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Note: This page contains sample records for the topic "open automated demand" 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.


1

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...

2

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...

3

Open Automated Demand Response for Small Commerical Buildings  

E-Print Network (OSTI)

of Fully Automated Demand  Response in Large Facilities.  Fully Automated Demand Response Tests in Large Facilities.  Open Automated  Demand Response Communication Standards: 

Dudley, June Han

2009-01-01T23:59:59.000Z

4

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

Goodin. 2009. “Open Automated Demand Response Communicationsin Demand Response for Wholesale Ancillary Services. ” InOpen Automated Demand Response Demonstration Project. LBNL-

Ghatikar, Girish

2010-01-01T23:59:59.000Z

5

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

6

Design and Operation of an Open, Interoperable Automated Demand...  

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

Design and Operation of an Open, Interoperable Automated Demand Response Infrastructure for Commercial Buildings Title Design and Operation of an Open, Interoperable Automated...

7

Linking Continuous Energy Management and Open Automated Demand Response  

E-Print Network (OSTI)

A. Barat, D. Watson. Demand Response Spinning ReserveOpen Automated Demand Response Communication Standards:Dynamic Controls for Demand Response in a New Commercial

Piette, Mary Ann

2009-01-01T23:59:59.000Z

8

Open Automated Demand Response Technologies for Dynamic Pricing and Smart Grid  

E-Print Network (OSTI)

for Automated Demand Response in Commercial Buildings. ” In2010. “Open Automated Demand Response Dynamic Pricing2009. “Open Automated Demand Response Communications

Ghatikar, Girish

2010-01-01T23:59:59.000Z

9

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

10

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

11

Design and Operation of an Open, Interoperable Automated Demand Response  

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

Design and Operation of an Open, Interoperable Automated Demand Response Design and Operation of an Open, Interoperable Automated Demand Response Infrastructure for Commercial Buildings Title Design and Operation of an Open, Interoperable Automated Demand Response Infrastructure for Commercial Buildings Publication Type Journal Article LBNL Report Number LBNL-2340e Year of Publication 2009 Authors Piette, Mary Ann, Girish Ghatikar, Sila Kiliccote, David S. Watson, Edward Koch, and Dan Hennage Journal Journal of Computing Science and Information Engineering Volume 9 Issue 2 Keywords communication and standards, market sectors, openadr Abstract This paper describes the concept for and lessons from the development and field-testing of an open, interoperable communications infrastructure to support automated demand response (auto-DR). Automating DR allows greater levels of participation, improved reliability, and repeatability of the DR in participating facilities. This paper also presents the technical and architectural issues associated with auto-DR and description of the demand response automation server (DRAS), the client/server architecture-based middle-ware used to automate the interactions between the utilities or any DR serving entity and their customers for DR programs. Use case diagrams are presented to show the role of the DRAS between utility/ISO and the clients at the facilities.

12

Open Automated Demand Response Communications Specification (Version 1.0)  

E-Print Network (OSTI)

and Techniques for Demand Response. May 2007. LBNL-59975.to facilitate automating  demand response actions at the Interoperable Automated Demand Response Infrastructure,

Piette, Mary Ann

2009-01-01T23:59:59.000Z

13

Open Automated Demand Response for Small Commerical Buildings  

Science Conference Proceedings (OSTI)

This report characterizes small commercial buildings by market segments, systems and end-uses; develops a framework for identifying demand response (DR) enabling technologies and communication means; and reports on the design and development of a low-cost OpenADR enabling technology that delivers demand reductions as a percentage of the total predicted building peak electric demand. The results show that small offices, restaurants and retail buildings are the major contributors making up over one third of the small commercial peak demand. The majority of the small commercial buildings in California are located in southern inland areas and the central valley. Single-zone packaged units with manual and programmable thermostat controls make up the majority of heating ventilation and air conditioning (HVAC) systems for small commercial buildings with less than 200 kW peak electric demand. Fluorescent tubes with magnetic ballast and manual controls dominate this customer group's lighting systems. There are various ways, each with its pros and cons for a particular application, to communicate with these systems and three methods to enable automated DR in small commercial buildings using the Open Automated Demand Response (or OpenADR) communications infrastructure. Development of DR strategies must consider building characteristics, such as weather sensitivity and load variability, as well as system design (i.e. under-sizing, under-lighting, over-sizing, etc). Finally, field tests show that requesting demand reductions as a percentage of the total building predicted peak electric demand is feasible using the OpenADR infrastructure.

Dudley, June Han; Piette, Mary Ann; Koch, Ed; Hennage, Dan

2009-05-01T23:59:59.000Z

14

Northwest Open Automated Demand Response Technology Demonstration...  

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

morning and summer afternoon peak electricity demand in commercial buildings the Seattle area. LBNL performed this demonstration for the Bonneville Power Administration (BPA)...

15

Northwest Open Automated Demand Response Technology Demonstration Project  

E-Print Network (OSTI)

reliability signals for demand response GTA HTTPS HVAC IT kWand Commissioning Automated Demand Response Systems. ”and Techniques for Demand Response. California Energy

Kiliccote, Sila

2010-01-01T23:59:59.000Z

16

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

17

Linking Continuous Energy Management and Open Automated Demand Response  

Science Conference Proceedings (OSTI)

Advances in communications and control technology, the strengthening of the Internet, and the growing appreciation of the urgency to reduce demand side energy use are motivating the development of improvements in both energy efficiency and demand response (DR) systems. This paper provides a framework linking continuous energy management and continuous communications for automated demand response (Auto-DR) in various times scales. We provide a set of concepts for monitoring and controls linked to standards and procedures such as Open Automation Demand Response Communication Standards (Open Auto-DR or OpenADR). Basic building energy science and control issues in this approach begin with key building components, systems, end-uses and whole building energy performance metrics. The paper presents a framework about when energy is used, levels of services by energy using systems, granularity of control, and speed of telemetry. DR, when defined as a discrete event, requires a different set of building service levels than daily operations. We provide examples of lessons from DR case studies and links to energy efficiency.

Piette, Mary Ann; Kiliccote, Sila; Ghatikar, Girish

2008-10-03T23:59:59.000Z

18

Automation of Capacity Bidding with an Aggregator Using Open Automated Demand Response  

Science Conference Proceedings (OSTI)

This report summarizes San Diego Gas& Electric Company?s collaboration with the Demand Response Research Center to develop and test automation capability for the Capacity Bidding Program in 2007. The report describes the Open Automated Demand Response architecture, summarizes the history of technology development and pilot studies. It also outlines the Capacity Bidding Program and technology being used by an aggregator that participated in this demand response program. Due to delays, the program was not fully operational for summer 2007. However, a test event on October 3, 2007, showed that the project successfully achieved the objective to develop and demonstrate how an open, Web?based interoperable automated notification system for capacity bidding can be used by aggregators for demand response. The system was effective in initiating a fully automated demand response shed at the aggregated sites. This project also demonstrated how aggregators can integrate their demand response automation systems with San Diego Gas& Electric Company?s Demand Response Automation Server and capacity bidding program.

Kiliccote, Sila; Piette, Mary Ann

2008-10-01T23:59:59.000Z

19

Northwest Open Automated Demand Response Technology Demonstration Project  

SciTech Connect

The Lawrence Berkeley National Laboratory (LBNL) Demand Response Research Center (DRRC) demonstrated and evaluated open automated demand response (OpenADR) communication infrastructure to reduce winter morning and summer afternoon peak electricity demand in commercial buildings the Seattle area. LBNL performed this demonstration for the Bonneville Power Administration (BPA) in the Seattle City Light (SCL) service territory at five sites: Seattle Municipal Tower, Seattle University, McKinstry, and two Target stores. This report describes the process and results of the demonstration. OpenADR is an information exchange model that uses a client-server architecture to automate demand-response (DR) programs. These field tests evaluated the feasibility of deploying fully automated DR during both winter and summer peak periods. DR savings were evaluated for several building systems and control strategies. This project studied DR during hot summer afternoons and cold winter mornings, both periods when electricity demand is typically high. This is the DRRC project team's first experience using automation for year-round DR resources and evaluating the flexibility of commercial buildings end-use loads to participate in DR in dual-peaking climates. The lessons learned contribute to understanding end-use loads that are suitable for dispatch at different times of the year. The project was funded by BPA and SCL. BPA is a U.S. Department of Energy agency headquartered in Portland, Oregon and serving the Pacific Northwest. BPA operates an electricity transmission system and markets wholesale electrical power at cost from federal dams, one non-federal nuclear plant, and other non-federal hydroelectric and wind energy generation facilities. Created by the citizens of Seattle in 1902, SCL is the second-largest municipal utility in America. SCL purchases approximately 40% of its electricity and the majority of its transmission from BPA through a preference contract. SCL also provides ancillary services within its own balancing authority. The relationship between BPA and SCL creates a unique opportunity to create DR programs that address both BPA's and SCL's markets simultaneously. Although simultaneously addressing both market could significantly increase the value of DR programs for BPA, SCL, and the end user, establishing program parameters that maximize this value is challenging because of complex contractual arrangements and the absence of a central Independent System Operator or Regional Transmission Organization in the northwest.

Kiliccote, Sila; Piette, Mary Ann; Dudley, Junqiao

2010-03-17T23:59:59.000Z

20

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

SciTech Connect

This study examines the use of OpenADR communications specification, related data models, technologies, and strategies to send dynamic prices (e.g., real time prices and peak prices) and Time of Use (TOU) rates to commercial and industrial electricity customers. OpenADR v1.0 is a Web services-based flexible, open information model that has been used in California utilities' commercial automated demand response programs since 2007. We find that data models can be used to send real time prices. These same data models can also be used to support peak pricing and TOU rates. We present a data model that can accommodate all three types of rates. For demonstration purposes, the data models were generated from California Independent System Operator's real-time wholesale market prices, and a California utility's dynamic prices and TOU rates. Customers can respond to dynamic prices by either using the actual prices, or prices can be mapped into"operation modes," which can act as inputs to control systems. We present several different methods for mapping actual prices. Some of these methods were implemented in demonstration projects. The study results demonstrate show that OpenADR allows interoperability with existing/future systems/technologies and can be used within related dynamic pricing activities within Smart Grid.

Ghatikar, Girish; Mathieu, Johanna L.; Piette, Mary Ann; Koch, Ed; Hennage, Dan

2010-08-02T23:59:59.000Z

Note: This page contains sample records for the topic "open automated demand" 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

Opportunities for Energy Efficiency and Open Automated Demand Response in Wastewater Treatment Facilities in California -- Phase I Report  

E-Print Network (OSTI)

your Power. (2008). "Demand Response Programs." RetrievedUsing Open Automated Demand Response, Lawrence Berkeley2008). "What is Demand Response?" Retrieved 10/10/2008, from

Lekov, Alex

2010-01-01T23:59:59.000Z

22

Northwest Open Automated Demand Response Technology Demonstration Project  

SciTech Connect

Lawrence Berkeley National Laboratory (LBNL) and the Demand Response Research Center (DRRC) performed a technology demonstration and evaluation for Bonneville Power Administration (BPA) in Seattle City Light's (SCL) service territory. This report summarizes the process and results of deploying open automated demand response (OpenADR) in Seattle area with winter morning peaking commercial buildings. The field tests were designed to evaluate the feasibility of deploying fully automated demand response (DR) in four to six sites in the winter and the savings from various building systems. The project started in November of 2008 and lasted 6 months. The methodology for the study included site recruitment, control strategy development, automation system deployment and enhancements, and evaluation of sites participation in DR test events. LBNL subcontracted McKinstry and Akuacom for this project. McKinstry assisted with recruitment, site survey collection, strategy development and overall participant and control vendor management. Akuacom established a new server and enhanced its operations to allow for scheduling winter morning day-of and day-ahead events. Each site signed a Memorandum of Agreement with SCL. SCL offered each site $3,000 for agreeing to participate in the study and an additional $1,000 for each event they participated. Each facility and their control vendor worked with LBNL and McKinstry to select and implement control strategies for DR and developed their automation based on the existing Internet connectivity and building control system. Once the DR strategies were programmed, McKinstry commissioned them before actual test events. McKinstry worked with LBNL to identify control points that can be archived at each facility. For each site LBNL collected meter data and trend logs from the energy management and control system. The communication system allowed the sites to receive day-ahead as well as day-of DR test event signals. Measurement of DR was conducted using three different baseline models for estimation peak load reductions. One was three-in-ten baseline, which is based on the site electricity consumption from 7 am to 10 am for the three days with the highest consumption of the previous ten business days. The second model, the LBNL outside air temperature (OAT) regression baseline model, is based on OAT data and site electricity consumption from the previous ten days, adjusted using weather regressions from the fifteen-minute electric load data during each DR test event for each site. A third baseline that simply averages the available load data was used for sites less with less than 10 days of historical meter data. The evaluation also included surveying sites regarding any problems or issues that arose during the DR test events. Question covered occupant comfort, control issues and other potential problems.

Kiliccote, Sila; Dudley, Junqiao Han; Piette, Mary Ann

2009-08-01T23:59:59.000Z

23

Demand Response and Open Automated Demand Response Opportunities for Data Centers  

SciTech Connect

This study examines data center characteristics, loads, control systems, and technologies to identify demand response (DR) and automated DR (Open Auto-DR) opportunities and challenges. The study was performed in collaboration with technology experts, industrial partners, and data center facility managers and existing research on commercial and industrial DR was collected and analyzed. The results suggest that data centers, with significant and rapidly growing energy use, have significant DR potential. Because data centers are highly automated, they are excellent candidates for Open Auto-DR. 'Non-mission-critical' data centers are the most likely candidates for early adoption of DR. Data center site infrastructure DR strategies have been well studied for other commercial buildings; however, DR strategies for information technology (IT) infrastructure have not been studied extensively. The largest opportunity for DR or load reduction in data centers is in the use of virtualization to reduce IT equipment energy use, which correspondingly reduces facility cooling loads. DR strategies could also be deployed for data center lighting, and heating, ventilation, and air conditioning. Additional studies and demonstrations are needed to quantify benefits to data centers of participating in DR and to address concerns about DR's possible impact on data center performance or quality of service and equipment life span.

Ghatikar, Girish; Piette, Mary Ann; Fujita, Sydny; McKane, Aimee; Dudley, Junqiao Han; Radspieler, Anthony; Mares, K.C.; Shroyer, Dave

2009-12-30T23:59:59.000Z

24

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

25

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

26

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

27

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

28

Design and Operation of an Open, Interoperable Automated Demand Response Infrastructure for Commercial Buildings  

E-Print Network (OSTI)

below. Fig. 4 Automated demand response general features Thearchitecture Automated Demand Response System ArchitectureCould Bene?t for Demand Response Programs, But Challenges

Piette, Mary Ann

2010-01-01T23:59:59.000Z

29

Design and Implementation of an Open, Interoperable Automated Demand Response Infrastructure  

E-Print Network (OSTI)

of Fully Automated Demand Response in Large Facilities. CEC-Fully Automated Demand Response Tests in Large Facilities.Management and Demand Response in Commercial Building. ,

Piette, Mary Ann; Kiliccote, Sila; Ghatikar, Girish

2008-01-01T23:59:59.000Z

30

Northwest Open Automated Demand Response Technology Demonstration Project  

E-Print Network (OSTI)

5. Average, minimum, and maximum demand reduction at eachshow the minimum and maximum demand reduction during the7. Average, minimum, and maximum demand reduction at each

Kiliccote, Sila

2010-01-01T23:59:59.000Z

31

Northwest Open Automated Demand Response Technology Demonstration Project  

E-Print Network (OSTI)

as 15-minute minimum and maximum demand values are provided.8. Hourly average and maximum demand savings of McKinstry on9. Hourly average and maximum demand savings of McKinstry on

Kiliccote, Sila

2010-01-01T23:59:59.000Z

32

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

if the customer’s maximum demand has exceeded 999 kilowattswhose meter indicates a maximum demand of 200 kW or greater2) the customer's maximum billing demand has exceeded 499

Ghatikar, Girish

2010-01-01T23:59:59.000Z

33

Open Automated Demand Response for Small Commerical Buildings  

E-Print Network (OSTI)

the average, minimum and maximum demand reduction for each Average, Minimum and Maximum Demand Reduction Based on 3/1016 Average, Minimum and Maximum Demand Reduction Based on

Dudley, June Han

2009-01-01T23:59:59.000Z

34

Open Automated Demand Response for Small Commerical Buildings  

E-Print Network (OSTI)

of the small commercial peak demand.  The majority of the less than 200 kW of peak demand, make up 20?25% of  peak the small commercial  peak demand.  A ten percent reduction 

Dudley, June Han

2009-01-01T23:59:59.000Z

35

Linking Continuous Energy Management and Open Automated Demand Response  

E-Print Network (OSTI)

minimization Monthly peak demand management Daily time-of-Some tariff designs have peak demand charges that apply tothat may result in a peak demand that occurs in one month to

Piette, Mary Ann

2009-01-01T23:59:59.000Z

36

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

1. The “Utility or ISO Operator OpenADR Interface” lists1. The “Utility or ISO Operator OpenADR Interface” listsSheets List of Figures Figure 1. OpenADR Version 1.0 Utility

Ghatikar, Girish

2010-01-01T23:59:59.000Z

37

Open Automated Demand Response Communications Specification (Version 1.0)  

E-Print Network (OSTI)

OpenADR  is one element of the Smart Grid information and OpenADR is  one element of the Smart Grid information and OpenADR is  one element of the Smart Grid information and 

Piette, Mary Ann

2009-01-01T23:59:59.000Z

38

Northwest Open Automated Demand Response Technology Demonstration Project  

E-Print Network (OSTI)

summer peak demand, with hydro power and wind integration,of its hydro system, continued load growth, wind power

Kiliccote, Sila

2010-01-01T23:59:59.000Z

39

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

NIST Framework and Roadmap for Smart Grid Interoperabilityaccessed: 6/16/2010. Open Smart Grid Users Group. “OpenADRactivities within the Smart Grid. Keywords: Commercial and

Ghatikar, Girish

2010-01-01T23:59:59.000Z

40

Open Automated Demand Response Technologies for Dynamic Pricing and Smart Grid  

Science Conference Proceedings (OSTI)

We present an Open Automated Demand Response Communications Specifications (OpenADR) data model capable of communicating real-time prices to electricity customers. We also show how the same data model could be used to for other types of dynamic pricing tariffs (including peak pricing tariffs, which are common throughout the United States). Customers participating in automated demand response programs with building control systems can respond to dynamic prices by using the actual prices as inputs to their control systems. Alternatively, prices can be mapped into"building operation modes," which can act as inputs to control systems. We present several different strategies customers could use to map prices to operation modes. Our results show that OpenADR can be used to communicate dynamic pricing within the Smart Grid and that OpenADR allows for interoperability with existing and future systems, technologies, and electricity markets.

Ghatikar, Girish; Mathieu, Johanna L.; Piette, Mary Ann; Kiliccote, Sila

2010-06-02T23:59:59.000Z

Note: This page contains sample records for the topic "open automated demand" 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

Design and Operation of an Open, Interoperable Automated Demand Response Infrastructure for Commercial Buildings  

Science Conference Proceedings (OSTI)

This paper describes the concept for and lessons from the development and field-testing of an open, interoperable communications infrastructure to support automated demand response (auto-DR). Automating DR allows greater levels of participation, improved reliability, and repeatability of the DR in participating facilities. This paper also presents the technical and architectural issues associated with auto-DR and description of the demand response automation server (DRAS), the client/server architecture-based middle-ware used to automate the interactions between the utilities or any DR serving entity and their customers for DR programs. Use case diagrams are presented to show the role of the DRAS between utility/ISO and the clients at the facilities.

Piette, Mary Ann; Ghatikar, Girish; Kiliccote, Sila; Watson, David; Koch, Ed; Hennage, Dan

2009-05-01T23:59:59.000Z

42

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

Comes to Demand Response is FERC is own Worst Enemy? ” The2009. Report No. 1018895. FERC. 2009. A National AssessmentLast accessed: 6/22/10. FERC. 2010. National Action Plan on

Ghatikar, Girish

2010-01-01T23:59:59.000Z

43

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

oasis. Last accessed: Con Edison. 2010. “Demand Response/is Southern California Edison’s real-time pricing tariff. 2.and Southern California Edison’s Critical Peak Pricing

Ghatikar, Girish

2010-01-01T23:59:59.000Z

44

Northwest Open Automated Demand Response Technology Demonstration Project  

E-Print Network (OSTI)

profile (Figure 10), the load profile over the test day isbelow as well as the load profile of the aggregate demandbelow as well as the load profile of the aggregate demand

Kiliccote, Sila

2010-01-01T23:59:59.000Z

45

Architecture Concepts and Technical Issues for an Open, Interoperable Automated Demand Response Infrastructure  

E-Print Network (OSTI)

energy efficiency and demand response in large facilities.was sponsored by the Demand Response Research Center whichInteroperable Automated Demand Response Infrastructure Ed

Koch, Ed; Piette, Mary Ann

2008-01-01T23:59:59.000Z

46

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

47

Automation of Capacity Bidding with an Aggregator Using Open Automated Demand Response  

E-Print Network (OSTI)

S.  Kiliccote.   Estimating Demand Response Load  Impacts: in California.   Demand Response Research Center, Lawrence and Techniques for Demand Response.  LBNL Report 59975.  

Kiliccote, Sila

2011-01-01T23:59:59.000Z

48

Automation of Capacity Bidding with an Aggregator Using Open Automated Demand Response  

E-Print Network (OSTI)

Project Committee 135  Capacity Bidding Program  Client and REPORT AUTOMATION OF CAPACITY BIDDING WITH AN AGGREGATORDevelopment and Testing of the Capacity Bidding Program for 

Kiliccote, Sila

2011-01-01T23:59:59.000Z

49

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

50

Findings from Seven Years of Field Performance Data for Automated Demand Response in Commercial Buildings  

E-Print Network (OSTI)

Open Automated Demand Response Demonstration Project” LBNL-2009a). “Open Automated Demand Response Communications inand Actions for Industrial Demand Response in California. ”

Kiliccote, Sila

2010-01-01T23:59:59.000Z

51

Design and Operation of an Open, Interoperable Automated Demand Response Infrastructure for Commercial Buildings  

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

340E 340E Design and Operation of an Open, Interoperable Automated Demand Response Infrastructure for Commercial Buildings M.A. Piette, G. Ghatikar, S. Kiliccote, D. Watson Lawrence Berkeley National Laboratory E. Koch, D. Hennage Akuacom June 2009 Journal of Computing Science and Information Engineering, Vol. 9, Issue 2 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,

52

Opportunities for Energy Efficiency and Open Automated Demand Response in Wastewater Treatment Facilities in California -- Phase I Report  

SciTech Connect

This report summarizes the Lawrence Berkeley National Laboratory?s research to date in characterizing energy efficiency and automated demand response opportunities for wastewater treatment facilities in California. The report describes the characteristics of wastewater treatment facilities, the nature of the wastewater stream, energy use and demand, as well as details of the wastewater treatment process. It also discusses control systems and energy efficiency and automated demand response opportunities. In addition, several energy efficiency and load management case studies are provided for wastewater treatment facilities.This study shows that wastewater treatment facilities can be excellent candidates for open automated demand response and that facilities which have implemented energy efficiency measures and have centralized control systems are well-suited to shift or shed electrical loads in response to financial incentives, utility bill savings, and/or opportunities to enhance reliability of service. Control technologies installed for energy efficiency and load management purposes can often be adapted for automated demand response at little additional cost. These improved controls may prepare facilities to be more receptive to open automated demand response due to both increased confidence in the opportunities for controlling energy cost/use and access to the real-time data.

Lekov, Alex; Thompson, Lisa; McKane, Aimee; Song, Katherine; Piette, Mary Ann

2009-04-01T23:59:59.000Z

53

Opportunities for Energy Efficiency and Automated Demand Response in Industrial Refrigerated Warehouses in California  

E-Print Network (OSTI)

your Power. (2008). "Demand Response Programs." RetrievedS. (2008). Automated Demand Response Results from Multi-Yearusing Open Automated Demand Response, California Energy

Lekov, Alex

2009-01-01T23:59:59.000Z

54

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...

