National Library of Energy BETA

Sample records for open automated demand

  1. Demand Response and Open Automated Demand Response

    E-Print Network [OSTI]

    LBNL-3047E Demand Response and Open Automated Demand Response Opportunities for Data Centers G described in this report was coordinated by the Demand Response Research Center and funded by the California. Demand Response and Open Automated Demand Response Opportunities for Data Centers. California Energy

  2. Open Automated Demand Response for Small Commerical Buildings

    E-Print Network [OSTI]

    Dudley, June Han

    2009-01-01

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

  3. Open Automated Demand Response Dynamic Pricing Technologies and Demonstration

    E-Print Network [OSTI]

    Ghatikar, Girish

    2010-01-01

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

  4. Northwest Open Automated Demand Response Technology Demonstration Project

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01

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

  5. Analysis of Open Automated Demand Response Deployments in California

    E-Print Network [OSTI]

    LBNL-6560E Analysis of Open Automated Demand Response Deployments in California and Guidelines The work described in this report was coordinated by the Demand Response Research. #12; #12;Abstract This report reviews the Open Automated Demand Response

  6. Linking Continuous Energy Management and Open Automated Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01

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

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2011-01-01

    program, demand  response aggregator, demand response  vii WITH AN AGGREGATOR USING OPEN AUTOMATED DEMAND RESPONSE ThisWith an Aggregator Using Open Automated Demand Response is 

  8. Open Automated Demand Response Technologies for Dynamic Pricing and Smart Grid

    E-Print Network [OSTI]

    Ghatikar, Girish

    2010-01-01

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

  9. Analysis of Open Automated Demand Response Deployments in California and Guidelines to Transition to Industry Standards

    E-Print Network [OSTI]

    Ghatikar, Girish

    2014-01-01

    to  Automated  Demand   Response  and  the  OpenADR  ®  Automated  Demand  Response  Program.   https://2009a.   Open  Automated  Demand  Response  Communications  

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01

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

  11. Analysis of Open Automated Demand Response Deployments in California and Guidelines to Transition to Industry Standards

    E-Print Network [OSTI]

    Ghatikar, Girish

    2014-01-01

    to  Automated  Demand   Response  and  the  OpenADR  ®  Automated  Demand  Response  Program.   https://Data  for  Automated  Demand  Response  in  Commercial  

  12. Automated Price and Demand Response Demonstration for Large Customers in New York City using OpenADR

    E-Print Network [OSTI]

    Kim, Joyce Jihyun

    2014-01-01

    2009. Open Automated Demand Response Communications2010. Open Automated Demand Response Technologies forenergy efficiency and demand response: Framework concepts

  13. Open Automated Demand Response Communications Specification (Version 1.0)

    SciTech Connect (OSTI)

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

    2009-02-28

    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.

  14. Development and Demonstration of the Open Automated Demand Response Standard for the Residential Sector

    E-Print Network [OSTI]

    Herter, Karen

    2014-01-01

    of the Open Automated Demand Response Standard for theOpen Automated Demand Response (OpenADR) Price Schedule Time3.3.2. General Electric Demand Response Module Figure 7. GE’

  15. Open Automated Demand Response Communications Specification (Version 1.0)

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01

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

  16. Automated Demand Response Technologies and Demonstration in New York City using OpenADR

    E-Print Network [OSTI]

    Kim, Joyce Jihyun

    2014-01-01

    C. McParland, "Open Automated Demand Response Communications2011. Utility & Demand Response Programs Energy ProviderAnnual Consumption (kWh) Demand Response Program Curtailment

  17. Open Automated Demand Response for Small Commerical Buildings

    SciTech Connect (OSTI)

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

    2009-05-01

    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.

  18. Linking Continuous Energy Management and Open Automated Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01

    Linking Continuous Energy Management and Open AutomatedKeywords: Continuous Energy Management, Automated Demandlinking continuous energy management and continuous

  19. Linking Continuous Energy Management and Open Automated Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01

    Protocol for Building Automation and Control Networks.existing open building automation and control networkingare in use by the Building Automation and Controls Network (

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01

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

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2011-01-01

    Protocol for Building Automation and Control  Networks.  Protocol for Building Automation and Control  Networks, Demand Response Automation Server  Demand Response Research 

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

    SciTech Connect (OSTI)

    Kiliccote, Sila; Piette, Mary Ann

    2008-10-01

    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.

  3. Northwest Open Automated Demand Response Technology Demonstration Project

    SciTech Connect (OSTI)

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

    2010-03-17

    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.

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

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

  5. Open Automated Demand Response Dynamic Pricing Technologies and Demonstration

    SciTech Connect (OSTI)

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

    2010-08-02

    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.

  6. Northwest Open Automated Demand Response Technology Demonstration Project

    SciTech Connect (OSTI)

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

    2009-08-01

    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.

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

    SciTech Connect (OSTI)

    Kiliccote, Sila; Piette, Mary Ann; Ghatikar, Girish; Koch, Ed; Hennage, Dan; Hernandez, John; Chiu, Albert; Sezgen, Osman; Goodin, John

    2009-11-06

    The Pacific Gas and Electric Company (PG&E) is conducting a pilot program to investigate the technical feasibility of bidding certain demand response (DR) resources into the California Independent System Operator's (CAISO) day-ahead market for ancillary services nonspinning reserve. Three facilities, a retail store, a local government office building, and a bakery, are recruited into the pilot program. For each facility, hourly demand, and load curtailment potential are forecasted two days ahead and submitted to the CAISO the day before the operation as an available resource. These DR resources are optimized against all other generation resources in the CAISO ancillary service. Each facility is equipped with four-second real time telemetry equipment to ensure resource accountability and visibility to CAISO operators. When CAISO requests DR resources, PG&E's OpenADR (Open Automated DR) communications infrastructure is utilized to deliver DR signals to the facilities energy management and control systems (EMCS). The pre-programmed DR strategies are triggered without a human in the loop. This paper describes the automated system architecture and the flow of information to trigger and monitor the performance of the DR events. We outline the DR strategies at each of the participating facilities. At one site a real time electric measurement feedback loop is implemented to assure the delivery of CAISO dispatched demand reductions. Finally, we present results from each of the facilities and discuss findings.

  8. Open Automated Demand Response Technologies for Dynamic Pricing and Smart Grid

    E-Print Network [OSTI]

    Ghatikar, Girish

    2010-01-01

    Protocol for Building Automation and Control Networks. ”existing open building automation and control networkingbuildings using open communications and automation

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

    SciTech Connect (OSTI)

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

    2009-12-30

    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.

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

    E-Print Network [OSTI]

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

    2008-01-01

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

  11. Design and Implementation of an Open, Interoperable AutomatedDemand Response Infrastructure

    SciTech Connect (OSTI)

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

    2007-10-01

    This paper describes the concept for and lessons from the development and field-testing of an open, interoperable communications infrastructure to support automating demand response (DR). Automating DR allows greater levels of participation and improved reliability and repeatability of the demand response and customer facilities. Automated DR systems have been deployed for critical peak pricing and demand bidding and are being designed for real time pricing. The system is designed to generate, manage, and track DR signals between utilities and Independent System Operators (ISOs) to aggregators and end-use customers and their control systems.

  12. Open Automated Demand Response for Small Commerical Buildings

    E-Print Network [OSTI]

    Dudley, June Han

    2009-01-01

    Protocol for Building  Automation and Control Networks.  to small buildings owners to enable automation of DR.   7.0for small buildings owners to  enable automation of DR can 

  13. Linking Continuous Energy Management and Open Automated Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01

    building automation and control networking protocols to facilitate integration of utility/ISO information systems

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

    E-Print Network [OSTI]

    Koch, Ed; Piette, Mary Ann

    2008-01-01

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

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

    SciTech Connect (OSTI)

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

    2009-05-01

    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.

  16. Open Automated Demand Response Technologies for Dynamic Pricing and Smart Grid

    SciTech Connect (OSTI)

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

    2010-06-02

    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.

  17. Analysis of Open Automated Demand Response Deployments in California and Guidelines to Transition to Industry Standards

    SciTech Connect (OSTI)

    Ghatikar, Girish; Riess, David; Piette, Mary Ann

    2014-01-02

    This report reviews the Open Automated Demand Response (OpenADR) deployments within the territories serviced by California?s investor-owned utilities (IOUs) and the transition from the OpenADR 1.0 specification to the formal standard?OpenADR 2.0. As demand response service providers and customers start adopting OpenADR 2.0, it is necessary to ensure that the existing Automated Demand Response (AutoDR) infrastructure investment continues to be useful and takes advantage of the formal standard and its many benefits. This study focused on OpenADR deployments and systems used by the California IOUs and included a summary of the OpenADR deployment from the U.S. Department of Energy-funded demonstration conducted by the Sacramento Municipal Utility District (SMUD). Lawrence Berkeley National Laboratory collected and analyzed data about OpenADR 1.0 deployments, categorized architectures, developed a data model mapping to understand the technical compatibility of each version, and compared the capabilities and features of the two specifications. The findings, for the first time, provided evidence of the total enabled load shed and average first cost for system enablement in the IOU and SMUD service territories. The OpenADR 2.0a profile specification semantically supports AutoDR system architectures and data propagation with a testing and certification program that promotes interoperability, scaled deployments by multiple vendors, and provides additional features that support future services.

  18. Open Automated Demand Response for Small Commerical Buildings

    E-Print Network [OSTI]

    Dudley, June Han

    2009-01-01

    system  Ethernet, Z?wave, Zigbee  OpenADR  Yes  Yes  Yes but seem to support Zigbee.   Model  Vendor  Load Type Comms Interfaces  Ethernet, Zigbee  Standards  Integrate 

  19. Automated Demand Response and Commissioning

    E-Print Network [OSTI]

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

    2005-01-01

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

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2011-01-01

    Protocol for Building Automation and Control  Networks.  Protocol for Building Automation and Control  Networks, Protocol for building Automation and Controls Networks.   

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

    SciTech Connect (OSTI)

    Koch, Ed; Piette, Mary Ann

    2007-10-01

    This paper presents the technical and architectural issues associated with automating Demand Response (DR) programs. The paper focuses on a description of the Demand Response Automation Server (DRAS), which is the main component used to automate the interactions between the Utilities and their customers for DR programs. Use cases are presented that show the role of the DRAS in automating various aspects of DR programs. This paper also describes the various technical aspects of the DRAS including its interfaces and major modes of operation. This includes how the DRAS supports automating such Utility/Customer interactions as automated DR bidding, automated DR event handling, and finally real-time pricing.

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2011-01-01

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

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01

    and data acquisition (SCADA) systems, these automation andPG&E PIER PSRR PUE R&D SAN SCADA SCE SDG&E SI-EER SLA SNMP

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2011-01-01

    and industrial facilities.   The long?term vision is to embed the  automation Industrial/Agricultural/Water End?Use Energy Efficiency  Renewable Energy Technologies  Transportation  The Automation 

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01

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

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

    E-Print Network [OSTI]

    Piette, Mary Ann

    2014-01-01

    of fully automated demand response in large facilities,2009). Open Automated Demand Response CommunicationsOpen Automated Demand Response Technology Demonstration

  7. Development and Demonstration of the Open Automated Demand Response Standard for the Residential Sector

    SciTech Connect (OSTI)

    Herter, Karen; Rasin, Josh; Perry, Tim

    2009-11-30

    The goal of this study was to demonstrate a demand response system that can signal nearly every customer in all sectors through the integration of two widely available and non- proprietary communications technologies--Open Automated Demand Response (OpenADR) over lnternet protocol and Utility Messaging Channel (UMC) over FM radio. The outcomes of this project were as follows: (1) a software bridge to allow translation of pricing signals from OpenADR to UMC; and (2) a portable demonstration unit with an lnternet-connected notebook computer, a portfolio of DR-enabling technologies, and a model home. The demonstration unit provides visitors the opportunity to send electricity-pricing information over the lnternet (through OpenADR and UMC) and then watch as the model appliances and lighting respond to the signals. The integration of OpenADR and UMC completed and demonstrated in this study enables utilities to send hourly or sub-hourly electricity pricing information simultaneously to the residential, commercial and industrial sectors.

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01

    Response for Wholesale Ancillary Services Sila Kiliccote,Response for Wholesale Ancillary Services Sila Kiliccotedemand response, OpenADR, ancillary services Abstract The

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

    SciTech Connect (OSTI)

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

    2009-04-01

    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.

  10. Automated Price and Demand Response Demonstration for Large Customers in New York City using OpenADR

    SciTech Connect (OSTI)

    Kim, Joyce Jihyun; Yin, Rongxin; Kiliccote, Sila

    2013-10-01

    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.

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

    E-Print Network [OSTI]

    Lekov, Alex

    2009-01-01

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

  12. Honeywell Demonstrates Automated Demand Response Benefits for...

    Office of Environmental Management (EM)

    Honeywell Demonstrates Automated Demand Response Benefits for Utility, Commercial, and Industrial Customers Honeywell Demonstrates Automated Demand Response Benefits for Utility,...

  13. Installation and Commissioning Automated Demand Response Systems

    E-Print Network [OSTI]

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

    2008-01-01

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

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

    E-Print Network [OSTI]

    Piette, Mary Ann

    2010-01-01

    such as CEC, EPRI, Building Automation Control Network (is the lack of automation of building systems. Most DRresponse, automation, commercial, industrial buildings, peak

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

    E-Print Network [OSTI]

    McKane, Aimee

    2010-01-01

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

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

    E-Print Network [OSTI]

    Watson, David S.

    2013-01-01

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

  17. Automated Demand Response Opportunities in Wastewater Treatment Facilities

    E-Print Network [OSTI]

    Thompson, Lisa

    2008-01-01

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

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

    E-Print Network [OSTI]

    Piette, Mary Ann

    2010-01-01

    response, automation, commercial, industrial buildings, peakautomation system design. Auto-DR for commercial and industrialautomation server renamed as the DRAS. This server was operated at a secure industrial

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

    E-Print Network [OSTI]

    Koch, Ed; Piette, Mary Ann

    2008-01-01

    of DR signals. Industry Standards - Automated DR consistsinteroperable industry standard control and communicationsthat the standard produced by this industry consortium may

  20. Open Automated Demand Response Technologies for Dynamic Pricing and Smart Grid

    E-Print Network [OSTI]

    Ghatikar, Girish

    2010-01-01

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

  1. Open Automated Demand Response Technologies for Dynamic Pricing and Smart Grid

    E-Print Network [OSTI]

    Ghatikar, Girish

    2010-01-01

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

  2. Automated Demand Response Opportunities in Wastewater Treatment Facilities

    SciTech Connect (OSTI)

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

    2008-11-19

    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.

  3. Results and commissioning issues from an automated demand response pilot

    E-Print Network [OSTI]

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

    2004-01-01

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

  4. 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 (OSTI)

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

    2010-08-20

    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.

  5. Price Responsive Demand in New York Wholesale Electricity Market using OpenADR

    E-Print Network [OSTI]

    Kim, Joyce Jihyun

    2013-01-01

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

  6. Direct versus Facility Centric Load Control for Automated Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2010-01-01

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

  7. Automated Demand Response Strategies and Commissioning Commercial Building Controls

    E-Print Network [OSTI]

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

    2006-01-01

    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"

  8. LEED Demand Response Credit: A Plan for Research towards Implementation

    E-Print Network [OSTI]

    Kiliccote, Sila

    2014-01-01

    C. McParland, Open Automated Demand Response Communicationsand Open Automated Demand Response", Grid Interop Forum,Testing of Automated Demand Response for Integration of

  9. Installation and Commissioning Automated Demand Response Systems

    SciTech Connect (OSTI)

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

    2008-04-21

    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.

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

    nature of the wastewater stream, energy use and demand, asPakenas Energy extraction from municipal effluent streamsthe waste stream also greatly reduces the amount of energy

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

    2005). "Energy Demand in Sludge Dewatering." Water Researchand F. Bloetscher (1999). "Sludge Management, Processing,manufacturers can also use sludge and wastewater generated

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

    E-Print Network [OSTI]

    LBNL-6216E Field Demonstration of Automated Demand Response for Both Winter and Summer Events;1 Field Demonstration of Automated Demand Response for Both Winter and Summer Events in Large Buildings of a series of field test of automated demand response systems in large buildings in the Pacific Northwest

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

    energy efficiency and load management case studies areenergy efficiency and load management purposes can often bein OpenADR and load management require facility operators to

  14. Home Network Technologies and Automating Demand Response

    SciTech Connect (OSTI)

    McParland, Charles

    2009-12-01

    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.

  15. AUTOMATION OF ENERGY DEMAND FORECASTING Sanzad Siddique, B.S.

    E-Print Network [OSTI]

    Povinelli, Richard J.

    AUTOMATION OF ENERGY DEMAND FORECASTING by Sanzad Siddique, B.S. A Thesis submitted to the Faculty OF ENERGY DEMAND FORECASTING Sanzad Siddique, B.S. Marquette University, 2013 Automation of energy demand of the energy demand forecasting are achieved by integrating nonlinear transformations within the models

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

    E-Print Network [OSTI]

    LBNL-5555E Fast Automated Demand Response to Enable the Integration of Renewable Resources David S The work described in this report was coordinated by the Demand Response Research Center and funded ABSTRACT This study examines how fast automated demand response (AutoDR) can help mitigate grid balancing

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

    SciTech Connect (OSTI)

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

    2009-05-11

    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.

