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

    2009-01-01T23:59:59.000Z

    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

    2010-01-01T23:59:59.000Z

    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

    2010-01-01T23:59:59.000Z

    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

    2009-01-01T23:59:59.000Z

    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

    2011-01-01T23:59:59.000Z

    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

    2010-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01T23:59:59.000Z

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

  10. 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

    2009-02-28T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01T23:59:59.000Z

    Keywords: demand response, buildings, electricity use, Interface  Automated Demand Response  Building Automation of demand response in  commercial buildings.   One key 

  13. Open Automated Demand Response for Small Commerical Buildings

    E-Print Network [OSTI]

    Dudley, June Han

    2009-01-01T23:59:59.000Z

    Demand  Response for Small Commercial Buildings.   CEC?500?automated demand response  For small commercial buildings, AUTOMATED DEMAND RESPONSE FOR SMALL COMMERCIAL BUILDINGS

  14. Open Automated Demand Response for Small Commerical Buildings

    SciTech Connect (OSTI)

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

    2009-05-01T23:59:59.000Z

    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.

  15. Linking Continuous Energy Management and Open Automated Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01T23:59:59.000Z

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

  16. Northwest Open Automated Demand Response Technology Demonstration Project

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01T23:59:59.000Z

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

  17. Open Automated Demand Response Dynamic Pricing Technologies and Demonstration

    E-Print Network [OSTI]

    Ghatikar, Girish

    2010-01-01T23:59:59.000Z

    for Automated Demand Response in Commercial Buildings. Inbased demand response information to building controlDemand Response Standard for the Residential Sector. California Energy Commission, PIER Buildings

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01T23:59:59.000Z

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

  19. Linking Continuous Energy Management and Open Automated Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01T23:59:59.000Z

    January 2008. Biography Mary Ann Piette is a Staff ScientistAutomated Demand Response Mary Ann Piette, Sila Kiliccote,

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2011-01-01T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

    Kiliccote, Sila; Piette, Mary Ann

    2008-10-01T23:59:59.000Z

    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.

  2. Linking Continuous Energy Management and Open Automated Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01T23:59:59.000Z

    building electric load management concepts and faster scale dynamic DR using open automation systems.systems are being designed to be compatible with existing open building automationbuilding controls, weather sensitivity and occupancy patterns. Automation - Historically many energy management systems

  3. Northwest Open Automated Demand Response Technology Demonstration Project

    SciTech Connect (OSTI)

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

    2010-03-17T23:59:59.000Z

    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. Role of Standard Demand Response Signals for Advanced Automated Aggregation

    E-Print Network [OSTI]

    Kiliccote, Sila

    2013-01-01T23:59:59.000Z

    for the Open Automated Demand Response (OpenADR) StandardsControl for Automated Demand Response, Grid Interop, 2009. [C. McParland, Open Automated Demand Response Communications

  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

    2010-08-02T23:59:59.000Z

    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. Open Automated Demand Response Dynamic Pricing Technologies and Demonstration

    E-Print Network [OSTI]

    Ghatikar, Girish

    2010-01-01T23:59:59.000Z

    congress_1252d.pdf. EPRI. 2009. Automated DemandProject – Revision 1. EPRI. Palo Alto, California: 2009.DOE DR DRAS DRRC EIS EMCS EPRI FERC FM HASP ISO ISONE LBNL

  7. Northwest Open Automated Demand Response Technology Demonstration Project

    SciTech Connect (OSTI)

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

    2009-08-01T23:59:59.000Z

    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.

  8. 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

    2009-11-06T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Ghatikar, Girish

    2010-01-01T23:59:59.000Z

    for Automated Demand Response in Commercial Buildings. ” InAutomated Demand Response for Small Commercial Buildings. ”in automated demand response programs with building control

  10. 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

    2009-12-30T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Ghatikar, Girish

    2014-01-01T23:59:59.000Z

    Automated  Demand  Response  in  Commercial  Buildings.  Demand  Response  Infrastructure  for   Commercial  Buildings.  

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

    E-Print Network [OSTI]

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

    2008-01-01T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

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

    2007-10-01T23:59:59.000Z

    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.

  14. Linking Continuous Energy Management and Open Automated Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01T23:59:59.000Z

    description of six energy and demand management concepts.how quickly it can modify energy demand. This is not a newimprovements in both energy efficiency and demand response (

  15. Northwest Open Automated Demand Response Technology Demonstration Project

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01T23:59:59.000Z

    Building Control Strategies and Techniques for Demand Response.Building Systems and DR Strategies 16 Demand ResponseDemand Response Systems. ” Proceedings, 16 th National Conference on Building

  16. Northwest Open Automated Demand Response Technology Demonstration Project

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01T23:59:59.000Z

    is manual demand response where building staff receive acommercial buildings’ demand response technologies andBuilding Control Strategies and Techniques for Demand Response.

  17. Northwest Open Automated Demand Response Technology Demonstration Project

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01T23:59:59.000Z

    gateway device or building automation system. DR automationautomation of DR systems. Most DR activities are manual and require building

  18. Linking Continuous Energy Management and Open Automated Demand Response

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01T23:59:59.000Z

    for Demand Response in a New Commercial Building in NewDemand Response and Energy Efficiency in Commercial Buildings.Demand Response Mary Ann Piette, Sila Kiliccote, and Girish Ghatikar Lawrence Berkeley National Laboratory Building

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

    E-Print Network [OSTI]

    Tronci, Enrico

    solar panels)], for each time slot (say each hour) the DNO price policy defines an interval of energySmartHG: Energy Demand Aware Open Services for Smart Grid Intelligent Automation Enrico Tronci.prodanovic,jorn.gruber, barry.hayes}@imdea.org I. INTRODUCTION The SmartHG project [1], [2] has the goal of developing

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

    E-Print Network [OSTI]

    Koch, Ed; Piette, Mary Ann

    2008-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

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

    2008-01-01T23:59:59.000Z

    is manual demand response -- where building staff receives aand Demand Response in Commercial Building. ,April, LBNL-Keywords: Demand response, automation, commercial buildings,

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

    E-Print Network [OSTI]

    Koch, Ed; Piette, Mary Ann

    2008-01-01T23:59:59.000Z

    is manual demand response -- where building staff receives aKeywords: Demand response, automation, commercial buildings,buildings, especially as it applies to Demand Response

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

    SciTech Connect (OSTI)

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

    2010-06-02T23:59:59.000Z

    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.

  4. 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

    2009-05-01T23:59:59.000Z

    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.