55

Automation systems for Demand Response, ForskEL (Smart Grid Project) | Open  

Open Energy Info (EERE)

systems for Demand Response, ForskEL (Smart Grid Project) systems for Demand Response, ForskEL (Smart Grid Project) Jump to: navigation, search Project Name Automation systems for Demand Response, ForskEL Country Denmark Coordinates 56.26392°, 9.501785° 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":56.26392,"lon":9.501785,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

56

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

57

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

58

Automated Demand Response: The Missing Link in the Electricity Value Chain  

E-Print Network (OSTI)

and Open Automated Demand Response. In Grid Interop Forum.Berkeley National Laboratory. Demand Response ResearchCenter, Demand Response Research Center PIER Team Briefing,

McKane, Aimee

2010-01-01T23:59:59.000Z

59

Fast Automated Demand Response to Enable the Integration of Renewable Resources  

E-Print Network (OSTI)

Consulting), and Dave Shroyer (SCG). Demand Response andOpen Automated Demand Response Opportunities for DataIAW Research Team, Demand Response Research Center, Lawrence

Watson, David S.

2013-01-01T23:59:59.000Z

60

Open Automated Demand Response Technologies for Dynamic Pricing and Smart Grid  

E-Print Network (OSTI)

Signals. ” SGIP NIST Smart Grid Collaboration Site. http://emix/. Last accessed: Open Smart Grid Users Group. “OpenADROpenADR technologies and Smart Grid standards activities.

Ghatikar, Girish

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "open automated demand" 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

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

62

Open Automated Demand Response Technologies for Dynamic Pricing and Smart Grid  

E-Print Network (OSTI)

of communicating real-time prices to electricity customers.and demand conditions. Real-time prices can be set with day-e.g. , mapping real-time prices to “normal, moderate, or

Ghatikar, Girish

2010-01-01T23:59:59.000Z

63

Scenarios for Consuming Standardized Automated Demand Response Signals  

E-Print Network (OSTI)

of Fully Automated Demand Response in Large Facilities.Fully Automated Demand Response Tests in Large Facilities.Interoperable Automated Demand Response Infrastructure.

Koch, Ed

2009-01-01T23:59:59.000Z

64

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

65

Automated Demand Response Opportunities in Wastewater Treatment Facilities  

Science Conference Proceedings (OSTI)

Wastewater treatment is an energy intensive process which, together with water treatment, comprises about three percent of U.S. annual energy use. Yet, since wastewater treatment facilities are often peripheral to major electricity-using industries, they are frequently an overlooked area for automated demand response opportunities. Demand response is a set of actions taken to reduce electric loads when contingencies, such as emergencies or congestion, occur that threaten supply-demand balance, and/or market conditions occur that raise electric supply costs. Demand response programs are designed to improve the reliability of the electric grid and to lower the use of electricity during peak times to reduce the total system costs. Open automated demand response is a set of continuous, open communication signals and systems provided over the Internet to allow facilities to automate their demand response activities without the need for manual actions. Automated demand response strategies can be implemented as an enhanced use of upgraded equipment and facility control strategies installed as energy efficiency measures. Conversely, installation of controls to support automated demand response may result in improved energy efficiency through real-time access to operational data. This paper argues that the implementation of energy efficiency opportunities in wastewater treatment facilities creates a base for achieving successful demand reductions. This paper characterizes energy use and the state of demand response readiness in wastewater treatment facilities and outlines automated demand response opportunities.

Thompson, Lisa; Song, Katherine; Lekov, Alex; McKane, Aimee

2008-11-19T23:59:59.000Z

66

Automated Demand Response Technology Demonstration Project for...  

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

Demonstration Project for Small and Medium Commercial Buildings Title Automated Demand Response Technology Demonstration Project for Small and Medium Commercial Buildings...

67

Home Network Technologies and Automating Demand Response  

E-Print Network (OSTI)

networks_in_the_home_the_new_growth_market.htm [12] NationalHome Network Technologies and Automating Demand Responsethe University of California. Home Network Technologies and

McParland, Charles

2010-01-01T23:59:59.000Z

68

Results and commissioning issues from an automated demand response pilot  

E-Print Network (OSTI)

of Fully Automated Demand Response in Large Facilities"Management and Demand Response in Commercial Buildings", L Band Commissioning Issues from an Automated Demand Response.

Piette, Mary Ann; Watson, Dave; Sezgen, Osman; Motegi, Naoya

2004-01-01T23:59:59.000Z

69

Opportunities for Open Automated Demand Response in Wastewater Treatment Facilities in California - Phase II Report. San Luis Rey Wastewater Treatment Plant Case Study  

SciTech Connect

This case study enhances the understanding of open automated demand response opportunities in municipal wastewater treatment facilities. The report summarizes the findings of a 100 day submetering project at the San Luis Rey Wastewater Treatment Plant, a municipal wastewater treatment facility in Oceanside, California. The report reveals that key energy-intensive equipment such as pumps and centrifuges can be targeted for large load reductions. Demand response tests on the effluent pumps resulted a 300 kW load reduction and tests on centrifuges resulted in a 40 kW load reduction. Although tests on the facility?s blowers resulted in peak period load reductions of 78 kW sharp, short-lived increases in the turbidity of the wastewater effluent were experienced within 24 hours of the test. The results of these tests, which were conducted on blowers without variable speed drive capability, would not be acceptable and warrant further study. This study finds that wastewater treatment facilities have significant open automated demand response potential. However, limiting factors to implementing demand response are the reaction of effluent turbidity to reduced aeration load, along with the cogeneration capabilities of municipal facilities, including existing power purchase agreements and utility receptiveness to purchasing electricity from cogeneration facilities.

Thompson, Lisa; Lekov, Alex; McKane, Aimee; Piette, Mary Ann

2010-08-20T23:59:59.000Z

70

Automated Demand Response Technologies and Demonstration in New York City  

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

Technologies and Demonstration in New York City Technologies and Demonstration in New York City using OpenADR Title Automated Demand Response Technologies and Demonstration in New York City using OpenADR Publication Type Report LBNL Report Number LBNL-6470E Year of Publication 2013 Authors Kim, Joyce Jihyun, Sila Kiliccote, and Rongxin Yin Date Published 09/2013 Publisher LBNL/NYSERDA Abstract Demand response (DR) - allowing customers to respond to reliability requests and market prices by changing electricity use from their normal consumption pattern - continues to be seen as an attractive means of demand-side management and a fundamental smart-grid improvement that links supply and demand. Since October 2011, the Demand Response Research Center at Lawrence Berkeley National Laboratory and New York State Energy Research and Development Authority have conducted a demonstration project enabling Automated Demand Response (Auto-DR) in large commercial buildings located in New York City using Open Automated Demand Response (OpenADR) communication protocols. In particular, this project focuses on demonstrating how OpenADR can automate and simplify interactions between buildings and various stakeholders in New York State including the independent system operator, utilities, retail energy providers, and curtailment service providers. In this paper, we present methods to automate control strategies via building management systems to provide event-driven demand response, price response and demand management based on OpenADR signals. We also present cost control opportunities under day-ahead hourly pricing for large customers and Auto-DR control strategies developed for demonstration buildings. Lastly, we discuss the communication architecture and Auto-DR system designed for the demonstration project to automate price response and DR participation.

71

Distributed Intelligent Automated Demand Response (DIADR) Building  

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

Distributed Intelligent Automated Demand Distributed Intelligent Automated Demand Response (DIADR) Building Management System Distributed Intelligent Automated Demand Response (DIADR) Building Management System The U.S. Department of Energy (DOE) is currently conducting research into distributed intelligent-automated demand response (DIADR) building management systems. Project Description This project aims to develop a DIADR building management system with intelligent optimization and control algorithms for demand management, taking into account a multitude of factors affecting cost including: Comfort Heating, ventilating, and air conditioning (HVAC) Lighting Other building systems Climate Usage and occupancy patterns. The key challenge is to provide the demand response the ability to address more and more complex building systems that include a variety of loads,

72

Price Responsive Demand in New York Wholesale Electricity Market using OpenADR  

E-Print Network (OSTI)

Advanced Metering, and Demand Response in Electricity2006. Benefits of Demand Response in Electricity Markets and2010. Open Automated Demand Response Technologies for

Kim, Joyce Jihyun

2013-01-01T23:59:59.000Z

73

Automated Demand Response for Critical Peak Pricing  

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

Automated Demand Response for Critical Peak Pricing Speaker(s): Naoya Motegi Date: June 9, 2005 - 12:00pm Location: Bldg. 90 California utilities have been exploring the use of...

74

Findings from the 2004 Fully Automated Demand Response Tests...  

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

the 2004 Fully Automated Demand Response Tests in Large Facilities Title Findings from the 2004 Fully Automated Demand Response Tests in Large Facilities Publication Type Report...

75

A Demand Response (DR) Event: Benefits, Strategies, Automation...  

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

A Demand Response (DR) Event: Benefits, Strategies, Automation and Future of DR Title A Demand Response (DR) Event: Benefits, Strategies, Automation and Future of DR Publication...

76

Field Demonstration of Automated Demand Response for Both Winter...  

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

Demonstration of Automated Demand Response for Both Winter and Summer Events in Large Buildings in the Pacific Northwest Title Field Demonstration of Automated Demand Response for...

77

Fast Automated Demand Response to Enable the Integration of Renewable...  

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

Fast Automated Demand Response to Enable the Integration of Renewable Resources Title Fast Automated Demand Response to Enable the Integration of Renewable Resources Publication...

78

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

79

Direct versus Facility Centric Load Control for Automated Demand Response  

E-Print Network (OSTI)

Interoperable Automated Demand Response Infrastructure.and Techniques for Demand Response. LBNL Report 59975. Mayand Communications for Demand Response and Energy Efficiency

Piette, Mary Ann

2010-01-01T23:59:59.000Z

80

Measurement and evaluation techniques for automated demand response demonstration  

E-Print Network (OSTI)

Development for Demand Response Calculation – Findings andManagement and Demand Response in Commercial Buildings. ”of Fully Automated Demand Response in Large Facilities. ”

Motegi, Naoya; Piette, Mary Ann; Watson, David S.; Sezgen, Osman; ten Hope, Laurie

2004-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "open automated demand" 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

Automated Demand Response Strategies and Commissioning Commercial Building Controls  

E-Print Network (OSTI)

4 9 . Piette et at Automated Demand Response Strategies andDynamic Controls for Demand Response in New and ExistingFully Automated Demand Response Tests in Large Facilities"

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

2006-01-01T23:59:59.000Z

82

Field Testing of Automated Demand Response for Integration of...  

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

Field Testing of Automated Demand Response for Integration of Renewable Resources in California's Ancillary Services Market for Regulation Products Title Field Testing of Automated...

83

Installation and Commissioning Automated Demand Response Systems  

Science Conference Proceedings (OSTI)

Demand Response (DR) can be defined as actions taken to reduce electric loads when contingencies, such as emergencies and congestion, occur that threaten supply-demand balance, or market conditions raise supply costs. California utilities have offered price and reliability DR based programs to customers to help reduce electric peak demand. The lack of knowledge about the DR programs and how to develop and implement DR control strategies is a barrier to participation in DR programs, as is the lack of automation of DR systems. Most DR activities are manual and require people to first receive notifications, and then act on the information to execute DR strategies. Levels of automation in DR can be defined as follows. Manual Demand Response involves a labor-intensive approach such as manually turning off or changing comfort set points at each equipment switch or controller. Semi-Automated Demand Response involves a pre-programmed demand response strategy initiated by a person via centralized control system. Fully-Automated Demand Response does not involve human intervention, but is initiated at a home, building, or facility through receipt of an external communications signal. The receipt of the external signal initiates pre-programmed demand response strategies. We refer to this as Auto-DR (Piette et. al. 2005). Auto-DR for commercial and industrial facilities can be defined as fully automated DR initiated by a signal from a utility or other appropriate entity and that provides fully-automated connectivity to customer end-use control strategies. One important concept in Auto-DR is that a homeowner or facility manager should be able to 'opt out' or 'override' a DR event if the event comes at time when the reduction in end-use services is not desirable. Therefore, Auto-DR is not handing over total control of the equipment or the facility to the utility but simply allowing the utility to pass on grid related information which then triggers facility defined and programmed strategies if convenient to the facility. From 2003 through 2006 Lawrence Berkeley National Laboratory (LBNL) and the Demand Response Research Center (DRRC) developed and tested a series of demand response automation communications technologies known as Automated Demand Response (Auto-DR). In 2007, LBNL worked with three investor-owned utilities to commercialize and implement Auto-DR programs in their territories. This paper summarizes the history of technology development for Auto-DR, and describes the DR technologies and control strategies utilized at many of the facilities. It outlines early experience in commercializing Auto-DR systems within PG&E DR programs, including the steps to configure the automation technology. The paper also describes the DR sheds derived using three different baseline methodologies. Emphasis is given to the lessons learned from installation and commissioning of Auto-DR systems, with a detailed description of the technical coordination roles and responsibilities, and costs.

Global Energy Partners; Pacific Gas and Electric Company; Kiliccote, Sila; Kiliccote, Sila; Piette, Mary Ann; Wikler, Greg; Prijyanonda, Joe; Chiu, Albert

2008-04-21T23:59:59.000Z

84

Home Network Technologies and Automating Demand Response  

Science Conference Proceedings (OSTI)

Over the past several years, interest in large-scale control of peak energy demand and total consumption has increased. While motivated by a number of factors, this interest has primarily been spurred on the demand side by the increasing cost of energy and, on the supply side by the limited ability of utilities to build sufficient electricity generation capacity to meet unrestrained future demand. To address peak electricity use Demand Response (DR) systems are being proposed to motivate reductions in electricity use through the use of price incentives. DR systems are also be design to shift or curtail energy demand at critical times when the generation, transmission, and distribution systems (i.e. the 'grid') are threatened with instabilities. To be effectively deployed on a large-scale, these proposed DR systems need to be automated. Automation will require robust and efficient data communications infrastructures across geographically dispersed markets. The present availability of widespread Internet connectivity and inexpensive, reliable computing hardware combined with the growing confidence in the capabilities of distributed, application-level communications protocols suggests that now is the time for designing and deploying practical systems. Centralized computer systems that are capable of providing continuous signals to automate customers reduction of power demand, are known as Demand Response Automation Servers (DRAS). The deployment of prototype DRAS systems has already begun - with most initial deployments targeting large commercial and industrial (C & I) customers. An examination of the current overall energy consumption by economic sector shows that the C & I market is responsible for roughly half of all energy consumption in the US. On a per customer basis, large C & I customers clearly have the most to offer - and to gain - by participating in DR programs to reduce peak demand. And, by concentrating on a small number of relatively sophisticated energy consumers, it has been possible to improve the DR 'state of the art' with a manageable commitment of technical resources on both the utility and consumer side. Although numerous C & I DR applications of a DRAS infrastructure are still in either prototype or early production phases, these early attempts at automating DR have been notably successful for both utilities and C & I customers. Several factors have strongly contributed to this success and will be discussed below. These successes have motivated utilities and regulators to look closely at how DR programs can be expanded to encompass the remaining (roughly) half of the state's energy load - the light commercial and, in numerical terms, the more important residential customer market. This survey examines technical issues facing the implementation of automated DR in the residential environment. In particular, we will look at the potential role of home automation networks in implementing wide-scale DR systems that communicate directly to individual residences.

McParland, Charles

2009-12-01T23:59:59.000Z

85

TY CONF T1 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 T2 International Conference for Enhanced Building Operations ICEBO A1 Joyce Jihyun Kim A1 Rongxin Yin A1 Sila Kiliccote AB 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

86

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

87

Intelligent Building Automation: A Demand Response Management Perspective  

E-Print Network (OSTI)

In recent years intelligent Building Automation Systems, based on best practice open technology, have succeeded in helping facilities reduce their infrastructure, installation and operating costs. The idea was - 'the less the human intervention and the more automated the system then the more efficient the building'. With that in mind a question may arise as to whether this philosophy has been successful in educating the consumer on the importance of energy efficiency or has it actually alienated him? Would it be more effective if the consumer were to be part of the efficiency process? What about if the energy savings could be passed on to the consumer directly depending on how efficient he was? Demand response is a mechanism by which consumers change the energy consumption in response to energy price fluctuations, demand charges, or a direct request to reduce demand when the power grid reaches critical levels. However, in order for a demand response regime to be effective the building will need to have a number of 'pre-requisites' in place.

Qazi, T.

2010-01-01T23:59:59.000Z

88

Opportunities for Energy Efficiency and Automated Demand Response in Industrial Refrigerated Warehouses in California  

Science Conference Proceedings (OSTI)

This report summarizes the Lawrence Berkeley National Laboratory's research to date in characterizing energy efficiency and open automated demand response opportunities for industrial refrigerated warehouses in California. The report describes refrigerated warehouses characteristics, energy use and demand, and control systems. It also discusses energy efficiency and open automated demand response opportunities and provides analysis results from three demand response studies. In addition, several energy efficiency, load management, and demand response case studies are provided for refrigerated warehouses. This study shows that refrigerated warehouses can be excellent candidates for open automated demand response and that facilities which have implemented energy efficiency measures and have centralized control systems are well-suited to shift or shed electrical loads in response to financial incentives, utility bill savings, and/or opportunities to enhance reliability of service. Control technologies installed for energy efficiency and load management purposes can often be adapted for open automated demand response (OpenADR) at little additional cost. These improved controls may prepare facilities to be more receptive to OpenADR due to both increased confidence in the opportunities for controlling energy cost/use and access to the real-time data.

Lekov, Alex; Thompson, Lisa; McKane, Aimee; Rockoff, Alexandra; Piette, Mary Ann

2009-05-11T23:59:59.000Z

89

Automated Demand Response Technology Demonstration Project for Small and  

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

Technology Demonstration Project for Small and Technology Demonstration Project for Small and Medium Commercial Buildings Title Automated Demand Response Technology Demonstration Project for Small and Medium Commercial Buildings Publication Type Report LBNL Report Number LBNL-4982E Year of Publication 2011 Authors Page, Janie, Sila Kiliccote, Junqiao Han Dudley, Mary Ann Piette, Albert K. Chiu, Bashar Kellow, Edward Koch, and Paul Lipkin Date Published 07/2011 Publisher CEC/LBNL Keywords demand response, emerging technologies, market sectors, medium commercial business, openadr, small commercial, small commercial business, technologies Abstract Small and medium commercial customers in California make up about 20-25% of electric peak load in California. With the roll out of smart meters to this customer group, which enable granular measurement of electricity consumption, the investor-owned utilities will offer dynamic prices as default tariffs by the end of 2011. Pacific Gas and Electric Company, which successfully deployed Automated Demand Response (AutoDR) Programs to its large commercial and industrial customers, started investigating the same infrastructures application to the small and medium commercial customers. This project aims to identify available technologies suitable for automating demand response for small-medium commercial buildings; to validate the extent to which that technology does what it claims to be able to do; and determine the extent to which customers find the technology useful for DR purpose. Ten sites, enabled by eight vendors, participated in at least four test AutoDR events per site in the summer of 2010. The results showed that while existing technology can reliably receive OpenADR signals and translate them into pre-programmed response strategies, it is likely that better levels of load sheds could be obtained than what is reported here if better understanding of the building systems were developed and the DR response strategies had been carefully designed and optimized for each site.

90

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...

91

Summary of the 2006 Automated Demand Response Pilot  

E-Print Network (OSTI)

This paper discusses the specific concept for, design of, and results from a pilot program to automate demand response with critical peak pricing. California utilities have been exploring the use of critical peak pricing (CPP) to help reduce peak day summer time electric loads. CPP is a form of price-responsive demand response. This Automated Critical Peak Pricing (Auto-CPP) project from 2006 draws upon three years of previous research and demonstrations from the years of 2003, 2004, and 2005. The purpose of automated demand response (DR) is to improve the responsiveness and participation of electricity customers in DR programs and lower overall costs to achieve DR. Auto-CPP is a form of automated demand response (Auto-DR).

Piette, M.; Kiliccote, S.

2007-01-01T23:59:59.000Z

92

Automated electricity demand response - Tech Close-Up  

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

Automated electricity demand response - Tech Close-Up Click here to view this video Date: August 27, 2013 Presenter(s): Many, including EETD's Mary Ann Piette. A Tech Close-Up news...

93

Opportunities for Open Automated Demand Response in Wastewater Treatment Facilities in California - Phase II Report. San Luis Rey Wastewater Treatment Plant Case Study  

E-Print Network (OSTI)

your Power. (2008). "Demand Response Programs." RetrievedTool Berkeley, CA, Demand Response Research Center.2008). "What is Demand Response?" Retrieved 10/10/2008, from

Thompson, Lisa

2010-01-01T23:59:59.000Z

94

Opportunities for Open Automated Demand Response in Wastewater Treatment Facilities in California - Phase II Report. San Luis Rey Wastewater Treatment Plant Case Study  

E-Print Network (OSTI)

and have significant electricity demand during utility peakoperates at an average electricity demand of 1.3 MW, withalso has a high electricity demand. In many wastewater

Thompson, Lisa

2010-01-01T23:59:59.000Z

95

Opportunities for Automated Demand Response in Wastewater Treatment  

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

Opportunities for Automated Demand Response in Wastewater Treatment Opportunities for Automated Demand Response in Wastewater Treatment Facilities in California - Southeast Water Pollution Control Plant Case Study Title Opportunities for Automated Demand Response in Wastewater Treatment Facilities in California - Southeast Water Pollution Control Plant Case Study Publication Type Report LBNL Report Number LBNL-6056E Year of Publication 2012 Authors Olsen, Daniel, Sasank Goli, David Faulkner, and Aimee T. McKane Date Published 12/2012 Publisher CEC/LBNL Keywords market sectors, technologies Abstract This report details a study into the demand response potential of a large wastewater treatment facility in San Francisco. Previous research had identified wastewater treatment facilities as good candidates for demand response and automated demand response, and this study was conducted to investigate facility attributes that are conducive to demand response or which hinder its implementation. One years' worth of operational data were collected from the facility's control system, submetered process equipment, utility electricity demand records, and governmental weather stations. These data were analyzed to determine factors which affected facility power demand and demand response capabilities.

96

Role of Standard Demand Response Signals for Advanced Automated Aggregation  

Science Conference Proceedings (OSTI)

Emerging standards such as OpenADR enable Demand Response (DR) Resources to interact directly with Utilities and Independent System Operators to allow their facility automation equipment to respond to a variety of DR signals ranging from day ahead to real time ancillary services. In addition, there are Aggregators in today’s markets who are capable of bringing together collections of aggregated DR assets and selling them to the grid as a single resource. However, in most cases these aggregated resources are not automated and when they are, they typically use proprietary technologies. There is a need for a framework for dealing with aggregated resources that supports the following requirements: • Allows demand-side resources to participate in multiple DR markets ranging from wholesale ancillary services to retail tariffs without being completely committed to a single entity like an Aggregator; • Allow aggregated groups of demand-side resources to be formed in an ad hoc fashion to address specific grid-side issues and support the optimization of the collective response of an aggregated group along a number of different dimensions. This is important in order to taylor the aggregated performance envelope to the needs to of the grid; • Allow aggregated groups to be formed in a hierarchical fashion so that each group can participate in variety of markets from wholesale ancillary services to distribution level retail tariffs. This paper explores the issues of aggregated groups of DR resources as described above especially within the context of emerging smart grid standards and the role they will play in both the management and interaction of various grid-side entities with those resources.

Lawrence Berkeley National Laboratory; Kiliccote, Sila

2011-11-18T23:59:59.000Z

97

Opportunities for Open Automated Demand Response in Wastewater Treatment Facilities in California - Phase II Report. San Luis Rey Wastewater Treatment Plant Case Study  

E-Print Network (OSTI)

demand of 1.3 MW, with peak demand reaching 2 MW. Figure 1summer period. SDG&E’s peak demand period is between 11 AMlast 10 with the highest peak demand (Coughlin 2008). Unlike

Thompson, Lisa

2010-01-01T23:59:59.000Z

98

Findings from Seven Years of Field Performance Data for Automated Demand  

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

Seven Years of Field Performance Data for Automated Demand Seven Years of Field Performance Data for Automated Demand Response in Commercial Buildings Title Findings from Seven Years of Field Performance Data for Automated Demand Response in Commercial Buildings Publication Type Conference Paper LBNL Report Number LBNL-3643E Year of Publication 2010 Authors Kiliccote, Sila, Mary Ann Piette, Johanna L. Mathieu, and Kristen Parrish Conference Name 2010 ACEEE Summer Study on Energy Efficiency in Buildings Conference Location Pacific Grove, CA Keywords market sectors, openadr Abstract California is a leader in automating demand response (DR) to promote low-cost, consistent, and predictable electric grid management tools. Over 250 commercial and industrial facilities in California participate in fully-automated programs providing over 60 MW of peak DR savings. This paper presents a summary of Open Automated DR (OpenADR) implementation by each of the investor-owned utilities in California. It provides a summary of participation, DR strategies and incentives. Commercial buildings can reduce peak demand from 5 to 15% with an average of 13%. Industrial facilities shed much higher loads. For buildings with multi-year savings we evaluate their load variability and shed variability. We provide a summary of control strategies deployed, along with costs to install automation. We report on how the electric DR control strategies perform over many years of events. We benchmark the peak demand of this sample of buildings against their past baselines to understand the differences in building performance over the years. This is done with peak demand intensities and load factors. The paper also describes the importance of these data in helping to understand possible techniques to reach net zero energy using peak day dynamic control capabilities in commercial buildings. We present an example in which the electric load shape changed as a result of a lighting retrofit.