  18. Installation and Commissioning Automated Demand Response Systems

    E-Print Network [OSTI]

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

    2008-01-01

    Protocol for Building Automation and Control Networks.EPRI), and C&C Building Automation, Inc. PG&E directlyLBNL hired C&C Building Automation to start transferring

  19. Home Network Technologies and Automating Demand Response

    E-Print Network [OSTI]

    McParland, Charles

    2010-01-01

    LBNL Commercial and Residential Demand Response Overview ofmarket [5]. Residential demand reduction programs have beenin the domain of residential demand response. There are a

  20. A DISTRIBUTED INTELLIGENT AUTOMATED DEMAND RESPONSE BUILDING MANAGEMENT SYSTEM

    SciTech Connect (OSTI)

    Auslander, David; Culler, David; Wright, Paul; Lu, Yan; Piette, Mary

    2013-12-30

    The goal of the 2.5 year Distributed Intelligent Automated Demand Response (DIADR) project was to reduce peak electricity load of Sutardja Dai Hall at UC Berkeley by 30% while maintaining a healthy, comfortable, and productive environment for the occupants. We sought to bring together both central and distributed control to provide “deep” demand response1 at the appliance level of the building as well as typical lighting and HVAC applications. This project brought together Siemens Corporate Research and Siemens Building Technology (the building has a Siemens Apogee Building Automation System (BAS)), Lawrence Berkeley National Laboratory (leveraging their Open Automated Demand Response (openADR), Auto-­Demand Response, and building modeling expertise), and UC Berkeley (related demand response research including distributed wireless control, and grid-­to-­building gateway development). Sutardja Dai Hall houses the Center for Information Technology Research in the Interest of Society (CITRIS), which fosters collaboration among industry and faculty and students of four UC campuses (Berkeley, Davis, Merced, and Santa Cruz). The 141,000 square foot building, occupied in 2009, includes typical office spaces and a nanofabrication laboratory. Heating is provided by a district heating system (steam from campus as a byproduct of the campus cogeneration plant); cooling is provided by one of two chillers: a more typical electric centrifugal compressor chiller designed for the cool months (Nov-­ March) and a steam absorption chiller for use in the warm months (April-­October). Lighting in the open office areas is provided by direct-­indirect luminaries with Building Management System-­based scheduling for open areas, and occupancy sensors for private office areas. For the purposes of this project, we focused on the office portion of the building. Annual energy consumption is approximately 8053 MWh; the office portion is estimated as 1924 MWh. The maximum peak load during the study period was 1175 kW. Several new tools facilitated this work, such as the Smart Energy Box, the distributed load controller or Energy Information Gateway, the web-­based DR controller (dubbed the Central Load-­Shed Coordinator or CLSC), and the Demand Response Capacity Assessment & Operation Assistance Tool (DRCAOT). In addition, an innovative data aggregator called sMAP (simple Measurement and Actuation Profile) allowed data from different sources collected in a compact form and facilitated detailed analysis of the building systems operation. A smart phone application (RAP or Rapid Audit Protocol) facilitated an inventory of the building’s plug loads. Carbon dioxide sensors located in conference rooms and classrooms allowed demand controlled ventilation. The extensive submetering and nimble access to this data provided great insight into the details of the building operation as well as quick diagnostics and analyses of tests. For example, students discovered a short-­cycling chiller, a stuck damper, and a leaking cooling coil in the first field tests. For our final field tests, we were able to see how each zone was affected by the DR strategies (e.g., the offices on the 7th floor grew very warm quickly) and fine-­tune the strategies accordingly.

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

    E-Print Network [OSTI]

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

    2013-01-01

    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...

  2. Summary of the 2006 Automated Demand Response Pilot 

    E-Print Network [OSTI]

    Piette, M.; Kiliccote, S.

    2007-01-01

    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...

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

    SciTech Connect (OSTI)

    Lawrence Berkeley National Laboratory; Kiliccote, Sila

    2011-11-18

    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.

  4. Scenarios for Consuming Standardized Automated Demand Response Signals

    SciTech Connect (OSTI)

    Koch, Ed; Piette, Mary Ann

    2008-10-03

    Automated Demand Response (DR) programs require that Utility/ISO's deliver DR signals to participants via a machine to machine communications channel. Typically these DR signals constitute business logic information (e.g. prices and reliability/shed levels) as opposed to commands to control specific loads in the facility. At some point in the chain from the Utility/ISO to the loads in a facility, the business level information sent by the Utility/ISO must be processed and used to execute a DR strategy for the facility. This paper explores the various scenarios and types of participants that may utilize DR signals from the Utility/ISO. Specifically it explores scenarios ranging from single end user facility, to third party facility managers and DR Aggregators. In each of these scenarios it is pointed out where the DR signal sent from the Utility/ISO is processed and turned into the specific load control commands that are part of a DR strategy for a facility. The information in these signals is discussed. In some cases the DR strategy will be completely embedded in the facility while in others it may be centralized at a third party (e.g. Aggregator) and part of an aggregated set of facilities. This paper also discusses the pros and cons of the various scenarios and discusses how the Utility/ISO can use an open standardized method (e.g. Open Automated Demand Response Communication Standards) for delivering DR signals that will promote interoperability and insure that the widest range of end user facilities can participate in DR programs regardless of which scenario they belong to.

  5. 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]

    Thompson, Lisa

    2010-01-01

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

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

    E-Print Network [OSTI]

    McKane, Aimee

    2010-01-01

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

  7. Measurement and evaluation techniques for automated demand response demonstration

    SciTech Connect (OSTI)

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

    2004-08-01

    The recent electricity crisis in California and elsewhere has prompted new research to evaluate demand response strategies in large facilities. This paper describes an evaluation of fully automated demand response technologies (Auto-DR) in five large facilities. Auto-DR does not involve human intervention, but is initiated at a facility through receipt of an external communications signal. This paper summarizes the measurement and evaluation of the performance of demand response technologies and strategies in five large facilities. All the sites have data trending systems such as energy management and control systems (EMCS) and/or energy information systems (EIS). Additional sub-metering was applied where necessary to evaluate the facility's demand response performance. This paper reviews the control responses during the test period, and analyzes demand savings achieved at each site. Occupant comfort issues are investigated where data are available. This paper discusses methods to estimate demand savings and results from demand response strategies at five large facilities.

  8. Opportunities for Automated Demand Response in California Wastewater Treatment Facilities

    SciTech Connect (OSTI)

    Aghajanzadeh, Arian; Wray, Craig; McKane, Aimee

    2015-08-30

    Previous research over a period of six years has identified wastewater treatment facilities as good candidates for demand response (DR), automated demand response (Auto-­DR), and Energy Efficiency (EE) measures. This report summarizes that work, including the characteristics of wastewater treatment facilities, the nature of the wastewater stream, energy used and demand, as well as details of the wastewater treatment process. It also discusses control systems and automated demand response opportunities. Furthermore, this report summarizes the DR potential of three wastewater treatment facilities. In particular, Lawrence Berkeley National Laboratory (LBNL) has collected data at these facilities from control systems, submetered process equipment, utility electricity demand records, and governmental weather stations. The collected data were then used to generate a summary of wastewater power demand, factors affecting that demand, and demand response capabilities. These case studies show that facilities that have implemented energy efficiency measures and that have centralized control systems are well suited to shed or shift electrical loads in response to financial incentives, utility bill savings, and/or opportunities to enhance reliability of service. In summary, municipal wastewater treatment energy demand in California is large, and energy-­intensive equipment offers significant potential for automated demand response. In particular, large load reductions were achieved by targeting effluent pumps and centrifuges. One of the limiting factors to implementing demand response is the reaction of effluent turbidity to reduced aeration at an earlier stage of the process. Another limiting factor is that cogeneration capabilities of municipal facilities, including existing power purchase agreements and utility receptiveness to purchasing electricity from cogeneration facilities, limit a facility’s potential to participate in other DR activities.

  9. Market and Policy Barriers for Demand Response Providing Ancillary Services in U.S. Markets

    E-Print Network [OSTI]

    Cappers, Peter

    2014-01-01

    Wholesale Electricity Demand Response Program Comparison,J. (2009) Open Automated Demand Response Communicationsin Demand Response for Wholesale Ancillary Services.

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

    SciTech Connect (OSTI)

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

    2011-11-11

    There are growing strains on the electric grid as cooling peaks grow and equipment ages. Increased penetration of renewables on the grid is also straining electricity supply systems and the need for flexible demand is growing. This paper summarizes results of a series of field test of automated demand response systems in large buildings in the Pacific Northwest. The objective of the research was two fold. One objective was to evaluate the use demand response automation technologies. A second objective was to evaluate control strategies that could change the electric load shape in both winter and summer conditions. Winter conditions focused on cold winter mornings, a time when the electric grid is often stressed. The summer test evaluated DR strategies in the afternoon. We found that we could automate both winter and summer control strategies with the open automated demand response communication standard. The buildings were able to provide significant demand response in both winter and summer events.

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

    SciTech Connect (OSTI)

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

    2010-05-14

    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.

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

    E-Print Network [OSTI]

    LBNL-5556E Field Testing of Automated Demand Response for Integration of Renewable Resources responsibility for the accuracy, completeness, or usefulness of any information TCP/IP over CDMA CAISO Utility Aggregator NOC Proprietary Comm. EMS GridLink Loads Interval Meter

  13. 2008-2010 Research Summary: Analysis of Demand Response Opportunities in California Industry

    E-Print Network [OSTI]

    Goli, Sasank

    2013-01-01

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

  14. Intelligent Building Energy Information and Control Systems for Low-Energy Operations and Optimal Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2014-01-01

    Open  Automated  Demand  Response  Communications from  7 Years of Automated Demand Response in Commercial Management and Demand Response in Commercial  Buildings. , 

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

    E-Print Network [OSTI]

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

    2004-01-01

    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

  16. Findings from the 2004 Fully Automated Demand Response Tests in Large

    E-Print Network [OSTI]

    LBNL-58178 Findings from the 2004 Fully Automated Demand Response Tests in Large Facilities M;Findings from the 2004 Fully Automated Demand Response Tests in Large Facilities September 7, 2005 Mary Ann Manager Dave Michel Contract 500-03-026 Sponsored by the California Energy Commission PIER Demand Response

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

    SciTech Connect (OSTI)

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

    2009-08-01

    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.

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

    SciTech Connect (OSTI)

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

    2008-08-01

    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.

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2013-01-01

    and Techniques for Demand Response”, May 2007. LBNL-59975 38the Role of Automated Demand Response, 2010. Watson, D. , N.Fast Automated Demand Response to Enable Integration of

  20. Demand Response Opportunities in Industrial Refrigerated Warehouses in California

    E-Print Network [OSTI]

    Goli, Sasank

    2012-01-01

    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).

  1. Demand Response Research Center and Open Automated Demand Response

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: AlternativeCommunication &20081-929-200499

  2. Price Responsive Demand in New York Wholesale Electricity Market using OpenADR

    SciTech Connect (OSTI)

    Kim, Joyce Jihyun; Kiliccote, Sila

    2012-06-01

    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.

  3. TJ Automation | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION JEnvironmental Jump to:EA EISTJ Automation Jump to: navigation, search Name TJ Automation Facility

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

    SciTech Connect (OSTI)

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

    2011-07-01

    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.

  5. Automated Demand Response Strategies and Commissioning Commercial Building Controls

    E-Print Network [OSTI]

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

    2006-01-01

    efficiency, daily peak load management and demand response.Loads Efficiency, Daily Load Management and Demand ResponseOperations Peak Load Management (Daily) - TOU Savings - Peak

  6. Direct versus Facility Centric Load Control for Automated Demand Response

    SciTech Connect (OSTI)

    Koch, Ed; Piette, Mary Ann

    2009-11-06

    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.

  7. OPPORTUNITIES FOR AUTOMATED DEMAND RESPONSE IN CALIFORNIA’S DAIRY PROCESSING INDUSTRY

    SciTech Connect (OSTI)

    Homan, Gregory K.; Aghajanzadeh, Arian; McKane, Aimee

    2015-08-30

    During periods of peak electrical demand on the energy grid or when there is a shortage of supply, the stability of the grid may be compromised or the cost of supplying electricity may rise dramatically, respectively. Demand response programs are designed to mitigate the severity of these problems and improve reliability by reducing the demand on the grid during such critical times. In 2010, the Demand Response Research Center convened a group of industry experts to suggest potential industries that would be good demand response program candidates for further review. The dairy industry was suggested due to the perception that the industry had suitable flexibility and automatic controls in place. The purpose of this report is to provide an initial description of the industry with regard to demand response potential, specifically automated demand response. This report qualitatively describes the potential for participation in demand response and automated demand response by dairy processing facilities in California, as well as barriers to widespread participation. The report first describes the magnitude, timing, location, purpose, and manner of energy use. Typical process equipment and controls are discussed, as well as common impediments to participation in demand response and automated demand response programs. Two case studies of demand response at dairy facilities in California and across the country are reviewed. Finally, recommendations are made for future research that can enhance the understanding of demand response potential in this industry.

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

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01

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

  9. Demand Response Opportunities and Enabling Technologies for Data Centers: Findings From Field Studies

    E-Print Network [OSTI]

    Ghatikar, Girish

    2014-01-01

    2008. Estimating Demand Response Load Impacts: Evaluation ofK. C. Mares, and D. Shroyer. 2010. Demand Response andOpen Automated Demand Response Opportunities for Data

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

    E-Print Network [OSTI]

    Polany, Rany

    2012-01-01

    Automation . . . . . . . . . . . . . . . . . . . . . . iii 3Automation . . . . . . . . . . . . . . . . . . . . . . 5Dashboard/Cockpit Automation . . . . . . . . . . . . .

  11. OpenADR Open Source Toolkit: Developing Open Source Software for the Smart Grid

    E-Print Network [OSTI]

    McParland, Charles

    2012-01-01

    McParland. “Open Automated Demand Response Communicationshttp://openadr.lbl.gov PIER Demand Response Research Center,Laboratory Abstract—Demand response (DR) is becoming an

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

    SciTech Connect (OSTI)

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

    2011-08-15

    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.

  13. Solar Automation Inc | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Information Serbia-Enhancing Capacity forSilicium deEnergy Information North|SolaireAutomation Inc

  14. Opportunities for Automated Demand Response in Wastewater Treatment

    E-Print Network [OSTI]

    for North Shore Pumping Station shutdowns Cogeneration Plant Active #12;Figure 16: Demand from utility meter, solar generation, and cogeneration during days where cogeneration unit is running throughout the day Figure 17: Cogeneration unit ramp-up profile #12;CHAPTER 7: Conclusions #12;#12;References #12;Glossary

  15. Intelligent Building Automation: A Demand Response Management Perspective 

    E-Print Network [OSTI]

    Qazi, T.

    2010-01-01

    ? 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...

  16. Demonstration and Results of Grid Integrated Technologies at the Demand to Grid Laboratory (D2G Lab): Phase I Operations Report

    E-Print Network [OSTI]

    Ghatikar, Girish

    2014-01-01

    and Techniques for Demand Response. California Energyreleased by LBNL Demand Response Research Center (DRRC) on2009. Open Automated Demand Response Communications

  17. Abstract--Implementation of Distribution Automation (DA) and Demand Side Management (DSM) intended to serve both

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    by the distribution utility for the security. REMPLI (Remote Energy Management over Power Lines and Internet) system of distribution network. They should be monitored and controlled in DA system. The load monitoring and estimationAbstract--Implementation of Distribution Automation (DA) and Demand Side Management (DSM) intended

  18. Transportation Demand Management (TDM) Encyclopedia | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Information Serbia-EnhancingEt Al., 2013)OpenEnergyTrail Canyonsource History ViewCaseInformation

  19. Development and evaluation of fully automated demand response in large facilities

    SciTech Connect (OSTI)

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

    2004-03-30

    This report describes the results of a research project 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 activities to reduce or shift electricity use to improve electric grid reliability, manage electricity costs, and ensure that customers receive signals that encourage load reduction during times when the electric grid is near its capacity. The two main drivers for widespread demand responsiveness are the prevention of future electricity crises and the reduction of electricity prices. Additional goals for price responsiveness include equity through cost of service pricing, and customer control of electricity usage and bills. The technology developed and evaluated in this report could be used to support numerous forms of DR programs and tariffs. For the purpose of this report, we have defined three levels of Demand Response automation. Manual Demand Response involves manually turning off lights or equipment; this can be a labor-intensive approach. Semi-Automated Response involves the use of building energy management control systems for load shedding, where a preprogrammed load shedding strategy is initiated by facilities staff. Fully-Automated Demand Response is initiated at a building or facility through receipt of an external communications signal--facility staff set up a pre-programmed load shedding strategy which is automatically initiated by the system without the need for human intervention. We have defined this approach to be Auto-DR. An important concept in Auto-DR is that a facility manager is able to ''opt out'' or ''override'' an individual DR event if it occurs at a time when the reduction in end-use services is not desirable. This project sought to improve the feasibility and nature of Auto-DR strategies in large facilities. The research focused on technology development, testing, characterization, and evaluation relating to Auto-DR. This evaluation also included the related decisionmaking perspectives of the facility owners and managers. Another goal of this project was to develop and test a real-time signal for automated demand response that provided a common communication infrastructure for diverse facilities. The six facilities recruited for this project were selected from the facilities that received CEC funds for new DR technology during California's 2000-2001 electricity crises (AB970 and SB-5X).

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01

    In cases where a building automation system is notyears. Automation guarantees that each building is operatedstreamline automation by providing guidance on DR building

  1. Open Automated Demand Response Dynamic Pricing Technologies and Demonstration

    E-Print Network [OSTI]

    Ghatikar, Girish

    2010-01-01

    Market); RUC (Residual Unit Commitment); and RTM (Real TimeData Systems Residual Unit Commitment Southern California

  2. Open Automated Demand Response Dynamic Pricing Technologies and Demonstration

    E-Print Network [OSTI]

    Ghatikar, Girish

    2010-01-01

    and Technology Architecture Pricing Technology Architecture and Client Interfaces..Pricing Technology Architecture and Client Interfaces 3.3.

  3. Open Automated Demand Response Communications Specification (Version 1.0)

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01

    105   Use of BACnet Web Services to Exchange DR EventState Protocol (SOAP) and Web services form the basis of the Protocol (SOAP) and Web services form the basis of the 

  4. Northwest Open Automated Demand Response Technology Demonstration Project

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01

    was funded by Bonneville Power Administration and SeattlePam Sporborg (Bonneville Power Administration) David Hsu andfor the Bonneville Power Administration (BPA) in Seattle

  5. Northwest Open Automated Demand Response Technology Demonstration Project

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01

    Funded by: Bonneville Power Administration Contact: Joshuawas funded by Bonneville Power Administration and SeattlePam Sporborg (Bonneville Power Administration) David Hsu and

  6. Linking Continuous Energy Management and Open Automated Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01

    end-uses and whole building energy performance metrics. Theperformance metrics associated with each of the domains. For example, whole-building energy

  7. Northwest Open Automated Demand Response Technology Demonstration Project

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01

    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

  8. Linking Continuous Energy Management and Open Automated Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01

    efficiency, daily peak load management, and DR [2 & 3]. Thisbuilding electric load management concepts and faster scale

  9. Linking Continuous Energy Management and Open Automated Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01

    forthcoming. May 2008. [10] ANSI/ASHRAE 135-2001. BACnet: AJune 2001; REPLACED by ANSI/ASHRAE 135-2004. [11] Rubinstein

  10. Northwest Open Automated Demand Response Technology Demonstration Project

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01

    BPA operates an electricity transmission system and marketsBPA operates an electricity transmission system and marketsof its electricity and the majority of its transmission from

  11. Energy Automation Systems Inc | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTIONRobertsdale, AlabamaETEC GmbH JumpEllenville, NewLtd EIL JumpAutomation Systems Inc Jump to:

  12. Ditec Automation Group | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LISTStar2-0057-EA Jump to: navigation,DepartmentCalculator JumpDitec Automation Group

  13. Simulating an Automated Approach to Discovery and Modeling of Open Source Software Development Processes

    E-Print Network [OSTI]

    Scacchi, Walt

    of automated process discovery and modeling mechanisms that can be applied to Web-based software development projects. Keywords: Automated Process Discovery, Process Modeling and Simulation, Open Source SoftwareSimulating an Automated Approach to Discovery and Modeling of Open Source Software Development

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

    E-Print Network [OSTI]

    Watson, David S.