  5. Open Automated Demand Response Dynamic Pricing Technologies and Demonstration

    E-Print Network [OSTI]

    Ghatikar, Girish

    2010-01-01T23:59:59.000Z

    in Demand Response for Wholesale Ancillary Services. ” Incan be used to link wholesale and retail real-time prices.11 Wholesale Electricity Market Information

  6. 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

    2014-01-02T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01T23:59:59.000Z

    Reports X BIP X X X X X X X X PDC X X X X X X X X PCT X X XNOC  OASIS  PG&E  PCT  OpenADR  PDC  REST  RFC  RTP  SDG&E D.2.5 Peak Day Credit (PDC) Peak Day Credit (PDC) is a DR 

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

    SciTech Connect (OSTI)

    Koch, Ed; Piette, Mary Ann

    2007-10-01T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01T23:59:59.000Z

    in Demand Response for Wholesale Ancillary Services Silain Demand Response for Wholesale Ancillary Services Silasuccessfully in the wholesale non- spinning ancillary

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2011-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2011-01-01T23:59:59.000Z

    high.  Demand response helps to manage building electricity Building  Control Strategies and Techniques for Demand Response.  Non?Residential Building in California.   Demand Response 

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2011-01-01T23:59:59.000Z

    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 

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

    SciTech Connect (OSTI)

    Herter, Karen; Rasin, Josh; Perry, Tim

    2009-11-30T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01T23:59:59.000Z

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

  16. 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

    2009-04-01T23:59:59.000Z

    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.

  17. 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

    2013-10-01T23:59:59.000Z

    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.

  18. Installation and Commissioning Automated Demand Response Systems

    E-Print Network [OSTI]

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

    2008-01-01T23:59:59.000Z

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

  19. Home Network Technologies and Automating Demand Response

    E-Print Network [OSTI]

    McParland, Charles

    2010-01-01T23:59:59.000Z

    and Automating Demand Response Charles McParland, Lawrenceand Automating Demand Response Charles McParland, LBNLCommercial and Residential Demand Response Overview of the

  20. Installation and Commissioning Automated Demand Response Systems

    E-Print Network [OSTI]

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

    2008-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    McKane, Aimee

    2010-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Watson, David S.

    2013-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Kim, Joyce Jihyun

    2014-01-01T23:59:59.000Z

    customers need to reduce energy demand during expensiveadditive) $11.42 / kW-max demand Energy Delivery Charges Alltype, floor space, peak demand, energy supplier, DR program

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

    E-Print Network [OSTI]

    Piette, Mary Ann

    2010-01-01T23:59:59.000Z

    and Demand Response in Commercial Building,” Report No.Demand Response Infrastructure for Commercial Buildings MaryDemand Response Infrastructure for Commercial Buildings Mary

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

    E-Print Network [OSTI]

    Kim, Joyce Jihyun

    2014-01-01T23:59:59.000Z

    commercial building, demand response, dynamic pricing,demand response (Auto-DR) in large commercial buildingsdemand response (Auto-DR) in large commercial buildings

  6. Automated Demand Response and Commissioning

    SciTech Connect (OSTI)

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

    2005-04-01T23:59:59.000Z

    This paper describes the results from the second season of research to develop and evaluate the performance of new Automated Demand Response (Auto-DR) hardware and software technology in large facilities. Demand Response (DR) is a set of activities to reduce or shift electricity use to improve the electric grid reliability and manage electricity costs. 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. We refer to this as Auto-DR. The evaluation of the control and communications must be properly configured and pass through a set of test stages: Readiness, Approval, Price Client/Price Server Communication, Internet Gateway/Internet Relay Communication, Control of Equipment, and DR Shed Effectiveness. New commissioning tests are needed for such systems to improve connecting demand responsive building systems to the electric grid demand response systems.

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

    E-Print Network [OSTI]

    Piette, Mary Ann

    2010-01-01T23:59:59.000Z

    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

  8. Automated Demand Response Opportunities in Wastewater Treatment Facilities

    E-Print Network [OSTI]

    Thompson, Lisa

    2008-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Ghatikar, Girish

    2010-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Ghatikar, Girish

    2010-01-01T23:59:59.000Z

    Signals. ” SGIP NIST Smart Grid Collaboration Site. http://Presented at the Grid Interop Forum, Albuquerque, NM.Last accessed: Open Smart Grid Users Group. “OpenADR Task

  11. Scenarios for Consuming Standardized Automated Demand Response Signals

    E-Print Network [OSTI]

    Koch, Ed

    2009-01-01T23:59:59.000Z

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

  12. Automated Demand Response Opportunities in Wastewater Treatment Facilities

    SciTech Connect (OSTI)

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

    2008-11-19T23:59:59.000Z

    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.

  13. automated demand response: Topics by E-print Network

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

    automated demand response First Page Previous Page 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Next Page Last Page Topic Index 1 Analysis of Open Automated...

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

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

    Honeywell's Smart Grid Investment Grant (SGIG) project demonstrates utility-scale performance of a hardwaresoftware platform for automated demand response (ADR). This project...

  15. 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

    2010-08-20T23:59:59.000Z

    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.

  16. Automated Demand Response and Commissioning

    E-Print Network [OSTI]

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

    2005-01-01T23:59:59.000Z

    and Demand Response in Commercial Buildings”, Lawrencesystems. Demand Response using HVAC in Commercial BuildingsDemand Response Test in Large Facilities13 National Conference on Building

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01T23:59:59.000Z

    produce the greatest energy and demand savings. Aeration andand C.Y. Chang (2005). "Energy Demand in Sludge Dewatering."be modified to reduce energy demand during demand response

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

    E-Print Network [OSTI]

    Kim, Joyce Jihyun

    2014-01-01T23:59:59.000Z

    Dynamic controls for energy efficiency and demand response:to evaluate continuous energy management and demand responseBldg Energy (kWh) Energy (kWh) Demand (kW) Office Bldg Of f

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

    E-Print Network [OSTI]

    Piette, Mary Ann

    2010-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

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

    2004-01-01T23:59:59.000Z

    Management and Demand Response in Commercial Buildings", L BAutomated Demand Response National Conference on BuildingAutomated Demand Response National Conference on Building

  1. Scenarios for Consuming Standardized Automated Demand Response Signals

    E-Print Network [OSTI]

    Koch, Ed

    2009-01-01T23:59:59.000Z

    Keywords: Demand response, automation, commercial buildings,Demand Response and Energy Efficiency in Commercial Buildings,Building Control Strategies and Techniques for Demand Response.

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

    E-Print Network [OSTI]

    Piette, Mary Ann

    2010-01-01T23:59:59.000Z

    Keywords: Demand response, automation, commercial buildings,Demand Response and Energy Efficiency in Commercial Buildings,Building Control Strategies and Techniques for Demand Response.

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

    E-Print Network [OSTI]

    Kim, Joyce Jihyun

    2014-01-01T23:59:59.000Z

    and demand response for large commercial buildings in Newdemand response and energy efficiency in commercial buildings.demand response participation for large commercial buildings

  4. Automated Demand Response Strategies and Commissioning Commercial Building Controls

    E-Print Network [OSTI]

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

    2006-01-01T23:59:59.000Z

    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"

  5. Automated Demand Response and Commissioning

    E-Print Network [OSTI]

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

    2005-01-01T23:59:59.000Z

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

  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

    2010-01-01T23:59:59.000Z

    best practices that could be applicable in improving the energy efficiency and demand responsebest practices that could be applied to form the basis for demand responsedemand response activities. The following case studies illustrate best practices

  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

    2010-01-01T23:59:59.000Z

    best practices that could be applied to form the basis for demand responsebest practices that could be applicable in improving the energy efficiency and demand responsedemand response activities. The following case studies illustrate best practices

  8. 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

    2008-04-21T23:59:59.000Z

    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.