99

Automated Demand Response Strategies and Commissioning CommercialBuilding Controls  

SciTech Connect

California electric utilities have been exploring the use of dynamic critical peak pricing (CPP) and other demand response programs to help reduce peaks in customer electric loads. CPP is a new electricity tariff design to promote demand response. This paper begins with a brief review of terminology regarding energy management and demand response, followed by a discussion of DR control strategies and a preliminary overview of a forthcoming guide on DR strategies. The final section discusses experience to date with these strategies, followed by a discussion of the peak electric demand savings from the 2005 Automated CPP program. An important concept identified in the automated DR field tests is that automated DR will be most successful if the building commissioning industry improves the operational effectiveness of building controls. Critical peak pricing and even real time pricing are important trends in electricity pricing that will require new functional tests for building commissioning.

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

2006-05-01T23:59:59.000Z

100

Automated Demand Response: The Missing Link in the Electricity Value Chain  

E-Print Network (OSTI)

Laboratory. Berkeley. Demand Response Research Center,and Automated Demand Response in Wastewater TreatmentLaboratory. Berkeley. Demand Response Research Center,

McKane, Aimee

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "open automated demand" 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

Findings from Seven Years of Field Performance Data for Automated Demand Response in Commercial Buildings  

SciTech Connect

California is a leader in automating demand response (DR) to promote low-cost, consistent, and predictable electric grid management tools. Over 250 commercial and industrial facilities in California participate in fully-automated programs providing over 60 MW of peak DR savings. This paper presents a summary of Open Automated DR (OpenADR) implementation by each of the investor-owned utilities in California. It provides a summary of participation, DR strategies and incentives. Commercial buildings can reduce peak demand from 5 to 15percent with an average of 13percent. Industrial facilities shed much higher loads. For buildings with multi-year savings we evaluate their load variability and shed variability. We provide a summary of control strategies deployed, along with costs to install automation. We report on how the electric DR control strategies perform over many years of events. We benchmark the peak demand of this sample of buildings against their past baselines to understand the differences in building performance over the years. This is done with peak demand intensities and load factors. The paper also describes the importance of these data in helping to understand possible techniques to reach net zero energy using peak day dynamic control capabilities in commercial buildings. We present an example in which the electric load shape changed as a result of a lighting retrofit.

Kiliccote, Sila; Piette, Mary Ann; Mathieu, Johanna; Parrish, Kristen

2010-05-14T23:59:59.000Z

102

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)

103

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

104

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"

105

Market transformation lessons learned from an automated demand response test in the Summer and Fall of 2003  

E-Print Network (OSTI)

of Fully Automated Demand Response in Large Facilities”Learned from an Automated Demand Response Test in the SummerLearned from an Automated Demand Response Test in the Summer

Shockman, Christine; Piette, Mary Ann; ten Hope, Laurie

2004-01-01T23:59:59.000Z

106

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

107

2008-2010 Research Summary: Analysis of Demand Response Opportunities in California Industry  

E-Print Network (OSTI)

K.C. Mares, D. Shroyer. 2010. Demand Response andOpen Automated Demand Response Opportunities for Dataand the Role of Automated Demand Response. Lawrence Berkeley

Goli, Sasank

2013-01-01T23:59:59.000Z

108

TJ Automation | Open Energy Information  

Open Energy Info (EERE)

TJ Automation TJ Automation Jump to: navigation, search Name TJ Automation Facility TJ Automation Sector Wind energy Facility Type Small Scale Wind Facility Status In Service Owner TJ Automation Energy Purchaser TJ Automation Location Archbold OH Coordinates 41.45823855°, -84.30666804° 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":41.45823855,"lon":-84.30666804,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

109

Northwest Open Automated Demand Response Technology Demonstration...  

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

in Portland, Oregon and serving the Pacific Northwest. BPA operates an electricity transmission system and markets wholesale electrical power at cost from federal dams, one...

110

Northwest Open Automated Demand Response Technology Demonstration...  

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

units as well as adjusting temperature setpoints. McKinstry duty cycled roof-top units. HVAC and lighting systems in each of the facilities are summarized in Table 5 . Table 5....

111

Automated Demand Response: The Missing Link in the Electricity Value Chain  

SciTech Connect

In 2006, the Public Interest Energy Research Program (PIER) Demand Response Research Center (DRRC) at Lawrence Berkeley National Laboratory initiated research into Automated Demand Response (OpenADR) applications in California industry. The goal is to improve electric grid reliability and lower electricity use during periods of peak demand. The purpose of this research is to begin to define the relationship among a portfolio of actions that industrial facilities can undertake relative to their electricity use. This 'electricity value chain' defines energy management and demand response (DR) at six levels of service, distinguished by the magnitude, type, and rapidity of response. One element in the electricity supply chain is OpenADR, an open-standards based communications system to send signals to customers to allow them to manage their electric demand in response to supply conditions, such as prices or reliability, through a set of standard, open communications. Initial DRRC research suggests that industrial facilities that have undertaken energy efficiency measures are probably more, not less, likely to initiate other actions within this value chain such as daily load management and demand response. Moreover, OpenADR appears to afford some facilities the opportunity to develop the supporting control structure and to 'demo' potential reductions in energy use that can later be applied to either more effective load management or a permanent reduction in use via energy efficiency. Under the right conditions, some types of industrial facilities can shift or shed loads, without any, or minimal disruption to operations, to protect their energy supply reliability and to take advantage of financial incentives. In 2007 and 2008, 35 industrial facilities agreed to implement OpenADR, representing a total capacity of nearly 40 MW. This paper describes how integrated or centralized demand management and system-level network controls are linked to OpenADR systems. Case studies of refrigerated warehouses and wastewater treatment facilities are used to illustrate OpenADR load reduction potential. Typical shed and shift strategies include: turning off or operating compressors, aerator blowers and pumps at reduced capacity, increasing temperature set-points or pre-cooling cold storage areas and over-oxygenating stored wastewater prior to a DR event. This study concludes that understanding industrial end-use processes and control capabilities is a key to support reduced service during DR events and these capabilities, if DR enabled, hold significant promise in reducing the electricity demand of the industrial sector during utility peak periods.

McKane, Aimee; Rhyne, Ivin; Piette, Mary Ann; Thompson, Lisa; Lekov, Alex

2008-08-01T23:59:59.000Z

112

Automated Demand Response: The Missing Link in the Electricity Value Chain  

SciTech Connect

In 2006, the Public Interest Energy Research Program (PIER) Demand Response Research Center (DRRC) at Lawrence Berkeley National Laboratory initiated research into Automated Demand Response (OpenADR) applications in California industry. The goal is to improve electric grid reliability and lower electricity use during periods of peak demand. The purpose of this research is to begin to define the relationship among a portfolio of actions that industrial facilities can undertake relative to their electricity use. This 'electricity value chain' defines energy management and demand response (DR) at six levels of service, distinguished by the magnitude, type, and rapidity of response. One element in the electricity supply chain is OpenADR, an open-standards based communications system to send signals to customers to allow them to manage their electric demand in response to supply conditions, such as prices or reliability, through a set of standard, open communications. Initial DRRC research suggests that industrial facilities that have undertaken energy efficiency measures are probably more, not less, likely to initiate other actions within this value chain such as daily load management and demand response. Moreover, OpenADR appears to afford some facilities the opportunity to develop the supporting control structure and to 'demo' potential reductions in energy use that can later be applied to either more effective load management or a permanent reduction in use via energy efficiency. Under the right conditions, some types of industrial facilities can shift or shed loads, without any, or minimal disruption to operations, to protect their energy supply reliability and to take advantage of financial incentives. In 2007 and 2008, 35 industrial facilities agreed to implement OpenADR, representing a total capacity of nearly 40 MW. This paper describes how integrated or centralized demand management and system-level network controls are linked to OpenADR systems. Case studies of refrigerated warehouses and wastewater treatment facilities are used to illustrate OpenADR load reduction potential. Typical shed and shift strategies include: turning off or operating compressors, aerator blowers and pumps at reduced capacity, increasing temperature set-points or pre-cooling cold storage areas and over-oxygenating stored wastewater prior to a DR event. This study concludes that understanding industrial end-use processes and control capabilities is a key to support reduced service during DR events and these capabilities, if DR enabled, hold significant promise in reducing the electricity demand of the industrial sector during utility peak periods.

McKane, Aimee; Rhyne, Ivin; Piette, Mary Ann; Thompson, Lisa; Lekov, Alex

2008-08-01T23:59:59.000Z

113

Automated Demand Response: The Missing Link in the Electricity Value Chain  

Science Conference Proceedings (OSTI)

In 2006, the Public Interest Energy Research Program (PIER) Demand Response Research Center (DRRC) at Lawrence Berkeley National Laboratory initiated research into Automated Demand Response (OpenADR) applications in California industry. The goal is to improve electric grid reliability and lower electricity use during periods of peak demand. The purpose of this research is to begin to define the relationship among a portfolio of actions that industrial facilities can undertake relative to their electricity use. This ?electricity value chain? defines energy management and demand response (DR) at six levels of service, distinguished by the magnitude, type, and rapidity of response. One element in the electricity supply chain is OpenADR, an open-standards based communications system to send signals to customers to allow them to manage their electric demand in response to supply conditions, such as prices or reliability, through a set of standard, open communications. Initial DRRC research suggests that industrial facilities that have undertaken energy efficiency measures are probably more, not less, likely to initiate other actions within this value chain such as daily load management and demand response. Moreover, OpenADR appears to afford some facilities the opportunity to develop the supporting control structure and to"demo" potential reductions in energy use that can later be applied to either more effective load management or a permanent reduction in use via energy efficiency. Under the right conditions, some types of industrial facilities can shift or shed loads, without any, or minimal disruption to operations, to protect their energy supply reliability and to take advantage of financial incentives.1 In 2007 and 2008, 35 industrial facilities agreed to implement OpenADR, representing a total capacity of nearly 40 MW. This paper describes how integrated or centralized demand management and system-level network controls are linked to OpenADR systems. Case studies of refrigerated warehouses and wastewater treatment facilities are used to illustrate OpenADR load reduction potential. Typical shed and shift strategies include: turning off or operating compressors, aerator blowers and pumps at reduced capacity, increasing temperature set-points or pre-cooling cold storage areas and over-oxygenating stored wastewater prior to a DR event. This study concludes that understanding industrial end-use processes and control capabilities is a key to support reduced service during DR events and these capabilities, if DR enabled, hold significant promise in reducing the electricity demand of the industrial sector during utility peak periods.

McKane, Aimee; Rhyne, Ivin; Lekov, Alex; Thompson, Lisa; Piette, MaryAnn

2009-08-01T23:59:59.000Z

114

Solar Automation Inc | Open Energy Information  

Open Energy Info (EERE)

Inc Inc Jump to: navigation, search Name Solar Automation Inc Place Albuquerque, New Mexico Zip NM 8110 Product Produces manufacturing equipment for PV cells. References Solar Automation Inc[1] LinkedIn Connections CrunchBase Profile No CrunchBase profile. Create one now! This article is a stub. You can help OpenEI by expanding it. Solar Automation Inc is a company located in Albuquerque, New Mexico . References ↑ "Solar Automation Inc" Retrieved from "http://en.openei.org/w/index.php?title=Solar_Automation_Inc&oldid=351247" Categories: Clean Energy Organizations Companies Organizations Stubs What links here Related changes Special pages Printable version Permanent link Browse properties About us Disclaimers Energy blogs Linked Data Developer services

115

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

116

An Open Architecture Platform for Demand Resources from AutoDR and MBCx:  

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

An Open Architecture Platform for Demand Resources from AutoDR and MBCx: An Open Architecture Platform for Demand Resources from AutoDR and MBCx: National Virtual Power Plant Speaker(s): Jung In Choi Date: December 20, 2013 - 2:00pm - 3:00pm Location: 90-3122 Seminar Host/Point of Contact: Philip Haves The presentation lays out the technology and business model for National Virtual Power Plant (NVPP). NAPP is a Korean initiative to develop a cluster of demand resources from consumers by peak reduction or energy saving. Demand resources from NVPP are collectively traded in the open architecture platform for energy market. The platform enables 3rd parties to develop new business models and applications through open API s. It will bring a long tail market for demand response and energy efficiency in small and medium size buildings as well as large ones. Automated Demand

117

Demand Response and Open Automated Demand Response Opportunities...  

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

devices, and, in California, will likely be on a time-of-use commercial or industrial electricity tariff. Most will have control andor monitoring systems in place. Data Center...

118

Definition: Automated Distribution Circuit Switches | Open Energy  

Open Energy Info (EERE)

Circuit Switches Circuit Switches Jump to: navigation, search Dictionary.png Automated Distribution Circuit Switches Distribution circuit switches that can be operated automatically in response to control signals from local sensors, distribution automation systems, or grid control systems. Such switches can be installed as automated devices or existing equipment can be retrofitted with controls and communications. The degree of automation depends on the controls and communications system implemented. These switches can be opened or closed to isolate portions of a distribution circuit that has experienced a short circuit (fault), or must be taken out of service for maintenance or other operations. When used in combination, these switches can reroute power from other substations or nearby distribution circuits.[1]

119

Assessment of Commercial Building Automation and Energy Management Systems for Demand Response Applications  

Science Conference Proceedings (OSTI)

This Technical Update is an overview of commercial building automation and energy management systems with a focus on their capabilities (current and future), especially in support of demand response (DR). The report includes background on commercial building automation and energy management systems; a discussion of demand response applications in commercial buildings, including building loads and control strategies; and a review of suppliers’ building automation and energy management systems to support d...

2009-12-14T23:59:59.000Z

120

Demand Response Opportunities in Industrial Refrigerated Warehouses in California  

E-Print Network (OSTI)

and Open Automated Demand Response. In Grid Interop Forum.work was sponsored by the Demand Response Research Center (load-management.php. Demand Response Research Center (2009).

Goli, Sasank

2012-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "open automated demand" 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

Automated Demand Response Opportunities in Wastewater Treatment Facilities  

E-Print Network (OSTI)

power generators during peak demand periods. 13 Onsite powerit can be used during peak-demand periods. 15 Implementingtreatment loads from peak demand hours to off-peak hours is

Thompson, Lisa

2008-01-01T23:59:59.000Z

122

Direct versus Facility Centric Load Control for Automated Demand Response  

SciTech Connect

Direct load control (DLC) refers to the scenario where third party entities outside the home or facility are responsible for deciding how and when specific customer loads will be controlled in response to Demand Response (DR) events on the electric grid. Examples of third parties responsible for performing DLC may be Utilities, Independent System Operators (ISO), Aggregators, or third party control companies. DLC can be contrasted with facility centric load control (FCLC) where the decisions for how loads are controlled are made entirely within the facility or enterprise control systems. In FCLC the facility owner has more freedom of choice in how to respond to DR events on the grid. Both approaches are in use today in automation of DR and both will continue to be used in future market segments including industrial, commercial and residential facilities. This paper will present a framework which can be used to differentiate between DLC and FCLC based upon where decisions are made on how specific loads are controlled in response to DR events. This differentiation is then used to compare and contrast the differences between DLC and FCLC to identify the impact each has on:(1)Utility/ISO and third party systems for managing demand response, (2)Facility systems for implementing load control, (3)Communications networks for interacting with the facility and (4)Facility operators and managers. Finally a survey of some of the existing DR related specifications and communications standards is given and their applicability to DLC or FCLC. In general FCLC adds more cost and responsibilities to the facilities whereas DLC represents higher costs and complexity for the Utility/ISO. This difference is primarily due to where the DR Logic is implemented and the consequences that creates. DLC may be more certain than FCLC because it is more predictable - however as more loads have the capability to respond to DR signals, people may prefer to have their own control of end-use loads and FCLC systems. Research is needed to understand the predictability of FCLC which is related to the perceived value of the DR from the facility manager or home owner's perspective.

Koch, Ed; Piette, Mary Ann

2009-11-06T23:59:59.000Z

123

Automated Demand Response Technology Demonstration Project for Small and Medium Commercial Buildings  

Science Conference Proceedings (OSTI)

Small and medium commercial customers in California make up about 20-25% of electric peak load in California. With the roll out of smart meters to this customer group, which enable granular measurement of electricity consumption, the investor-owned utilities will offer dynamic prices as default tariffs by the end of 2011. Pacific Gas and Electric Company, which successfully deployed Automated Demand Response (AutoDR) Programs to its large commercial and industrial customers, started investigating the same infrastructures application to the small and medium commercial customers. This project aims to identify available technologies suitable for automating demand response for small-medium commercial buildings; to validate the extent to which that technology does what it claims to be able to do; and determine the extent to which customers find the technology useful for DR purpose. Ten sites, enabled by eight vendors, participated in at least four test AutoDR events per site in the summer of 2010. The results showed that while existing technology can reliably receive OpenADR signals and translate them into pre-programmed response strategies, it is likely that better levels of load sheds could be obtained than what is reported here if better understanding of the building systems were developed and the DR response strategies had been carefully designed and optimized for each site.

Page, Janie; Kiliccote, Sila; Dudley, Junqiao Han; Piette, Mary Ann; Chiu, Albert K.; Kellow, Bashar; Koch, Ed; Lipkin, Paul

2011-07-01T23:59:59.000Z

124

Results and commissioning issues from an automated demand response pilot  

E-Print Network (OSTI)

Conference on Building Commissioning. May 2002. Motegi,et al: Results and Commissioning Issues from an AutomatedConference on Building Commissioning: May 1e-20, 2004

Piette, Mary Ann; Watson, Dave; Sezgen, Osman; Motegi, Naoya

2004-01-01T23:59:59.000Z

125

home automation | OpenEI Community  

Open Energy Info (EERE)

91 91 Varnish cache server Home Groups Community Central Green Button Applications Developer Utility Rate FRED: FRee Energy Database More Public Groups Private Groups Features Groups Blog posts Content Stream Documents Discussions Polls Q & A Events Notices My stuff Energy blogs 429 Throttled (bot load) Error 429 Throttled (bot load) Throttled (bot load) Guru Meditation: XID: 2142285591 Varnish cache server home automation Home Graham7781's picture Submitted by Graham7781(2002) Super contributor 23 January, 2013 - 13:57 The Consumer Electronics Show round-up CES electronics home automation Las Vegas OpenEI Smart Grid Every January, Las Vegas hosts the Consumer Electronics Show. The CES is the world's largest technology-related trade show. The highlights of this year's show were OLED TVs, ultra-thin laptops,

126

Findings from the 2004 Fully Automated Demand Response Tests in Large  

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

the 2004 Fully Automated Demand Response Tests in Large the 2004 Fully Automated Demand Response Tests in Large Facilities Title Findings from the 2004 Fully Automated Demand Response Tests in Large Facilities Publication Type Report LBNL Report Number LBNL-58178 Year of Publication 2005 Authors Piette, Mary Ann, David S. Watson, Naoya Motegi, and Norman Bourassa Date Published 10/18/2005 Keywords market sectors, technologies Abstract This report describes the results of the second season of research to develop and evaluate the performance of new Automated Demand Response (Auto-DR) hardware and software technology in large facilities. Demand Response (DR) is a set of time dependant activities that reduce or shift electricity use to improve electric grid reliability, manage electricity costs, and provide systems that encourage load shifting or shedding during times when the electric grid is near its capacity or electric prices are high. Demand Response is a subset of demand side management, which also includes energy efficiency and conservation. The overall goal of this research project was to support increased penetration of DR in large facilities through the use of automation and better understanding of DR technologies and strategies in large facilities. To achieve this goal, a set of field tests were designed and conducted. These tests examined the performance of Auto-DR systems that covered a diverse set of building systems, ownership and management structures, climate zones, weather patterns, and control and communication configurations.

127

Fourth international symposium on distribution automation and demand side management (DA/DSM 94)  

SciTech Connect

This document is the conference proceedings from the 1994 Distribution Automation/Demand Side Management meeting in Orlando, Florida. There are 87 papers presented, and topics include: (1) improved feeder efficiency, (2) automation of older substations, (3) modeling tools for distribution, planning, and operations, (4) sensing and fault detection, (5) outage monitoring, (6) cost and benefits of distribution automation, (7) communications, (8) optimization of feeder systems operations, (9) information technology, (10) demand-side management applications in the industrial, commercial, and residential sectors, (11) pricing and regulation, and (12) applications to the natural gas industry.

NONE

1994-12-31T23:59:59.000Z

128

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

129

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

130

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

131

What China Can Learn from International Experiences in Developing a Demand Response Program  

E-Print Network (OSTI)

K.C. Mares, D. Shroyer. , 2010. Demand Response andOpen Automated Demand Response Opportunities for DataProcessing Industry Demand Response Participation: A Scoping

Shen, Bo

2013-01-01T23:59:59.000Z

132

Definition: Demand Side Management | Open Energy Information  

Open Energy Info (EERE)

Side Management Side Management Jump to: navigation, search Dictionary.png Demand Side Management The term for all activities or programs undertaken by Load-Serving Entity or its customers to influence the amount or timing of electricity they use.[1] View on Wikipedia Wikipedia Definition Energy demand management, also known as demand side management (DSM), is the modification of consumer demand for energy through various methods such as financial incentives and education. Usually, the goal of demand side management is to encourage the consumer to use less energy during peak hours, or to move the time of energy use to off-peak times such as nighttime and weekends. Peak demand management does not necessarily decrease total energy consumption, but could be expected to reduce the need

133

Automation Alley Technology Center | Open Energy Information  

Open Energy Info (EERE)

Automation Alley Technology Center Jump to: navigation, search Name Automation Alley Technology Center Place United States Sector Services Product General Financial & Legal...

134

Transportation Demand Management (TDM) Encyclopedia | Open Energy  

Open Energy Info (EERE)

Transportation Demand Management (TDM) Encyclopedia Transportation Demand Management (TDM) Encyclopedia Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Transportation Demand Management (TDM) Encyclopedia Agency/Company /Organization: Victoria Transport Policy Institute Sector: Energy Focus Area: Transportation Topics: Implementation Resource Type: Guide/manual Website: www.vtpi.org/tdm/tdm12.htm Cost: Free Language: English References: Victoria Transport Policy Institute[1] "The Online TDM Encyclopedia is the world's most comprehensive information resource concerning innovative transportation management strategies. It describes dozens of Transportation Demand Management (TDM) strategies and contains information on TDM planning, evaluation and implementation. It has thousands of hyperlinks that provide instant access

135

Role of home automation in demand-side management. Topical report, May 1994  

Science Conference Proceedings (OSTI)

The report explores the role of home automation (HA) in utility demand-side management (DSM) programs, in order to demonstrate the potential usefulness of a combined HA/DSM strategy in meeting the changing needs of the gas industry and providing the industry with a timely and competitive edge in the coming decade. Research was conducted using primary and secondary sources, on-line databases, and documentary research. Factors leading to the development and implementation of demand-side management and home automation were analyzed in order to best define opportunities and interests for the gas industry.

Davis, K.W.

1994-05-01T23:59:59.000Z

136

Autimated Price and Demand Response Demonstration for Large Customers in New York City using OpenADR  

E-Print Network (OSTI)

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 and demand response capabilities of two commercial buildings in New York City. Preliminary results reveal that providing machine-readable prices to commercial buildings can facilitate both demand response participation and continuous energy cost savings. Hence, efforts should be made to develop more sophisticated algorithms for building control systems to minimize customer's utility bill based on price and reliability information from the electricity grid.

Kim, J. J.; Yin, R.; Kiliccote, S.