    2013-01-01

    Water Supply Related Electricity Demand in California. CECbuildings, heating electricity demand is not included incenter-related electricity demand, or 573.4 MW, corresponds

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

    E-Print Network [OSTI]

    Page, Janie

    2012-01-01

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

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

    E-Print Network [OSTI]

    Han, Junqiao

    2008-01-01

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

  17. Development and evaluation of fully automated demand response in large facilities

    E-Print Network [OSTI]

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

    2004-01-01

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

  18. Manz Automation AG | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History View NewTexas:Montezuma,Information MHKMHK5TransportManitouChange | Open EnergyManville,Manz

  19. Opportunities for Automated Demand Response in Wastewater Treatment Facilities in California - Southeast Water Pollution Control Plant Case Study

    SciTech Connect (OSTI)

    Olsen, Daniel; Goli, Sasank; Faulkner, David; McKane, Aimee

    2012-12-20

    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 The average baseline demand at the Southeast facility was approximately 4 MW. During the rainy season (October-March) the facility treated 40% more wastewater than the dry season, but demand only increased by 4%. Submetering of the facility's lift pumps and centrifuges predicted load shifts capabilities of 154 kW and 86 kW, respectively, with large lift pump shifts in the rainy season. Analysis of demand data during maintenance events confirmed the magnitude of these possible load shifts, and indicated other areas of the facility with demand response potential. Load sheds were seen to be possible by shutting down a portion of the facility's aeration trains (average shed of 132 kW). Load shifts were seen to be possible by shifting operation of centrifuges, the gravity belt thickener, lift pumps, and external pump stations These load shifts were made possible by the storage capabilities of the facility and of the city's sewer system. Large load reductions (an average of 2,065 kW) were seen from operating the cogeneration unit, but normal practice is continuous operation, precluding its use for demand response. The study also identified potential demand response opportunities that warrant further study: modulating variable-demand aeration loads, shifting operation of sludge-processing equipment besides centrifuges, and utilizing schedulable self-generation.

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

    E-Print Network [OSTI]

    Watson, David S.

    2013-01-01

    California Energy Demand 2010 2020 Adopted Forecast presentsEnergy Commission, Demand Analysis Office. Ag and Water Pumping Energy Forecasts (

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

    E-Print Network [OSTI]

    McKane, Aimee

    2010-01-01

    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.

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

    E-Print Network [OSTI]

    McKane, Aimee

    2010-01-01

    chain such as daily load management and demand response.more effective load management or a permanent reduction inother actions such as load management and demand response (

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01

    lowered energy use could be significant. Rebound avoidanceenergy savings and scalability needs to be quantified. Summary of Potential Strategy Reboundenergy savings and scalability needs to be quantified. Summary of Potential Strategy Rebound

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01

    weather-dependent loads than other types of data centers. Even within mixed-use data centers, however, the base

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01

    LBNL-1335E. Modius Data Center Infrastructure Manager (and Accenture. 2008. Data Center Energy Forecast. Stanley,Koomey. Four Metrics Define Data Center “Greenness. ” Uptime

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01

    and air conditioning internet protocol independent system operator information technology information technology energy-efficiencyenergy efficiency, development of a certified practitioner program and a joint American Society of Heating, Refrigerating, and Air-conditioning

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01

    to provide for energy and grid security and reliability.allowed to run and grid security and higher energy prices

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01

    directly by the switchgear (i.e. , electric power system),from the grid through the switchgear is first passed through

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01

    and studies of uninterruptible power supply and DR powerdata centers have uninterruptible power supply (UPS) andsystems such as uninterruptible power supplies (UPS),

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01

    Savings from Efficiency Measures..4.1.4. Potential Savings from Efficiency Measures Using theefficiency measures may not recognize that additional savings

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01

    consolidated. 9 Water or air-side economizers can be used inAnalysis for Water or Air-Side Economizers) discusses thisfor water or air-side economizers, power delivery systems,

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01

    cogeneration (natural-gas-fired reciprocating engines or turbines) capabilities integrated with conventional systems.

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01

    and studies of uninterruptible power supply and DR powerdata centers have uninterruptible power supply (UPS) andcontrol protocol uninterruptible power supply variable air

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

    E-Print Network [OSTI]

    Han, Junqiao

    2008-01-01

    operation of the power grid. Keywords Demand Response,ensures stability of the power grid. Auto-DR implementations

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

    E-Print Network [OSTI]

    Watson, David S.

    2013-01-01

    and thus available for irrigation pump-based demand responseeach) and booster pumps used in crop irrigation also make up

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2011-01-01

    event are broadcast by a web services server through  the common signal using a web services client application.   control  systems.    A Web Services software or smart 

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2011-01-01

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

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

    E-Print Network [OSTI]

    McKane, Aimee

    2010-01-01

    National Laboratory ABSTRACT In 2006, the Public Interest Energy ResearchEnergy Research Program Demand Response Research Center (DRRC) at Lawrence Berkeley National Laboratory

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

    E-Print Network [OSTI]

    Lekov, Alex

    2009-01-01

    energy efficiency, load management, and demand response caseenergy efficiency and load management purposes can often bein place controls for load management programs as well as

  20. Strategies for Demand Response in Commercial Buildings

    E-Print Network [OSTI]

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

    2006-01-01

    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

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

    E-Print Network [OSTI]

    Mathieu, Johanna L.

    2012-01-01

    that pre-cool, rebound, or otherwise shift energy use to theexhibit almost no rebound and save some energy on DR days,kW) Rebound (kW) Daily Peak Demand (kW) Daily Energy (kWh) a

  2. EnergySolve Demand Response | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTIONRobertsdale, AlabamaETEC GmbH JumpEllenville, NewLtd EILEnergyInformationEnergySolve Demand

  3. Demand charge schedule data | OpenEI Community

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LISTStar2-0057-EA Jump to: navigation, searchDaimlerDayton Power(Maryland)PvtDemand

  4. Estimating Demand Response Market Potential | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX ECoopButtePowerEdisto Electric Coop,Erosion Flume Jump to: navigation,NewDemand Response

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

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01

    DECC aggregator managed portfolio automated demand responseaggregator designs their own programs, and offers demand responseaggregator is responsible for designing and implementing their own demand response

  6. Simulating an Automated Approach to Discovery and Modeling of Open Source Software Development Processes

    E-Print Network [OSTI]

    Scacchi, Walt

    Process Discovery, Process Modeling and Simulation, Open Source Software Development 1. Introduction to that can more readily facilitate process discovery and modeling. In our approach, we identify the kindsSimulating an Automated Approach to Discovery and Modeling of Open Source Software Development

  7. AUTOMATED ACTIN FILAMENT SEGMENTATION, TRACKING AND TIP ELONGATION MEASUREMENTS BASED ON OPEN ACTIVE CONTOUR MODELS

    E-Print Network [OSTI]

    Huang, Xiaolei

    AUTOMATED ACTIN FILAMENT SEGMENTATION, TRACKING AND TIP ELONGATION MEASUREMENTS BASED ON OPEN use a novel open active contour model for filament segmentation and tracking, which is fast and robust against noise; and (ii) different strategies are proposed to solve the filament intersection problem

  8. Managing water demand as a regulated open MAS. (Work in progress)

    E-Print Network [OSTI]

    Garrido, Antonio

    1 Managing water demand as a regulated open MAS. (Work in progress) Vicente Botti 1 , Antonio Scientific Research Council, {vbotti,agarridot,agiret}@dsic.upv.es, pablo@iiia.csic.es I. WATER MANAGEMENT of water policy is the need to foster a more rational use of the resource and this may be addressed

  9. Managing water demand as a regulated open MAS. (Work in progress)

    E-Print Network [OSTI]

    Garrido, Antonio

    1 Managing water demand as a regulated open MAS. (Work in progress) Vicente Botti1 , Antonio Scientific Research Council, {vbotti,agarridot,agiret}@dsic.upv.es, pablo@iiia.csic.es I. WATER MANAGEMENT of water policy is the need to foster a more rational use of the resource and this may be addressed

  10. Property:OpenEI/UtilityRate/DemandChargePeriod7FAdj | Open Energy...

    Open Energy Info (EERE)

    Jump to: navigation, search This is a property of type Number. Name: Demand Charge Period 7 Fuel Adj Retrieved from "http:en.openei.orgwindex.php?titleProper...

  11. Search for Human Competitive Results in Open Ended Automated Synthesis of a Primordial Mechatronic System

    E-Print Network [OSTI]

    Fernandez, Thomas

    Search for Human Competitive Results in Open Ended Automated Synthesis of a Primordial Mechatronic@ceme.edu.pk ABSTRACT The multi domain nature of a mechatronic system makes it difficult to model using a single-Graph model of the mechatronic system can be directly simulated on a digital computer using simulation

  12. Automated analysis and validation of open chemical data

    E-Print Network [OSTI]

    Day, Nicholas E

    2009-01-13

    Description Framework attributes REST Representational State Transfer RHF Restricted Hartree-Fock RMS Root Mean Squared RSC Royal Society of Chemistry RSS Rich Site Summary RTECS Registry of Toxic Effects of Chemical Substances SAX Simple API for XML SMILES... copyright, patents or other mechanisms of control.[9] A number of Open Data licenses have been developed, including Science Commons[10] and Open Data Commons[11], which data providers can use to tag their data. We now see several different steps being taken...

  13. Property:OpenEI/UtilityRate/FlatDemandMonth7 | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2FlatDemandMonth3 Jump to: navigation, search This is aFlatDemandMonth7 Jump

  14. Property:OpenEI/UtilityRate/FlatDemandMonth8 | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2FlatDemandMonth3 Jump to: navigation, search This is aFlatDemandMonth7

  15. Demand Response Projects: Technical and Market Demonstrations

    E-Print Network [OSTI]

    signal · Reinforces conservation program efforts in areas such as insulation and heat pumps Voluntary DR Commercial & Industrial DR Pilot ­ Internet communication/control of imbedded load control devices on up storage Commercial & Industrial DR Pilot (8 customers)* · Open Automated Demand Response Communication

  16. Demand Responsive Lighting: A Scoping Study

    E-Print Network [OSTI]

    Rubinstein, Francis; Kiliccote, Sila

    2007-01-01

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

  17. Price Responsive Demand in New York Wholesale Electricity Market using OpenADR

    E-Print Network [OSTI]

    Kim, Joyce Jihyun

    2013-01-01

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

  18. Property:OpenEI/UtilityRate/DemandChargePeriod7 | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2 JumpPublicationDate Jump to: navigation,DemandChargePeriod7 Jump to:

  19. Property:OpenEI/UtilityRate/DemandChargePeriod8 | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2 JumpPublicationDate Jump to: navigation,DemandChargePeriod7 Jump

  20. Property:OpenEI/UtilityRate/DemandChargePeriod8FAdj | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2 JumpPublicationDate Jump to: navigation,DemandChargePeriod7

  1. Property:OpenEI/UtilityRate/DemandChargePeriod9 | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2 JumpPublicationDate Jump to: navigation,DemandChargePeriod7 Jump to:

  2. Property:OpenEI/UtilityRate/DemandChargePeriod9FAdj | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2 JumpPublicationDate Jump to: navigation,DemandChargePeriod7 Jump

  3. Property:OpenEI/UtilityRate/DemandRatchetPercentage | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2 JumpPublicationDate Jump to: navigation,DemandChargePeriod7

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

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2 JumpPublicationDate Jump to: navigation,DemandChargePeriod7Information

  5. Property:OpenEI/UtilityRate/DemandWindow | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2 JumpPublicationDate JumpEnergy InformationDemandWindow Jump to:

  6. Property:OpenEI/UtilityRate/EnableDemandCharge | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2 JumpPublicationDate JumpEnergy InformationDemandWindow Jump

  7. Property:OpenEI/UtilityRate/FixedDemandChargeMonth9 | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2 JumpPublicationDateInformationInformation FixedDemandChargeMonth9

  8. Property:OpenEI/UtilityRate/FlatDemandMonth1 | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2 JumpPublicationDateInformationInformationFlatDemandMonth1 Jump to:

  9. Property:OpenEI/UtilityRate/FlatDemandMonth10 | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2 JumpPublicationDateInformationInformationFlatDemandMonth1 Jump

  10. Property:OpenEI/UtilityRate/FlatDemandMonth11 | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2 JumpPublicationDateInformationInformationFlatDemandMonth1 JumpThis

  11. Property:OpenEI/UtilityRate/FlatDemandMonth12 | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2 JumpPublicationDateInformationInformationFlatDemandMonth1

  12. Property:OpenEI/UtilityRate/FlatDemandMonth3 | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2FlatDemandMonth3 Jump to: navigation, search This is a property of type

  13. Property:OpenEI/UtilityRate/FlatDemandMonth4 | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2FlatDemandMonth3 Jump to: navigation, search This is a property of

  14. Property:OpenEI/UtilityRate/FlatDemandMonth5 | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2FlatDemandMonth3 Jump to: navigation, search This is a property

  15. Property:OpenEI/UtilityRate/FlatDemandMonth6 | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2FlatDemandMonth3 Jump to: navigation, search This is a

  16. Property:OpenEI/UtilityRate/FlatDemandMonth9 | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2FlatDemandMonth3 Jump to: navigation, search This is

  17. Property:OpenEI/UtilityRate/FlatDemandStructure/Period | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo,AltFuelVehicle2FlatDemandMonth3 Jump to: navigation, search This isInformation

  18. Automated Demand Response Technologies and Demonstration in New York City using OpenADR

    E-Print Network [OSTI]

    Kim, Joyce Jihyun

    2014-01-01

    system operators, energy suppliers, etc. ) to subscribingor retail third-party energy supplier contract. In NYS, MHPfrom a retail energy supplier. Con Edison’s customers who

  19. Analysis of Open Automated Demand Response Deployments in California and Guidelines to Transition to Industry Standards

    E-Print Network [OSTI]

    Ghatikar, Girish

    2014-01-01

    pub/SmartGrid/SmartGridTestingAndCertificationCommittee/http://www.nist.gov/smartgrid/upload/NIST_Framework_

  20. SmartHG: Energy Demand Aware Open Services for Smart Grid Intelligent Automation

    E-Print Network [OSTI]

    Tronci, Enrico

    : to minimise energy usage and cost for each home, and to support the Distribution Network Operator (DNO) in optimising operation of the Electric Distribution Network (EDN). SmartHG goals are achieved solar panels)], for each time slot (say each hour) the DNO price policy defines an interval of energy

  1. Automated Demand Response Technologies and Demonstration in New York City using OpenADR

    E-Print Network [OSTI]

    Kim, Joyce Jihyun

    2014-01-01

    Powerline Alliance. Smart Energy Pro?le 2.0 Technicalprotocols, like Smart Energy Profile (SEP) intended forprotocols, like Smart Energy Profile (SEP) intended for

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

    E-Print Network [OSTI]

    Piette, Mary Ann

    2010-01-01

    system is built on industry-standard SOA using secure WS.working to advance common industry standards, protocols, andto meet current industry standards for ?nancially binding

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

    E-Print Network [OSTI]

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

    2008-01-01

    receipt of DR signals. Industry Standards - AutoDR consistsinteroperable industry standard control and communications

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

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LISTStar Energy LLC Jump to:Greece: Energy ResourcesCooperativeEnergy Information

  5. Advanced Control Technologies and Strategies Linking Demand Response and Energy Efficiency

    E-Print Network [OSTI]

    Kiliccote, Sila; Piette, Mary Ann

    2005-01-01

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

  6. Risk Management and Combinatorial Optimization for Large-Scale Demand Response and Renewable Energy Integration

    E-Print Network [OSTI]

    Yang, Insoon

    2015-01-01

    results: demand response . . . . . . . . . . . . . . . . . .Institute. “Automated Demand Response Today”. In: (2012). [Energy. “Benefits of demand response in electricity markets

  7. Price Responsive Demand in New York Wholesale Electricity Market using OpenADR

    E-Print Network [OSTI]

    Kim, Joyce Jihyun

    2013-01-01

    and Demand Response in Electricity Markets." University ofDemand Response in Electricity Markets and Recommendationsof Wholesale Electricity Markets for NYC in Summer

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

    SciTech Connect (OSTI)

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

    2004-08-01

    A recent pilot test to enable an Automatic Demand Response system in California has revealed several lessons that are important to consider for a wider application of a regional or statewide Demand Response Program. The six facilities involved in the site testing were from diverse areas of our economy. The test subjects included a major retail food marketer and one of their retail grocery stores, financial services buildings for a major bank, a postal services facility, a federal government office building, a state university site, and ancillary buildings to a pharmaceutical research company. Although these organizations are all serving diverse purposes and customers, they share some underlying common characteristics that make their simultaneous study worthwhile from a market transformation perspective. These are large organizations. Energy efficiency is neither their core business nor are the decision makers who will enable this technology powerful players in their organizations. The management of buildings is perceived to be a small issue for top management and unless something goes wrong, little attention is paid to the building manager's problems. All of these organizations contract out a major part of their technical building operating systems. Control systems and energy management systems are proprietary. Their systems do not easily interact with one another. Management is, with the exception of one site, not electronically or computer literate enough to understand the full dimensions of the technology they have purchased. Despite the research team's development of a simple, straightforward method of informing them about the features of the demand response program, they had significant difficulty enabling their systems to meet the needs of the research. The research team had to step in and work directly with their vendors and contractors at all but one location. All of the participants have volunteered to participate in the study for altruistic reasons, that is, to help find solutions to California's energy problems. They have provided support in workmen, access to sites and vendors, and money to participate. Their efforts have revealed organizational and technical system barriers to the implementation of a wide scale program. This paper examines those barriers and provides possible avenues of approach for a future launch of a regional or statewide Automatic Demand Response Program.