  9. Home Network Technologies and Automating Demand Response

    SciTech Connect (OSTI)

    McParland, Charles

    2009-12-01T23:59:59.000Z

    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.

  10. 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

  11. 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

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

    E-Print Network [OSTI]

    ) is a demand-side management strategy to reduce electricity use during times of high peak electric loads;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. Findings from Seven Years of Field Performance Data for Automated Demand Response in Commercial Buildings

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01T23:59:59.000Z

    Automated Demand Response for Small Commercial Buildings. ”Demand Response Strategies and Commissioning Commercial Buildingfor Automated Demand Response in Commercial Buildings Sila

  14. 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

    2009-05-11T23:59:59.000Z

    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.

  15. 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.

    2013-01-01T23:59:59.000Z

    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...

  16. 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.

    2013-01-01T23:59:59.000Z

    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...

  17. Installation and Commissioning Automated Demand Response Systems

    E-Print Network [OSTI]

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

    2008-01-01T23:59:59.000Z

    Demand Response Systems National Conference on BuildingDemand Response Systems National Conference on BuildingDemand Response Systems National Conference on Building

  18. A DISTRIBUTED INTELLIGENT AUTOMATED DEMAND RESPONSE BUILDING MANAGEMENT SYSTEM

    SciTech Connect (OSTI)

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

    2013-12-30T23:59:59.000Z

    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.

  19. 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

    2010-01-01T23:59:59.000Z

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

  20. Summary of the 2006 Automated Demand Response Pilot

    E-Print Network [OSTI]

    Piette, M.; Kiliccote, S.

    2007-01-01T23:59:59.000Z

    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...

  1. Scenarios for Consuming Standardized Automated Demand Response Signals

    SciTech Connect (OSTI)

    Koch, Ed; Piette, Mary Ann

    2008-10-03T23:59:59.000Z

    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.

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

    SciTech Connect (OSTI)

    Lawrence Berkeley National Laboratory; Kiliccote, Sila

    2011-11-18T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    McKane, Aimee

    2010-01-01T23:59:59.000Z

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

  4. 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

    2004-01-01T23:59:59.000Z

    and Demand Response in Commercial Buildings. ” LBNL Reportautomated Demand Response (DR) technologies in buildings.Automated Demand Response is initiated at a building or

  5. 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

    2004-08-01T23:59:59.000Z

    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.

  6. 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.

    2011-11-11T23:59:59.000Z

    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.

  7. 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

    2010-05-14T23:59:59.000Z

    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.

  8. 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

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

    E-Print Network [OSTI]

    Lekov, Alex

    2009-01-01T23:59:59.000Z

    Best Practices. Kiliccote, S. (2008). Automated Demand Responsebest operation practices and behaviors to enhance the impact of DR activities. 1.0 Introduction Background and Overview Demand Response (

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

    E-Print Network [OSTI]

    Han, Junqiao

    2008-01-01T23:59:59.000Z

    Building Control Strategies and Techniques for Demand Response,Automated Demand Response in a Large Office Building JunqiaoDemand Response Load Impacts: Evaluation of Baseline Load Models for Non-Residential Building

  11. 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

    2014-01-01T23:59:59.000Z

    automated demand response systems in large buildings in theBuilding Control Strategies and Techniques for Demand Response,buildings were able to provide significant demand response

  12. Results and commissioning issues from an automated demand responsepilot

    SciTech Connect (OSTI)

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

    2004-08-05T23:59:59.000Z

    This paper describes a research project to develop and test Automated Demand Response hardware and software technology in large facilities. We describe the overall project and some of the commissioning and system design problems that took place. Demand Response (DR) is a set of activities to reduce or shift electricity use to improve the electric grid reliability purposes, manage electricity costs, and ensure that customers receive signals that encourage load reduction during times when the electric grid is near its capacity. There were a number of specific commissioning challenges in conducting this test including software compatibility, incorrect time zones, IT and EMCS failures, and hardware issues. The knowledge needed for this type of system commissioning combines knowledge of building controls with network management and knowledge of emerging information technologies.

  13. 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

  14. 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

    2008-08-01T23:59:59.000Z

    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.

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

    SciTech Connect (OSTI)

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

    2009-08-01T23:59:59.000Z

    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.

  16. 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

    2013-01-01T23:59:59.000Z

    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

  17. Demand Response Opportunities in Industrial Refrigerated Warehouses in California

    E-Print Network [OSTI]

    Goli, Sasank

    2012-01-01T23:59:59.000Z

    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).

  18. Automated Demand Response Strategies and Commissioning Commercial Building Controls

    E-Print Network [OSTI]

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

    2006-01-01T23:59:59.000Z

    for Demand Response in New and Existing Commercial BuildingsDemand Response Strategies and National Conference on BuildingDemand Response Strategies and Commissioning Commercial Building

  19. 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

    2011-07-01T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

    Koch, Ed; Piette, Mary Ann

    2009-11-06T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Polany, Rany

    2012-01-01T23:59:59.000Z

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

  3. 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

    2004-01-01T23:59:59.000Z

    and Demand Response in Commercial Buildings. ” Highdemand-response technologies in large commercial and institutional buildings.building method California Independent System Operator (Cal ISO)’s Demand Response

  4. Opportunities for Automated Demand Response in Wastewater Treatment

    E-Print Network [OSTI]

    ;CHAPTER 4: Facility Baseline Analysis Net Plant Demand Figure 5: Average load profile for net plant demand characteristics and estimated shed potential for six submetered centrifuge Lift Pumps #12;Figure 7: Daily profile on event days compared to average dry season demand Partial-day complete plant shutdown Table 5: Load sheds

  5. Intelligent Building Automation: A Demand Response Management Perspective

    E-Print Network [OSTI]

    Qazi, T.

    2010-01-01T23:59:59.000Z

    the energy consumption in response to energy price fluctuations, demand charges, or a direct request to reduce demand when the power grid reaches critical levels. However, in order for a demand response regime to be effective the building will need to have a...

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

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Abstract--Implementation of Distribution Automation (DA) and Demand Side Management (DSM) intended with differentiate QoS in a multitasking environment. I. INTRODUCTION ODERN society demands a reliable and high by the distribution utility for the security. REMPLI (Remote Energy Management over Power Lines and Internet) system

  7. 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

    2011-08-15T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Kim, Joyce Jihyun

    2013-01-01T23:59:59.000Z

    and provide demand response (DR) through building controland provide demand response (DR) through building controlDemand Response Automation Server (DRAS) in a 15-minute interval. This allows the continuous monitoring of the building's

  9. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluating A Potential Microhydro SiteDayton Power & LightDemand Management

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

    SciTech Connect (OSTI)

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

    2008-10-20T23:59:59.000Z

    Demand response (DR) is an emerging research field and an effective tool that improves grid reliability and prevents the price of electricity from rising, especially in deregulated markets. This paper introduces the definition of DR and Automated Demand Response (Auto-DR). It describes the Auto-DR technology utilized at a commercial building in the summer of 2006 and the methodologies to evaluate associated demand savings. On the basis of field tests in a large office building, Auto-DR is proven to be a reliable and credible resource that ensures a stable and economical operation of the power grid.