2013-01-01T23:59:59.000Z

137

DemandDirect | Open Energy Information  

Open Energy Info (EERE)

DemandDirect DemandDirect Jump to: navigation, search Name DemandDirect Place Woodbury, Connecticut Zip 6798 Sector Efficiency, Renewable Energy, Services Product DemandDirect provides demand response, energy efficiency, load management, and distributed generation services to end-use electricity customers in order to reduce electricity consumption, improve grid reliability, and promote renewable energy. Coordinates 44.440496°, -72.414991° 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":44.440496,"lon":-72.414991,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

138

Demand Management Institute (DMI) | Open Energy Information  

Open Energy Info (EERE)

Demand Management Institute (DMI) Demand Management Institute (DMI) Jump to: navigation, search Name Demand Management Institute (DMI) Address 35 Walnut Street Place Wellesley, Massachusetts Zip 02481 Sector Buildings Product Provides analysis for buildings on reducing energy use Website http://www.dmiinc.com/ Coordinates 42.3256508°, -71.2530294° 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":42.3256508,"lon":-71.2530294,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

139

Definition: Distribution Automation Communications Network | Open Energy  

Open Energy Info (EERE)

Automation Communications Network Automation Communications Network Jump to: navigation, search Dictionary.png Distribution Automation Communications Network A communications network or networks designed to deliver control signals and information between distribution automation devices, and between these devices and utility grid control systems. These networks can utilize wired or wireless connections, and can be utility-owned or provided as services by a third party.[1] Related Terms distribution automation References ↑ https://www.smartgrid.gov/category/technology/distribution_automation_communications_network [[C LikeLike UnlikeLike You like this.Sign Up to see what your friends like. ategory: Smart Grid Definitionssmart grid,smart grid, |Template:BASEPAGENAME]]smart grid,smart grid, Retrieved from

140

Definition: Distribution Automation | Open Energy Information  

Open Energy Info (EERE)

Automation Automation Jump to: navigation, search Dictionary.png Distribution Automation DA is a family of technologies including sensors, processors, communication networks, and switches that can perform a number of distribution system functions depending on how they are implemented. Over the last 20 years, utilities have been applying DA to improve reliability, service quality and operational efficiency. More recently, DA is being applied to perform automatic switching, reactive power compensation coordination, or other feeder operations/control.[1] Related Terms sustainability, smart grid References ↑ https://www.smartgrid.gov/category/technology/distribution_automation [[Ca LikeLike UnlikeLike You and one other like this.One person likes this. Sign Up to see what your friends like.

Note: This page contains sample records for the topic "open automated demand" 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

Mirle Automation Corporation | Open Energy Information  

Open Energy Info (EERE)

Mirle Automation Corporation Mirle Automation Corporation Jump to: navigation, search Name Mirle Automation Corporation Place Hsinchu, Taiwan Zip 30077 Sector Solar Product A Taiwan-based automation system integrators and related product manufacturers who have entered thin film solar cell manufacturing equipment business. Coordinates 24.69389°, 121.148064° 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":24.69389,"lon":121.148064,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

142

AIS Automation Dresden | Open Energy Information  

Open Energy Info (EERE)

AIS Automation Dresden AIS Automation Dresden Jump to: navigation, search Name AIS Automation Dresden Place Dresden, Germany Zip D-01237 Product Provides software for industrial process control for the semiconductor and photovoltaic industries. Coordinates 51.053645°, 13.740815° 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":51.053645,"lon":13.740815,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

143

Definition: Automated Capacitors | Open Energy Information  

Open Energy Info (EERE)

Capacitors Capacitors Jump to: navigation, search Dictionary.png Automated Capacitors Capacitors can increase the voltage on a distribution circuit by providing reactive power (often referred to as volt-amperes-reactive or VArs). Capacitor banks are switched in discrete steps, either manually, or in response to the voltage at the location where they are connected; typically, distribution capacitor banks are switched in a single step. If the voltage falls too far below the set point, the capacitor is switched in to raise the voltage. If the voltage rises too high above the set point, the capacitor is switched out to lower the voltage. Automated capacitors can be switched in coordination with other voltage control devices with signals from local sensors, distribution automation systems, or grid

144

Definition: Automated Voltage Regulators | Open Energy Information  

Open Energy Info (EERE)

Regulators Regulators Jump to: navigation, search Dictionary.png Automated Voltage Regulators Voltage regulators are transformers that can increase or decrease the voltage on a distribution circuit to help keep the voltage within a pre-determined band. Unlike capacitor banks, voltage regulators cannot adjust power factor. These devices typically monitor the voltage at the location where they are connected, and compare it to a programmed set point. If the voltage deviates too far from the set point, the voltage regulator can increase or decrease its output voltage by moving the tap on the secondary side up or down. An automated voltage regulator can operate with remote control signals, or in concert with other area voltage control devices, to help regulate distribution voltage in a coordinated fashion.

145

Energy Automation Systems Inc | Open Energy Information  

Open Energy Info (EERE)

Energy Automation Systems Inc Energy Automation Systems Inc Jump to: navigation, search Name Energy Automation Systems Inc. Place Hendersonville, Tennessee Zip 37075 Sector Buildings, Efficiency Product An energy efficiency consultancy firm focusing on analysis of energy consumption in buildings and providing improvments in the efficiency of the distribution system and equipment loads, similar to an ESCO. Coordinates 36.304861°, -86.620214° 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":36.304861,"lon":-86.620214,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

146

Open Automated Demand Response Communications Specification (Version 1.0)  

E-Print Network (OSTI)

Utility Issued DR Event Entity Each UtilityDREvent may contain a list utility IT system that initiated the DR event.   destinations–This is a list Utility Advanced  Metering Infrastructure).   APC?8 Appendix D: DR Program Use Cases This section lists 

Piette, Mary Ann

2009-01-01T23:59:59.000Z

147

Linking Continuous Energy Management and Open Automated Demand Response  

E-Print Network (OSTI)

Previous papers have discussed definitions of energyThis paper provides a framework linking continuous energyThe paper presents a framework about when energy is used,

Piette, Mary Ann

2009-01-01T23:59:59.000Z

148

Open Automated Demand Response Communications Specification (Version 1.0)  

E-Print Network (OSTI)

Research  Energy Systems Integration  Environmentally contributes to PIER’s Energy Systems Integration  Program.  integration of common Energy Management and  Control Systems (

Piette, Mary Ann

2009-01-01T23:59:59.000Z

149

Linking Continuous Energy Management and Open Automated Demand Response  

E-Print Network (OSTI)

optimized relative to the energy services begin delivered.energy is used, levels of services by energy using systems,energy efficiency and advances in controls and service level

Piette, Mary Ann

2009-01-01T23:59:59.000Z

150

Open Automated Demand Response for Small Commerical Buildings  

E-Print Network (OSTI)

customers to  view their energy usage, but may be  used as on  the  three highest energy usage days with the highest of  the  three  highest energy usage days of 2P2825, and 

Dudley, June Han

2009-01-01T23:59:59.000Z

151

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

aim to minimize energy costs over time. Note that becausedue to increased energy costs over time. Customers couldtime prices and price duration curves for a facility in Year 1 (low energy cost

Ghatikar, Girish

2010-01-01T23:59:59.000Z

152

Open Automated Demand Response Communications Specification (Version 1.0)  

E-Print Network (OSTI)

the  following:  Customer account number  Load reduction the following:  Customer account number  Load reduction the following:  Customer account number  Load reduction 

Piette, Mary Ann

2009-01-01T23:59:59.000Z

153

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

With such a large number of customers who are now on thebe to have customer-specified daily number of hours for eachnumber or percentage) based on historical prices and customer

Ghatikar, Girish

2010-01-01T23:59:59.000Z

154

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

left) and High (right) Electricity Price References..32 Listin response to dynamic electricity prices using the Opena variety of dynamic electricity price structures. In this

Ghatikar, Girish

2010-01-01T23:59:59.000Z

155

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

In peak pricing tariffs, electricity prices on peak days areIn peak pricing tariffs, electricity prices on peak days areof California electricity pricing tariffs (including RTP,

Ghatikar, Girish

2010-01-01T23:59:59.000Z

156

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

prices and time-of-use rates to commercial and industrial electricityprices to commercial and industrial facilities. 2. Evaluate if existing static electricity

Ghatikar, Girish

2010-01-01T23:59:59.000Z

157

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

California utility retail electricity rates and independentCalifornia utility retail electricity rates and independenttime-of-use rates to commercial and industrial electricity

Ghatikar, Girish

2010-01-01T23:59:59.000Z

158

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

Center for the Study of Energy Markets Paper CSEMWP-105.OASIS SDO. 2010b. “Energy Market Information Exchange (eMIX)charges. • Wholesale energy market prices are volatile, and

Ghatikar, Girish

2010-01-01T23:59:59.000Z

159

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

Version 1.0). ” California Energy Commission, PIER Program.Markets. ” University of California Energy Institute: CenterSystem Communications. California Energy Commission, PIER

Ghatikar, Girish

2010-01-01T23:59:59.000Z

160

Northwest Open Automated Demand Response Technology Demonstration Project  

E-Print Network (OSTI)

lighting control systems that integrate with daylighting inlighting control systems that integrate with daylighting in

Kiliccote, Sila

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "open automated demand" 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

Northwest Open Automated Demand Response Technology Demonstration Project  

E-Print Network (OSTI)

cost from federal dams, one non-federal nuclear plant, and other non-federal hydroelectric and wind energycost from federal dams, one non-federal nuclear plant, and other non-federal hydroelectric and wind energy

Kiliccote, Sila

2010-01-01T23:59:59.000Z

162

Northwest Open Automated Demand Response Technology Demonstration Project  

E-Print Network (OSTI)

within the emerging “Smart Grid” domain. With the ongoingwithin the emerging “Smart Grid” domain. With the ongoing

Kiliccote, Sila

2010-01-01T23:59:59.000Z

163

Northwest Open Automated Demand Response Technology Demonstration Project  

E-Print Network (OSTI)

active within the emerging “Smart Grid” domain. With ongoingactive within the emerging “Smart Grid” domain. With ongoing

Kiliccote, Sila

2010-01-01T23:59:59.000Z

164

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

OASIS SDO. 2010b. “Energy Market Information Exchange (eMIX)11 Wholesale Electricity Market InformationWholesale Electricity Market Information Systems Several

Ghatikar, Girish

2010-01-01T23:59:59.000Z

165

Open Automated Demand Response for Small Commerical Buildings  

E-Print Network (OSTI)

to use the  Energy Management Control Systems (EMCS) to Facility Energy Management Control System (EMCS) carries either a small energy management control system (EMCS) or 

Dudley, June Han

2009-01-01T23:59:59.000Z

166

Northwest Open Automated Demand Response Technology Demonstration Project  

E-Print Network (OSTI)

s) and the energy management systems within the facilities.use of an energy management control system (EMCS) or energyor hardwire energy management control systems to curtail

Kiliccote, Sila

2010-01-01T23:59:59.000Z

167

Northwest Open Automated Demand Response Technology Demonstration Project  

E-Print Network (OSTI)

with hydro power and wind integration, more DR may be neededload growth, wind power integration, and fish operations are

Kiliccote, Sila

2010-01-01T23:59:59.000Z

168

Open Automated Demand Response Communications Specification (Version 1.0)  

E-Print Network (OSTI)

may include things like real time prices, shed or  shift participant loads:  1. Real?time prices are propagated from shed or shift loads.   2. Real?time prices are sent by the 

Piette, Mary Ann

2009-01-01T23:59:59.000Z

169

Linking Continuous Energy Management and Open Automated Demand Response  

E-Print Network (OSTI)

though day-ahead hourly real- time prices can be continuous,or 15-minute ahead real time price. A facility manager hasyr (though hour-ahead real-time prices can be continuous,

Piette, Mary Ann

2009-01-01T23:59:59.000Z

170

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

link wholesale and retail real-time prices. 6.0 Referencesdynamic prices such as real-time prices and peak prices andFigure 5. Average Daily Real-Time Prices and Price Duration

Ghatikar, Girish

2010-01-01T23:59:59.000Z

171

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

charges. • Wholesale energy market prices are volatile, andCAISO’s Wholesale Energy Market Prices PG&E’s PDP RetailWe used the CAISO wholesale energy market prices for the RTP

Ghatikar, Girish

2010-01-01T23:59:59.000Z

172

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

peak prices) to reduce electricity bill volatility. Smallwith higher or lower electricity bills than desired, since

Ghatikar, Girish

2010-01-01T23:59:59.000Z

173

Manz Automation AG | Open Energy Information  

Open Energy Info (EERE)

AG AG Jump to: navigation, search Name Manz Automation AG Place Reutlingen, Baden-Württemberg, Germany Zip D-72768 Sector Solar Product German manufacturer of solar and LCD capital equipment. Coordinates 48.49159°, 9.21487° 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":48.49159,"lon":9.21487,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

174

Findings from Seven Years of Field Performance Data for Automated Demand Response in Commercial Buildings  

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

3E 3E Findings from Seven Years of Field Performance Data for Automated Demand Response in Commercial Buildings S. Kiliccote, M.A. Piette, J. Mathieu, K. Parrish Environmental Energy Technologies Division 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,

175

Development and evaluation of fully automated demand response in large facilities  

E-Print Network (OSTI)

Development for Demand Response Calculation - Findings and2003. “Dividends with Demand Response. ” ASHRAE Journal,Management and Demand Response in Commercial Buildings. ”

Piette, Mary Ann; Sezgen, Osman; Watson, David S.; Motegi, Naoya; Shockman, Christine; ten Hope, Laurie

2004-01-01T23:59:59.000Z

176

Automated Demand Response Technology Demonstration Project for Small and Medium Commercial Buildings  

E-Print Network (OSTI)

2010 Assessment of Demand Response and  Advanced Metering:  Development for Demand Response  Calculation ? Findings and Energy  Efficiency and  Demand Response with Communicating 

Page, Janie

2012-01-01T23:59:59.000Z

177

Field Test Results of Automated Demand Response in a Large Office Building  

E-Print Network (OSTI)

and Techniques for Demand Response, LBNL-59975, May 2007 [Protocol Development for Demand Response Calculation – Findsand S. Kiliccote, Estimating Demand Response Load Impacts:

Han, Junqiao

2008-01-01T23:59:59.000Z

178

An Automation System for Optimizing a Supply Chain Network Design under the Influence of Demand Uncertainty  

E-Print Network (OSTI)

threshold for the maximum variation in demand that can bethreshold for the maximum variation in demand that can bethreshold for the maximum variation in demand that can be

Polany, Rany

2012-01-01T23:59:59.000Z

179

Fast Automated Demand Response to Enable the Integration of Renewable Resources  

E-Print Network (OSTI)

Water Supply Related Electricity Demand in California. CEC33 percent of our electricity demand in 2020 from renewablebuildings, heating electricity demand is not included in

Watson, David S.

2013-01-01T23:59:59.000Z

180

Demand Response and Open Automated Demand Response Opportunities for Data Centers  

E-Print Network (OSTI)

pumps, improved power supplies and transformers, and freeby adding power delivery (transformer and UPS losses),as uninterruptible power supplies (UPS), transformers, and

Mares, K.C.

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "open automated demand" 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

Demand Response and Open Automated Demand Response Opportunities for Data Centers  

E-Print Network (OSTI)

data centers that are part of large campuses or office complexes have renewable energy (photovoltaic, fuel cell, thermal storage) and cogeneration (

Mares, K.C.

2010-01-01T23:59:59.000Z

182

Demand Response and Open Automated Demand Response Opportunities for Data Centers  

E-Print Network (OSTI)

data centers, field tests and sub-metering are necessary toAuto-DR. Due to the lack of sub-metering and availability of

Mares, K.C.

2010-01-01T23:59:59.000Z

183

Demand Response and Open Automated Demand Response Opportunities for Data Centers  

E-Print Network (OSTI)

DOE. 2009. Press release on Smart Grid standards. http://Report to NIST on Smart Grid Interoperability Standardsas a national Smart Grid (U.S. DOE 2009) standard for DR (

Mares, K.C.

2010-01-01T23:59:59.000Z

184

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

E-Print Network (OSTI)

Day-Ahead Schedule) x Real-Time Price This may result in areal-time dispatch of the CAISO controlled grid. Participating load program relies on a simple price-

Kiliccote, Sila

2010-01-01T23:59:59.000Z

185

Findings from Seven Years of Field Performance Data for Automated Demand Response in Commercial Buildings  

E-Print Network (OSTI)

indicate minimum and maximum demand reduction. There is nopackaged units. In 2009, maximum demand for this facilityat 1.4 MW. Weekday maximum demand is 1.2 MW. Over the last

Kiliccote, Sila

2010-01-01T23:59:59.000Z

186

Automated Demand Response: The Missing Link in the Electricity Value Chain  

E-Print Network (OSTI)

promise in reducing the electricity demand of the industrialchanges the time of electricity demand to off-peak hours.Load shedding curtails electricity demand during a DR event.

McKane, Aimee

2010-01-01T23:59:59.000Z

187

Findings from Seven Years of Field Performance Data for Automated Demand Response in Commercial Buildings  

E-Print Network (OSTI)

buildings can reduce peak demand from 5 to 15% with anof events. We benchmark the peak demand of this sample ofyears. This is done with peak demand intensities and load

Kiliccote, Sila

2010-01-01T23:59:59.000Z

188

Fast Automated Demand Response to Enable the Integration of Renewable Resources  

E-Print Network (OSTI)

peak demand, and natural gas demand forecasts for eachnatural gas and other fossil fuels are the predominant heating fuels for California’s commercial buildings, heating electricity demandDemand. The California End Use Survey 2004 (CEUS 2004) provides statewide hourly electricity and natural gas

Watson, David S.

2013-01-01T23:59:59.000Z

189

Automation of Capacity Bidding with an Aggregator Using Open Automated Demand Response  

E-Print Network (OSTI)

Program Area Lead Energy Systems Integration Mike GravelyCommission, PIER Energy Systems Integration Program.  CEC?Research  Energy Systems Integration  Environmentally 

Kiliccote, Sila

2011-01-01T23:59:59.000Z

190

Automation of Capacity Bidding with an Aggregator Using Open Automated Demand Response  

E-Print Network (OSTI)

Site Controls 21  Aggregated Load Profile of the  Test on 24  Retail C3 Load Profile with Its C6 Figure 5. Aggregated Load Profile of the Test on October

Kiliccote, Sila

2011-01-01T23:59:59.000Z

191

Automation of Capacity Bidding with an Aggregator Using Open Automated Demand Response  

E-Print Network (OSTI)

implementation in energy management systems.   This effort linked to energy management control systems (EMCS) 4  or Systems  Energy Management and Control Systems  Electric 

Kiliccote, Sila

2011-01-01T23:59:59.000Z

192

Estimating Demand Response Market Potential | Open Energy Information  

Open Energy Info (EERE)

Estimating Demand Response Market Potential Estimating Demand Response Market Potential Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Estimating Demand Response Market Potential Focus Area: Energy Efficiency, - Utility Topics: Socio-Economic Website: www.ieadsm.org/Files/Tasks/Task%20XIII%20-%20Demand%20Response%20Resou Equivalent URI: cleanenergysolutions.org/content/estimating-demand-response-market-pot Language: English Policies: "Deployment Programs,Regulations" is not in the list of possible values (Deployment Programs, Financial Incentives, Regulations) for this property. DeploymentPrograms: Demonstration & Implementation Regulations: Resource Integration Planning This resource presents demand response (DR) potential results from top-performing programs in the United States and Canada, as well as a DR

193

Network-Driven Demand Side Management Website | Open Energy Informatio...  

Open Energy Info (EERE)

Side Management Website Jump to: navigation, search Name Network-Driven Demand Side Management Website Abstract This task of the International Energy Agency is a broad,...

194

ranking of utilities by demand charge? | OpenEI Community  

Open Energy Info (EERE)

ranking of utilities by demand charge? ranking of utilities by demand charge? Home > Groups > Utility Rate Sorry..simple question because i am a bit dumb. How do I download the utility rate data in CSV so i can sort by demand charge? Or can i sort by demand charge in the API? New to this API stuff. Many thanks/ Submitted by Apin101 on 26 November, 2013 - 07:12 1 answer Points: 0 There is currently no way to sort the responses, but since you are downloading in a CSV format you can sort most responses in Excel (or a spreadsheet editor). Another option is to run direct Ask queries and specify a property to sort on (see massive URL below). To do any sorting on an element of a packed array like DemandWeekdaySchedule would require custom logic in the result spreadsheet, or custom scripting of some kind. The new utility rate custom

195

ranking of utilities by demand charge? | OpenEI Community  

Open Energy Info (EERE)

ranking of utilities by demand charge? ranking of utilities by demand charge? Home > Groups > Utility Rate Sorry..simple question because i am a bit dumb. How do I download the utility rate data in CSV so i can sort by demand charge? Or can i sort by demand charge in the API? New to this API stuff. Many thanks/ Submitted by Apin101 on 26 November, 2013 - 07:12 1 answer Points: 0 There is currently no way to sort the responses, but since you are downloading in a CSV format you can sort most responses in Excel (or a spreadsheet editor). Another option is to run direct Ask queries and specify a property to sort on (see massive URL below). To do any sorting on an element of a packed array like DemandWeekdaySchedule would require custom logic in the result spreadsheet, or custom scripting of some kind. The new utility rate custom

196

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

197

Definition: Interruptible Load Or Interruptible Demand | Open Energy  

Open Energy Info (EERE)

Interruptible Load Or Interruptible Demand Interruptible Load Or Interruptible Demand Jump to: navigation, search Dictionary.png Interruptible Load Or Interruptible Demand Demand that the end-use customer makes available to its Load-Serving Entity via contract or agreement for curtailment.[1] View on Wikipedia Wikipedia Definition View on Reegle Reegle Definition No reegle definition available. Also Known As non-firm service Related Terms transmission lines, electricity generation, transmission line, firm transmission service, 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 "http://en.openei.org/w/index.php?title=Definition:Interruptible_Load_Or_Interruptible_Demand&oldid=502615"

198

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

199

Property:OpenEI/UtilityRate/DemandRateStructure/Tier6Adjustment | Open  

Open Energy Info (EERE)

Property Property Edit with form History Facebook icon Twitter icon » Property:OpenEI/UtilityRate/DemandRateStructure/Tier6Adjustment Jump to: navigation, search This is a property of type Number. Pages using the property "OpenEI/UtilityRate/DemandRateStructure/Tier6Adjustment" Showing 13 pages using this property. 4 4b524791-bef2-49b1-850b-458730755203 + 8 + 4b524791-bef2-49b1-850b-458730755203 + 8 +, 9 +, 67 +, ... 4b524791-bef2-49b1-850b-458730755203 + 9 + 4b524791-bef2-49b1-850b-458730755203 + 2 + 4b524791-bef2-49b1-850b-458730755203 + 67 + 4b524791-bef2-49b1-850b-458730755203 + 2 + 4b524791-bef2-49b1-850b-458730755203 + 89 + 4b524791-bef2-49b1-850b-458730755203 + 3 + 4b524791-bef2-49b1-850b-458730755203 + 3 + 4b524791-bef2-49b1-850b-458730755203 + 3 +

200

EnergySolve Demand Response | Open Energy Information  

Open Energy Info (EERE)

EnergySolve Demand Response EnergySolve Demand Response Jump to: navigation, search Name EnergySolve Demand Response Place Somerset, New Jersey Product Somerset-based utility bill outsourcing company that provides electronic utility bill auditing, tariff analysis, late fee avoidance, and flexible bill payment solutions. Coordinates 45.12402°, -92.675379° 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":45.12402,"lon":-92.675379,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

Note: This page contains sample records for the topic "open automated demand" 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

Definition: Automated Islanding And Reconnection | Open Energy Information  

Open Energy Info (EERE)

Islanding And Reconnection Islanding And Reconnection Jump to: navigation, search Dictionary.png Automated Islanding And Reconnection Automated Islanding and Reconnection Automated islanding and reconnection is achieved by automated separation and subsequent reconnection (autonomous synchronization) of an independently operated portion of the T&D system (i.e., microgrid) from the interconnected electric grid. A microgrid is an integrated energy system consisting of interconnected loads and distributed energy resources which, as an integrated system, can operate in parallel with the grid or as an island.[1] View on Wikipedia Wikipedia Definition Islanding refers to the condition in which a distributed (DG) generator continues to power a location even though electrical grid power

202

ADB-Methods and Tools for Energy Demand Projection | Open Energy  

Open Energy Info (EERE)

ADB-Methods and Tools for Energy Demand Projection ADB-Methods and Tools for Energy Demand Projection Jump to: navigation, search Tool Summary Name: Methods and Tools for Energy Demand Projection Agency/Company /Organization: Asian Development Bank Sector: Energy Topics: Pathways analysis Resource Type: Presentation, Software/modeling tools Website: cdm-mongolia.com/files/2_Methods_Hoseok_16May2010.pdf Cost: Free Methods and Tools for Energy Demand Projection Screenshot References: Methods and Tools for Energy Demand Projection[1] This article is a stub. You can help OpenEI by expanding it. References ↑ "Methods and Tools for Energy Demand Projection" Retrieved from "http://en.openei.org/w/index.php?title=ADB-Methods_and_Tools_for_Energy_Demand_Projection&oldid=398945" Categories:

203

Demand Response Energy Consulting LLC | Open Energy Information  

Open Energy Info (EERE)