  9. The Impact of Control Technology on the Demand Response Potential of California Industrial Refrigerated Facilities Final Report

    E-Print Network [OSTI]

    Scott, Doug

    2014-01-01

    and Automated Demand Response in Industrial RefrigeratedDemand Response .. ..Technology on the Demand Response Potential of California

  10. Price Responsive Demand in New York Wholesale Electricity Market using OpenADR

    E-Print Network [OSTI]

    Kim, Joyce Jihyun

    2013-01-01

    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."

  11. Test Automation Test Automation

    E-Print Network [OSTI]

    Mousavi, Mohammad

    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

  12. Simplified programming and control of automated radiosynthesizers through unit operations

    E-Print Network [OSTI]

    Claggett, SB; Quinn, KM; Lazari, M; Moore, MD; van Dam, RM

    2013-01-01

    et al. : Simplified programming and control of automatedRM: A new paradigm for programming and controlling automatedOpen Access Simplified programming and control of automated

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

    E-Print Network [OSTI]

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

    2006-01-01

    Figure 2. Demand Response Automation Server and BuildingII system to notify the Automation Server of an up comingoccurs day-ahead). 2. The Automation Server posts two pieces

  14. Analytical Frameworks to Incorporate Demand Response in Long-term Resource Planning

    E-Print Network [OSTI]

    Satchwell, Andrew

    2014-01-01

    Cost- effectiveness of Demand Response. ” Prepared for theon the National Action Plan on Demand Response, February.Role of Automated Demand Response. ” LBNL-4189E, November.

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

    E-Print Network [OSTI]

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

    2006-01-01

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

  16. A Cooperative Demand Response Scheme Using Punishment Mechanism and Application to Industrial Refrigerated Warehouses

    E-Print Network [OSTI]

    Ma, Kai; Hu, Guoqiang; Spanos, Costas J

    2014-01-01

    Z. Yang, and Y. Zhang, “Demand response man- agement withwith application to demand response,” IEEE Transactions onand automated demand response in industrial refrigerated

  17. Interoperable and Secure Communication for Cyber Physical Systems in the Energy Grid

    E-Print Network [OSTI]

    Lee, Eun Kyu

    2014-01-01

    Demand Response . . . . . . . . . . . . . . . . . . . . . .Field Study - Automated Demand Response . . . . . . .Open Automated Demand Response Communications Specification

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

    2007) Installing VFDs on centrifugal pumps is also likely tosavings (Offik 2006). Centrifugal pumps are commonly used inare more efficient for centrifugal pumps compared to control

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

    Anaerobic digestion: Anaerobic digesters break down thefeeders, mixers for anaerobic digesters and blending tanks,are planning to install anaerobic digesters should consider

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

    2005). California's Water-Energy Relationship. CaliforniaThrough Integrated Water-Energy Efficiency Measures,supply, its impact on water energy use in specific regions (

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

    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).

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

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

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

    PG&E PID PIER PLC RTU SCADA SO 3 TSS U.S. UV VFD Pacific GasThe Fundamentals of SCADA. Benyahia, F. , M. Abdulkarim, A.53 Overview of SCADA

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

    Fuller, J. (2003). Energy Efficient Alternative for theAgricultural/Water End-Use Energy Efficiency Program. LycoWastewater Industry Energy Efficiency: A Research Roadmap,

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

    Wastewater flows are also equalized during primary treatment.Primary treatment Secondary treatment Disinfection Advanced wastewaterthe wastewater treatment process. Primary treatment removes

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

    5.3.2. Food Processing - Meat and Poultry The waste stream5.3.3. Food Processing – Dairy High amounts of dairy wasteQuality and Waste Management. 1996. In 1999, food processing

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

    50 Effluent Hydropower- Kilowatt Output as Function of HeadDepartment of Energy (2003). Hydropower Setting a Course forEnergy Commission). Hydropower: Hydropower turbines for low-

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

    energy sources include cogeneration and fuel cells. Onsitedigesters should consider cogeneration as a reliable andhave a potential for cogeneration of less than 1000 kW (

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

    pumps on and off to distribute wastewater, the system now provides a constant flow, using less energy, reducing motor wear

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

    for Wineries- Energy Management East Bay Municipal UtilityWastewater Process Energy, Case Studies: East Bay MunicipalEast Bay Municipal Utilities District Metropolitan Water District Methodology for Analysis of Energy

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

    Winter 4.1.2. Energy Use in the Petroleum Refining Industry2004). Energy Efficiency Roadmap for Petroleum Refineries in35 4.1.2. Energy Use in the Petroleum Refining

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01

    water efficiency, reclaiming and recycling wastewater, or subsidizing the expansion of municipal treatment capacity (Pacific Northwest Pollution

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

    E-Print Network [OSTI]

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

    2007-01-01

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

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

    E-Print Network [OSTI]

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

    2006-01-01

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

  15. Progress toward Producing Demand-Response-Ready Appliances

    SciTech Connect (OSTI)

    Hammerstrom, Donald J.; Sastry, Chellury

    2009-12-01

    This report summarizes several historical and ongoing efforts to make small electrical demand-side devices like home appliances more responsive to the dynamic needs of electric power grids. Whereas the utility community often reserves the word demand response for infrequent 2 to 6 hour curtailments that reduce total electrical system peak load, other beneficial responses and ancillary services that may be provided by responsive electrical demand are of interest. Historically, demand responses from the demand side have been obtained by applying external, retrofitted, controlled switches to existing electrical demand. This report is directed instead toward those manufactured products, including appliances, that are able to provide demand responses as soon as they are purchased and that require few, or no, after-market modifications to make them responsive to needs of power grids. Efforts to be summarized include Open Automated Demand Response, the Association of Home Appliance Manufacturer standard CHA 1, a simple interface being developed by the U-SNAP Alliance, various emerging autonomous responses, and the recent PinBus interface that was developed at Pacific Northwest National Laboratory.

  16. Demand Reduction

    Office of Energy Efficiency and Renewable Energy (EERE)

    Grantees may use funds to coordinate with electricity supply companies and utilities to reduce energy demands on their power systems. These demand reduction programs are usually coordinated through...

  17. Examining Uncertainty in Demand Response Baseline Models and

    E-Print Network [OSTI]

    LBNL-5096E Examining Uncertainty in Demand Response Baseline Models and Variability in Automated of California. #12;Examining Uncertainty in Demand Response Baseline Models and Variability in Automated.e. dynamic prices). Using a regression-based baseline model, we define several Demand Response (DR

  18. FEMP Presents Its Newest On-Demand eTraining Course on Building...

    Office of Environmental Management (EM)

    Presents Its Newest On-Demand eTraining Course on Building Automation Systems FEMP Presents Its Newest On-Demand eTraining Course on Building Automation Systems November 19, 2013 -...

  19. Los Angeles Air Force Base Vehicle to Grid Pilot Project

    E-Print Network [OSTI]

    Marnay, Chris

    2014-01-01

    Akuacom Inc. operated Demand Response Automated Server (the Open Automated Demand Response (OpenADR) protocol. PEVstandard for Automated Demand Response, OpenADR has achieved

  20. Opportunities, Barriers and Actions for Industrial Demand Response in California

    SciTech Connect (OSTI)

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

    2008-01-31

    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.

  1. Automate avec sortie Automates et le temps

    E-Print Network [OSTI]

    Grigoras, .Romulus

    Automate avec sortie Automates et le temps Automates et l'inni Automates avec contrôle auxiliaire Généralisations Philippe Quéinnec 3 janvier 2011 1 / 26 #12;Automate avec sortie Automates et le temps Automates et l'inni Automates avec contrôle auxiliaire Plan 1 Automate avec sortie 2 Automates et le temps 3

  2. Installation and Commissioning Automated Demand Response Systems

    E-Print Network [OSTI]

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

    2008-01-01

    of the Auto-DR architecture and technology. Steps forPG&E Auto-DR Technology Architecture Auto-DR and Technical

  3. Home Network Technologies and Automating Demand Response

    E-Print Network [OSTI]

    McParland, Charles

    2010-01-01

    Prices to Devices'", EPRI Energy Technology Assessment Center [8] Residential Buildings Committee (Northern California

  4. Home Network Technologies and Automating Demand Response

    E-Print Network [OSTI]

    McParland, Charles

    2010-01-01

    C •Security panel HAN Protocols Zigbee Z-wave Insteon Wi-Fiand MAC layer (e.g. ZigBee and 6LowWPAN), interoperabilityApplication Proprietary ZigBee Application / Profile API

  5. Installation and Commissioning Automated Demand Response Systems

    E-Print Network [OSTI]

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

    2008-01-01

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

  6. Distributed Automated Demand Response - Energy Innovation Portal

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantityBonneville Power Administration would like submit theCovalentLaboratory | National(TechnicalNISACDisruptionEnergy Analysis

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

    SciTech Connect (OSTI)

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

    2013-01-01

    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.

  8. Demand Response Opportunities in Industrial Refrigerated Warehouses in California

    SciTech Connect (OSTI)

    Goli, Sasank; McKane, Aimee; Olsen, Daniel

    2011-06-14

    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.

  9. Demand Response Opportunities in Industrial Refrigerated Warehouses in

    E-Print Network [OSTI]

    LBNL-4837E Demand Response Opportunities in Industrial Refrigerated Warehouses in California Sasank thereof or The Regents of the University of California. #12;Demand Response Opportunities in Industrial centralized control systems can be excellent candidates for Automated Demand Response (Auto- DR) due

  10. 2008-2010 Research Summary: Analysis of Demand Response

    E-Print Network [OSTI]

    LBNL-5680E 2008-2010 Research Summary: Analysis of Demand Response Opportunities in California. · #12;· · · 1.1. Role of the Demand Response Research Center · · · · · · #12;Figure 2: Discovery Process Treatment Facility Controls #12;2.1.2. Automated Demand Response Strategies #12;2.1.3. San Luis Rey

  11. Test Automation Ant JUnit Test Automation

    E-Print Network [OSTI]

    Mousavi, Mohammad

    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

  12. 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]

    Thompson, Lisa

    2010-01-01

    There are four anaerobic digesters at the facility, whichis used to warm-up the anaerobic digesters. The San Luis Raywill all the digester gas produced from anaerobic digestion

  13. 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]

    Thompson, Lisa

    2010-01-01

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

  14. 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]

    Thompson, Lisa

    2010-01-01

    Control and Data Acquisition (SCADA) Systems." NCS TechnicalPG&E PID PIER PLC PPA R&D RTU SCADA SDG&E TOU TSS US VFDControl and Data Acquisition (SCADA) system which is capable

  15. 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]

    Thompson, Lisa

    2010-01-01

    including existing power purchase agreements and utilityincluding existing power purchase agreements and utilityincluding existing power purchase agreements and utility

  16. 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]

    Thompson, Lisa

    2010-01-01

    wastewater treatment at San Luis Rey are primary and secondary treatment.primary treatment at the San Luis Rey facility, the wastewater

  17. 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]

    Thompson, Lisa

    2010-01-01

    22 Load Management at the San Luis ReyEnergy efficiency and load management technologies alreadyfor energy efficiency and load management purposes may also

  18. 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]

    Thompson, Lisa

    2010-01-01

    requires that the cogeneration plant produce a minimum ofby SDG&E. The cogeneration plant line is tied directly backunit, so after the cogeneration plant was commissioned, the

  19. InDemandInDemandInDemand Energize Your Career

    E-Print Network [OSTI]

    Wolberg, George

    InDemandInDemandInDemand Energize Your Career You can join the next generation of workers who in Energy #12;#12;In Demand | 1 No, this isn't a quiz...but if you answered yes to any or all and Training Administration wants you to have this publication, In Demand: Careers in Energy. It will let you

  20. Automated Synthesis of Mechanical Vibration Absorbers Using Genetic Programming

    E-Print Network [OSTI]

    Hu, Jianjun

    Automated Synthesis of Mechanical Vibration Absorbers Using Genetic Programming Jianjun Hu Program an automated methodology for open-ended synthesis of mechanical vibration absorbers based on genetic, 48824 Ronald Rosenberg Department of Mechanical Engineering, Michigan State University, East Lansing, MI

  1. VideoonDemandVideoonDemandVideoonDemand Video on Demand Testbed

    E-Print Network [OSTI]

    Eleftheriadis, Alexandros

    VideoonDemandVideoonDemandVideoonDemand Columbia's Video on Demand Testbed and Interoperability Experiment Columbia's Video on Demand Testbed and Interoperability Experiment S.-F. Chang and A Columbia UniversityColumbia University www.www.ctrctr..columbiacolumbia..eduedu/advent/advent #12;VideoonDemandVideoonDemandVideoonDemand

  2. VideoonDemandVideoonDemandVideoonDemand Video on Demand Testbed

    E-Print Network [OSTI]

    Eleftheriadis, Alexandros

    #12;VideoonDemandVideoonDemandVideoonDemand Columbia's Video on Demand Testbed and Interoperability Experiment Columbia's Video on Demand Testbed and Interoperability Experiment H.H. KalvaKalva, A.www.eeee..columbiacolumbia..eduedu/advent/advent #12;VideoonDemandVideoonDemandVideoonDemand VoD Testbed ArchitectureVoD Testbed Architecture Video

  3. Demand Charges | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTIONRobertsdale, Alabama (UtilityInstruments IncMississippi:Delta Electric Power AssnDeluge Inc Jump

  4. Energy Demand | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX ECoopButtePowerEdisto Electric Coop, Inc JumpElko,ServiziEnergy

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

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01

    as energy monitoring, building automation systems and loadhave the necessary building automation systems, it is likely

  6. 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

  7. Advanced Demand Responsive Lighting

    E-Print Network [OSTI]

    Advanced Demand Responsive Lighting Host: Francis Rubinstein Demand Response Research Center demand responsive lighting systems ­ Importance of dimming ­ New wireless controls technologies · Advanced Demand Responsive Lighting (commenced March 2007) #12;Objectives · Provide up-to-date information

  8. Automation Status

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    member August 11, 2011 Automation Status ing NREL Manufacturing R&D Workshop Fuel Cell Proton Exchange Membrane (PEM) and Solid Oxide Fuel Cell (SOFC) Manufacturing Lines and test...

  9. Wireless Demand Response Controls for HVAC Systems

    SciTech Connect (OSTI)

    Federspiel, Clifford

    2009-06-30

    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.

  10. Commercial and Industrial Base Intermittent Resource Management Pilot

    E-Print Network [OSTI]

    Kiliccote, Sila

    2011-01-01

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

  11. Automated Critical Peak Pricing Field Tests: Program Descriptionand Results

    SciTech Connect (OSTI)

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

    2006-04-06

    California utilities have been exploring the use of critical peak prices (CPP) to help reduce needle peaks in customer end-use loads. CPP is a form of price-responsive demand response (DR). Recent experience has shown that customers have limited knowledge of how to operate their facilities in order to reduce their electricity costs under CPP (Quantum 2004). While the lack of knowledge about how to develop and implement DR control strategies is a barrier to participation in DR programs like CPP, another barrier is the lack of automation of DR systems. During 2003 and 2004, the PIER Demand Response Research Center (DRRC) conducted a series of tests of fully automated electric demand response (Auto-DR) at 18 facilities. Overall, the average of the site-specific average coincident demand reductions was 8% from a variety of building types and facilities. Many electricity customers have suggested that automation will help them institutionalize their electric demand savings and improve their overall response and DR repeatability. This report focuses on and discusses the specific results of the Automated Critical Peak Pricing (Auto-CPP, a specific type of Auto-DR) tests that took place during 2005, which build on the automated demand response (Auto-DR) research conducted through PIER and the DRRC in 2003 and 2004. The long-term goal of this project is to understand the technical opportunities of automating demand response and to remove technical and market impediments to large-scale implementation of automated demand response (Auto-DR) in buildings and industry. A second goal of this research is to understand and identify best practices for DR strategies and opportunities. The specific objectives of the Automated Critical Peak Pricing test were as follows: (1) Demonstrate how an automated notification system for critical peak pricing can be used in large commercial facilities for demand response (DR). (2) Evaluate effectiveness of such a system. (3) Determine how customers will respond to this form of automation for CPP. (4) Evaluate what type of DR shifting and shedding strategies can be automated. (5) Explore how automation of control strategies can increase participation rates and DR saving levels with CPP. (6) Identify optimal demand response control strategies. (7) Determine occupant and tenant response.

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

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01

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

  13. Variability in Automated Responses of Commercial Buildings and Industrial

    E-Print Network [OSTI]

    LBNL-5129E Variability in Automated Responses of Commercial Buildings and Industrial Facilities;Variability in Automated Responses of Commercial Buildings and Industrial Facilities to Dynamic Electricity consumption of commercial buildings and industrial facilities (C&I facilities) during Demand Response (DR

  14. Demand Response Valuation Frameworks Paper

    E-Print Network [OSTI]

    Heffner, Grayson

    2010-01-01

    benefits of Demand Side Management (DSM) are insufficient toefficiency, demand side management (DSM) cost effectivenessResearch Center Demand Side Management Demand Side Resources

  15. Participation through Automation: Fully Automated Critical PeakPricing in Commercial Buildings

    SciTech Connect (OSTI)

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

    2006-06-20

    California electric utilities have been exploring the use of dynamic critical peak prices (CPP) and other demand response programs to help reduce peaks in customer electric loads. CPP is a tariff design to promote demand response. Levels of automation in DR can be defined as follows: Manual Demand Response involves a potentially 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. They refer to this as Auto-DR. This paper describes the development, testing, and results from automated CPP (Auto-CPP) as part of a utility project in California. The paper presents the project description and test methodology. This is followed by a discussion of Auto-DR strategies used in the field test buildings. They present a sample Auto-CPP load shape case study, and a selection of the Auto-CPP response data from September 29, 2005. If all twelve sites reached their maximum saving simultaneously, a total of approximately 2 MW of DR is available from these twelve sites that represent about two million ft{sup 2}. The average DR was about half that value, at about 1 MW. These savings translate to about 0.5 to 1.0 W/ft{sup 2} of demand reduction. They are continuing field demonstrations and economic evaluations to pursue increasing penetrations of automated DR that has demonstrated ability to provide a valuable DR resource for California.