  11. Decon2LS: An Open-Source Software Package for Automated Processing...

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

    Software Package for Automated Processing and Visualization of High Resolution Mass Spectrometry Data. Decon2LS: An Open-Source Software Package for Automated Processing and...

  12. 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

    2004-03-30T23:59:59.000Z

    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).

  13. Mass Market Demand Response and Variable Generation Integration Issues: A Scoping Study

    E-Print Network [OSTI]

    Cappers, Peter

    2012-01-01T23:59:59.000Z

    Reliability Corporation National Institute of Standards and Technology Open Access Transmission Tariff Open Automated Demand Response Protocol Public Utility Commission Photovoltaic

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

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01T23:59:59.000Z

    Name of the data entity  Primary Key of the entity (“PK”).  foreign keys are the primary keys  of the entities that 

  15. Open Automated Demand Response Dynamic Pricing Technologies and Demonstration

    E-Print Network [OSTI]

    Ghatikar, Girish

    2010-01-01T23:59:59.000Z

    locational marginal price (LMP) for the Hourly DA_RTP pricingpricing structure for wholesale markets. For example, ISO-NE’s hourly locational marginal

  16. Open Automated Demand Response for Small Commerical Buildings

    E-Print Network [OSTI]

    Dudley, June Han

    2009-01-01T23:59:59.000Z

    15  Heating Ventilation and Air Conditioning (HVAC) Institute  Heating, Ventilation and Air Conditioning the  majority of heating ventilation and air conditioning (

  17. Northwest Open Automated Demand Response Technology Demonstration Project

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01T23:59:59.000Z

    loop controls. Heating ventilation and air conditioning (with varying heating, ventilation, and air conditioning (text protocol heating, ventilation, and air conditioning

  18. Open Automated Demand Response for Small Commerical Buildings

    E-Print Network [OSTI]

    Dudley, June Han

    2009-01-01T23:59:59.000Z

    HVAC Control System Advanced Telemetry No DDC Zone Control Data Trending DDC Zone Control Data Trending Detail EMCS Trends

  19. Open Automated Demand Response Dynamic Pricing Technologies and Demonstration

    E-Print Network [OSTI]

    Ghatikar, Girish

    2010-01-01T23:59:59.000Z

    Schedules. ” www.pge.com/tariffs/. Last accessed: 4/26/10.and PG&E’s PDP rates (as a peak pricing tariff and as aproxy for TOU pricing tariff) were used. The technology

  20. Open Automated Demand Response for Small Commerical Buildings

    E-Print Network [OSTI]

    Dudley, June Han

    2009-01-01T23:59:59.000Z

    Power Systems) Cannon & Honeywell together for smart rd  party vendors such as  Honeywell.   Mostly a remote web of wireless  connections.   Honeywell Honeywell has many

  1. Northwest Open Automated Demand Response Technology Demonstration Project

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01T23:59:59.000Z

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

  2. Northwest Open Automated Demand Response Technology Demonstration Project

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Piette, Mary Ann

    2009-01-01T23:59:59.000Z

    Models DRAS Client API Simple REST Services Simple SOAPAPI SPECIFICATIONS 103   Utility Program Operator APIs .. 103   Participant Operator APIs.. 103   DRAS Client APIs 103   Use of Simple REST API and the minimum methods that must be supported are  as described in Section 9.3.1 that are similar for simple REST 

  4. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model,DOEHazel Crest,EnergySerranopolis Jump to: navigation,NouvellesAutomation Systems

  5. automated worm response: Topics by E-print Network

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

    a means to accurately model the early phase Bagchi, Saurabh 11 Analysis of Open Automated Demand Response Deployments in California Energy Storage, Conversion and Utilization...

  6. automated perfit analysis: Topics by E-print Network

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

    12;The Big Picture X-ray Microanalysis Perkins, Richard A. 10 Analysis of Open Automated Demand Response Deployments in California Energy Storage, Conversion and Utilization...

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

    E-Print Network [OSTI]

    Lekov, Alex

    2009-01-01T23:59:59.000Z

    in significant energy and demand savings for refrigeratedbe modified to reduce energy demand during demand responsein refrigerated warehouse energy demand if they are not

  8. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag JumpID-fTriWildcat 1 WindtheEnergySulfonate asAEEOpenOpen EnergyAGL EnergyAHAIGAIS

  9. Barrier Immune Radio Communications for Demand Response

    E-Print Network [OSTI]

    Rubinstein, Francis

    2010-01-01T23:59:59.000Z

    of Fully Automated Demand Response in Large Facilities,”Fully Automated Demand Response Tests in Large Facilities.for Automated Demand Response. Technical Document to

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

    E-Print Network [OSTI]

    Page, Janie

    2012-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Han, Junqiao

    2008-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Mathieu, Johanna L.

    2012-01-01T23:59:59.000Z

    demand response and energy ef?ciency in commercial buildings,”building control strategies and techniques for demand response,”building electricity use with application to demand response,”

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

    E-Print Network [OSTI]

    Page, Janie

    2012-01-01T23:59:59.000Z

    Demand Response for Small Commercial Buildings.   Lawrence small?medium buildings’ roles in demand response  efforts.  demand response for small? medium commercial buildings 

  14. Hirschmann Automation and Control GmbH | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup | Open EnergyInformation HessHirschmann Automation and Control GmbH

  15. 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

    2012-12-20T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01T23:59:59.000Z

    not as energy efficient as state-of-the-art data centers andHere Data Center 5 likely installed energy efficient ITart data center is likely to be energy efficient, employing

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01T23:59:59.000Z

    lighting, and heating, ventilation, and air conditioning.affect heating, ventilation, and air conditioning (HVAC)per second heating, ventilation, and air conditioning

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01T23:59:59.000Z

    Innovative Cooling System Management Some data centers thatConditions Guidelines Data center cooling systems attempt tocooling system efficiency measures for data centers. Power

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01T23:59:59.000Z

    greatest perceived equipment reliability and continuity-of-center performance or equipment reliability and life span.equipment, and are mature enough to meet performance and reliability

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01T23:59:59.000Z

    and need further research. Cooling system strategies arecooling systems and lighting controls. This scoping study builds on ongoing DRRC research,

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

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01T23:59:59.000Z

    is often 60% to 80%. Flywheel-based units typically have 2%systems (static UPS) or flywheels for energy storage. Staticat full load whereas flywheel-based systems provide from two

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2011-01-01T23:59:59.000Z

    U-tube fixtures. Shut off hot water heater. Increase zoneU-tube fixtures. Shut off hot water heater. Increase zoneof all lighting. Shut off hot water heater. Increase zone

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2011-01-01T23:59:59.000Z

    Institute  Heating, Ventilation and Air Conditioning primarily for heating, ventilation, and air conditioning lights and b) heating, ventilation, and air conditioning  (

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

    E-Print Network [OSTI]

    Watson, David S.

    2013-01-01T23:59:59.000Z

    demand response is more environmentally friendly than fossil fueldemand response (DR) used in the commercial and industrial sectors is more environmentally friendly than fossil fuelfossil fuels are the predominant heating fuels for California’s commercial buildings, heating electricity demand

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

    E-Print Network [OSTI]

    Watson, David S.