Response Energy Consulting LLC Response Energy Consulting LLC Jump to: navigation, search Name Demand Response & Energy Consulting LLC Place Delanson, New York Zip NY 12053 Sector Efficiency Product Delanson-based demand response and energy efficiency consultants. Coordinates 42.748995°, -74.185794° 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":42.748995,"lon":-74.185794,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

204

Findings from Seven Years of Field Performance Data for Automated Demand Response in Commercial Buildings  

E-Print Network (OSTI)

Department of Energy Smart Grid Investment Grant to deliverstakeholders and current Smart Grid leaders, it was furtherthe key standards for Smart Grid. The OpenADR specification

Kiliccote, Sila

2010-01-01T23:59:59.000Z

205

Property:OpenEI/UtilityRate/FlatDemandMonth8 | Open Energy Information  

Open Energy Info (EERE)

Property Property Edit with form History Facebook icon Twitter icon » Property:OpenEI/UtilityRate/FlatDemandMonth8 Jump to: navigation, search This is a property of type Number. The allowed values for this property are: 1 2 3 4 5 6 7 8 9 Pages using the property "OpenEI/UtilityRate/FlatDemandMonth8" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 1 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 00101108-073b-4503-9cd4-01769611c26f + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 001ea8be-7a59-4bcb-a923-e8f1015946ee + 1 + 001eaca9-6ce7-4c0f-8578-44fc29654e97 + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 +

206

Property:OpenEI/UtilityRate/FlatDemandMonth5 | Open Energy Information  

Open Energy Info (EERE)

FlatDemandMonth5 FlatDemandMonth5 Jump to: navigation, search This is a property of type Number. The allowed values for this property are: 1 2 3 4 5 6 7 8 9 Pages using the property "OpenEI/UtilityRate/FlatDemandMonth5" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 1 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 00101108-073b-4503-9cd4-01769611c26f + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 001ea8be-7a59-4bcb-a923-e8f1015946ee + 1 + 001eaca9-6ce7-4c0f-8578-44fc29654e97 + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 + 002661b8-2e71-48b8-b657-44ff7372f757 + 1 + 0026b4d3-dd02-4423-95e1-56430d887b28 + 1 +

207

Property:OpenEI/UtilityRate/FixedDemandChargeMonth1 | Open Energy  

Open Energy Info (EERE)

Fixed Demand Charge Month 1 Fixed Demand Charge Month 1 Pages using the property "OpenEI/UtilityRate/FixedDemandChargeMonth1" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 7 + 00101108-073b-4503-9cd4-01769611c26f + 1.71 + 0030a241-5084-4404-9fe4-ed558aad8b59 + 8.28 + 0049111b-fba2-46ba-827d-7ce95609a1d9 + 9.51 + 0055db46-f535-4dc9-a192-920d1bdf382b + 3.2 + 0070a37f-0d41-4331-8115-df40c62e00f3 + 13.24 + 007f7b1f-0cba-450c-9023-df962aa387a4 + 5.28 + 008960d4-14ad-4822-b293-140640cf0bcf + 4.924 + 00cdded9-47a1-49b6-a217-10941ffbefc6 + 1.468 + 00e0b930-90c6-43c2-971a-91dade33f76a + 3.35 + 010f37ad-90a9-4aa8-bbdf-c55e72ee1495 + 4.74 + 017a32a0-140a-4e0b-a10c-f6f67905829c + 4.5 + 019941c8-cc3b-452c-b12e-201301099603 + 11.95 +

208

Property:OpenEI/UtilityRate/DemandReactivePowerCharge | Open Energy  

Open Energy Info (EERE)

DemandReactivePowerCharge DemandReactivePowerCharge Jump to: navigation, search This is a property of type Number. Pages using the property "OpenEI/UtilityRate/DemandReactivePowerCharge" Showing 25 pages using this property. (previous 25) (next 25) 0 00b7ccdc-c7e0-40d2-907f-acb6ae828292 + 0.25 + 00e0b930-90c6-43c2-971a-91dade33f76a + 0.32 + 00e2a43f-6844-417a-b459-edf32d33b051 + 0.0092 + 00fb7dca-d0a6-4b11-b7de-791c2fb9f2e1 + 2.7 + 01a64840-7edc-4193-8073-ed5604e098ca + 0.83 + 035f3d22-3650-47cc-a427-bb35170db128 + 0.3 + 042f06f4-6a5b-424f-a31f-8e1c5a838700 + 0.27 + 0479cd85-894d-412b-b2ce-3b96912e9014 + 0.2 + 04bab597-fe1e-4507-8d90-144980aeba73 + 0.3 + 05211bd7-b6d3-425c-9f96-0845b7828c3c + 0.27 + 052fbe23-ac02-4195-b76d-e572cc53f669 + 0.68 + 05490683-8158-4d2f-ad96-66d5e4980890 + 0.25 +

209

Property:OpenEI/UtilityRate/FlatDemandMonth3 | Open Energy Information  

Open Energy Info (EERE)

FlatDemandMonth3 FlatDemandMonth3 Jump to: navigation, search This is a property of type Number. The allowed values for this property are: 1 2 3 4 5 6 7 8 9 Pages using the property "OpenEI/UtilityRate/FlatDemandMonth3" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 1 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 00101108-073b-4503-9cd4-01769611c26f + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 001ea8be-7a59-4bcb-a923-e8f1015946ee + 1 + 001eaca9-6ce7-4c0f-8578-44fc29654e97 + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 + 002661b8-2e71-48b8-b657-44ff7372f757 + 1 + 0026b4d3-dd02-4423-95e1-56430d887b28 + 2 +

210

Property:OpenEI/UtilityRate/FlatDemandMonth2 | Open Energy Information  

Open Energy Info (EERE)

FlatDemandMonth2 FlatDemandMonth2 Jump to: navigation, search This is a property of type Number. The allowed values for this property are: 1 2 3 4 5 6 7 8 9 Pages using the property "OpenEI/UtilityRate/FlatDemandMonth2" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 1 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 00101108-073b-4503-9cd4-01769611c26f + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 001ea8be-7a59-4bcb-a923-e8f1015946ee + 1 + 001eaca9-6ce7-4c0f-8578-44fc29654e97 + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 + 002661b8-2e71-48b8-b657-44ff7372f757 + 1 + 0026b4d3-dd02-4423-95e1-56430d887b28 + 2 +

211

Property:OpenEI/UtilityRate/FlatDemandMonth4 | Open Energy Information  

Open Energy Info (EERE)

FlatDemandMonth4 FlatDemandMonth4 Jump to: navigation, search This is a property of type Number. The allowed values for this property are: 1 2 3 4 5 6 7 8 9 Pages using the property "OpenEI/UtilityRate/FlatDemandMonth4" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 1 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 00101108-073b-4503-9cd4-01769611c26f + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 001ea8be-7a59-4bcb-a923-e8f1015946ee + 1 + 001eaca9-6ce7-4c0f-8578-44fc29654e97 + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 + 002661b8-2e71-48b8-b657-44ff7372f757 + 1 + 0026b4d3-dd02-4423-95e1-56430d887b28 + 2 +

212

Property:OpenEI/UtilityRate/FlatDemandMonth7 | Open Energy Information  

Open Energy Info (EERE)

FlatDemandMonth7 FlatDemandMonth7 Jump to: navigation, search This is a property of type Number. The allowed values for this property are: 1 2 3 4 5 6 7 8 9 Pages using the property "OpenEI/UtilityRate/FlatDemandMonth7" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 1 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 00101108-073b-4503-9cd4-01769611c26f + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 001ea8be-7a59-4bcb-a923-e8f1015946ee + 1 + 001eaca9-6ce7-4c0f-8578-44fc29654e97 + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 + 002661b8-2e71-48b8-b657-44ff7372f757 + 1 + 0026b4d3-dd02-4423-95e1-56430d887b28 + 1 +

213

Property:OpenEI/UtilityRate/FlatDemandMonth1 | Open Energy Information  

Open Energy Info (EERE)

FlatDemandMonth1 FlatDemandMonth1 Jump to: navigation, search This is a property of type Number. The allowed values for this property are: 1 2 3 4 5 6 7 8 9 Pages using the property "OpenEI/UtilityRate/FlatDemandMonth1" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 1 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 00101108-073b-4503-9cd4-01769611c26f + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 001ea8be-7a59-4bcb-a923-e8f1015946ee + 1 + 001eaca9-6ce7-4c0f-8578-44fc29654e97 + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 + 002661b8-2e71-48b8-b657-44ff7372f757 + 1 + 0026b4d3-dd02-4423-95e1-56430d887b28 + 2 +

214

Property:OpenEI/UtilityRate/FlatDemandMonth6 | Open Energy Information  

Open Energy Info (EERE)

FlatDemandMonth6 FlatDemandMonth6 Jump to: navigation, search This is a property of type Number. The allowed values for this property are: 1 2 3 4 5 6 7 8 9 Pages using the property "OpenEI/UtilityRate/FlatDemandMonth6" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 1 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 00101108-073b-4503-9cd4-01769611c26f + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 001ea8be-7a59-4bcb-a923-e8f1015946ee + 1 + 001eaca9-6ce7-4c0f-8578-44fc29654e97 + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 + 002661b8-2e71-48b8-b657-44ff7372f757 + 1 + 0026b4d3-dd02-4423-95e1-56430d887b28 + 1 +

215

Demand response medium sized industry consumers (Smart Grid Project) | Open  

Open Energy Info (EERE)

response medium sized industry consumers (Smart Grid Project) response medium sized industry consumers (Smart Grid Project) Jump to: navigation, search Project Name Demand response medium sized industry consumers Country Denmark Headquarters Location Aarhus, Denmark Coordinates 56.162937°, 10.203921° 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":56.162937,"lon":10.203921,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

216

Property:FlatDemandStructure | Open Energy Information  

Open Energy Info (EERE)

Property Property Edit with form History Facebook icon Twitter icon » Property:FlatDemandStructure Jump to: navigation, search This is a property of type Page. Pages using the property "FlatDemandStructure" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 0000827d-84d0-453d-b659-b86869323897 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 00101108-073b-4503-9cd4-01769611c26f + 00101108-073b-4503-9cd4-01769611c26f + 001361ca-50d2-49bc-b331-08755a2c7c7d + 001361ca-50d2-49bc-b331-08755a2c7c7d + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 001d1952-955c-411b-8ce4-3d146852a75e + 001d1952-955c-411b-8ce4-3d146852a75e +

217

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

218

Definition: Automated Feeder And Line Switching | Open Energy Information  

Open Energy Info (EERE)

Definition Definition Edit with form History Facebook icon Twitter icon » Definition: Automated Feeder And Line Switching Jump to: navigation, search Dictionary.png Automated Feeder And Line Switching Automated feeder and line switching is realized through automatic isolation and reconfiguration of faulted segments of distribution feeders or transmission lines via sensors, controls, switches, and communications systems. These devices can operate autonomously in response to local events or in response to signals from a central control system.[1] Related Terms transmission lines, transmission line References ↑ SmartGrid.gov 'Description of Functions' An i LikeLike UnlikeLike 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:Automated_Feeder_And_Line_Switching&oldid=480562"

219

Property:OpenEI/UtilityRate/FixedDemandChargeMonth11 | Open Energy  

Open Energy Info (EERE)

Name: Fixed Demand Charge Month 11 Pages using the property "OpenEI/UtilityRate/FixedDemandChargeMonth11" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 7 + 00101108-073b-4503-9cd4-01769611c26f + 1.71 + 0030a241-5084-4404-9fe4-ed558aad8b59 + 8.28 + 0049111b-fba2-46ba-827d-7ce95609a1d9 + 9.51 + 0055db46-f535-4dc9-a192-920d1bdf382b + 3.2 + 0070a37f-0d41-4331-8115-df40c62e00f3 + 13.24 + 007f7b1f-0cba-450c-9023-df962aa387a4 + 5.28 + 008960d4-14ad-4822-b293-140640cf0bcf + 4.924 + 00cdded9-47a1-49b6-a217-10941ffbefc6 + 1.468 + 00e0b930-90c6-43c2-971a-91dade33f76a + 3.35 + 010f37ad-90a9-4aa8-bbdf-c55e72ee1495 + 4.74 + 017a32a0-140a-4e0b-a10c-f6f67905829c + 4.5 + 019941c8-cc3b-452c-b12e-201301099603 + 11.95 +

220

Property:OpenEI/UtilityRate/FixedDemandChargeMonth12 | Open Energy  

Open Energy Info (EERE)

Name: Fixed Demand Charge Month 12 Pages using the property "OpenEI/UtilityRate/FixedDemandChargeMonth12" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 7 + 00101108-073b-4503-9cd4-01769611c26f + 1.71 + 0030a241-5084-4404-9fe4-ed558aad8b59 + 8.28 + 0049111b-fba2-46ba-827d-7ce95609a1d9 + 9.51 + 0055db46-f535-4dc9-a192-920d1bdf382b + 3.2 + 0070a37f-0d41-4331-8115-df40c62e00f3 + 13.24 + 007f7b1f-0cba-450c-9023-df962aa387a4 + 5.28 + 008960d4-14ad-4822-b293-140640cf0bcf + 4.924 + 00cdded9-47a1-49b6-a217-10941ffbefc6 + 1.468 + 00e0b930-90c6-43c2-971a-91dade33f76a + 3.35 + 010f37ad-90a9-4aa8-bbdf-c55e72ee1495 + 4.74 + 017a32a0-140a-4e0b-a10c-f6f67905829c + 4.5 + 019941c8-cc3b-452c-b12e-201301099603 + 11.95 +

Note: This page contains sample records for the topic "open automated demand" 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

Property:OpenEI/UtilityRate/FixedDemandChargeMonth10 | Open Energy  

Open Energy Info (EERE)

Name: Fixed Demand Charge Month 10 Pages using the property "OpenEI/UtilityRate/FixedDemandChargeMonth10" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 7 + 00101108-073b-4503-9cd4-01769611c26f + 1.71 + 0030a241-5084-4404-9fe4-ed558aad8b59 + 10.59 + 0049111b-fba2-46ba-827d-7ce95609a1d9 + 9.51 + 0055db46-f535-4dc9-a192-920d1bdf382b + 3.2 + 0070a37f-0d41-4331-8115-df40c62e00f3 + 13.24 + 007f7b1f-0cba-450c-9023-df962aa387a4 + 5.28 + 008960d4-14ad-4822-b293-140640cf0bcf + 4.924 + 00cdded9-47a1-49b6-a217-10941ffbefc6 + 1.468 + 00e0b930-90c6-43c2-971a-91dade33f76a + 2.71 + 010f37ad-90a9-4aa8-bbdf-c55e72ee1495 + 4.74 + 017a32a0-140a-4e0b-a10c-f6f67905829c + 4.5 + 019941c8-cc3b-452c-b12e-201301099603 + 11.95 +

222

Property:OpenEI/UtilityRate/DemandChargePeriod6 | Open Energy Information  

Open Energy Info (EERE)

Jump to: navigation, search This is a property of type Number. Name: Demand Charge Period 6 Pages using the property "OpenEI/UtilityRate/DemandChargePeriod6" Showing 13 pages using this property. 0 0cbf0ab5-6819-42a2-bec6-1474dedf49c7 + 4.94 + 2 243d213c-25ea-4709-b421-6ff602b22d53 + 4.94 + 3 3436a635-b3b2-43a5-93ea-e0df37ef26c0 + 0 + 37ba48cd-8228-413b-b67c-8924492a64ce + 4.94 + 4 479553d6-3efc-4773-88d7-7c87804c0a65 + 0.13 + 4bc8edda-d0e1-40ee-aac2-c2b32603a6b4 + 0.406 + 4d4a192d-b047-4a30-b719-27b28886d52b + 0 + C C65fb7a2-3639-410b-9164-fc6aa9e8e68c + 0.18 + D D21bf95c-9259-4058-ba7c-21aabd1edf31 + 0 + Df73a354-dd92-4e20-91b2-db16bde25dbb + 6 + E E0f831df-88a7-45a7-853c-d3958e41be83 + 1.2 + F F43273e8-6ef9-443f-9cee-9e20ab9b47d0 + 4.94 + F71b0b63-1b9c-4afd-8481-7af45939042a + 0 +

223

Property:OpenEI/UtilityRate/DemandChargePeriod2 | Open Energy Information  

Open Energy Info (EERE)

Pages using the property "OpenEI/UtilityRate/DemandChargePeriod2" Pages using the property "OpenEI/UtilityRate/DemandChargePeriod2" Showing 25 pages using this property. (previous 25) (next 25) 0 0044fc17-f119-47eb-ae5d-0f489e09b203 + 12.94 + 0070a37f-0d41-4331-8115-df40c62e00f3 + 3.49 + 00cdded9-47a1-49b6-a217-10941ffbefc6 + 10.865 + 00fb7dca-d0a6-4b11-b7de-791c2fb9f2e1 + 8.15 + 00ff280d-1664-4b09-979b-5ee1e370b704 + 0.26 + 018673f0-093a-4a53-869d-3ac77d260efb + 0 + 01dd3bae-411e-40ee-b067-b2a0430baba3 + 6.75 + 01f6f9b2-3658-45e2-aa3e-f7afaf9b481d + 17.96 + 024ac306-1e30-4870-94f8-ef12908abe23 + 16.89 + 0253037f-3371-4224-b225-523d48a5e4c8 + 0.0267 + 02f09bc0-ae05-47af-a5ec-0074226c199b + 4.03 + 0385ea12-8fa5-45aa-8fc9-05df0358cd07 + 23.65 + 05146a64-a5a4-4271-a5ad-cb3a9a1e9345 + 33.94 + 05490683-8158-4d2f-ad96-66d5e4980890 + 0 +

224

Property:OpenEI/UtilityRate/DemandChargePeriod5 | Open Energy Information  

Open Energy Info (EERE)

Pages using the property "OpenEI/UtilityRate/DemandChargePeriod5" Pages using the property "OpenEI/UtilityRate/DemandChargePeriod5" Showing 25 pages using this property. 0 0934dd86-7cbe-437a-8cc5-47f469d3a745 + 8.516 + 0cbf0ab5-6819-42a2-bec6-1474dedf49c7 + 12.05 + 1 15d745ce-504b-4b58-8398-bd0feecd6cd3 + 12.08 + 16c96f08-175e-4914-b959-38a16682f377 + 12.178 + 1f892ab7-b5e8-4c7d-9e3d-d8fd46472ccc + 1.66 + 2 243d213c-25ea-4709-b421-6ff602b22d53 + 11.89 + 3 3436a635-b3b2-43a5-93ea-e0df37ef26c0 + 15.42 + 37ba48cd-8228-413b-b67c-8924492a64ce + 12.34 + 4 479553d6-3efc-4773-88d7-7c87804c0a65 + 0.27 + 4bc8edda-d0e1-40ee-aac2-c2b32603a6b4 + 0.408 + 4d4a192d-b047-4a30-b719-27b28886d52b + 0 + 6 6431b6d0-4fce-4b94-ac92-b8e1634e144f + 1.66 + 9 98c27d12-986e-49f2-bba0-c6a507f49195 + 13.1 + A A8443e10-6622-42f0-ad0b-5dbf429bf993 + 11.778 +

225

Property:OpenEI/UtilityRate/DemandRateStructure/Tier4Rate | Open Energy  

Open Energy Info (EERE)

Rate" Rate" Showing 13 pages using this property. 4 4b524791-bef2-49b1-850b-458730755203 + 5 +, 6 +, 3 +, ... 4b524791-bef2-49b1-850b-458730755203 + 6 + 4b524791-bef2-49b1-850b-458730755203 + 3 + 4b524791-bef2-49b1-850b-458730755203 + 7 + 4b524791-bef2-49b1-850b-458730755203 + 5 + 4b524791-bef2-49b1-850b-458730755203 + 8 + 4b524791-bef2-49b1-850b-458730755203 + 5 + 4b524791-bef2-49b1-850b-458730755203 + 5 + 4b524791-bef2-49b1-850b-458730755203 + 6 + 4b524791-bef2-49b1-850b-458730755203 + 6 + E E40880ac-c27b-4cbf-a011-b0d7d6e10fe9 + 1 + E40880ac-c27b-4cbf-a011-b0d7d6e10fe9 + 1 + E40880ac-c27b-4cbf-a011-b0d7d6e10fe9 + 1 + Retrieved from "http://en.openei.org/w/index.php?title=Property:OpenEI/UtilityRate/DemandRateStructure/Tier4Rate&oldid=53975

226

Property:OpenEI/UtilityRate/DemandRateStructure/Tier3Max | Open Energy  

Open Energy Info (EERE)

Max" Max" Showing 13 pages using this property. 4 4b524791-bef2-49b1-850b-458730755203 + 5 + 4b524791-bef2-49b1-850b-458730755203 + 5 + 4b524791-bef2-49b1-850b-458730755203 + 6 + 4b524791-bef2-49b1-850b-458730755203 + 7 + 4b524791-bef2-49b1-850b-458730755203 + 3 +, 4 +, 5 +, ... 4b524791-bef2-49b1-850b-458730755203 + 9 + 4b524791-bef2-49b1-850b-458730755203 + 3 + 4b524791-bef2-49b1-850b-458730755203 + 30 + 4b524791-bef2-49b1-850b-458730755203 + 4 + 4b524791-bef2-49b1-850b-458730755203 + 36 + E E40880ac-c27b-4cbf-a011-b0d7d6e10fe9 + 200 + E40880ac-c27b-4cbf-a011-b0d7d6e10fe9 + 200 + E40880ac-c27b-4cbf-a011-b0d7d6e10fe9 + 200 + Retrieved from "http://en.openei.org/w/index.php?title=Property:OpenEI/UtilityRate/DemandRateStructure/Tier3Max&oldid=539747

227

KSL Kuttler Automation Systems GmbH | Open Energy Information  

Open Energy Info (EERE)

KSL Kuttler Automation Systems GmbH KSL Kuttler Automation Systems GmbH Jump to: navigation, search Name KSL-Kuttler Automation Systems GmbH Place Dauchingen, Baden-Württemberg, Germany Zip 78083 Sector Solar Product KSL-Kuttler Automation Systems GmbH is a manufacturer of automation systems for the PCB-industry and solar industry. KSL Kuttler was acquired by Suntech in April 2008. Coordinates 48.090319°, 8.552377° 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":48.090319,"lon":8.552377,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

228

Aescusoft GmbH Automation | Open Energy Information  

Open Energy Info (EERE)

Aescusoft GmbH Automation Aescusoft GmbH Automation Jump to: navigation, search Name Aescusoft GmbH Automation Place Ettenheim, Germany Zip 77955 Product Offers PV cell testing lines. Coordinates 48.256309°, 7.813654° 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":48.256309,"lon":7.813654,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

229

DA (Distribution Automation) (Smart Grid Project) | Open Energy Information  

Open Energy Info (EERE)

DA (Distribution Automation) (Smart Grid Project) DA (Distribution Automation) (Smart Grid Project) Jump to: navigation, search Project Name DA (Distribution Automation) Country Netherlands Coordinates 52.132633°, 5.291266° 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":52.132633,"lon":5.291266,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

230

International Automated Systems Inc IAUS | Open Energy Information  

Open Energy Info (EERE)

International Automated Systems Inc IAUS International Automated Systems Inc IAUS Jump to: navigation, search Name International Automated Systems Inc (IAUS) Place Salem, Utah Zip 84653 Product Has developed a lens-based technology for concentrating heat in STEGSs, which it plans to deploy at projects. Coordinates 42.554485°, -88.110549° 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":42.554485,"lon":-88.110549,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

231

Automation and security of Supply (Smart Grid Project) | Open Energy  

Open Energy Info (EERE)

Automation and security of Supply (Smart Grid Project) Automation and security of Supply (Smart Grid Project) Jump to: navigation, search Project Name Automation and security of Supply Country Denmark Coordinates 56.26392°, 9.501785° 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":56.26392,"lon":9.501785,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

232

Property:OpenEI/UtilityRate/DemandChargePeriod2FAdj | Open Energy  

Open Energy Info (EERE)

Fuel Adj Fuel Adj Pages using the property "OpenEI/UtilityRate/DemandChargePeriod2FAdj" Showing 25 pages using this property. (previous 25) (next 25) 0 02317cd6-a0ec-4111-8627-09664a2c083c + 0.84 + 1 13087919-93aa-4ea4-a980-9651069273c7 + 7.31 + 16aa4028-86d4-4e27-be38-fe817b497238 + 0.497 + 1a72490d-bb6a-4115-99a7-7dbc54cb1824 + 11.49 + 2 2367240f-bd28-4b73-ae88-b8f1d7ed70c1 + 0.497 + 24f48897-8a68-4ae0-99d9-ecc0281f7ece + 8.73 + 3 3bbd220c-c3da-4420-99dc-f2eeb44ce2e3 + 0.0295 + 4 448aa8c8-e896-439a-82c8-b61a66a80429 + 0.412 + 479553d6-3efc-4773-88d7-7c87804c0a65 + 0.91 + 4bc8edda-d0e1-40ee-aac2-c2b32603a6b4 + 6.5e-4 + 4d4a192d-b047-4a30-b719-27b28886d52b + 1.5 + 4e7a224a-8960-4bbf-8843-321a81d7c3a8 + 0.888 + 4f0014b5-64b1-4487-8c74-3e19564df58e + 0.402 +

233

Property:OpenEI/UtilityRate/DemandRateStructure/Period | Open Energy  

Open Energy Info (EERE)

Period Period Jump to: navigation, search This is a property of type Number. The allowed values for this property are: 1 2 3 4 5 6 7 8 9 Pages using the property "OpenEI/UtilityRate/DemandRateStructure/Period" Showing 25 pages using this property. (previous 25) (next 25) 0 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 +

234

Definition: Automated Voltage And Var Control | Open Energy Information  

Open Energy Info (EERE)

Voltage And Var Control Voltage And Var Control Jump to: navigation, search Dictionary.png Automated Voltage And Var Control Automated voltage and VAR control requires coordinated operation of reactive power resources such as capacitor banks, voltage regulators, transformer load-tap changers, and distributed generation (DG) with sensors, controls, and communications systems. These devices could operate autonomously in response to local events or in response to signals from a central control system.[1] View on Wikipedia Wikipedia Definition Also Known As Volt-VAR Control (VVC) Related Terms smart grid, Reactive Power References ↑ SmartGrid.gov 'Description of Functions' An i LikeLike UnlikeLike You like this.Sign Up to see what your friends like. nline Glossary Definition Retrieved from

235

Distributed Automated Demand Response  

... could offer the adaptors under incentive programs in a manner similar to the programs in place for compact fluorescent light bulbs.