  16. Residential Demand Sector Data, Commercial Demand Sector Data, Industrial Demand Sector Data - Annual Energy Outlook 2006

    SciTech Connect (OSTI)

    2009-01-18

    Tables describing consumption and prices by sector and census division for 2006 - includes residential demand, commercial demand, and industrial demand

  17. DEMAND INTERPROCEDURAL PROGRAM ANALYSIS

    E-Print Network [OSTI]

    Reps, Thomas W.

    1 DEMAND INTERPROCEDURAL PROGRAM ANALYSIS USING LOGIC DATABASES Thomas W. Reps Computer Sciences@cs.wisc.edu ABSTRACT This paper describes how algorithms for demand versions of inerprocedural program­ analysis for all elements of the program. This paper concerns the solution of demand versions of interprocedural

  18. Capacity Demand Power (GW)

    E-Print Network [OSTI]

    California at Davis, University of

    Capacity Demand Power (GW) Hour of the Day The "Dip" Electricity Demand in Electricity Demand Every weekday, Japan's electricity use dips about 6 GW at 12 but it also shows that: · Behavior affects naHonal electricity use in unexpected ways

  19. Demand Response Assessment INTRODUCTION

    E-Print Network [OSTI]

    Demand Response Assessment INTRODUCTION This appendix provides more detail on some of the topics raised in Chapter 4, "Demand Response" of the body of the Plan. These topics include 1. The features, advantages and disadvantages of the main options for stimulating demand response (price mechanisms

  20. Demand Response Spinning Reserve Demonstration

    E-Print Network [OSTI]

    2007-01-01

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

  1. Demand Response Programs for Oregon

    E-Print Network [OSTI]

    Demand Response Programs for Oregon Utilities Public Utility Commission May 2003 Public Utility ....................................................................................................................... 1 Types of Demand Response Programs............................................................................ 3 Demand Response Programs in Oregon

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

    E-Print Network [OSTI]

    Lewis, Glen

    2010-01-01

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

  3. Building operating systems services: An architecture for programmable buildings.

    E-Print Network [OSTI]

    Dawson-Haggerty, Stephen

    2014-01-01

    interoperable automated demand response infrastructure. InMcParland. Open automated demand response communicationsof-use pricing, and demand response. In an electric grid

  4. Enabling Efficient, Responsive, and Resilient Buildings: Collaboration Between the United States and India

    E-Print Network [OSTI]

    Basu, Chandrayee

    2014-01-01

    Fast Automated Demand Response to Enable the Integration ofCallaway, S. Kiliccote. “Demand Response Providing Ancillaryand Open Automated Demand Response. ” Grid Interop Forum.

  5. Deploying Systems Interoperability and Customer Choice within Smart Grid

    E-Print Network [OSTI]

    Ghatikar, Girish

    2014-01-01

    National Laboratory’s Demand Response Research Center (2009a). “Open Automated Demand Response CommunicationsInteroperable Automated Demand Response Infrastructure for

  6. Exponential Demand Simulation Tool

    E-Print Network [OSTI]

    Reed, Derek D.

    2015-05-15

    Operant behavioral economics investigates the relation between environmental constraint and reinforcer consumption. The standard approach to quantifying this relation is through the use of behavioral economic demand curves. ...

  7. Managing Increased Charging Demand

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Managing Increased Charging Demand Carrie Giles ICF International, Supporting the Workplace Charging Challenge Workplace Charging Challenge Do you already own an EV? Are you...

  8. Automated Demand Response Opportunities in Wastewater Treatment Facilities

    E-Print Network [OSTI]

    Thompson, Lisa

    2008-01-01

    generators running on anaerobic digester gas, a byproduct ofcover that stores anaerobic digester gas until it can be

  9. Direct versus Facility Centric Load Control for Automated Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2010-01-01

    is being developed by the Zigbee/Homeplug alliance. It hasautomation including BACnet, Zigbee (including Smart EnergyOASIS, UCAIug, NAESB, and the Zigbee/Homeplug alliance, just

  10. Measurement and evaluation techniques for automated demand response demonstration

    E-Print Network [OSTI]

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

    2004-01-01

    Study on Energy Efficiency in Buildings. American CouncilStudy on Energy Efficiency in Buildings. American CouncilSummer Study on Energy Efficiency in Buildings: Breaking out

  11. Automated Demand Response Opportunities in Wastewater Treatment Facilities

    E-Print Network [OSTI]

    Thompson, Lisa

    2008-01-01

    > ARC Advisory Group, SCADA Market for Water & Wastewater toand Data Acquisition (SCADA) systems in wastewater treatmenttreatment facilities, SCADA systems direct when to operate

  12. Direct versus Facility Centric Load Control for Automated Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2010-01-01

    http://www.multispeak.org) Smart Energy Profile Marketingstandard fashion (i.e. Smart Energy Profile - SEP). Inaspects of FCLC and DLC. Smart Energy Profile (SEP) versions

  13. Automated Demand Response Opportunities in Wastewater Treatment Facilities

    E-Print Network [OSTI]

    Thompson, Lisa

    2008-01-01

    has implemented a load management strategy which includes aand proven as successful load management strategies, similar

  14. Honeywell Demonstrates Automated Demand Response Benefits for Utility,

    Broader source: Energy.gov (indexed) [DOE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergy A plug-inPPLforLDRD Report11,SecurityHome solar systems can saveCommercial, and

  15. Electrical Demand Management 

    E-Print Network [OSTI]

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

    1983-01-01

    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...

  16. Multiplex automated genome engineering

    DOE Patents [OSTI]

    Church, George M; Wang, Harris H; Isaacs, Farren J

    2013-10-29

    The present invention relates to automated methods of introducing multiple nucleic acid sequences into one or more target cells.

  17. Demand Dispatch-Intelligent

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    such as wind, solar, and electric vehicles as well as dispatchable loads and microgrids. Many of these resources will be "behind-the-meter" (i.e., demand resources) and...

  18. Demand and Price Uncertainty: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2013-01-01

    World crude oil and natural gas: a demand and supply model.analysis of the demand for oil in the Middle East. EnergyEstimates elasticity of demand for crude oil, not gasoline.

  19. Demand and Price Volatility: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2011-01-01

    World crude oil and natural gas: a demand and supply model.analysis of the demand for oil in the Middle East. EnergyEstimates elasticity of demand for crude oil, not gasoline.

  20. Demand and Price Volatility: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2011-01-01

    H. , and James M. Gri¢ n. 1983. Gasoline demand in the OECDof dynamic demand for gasoline. Journal of Econometrics 77(An empirical analysis of gasoline demand in Denmark using

  1. Demand and Price Volatility: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2011-01-01

    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

  2. Turkey opens electricity markets as demand grows

    SciTech Connect (OSTI)

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

    2009-06-15

    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.

  3. DemandDirect | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTIONRobertsdale, Alabama (UtilityInstruments IncMississippi:Delta Electric Power AssnDeluge

  4. Demand Management Institute (DMI) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LISTStar2-0057-EA Jump to: navigation, searchDaimlerDayton Power(Maryland)Pvt

  5. Joint Genome Institute's Automation Approach and History

    E-Print Network [OSTI]

    Roberts, Simon

    2006-01-01

    Joint Genome Institute’s Automation Approach and Historythroughput environment; – automation does not necessarilyissues “Islands of Automation” – modular instruments with

  6. A National Forum on Demand Response: What Remains to Be Done...

    Office of Environmental Management (EM)

    needs are changing as additional opportunities have opened up in wholesale and retail electricity markets for demand response resources. The working group focused on two key...

  7. AiiDA: Automated Interactive Infrastructure and Database for Computational Science

    E-Print Network [OSTI]

    Pizzi, Giovanni; Sabatini, Riccardo; Marzari, Nicola; Kozinsky, Boris

    2015-01-01

    Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing, and discuss its implementation in the open-source AiiDA platform (http://www.aiida.net). The platform is tuned first to the demands of computational materials science: coupling remote management with automatic data generation; ensuring provenance, preservation, and searchability of heterogeneous data through a design based on directed acyclic graphs; encoding complex sequences of low-level codes into scientific w...

  8. Demand and Price Uncertainty: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2013-01-01

    Sterner. 1991. Analysing gasoline demand elasticities: A2011. Measuring global gasoline and diesel price and incomeMutairi. 1995. Demand for gasoline in Kuwait: An empirical

  9. Demand Response Valuation Frameworks Paper

    E-Print Network [OSTI]

    Heffner, Grayson

    2010-01-01

    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

  10. Optimal Demand Response Libin Jiang

    E-Print Network [OSTI]

    Optimal Demand Response Libin Jiang Steven Low Computing + Math Sciences Electrical Engineering Caltech Oct 2011 #12;Outline Caltech smart grid research Optimal demand response #12;Global trends 1

  11. ENERGY DEMAND FORECAST METHODS REPORT

    E-Print Network [OSTI]

    ....................................................................................................1-16 Energy Consumption Data...............................................1-15 Data Sources for Energy Demand Forecasting ModelsCALIFORNIA ENERGY COMMISSION ENERGY DEMAND FORECAST METHODS REPORT Companion Report

  12. Estimating a Demand System with Nonnegativity Constraints: Mexican Meat Demand

    E-Print Network [OSTI]

    Carlini, David

    Estimating a Demand System with Nonnegativity Constraints: Mexican Meat Demand Amos Golan* Jeffrey an almost ideal demand system for five types of meat using cross-sectional data from Mexico, where most households did not buy at least one type of meat during the survey week. The system of demands is shown

  13. Peer-Assisted On-Demand Streaming: Characterizing Demands and

    E-Print Network [OSTI]

    Li, Baochun

    Peer-Assisted On-Demand Streaming: Characterizing Demands and Optimizing Supplies Fangming Liu Abstract--Nowadays, there has been significant deployment of peer-assisted on-demand streaming services over the Internet. Two of the most unique and salient features in a peer-assisted on-demand streaming

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

    SciTech Connect (OSTI)

    2012-02-11

    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.

  15. Evaluation of Representative Smart Grid Investment Project Technologies: Demand Response

    SciTech Connect (OSTI)

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

    2012-02-14

    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.

  16. Energy Demand Staff Scientist

    E-Print Network [OSTI]

    Eisen, Michael

    #12;Sources: China National Bureau of Statistics; U.S. Energy Information Administration, Annual Energy Outlook. Overview:Overview: Energy Use in China and the U.S.Energy Use in China and the U.S. 5 0Energy Demand in China Lynn Price Staff Scientist February 2, 2010 #12;Founded in 1988 Focused

  17. Copyright (c) Cem Kaner, Automated Testing. 1 Software Test Automation:Software Test Automation

    E-Print Network [OSTI]

    Copyright (c) Cem Kaner, Automated Testing. 1 Software Test Automation:Software Test Automation: A RealA Real--World ProblemWorld Problem Cem Kaner, Ph.D., J.D. #12;Copyright (c) Cem Kaner, Automated Testing. 2 This TalkThis Talk The most widely used class of automated testing tools leads senior software

  18. Architectures of Test Automation 1 High Volume Test AutomationHigh Volume Test Automation

    E-Print Network [OSTI]

    Architectures of Test Automation 1 High Volume Test AutomationHigh Volume Test Automation Cem Kaner Institute of Technology October 2003 #12;Architectures of Test Automation 2 Acknowledgements developed a course on test automation architecture, and in the Los Altos Workshops on Software Testing

  19. High Volume Test Automation 1 High Volume Test AutomationHigh Volume Test Automation

    E-Print Network [OSTI]

    High Volume Test Automation 1 High Volume Test AutomationHigh Volume Test Automation Keynote Automation 2 AcknowledgementsAcknowledgements · Many of the ideas in this presentation were initially jointly developed with Doug Hoffman,as we developed a course on test automation architecture, and in the Los Altos

  20. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

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

    2008-01-01

    fraction of residential and commercial demands, leading16 Residential electricity demand endspecific residential electricity demands into electricity

  1. Demand Forecast INTRODUCTION AND SUMMARY

    E-Print Network [OSTI]

    Demand Forecast INTRODUCTION AND SUMMARY A 20-year forecast of electricity demand is a required in electricity demand is, of course, crucial to determining the need for new electricity resources and helping of any forecast of electricity demand and developing ways to reduce the risk of planning errors

  2. Analysis of Residential Demand Response and Double-Auction Markets

    SciTech Connect (OSTI)

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

    2011-10-10

    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.

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

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01

    retail regulatory authority prohibit such activity. Demand response integration into US wholesale power marketsretail or wholesale level. 17 While demand response began participating at scale in wholesale power markets

  4. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    3-4 Table 4-1. How Building Automation Systems (i.e. , EMCS)are regularly used for building automation and temperaturepurpose versions of a building automation system (BAS). 1

  5. Demand Dispatch-Intelligent

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantityBonneville Power Administration would like submit theCovalent Bonding Low-Cost2DepartmentDelta Dental Claim Form PDF iconDemand

  6. RF test bench automation Description

    E-Print Network [OSTI]

    Dobigeon, Nicolas

    RF test bench automation Description: Callisto would like to implement automated RF test bench. Three RF test benches have to be studied and automated: LNA noise temperature test bench LNA gain phase of the test benches and an implementation of the automation phase. Tasks: Noise temperature

  7. Automation of Feynman Diagram Evaluation

    E-Print Network [OSTI]

    M. Tentyukov; J. Fleischer

    1998-02-04

    A C-program DIANA (DIagram ANAlyser) for the automation of Feynman diagram evaluations is presented.

  8. Revelation on Demand Nicolas Anciaux

    E-Print Network [OSTI]

    Revelation on Demand Nicolas Anciaux 1 · Mehdi Benzine1,2 · Luc Bouganim1 · Philippe Pucheral1 "revelation on demand". Keywords: Confidentiality and privacy, Secure device, Data warehousing, Indexing model

  9. by popular demand: Addiction II

    E-Print Network [OSTI]

    Niv, Yael

    by popular demand: Addiction II PSY/NEU338:Animal learning and decision making: Psychological, size of other non-drug rewards, and cost (but ultimately the demand is inelastic, or at least

  10. Demand Response: Load Management Programs 

    E-Print Network [OSTI]

    Simon, J.

    2012-01-01

    Management Programs CATEE Conference October, 2012 Agenda Outline I. General Demand Response Definition II. General Demand Response Program Rules III. CenterPoint Commercial Program IV. CenterPoint Residential Programs V. Residential Discussion... Points Demand Response Definition of load management per energy efficiency rule 25.181: ? Load control activities that result in a reduction in peak demand, or a shifting of energy usage from a peak to an off-peak period or from high-price periods...

  11. Energy Efficiency, Demand Response, and Volttron

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    r a a v ave com BAS - BUILDING AUTOMATION SYSTEMS Small and Medium Size Building Automation and Control System Needs: Scoping Study Michael Brambley - April 2, 2013 T...

  12. Chord on Demand Alberto Montresor

    E-Print Network [OSTI]

    Jelasity, Márk

    Chord on Demand Alberto Montresor University of Bologna, Italy montresor@cs.unibo.it M´ark Jelasity to solve a specific task on demand. We introduce T- CHORD, that can build a Chord network efficiently to solve a specific task on demand. Existing join protocols are not designed to handle the massive

  13. Supply Chain Supernetworks Random Demands

    E-Print Network [OSTI]

    Nagurney, Anna

    Supply Chain Supernetworks with Random Demands June Dong and Ding Zhang Department of Marketing of three tiers of decision-makers: the manufacturers, the distributors, and the retailers, with the demands equilibrium model with electronic commerce and with random demands for which modeling, qualitative analysis

  14. Chord on Demand Alberto Montresor

    E-Print Network [OSTI]

    Chord on Demand Alberto Montresor University of Bologna, Italy montresor@cs.unibo.it Mark Jelasity to solve a specific task on demand. We introduce T- CHORD, that can build a Chord network efficiently on demand. Existing join protocols are not designed to handle the massive concurrency involved in a jump

  15. ERCOT Demand Response Paul Wattles

    E-Print Network [OSTI]

    Mohsenian-Rad, Hamed

    ERCOT Demand Response Paul Wattles Senior Analyst, Market Design & Development, ERCOT Whitacre;Definitions of Demand Response · `The short-term adjustment of energy use by consumers in response to price to market or reliability conditions.' (NAESB) #12;Definitions of Demand Response · The common threads

  16. Assessment of Demand Response Resource

    E-Print Network [OSTI]

    Assessment of Demand Response Resource Potentials for PGE and Pacific Power Prepared for: Portland January 15, 2004 K:\\Projects\\2003-53 (PGE,PC) Assess Demand Response\\Report\\Revised Report_011504.doc #12;#12;quantec Assessment of Demand Response Resource Potentials for I-1 PGE and Pacific Power I. Introduction

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

    SciTech Connect (OSTI)

    Lu, Shuai; Kintner-Meyer, Michael CW

    2008-06-06

    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.

  18. Automation of Diagrammatic Reasoning 

    E-Print Network [OSTI]

    Jamnik, Mateja; Bundy, Alan; Green, Ian

    1997-01-01

    Theorems in automated theorem proving are usually proved by logical formal proofs. However, there is a subset of problems which humans can prove in a different way by the use of geometric operations on diagrams, so called diagrammatic proofs...

  19. Automated Lattice Perturbation Theory

    SciTech Connect (OSTI)

    Monahan, Christopher

    2014-11-01

    I review recent developments in automated lattice perturbation theory. Starting with an overview of lattice perturbation theory, I focus on the three automation packages currently "on the market": HiPPy/HPsrc, Pastor and PhySyCAl. I highlight some recent applications of these methods, particularly in B physics. In the final section I briefly discuss the related, but distinct, approach of numerical stochastic perturbation theory.

  20. ON-DEMAND SERIAL DILUTION USING QUANTIZED NANO/PICOLITER-SCALE DROPLETS

    SciTech Connect (OSTI)

    Jambovane, Sachin R.; Prost, Spencer A.; Sheen, Allison M.; Magnuson, Jon K.; Kelly, Ryan T.

    2014-10-29

    This paper describes a fully automated droplet-based microfluidic device for on-demand serial dilution that is capable of achieving a dilution ratio of >6000 (concentration ranges from 1 mM to 160nM) over 35 nanoliter-scale droplets. This serial diluter can be applied to high throughput and label-free kinetic assays by integrating with our previously developed on-demand droplet-based microfluidic with mass spectrometry detection.