    2013-01-01T23:59:59.000Z

    Sodium Sulfur (NaS) Zinc- Air Battery Demand Response CostsSodium Sulfur (NaS) Zinc- Air Battery Low High AverageSodium Sulfur (NaS) Zinc- Air Battery Demand Response Costs

  6. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluating A Potential Microhydro SiteDayton Power & LightDemand ManagementDemand

  7. Strategies for Demand Response in Commercial Buildings

    E-Print Network [OSTI]

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

    2006-01-01T23:59:59.000Z

    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

  8. Changes in worldwide demand for metals (final). Open File report

    SciTech Connect (OSTI)

    Faucett, J.G.; Chmelynski, H.J.

    1986-08-01T23:59:59.000Z

    Worldwide demand for metals was analyzed to identify the important factors that explain differences in the level of demand among world countries. The per capita demand for steel, aluminum, copper, and total nonferrous metals was investigated for 40 to 50 countries over a 22-year period. These countries have been further grouped into four world regions for purposes of making generalizations about the importance of these factors for countries in different stages of development and with dissimilar levels of per capita gross domestic product (GDP). Intercountry and intertemporal differences are explained largely by differences in per capita GDP and changes over time in per capita GDP, oil real prices, and to a lesser extent, metal real prices. The trend in world consumption is dramatically different in the last decade than the previous one. In 1962-73, per capita consumption increased in all areas and consumption intensity (consumption divided by (GDP) increased in most areas). In 1973-84, per capita consumption fell in most areas and intensity fell dramatically, except in developing nations.

  9. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag Jump to: navigation, search Property NameFirstWellDepth JumpFlatDemandStructure

  10. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluating A Potential Microhydro SiteDayton Power & LightDemand

  11. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualPropertyd8c-a9ae-f8521cbb8489 No revision|LLCInsulation IncentivesEshone EnergyEstero,Demand

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2014-01-01T23:59:59.000Z

    demand-side management activities and commercial buildings’demand-side management (DSM) framework presented in Figure 1 provides continuous energy management concepts for shaping electric loads in buildings,demand-side management activities, DR methods and levels of automation. We highlight OpenADR as a standard for commercial buildings

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

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01T23:59:59.000Z

    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

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

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01T23:59:59.000Z

    Data for Automated Demand Response in Commercial Buildings,Demand Response Infrastructure for Commercial Buildings",demand response and energy efficiency functions into the design of buildings,

  15. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformationInformation FixedDemandChargeMonth8 Jump to:FlatDemandMonth1 Jump

  16. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformationInformation FixedDemandChargeMonth8 Jump to:FlatDemandMonth1

  17. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformationInformation FixedDemandChargeMonth8 Jump to:FlatDemandMonth1This is

  18. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformationInformation FixedDemandChargeMonth8 Jump to:FlatDemandMonth1This

  19. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformationInformation FixedDemandChargeMonth8 JumpFlatDemandMonth3 Jump to:

  20. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformationInformation FixedDemandChargeMonth8 JumpFlatDemandMonth3 Jump

  1. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformationInformation FixedDemandChargeMonth8 JumpFlatDemandMonth3

  2. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformationInformation FixedDemandChargeMonth8FlatDemandMonth7 Jump to:

  3. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformationInformation FixedDemandChargeMonth8FlatDemandMonth7 Jump

  4. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformationInformation FixedDemandChargeMonth8FlatDemandMonth7

  5. DA (Distribution Automation) (Smart Grid Project) | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model, clickInformationNew|CoreCpWingCushing,DA (Distribution Automation) (Smart

  6. Manz Automation India Pvt Ltd | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup |JilinLu an Group JumpNew Hampshire: EnergyManz Automation India Pvt Ltd

  7. Property:OpenEI/UtilityRate/DemandChargePeriod3 | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkins County,NumberOfNonCorporateOrganizations Jump Jump to:DemandChargePeriod3 Jump to:

  8. Property:OpenEI/UtilityRate/DemandChargePeriod3FAdj | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkins County,NumberOfNonCorporateOrganizations Jump Jump to:DemandChargePeriod3 Jump

  9. Property:OpenEI/UtilityRate/DemandChargePeriod4 | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkins County,NumberOfNonCorporateOrganizations Jump Jump to:DemandChargePeriod3

  10. Property:OpenEI/UtilityRate/DemandChargePeriod5 | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkins County,NumberOfNonCorporateOrganizations Jump JumpDemandChargePeriod5 Jump to:

  11. Property:OpenEI/UtilityRate/DemandChargePeriod5FAdj | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkins County,NumberOfNonCorporateOrganizations Jump JumpDemandChargePeriod5 Jump

  12. Property:OpenEI/UtilityRate/DemandChargePeriod6 | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkins County,NumberOfNonCorporateOrganizations Jump JumpDemandChargePeriod5

  13. Property:OpenEI/UtilityRate/DemandChargePeriod6FAdj | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkins County,NumberOfNonCorporateOrganizations Jump JumpDemandChargePeriod5Information

  14. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkins County,NumberOfNonCorporateOrganizations JumpInformation DemandRatchetPercentage

  15. Property:OpenEI/UtilityRate/DemandReactivePowerCharge | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformation DemandReactivePowerCharge Jump to: navigation, search This is a

  16. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformation DemandReactivePowerCharge Jump to: navigation, search This is

  17. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformation DemandReactivePowerCharge Jump to: navigation, search This

  18. Property:OpenEI/UtilityRate/FixedDemandChargeMonth1 | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformation DemandReactivePowerChargeInformation Rate Jump to:Information

  19. Property:OpenEI/UtilityRate/FixedDemandChargeMonth10 | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformation DemandReactivePowerChargeInformation Rate Jump

  20. Property:OpenEI/UtilityRate/FixedDemandChargeMonth11 | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformation DemandReactivePowerChargeInformation Rate JumpInformation 1

  1. Property:OpenEI/UtilityRate/FixedDemandChargeMonth12 | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformation DemandReactivePowerChargeInformation Rate JumpInformation

  2. Property:OpenEI/UtilityRate/FixedDemandChargeMonth2 | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformation DemandReactivePowerChargeInformation Rate

  3. Property:OpenEI/UtilityRate/FixedDemandChargeMonth3 | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformation DemandReactivePowerChargeInformation RateInformation

  4. Property:OpenEI/UtilityRate/FixedDemandChargeMonth4 | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformation DemandReactivePowerChargeInformation

  5. Property:OpenEI/UtilityRate/FixedDemandChargeMonth5 | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformation DemandReactivePowerChargeInformationInformation

  6. Property:OpenEI/UtilityRate/FixedDemandChargeMonth6 | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformation DemandReactivePowerChargeInformationInformationInformation

  7. Property:OpenEI/UtilityRate/FixedDemandChargeMonth8 | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformationInformation FixedDemandChargeMonth8 Jump to: navigation, search

  8. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformationInformation FixedDemandChargeMonth8 Jump to: navigation,

  9. Property:OpenEI/UtilityRate/FlatDemandMonth2 | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformationInformation FixedDemandChargeMonth8 Jump

  10. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkinsInformationInformation FixedDemandChargeMonth8