236

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.

237

Price Responsive Demand in New York Wholesale Electricity Market using OpenADR  

E-Print Network (OSTI)

months when buildings' electricity demand is also high dueoptimize buildings' electricity demand according to hourlymonths when buildings' electricity demand is also high due

Kim, Joyce Jihyun

2013-01-01T23:59:59.000Z

238

Open Automated Demand Response Technologies for Dynamic Pricing and Smart Grid  

E-Print Network (OSTI)

OASIS SDO. “Energy Market Information Exchange (eMIX)information are published: For each of these wholesale markets, wholesale prices for energy andInformation System (OASIS) [7]. We used the CAISO wholesale energy market

Ghatikar, Girish

2010-01-01T23:59:59.000Z

239

Design and Operation of an Open, Interoperable Automated Demand Response Infrastructure for Commercial Buildings  

E-Print Network (OSTI)

14] Common Information Model (CIM) Standards, Distributedwww.dmtf.org/standards/cim/. [15] Motegi, N. , Piette, M.a common information model (CIM). The CIM de?nitions manage

Piette, Mary Ann

2010-01-01T23:59:59.000Z

240

Open Automated Demand Response Technologies for Dynamic Pricing and Smart Grid  

E-Print Network (OSTI)

communicate dynamic electricity prices to facilities and howdoes not know the electricity prices more than a day inTime Pricing (RTP): Electricity prices vary continuously

Ghatikar, Girish

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "open automated demand" 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

Open Automated Demand Response Technologies for Dynamic Pricing and Smart Grid  

E-Print Network (OSTI)

existing TOU tariff and a peak energy charge ( $1.20/kWh) isWholesale Energy Market Prices PG&E’s PDP Tariff PG&E’s TOU

Ghatikar, Girish

2010-01-01T23:59:59.000Z

242

The Regents of the University of California. Linking Continuous Energy Management and Open Automated Demand Response  

E-Print Network (OSTI)

and to be published in the ProceedingsDISCLAIMER 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, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its 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, or The Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or

M. A. Piette; S. Kiliccote; G. Ghatikar; Mary Ann Piette; Sila Kiliccote; Girish Ghatikar

2008-01-01T23:59:59.000Z

243

Design and Operation of an Open, Interoperable Automated Demand Response Infrastructure for Commercial Buildings  

E-Print Network (OSTI)

No. LBNL-52510. [16] California Energy Commission (CEC),21_EAP2_FINAL.PDF. [2] California Energy Commission (CEC),Company, Apr. 28, California Energy Commission, Jan. 2. [5

Piette, Mary Ann

2010-01-01T23:59:59.000Z

244

Architecture Concepts and Technical Issues for an Open, Interoperable Automated Demand Response Infrastructure  

E-Print Network (OSTI)

networks used in energy management systems to IP networkssystem (e.g. Energy Management Control Systems) responsiblebuilding energy management and controls systems (EMCS) and

Koch, Ed; Piette, Mary Ann

2008-01-01T23:59:59.000Z

245

Open Automated Demand Response Technologies for Dynamic Pricing and Smart Grid  

E-Print Network (OSTI)

CAISO’s Wholesale Energy Market Prices PG&E’s PDP TariffWe used the CAISO wholesale energy market prices for the RTPusing CAISO wholesale energy market prices allowed us to

Ghatikar, Girish

2010-01-01T23:59:59.000Z

246

Design and Operation of an Open, Interoperable Automated Demand Response Infrastructure for Commercial Buildings  

E-Print Network (OSTI)

architecture and is a comprehensive technical framework that links communications infrastructure and electricity markets into a “smart grid”

Piette, Mary Ann

2010-01-01T23:59:59.000Z

247

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

248

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

249

Terminal automation system maintenance  

SciTech Connect

Nothing has improved petroleum product loading in recent years more than terminal automation systems. The presence of terminal automation systems (TAS) at loading racks has increased operational efficiency and safety and enhanced their accounting and management capabilities. However, like all finite systems, they occasionally malfunction or fail. Proper servicing and maintenance can minimize this. And in the unlikely event a TAS breakdown does occur, prompt and effective troubleshooting can reduce its impact on terminal productivity. To accommodate around-the-clock loading at racks, increasingly unattended by terminal personnel, TAS maintenance, servicing and troubleshooting has become increasingly demanding. It has also become increasingly important. After 15 years of trial and error at petroleum and petrochemical storage and transfer terminals, a number of successful troubleshooting programs have been developed. These include 24-hour {open_quotes}help hotlines,{close_quotes} internal (terminal company) and external (supplier) support staff, and {open_quotes}layered{close_quotes} support. These programs are described.

Coffelt, D.; Hewitt, J. [Engineered Systems Inc., Tempe, AZ (United States)

1997-01-01T23:59:59.000Z

250

Advanced Control Technologies and Strategies Linking Demand Response and Energy Efficiency  

E-Print Network (OSTI)

Fully Automated Demand Response Tests in Large Facilities”.also provided through the Demand Response Research Center (of Fully Automated Demand Response in Large Facilities”

Kiliccote, Sila; Piette, Mary Ann

2005-01-01T23:59:59.000Z

251

New Zealand Energy Data: Electricity Demand and Consumption | OpenEI  

Open Energy Info (EERE)

Electricity Demand and Consumption Electricity Demand and Consumption 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). The sectors included are: agriculture, forestry and fishing; industrial (mining, food processing, wood and paper, chemicals, basic metals, other minor sectors); commercial; and residential. Source New Zealand Ministry of Economic Development Date Released Unknown Date Updated July 03rd, 2009 (5 years ago)

252

Price Responsive Demand in New York Wholesale Electricity Market using OpenADR  

E-Print Network (OSTI)

charges to avoid high electricity bills. Demand Responseaffect customers' electricity bills negatively. Therefore,charges to avoid high electricity bills Under ConEd's SC-9

Kim, Joyce Jihyun

2013-01-01T23:59:59.000Z

253

Findings from Seven Years of Field Performance Data for Automated...  

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

Seven Years of Field Performance Data for Automated Demand Response in Commercial Buildings Title Findings from Seven Years of Field Performance Data for Automated Demand Response...

254

Price Responsive Demand in New York Wholesale Electricity Market using OpenADR  

E-Print Network (OSTI)

and Demand Response in Electricity Markets." University ofRates and Tariffs /Schedule for Electricity Service, P.S.C.no. 10- Electricity/Rules 24 (Riders)/Leaf No. 177-327."

Kim, Joyce Jihyun

2013-01-01T23:59:59.000Z

255

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,...

256

Participation through Automation: Fully Automated Critical Peak Pricing in Commercial Buildings  

E-Print Network (OSTI)

2004. Working Group 2 Demand Response Evaluation – Program2006. Au tomated Demand Response Control Strategies inrequired. Figure 2. Demand Response Automation Server and

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

2006-01-01T23:59:59.000Z

257

Test Automation Test Automation  

E-Print Network (OSTI)

Test Automation Test Automation Mohammad Mousavi Eindhoven University of Technology, The Netherlands Software Testing 2013 Mousavi: Test Automation #12;Test Automation Outline Test Automation Mousavi: Test Automation #12;Test Automation Why? Challenges of Manual Testing Test-case design: Choosing inputs

Mousavi, Mohammad

258

Opportunities for Energy Efficiency and Open Automated Demand Response in Wastewater Treatment Facilities in California -- Phase I Report  

E-Print Network (OSTI)

40 CFR Part 419 Petroleum Refining Point Source Category.40 CFR Part 419 Petroleum Refining Point Source Category.source, and operating procedures (Wang 2005). Petroleum

Lekov, Alex

2010-01-01T23:59:59.000Z

259

Opportunities for Energy Efficiency and Open Automated Demand Response in Wastewater Treatment Facilities in California -- Phase I Report  

E-Print Network (OSTI)

Commission. (2008). "Anaerobic Digestion." Retrieved AugustRENEWABLE / BIOMASS / ANAEROBIC DIGESTION /. CaliforniaResearch: Biomass - Anaerobic Digestion." Retrieved December

Lekov, Alex

2010-01-01T23:59:59.000Z

260

Opportunities for Energy Efficiency and Open Automated Demand Response in Wastewater Treatment Facilities in California -- Phase I Report  

E-Print Network (OSTI)

A byproduct of this process is biogas which contains 50– 70%Partners LLC 2007). This biogas can be used to generate heatmethane fermentation and biogas recovery (Green 1995).

Lekov, Alex

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "open automated demand" 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

Opportunities for Energy Efficiency and Open Automated Demand Response in Wastewater Treatment Facilities in California -- Phase I Report  

E-Print Network (OSTI)

Processing Industry Energy Efficiency Initiative, CaliforniaK. (2004). Bringing Energy Efficiency to the Water andAgricultural/Water End-Use Energy Efficiency Program. Lyco

Lekov, Alex

2010-01-01T23:59:59.000Z

262

Opportunities for Energy Efficiency and Open Automated Demand Response in Wastewater Treatment Facilities in California -- Phase I Report  

E-Print Network (OSTI)

state.aspx? id=124. California Energy Commission. (2000). "pubs/fuelcell.pdf. California Energy Commission (2003).Wastewater Treatment. California Energy Commission (2003).

Lekov, Alex

2010-01-01T23:59:59.000Z

263

Opportunities for Energy Efficiency and Open Automated Demand Response in Wastewater Treatment Facilities in California -- Phase I Report  

E-Print Network (OSTI)

to remove large quantities of oil and grease. Wastewateroils, and feedstocks for the petrochemical industry (Benyahia 2006). Petroleum refining uses large quantities

Lekov, Alex

2010-01-01T23:59:59.000Z

264

Opportunities for Energy Efficiency and Open Automated Demand Response in Wastewater Treatment Facilities in California -- Phase I Report  

E-Print Network (OSTI)

units per 100 mL Oil and Grease TSS Source: EnvironmentalTSS removal pH pH Oil and Grease mg/L Source: CaliforniaOil and Grease Phenolic compounds Ammonia Sulfide Total chromium Hexavalent chromium pH Source:

Lekov, Alex

2010-01-01T23:59:59.000Z

265

Demand response : Daylighting The New York Times Building  

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

Demand response Overview The architectural approach The owner's approach Daylighting field study Daylighting control systems Automated roller shades Procurement specifications...

266

Chilled Water Thermal Storage System and Demand Response at the University of California at Merced  

E-Print Network (OSTI)

and Techniques for Demand Response. California EnergyTest Results of Automated Demand Response in a Large OfficeStorage System and Demand Response at the University of

Granderson, Jessica

2010-01-01T23:59:59.000Z

267

Advanced Controls and Communications for Demand Response and Energy Efficiency in Commercial Buildings  

E-Print Network (OSTI)

of Fully Automated Demand Response in Large Facilities”NYSERDA) and the Demand Response Research Center (LLC “Working Group 2 Demand Response Program Evaluation –

Kiliccote, Sila; Piette, Mary Ann; Hansen, David

2006-01-01T23:59:59.000Z

268

OpenEI - demand  

Open Energy Info (EERE)

are given by a location defined by the Typical Meteorological Year (TMY) for which the weather data was collected. Commercial load data is sorted by the (TMY) site as a...

269

Automated reaction mapping  

Science Conference Proceedings (OSTI)

Automated reaction mapping is a fundamental first step in the analysis of chemical reactions and opens the door to the development of sophisticated chemical kinetic tools. This article formulates the reaction mapping problem as an optimization problem. ... Keywords: Cheminformatics, mechanisms

John D. Crabtree; Dinesh P. Mehta

2009-02-01T23:59:59.000Z

270

2012 Portland General Electric. All rights reserved. Planning for Demand  

E-Print Network (OSTI)

2/13/2013 1 © 2012 Portland General Electric. All rights reserved. Planning for Demand Response their usage. Demand Response ­ PGE Current Status 10 Automated Demand R

271

Automated Critical Peak Pricing Field Tests: 2006 Pilot Program Description and Results  

E-Print Network (OSTI)

Techniques  for  Demand  Response.   Lawrence  Berkeley Communications  for  Demand Response and Energy Efficiency for  Automated  Demand  Response  Demonstration.   2004.  

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

2007-01-01T23:59:59.000Z

272

Dynamic Controls for Energy Efficiency and Demand Response: Framework Concepts and a New Construction Study Case in New York  

E-Print Network (OSTI)

of Fully Automated Demand Response in Large Facilities.for Energy Efficiency and Demand Response”, Proceedings ofAuthority (NYSERDA), the Demand Response Research Center (

Kiliccote, Sila; Piette, Mary Ann; Watson, David S.; Hughes, Glenn

2006-01-01T23:59:59.000Z

273

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...

274

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...

275

Building Technologies Office: Distributed Intelligent-Automated...  

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

Distributed Intelligent-Automated Demand Response Building Management System Research Project to someone by E-mail Share Building Technologies Office: Distributed...

276

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...

277

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...

278

Home Network Technologies and Automating Demand Response  

E-Print Network (OSTI)

As noted earlier, the number of residential customers willto residential customers, aggregate numbers of DR clientsexceed the number of large commercial customers by at

McParland, Charles

2010-01-01T23:59:59.000Z

279

Distributed Automated Demand Response - Energy Innovation Portal  

... offer the adaptors under incentive programs in a manner similar to the programs in place for compact fluorescent light bulbs.

280

Home Network Technologies and Automating Demand Response  

E-Print Network (OSTI)

security "to" the home from security "in" the home. )homes can be controlled to optimize convenience, comfort and securityhome network protocols. Some protocols are adding security

McParland, Charles

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "open automated demand" 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

Home Network Technologies and Automating Demand Response  

E-Print Network (OSTI)

freed from this communications technology constraint. Homeinvestigating several communications technologies capable ofdedicated-wire communications technologies as impractical

McParland, Charles

2010-01-01T23:59:59.000Z

282

Installation and Commissioning Automated Demand Response Systems  

E-Print Network (OSTI)

Information System or Energy Management System (EMCS) so aimplementation in energy management systems. This effort isTC): Trained energy management control system vendors were

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

2008-01-01T23:59:59.000Z

283

Opportunities, Barriers and Actions for Industrial Demand Response in California  

SciTech Connect

In 2006 the Demand Response Research Center (DRRC) formed an Industrial Demand Response Team to investigate opportunities and barriers to implementation of Automated Demand Response (Auto-DR) systems in California industries. Auto-DR is an open, interoperable communications and technology platform designed to: Provide customers with automated, electronic price and reliability signals; Provide customers with capability to automate customized DR strategies; Automate DR, providing utilities with dispatchable operational capability similar to conventional generation resources. This research began with a review of previous Auto-DR research on the commercial sector. Implementing Auto-DR in industry presents a number of challenges, both practical and perceived. Some of these include: the variation in loads and processes across and within sectors, resource-dependent loading patterns that are driven by outside factors such as customer orders or time-critical processing (e.g. tomato canning), the perceived lack of control inherent in the term 'Auto-DR', and aversion to risk, especially unscheduled downtime. While industry has demonstrated a willingness to temporarily provide large sheds and shifts to maintain grid reliability and be a good corporate citizen, the drivers for widespread Auto-DR will likely differ. Ultimately, most industrial facilities will balance the real and perceived risks associated with Auto-DR against the potential for economic gain through favorable pricing or incentives. Auto-DR, as with any ongoing industrial activity, will need to function effectively within market structures. The goal of the industrial research is to facilitate deployment of industrial Auto-DR that is economically attractive and technologically feasible. Automation will make DR: More visible by providing greater transparency through two-way end-to-end communication of DR signals from end-use customers; More repeatable, reliable, and persistent because the automated controls strategies that are 'hardened' and pre-programmed into facility's software and hardware; More affordable because automation can help reduce labor costs associated with manual DR strategies initiated by facility staff and can be used for long-term.

McKane, Aimee T.; Piette, Mary Ann; Faulkner, David; Ghatikar, Girish; Radspieler Jr., Anthony; Adesola, Bunmi; Murtishaw, Scott; Kiliccote, Sila

2008-01-31T23:59:59.000Z

284

Automating Natural Disaster Impact Analysis: An Open Resource to Visually Estimate a Hurricane s Impact on the Electric Grid  

SciTech Connect

An ORNL team working on the Energy Awareness and Resiliency Standardized Services (EARSS) project developed a fully automated procedure to take wind speed and location estimates provided by hurricane forecasters and provide a geospatial estimate on the impact to the electric grid in terms of outage areas and projected duration of outages. Hurricane Sandy was one of the worst US storms ever, with reported injuries and deaths, millions of people without power for several days, and billions of dollars in economic impact. Hurricane advisories were released for Sandy from October 22 through 31, 2012. The fact that the geoprocessing was automated was significant there were 64 advisories for Sandy. Manual analysis typically takes about one hour for each advisory. During a storm event, advisories are released every two to three hours around the clock, and an analyst capable of performing the manual analysis has other tasks they would like to focus on. Initial predictions of a big impact and landfall usually occur three days in advance, so time is of the essence to prepare for utility repair. Automated processing developed at ORNL allowed this analysis to be completed and made publicly available within minutes of each new advisory being released.

Barker, Alan M [ORNL; Freer, Eva B [ORNL; Omitaomu, Olufemi A [ORNL; Fernandez, Steven J [ORNL; Chinthavali, Supriya [ORNL; Kodysh, Jeffrey B [ORNL

2013-01-01T23:59:59.000Z

285

Automation Status  

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

NREL Manufacturing R&D Workshop NREL Manufacturing R&D Workshop NREL H2/FC Manufacturing R&D Workshop Automation Status Garry Sperrick garry@sperkllc.com 585-259-0311 August 11, 2011 Automation Status NREL Manufacturing R&D Workshop Presentation Overview ƒ Brief Introduction ƒ DOE / NREL - Review & Discussion ƒ Automation Platforms ƒ Automation Processes ƒ Automation Considerations of the Manufacturer ƒ Manufacturer and the Supplier ƒ Three (3) Automation Programs Following these Guidelines ƒ Automotive Component Manufacturing ƒ Medical Manufacturing ƒ Membrane Electrode Assembly Manufacturing ƒ Hypothetical Fuel Cell Manufacturing Platforms August 11, 2011 Automation Status NREL Manufacturing R&D Workshop Professional Bio ƒ Automation Technician - Mobil Chemical

286

Automated roller shades : Daylighting The New York Times Building  

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

Automated roller shades Automated roller shades Overview The architectural approach The owner's approach Daylighting field study Daylighting control systems Automated roller shades Procurement specifications Shades and Shade Controls Lighting Controls Visualizing daylight Commissioning/ verification Demand response Mainstream solutions Post-occupancy evaluation Publications Sponsors Project team Automated roller shades Automated roller shades manage daylight, solar heat gains, view, glare, privacy, and building appearance through changes in shade height. A roller shade consists of a fabric wrapped around a horizontal tube, which contains a tubular motor. The tubular motor rotates when ac- or dc- power is applied to it causing modulation in shade height. The control system can be designed to optimize numerous variables, the most common being control of direct sun. In this application, the shades were controlled to five preset heights that aligned with the architectural features of the façade - i.e., the vision portion of the window wall - so that direct sun penetrated no more than a specified distance from the window wall. The Times Company also requested that the shades control window glare, yet maximize the opportunity for view and daylight admission. Unnecessary up/down movement of the shade was to be minimized. Fabric choices (openness of the weave, color of the fabric facing toward the interior and exterior) were additional considerations in terms of appearance as well as visual comfort and daylighting benefits.

287

Home Automation  

E-Print Network (OSTI)

In this paper I briefly discuss the importance of home automation system. Going in to the details I briefly present a real time designed and implemented software and hardware oriented house automation research project, capable of automating house's electricity and providing a security system to detect the presence of unexpected behavior.

Ahmed, Zeeshan

2010-01-01T23:59:59.000Z

288

Opportunities for Open Automated Demand Response in Wastewater Treatment Facilities in California - Phase II Report. San Luis Rey Wastewater Treatment Plant Case Study  

E-Print Network (OSTI)

2007). A byproduct of anaerobic digestion is biogas whichthe byproduct of the anaerobic digestion of solids removedgas produced from anaerobic digestion at no cost. CalPower,

Thompson, Lisa

2010-01-01T23:59:59.000Z

289

Opportunities for Open Automated Demand Response in Wastewater Treatment Facilities in California - Phase II Report. San Luis Rey Wastewater Treatment Plant Case Study  

E-Print Network (OSTI)

anaerobic digestion is biogas which contains 50–70 percentPlant collects this biogas and uses it in the cogeneration

Thompson, Lisa

2010-01-01T23:59:59.000Z

290

Guidelines for Planning Interoperable Distribution Automation Systems  

Science Conference Proceedings (OSTI)

Fully integrated distribution automation is a relatively new challenge for the utility industry, and the full range of new capabilities it enables are not well understood. In addition, developing an overall strategy to long-term automation integration and deployment through the use of open standards adds to the challenge. These guidelines were developed to describe the overall vision of distribution automation and to assist in application of emerging open standards to the procurement and integration of a...

2004-04-26T23:59:59.000Z

291

Test Automation Ant JUnit Test Automation  

E-Print Network (OSTI)

Test Automation Ant JUnit Test Automation Mohammad Mousavi Eindhoven University of Technology, The Netherlands Software Testing 2012 Mousavi: Test Automation #12;Test Automation Ant JUnit Outline Test Automation Ant JUnit Mousavi: Test Automation #12;Test Automation Ant JUnit Why? Challenges of Manual Testing

Mousavi, Mohammad

292

OpenEI - building demand  

Open Energy Info (EERE)

are given by a location defined by the Typical Meteorological Year (TMY) for which the weather data was collected. Commercial load data is sorted by the (TMY) site as a...

293

Akuacom | Open Energy Information  

Open Energy Info (EERE)

Services Product California-based provider of technology and services for Automated Demand Response (Auto-DR). References Akuacom1 LinkedIn Connections CrunchBase Profile...

294

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

295

Demand Response Opportunities in Industrial Refrigerated Warehouses in California  

Science Conference Proceedings (OSTI)

Industrial refrigerated warehouses that implemented energy efficiency measures and have centralized control systems can be excellent candidates for Automated Demand Response (Auto-DR) due to equipment synergies, and receptivity of facility managers to strategies that control energy costs without disrupting facility operations. Auto-DR utilizes OpenADR protocol for continuous and open communication signals over internet, allowing facilities to automate their Demand Response (DR). Refrigerated warehouses were selected for research because: They have significant power demand especially during utility peak periods; most processes are not sensitive to short-term (2-4 hours) lower power and DR activities are often not disruptive to facility operations; the number of processes is limited and well understood; and past experience with some DR strategies successful in commercial buildings may apply to refrigerated warehouses. This paper presents an overview of the potential for load sheds and shifts from baseline electricity use in response to DR events, along with physical configurations and operating characteristics of refrigerated warehouses. Analysis of data from two case studies and nine facilities in Pacific Gas and Electric territory, confirmed the DR abilities inherent to refrigerated warehouses but showed significant variation across facilities. Further, while load from California's refrigerated warehouses in 2008 was 360 MW with estimated DR potential of 45-90 MW, actual achieved was much less due to low participation. Efforts to overcome barriers to increased participation may include, improved marketing and recruitment of potential DR sites, better alignment and emphasis on financial benefits of participation, and use of Auto-DR to increase consistency of participation.