  1. LABORATORY EQUIPMENT Most of the work at the Automation and Control Institute is done with the

    E-Print Network [OSTI]

    15 4 LABORATORY EQUIPMENT Most of the work at the Automation and Control Institute is done-time software applications, too. Figure 1. Distillation process. A semi-industrial scale binary (ethanol with MetsoDNA open automation system. When testing more sophisticated control algorithms the process data

  2. Automated Continuous Commissioning of Commercial Buildings

    E-Print Network [OSTI]

    Bailey, Trevor

    2013-01-01

    Engineers BACnet: Building Automation and Control Networksprotocol for building automation and control networks. It iscommunication of building automation and control systems for

  3. Development of Building Automation and Control Systems

    E-Print Network [OSTI]

    Yang, Yang; Zhu, Qi; Maasoumy, Mehdi; Sangiovanni-Vincentelli, Alberto

    2012-01-01

    A design flow for building automation and control systems,’’Development of Building Automation and Control Systems Yanga design flow for building automation systems that focuses

  4. Development of Building Automation and Control Systems

    E-Print Network [OSTI]

    Yang, Yang; Zhu, Qi; Maasoumy, Mehdi; Sangiovanni-Vincentelli, Alberto

    2012-01-01

    A design flow for building automation and control systems,’’Development of Building Automation and Control Systems Yangdesign of the build- ing automation system (including the

  5. Demand Response Programs, 6. edition

    SciTech Connect (OSTI)

    2007-10-15

    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.

  6. Generating Demand for Multifamily Building Upgrades | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Demand for Multifamily Building Upgrades Generating Demand for Multifamily Building Upgrades Better Buildings Residential Network Peer Exchange Call Series: Generating Demand for...

  7. China's Coal: Demand, Constraints, and Externalities

    E-Print Network [OSTI]

    Aden, Nathaniel

    2010-01-01

    raising transportation oil demand. Growing internationalcoal by wire could reduce oil demand by stemming coal roadEastern oil production. The rapid growth of coal demand

  8. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    of Energy demand-side management energy information systemdemand response. Demand-side management (DSM) program goalsa goal for demand-side management (DSM) coordination and

  9. Demand Responsive Lighting: A Scoping Study

    E-Print Network [OSTI]

    Rubinstein, Francis; Kiliccote, Sila

    2007-01-01

    3 2.1 Demand-Side Managementbuildings. The demand side management framework is discussedIssues 2.1 Demand-Side Management Framework Forecasting

  10. Coupling Renewable Energy Supply with Deferrable Demand

    E-Print Network [OSTI]

    Papavasiliou, Anthony

    2011-01-01

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

  11. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

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

  12. Hawaiian Electric Company Demand Response Roadmap Project

    E-Print Network [OSTI]

    Levy, Roger

    2014-01-01

    Like HECO actual utility demand response implementations canindustry-wide utility demand response applications tend toobjective. Figure 4. Demand Response Objectives 17  

  13. Retail Demand Response in Southwest Power Pool

    E-Print Network [OSTI]

    Bharvirkar, Ranjit

    2009-01-01

    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

  14. Demand Response - Policy | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Coordination of Energy Efficiency and Demand Response Demand Response in U.S. Electricity Markets: Empirical Evidence 2009 Retail Demand Response in Southwest Power Pool (January...

  15. Demand Response as a System Reliability Resource

    E-Print Network [OSTI]

    Joseph, Eto

    2014-01-01

    Barat, and D. Watson. 2007. Demand Response Spinning ReserveKueck, and B. Kirby. 2009. Demand Response Spinning Reserveand B. Kirby. 2012. The Demand Response Spinning Reserve

  16. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

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

    2008-01-01

    duty fuel demand in alternate scenarios. ..for light-duty fuel demand in alternate scenarios. Minimum52 Heavy-duty vehicle fuel demand for each alternate

  17. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

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

    2008-01-01

    2006-2016: Staff energy demand forecast (Revised SeptemberCEC (2005b) Energy demand forecast methods report.California energy demand 2003-2013 forecast. California

  18. Assessing the Control Systems Capacity for Demand Response in California Industries

    SciTech Connect (OSTI)

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

    2012-01-18

    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.

  19. Mirle Automation Corporation | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo, Maryland: Energy ResourcesDec 2005Minnehaha County,EnergyII Geothermal PowerMirle

  20. Brooks Automation Inc | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LISTStar Energy LLC JumpBiossence JumpJerseyEconomyBridgerNational Laboratory Jump

  1. Meikle Automation Inc | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX ECoop Inc Jump to: navigation, searchScotlandRecentchangestext JumpEnergy,Meikle

  2. Automation Alley Technology Center | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION JEnvironmental Jump to:EAandAmminex AAustria Geothermal Region Jump to: navigation,AutaugaAlley

  3. AIS Automation Dresden | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION JEnvironmental Jump to:EAand Dalton JumpProgram |RecentSulfonateAFVAGO AG Energie AnlagenAIS

  4. home automation | OpenEI Community

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX ECoop IncIowa (UtilityMichigan)data book Homefuel efficiencyhacking Homehipster

  5. OpenEI Community - home automation

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIXsourceII JumpQuarterly Smart Grid Data available for download onst,/0 enBig

  6. Demand Response Technology Roadmap A

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    meetings and workshops convened to develop content for the Demand Response Technology Roadmap. The project team has developed this companion document in the interest of providing...

  7. Design Automation Challenges in Automotive CPS Sayan Mitra

    E-Print Network [OSTI]

    Rajkumar, Ragunathan "Raj"

    Design Automation Challenges in Automotive CPS Sayan Mitra mitras@illinois.edu In principle, best theorem proving. Unfortunately, a stan- dardized open repository of benchmarks for automotive CPS-up companies, in which each play a role and the automotive CPS community flourishes. A good benchmark

  8. Automated Transportation Logistics and Analysis System (ATLAS...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Packaging and Transportation Automated Transportation Logistics and Analysis System (ATLAS) Automated Transportation Logistics and Analysis System (ATLAS) The Department of...

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

    SciTech Connect (OSTI)

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

    2009-06-28

    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.

  10. Supply Chain Supernetworks With Random Demands

    E-Print Network [OSTI]

    Nagurney, Anna

    Supply Chain Supernetworks With Random Demands June Dong Ding Zhang School of Business State Field Warehouses: stocking points Customers, demand centers sinks Production/ purchase costs Inventory Customer Demand Customer Demand Retailer OrdersRetailer Orders Distributor OrdersDistributor Orders

  11. Automated gas chromatography

    DOE Patents [OSTI]

    Mowry, Curtis D. (Albuquerque, NM); Blair, Dianna S. (Albuquerque, NM); Rodacy, Philip J. (Albuquerque, NM); Reber, Stephen D. (Corrales, NM)

    1999-01-01

    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.

  12. Marketing & Driving Demand Collaborative - Social Media Tools...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    & Driving Demand Collaborative - Social Media Tools & Strategies Marketing & Driving Demand Collaborative - Social Media Tools & Strategies Presentation slides from the Better...

  13. Retail Demand Response in Southwest Power Pool

    E-Print Network [OSTI]

    Bharvirkar, Ranjit

    2009-01-01

    Data Collection for Demand-side Management for QualifyingPrepared by Demand-side Management Task Force of the

  14. Effects of the drought on California electricity supply and demand

    E-Print Network [OSTI]

    Benenson, P.

    2010-01-01

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

  15. Demand Response for Ancillary Services

    SciTech Connect (OSTI)

    Alkadi, Nasr E; Starke, Michael R

    2013-01-01

    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.

  16. Demand and Price Volatility: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2011-01-01

    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

  17. Demand and Price Uncertainty: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2013-01-01

    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

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

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01

    electricity. In this manner, demand side management is directly integrated into the wholesale capacity marketcapacity market U.S. Federal Energy Regulatory Commission Florida Reliability Coordinating Council incremental auctions independent electricity

  19. Demand and Price Uncertainty: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2013-01-01

    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-

  20. California Baseline Energy Demands to 2050 for Advanced Energy Pathways

    E-Print Network [OSTI]

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

    2008-01-01

    demands. Residential and commercial demand has a significantDemand by Sector Residential Peak Demand (MW) Commercialwe convert residential electricity demand based upon climate

  1. Demand Response for Ancillary Services

    Broader source: Energy.gov [DOE]

    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 implement a methodology to construct detailed temporal and spatial representations of demand response resources and 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 assess economic value of the realizable potential of demand response for ancillary services.

  2. Physically-based demand modeling 

    E-Print Network [OSTI]

    Calloway, Terry Marshall

    1980-01-01

    nts on the demand. Of course the demand of a real a1r cond1t1oner has lower and upper bounds equal to 0 and 0 , respec- u tively. A constra1ned system can be simulated numerically, but there 1s no explicit system response formula s1m11ar... sect1on. It may now be instruct1ve to relate this model to that of Jones and Bri ce [5] . The average demand pred1 cted by their model is the expected value of the product of a load response factor 0 and a U sw1tching process H(t), which depends...

  3. The Pacific Northwest Demand Response Market Demonstration

    SciTech Connect (OSTI)

    Chassin, David P.; Hammerstrom, Donald J.; DeSteese, John G.

    2008-07-20

    This paper describes the implementation and results of a field demonstration wherein residential electric water heaters and thermostats, commercial building space conditioning, municipal water pump loads, and several distributed generators were coordinated to manage constrained feeder electrical distribution through the two-way communication of load status and electric price signals. The field demonstration took place in Washington and Oregon and was paid for by the U.S. Department of Energy and several northwest utilities. Price is found to be an effective control signal for managing transmission or distribution congestion. Real-time signals at 5-minute intervals are shown to shift controlled load in time. The behaviors of customers and their responses under fixed, time-ofuse, and real-time price contracts are compared. Peak loads are effectively reduced on the experimental feeder. A novel application of portfolio theory is applied to the selection of an optimal mix of customer contract types. Index Terms—demand response, power markets, retail markets, distribution automation, distributed resources, load control.

  4. Seasonality in air transportation demand

    E-Print Network [OSTI]

    Reichard Megwinoff, H?tor Nicolas

    1988-01-01

    This thesis investigates the seasonality of demand in air transportation. It presents three methods for computing seasonal indices. One of these methods, the Periodic Average Method, is selected as the most appropriate for ...

  5. Demand response enabling technology development

    E-Print Network [OSTI]

    2006-01-01

    Monitoring in an Agent-Based Smart Home, Proceedings of theConference on Smart Homes and Health Telematics, September,Smart Meter Motion sensors Figure 1: Schematic of the Demand Response Electrical Appliance Manager in a Home.

  6. Full Rank Rational Demand Systems

    E-Print Network [OSTI]

    LaFrance, Jeffrey T; Pope, Rulon D.

    2006-01-01

    Dover Publications 1972. Barnett, W.A. and Y.W. Lee. “TheEconometrica 53 (1985): 1421- Barnett, W.A. , Lee, Y.W. ,Laurent demand systems (Barnett and Lee 1985; Barnett, Lee,

  7. Residential Demand Module - NEMS Documentation

    Reports and Publications (EIA)

    2014-01-01

    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.

  8. Marketing Demand-Side Management 

    E-Print Network [OSTI]

    O'Neill, M. L.

    1988-01-01

    Demand-Side Management is an organizational tool that has proven successful in various realms of the ever changing business world in the past few years. It combines the multi-faceted desires of the customers with the increasingly important...

  9. Demand Response Spinning Reserve Demonstration

    SciTech Connect (OSTI)

    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.

    2007-05-01

    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.

  10. Automated gas chromatography

    DOE Patents [OSTI]

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

    1999-07-13

    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.

  11. Automated macromolecular crystallization screening

    DOE Patents [OSTI]

    Segelke, Brent W.; Rupp, Bernhard; Krupka, Heike I.

    2005-03-01

    An automated macromolecular crystallization screening system wherein a multiplicity of reagent mixes are produced. A multiplicity of analysis plates is produced utilizing the reagent mixes combined with a sample. The analysis plates are incubated to promote growth of crystals. Images of the crystals are made. The images are analyzed with regard to suitability of the crystals for analysis by x-ray crystallography. A design of reagent mixes is produced based upon the expected suitability of the crystals for analysis by x-ray crystallography. A second multiplicity of mixes of the reagent components is produced utilizing the design and a second multiplicity of reagent mixes is used for a second round of automated macromolecular crystallization screening. In one embodiment the multiplicity of reagent mixes are produced by a random selection of reagent components.

  12. Automated Job Hazards Analysis

    Broader source: Energy.gov [DOE]

    AJHA Program - The Automated Job Hazard Analysis (AJHA) computer program is part of an enhanced work planning process employed at the Department of Energy's Hanford worksite. The AJHA system is routinely used to performed evaluations for medium and high risk work, and in the development of corrective maintenance work packages at the site. The tool is designed to ensure that workers are fully involved in identifying the hazards, requirements, and controls associated with tasks.

  13. Optimal Demand Response and Power Flow

    E-Print Network [OSTI]

    Willett, Rebecca

    Optimal Demand Response and Power Flow Steven Low Computing + Math Sciences Electrical Engineering #12;Outline Optimal demand response n With L. Chen, L. Jiang, N. Li Optimal power flow n With S. Bose;Optimal demand response Model Results n Uncorrelated demand: distributed alg n Correlated demand

  14. Methodology for Prototyping Increased Levels of Automation

    E-Print Network [OSTI]

    Valasek, John

    Methodology for Prototyping Increased Levels of Automation for Spacecraft Rendezvous Functions of automation than previous NASA vehicles, due to program requirements for automation, including Automated Ren authority between humans and computers (i.e. automation) as a prime driver for cost, safety, and mission

  15. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

    relatively high economic/demographic growth, relatively low electricity and natural gas rates REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 1: Statewide Electricity Demand Bill Junker Manager DEMAND ANALYSIS OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS

  16. CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST

    E-Print Network [OSTI]

    high economic/demographic growth, relatively low electricity and natural gas rates, and relatively low CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST Volume 2: Electricity Demand Manager DEMAND ANALYSIS OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION

  17. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

    relatively high economic/demographic growth, relatively low electricity and natural gas rates CALIFORNIA ENERGY DEMAND 2014­2024 FINAL FORECAST Volume 1: Statewide Electricity Demand Gough Office Manager DEMAND ANALYSIS OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS

  18. Demand Response as a System Reliability Resource

    E-Print Network [OSTI]

    Joseph, Eto

    2014-01-01

    Barat, and D. Watson. 2007. Demand Response Spinning ReserveKueck, and B. Kirby. 2009. Demand Response Spinning ReserveFormat of 2009-2011 Demand Response Activity Applications.

  19. Exponential Communication Ine ciency of Demand Queries

    E-Print Network [OSTI]

    Sandholm, Tuomas W.

    FORECAST COMBINATION IN REVENUE MANAGEMENT DEMAND FORECASTING SILVIA RIEDEL A thesissubmitted Combination in RevenueManagement Demand Forecasting Abstract The domain of multi level forecastcombination

  20. Generating Demand for Multifamily Building Upgrades | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Generating Demand for Multifamily Building Upgrades Generating Demand for Multifamily Building Upgrades Better Buildings Residential Network Peer Exchange Call Series: Generating...

  1. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    demand response: ? Distribution utility ? ISO ? Aggregator (demand response less obstructive and inconvenient for the customer (particularly if DR resources are aggregated by a load aggregator).

  2. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

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

    2008-01-01

    annual per-capita electricity consumption by demand15 California electricity consumption projections by demandannual per-capita electricity consumption by demand

  3. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

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

  4. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    Energy Efficiency, Demand Response, and Peak Load Managementdemand response, and load management programs in the Ebefore they undertake load management and demand response

  5. Supply chain planning decisions under demand uncertainty

    E-Print Network [OSTI]

    Huang, Yanfeng Anna

    2008-01-01

    Sales and operational planning that incorporates unconstrained demand forecasts has been expected to improve long term corporate profitability. Companies are considering such unconstrained demand forecasts in their decisions ...

  6. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    > B-2 Coordination of Energy Efficiency and Demand Response> B-4 Coordination of Energy Efficiency and Demand Responseand integration is: Energy efficiency, energy conservation,

  7. Generating Demand for Multifamily Building Upgrades | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Generating Demand for Multifamily Building Upgrades Generating Demand for Multifamily Building Upgrades May 14, 2015 12:30PM to 2:00PM EDT Learn more...

  8. Demand Response Programs Oregon Public Utility Commission

    E-Print Network [OSTI]

    Demand Response Programs Oregon Public Utility Commission January 6, 2005 Mike Koszalka Director;Demand Response Results, 2004 Load Control ­ Cool Keeper ­ ID Irrigation Load Control Price Responsive

  9. Turkey's energy demand and supply

    SciTech Connect (OSTI)

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

    2009-07-01

    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.

  10. International Oil Supplies and Demands

    SciTech Connect (OSTI)

    Not Available

    1992-04-01

    The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--1990 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world's dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single view of the likely future path for oil prices. The model results guided the group's thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.

  11. International Oil Supplies and Demands

    SciTech Connect (OSTI)

    Not Available

    1991-09-01

    The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--90 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world's dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single view of the likely future path for oil prices. The model results guided the group's thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.

  12. Integrated, Automated Distributed Generation Technologies Demonstration

    SciTech Connect (OSTI)

    Jensen, Kevin

    2014-09-30

    The purpose of the NETL Project was to develop a diverse combination of distributed renewable generation technologies and controls and demonstrate how the renewable generation could help manage substation peak demand at the ATK Promontory plant site. The Promontory plant site is located in the northwestern Utah desert approximately 25 miles west of Brigham City, Utah. The plant encompasses 20,000 acres and has over 500 buildings. The ATK Promontory plant primarily manufactures solid propellant rocket motors for both commercial and government launch systems. The original project objectives focused on distributed generation; a 100 kW (kilowatt) wind turbine, a 100 kW new technology waste heat generation unit, a 500 kW energy storage system, and an intelligent system-wide automation system to monitor and control the renewable energy devices then release the stored energy during the peak demand time. The original goal was to reduce peak demand from the electrical utility company, Rocky Mountain Power (RMP), by 3.4%. For a period of time we also sought to integrate our energy storage requirements with a flywheel storage system (500 kW) proposed for the Promontory/RMP Substation. Ultimately the flywheel storage system could not meet our project timetable, so the storage requirement was switched to a battery storage system (300 kW.) A secondary objective was to design/install a bi-directional customer/utility gateway application for real-time visibility and communications between RMP, and ATK. This objective was not achieved because of technical issues with RMP, ATK Information Technology Department’s stringent requirements based on being a rocket motor manufacturing facility, and budget constraints. Of the original objectives, the following were achieved: • Installation of a 100 kW wind turbine. • Installation of a 300 kW battery storage system. • Integrated control system installed to offset electrical demand by releasing stored energy from renewable sources during peak hours of the day. Control system also monitors the wind turbine and battery storage system health, power output, and issues critical alarms. Of the original objectives, the following were not achieved: • 100 kW new technology waste heat generation unit. • Bi-directional customer/utility gateway for real time visibility and communications between RMP and ATK. • 3.4% reduction in peak demand. 1.7% reduction in peak demand was realized instead.