  11. Wireless Demand Response Controls for HVAC Systems

    E-Print Network [OSTI]

    Federspiel, Clifford

    2010-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Piette, Mary Ann

    2010-01-01T23:59:59.000Z

    Power Research Institute (EPRI) IntelliGrid Consortium, “ThePower Delivery System,” EPRI, Jan. 2, http://www.epri.com/Power Research Institute (EPRI) that started in 2004. The

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

    E-Print Network [OSTI]

    Kim, Joyce Jihyun

    2014-01-01T23:59:59.000Z

    between buildings and various stakeholders in NYS includingbetween buildings and various stakeholders in NYS includingin NYS’s wholesale energy market, innovations in building

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

    E-Print Network [OSTI]

    Ghatikar, Girish

    2014-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Kim, Joyce Jihyun

    2014-01-01T23:59:59.000Z

    Immunity PASS Class B IEC 61000-4-3 Radiated Electromagnetic2-3:2006 Radiated Emissions - Class A Compliant IEC 61000-4-

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

    E-Print Network [OSTI]

    Ghatikar, Girish

    2010-01-01T23:59:59.000Z

    AND SMART GRID The GridWise® interoperability framework [6] was developed to facilitate systems integration and

  17. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual Siteof EnergyInnovation in Carbon CaptureAtria Power Corporation LtdATIAustria:Energy

  18. 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

    2006-01-01T23:59:59.000Z

    Figure 2. Demand Response Automation Server and BuildingDemand Response Control Strategies in Commercial Buildings,X X Example of Demand Response from an Office Building This

  19. 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

    2004-08-01T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Kim, Joyce Jihyun

    2013-01-01T23:59:59.000Z

    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."

  1. Automated Critical Peak Pricing Field Tests: Program Description and Results

    E-Print Network [OSTI]

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

    2006-01-01T23:59:59.000Z

    is manual demand response -- where building staff receive afor demand response analysis ?ELECTRIC Whole building powerof automated demand response (Auto-DR) in buildings and

  2. 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

  3. Model for Analysis of Energy Demand (MAED-2) | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal Pwer Plant JumpMarysville,Missoula, Montana: EnergyAnalysis of Energy Demand (MAED-2) Jump to:

  4. 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

    2006-01-01T23:59:59.000Z

    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

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

    E-Print Network [OSTI]

    Piette, Mary Ann

    2014-01-01T23:59:59.000Z

    account  demand  response  signals,  building?integrated of Automated Demand Response in Commercial Buildings.  and Demand Response in Commercial  Buildings. , LBNL 

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

    E-Print Network [OSTI]

    Granderson, Jessica

    2010-01-01T23:59:59.000Z

    Building Control Strategies and Techniques for Demand Response.of Automated Demand Response in a Large Office Building.there demand response potential in commercial building that

  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

    2010-01-01T23:59:59.000Z

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

  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

    2010-01-01T23:59:59.000Z

    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).

  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

    2010-01-01T23:59:59.000Z

    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

  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

    2010-01-01T23:59:59.000Z

    oil. Findings suggest that there are substantial opportunities to reduce energy consumption in the petroleum refining industry

  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

    2010-01-01T23:59:59.000Z

    WWW.ENERGY.CA.GOV / PIER / RENEWABLE / BIOMASS / ANAEROBICwww.energy.ca.gov/research/renewable/biomass/anaerobic_2008). "Renewable Energy Research: Biomass - Anaerobic

  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

    2010-01-01T23:59:59.000Z

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

  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

    2010-01-01T23:59:59.000Z

    Embaby, and M. Rao (2006). Refinery Wastewater Treatment: Aand Assessment of Al Ruwais Refinery Wastewater." Journal ofThe Effects of Petroleum Refinery Wastewater on the Rate of

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01T23:59:59.000Z

    This also produces waste heat that is used for process30 to 70% by recovering waste heat and using it for space

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01T23:59:59.000Z

    sludge, and digested biosolids (Metcalf & Eddy Inc. 2003).hours. Processes such as biosolids thickening/dewatering and

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

    E-Print Network [OSTI]

    Lekov, Alex

    2010-01-01T23:59:59.000Z

    biological operations. Tertiary treatment processes wastewaterwastewater treatment system, called the Living Machine, uses natural non-chemical biologicalbiological (Wilkinson 2000). Each type generally refers to a certain point in the wastewater treatment

  17. 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

    2006-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

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

    2008-01-01T23:59:59.000Z

    commercial buildings participating in a demand?response (buildings participating in an Automated Demand Response buildings  participating  in  an  event?driven  demand?response  (

  19. Toward a Systematic Approach to the Design and Evaluation of Automated Mobility-on-Demand Systems: A Case Study in Singapore

    E-Print Network [OSTI]

    Spieser, Kevin

    2014-04-24T23:59:59.000Z

    The objective of this work is to provide analytical guidelines and financial justification for the design of shared-vehicle mobility-on-demand systems. Specifically, we consider the fundamental issue of determining the ...

  20. automated critical peakpricing: Topics by E-print Network

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

    of California eScholarship Repository Summary: to show the potential for automated demand response systems.of their demand response strategy. If the potential site...

  1. automated critical peak: Topics by E-print Network

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

    of California eScholarship Repository Summary: to show the potential for automated demand response systems.of their demand response strategy. If the potential site...

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

    E-Print Network [OSTI]

    McParland, Charles

    2012-01-01T23:59:59.000Z

    building and facilities managers implement automated demand responsedemand shedding strategies quickly and automatically when requested by utility operations. By integrating automated building responses

  3. Progress toward Producing Demand-Response-Ready Appliances

    SciTech Connect (OSTI)

    Hammerstrom, Donald J.; Sastry, Chellury

    2009-12-01T23:59:59.000Z

    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.

  4. automated analysis tool: Topics by E-print Network

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

    and Information Sciences Websites Summary: automating tools or an open source DBMS whose lack of automated tools might increase the operational cost their benefits. We...

  5. Demand Reduction

    Broader source: Energy.gov [DOE]

    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...

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

    Energy Savers [EERE]

    On-Demand eTraining Course on Building Automation Systems FEMP Presents Its Newest On-Demand eTraining Course on Building Automation Systems November 19, 2013 - 12:00am Addthis...

  7. 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

    2008-01-31T23:59:59.000Z

    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.

  8. 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

    2014-01-01T23:59:59.000Z

    ? min . [1] U. D. of Energy, “Benefits of demand response inHong, and X. Li, “A demand response energy management schemefor energy efficiency and automated demand response in

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01T23:59:59.000Z

    Automated Demand Response in a Large Office Building”, CECBuilding Control Strategies and Techniques for Demand Response”,Demand Response Load Impacts: Evaluation of Baseline Load Models for Non-Residential Buildings

  10. Home Network Technologies and Automating Demand Response

    E-Print Network [OSTI]

    McParland, Charles

    2010-01-01T23:59:59.000Z

    Application / Profile API included in standard PresentationApplication API included in standard Part of standard PartApplication / Profile API included in standard Part of

  11. 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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOEThe Bonneville Power AdministrationField Campaign:INEA : Papers SubfoldersU.S. Refining

  12. 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

    2013-01-01T23:59:59.000Z

    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.