Goli, Sasank; McKane, Aimee; Olsen, Daniel

2011-06-14T23:59:59.000Z

296

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

297

Capitalizing on Two-Way Communications for Demand Response -- Vendor Overview  

Science Conference Proceedings (OSTI)

This report is part of a two-volume study on communication technologies for demand response. Communications and controls technologies can automate much of the work in a utility's demand response program. This automation can make programs easier for customers to use, therefore making them more attractive and robust. This volume focuses on communication technologies currently available for automated demand response communications. The report also provides an overview of major technology vendors whose produ...

2003-11-25T23:59:59.000Z

298

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

299

Security Automation Conference & Workshop  

Science Conference Proceedings (OSTI)

... Security Automation Conference & Workshop. ... Richard Hale, DISA - Information Security & Security Automation in DoD (coming soon); ...

300

Demand Response Opportunities in Industrial Refrigerated Warehouses in  

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

Response Opportunities in Industrial Refrigerated Warehouses in Response Opportunities in Industrial Refrigerated Warehouses in California Title Demand Response Opportunities in Industrial Refrigerated Warehouses in California Publication Type Conference Paper LBNL Report Number LBNL-4837E Year of Publication 2011 Authors Goli, Sasank, Aimee T. McKane, and Daniel Olsen Conference Name 2011 ACEEE Summer Study on Energy Efficiency in Industry Date Published 08/2011 Conference Location Niagara Falls, NY Keywords market sectors, openadr, refrigerated warehouses Abstract Industrial refrigerated warehouses that implemented energy efficiency measures and have centralized control systems can be excellent candidates for Automated Demand Response (Auto-DR) due to equipment synergies, and receptivity of facility managers to strategies that control energy costs without disrupting facility operations. Auto-DR utilizes OpenADR protocol for continuous and open communication signals over internet, allowing facilities to automate their Demand Response (DR). Refrigerated warehouses were selected for research because: They have significant power demand especially during utility peak periods; most processes are not sensitive to short-term (2-4 hours) lower power and DR activities are often not disruptive to facility operations; the number of processes is limited and well understood; and past experience with some DR strategies successful in commercial buildings may apply to refrigerated warehouses. This paper presents an overview of the potential for load sheds and shifts from baseline electricity use in response to DR events, along with physical configurations and operating characteristics of refrigerated warehouses. Analysis of data from two case studies and nine facilities in Pacific Gas and Electric territory, confirmed the DR abilities inherent to refrigerated warehouses but showed significant variation across facilities. Further, while load from California's refrigerated warehouses in 2008 was 360 MW with estimated DR potential of 45-90 MW, actual achieved was much less due to low participation. Efforts to overcome barriers to increased participation may include, improved marketing and recruitment of potential DR sites, better alignment and emphasis on financial benefits of participation, and use of Auto-DR to increase consistency of participation.

Note: This page contains sample records for the topic "open automated demand" 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

Automated dispenser  

DOE Patents (OSTI)

An automated dispenser having a conventional pipette attached to an actuating cylinder through a flexible cable for delivering precise quantities of a liquid through commands from remotely located computer software. The travel of the flexible cable is controlled by adjustable stops and a locking shaft. The pipette can be positioned manually or by the hands of a robot. 1 fig.

Hollen, R.M.; Stalnaker, N.D.

1989-04-06T23:59:59.000Z

302

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

303

Take an integrated approach to refinery automation  

Science Conference Proceedings (OSTI)

An integrated approach to designing refinery automation systems is essential to guaranteeing systems compatibility and maximizing benefits. Several aspects of implementing integrated refinery automation should be considered early in the project. Many refineries have major parts of their business automated, starting from corporate planning at the higher level, down to DCS and field instrumentation. A typical refinery automation system architecture of the mid-eighties is shown. Automation systems help refineries improve their business through: Rationalization of man power; Increased throughputs; Reduced give-away; Reduced energy consumption; Better response to market demands and changes; Effective use of offsite areas through scheduling and automatic line-up systems; Reduced losses; and Decision support systems.

Wadi, I. (Abu Dhabi National Oil Co. (United Arab Emirates))

1993-09-01T23:59:59.000Z

304

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

305

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...

306

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...

307

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...

308

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 ...

309

Exploiting home automation protocols for load monitoring in smart buildings  

Science Conference Proceedings (OSTI)

Monitoring and controlling electrical loads is crucial for demand-side energy management in smart grids. Home automation (HA) protocols, such as X10 and Insteon, have provided programmatic load control for many years, and are being widely deployed in ...

David Irwin; Sean Barker; Aditya Mishra; Prashant Shenoy; Anthony Wu; Jeannie Albrecht

2011-11-01T23:59:59.000Z

310

Commercial and Industrial Base Intermittent Resource Management Pilot  

E-Print Network (OSTI)

2:1002–1004. FERC. Demand Response and Advanced Metering.and Open Automated Demand Response in Wastewater Treatmentand Open Automated Demand Response in Refrigerated

Kiliccote, Sila

2011-01-01T23:59:59.000Z

311

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

0 + OpenEIUtilityRateDemandRateUnit kW + OpenEIUtilityRateDescription LED 250 Watt Equivalent per Lamp OpenEIUtilityRateFixedMonthlyCharge 11.75 + OpenEI...

312

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

0 + OpenEIUtilityRateDemandRateUnit kW + OpenEIUtilityRateDescription LED 100 Watt Equivalent per Lamp OpenEIUtilityRateFixedMonthlyCharge 6.64 + OpenEI...

313

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

0 + OpenEIUtilityRateDemandRateUnit kW + OpenEIUtilityRateName GPD-Substation Ownership Credit + OpenEIUtilityRateSector Industrial + OpenEIUtilityRateSource...

314

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

315

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

316

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

0 + OpenEIUtilityRateDemandRateUnit kW + OpenEIUtilityRateDescription TERMS AND CONDITIONS Contracts for outdoor ... TERMS AND CONDITIONS Contracts for outdoor...

317

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 ...

318

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,

319

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

320

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.

Note: This page contains sample records for the topic "open automated demand" 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

An application of automated reasoning in natural language question answering  

Science Conference Proceedings (OSTI)

The LogAnswer system is an application of automated reasoning to the field of open domain question answering. In order to find answers to natural language questions regarding arbitrary topics, the system integrates an automated theorem prover in a framework ... Keywords: Question answering, theorem prover

Ulrich Furbach; Ingo Glöckner; Björn Pelzer

2010-04-01T23:59:59.000Z

322

Measurement and evaluation techniques for automated demand response demonstration  

E-Print Network (OSTI)

and Renewable Energy, Office of Building Technology, State andand Renewable Energy, Office of Building Technology, State and

Motegi, Naoya; Piette, Mary Ann; Watson, David S.; Sezgen, Osman; ten Hope, Laurie

2004-01-01T23:59:59.000Z

323

Automated Demand Response Strategies and Commissioning Commercial Building Controls  

E-Print Network (OSTI)

loads. C P P is a new electricity tariff design to promotethe structures of electricity tariffs considering the timeand tariffs provide even greater incentives to consider sophisticated building operational and control strategies that reduce electricity

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

2006-01-01T23:59:59.000Z

324

Role of Standard Demand Response Signals for Advanced Automated...  

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

each group can participate in variety of markets from wholesale ancillary services to distribution level retail tariffs. This paper explores the issues of aggregated groups of...

325

Automated Demand Response Strategies and Commissioning Commercial Building Controls  

E-Print Network (OSTI)

Conference on Building Commissioning: April 19-21, 2006Auto-DR Strategies and Commissioning One common questionConference on Building Commissioning: April 19-21, 2006

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

2006-01-01T23:59:59.000Z

326

Automated Demand Response Opportunities in Wastewater Treatment Facilities  

E-Print Network (OSTI)

Down the Drain: The Hidden Costs of California Energy. 2004,Pacific Institute. California Energy Commission,which is funded by the California Energy Commission (Energy

Thompson, Lisa

2008-01-01T23:59:59.000Z

327

Findings from the 2004 Fully Automated Demand Response Tests...  

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

test all of the sites had some sort of Web-based Energy Information System (EIS) and Energy Management and Control System (EMCS) with PC. During 2004, five of the 18 sites used...

328

Measurement and evaluation techniques for automated demand response demonstration  

E-Print Network (OSTI)

EIS) 1 . Additional sub-metering was applied where necessarydata at each site. The sub- metering for the Site B building

Motegi, Naoya; Piette, Mary Ann; Watson, David S.; Sezgen, Osman; ten Hope, Laurie

2004-01-01T23:59:59.000Z

329

Direct versus Facility Centric Load Control for Automated Demand Response  

E-Print Network (OSTI)

Standards & Technology (NIST) Smart Grid Roadmap there haveinvolved in a number of Smart Grid standardization effortsCommittee, and the NAESB Smart Grid Standards Taskforce.

Piette, Mary Ann

2010-01-01T23:59:59.000Z

330

Role of Standard Demand Response Signals for Advanced Automated Aggregation  

E-Print Network (OSTI)

involved in various Smart Grid standards efforts withinthe context of emerging smart grid standards and the rolewere developed during the Smart Grid Interoperability Panel’

Kiliccote, Sila

2013-01-01T23:59:59.000Z

331

Scenarios for Consuming Standardized Automated Demand Response Signals  

E-Print Network (OSTI)

actions to influence the load profiles of facilities thatISO needs to influence the load profile of a Facility areUtility/ISO influences the load profile of a facility is to

Koch, Ed

2009-01-01T23:59:59.000Z

332

Role of Standard Demand Response Signals for Advanced Automated Aggregation  

E-Print Network (OSTI)

control to allow specific load profiles to be created.More flexible load profiles. The increased number of DRmix and match different load profiles of the individual DR

Kiliccote, Sila

2013-01-01T23:59:59.000Z

333

Results and commissioning issues from an automated demand response pilot  

E-Print Network (OSTI)

show an intermittent load profile that started appearingdaily "cooker-like" load profiles that we observed. Rather

Piette, Mary Ann; Watson, Dave; Sezgen, Osman; Motegi, Naoya

2004-01-01T23:59:59.000Z

334

Direct versus Facility Centric Load Control for Automated Demand Response  

E-Print Network (OSTI)

actions to influence load profiles of their customers atISO needs to influence the load profile of a Facility arebe a specific target load profile to be achieved while in

Piette, Mary Ann

2010-01-01T23:59:59.000Z

335

Direct versus Facility Centric Load Control for Automated Demand Response  

E-Print Network (OSTI)

networks used in energy management systems to IP networkswith residential energy management systems and is beingequipment (e.g. energy management and control system - EMCS)

Piette, Mary Ann

2010-01-01T23:59:59.000Z

336

Scenarios for Consuming Standardized Automated Demand Response Signals  

E-Print Network (OSTI)

from single end user facility, to third party facilitywidest range of end user facilities can participate in DR

Koch, Ed

2009-01-01T23:59:59.000Z

337

Automated Demand Response Opportunities in Wastewater Treatment Facilities  

E-Print Network (OSTI)

05CH11231. References EPRI, Energy Audit Manual for Water/Research Institute, Energy Audit Manual for Water/Wastewater

Thompson, Lisa

2008-01-01T23:59:59.000Z

338

Measurement and evaluation techniques for automated demand response demonstration  

E-Print Network (OSTI)

static pressure min. Cooling tower, Pump status/VFD, Zonesystem Fans Chillers Pumps Cooling towers Fans Fans Chillerssecondary pumps, cooling towers, and fans were considered as

Motegi, Naoya; Piette, Mary Ann; Watson, David S.; Sezgen, Osman; ten Hope, Laurie

2004-01-01T23:59:59.000Z

339

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

340

Automated deduction for verification  

Science Conference Proceedings (OSTI)

Automated deduction uses computation to perform symbolic logical reasoning. It has been a core technology for program verification from the very beginning. Satisfiability solvers for propositional and first-order logic significantly automate the task ...

Natarajan Shankar

2009-10-01T23:59:59.000Z

Note: This page contains sample records for the topic "open automated demand" 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 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...

342

PUCK: an automated prompting system for smart environments: toward achieving automated prompting--challenges involved  

Science Conference Proceedings (OSTI)

The growth in popularity of smart environments has been quite steep in the last decade and so has the demand for smart health assistance systems. A smart home-based prompting system can enhance these technologies to deliver in-home interventions to users ... Keywords: Automated prompting, Imbalanced class distribution, Machine learning, Prompting systems, Smart environments

Barnan Das; Diane J. Cook; Maureen Schmitter-Edgecombe; Adriana M. Seelye

2012-10-01T23:59:59.000Z

343

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

344

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, ...

345

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

346

Making automation work  

Science Conference Proceedings (OSTI)

Today's automated systems provide enormous safety and convenience. However, when glitches, problems, or breakdowns occur, the results can be catastrophic.

Samuel Greengard

2009-12-01T23:59:59.000Z

347

Evolution of industrial automation  

Science Conference Proceedings (OSTI)

Automation has been of high priority for the manufacturing sector, from Ford's first set of Model-T Assembly lines in the early 1920s to the modern factory floor. With appropriate automation, the aim was to rationalise the production and keep ... Keywords: Ethernet, architecture, automated manufacturing, bus topology, control servers, distributed control, economies of scale, embedded intelligence, functionality, fuzzy logic, global village, graphic panel, industrial automation, networking, networks

R. Murugesan

2006-03-01T23:59:59.000Z

348

Automated task allocation  

Science Conference Proceedings (OSTI)

The goal of the paradigm shift in Air Traffic Management (ATM) is to increase its overall performance by means of redesigning processes, evolving to a more automated, autonomous and predictable system. Nevertheless, when dealing with automation, it is ... Keywords: ATM, anticipatory, autonomous, centric, compensatory, decision support tools, level of automation, operations research, optimisation, performance metrics, task allocation

Rocío Barragán Montes, Eduardo García, Francisco Javier Sáez Nieto

2013-05-01T23:59:59.000Z

349

Turkey opens electricity markets as demand grows  

Science Conference Proceedings (OSTI)

Turkey's growing power market has attracted investors and project developers for over a decade, yet their plans have been dashed by unexpected political or financial crises or, worse, obstructed by a lengthy bureaucratic approval process. Now, with a more transparent retail electricity market, government regulators and investors are bullish on Turkey. Is Turkey ready to turn the power on? This report closely examine Turkey's plans to create a power infrastructure capable of providing the reliable electricity supplies necessary for sustained economic growth. It was compiled with on-the-ground research and extensive interview with key industrial and political figures. Today, hard coal and lignite account for 21% of Turkey's electricity generation and gas-fired plants account for 50%. The Alfin Elbistan-B lignite-fired plant has attracted criticism for its lack of desulfurization units and ash dam facilities that have tarnished the industry's image. A 1,100 MW hard-coal fired plant using supercritical technology is under construction. 9 figs., 1 tab.

McKeigue, J.; Da Cunha, A.; Severino, D. [Global Business Reports (United States)

2009-06-15T23:59:59.000Z

350

Definition: Firm Demand | Open Energy Information  

Open Energy Info (EERE)

is obligated to provide except when system reliability is threatened or during emergency conditions.1 Related Terms system, power References Glossary of Terms Used in...

351

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

0 + OpenEIUtilityRateDemandRateUnit kW + OpenEIUtilityRateDescription Rate D LED-100 Watt Metal Halide Equivalent: On Timer - 2920 hoursyear OpenEIUtilityRate...

352

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

0 + OpenEIUtilityRateDemandRateUnit kW + OpenEIUtilityRateDescription Rate D LED-100 Watt Metal Halide Equivalent: Dusk to Dawn OpenEIUtilityRateFlatRateBuy 1.81 +...

353

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

DemandRateUnit kW + OpenEIUtilityRateDescription Note: Rate schedule specifies "84 LED 31 kWhMonth". OpenEIUtilityRateFixedMonthlyCharge 24.5 + OpenEIUtilityRateName...

354

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

0 + OpenEIUtilityRateDemandRateUnit kW + OpenEIUtilityRateDescription LED: available for all new and existing locations OpenEIUtilityRateFixedMonthlyCharge...

355

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

UtilityRateFlatDemandStructurePeriod 1 + OpenEIUtilityRateName Schedule A5- Farm & Home Service TOD Option A + OpenEIUtilityRateSector Residential + OpenEIUtilityRate...

356

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

UtilityRateFlatDemandStructurePeriod 1 + OpenEIUtilityRateName Schedule A5- Farm & Home Service TOD Option B + OpenEIUtilityRateSector Residential + OpenEIUtilityRate...

357

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

UtilityRateFlatDemandStructureTier1Rate 7.6 + OpenEIUtilityRateName Large Power (Shell Oil) + OpenEIUtilityRateSector Commercial + OpenEIUtilityRateSource Rate Binder...

358

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

359

Integrated Predictive Demand Response Controller Research Project |  

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

Predictive Demand Response Predictive Demand Response Controller Research Project Integrated Predictive Demand Response Controller Research Project The U.S. Department of Energy (DOE) is currently conducting research into integrated predictive demand response (IPDR) controllers. The project team will attempt to design an IPDR controller so that it can be used in new or existing buildings or in collections of buildings. In the case of collections of buildings, they may be colocated on a single campus or remotely located as long as they are served by a single utility or independent service operator. Project Description This project seeks to perform the necessary applied research, development, and testing to provide a communications interface using industry standard open protocols and emerging National Institute of Standards and Technology

360

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

Note: This page contains sample records for the topic "open automated demand" 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

Using Dimmable Lighting for Regulation Capacity and Non-Spinning Reserves in the Ancillary Services Market. A Feasibility Study.  

E-Print Network (OSTI)

Digital Addressable Lighting Interface Demand ResponseDemand Response Automation Server Energy Information Agency2009). “Open Automated Demand Response Communications in

Rubinstein, Francis

2011-01-01T23:59:59.000Z

362

California Food Processing Industry Wastewater Demonstration Project: Phase I Final Report  

E-Print Network (OSTI)

and Automated Demand Response in Wastewater TreatmentProcessing Industry Demand Response Participation: A Scopingand Open Automated Demand Response. Lawrence Berkeley

Lewis, Glen

2010-01-01T23:59:59.000Z

363

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

364

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...

365

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...

366

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

367

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

368

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

369

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

370

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

371

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

372

Distribution automation. (Latest citations from the INSPEC database). Published Search  

SciTech Connect

The bibliography contains citations concerning distribution automation for the electric power utility industry. Distribution automation comprises a combination of real-time load monitoring; detailed modeling of the distribution, generation, transmission, and customer systems; and remote control of devices. Distribution automation achieves energy conservation by reduction of consumption, lower losses in distribution and transmission mission circuits, and reduction of peak load. Topics covered include demand-side management, telecontrol, distribution networks, communications software, and cost-benefit analyses. (Contains a minimum of 93 citations and includes a subject term index and title list.)

Not Available

1994-12-01T23:59:59.000Z

373

H. R. 4604: a bill to promote competition in the natural gas market, to ensure open access to transportation service, to encourage production of natural gas, to provide natural gas consumers with adequate supplies at reasonable prices, to eliminate demand restraints, and for other purposes. Introduced in the House of Representatives, Ninety-Ninth Congress, Second Session, April 16, 1986  

Science Conference Proceedings (OSTI)

The Natural Gas Policy Act Amendments of 1986 promotes competition in the natural gas market. Title I ensures open access to transportation service by requiring that interstate pipelines not discriminate in providing transportation services. Title II encourages production of natural gas by removing wellhead price controls and repealing jurisdiction over first sales. Title III provides natural gas consumers with adequate supplies at reasonable prices and eliminates demand restraints. The bill was referred to the House Committee on Energy and Commerce.

Not Available

1986-01-01T23:59:59.000Z

374

Security Automation Developer Days  

Science Conference Proceedings (OSTI)

... not addressed is the actual distribution of standardized ... 3:30 – 4:45 Automation Content Repositories ... provide protocols for querying devices as to ...

2012-10-26T23:59:59.000Z

375

CAESAR: Computer Automated Resummations  

E-Print Network (OSTI)

This talk gives a brief discussion of the motivations and principles behind computer automated expert semi-analytical resummation (CAESAR) for QCD final states.

Gavin P. Salam

2004-07-30T23:59:59.000Z

376

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

377

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

378

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

379

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.

380

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.

Note: This page contains sample records for the topic "open automated demand" 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

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

382

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

383

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

384

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

385

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

386

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

387

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

388

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

389

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

390

Analysis of Distribution Level Residential Demand Response  

SciTech Connect

Control of end use loads has existed in the form of direct load control for decades. Direct load control systems allow a utility to interrupt power to a medium to large size commercial or industrial customer a set number of times a year. With the current proliferation of computing resources and communications systems the ability to extend the direct load control systems now exists. Demand response systems now have the ability to not only engage commercial and industrial customers, but also the individual residential customers. Additionally, the ability exists to have automated control systems which operate on a continual basis instead of the traditional load control systems which could only be operated a set number of times a year. These emerging demand response systems have the capability to engage a larger portion of the end use load and do so in a more controlled manner. This paper will examine the impact that demand response systems have on the operation of an electric power distribution system.

Schneider, Kevin P.; Fuller, Jason C.; Chassin, David P.

2009-03-23T23:59:59.000Z

391

Demand or Request: Will Load Behave?  

Science Conference Proceedings (OSTI)

Power planning engineers are trained to design an electric system that satisfies predicted electrical demand under stringent conditions of availability and power quality. Like responsible custodians, we plan for the provision of electrical sustenance and shelter to those in whose care regulators have given us the responsibility to serve. Though most customers accept this nurturing gladly, a growing number are concerned with the economic costs and environmental impacts of service at a time when technology (particularly distributed generation, storage, automation, and information networks) offers alternatives for localized control and competitive service. As customers’ and their systems mature, a new relationship with the electricity provider is emerging. Demand response is perhaps the first unsteady step where the customer participates as a partner in system operations. This paper explores issues system planners need to consider as demand response matures to significant levels beyond direct load control and toward a situation where service is requested and bargains are reached with the electricity provider based on desired load behavior. On one hand, predicting load growth and behavior appears more daunting than ever. On the other, for the first time load becomes a new resource whose behavior can be influenced during system operations to balance system conditions.

Widergren, Steven E.

2009-07-30T23:59:59.000Z

392

Process development for automated solar-cell and module production. Task 4. Automated array assembly. Quarterly report No. 3  

DOE Green Energy (OSTI)

The Automated Lamination Station is mechanically complete and is currently undergoing final wiring. The high current driver and isolator boards have been completed and installed, and the main interface board is under construction. The automated vacuum chamber has had a minor redesign to increase stiffness and improve the cover open/close mechanism. Design of the Final Assembly Station has been completed and construction is underway.

Hagerty, J. J.; Gifford, M.

1981-04-15T23:59:59.000Z

393

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

394

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

395

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

396

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

397

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

398

Evaluation of Representative Smart Grid Investment Project Technologies: Demand Response  

DOE Green Energy (OSTI)

This document is one of a series of reports estimating the benefits of deploying technologies similar to those implemented on the Smart Grid Investment Grant (SGIG) projects. Four technical reports cover the various types of technologies deployed in the SGIG projects, distribution automation, demand response, energy storage, and renewables integration. A fifth report in the series examines the benefits of deploying these technologies on a national level. This technical report examines the impacts of a limited number of demand response technologies and implementations deployed in the SGIG projects.

Fuller, Jason C.; Prakash Kumar, Nirupama; Bonebrake, Christopher A.

2012-02-14T23:59:59.000Z

399

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

400

Integration of Renewables Via Demand Management: Highly Dispatchable and Distributed Demand Response for the Integration of Distributed Generation  

Science Conference Proceedings (OSTI)

GENI Project: AutoGrid, in conjunction with Lawrence Berkeley National Laboratory and Columbia University, will design and demonstrate automated control software that helps manage real-time demand for energy across the electric grid. Known as the Demand Response Optimization and Management System - Real-Time (DROMS-RT), the software will enable personalized price signal to be sent to millions of customers in extremely short timeframes—incentivizing them to alter their electricity use in response to grid conditions. This will help grid operators better manage unpredictable demand and supply fluctuations in short time-scales —making the power generation process more efficient and cost effective for both suppliers and consumers. DROMS-RT is expected to provide a 90% reduction in the cost of operating demand response and dynamic pricing Projects in the U.S.

