmental impacts of development options for biomass feedstock production, biorefineries, and related infrastructure. [copied from https://www.bioenergykdf.net/content/about]

Holdings include datasets, models, and maps. This is a very new resource, but the collections will grow due to both DOE contributions and individualsÆ data uploads. Currently the Feedstock Logistics collection includes 38 items or links, of which eight are datasets.

492

A needs-based approach to activity generation for travel demand analysis/  

E-Print Network (OSTI)

This thesis develops a needs-based framework for behavioral enhancement of conventional activity-based travel demand models. Operational activity-based models specify activity generation models based on empirical considerations ...

Pattabhiraman, Varun R. (Varun Ramakrishna)

2012-01-01T23:59:59.000Z

493

Residential demand response using reinforcement learning  

E-Print Network (OSTI)

Abstract — We present a novel energy management system for residential demand response. The algorithm, named CAES, reduces residential energy costs and smooths energy usage. CAES is an online learning application that implicitly estimates the impact of future energy prices and of consumer decisions on long term costs and schedules residential device usage. CAES models both energy prices and residential device usage as Markov, but does not assume knowledge of the structure or transition probabilities of these Markov chains. CAES learns continuously and adapts to individual consumer preferences and pricing modifications over time. In numerical simulations CAES reduced average end-user financial costs from 16 % to 40 % with respect to a price-unaware energy allocation. I.

Marco Levorato; Andrea Goldsmith; Urbashi Mitra

2010-01-01T23:59:59.000Z

494

Measurement and Verification for Demand Response  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Measurement and Verification for Measurement and Verification for Demand Response Prepared for the National Forum on the National Action Plan on Demand Response: Measurement and Verification Working Group AUTHORS: Miriam L. Goldberg & G. Kennedy Agnew-DNV KEMA Energy and Sustainability National Forum of the National Action Plan on Demand Response Measurement and Verification for Demand Response was developed to fulfill part of the Implementation Proposal for The National Action Plan on Demand Response, a report to Congress jointly issued by the U.S. Department of Energy (DOE) and the Federal Energy Regulatory Commission (FERC) in June 2011. Part of that implementation proposal called for a "National Forum" on demand response to be conducted by DOE and FERC. Given that demand response has matured, DOE and FERC decided that a "virtual" project

495

Are they equal yet. [Demand side management  

Science Conference Proceedings (OSTI)

Demand-side management (DSM) is considered an important tool in meeting the load growth of many utilities. Northwest regional and utility resource plans forecast demand-side resources to meet from one-half to two-thirds of additional electrical energy needs over the next 10 years. Numerous sources have stated that barriers, both regulatory and financial, exist to utility acquisition of demand-side resources. Regulatory actions are being implemented in Oregon to make demand-side investments competitive with supply-side investments. In 1989, the Oregon Public Utility Commission (PUC) took two actions regarding demand-side investments. The PUC's Order 89-1700 directed utilities to capitalize demand-side investments to properly match amortization expense with the multiyear benefits provided by DSM. The PUC also began an informal investigation concerning incentives for Oregon's regulated electric utilities to acquire demand-side resources.

Irwin, K.; Phillips-Israel, K.; Busch, E.

1994-05-15T23:59:59.000Z

496

Estimating disaggregated price elasticities in industrial energy demand  

Science Conference Proceedings (OSTI)

Econometric energy models are used to evaluate past policy experiences, assess the impact of future policies and forecast energy demand. This paper estimates an industrial energy demand model for the province of Ontario using a linear-logit specification for fuel type equations which are embedded in an aggregate energy demand equation. Short-term, long-term, own- and cross-price elasticities are estimated for electricity, natural gas, oil and coal. Own- and cross-price elasticities are disaggregated to show that overall price elasticities and the energy-constant price elasticities when aggregate energy use is held unchanged. These disaggregations suggest that a substantial part of energy conservation comes from the higher aggregate price of energy and not from interfuel substitution. 13 refs., 2 tabs.

Elkhafif, M.A.T. (Ontario Ministry of Energy, Toronto (Canada))

1992-01-01T23:59:59.000Z

497

Weirton Steel Corporation logistics and integrated scheduling. Final report  

SciTech Connect

In order to remain competitive in the changing steel market, US steel producers restructured by taking on foreign and domestic partners, closing facilities and/or trimming work forces, and modernizing their steel making facilities. However, very little was done to develop production management technology to complement these changes. The Logistics and Integrated Scheduling program (LIS) was undertaken to address this issue. LIS is an information management system that delivers better customer service, better quality materials, and a just-in-time delivery system. It involves three major components: (1) material marking and sensing: advanced R&D applied to determining cost effective, feasible solutions to passive inventory; (2) material inventory and tracking: advanced technology applied to managing inventory movement; (3) planning and scheduling: beginning with annual production plans, order management, and operational constraints, the ability to build integrated schedules capable of pull through and push through scheduling for various plant capability levels and location configurations with rapid turnaround capability. LIS provides accurate, automated tracking of material flows throughout the mill, the collection and analysis of production data, and automated schedule optimization.

Guzzetta, M.B. [comp.

1996-06-01T23:59:59.000Z

498

Driving toward excellence in transportation and logistics operations and safety  

SciTech Connect

DoE's EM is the largest cleanup project in the world: 114 sites, 31 states, 2,000,000 acres. EM scope includes remediation, processing and transportation of approximately: 25 tons of plutonium, 108 tons of plutonium residues, 88 million gallons of radioactive liquid waste, 2,500 tons of spent nuclear fuel, 137,000 cubic meters of transuranic waste, 1.3 million cubic meters of low-level waste. This series of slides presents: the Rocky Flats Status, the Fernald Closure Project, the Mound/Miamisburg and Battelle Columbus statuses, the DUF{sub 6} (Depleted Uranium Hexafluoride) Conversion Project Overview, Conversion and Transport Logistics; DoE's EM Measures of Success and performance (transportation incident criteria); the application of technology to Enhance Motor Carrier Performance, Safety, and Emergency Preparedness (technological capabilities for DOE to improve driver performance, shipment safety, and emergency response); the Motor Carrier Tracking and Alert system; DOE Load Securement Field Guide and Checklist developed to ensure all shipments are secured prior to shipment; The transportation Emergency Preparedness Program (TEPP) and outreach support; the EM Transportation Community Awareness and Emergency Response (TransCAER); and the Commodity Flow Survey data of Tennessee, Flagstaff, and Texas/Louisiana.

Ashworth, D. [Office of Transportation, U.S. Dept. of Energy, Washington, DC (United States)

2007-07-01T23:59:59.000Z

499

2002 EIA Models Directory - Energy Information Administration  

U.S. Energy Information Administration (EIA)

The NEMS Residential Sector Demand Module is an integrated dynamic modeling system that projects residential energy demand by ... Energy Demand and Integration ...

500

Demand response enabling technology development  

E-Print Network (OSTI)

is normally of thermistor or RTD (resistance temperatureour temperature sensors (RTD’s) were prepackaged with matingsimulation model MZEST. The RTD temperature sensor boards

2006-01-01T23:59:59.000Z