  13. Demand Response and Energy Efficiency 

    E-Print Network [OSTI]

    2009-01-01

    stream_source_info ESL-IC-09-11-05.pdf.txt stream_content_type text/plain stream_size 14615 Content-Encoding ISO-8859-1 stream_name ESL-IC-09-11-05.pdf.txt Content-Type text/plain; charset=ISO-8859-1 Demand Response... 4 An Innovative Solution to Get the Ball Rolling ? Demand Response (DR) ? Monitoring Based Commissioning (MBCx) EnerNOC has a solution involving two complementary offerings. ESL-IC-09-11-05 Proceedings of the Ninth International Conference...

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

    SciTech Connect (OSTI)

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

    2009-10-08

    The 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. This makes demand response more subtle and challenges typical evaluation protocols. 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 summer electric loads are characterized; second, its participation in two demand response events is detailed. In each event a set of strategies were pre-programmed into the campus control system to enable semi-automated response. Finally, demand savings results are applied to the utility's DR incentives structure to calculate the financial savings under various DR programs and tariffs. A key conclusion to this research is that there is significant demand reduction using a zone temperature set point change event with the full off peak storage cooling in use.

  15. Analyzing Software Evolvability of an Industrial Automation Control System: A Case Study

    E-Print Network [OSTI]

    Becker, Steffen

    ], demands for distributed development, system migration to product line architecture, etc. The evolution a product line. We present our experiences of the development of the product line architecture in the formAnalyzing Software Evolvability of an Industrial Automation Control System: A Case Study Hongyu Pei

  16. Automated fiber pigtailing machine

    DOE Patents [OSTI]

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

    1999-01-05

    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.

  17. Automated fiber pigtailing machine

    DOE Patents [OSTI]

    Strand, Oliver T. (Castro Valley, CA); Lowry, Mark E. (Castro Valley, CA)

    1999-01-01

    The Automated Fiber Pigtailing Machine (AFPM) aligns and attaches optical fibers to optoelectonic (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.

  18. Beyond Leaks: Demand-side Strategies for Improving Compressed Air Efficiency 

    E-Print Network [OSTI]

    Howe, B.; Scales, B.

    1997-01-01

    stream_source_info ESL-IE-97-04-27.pdf.txt stream_content_type text/plain stream_size 18135 Content-Encoding ISO-8859-1 stream_name ESL-IE-97-04-27.pdf.txt Content-Type text/plain; charset=ISO-8859-1 Beyond Leaks: Demand.... Adding sensors to detect when compressed air is required, and then automating how the compressed air is applied, can dramatically reduce both compressed air use and peak demand. COMPRESSED AIR DELIVERY AND CONTROL How compressed air is delivered...

  19. Revelation on Demand Nicolas Anciaux

    E-Print Network [OSTI]

    is willing to reveal the aggregate response (according to his company's policy) to the customer dataRevelation on Demand Nicolas Anciaux 1 · Mehdi Benzine1,2 · Luc Bouganim1 · Philippe Pucheral1 time to support epidemiological studies. In these and many other situations, aggregate data or partial

  20. Demand Response Providing Ancillary Services

    E-Print Network [OSTI]

    1 Demand Response Providing Ancillary Services: A Comparison of Opportunities and Challenges in US to operate (likely price takers) ­ Statistical reliability (property of large aggregations of small resources size based on Mid-Atlantic Reserve Zone #12;Market Rules: Resource Size Min. Size (MW) Aggregation

  1. Projecting Electricity Demand in 2050

    SciTech Connect (OSTI)

    Hostick, Donna J.; Belzer, David B.; Hadley, Stanton W.; Markel, Tony; Marnay, Chris; Kintner-Meyer, Michael CW

    2014-07-01

    This paper describes the development of end-use electricity projections and load curves that were developed for the Renewable Electricity (RE) Futures Study (hereafter RE Futures), which explored the prospect of higher percentages (30% ? 90%) of total electricity generation that could be supplied by renewable sources in the United States. As input to RE Futures, two projections of electricity demand were produced representing reasonable upper and lower bounds of electricity demand out to 2050. The electric sector models used in RE Futures required underlying load profiles, so RE Futures also produced load profile data in two formats: 8760 hourly data for the year 2050 for the GridView model, and in 2-year increments for 17 time slices as input to the Regional Energy Deployment System (ReEDS) model. The process for developing demand projections and load profiles involved three steps: discussion regarding the scenario approach and general assumptions, literature reviews to determine readily available data, and development of the demand curves and load profiles.

  2. Water demand management in Kuwait

    E-Print Network [OSTI]

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

    2006-01-01

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

  3. On-demand data broadcasting 

    E-Print Network [OSTI]

    Kothandaraman, Kannan

    1998-01-01

    related to on-demand data broadcasting. We look at the problem of data broadcasting in an environment where clients make explicit requests to the server. The server broadcasts requested data items to all the clients, including those who have not requested...

  4. Promising Technology: Demand Control Ventilation

    Broader source: Energy.gov [DOE]

    Demand control ventilation (DCV) measures carbon dioxide concentrations in return air or other strategies to measure occupancy, and accurately matches the ventilation requirement. This system reduces ventilation when spaces are vacant or at lower than peak occupancy. When ventilation is reduced, energy savings are accrued because it is not necessary to heat, cool, or dehumidify as much outside air.

  5. Laboratory Testing of Demand-Response Enabled Household Appliances

    SciTech Connect (OSTI)

    Sparn, B.; Jin, X.; Earle, L.

    2013-10-01

    With the advent of the Advanced Metering Infrastructure (AMI) systems capable of two-way communications between the utility's grid and the building, there has been significant effort in the Automated Home Energy Management (AHEM) industry to develop capabilities that allow residential building systems to respond to utility demand events by temporarily reducing their electricity usage. Major appliance manufacturers are following suit by developing Home Area Network (HAN)-tied appliance suites that can take signals from the home's 'smart meter,' a.k.a. AMI meter, and adjust their run cycles accordingly. There are numerous strategies that can be employed by household appliances to respond to demand-side management opportunities, and they could result in substantial reductions in electricity bills for the residents depending on the pricing structures used by the utilities to incent these types of responses. The first step to quantifying these end effects is to test these systems and their responses in simulated demand-response (DR) conditions while monitoring energy use and overall system performance.

  6. Laboratory Testing of Demand-Response Enabled Household Appliances

    SciTech Connect (OSTI)

    Sparn, B.; Jin, X.; Earle, L.

    2013-10-01

    With the advent of the Advanced Metering Infrastructure (AMI) systems capable of two-way communications between the utility's grid and the building, there has been significant effort in the Automated Home Energy Management (AHEM) industry to develop capabilities that allow residential building systems to respond to utility demand events by temporarily reducing their electricity usage. Major appliance manufacturers are following suit by developing Home Area Network (HAN)-tied appliance suites that can take signals from the home's 'smart meter,' a.k.a. AMI meter, and adjust their run cycles accordingly. There are numerous strategies that can be employed by household appliances to respond to demand-side management opportunities, and they could result in substantial reductions in electricity bills for the residents depending on the pricing structures used by the utilities to incent these types of responses.The first step to quantifying these end effects is to test these systems and their responses in simulated demand-response (DR) conditions while monitoring energy use and overall system performance.

  7. Effects of the drought on California electricity supply and demand

    E-Print Network [OSTI]

    Benenson, P.

    2010-01-01

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

  8. Opportunities, Barriers and Actions for Industrial Demand Response in California

    E-Print Network [OSTI]

    McKane, Aimee T.

    2009-01-01

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

  9. Incorporating Demand Response into Western Interconnection Transmission Planning

    E-Print Network [OSTI]

    Satchwell, Andrew

    2014-01-01

    Aggregator Programs. Demand Response Measurement andIncorporating Demand Response into Western Interconnection13 Demand Response Dispatch

  10. Upply Chain Supernetworks with Random Demands

    E-Print Network [OSTI]

    Nagurney, Anna

    Upply Chain Supernetworks with Random Demands June Dong & Ding Zhang School of Business State Warehouses: stocking points Field Warehouses: stocking points Customers, demand centers sinks Production Commerce and Value Chain Management, 1998 Customer Demand Customer Demand Retailer OrdersRetailer Orders

  11. Assessment of Demand Response and Advanced Metering

    E-Print Network [OSTI]

    Tesfatsion, Leigh

    #12;#12;2008 Assessment of Demand Response and Advanced Metering Staff Report Federal Energy metering penetration and potential peak load reduction from demand response have increased since 2006. Significant activity to promote demand response or to remove barriers to demand response occurred at the state

  12. The alchemy of demand response: turning demand into supply

    SciTech Connect (OSTI)

    Rochlin, Cliff

    2009-11-15

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

  13. Addressing Energy Demand through Demand Response. International Experiences and Practices

    SciTech Connect (OSTI)

    Shen, Bo; Ghatikar, Girish; Ni, Chun Chun; Dudley, Junqiao; Martin, Phil; Wikler, Greg

    2012-06-01

    Demand response (DR) is a load management tool which provides a cost-effective alternative to traditional supply-side solutions to address the growing demand during times of peak electrical load. According to the US Department of Energy (DOE), demand response reflects “changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized.” 1 The California Energy Commission (CEC) defines DR as “a reduction in customers’ electricity consumption over a given time interval relative to what would otherwise occur in response to a price signal, other financial incentives, or a reliability signal.” 2 This latter definition is perhaps most reflective of how DR is understood and implemented today in countries such as the US, Canada, and Australia where DR is primarily a dispatchable resource responding to signals from utilities, grid operators, and/or load aggregators (or DR providers).

  14. Demand Response as a System Reliability Resource

    E-Print Network [OSTI]

    Joseph, Eto

    2014-01-01

    storage, combined heat and power (including both natural gas and biomass), AMI, DR capabilities, distribution, automation, electric vehicle accommodation, and microgrid

  15. Robust automated knowledge capture.

    SciTech Connect (OSTI)

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

    2011-10-01

    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.

  16. (No) Security in Automation!?

    E-Print Network [OSTI]

    Lüders, S

    2008-01-01

    Modern Information Technologies like Ethernet, TCP/IP, web server or FTP are nowadays increas-ingly used in distributed control and automation systems. Thus, information from the factory floor is now directly available at the management level (From Shop-Floor to Top-Floor) and can be ma-nipulated from there. Despite the benefits coming with this (r)evolution, new vulnerabilities are in-herited, too: worms and viruses spread within seconds via Ethernet and attackers are becoming interested in control systems. Unfortunately, control systems lack the standard security features that usual office PCs have. This contribution will elaborate on these problems, discuss the vulnerabilities of modern control systems and present international initiatives for mitigation.

  17. Communication in automation, including networking and wireless

    E-Print Network [OSTI]

    Antsaklis, Panos

    Communication in automation, including networking and wireless Nicholas Kottenstette and Panos J and networking in automation is given. Digital communication fundamentals are reviewed and networked control are presented. 1 Introduction 1.1 Why communication is necessary in automated systems Automated systems use

  18. Automating Personalized Battery Management on Smartphones

    E-Print Network [OSTI]

    Falaki, Mohamamd Hossein

    2012-01-01

    3 Automating Battery Management . . . . . . .122 Battery Goal Setting UI . . . . . . . . . . . . . . .Power and Battery Management . . . . . . . . . . . . . . .

  19. Real-time Pricing Demand Response in Operations

    SciTech Connect (OSTI)

    Widergren, Steven E.; Marinovici, Maria C.; Berliner, Teri; Graves, Alan

    2012-07-26

    Abstract—Dynamic pricing schemes have been implemented in commercial and industrial application settings, and recently they are getting attention for application to residential customers. Time-of-use and critical-peak-pricing rates are in place in various regions and are being piloted in many more. These programs are proving themselves useful for balancing energy during peak periods; however, real-time (5 minute) pricing signals combined with automation in end-use systems have the potential to deliver even more benefits to operators and consumers. Besides system peak shaving, a real-time pricing system can contribute demand response based on the locational marginal price of electricity, reduce load in response to a generator outage, and respond to local distribution system capacity limiting situations. The US Department of Energy (DOE) is teaming with a mid-west electricity service provider to run a distribution feeder-based retail electricity market that negotiates with residential automation equipment and clears every 5 minutes, thus providing a signal for lowering or raising electric consumption based on operational objectives of economic efficiency and reliability. This paper outlines the capability of the real-time pricing system and the operational scenarios being tested as the system is rolled-out starting in the first half of 2012.

  20. Automating component reuse and adaptation

    E-Print Network [OSTI]

    Alexander, Perry; Morel, B.

    2004-09-01

    framework for automating specification-based component retrieval and adaptation that has been successfully applied to synthesis of software for embedded and digital signal processing systems. Using specifications to abstractly represent implementations...

  1. Automated Assembly Using Feature Localization

    E-Print Network [OSTI]

    Gordon, Steven Jeffrey

    1986-12-01

    Automated assembly of mechanical devices is studies by researching methods of operating assembly equipment in a variable manner; that is, systems which may be configured to perform many different assembly operations ...

  2. Aspects of automation mode confusion

    E-Print Network [OSTI]

    Wheeler, Paul H. (Paul Harrison)

    2007-01-01

    Complex systems such as commercial aircraft are difficult for operators to manage. Designers, intending to simplify the interface between the operator and the system, have introduced automation to assist the operator. In ...

  3. STEO December 2012 - coal demand

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home RoomPreservation of Fe(II) byMultidayAlumni > The2/01/12 Page 1NEWSSupportcoal demand seen below

  4. Scaling Microblogging Services with Divergent Traffic Demands

    E-Print Network [OSTI]

    Fu, Xiaoming

    Scaling Microblogging Services with Divergent Traffic Demands Tianyin Xu, Yang Chen, Lei Jiao, Ben-server architecture has not scaled with user demands, lead- ing to server overload and significant impairment

  5. Michel Meulpolder Managing Supply and Demand of

    E-Print Network [OSTI]

    Michel Meulpolder Managing Supply and Demand of Bandwidth in Peer-to-Peer Communities #12;#12;Managing Supply and Demand of Bandwidth in Peer-to-Peer Communities Proefschrift ter verkrijging van de

  6. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

    relatively high economic/demographic growth, relatively low electricity and natural gas rates REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 2: Electricity Demand by Utility OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION Robert P

  7. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

    /demographic growth, relatively low electricity and natural gas rates, and relatively low efficiency program CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 1: Statewide Electricity Manager Bill Junker Manager DEMAND ANALYSIS OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY

  8. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

    incorporates relatively high economic/demographic growth, relatively low electricity and natural gas rates CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST Volume 2: Electricity Demand Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION Robert P. Oglesby Executive

  9. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

    incorporates relatively high economic/demographic growth, relatively low electricity and natural gas rates CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 2: Electricity Demand OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION Robert P

  10. Solar in Demand | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Solar in Demand Solar in Demand June 15, 2012 - 10:23am Addthis Kyle Travis, left and Jon Jackson, with Lighthouse Solar, install microcrystalline PV modules on top of Kevin...

  11. Demand Effects in Productivity and Efficiency Analysis 

    E-Print Network [OSTI]

    Lee, Chia-Yen

    2012-07-16

    Demand fluctuations will bias the measurement of productivity and efficiency. This dissertation described three ways to characterize the effect of demand fluctuations. First, a two-dimensional efficiency decomposition (2DED) of profitability...

  12. Industrial Equipment Demand and Duty Factors 

    E-Print Network [OSTI]

    Dooley, E. S.; Heffington, W. M.

    1998-01-01

    Demand and duty factors have been measured for selected equipment (air compressors, electric furnaces, injection molding machines, centrifugal loads, and others) in industrial plants. Demand factors for heavily loaded air ...

  13. Coordination of Energy Efficiency and Demand Response

    SciTech Connect (OSTI)

    none,

    2010-01-01

    Summarizes existing research and discusses current practices, opportunities, and barriers to coordinating energy efficiency and demand response programs.

  14. Decentralized demand management for water distribution 

    E-Print Network [OSTI]

    Zabolio, Dow Joseph

    1989-01-01

    OF THE DEMAND CURVE 30 31 35 39 Model Development Results 39 45 VI CONTROLLER DESIGN AND COSTS 49 Description of Controller Production and Installation Costs 49 50 VII SYSTEM EVALUATION AND ECONOMICS 53 System Response and Degree of Control... Patterns 9 Typical Winter Diurnal Patterns 10 Trace of Marginal Pump Efficiency and Hourly Demand 11 Original Demand Distribution and Possible Redistributions 33 34 40 41 43 46 12 Typical Nodal Responses to Demand Change 54 ix LIST OF TABLES...

  15. Automated Sorting of Transuranic Waste

    SciTech Connect (OSTI)

    Shurtliff, Rodney Marvin

    2001-03-01

    The HANDSS-55 Transuranic Waste Sorting Module is designed to sort out items found in 55-gallon drums of waste as determined by an operator. Innovative imaging techniques coupled with fast linear motor-based motion systems and a flexible end-effector system allow the operator to remove items from the waste stream by a touch of the finger. When all desired items are removed from the waste stream, the remaining objects are automatically moved to a repackaging port for removal from the glovebox/cell. The Transuranic Waste Sorting Module consists of 1) a high accuracy XYZ Stereo Measurement and Imaging system, 2) a vibrating/tilting sorting table, 3) an XY Deployment System, 4) a ZR Deployment System, 5) several user-selectable end-effectors, 6) a waste bag opening system, 7) control and instrumentation, 8) a noncompliant waste load-out area, and 9) a Human/Machine Interface (HMI). The system is modular in design to accommodate database management tools, additional load-out ports, and other enhancements. Manually sorting the contents of a 55-gallon drum takes about one day per drum. The HANDSS-55 Waste Sorting Module is designed to significantly increase the throughput of this sorting process by automating those functions that are strenuous and tiresome for an operator to perform. The Waste Sorting Module uses the inherent ability of an operator to identify the items that need to be segregated from the waste stream and then, under computer control, picks that item out of the waste and deposits it in the appropriate location. The operator identifies the object by locating the visual image on a large color display and touches the image on the display with his finger. The computer then determines the location of the object, and performing a highspeed image analysis determines its size and orientation, so that a robotic gripper can be deployed to pick it up. Following operator verification by voice or function key, the object is deposited into a specified location.