None

2012-02-11T23:59:59.000Z

Note: This page contains sample records for the topic "open automated demand" 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

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

11111111111111111111111111111 OpenEIUtilityRateDemandComments If customer owns its substation and facilities to receive power at a delivery point, then customer shall be entitled...

402

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

DemandRateUnit kW + OpenEIUtilityRateDescription LARGE POWER DISTRIBUTION SUBSTATION RATE - ... LARGE POWER DISTRIBUTION SUBSTATION RATE - CHOICE SCHEDULE LPDS-C *...

403

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

DemandRateUnit kW + OpenEIUtilityRateDescription LARGE POWER DISTRIBUTION SUBSTATION SCHEDU ... LARGE POWER DISTRIBUTION SUBSTATION SCHEDULE LPDS * This rate is...

404

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

405

Price Server System for Automated Critical Peak Pricing  

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

Price Server System for Automated Critical Peak Pricing Price Server System for Automated Critical Peak Pricing Speaker(s): David S. Watson Date: June 3, 2005 - 12:00pm Location: 90-3148 Overview of current California Energy Commission (CEC)/Demand Response Research Center (DRRC) Auto-CPP project: This summer, some select commercial CPP customers of PG&E will have the option of joining the Automated Critical Peak Pricing pilot. The pilot will have the same tariffs as standard CPP programs, but will include an added feature: automated shedding of electric loads. Through use of the Price Server System, day-ahead CPP event signals initiated by PG&E will ultimately cause electric loads to be automatically curtailed on commercial customer sites. These optional predetermined shed strategies will occur without

406

New Services for Building Maintenance Enabled by Building Automation  

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

New Services for Building Maintenance Enabled by Building Automation New Services for Building Maintenance Enabled by Building Automation Systems Speaker(s): Heikki Ihasalo Date: May 17, 2007 - 12:00pm Location: 90-3122 Seminar Host/Point of Contact: Peng Xu Today's building automation systems enable building systems integration and access to information from a variety of sources, e.g. fire alarm systems, access control, security systems, lighting and control systems. With the help of Internet technology and XML (Extensible Markup Language) building automation systems can also connect to other enterprise\ applications such as facility management software, maintenance management systems, ERP or financial systems. New applications are also emerging to manage energy consumption in buildings, for example demand response and fault detection

407

Automated Program Description  

E-Print Network (OSTI)

The Programmer's apprentice (PA) is an automated program development tool. The PA depends upon a library of common algorithms (cliches) as the source of its knowledge about programming. The PA uses these cliches to understand ...

Cyphers, D. Scott

408

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

409

Roxboro Integrated Automation Project  

Science Conference Proceedings (OSTI)

EPRI, Carolina Power & Light (CP&L), and ASEA Brown Boveri (ABB) formed an alliance to develop and demonstrate automation technologies at CP&LOs Roxboro Plant. This alliance is governed by a Memorandum of Understanding that allows all parties to share in the success of the products developed, and to contribute to their commercialization. This research project is intended to demonstrate the economic benefits of efficient and useful plant-wide automation technologies for the utility industry. Successful im...

1997-01-12T23:59:59.000Z

410

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

UtilityRateDemandRateUnit kW + OpenEIUtilityRateDescription Customers requiring steam service on premises adjacent to existing low pressure steam mains. OpenEIUtilityRate...

411

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

OpenEIUtilityRateDemandRateUnit kW + OpenEIUtilityRateDescription General Service - Coal Bed Methane (CBM) S ... General Service - Coal Bed Methane (CBM) Single Phase...

412

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

OpenEIUtilityRateDemandRateUnit kW + OpenEIUtilityRateDescription General Service - Coal Bed Methane Three P ... General Service - Coal Bed Methane Three Phase Applicability:...

413

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

0 + OpenEIUtilityRateDemandRateUnit kW + OpenEIUtilityRateDescription Special Terms and Conditions 1. Auxiliary buildings, detached from the building in which the...

414

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

0 + OpenEIUtilityRateDemandRateUnit kW + OpenEIUtilityRateDescription Special Terms and Conditions 1. Service un ... Special Terms and Conditions 1. Service under this...

415

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

0 + OpenEIUtilityRateDemandRateUnit kW + OpenEIUtilityRateDescription A. Terms and conditions of negotiated rate will be by contract. B. Will be based upon the...

416

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

UtilityRateDemandRateUnit kW + OpenEIUtilityRateDescription Energy adjustment base average 0.054kwh (E.A. base would vary each month based on projected power costs. OpenEI...

417

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

DemandRateUnit kW + OpenEIUtilityRateDescription Three Phase Energy adjustment base average 0.054kwh (E.A. base would vary each month based on projected power costs. OpenEI...

418

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

0 + OpenEIUtilityRateDemandRateUnit kW + OpenEIUtilityRateDescription If the energy usage in the monthly billing ... If the energy usage in the monthly billing period...

419

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

420

TNC: Open Standards for Network Security Automation  

Science Conference Proceedings (OSTI)

... Hash software into PCR before running it ? PCR value cannot be reset except via hard reboot ... TPM securely sends PCR value to PDP ...

2012-10-26T23:59:59.000Z

Note: This page contains sample records for the topic "open automated demand" 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

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

422

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.

423

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

424

Analysis of Residential Demand Response and Double-Auction Markets  

Science Conference Proceedings (OSTI)

Demand response and dynamic pricing programs are expected to play increasing roles in the modern Smart Grid environment. While direct load control of end-use loads has existed for decades, price driven response programs are only beginning to be explored at the distribution level. These programs utilize a price signal as a means to control demand. Active markets allow customers to respond to fluctuations in wholesale electrical costs, but may not allow the utility to control demand. Transactive markets, utilizing distributed controllers and a centralized auction can be used to create an interactive system which can limit demand at key times on a distribution system, decreasing congestion. With the current proliferation of computing and communication resources, the ability now exists to create transactive demand response programs at the residential level. With the combination of automated bidding and response strategies coupled with education programs and customer response, emerging demand response programs have the ability to reduce utility demand and congestion in a more controlled manner. This paper will explore the effects of a residential double-auction market, utilizing transactive controllers, on the operation of an electric power distribution system.

Fuller, Jason C.; Schneider, Kevin P.; Chassin, David P.

2011-10-10T23:59:59.000Z

425

An automated end-to-end lecture capture and broadcasting system  

Science Conference Proceedings (OSTI)

Remote viewing of lectures presented to a live audience is becoming increasingly popular. At the same time, the lectures can be recorded for subsequent on-demand viewing over the Internet. Providing such services, however, is often prohibitive due to ... Keywords: Automated lecture capture, lecture broadcasting, live/on-demand broadcasting

Cha Zhang; Yong Rui; Jim Crawford; Li-Wei He

2008-01-01T23:59:59.000Z

426

Automating DNA processing  

E-Print Network (OSTI)

Technology is currently available to identify the genetic codes responsible for physical traits and genetic diseases in both plants and animals. Regardless of whether the final goal is medical diagnosis or breeder selection, extensive time and resources must be spent in laboratory research to determine the genetic structure of the relevant organisms. DNA processing is riddled with time intensive laboratory techniques that must be improved or replaced if genotyping large numbers of samples is to be accomplished. This thesis identifies and explains modules in DNA processing and how they can be improved by automation. modules associated with genome mapping are the focus of most of the discussion. A functional biochemistry background is provided so that researchers in automation can be efficiently assimilated to future biochen-fistry/automation projects. The needs of biochemistry researchers at Texas A&M University are specifically addressed. Herein, DNA processing has been defined as a series of discrete sub-processes or process modules in order to aid scheduling of future automation projects. Target process modules (sub-processes with a high probability of automation success) have been identified. In addition, possible automation solutions have been proposed for each target module along with a characterization of fundamental design parameters. Concluding this text is a discussion of procedures in genome mapping that have not been sufficiently automated. The initial focus of this thesis is on short term solutions. However, attention is given to more conceptual solutions accompanied by the biochemistry background necessary to begin developing them. Though systems are proposed to improve the efficiency of many processes, no implementation has been attempted. Design specifications are based on observation of current laboratory techniques and the variance that is typically allowed in relevant process parameters in TAMU laboratories.

Wienen, Michael Jan

1994-01-01T23:59:59.000Z

427

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

428

Browse wiki | Open Energy Information  

Open Energy Info (EERE)

2-4e91-be74-1ecc748de493 OpenEIUtilityRateDemandRateUnit kW + OpenEIUtilityRateName test + OpenEIUtilityRateTieredRateMonth1 1 + OpenEIUtilityRateTieredRateMonth10 1 +...

429

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

430

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

431

HIGHLY AUTOMATED MACROMOLECULAR  

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

AUTOMATED MACROMOLECULAR AUTOMATED MACROMOLECULAR CRYSTALLOGRAPHY BEAMLINE (AMX) Group Leader: Dieter Schneider Proposal Team: M. Allaire 1 , L. Berman 1 , M. Chance 2 , W. Hendrickson 3 , A. Héroux 1 , J. Jakoncic 1 , A. Orville 1 , H. Robinson 1 , D. Schneider 1 , W. Shi 2 , A. Soares 1 , V. Stojanoff 1 , R. Sweet 1 1 Brookhaven National Laboratory, 2 Case Western Reserve University, 3 Columbia University MISSION APPLICATIONS AND CAPABILITIES ADDITIONAL INFORMATION * AMX at NSLS-II will provide structural biologists with ready access to an advanced macromolecular crystallography (MX) beamline for the elucidation of structure and function of macromolecular complexes. * Its high flux, tunable energy, and natively small focal spot will make it a crystallographer's preferred beamline. * Its high degree of automation will provide a high throughput

432

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

433

Automated software engineering: supporting understanding  

Science Conference Proceedings (OSTI)

The most important role for automation in software engineering is the support of human understanding. Some aspects of understanding and how it can be supported are discussed. Keywords: Automation, Complexity, Description, Fragmentation, Manipulation, Mechanisation

Michael Jackson

2008-12-01T23:59:59.000Z

434

Scoping Study for Demand Respose DFT II Project in Morgantown, WV  

Science Conference Proceedings (OSTI)

This scoping study describes the underlying data resources and an analysis tool for a demand response assessment specifically tailored toward the needs of the Modern Grid Initiatives Demonstration Field Test in Phase II in Morgantown, WV. To develop demand response strategies as part of more general distribution automation, automated islanding and feeder reconfiguration schemes, an assessment of the demand response resource potential is required. This report provides the data for the resource assessment for residential customers and describes a tool that allows the analyst to estimate demand response in kW for each hour of the day, by end-use, season, day type (weekday versus weekend) with specific saturation rates of residential appliances valid for the Morgantown, WV area.

Lu, Shuai; Kintner-Meyer, Michael CW

2008-06-06T23:59:59.000Z

435

Automated Transportation Management System (ATMS) | Department...  

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

Waste Management Packaging and Transportation Automated Transportation Management System (ATMS) Automated Transportation Management System (ATMS) The Department of Energy's...

436

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

437

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

438

Demand Response-Ready Technology Capabilities: A Summary of Multi-Stakeholder Workshop and Survey Perspectives  

Science Conference Proceedings (OSTI)

This technical update describes technology capabilities that support more automated and ubiquitous demand response. It begins by describing the Demand Response-Ready (DR-Ready) concept and related industry activities that support realization of the concept. In the DR-Ready vision, consumers receive DR-Ready end-use products at the point of purchase, thus eliminating the need for utility truck service visits to retrofit equipment and significantly reducing the cost of deploying DR-enabling technologies. ...

2012-04-06T23:59:59.000Z

439

Demand Response-Ready Capabilities Roadmap: A Summary of Multi-Stakeholder Workshop and Survey Perspectives  

Science Conference Proceedings (OSTI)

The report describes a high-level roadmap for premise-level migration towards more automated and ubiquitous demand response. It begins by describing the Demand Response Ready (DR-Ready) concept and related industry activities supporting realization of the concept. In the DR-Ready vision, consumers receive DR-Ready end-use products at the point of purchase, thus eliminating the need for utility truck rolls to retrofit equipment, and thereby significantly reducing costs of deploying DR enabling ...

2012-12-31T23:59:59.000Z

440

Portable Automated Mesonet II  

Science Conference Proceedings (OSTI)

The Portable Automated Mesonet II (PAM II) system was developed by NCAR to provide surface mesoscale data for the research needs of the atmospheric science community. The PAM system has 60 remote stations with planned growth to 300. In such a ...

Fred V. Brock; George H. Saum; Steven R. Semmer

1986-12-01T23:59:59.000Z

Note: This page contains sample records for the topic "open automated demand" 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

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

442

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

443

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

444

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

445

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

446

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...

447

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

448

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

449

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

450

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

451

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

452

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

453

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

454

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

455

Assessing the Control Systems Capacity for Demand Response in California Industries  

SciTech Connect

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.

Ghatikar, Girish; McKane, Aimee; Goli, Sasank; Therkelsen, Peter; Olsen, Daniel

2012-01-18T23:59:59.000Z

456

Automated gas chromatography  

DOE Patents (OSTI)

An apparatus and process for the continuous, near real-time monitoring of low-level concentrations of organic compounds in a liquid, and, more particularly, a water stream. A small liquid volume of flow from a liquid process stream containing organic compounds is diverted by an automated process to a heated vaporization capillary where the liquid volume is vaporized to a gas that flows to an automated gas chromatograph separation column to chromatographically separate the organic compounds. Organic compounds are detected and the information transmitted to a control system for use in process control. Concentrations of organic compounds less than one part per million are detected in less than one minute. 7 figs.

Mowry, C.D.; Blair, D.S.; Rodacy, P.J.; Reber, S.D.

1999-07-13T23:59:59.000Z

457

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...

458

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

459

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

460

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

Note: This page contains sample records for the topic "open automated demand" 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 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

462

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

463

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

464

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

465

Lime Energy formerly Electric City Corporation | Open Energy Information  

Open Energy Info (EERE)

Energy formerly Electric City Corporation Energy formerly Electric City Corporation Jump to: navigation, search Name Lime Energy (formerly Electric City Corporation) Place Elk Grove Village, Illinois Zip 60007 Product Developer, manufacturer and integrator of energy savings technologies and building automation systems. Specialist in demand response systems. References Lime Energy (formerly Electric City Corporation)[1] LinkedIn Connections CrunchBase Profile No CrunchBase profile. Create one now! This article is a stub. You can help OpenEI by expanding it. Lime Energy (formerly Electric City Corporation) is a company located in Elk Grove Village, Illinois . References ↑ "Lime Energy (formerly Electric City Corporation)" Retrieved from "http://en.openei.org/w/index.php?title=Lime_Energy_formerly_Electric_City_Corporation&oldid=348375"

466

Automated Centrifugal Chiller Diagnostician - Available ...  

Summary. Researchers and engineers at PNNL have developed an automated, sophisticated, multi-level, real-time centrifugal chiller diagnostician with ...

467

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.

468

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

469

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...

470

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

471

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...

472

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...

473

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...

474

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.

475

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

476

Automated fiber pigtailing machine  

DOE Patents (OSTI)

The Automated Fiber Pigtailing Machine (AFPM) aligns and attaches optical fibers to optoelectronic (OE) devices such as laser diodes, photodiodes, and waveguide devices without operator intervention. The so-called pigtailing process is completed with sub-micron accuracies in less than 3 minutes. The AFPM operates unattended for one hour, is modular in design and is compatible with a mass production manufacturing environment. This machine can be used to build components which are used in military aircraft navigation systems, computer systems, communications systems and in the construction of diagnostics and experimental systems. 26 figs.

Strand, O.T.; Lowry, M.E.

1999-01-05T23:59:59.000Z

477

Automated startup of the MIT research reactor  

SciTech Connect

This summary describes the development, implementation, and testing of a generic method for performing automated startups of nuclear reactors described by space-independent kinetics under conditions of closed-loop digital control. The technique entails first obtaining a reliable estimate of the reactor's initial degree of subcriticality and then substituting that estimate into a model-based control law so as to permit a power increase from subcritical on a demanded trajectory. The estimation of subcriticality is accomplished by application of the perturbed reactivity method. The shutdown reactor is perturbed by the insertion of reactivity at a known rate. Observation of the resulting period permits determination of the initial degree of subcriticality. A major advantage to this method is that repeated estimates are obtained of the same quantity. Hence, statistical methods can be applied to improve the quality of the calculation.

Kwok, K.S. (Massachusetts Inst. of Tech., Cambridge (United States))

1992-01-01T23:59:59.000Z

478

UV LED lighting for automated crystal centring  

E-Print Network (OSTI)

A direct outcome of the exponential growth of macromolecular crystallography is the continuously increasing demand for synchrotron beam time, both from academic and industrial users. As more and more projects entail screening a profusion of sample crystals, fully automated procedures at every level of the experiments are being implemented at all synchrotron facilities. One of the major obstacles to achieving such automation lies in the sample recognition and centring in the X-ray beam. The capacity of UV light to specifically react with aromatic residues present in proteins or with DNA base pairs is at the basis of UV-assisted crystal centring. Although very efficient, a well known side effect of illuminating biological samples with strong UV sources is the damage induced on the irradiated samples. In the present study the effectiveness of a softer UV light for crystal centring by taking advantage of low-power light-emitting diode (LED) sources has been investigated. The use of UV LEDs represents a lowcost solution for crystal centring with high specificity.

Leonard M. G. Chavas; Yusuke Yamada; Masahiko Hiraki; Noriyuki Igarashi; Naohiro Matsugaki; Soichi Wakatsuki

2010-01-01T23:59:59.000Z

479

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

480

Opportunities for Energy Efficiency and Demand Response in the California  

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

Opportunities for Energy Efficiency and Demand Response in the California Opportunities for Energy Efficiency and Demand Response in the California Cement Industry Title Opportunities for Energy Efficiency and Demand Response in the California Cement Industry Publication Type Report LBNL Report Number LBNL-4849E Year of Publication 2010 Authors Olsen, Daniel, Sasank Goli, David Faulkner, and Aimee T. McKane Date Published 12/2010 Publisher CEC/LBNL Keywords cement industry, cement sector, demand response, electricity use, energy efficiency, market sectors, mineral manufacturing, technologies Abstract This study examines the characteristics of cement plants and their ability to shed or shift load to participate in demand response (DR). Relevant factors investigated include the various equipment and processes used to make cement, the operational limitations cement plants are subject to, and the quantities and sources of energy used in the cement-making process. Opportunities for energy efficiency improvements are also reviewed. The results suggest that cement plants are good candidates for DR participation. The cement industry consumes over 400 trillion Btu of energy annually in the United States, and consumes over 150 MW of electricity in California alone. The chemical reactions required to make cement occur only in the cement kiln, and intermediate products are routinely stored between processing stages without negative effects. Cement plants also operate continuously for months at a time between shutdowns, allowing flexibility in operational scheduling. In addition, several examples of cement plants altering their electricity consumption based on utility incentives are discussed. Further study is needed to determine the practical potential for automated demand response (Auto-DR) and to investigate the magnitude and shape of achievable sheds and shifts.

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481

Rockwell Automation - Owens Corning Teaming Profile  

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

Rockwell Automation programmable automation controller to collect data on motor kilowatts, speed, and torque. Project Summary By using real time data collected by the...

482

Building Technologies Office: Automated Home Energy Management...  

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

Home Area Network Trends SmartGridCity(tm) Integration with Home Controls Automated HomeEnergy Management Automated Monitoring, Control, Diagnostics, Optimization and Soft Repair...

483

Characterizing the Response of Commercial and Industrial Facilities to Dynamic Pricing Signals from the Utility  

E-Print Network (OSTI)

2009, “Using Open Automated Demand Response Communicationsin Demand Response for Wholesale Ancillary Services,” Proc.and Techniques for Demand Response,” Lawrence Berkeley

Mathieu, Johanna L.

2010-01-01T23:59:59.000Z

484

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

485

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

486

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

487

Automating the analytical laboratory via the Chemical Analysis Automation paradigm  

Science Conference Proceedings (OSTI)

To address the need for standardization within the analytical chemistry laboratories of the nation, the Chemical Analysis Automation (CAA) program within the US Department of Energy, Office of Science and Technology`s Robotic Technology Development Program is developing laboratory sample analysis systems that will automate the environmental chemical laboratories. The current laboratory automation paradigm consists of islands-of-automation that do not integrate into a system architecture. Thus, today the chemist must perform most aspects of environmental analysis manually using instrumentation that generally cannot communicate with other devices in the laboratory. CAA is working towards a standardized and modular approach to laboratory automation based upon the Standard Analysis Method (SAM) architecture. Each SAM system automates a complete chemical method. The building block of a SAM is known as the Standard Laboratory Module (SLM). The SLM, either hardware or software, automates a subprotocol of an analysis method and can operate as a standalone or as a unit within a SAM. The CAA concept allows the chemist to easily assemble an automated analysis system, from sample extraction through data interpretation, using standardized SLMs without the worry of hardware or software incompatibility or the necessity of generating complicated control programs. A Task Sequence Controller (TSC) software program schedules and monitors the individual tasks to be performed by each SLM configured within a SAM. The chemist interfaces with the operation of the TSC through the Human Computer Interface (HCI), a logical, icon-driven graphical user interface. The CAA paradigm has successfully been applied in automating EPA SW-846 Methods 3541/3620/8081 for the analysis of PCBs in a soil matrix utilizing commercially available equipment in tandem with SLMs constructed by CAA.

Hollen, R.; Rzeszutko, C.

1997-10-01T23:59:59.000Z

488

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

489

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

490

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

491

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

492

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...

493

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

494

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

495

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

496

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

497

Chilled Water Thermal Storage System and Demand Response at the University of California at Merced  

E-Print Network (OSTI)

University of California at Merced is a unique campus that has benefited from intensive efforts to maximize energy efficiency, and has participated in a demand response program for the past two years. Campus demand response evaluations are often difficult because of the complexities introduced by central heating and cooling, non-coincident and diverse building loads, and existence of a single electrical meter for the entire campus. At the University of California at Merced, a two million gallon chilled water storage system is charged daily during off-peak price periods and used to flatten the load profile during peak demand periods, further complicating demand response scenarios. The goal of this research is to study demand response savings in the presence of storage systems in a campus setting. First, University of California at Merced is described and its participation in a demand response event during 2008 is detailed. Second, a set of demand response strategies were pre-programmed into the campus control system to enable semi-automated demand response during a 2009 event, which is also evaluated. Finally, demand savings results are applied to the utility’s DR incentives structure to calculate the financial savings under various DR programs and tariffs.

Granderson, J.; Dudley, J. H.; Kiliccote, S.; Piette, M. A.

2009-11-01T23:59:59.000Z

498

Automated closure system for nuclear reactor fuel assemblies  

DOE Patents (OSTI)

A welder for automated closure of fuel pins by a pulsed magnetic process in which the open end of a length of cladding is positioned within a complementary tube surrounded by a pulsed magnetic welder. Seals are provided at each end of the tube, which can be evacuated or can receive tag gas for direct introduction to the cladding interior. Loading of magnetic rings and end caps is accomplished automatically in conjunction with the welding steps carried out within the tube.

Christiansen, David W. (Kennewick, WA); Brown, William F. (West Richland, WA)

1985-01-01T23:59:59.000Z

499

Robust automated knowledge capture.  

SciTech Connect

This report summarizes research conducted through the Sandia National Laboratories Robust Automated Knowledge Capture Laboratory Directed Research and Development project. The objective of this project was to advance scientific understanding of the influence of individual cognitive attributes on decision making. The project has developed a quantitative model known as RumRunner that has proven effective in predicting the propensity of an individual to shift strategies on the basis of task and experience related parameters. Three separate studies are described which have validated the basic RumRunner model. This work provides a basis for better understanding human decision making in high consequent national security applications, and in particular, the individual characteristics that underlie adaptive thinking.

Stevens-Adams, Susan Marie; Abbott, Robert G.; Forsythe, James Chris; Trumbo, Michael Christopher Stefan; Haass, Michael Joseph; Hendrickson, Stacey M. Langfitt

2011-10-01T23:59:59.000Z

500

Berkeley automated supernova search  

SciTech Connect

The Berkeley automated supernova search employs a computer controlled 36-inch telescope and charge coupled device (CCD) detector to image 2500 galaxies per night. A dedicated minicomputer compares each galaxy image with stored reference data to identify supernovae in real time. The threshold for detection is m/sub v/ = 18.8. We plan to monitor roughly 500 galaxies in Virgo and closer every night, and an additional 6000 galaxies out to 70 Mpc on a three night cycle. This should yield very early detection of several supernovae per year for detailed study, and reliable premaximum detection of roughly 100 supernovae per year for statistical studies. The search should be operational in mid-1982.

Kare, J.T.; Pennypacker, C.R.; Muller, R.A.; Mast, T.S.; Crawford, F.S.; Burns, M.S.

1981-01-01T23:59:59.000Z