  13. Demand Response Opportunities in Industrial Refrigerated Warehouses in California

    SciTech Connect (OSTI)

    Goli, Sasank; McKane, Aimee; Olsen, Daniel

    2011-06-14T23:59:59.000Z

    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.

  14. 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

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

    E-Print Network [OSTI]

    Dudley, Junqiao Han

    2010-01-01T23:59:59.000Z

    of Automated Demand Response in a Large Office Building”, inBuilding Control Strategies and Techniques for Demand Response.Demand Response Performance with an EnergyPlus Model in a Low Energy Campus Building

  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

    2010-01-01T23:59:59.000Z

    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

  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

    2010-01-01T23:59:59.000Z

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

  18. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model,DOE Facility DatabaseMichigan: Energy Resources Jump to:Delta, Ohio:Charges Jump

  19. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualPropertyd8c-a9ae-f8521cbb8489 No revision|LLC Place: Ketchum, Idaho(1) Datapalooza (1)) EDI

  20. 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

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

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01T23:59:59.000Z

    monitoring, building automation systems and load controlthe necessary building automation systems, it is likely that

  2. 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

  3. automated computerized methodology: Topics by E-print Network

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

    functions. Open Access Theses and Dissertations Summary: ??The Crew Exploration Vehicle (CEV) necessitates higher levels of automation than previous NASA vehicles due to...

  4. automated zoning methodology: Topics by E-print Network

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

    functions. Open Access Theses and Dissertations Summary: ??The Crew Exploration Vehicle (CEV) necessitates higher levels of automation than previous NASA vehicles due to...

  5. automated docking screens: Topics by E-print Network

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

    functions. Open Access Theses and Dissertations Summary: ??The Crew Exploration Vehicle (CEV) necessitates higher levels of automation than previous NASA vehicles due to...

  6. automated rendezvous targeting: Topics by E-print Network

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

    functions. Open Access Theses and Dissertations Summary: ??The Crew Exploration Vehicle (CEV) necessitates higher levels of automation than previous NASA vehicles due to...

  7. Wireless Demand Response Controls for HVAC Systems

    SciTech Connect (OSTI)

    Federspiel, Clifford

    2009-06-30T23:59:59.000Z

    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.

  8. Demand Dispatch-Intelligent

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

    CA Control Areas CO 2 Carbon Dioxide CHP Combined Heat and Power CPP Critical Peak Pricing DG Distributed Generation DOE Department of Energy DR Demand Response DRCC Demand...

  9. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag JumpID-f <Maintained By FaultSunpodsSweetwater 4aSyntheticTAUTEST

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

    SciTech Connect (OSTI)

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

    2006-04-06T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01T23:59:59.000Z

    Addressing Energy Demand through Demand Response:both the avoided energy costs (and demand charges) as wellCoordination of Energy Efficiency and Demand Response,

  13. Coordinating Automated Vehicles via Communication

    E-Print Network [OSTI]

    Bana, Soheila Vahdati

    2001-01-01T23:59:59.000Z

    1.1 Vehicle Automation . . . . . . . . . . . 1.1.1 Controlareas of technology in vehicle automation and communicationChapter 1 Introduction Vehicle Automation Automation is an

  14. 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

  15. Demand Response Valuation Frameworks Paper

    E-Print Network [OSTI]

    Heffner, Grayson

    2010-01-01T23:59:59.000Z

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

  16. 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

    2006-06-20T23:59:59.000Z

    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.

  17. Demand response enabling technology development

    E-Print Network [OSTI]

    Arens, Edward; Auslander, David; Huizenga, Charlie

    2008-01-01T23:59:59.000Z

    behavior in developing a demand response future. Phase_II_Demand Response Enabling Technology Development Phase IIYi Yuan The goal of the Demand Response Enabling Technology

  18. Demand Response Spinning Reserve Demonstration

    E-Print Network [OSTI]

    2007-01-01T23:59:59.000Z

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

  19. Demand response enabling technology development

    E-Print Network [OSTI]

    2006-01-01T23:59:59.000Z

    Demand Response Enabling Technology Development Phase IEfficiency and Demand Response Programs for 2005/2006,Application to Demand Response Energy Pricing” SenSys 2003,

  20. Energy Demand Staff Scientist

    E-Print Network [OSTI]

    Eisen, Michael

    Energy Demand in China Lynn Price Staff Scientist February 2, 2010 #12;Founded in 1988 Focused,000 2,000 3,000 4,000 5,000 6,000 7,000 2007 USChina #12;Overview:Overview: Key Energy Demand DriversKey Energy Demand Drivers · 290 million new urban residents 1990-2007 · 375 million new urban residents 2007

  1. Industrial Demand Module

    Gasoline and Diesel Fuel Update (EIA)

    Boiler, Steam, and Cogeneration (BSC) Component. The BSC Component satisfies the steam demand from the PA and BLD Components. In some industries, the PA Component produces...

  2. Demand Response In California

    Broader source: Energy.gov [DOE]

    Presentation covers the demand response in California and is given at the FUPWG 2006 Fall meeting, held on November 1-2, 2006 in San Francisco, California.

  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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model,DOE Facility DatabaseMichigan: Energy Resources Jump to:Delta,

  4. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere IRaghuraji Agro IndustriesTown of Ladoga, Indiana (Utility Company)Library <InformationTopics Ask

  5. Automated search for galactic star clusters in large multiband surveys: I. Discovery of 15 new open clusters in the Galactic anticenter region

    E-Print Network [OSTI]

    S. E. Koposov; E. V. Glushkova; I. Yu. Zolotukhin

    2008-05-09T23:59:59.000Z

    Aims: According to some estimations, there are as many as 100000 open clusters in the Galaxy, but less than 2000 of them have been discovered, measured, and cataloged. We plan to undertake data mining of multiwavelength surveys to find new star clusters. Methods: We have developed a new method to search automatically for star clusters in very large stellar catalogs, which is based on convolution with density functions. We have applied this method to a subset of the Two Micron All Sky Survey catalog toward the Galactic anticenter. We also developed a method to verify whether detected stellar groups are real star clusters, which tests whether the stars that form the spatial density peak also fall onto a single isochrone in the color-magnitude diagram. By fitting an isochrone to the data, we estimate at the same time the main physical parameters of a cluster: age, distance, color excess. Results: For the present paper, we carried out a detailed analysis of 88 overdensity peaks detected in a field of $16\\times16$ degrees near the Galactic anticenter. From this analysis, 15 overdensities were confirmed to be new open clusters and the physical and structural parameters were determined for 12 of them; 10 of them were previously suspected to be open clusters by Kronberger (2006) and Froebrich (2007). The properties were also determined for 13 yet-unstudied known open clusters, thus almost tripling the sample of open clusters with studied parameters in the anticenter. The parameters determined with this method showed a good agreement with published data for a set of well-known clusters.