  16. Aggregate Model for Heterogeneous Thermostatically Controlled Loads with Demand Response

    SciTech Connect (OSTI)

    Zhang, Wei; Kalsi, Karanjit; Fuller, Jason C.; Elizondo, Marcelo A.; Chassin, David P.

    2012-07-22

    Due to the potentially large number of Distributed Energy Resources (DERs) – demand response, distributed generation, distributed storage - that are expected to be deployed, it is impractical to use detailed models of these resources when integrated with the transmission system. Being able to accurately estimate the fast transients caused by demand response is especially important to analyze the stability of the system under different demand response strategies. On the other hand, a less complex model is more amenable to design feedback control strategies for the population of devices to provide ancillary services. The main contribution of this paper is to develop aggregated models for a heterogeneous population of Thermostatic Controlled Loads (TCLs) to accurately capture their collective behavior under demand response and other time varying effects of the system. The aggregated model efficiently includes statistical information of the population and accounts for a second order effect necessary to accurately capture the collective dynamic behavior. The developed aggregated models are validated against simulations of thousands of detailed building models using GridLAB-D (an open source distribution simulation software) under both steady state and severe dynamic conditions caused due to temperature set point changes.

  17. Demand Response Valuation Frameworks Paper

    SciTech Connect (OSTI)

    Heffner, Grayson

    2009-02-01

    While there is general agreement that demand response (DR) is a valued component in a utility resource plan, there is a lack of consensus regarding how to value DR. Establishing the value of DR is a prerequisite to determining how much and what types of DR should be implemented, to which customers DR should be targeted, and a key determinant that drives the development of economically viable DR consumer technology. Most approaches for quantifying the value of DR focus on changes in utility system revenue requirements based on resource plans with and without DR. This ''utility centric'' approach does not assign any value to DR impacts that lower energy and capacity prices, improve reliability, lower system and network operating costs, produce better air quality, and provide improved customer choice and control. Proper valuation of these benefits requires a different basis for monetization. The review concludes that no single methodology today adequately captures the wide range of benefits and value potentially attributed to DR. To provide a more comprehensive valuation approach, current methods such as the Standard Practice Method (SPM) will most likely have to be supplemented with one or more alternative benefit-valuation approaches. This report provides an updated perspective on the DR valuation framework. It includes an introduction and four chapters that address the key elements of demand response valuation, a comprehensive literature review, and specific research recommendations.

  18. Demand Queries with Preprocessing Uriel Feige

    E-Print Network [OSTI]

    Demand Queries with Preprocessing Uriel Feige and Shlomo Jozeph May 1, 2014 )>IJH=?J Given a set of items and a submodular set-function f that determines the value of every subset of items, a demand query, the value of S minus its price. The use of demand queries is well motivated in the context of com

  19. DemandDriven Pointer Analysis Nevin Heintze

    E-Print Network [OSTI]

    Tardieu, Olivier

    Demand­Driven Pointer Analysis Nevin Heintze Research, Agere Systems (formerly Lucent Technologies analysis of a pro­ gram or program component. In this paper we introduce a demand­driven approach for pointer analysis. Specifically, we describe a demand­driven flow­insensitive, subset­based, context

  20. APPLICATION-FORM DEMANDED'ADMISSION

    E-Print Network [OSTI]

    Opportunities and Challenges for Data Center Demand Response Adam Wierman Zhenhua Liu Iris Liu of renewable energy into the grid as well as electric power peak-load shaving: data center demand response. Data center demand response sits at the intersection of two growing fields: energy efficient data

  1. Airline Pilot Demand Projections What this is-

    E-Print Network [OSTI]

    Bustamante, Fabián E.

    60 Mobile applications constantly demand additional memory, and traditional designs increase but also e-mail, Internet access, digital camera features, and video on demand. With feature expansion demanding additional storage and memory in all com- puting devices, DRAM and flash memory densities

  2. Algorithms Demands and Bounds Applications of Flow

    E-Print Network [OSTI]

    Kabanets, Valentine

    2/28/2014 1 Algorithms ­ Demands and Bounds Applications of Flow Networks Design and Analysis of Algorithms Andrei Bulatov Algorithms ­ Demands and Bounds 12-2 Lower Bounds The problem can be generalized) capacities (ii) demands (iii) lower bounds A circulation f is feasible if (Capacity condition) For each e E

  3. Adapton: Composable, Demand-Driven Incremental Computation

    E-Print Network [OSTI]

    Hicks, Michael

    Adapton: Composable, Demand-Driven Incremental Computation CS-TR-5027 -- July 12, 2013 Matthew A demands on the program output; that is, if a program input changes, all depen- dencies will be recomputed. To address these problems, we present cdd ic , a core calculus that applies a demand-driven seman- tics

  4. Pricing Cloud Bandwidth Reservations under Demand Uncertainty

    E-Print Network [OSTI]

    Li, Baochun

    Heap Assumptions on Demand Andreas Podelski1 , Andrey Rybalchenko2 , and Thomas Wies1 1 University analysis produces heap assumptions on demand to eliminate counterexamples, i.e., non-terminating abstract of a non-terminating abstract computation, i.e., it applies shape analysis on demand. The shape analysis

  5. Demand And Response Transportation Rider's Guide

    E-Print Network [OSTI]

    Acton, Scott

    Demand And Response Transportation Rider's Guide http://www.virginia.edu/parking/disabilities/dart Version 14.5 (8/13/14) Welcome DART Rider: The Demand and Response Transportation (DART) Service rides: #12;Demand And Response Transportation Rider's Guide http

  6. Scaling Microblogging Services with Divergent Traffic Demands

    E-Print Network [OSTI]

    Almeroth, Kevin C.

    Scaling Microblogging Services with Divergent Traffic Demands Tianyin Xu1 , Yang Chen1 , Lei Jiao1 client-server architecture has not scaled with user demands, leading to server overload and significant #12;Scaling Microblogging Services with Divergent Traffic Demands 21 producing effective predictions

  7. Demande de diplmes NOM,Prnom : ......................................................................................................................

    E-Print Network [OSTI]

    Chamroukhi, Faicel

    Optimal demand response: problem formulation and deterministic case Lijun Chen, Na Li, Libin Jiang load through real-time demand response and purchases balancing power on the spot market to meet the aggregate demand. Hence optimal supply procurement by the LSE and the consumption decisions by the users

  8. Precision On Demand: An Improvement in Probabilistic

    E-Print Network [OSTI]

    Precision On Demand: An Improvement in Probabilistic Hashing Igor Melatti, Robert Palmer approach Precision on Demand or POD). #12;This paper provides a scientific evaluation of the pros and cons time likely to increase by a factor of 1.8 or less. #12;Precision On Demand: An Improvement

  9. ADAPTON: Composable, Demand-Driven Incremental Computation

    E-Print Network [OSTI]

    Hicks, Michael

    ADAPTON: Composable, Demand- Driven Incremental Computation Abstract Many researchers have proposed important drawbacks. First, recomputation is oblivious to specific demands on the program output; that is ic , a core calculus that applies a demand-driven semantics to incremental computa- tion, tracking

  10. Constructing Speculative Demand Functions in Equilibrium Markets

    E-Print Network [OSTI]

    On the Convergence of Statistical Techniques for Inferring Network Traffic Demands Alberto Medina1 of traffic demands in a communication net- work enables or enhances a variety of traffic engineering and net set of these demands is prohibitively expensive because of the huge amounts of data that must

  11. Heap Assumptions on Demand Andreas Podelski1

    E-Print Network [OSTI]

    Wies, Thomas

    Heap Assumptions on Demand Andreas Podelski1 , Andrey Rybalchenko2 , and Thomas Wies1 1 University checker and shape analysis. The shape analysis pro- duces heap assumptions on demand to eliminate.e., it applies shape analysis on demand. The shape analysis produces a heap assumption, which is an assertion

  12. Appeld'offrespublic Demanded'approvisionnement

    E-Print Network [OSTI]

    Montréal, Université de

    ATM for Video and Audio on Demand David Greaves. University of Cambridge and ATM Ltd. email: djg fast, particularly for video- on-demand. These digital streams require constant-rate digi- tal channels of the Cambridge Digital Interactive Television Trial, where Video and Audio on demand are transported to the Home

  13. Precision On Demand: An Improvement in Probabilistic

    E-Print Network [OSTI]

    Precision On Demand: An Improvement in Probabilistic Hashing Igor Melatti, Robert Palmer approach Precision on Demand or POD). #12; This paper provides a scientific evaluation of the pros and cons time likely to increase by a factor of 1.8 or less. #12; Precision On Demand: An Improvement

  14. FORECAST COMBINATION IN REVENUE MANAGEMENT DEMAND FORECASTING

    E-Print Network [OSTI]

    Fernandez, Thomas

    Demandness in Rewriting and Narrowing Sergio Antoy1 and Salvador Lucas2 1 Computer Science by a strategy to compute a step. The notion of demandness provides a suitable framework for pre- senting that the notion of demandness is both atomic and fundamental to the study of strategies. 1 Introduction Modern

  15. Resolution on Demand Bianka BuschbeckWolf

    E-Print Network [OSTI]

    Reyle, Uwe

    Resolution on Demand Bianka Buschbeck­Wolf Universit¨at Stuttgart Report 196 May 1997 #12; May 1997¨ur den Inhalt dieser Arbeit liegt bei der Autorin. #12; Resolution on Demand Abstract Following the strategy of resolution on demand, the transfer component triggers inference processes in analysis

  16. Heap Assumptions on Demand Andreas Podelski1

    E-Print Network [OSTI]

    Wies, Thomas

    PROTOTYPE IMPLEMENTATION OF A DEMAND DRIVEN NETWORK MONITORING ARCHITECTURE Augusto Ciuffoletti for demand driven monitoring, named gd2, that can be potentially integrated in the gLite framework. We capable of managing the scalability challenge offered by a Grid environment: i) demand driven

  17. Pricing Cloud Bandwidth Reservations under Demand Uncertainty

    E-Print Network [OSTI]

    Li, Baochun

    Pricing Cloud Bandwidth Reservations under Demand Uncertainty Di Niu, Chen Feng, Baochun Li's utility depends not only on its bandwidth usage, but more importantly on the portion of its demand that can be made by all tenants and the cloud provider, even with the presence of demand uncertainty

  18. Transportation Energy: Supply, Demand and the Future

    E-Print Network [OSTI]

    Saldin, Dilano

    trends in China, India, Eastern Europe and other developing areas. China oil demand +104% by 2030, India 2000 2020 2040 2060 Supply demand Energy UWM-CUTS 14 U.S. DOE viewpoint, source:http://tonto.eia.doe.gov/FTPROOT/features/longterm.pdf#search='oilTransportation Energy: Supply, Demand and the Future http://www.uwm.edu/Dept/CUTS//2050/energy05

  19. Modeling Energy Demand Aggregators for Residential Consumers

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Modeling Energy Demand Aggregators for Residential Consumers G. Di Bella, L. Giarr`e, M. Ippolito, A. Jean-Marie, G. Neglia and I. Tinnirello § January 2, 2014 Abstract Energy demand aggregators- response paradigm. When the energy provider needs to reduce the current energy demand on the grid, it can

  20. INTEGRATION OF PV IN DEMAND RESPONSE

    E-Print Network [OSTI]

    Perez, Richard R.

    INTEGRATION OF PV IN DEMAND RESPONSE PROGRAMS Prepared by Richard Perez et al. NREL subcontract the case that distributed PV generation deserves a substantial portion of the credit allotted to demand response programs. This is because PV generation acts as a catalyst to demand response, markedly enhancing

  1. Demand Response for Computing Jerey S. Chase

    E-Print Network [OSTI]

    Chase, Jeffrey S.

    Chapter 1 Demand Response for Computing Centers Jerey S. Chase Duke University 1.1 Introduction ............................................................... 3 1.2 Demand Response in the Emerging Smart Grid .......................... 5 1.2.1 Importance of Demand Response for Energy E ciency .......... 6 1.2.2 The Role of Renewable Energy

  2. Response to changes in demand/supply

    E-Print Network [OSTI]

    Response to changes in demand/supply through improved marketing 21.2 http with the mill consuming 450 000 m3 , amounting to 30% of total plywood log demand in 1995. The composites board, statistics of demand and supply of wood, costs and competitiveness were analysed. The reactions

  3. Response to changes in demand/supply

    E-Print Network [OSTI]

    Response to changes in demand/supply through improved marketing 21.2 #12;#12;111 Impacts of changes log demand in 1995. The composites board mills operating in Korea took advantage of flexibility environment changes on the production mix, some economic indications, statistics of demand and supply of wood

  4. Demand Response and Ancillary Services September 2008

    E-Print Network [OSTI]

    Demand Response and Ancillary Services September 2008 #12;© 2008 EnerNOC, Inc. All Rights Reserved programs The purpose of this presentation is to offer insight into the mechanics of demand response and industrial demand response resources across North America in both regulated and restructured markets As of 6

  5. THE STATE OF DEMAND RESPONSE IN CALIFORNIA

    E-Print Network [OSTI]

    THE STATE OF DEMAND RESPONSE IN CALIFORNIA Prepared For: California Energy in this report. #12; ABSTRACT By reducing system loads during criticalpeak times, demand response (DR) can.S. and internationally and lay out ideas that could help move California forward. KEY WORDS demand response, peak

  6. Demand Response Resources in Pacific Northwest

    E-Print Network [OSTI]

    Demand Response Resources in Pacific Northwest Chuck Goldman Lawrence Berkeley National Laboratory cagoldman@lbl.gov Pacific Northwest Demand Response Project Portland OR May 2, 2007 #12;Overview · Typology Annual Reports ­ Journal articles/Technical reports #12;Demand Response Resources · Incentive

  7. Demand Responsive Lighting: A Scoping Study

    E-Print Network [OSTI]

    LBNL-62226 Demand Responsive Lighting: A Scoping Study F. Rubinstein, S. Kiliccote Energy Environmental Technologies Division January 2007 #12;LBNL-62226 Demand Responsive Lighting: A Scoping Study in this report was coordinated by the Demand Response Research Center and funded by the California Energy

  8. Barrier Immune Radio Communications for Demand Response

    E-Print Network [OSTI]

    LBNL-2294E Barrier Immune Radio Communications for Demand Response F. Rubinstein, G. Ghatikar, J Ann Piette of Lawrence Berkeley National Laboratory's (LBNL) Demand Response Research Center (DRRC and Environment's (CIEE) Demand Response Emerging Technologies Development (DRETD) Program, under Work for Others

  9. THE STATE OF DEMAND RESPONSE IN CALIFORNIA

    E-Print Network [OSTI]

    THE STATE OF DEMAND RESPONSE IN CALIFORNIA Prepared For: California Energy in this report. #12; ABSTRACT By reducing system loads during criticalpeak times, demand response can help reduce the threat of planned rotational outages. Demand response is also widely regarded as having

  10. Tuneable on-demand single-photon source

    E-Print Network [OSTI]

    Z. H. Peng; J. S. Tsai; O. V. Astafiev

    2015-05-21

    An on-demand single photon source is a key element in a series of prospective quantum technologies and applications. We demonstrate the operation of a tuneable on-demand microwave photon source based on a fully controllable superconducting artificial atom strongly coupled to an open-end transmission line (a 1D half-space). The atom emits a photon upon excitation by a short microwave $\\pi$-pulse applied through a control line weakly coupled to the atom. The emission and control lines are well decoupled from each other, preventing the direct leakage of radiation from the $\\pi$-pulses used for excitation. The estimated efficiency of the source is higher than 75\\% and remains to be about 50\\% or higher over a wide frequency range from 6.7 to 9.1 GHz continuously tuned by an external magnetic field.

  11. Hydrogen Demand and Resource Assessment Tool | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History View NewTexas: Energy Resources JumpNewTexas: EnergyHunterdonHutto,Fuel

  12. Demand Response Energy Consulting LLC | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTIONRobertsdale, Alabama (UtilityInstruments IncMississippi:Delta Electric Power AssnDeluge Inc

  13. OpenEI Community - Global DC Power System Market Demand

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo, Maryland:NPI VenturesNewSt.Information OlindaOnslow County,OpTICOpenBarter Jump

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

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LISTStar2-0057-EA Jump to: navigation, searchDaimlerDayton

  15. Assisting Mexico in Developing Energy Supply and Demand Projections | Open

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LISTStar Energy LLC Jump to: navigation,SummariesAshmanlaCommercial BuildingsEnergy

  16. ranking of utilities by demand charge? | OpenEI Community

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION JEnvironmental Jump to:EAand Dalton Jump to:Wylie, Texas: EnergyYBRZAPgftIDmirai Homeranking of

  17. Network-Driven Demand Side Management Website | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIXsourceII Jump to: navigation,National MarineUSAID Climate Activities JumpNestle

  18. Property:DemandRateStructure | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION JEnvironmental Jump to: navigation, search Property NameDefinition Jump to: navigation,

  19. Property:FlatDemandStructure | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION JEnvironmental Jump to: navigation,Property EditMimeType Jump to:FirstWellLog Jump

  20. Energy demand and population changes

    SciTech Connect (OSTI)

    Allen, E.L.; Edmonds, J.A.

    1980-12-01

    Since World War II, US energy demand has grown more rapidly than population, so that per capita consumption of energy was about 60% higher in 1978 than in 1947. Population growth and the expansion of per capita real incomes have led to a greater use of energy. The aging of the US population is expected to increase per capita energy consumption, despite the increase in the proportion of persons over 65, who consume less energy than employed persons. The sharp decline in the population under 18 has led to an expansion in the relative proportion of population in the prime-labor-force age groups. Employed persons are heavy users of energy. The growth of the work force and GNP is largely attributable to the growing participation of females. Another important consequence of female employment is the growth in ownership of personal automobiles. A third factor pushing up labor-force growth is the steady influx of illegal aliens.