  6. Automated Demand Response Opportunities in Wastewater Treatment Facilities

    E-Print Network [OSTI]

    Thompson, Lisa

    2008-01-01T23:59:59.000Z

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

  7. Scenarios for Consuming Standardized Automated Demand Response Signals

    E-Print Network [OSTI]

    Koch, Ed

    2009-01-01T23:59:59.000Z

    facility should respond. 3.3. Energy Service Company (ESCO)ESCO’s provide a broad category of services to facilities,

  8. Automated Demand Response Strategies and Commissioning Commercial Building Controls

    E-Print Network [OSTI]

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

    2006-01-01T23:59:59.000Z

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

  9. Automated Demand Response Opportunities in Wastewater Treatment Facilities

    E-Print Network [OSTI]

    Thompson, Lisa

    2008-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Piette, Mary Ann

    2010-01-01T23:59:59.000Z

    applications. Mary Ann Piette is a Staff Scientist atResponse Ed Koch, Akuacom Mary Ann Piette, Lawrence BerkeleyCA 94903 ed@akuacom.com Mary Ann Piette Lawrence Berkeley

  11. 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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742Energy ChinaofSchaefer To:Department of EnergySeacrist,theA12345Savings

  12. Controlling electric power demand

    SciTech Connect (OSTI)

    Eikenberry, J.

    1984-11-15T23:59:59.000Z

    Traditionally, demand control has not been viewed as an energy conservation measure, its intent being to reduce the demand peak to lower the electric bill demand charge by deferring the use of a block of power to another demand interval. Any energy savings were essentially incidental and unintentional, resulting from curtailment of loads that could not be assumed at another time. This article considers a microprocessor-based multiplexed system linked to a minicomputer to control electric power demand in a winery. In addition to delivering an annual return on investment of 55 percent in electric bill savings, the system provides a bonus in the form of alarm and monitoring capability for critical processes.

  13. A Supply-Demand Model Based Scalable Energy Management System for Improved Energy

    E-Print Network [OSTI]

    Bhunia, Swarup

    energy generation and consumption parameters. The system uses economics inspired supply-demand modelA Supply-Demand Model Based Scalable Energy Management System for Improved Energy Utilization Western Reserve University, *Cleveland State University, +Rockwell Automation, Cleveland, OR, USA Email

  14. An Automated, yet Interactive and Portable DB designer Ioannis Alagiannis1

    E-Print Network [OSTI]

    Polyzotis, Neoklis (Alkis)

    SQL open source DBMS. The tool suggests design features for both offline and online workloads. It providesSQL and MySQL. Thus, one has to face the dilemma of selecting an expensive commercial DBMS that provides automating tools or an open source DBMS whose lack of automated tools might increase the operational cost

  15. Building Energy Management Open-Source Software Development ...

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

    be able to optimize electricity usage to reduce energy consumption and help implement demand response (DR). This opens up demand side ancillary services markets and creates...

  16. Multiplex automated genome engineering

    DOE Patents [OSTI]

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

    2013-10-29T23:59:59.000Z

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

  17. Charmaine Toy Automation Engineer,

    E-Print Network [OSTI]

    Horowitz, Roberto

    @me.berkeley.edu Nonstationary Velocity Profiles for Emergency Vehicles on Automated Highways This paper explores the notion and usefulness of nonstationary velocity profiles for high priority emergency vehicle transit on automatedCharmaine Toy Automation Engineer, DiCon Fiberoptics, Inc., Richmond, CA 94804 e-mail: charm

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

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2013-01-01T23:59:59.000Z

    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

    2011-01-01T23:59:59.000Z

    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. AiiDA: Automated Interactive Infrastructure and Database for Computational Science

    E-Print Network [OSTI]

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

    2015-01-01T23:59:59.000Z

    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...

  1. Joint Genome Institute's Automation Approach and History

    E-Print Network [OSTI]

    Roberts, Simon

    2006-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2011-01-01T23:59:59.000Z

    An Exploration of Australian Petrol Demand: Unobserv- ableRelative Prices: Simulating Petrol Con- sumption Behavior.habit stock variable in a petrol demand regression, they

  3. Demand Response Valuation Frameworks Paper

    E-Print Network [OSTI]

    Heffner, Grayson

    2010-01-01T23:59:59.000Z

    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

  4. 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

  5. Convection automated logic oven control

    SciTech Connect (OSTI)

    Boyer, M.A.; Eke, K.I. [Apollo U.S.A. Inc., Orlando, FL (United States)] [Apollo U.S.A. Inc., Orlando, FL (United States)

    1998-03-01T23:59:59.000Z

    For the past few years, there has been a greater push to bring more automation to the cooling process. There have been attempts at automated cooking using a wide range of sensors and procedures, but with limited success. The authors have the answer to the automated cooking process; this patented technology is called Convection AutoLogic (CAL). The beauty of the technology is that it requires no extra hardware for the existing oven system. They use the existing temperature probe, whether it is an RTD, thermocouple, or thermistor. This means that the manufacturer does not have to be burdened with extra costs associated with automated cooking in comparison to standard ovens. The only change to the oven is the program in the central processing unit (CPU) on the board. As for its operation, when the user places the food into the oven, he or she is required to select a category (e.g., beef, poultry, or casseroles) and then simply press the start button. The CAL program then begins its cooking program. It first looks at the ambient oven temperature to see if it is a cold, warm, or hot start. CAL stores this data and then begins to look at the food`s thermal footprint. After CAL has properly detected this thermal footprint, it can calculate the time and temperature at which the food needs to be cooked. CAL then sets up these factors for the cooking stage of the program and, when the food has finished cooking, the oven is turned off automatically. The total time for this entire process is the same as the standard cooking time the user would normally set. The CAL program can also compensate for varying line voltages and detect when the oven door is opened. With all of these varying factors being monitored, CAL can produce a perfectly cooked item with minimal user input.

  6. Travel Demand Modeling

    SciTech Connect (OSTI)

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

    2011-01-01T23:59:59.000Z

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

  7. CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST

    E-Print Network [OSTI]

    CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST Manager Kae Lewis Acting Manager Demand Analysis Office Valerie T. Hall Deputy Director Energy Efficiency Demand Forecast report is the product of the efforts of many current and former California Energy

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

    SciTech Connect (OSTI)

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

    2012-02-14T23:59:59.000Z

    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.

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

    SciTech Connect (OSTI)

    None

    2012-02-11T23:59:59.000Z

    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.

  10. ENERGY DEMAND FORECAST METHODS REPORT

    E-Print Network [OSTI]

    CALIFORNIA ENERGY COMMISSION ENERGY DEMAND FORECAST METHODS REPORT Companion Report to the California Energy Demand 2006-2016 Staff Energy Demand Forecast Report STAFFREPORT June 2005 CEC-400. Hall Deputy Director Energy Efficiency and Demand Analysis Division Scott W. Matthews Acting Executive

  11. Demand Forecast INTRODUCTION AND SUMMARY

    E-Print Network [OSTI]

    electricity demand forecast means that the region's electricity needs would grow by 5,343 average megawattsDemand 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

  12. 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

  13. 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

  14. 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

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

    E-Print Network [OSTI]

    in Commercial Buildings S. Kiliccote, M.A. Piette, J. Mathieu, K. Parrish Environmental Energy Technologies;1 Findings from Seven Years of Field Performance Data for Automated Demand Response in Commercial Buildings. It provides a summary of participation, DR strategies and incentives. Commercial buildings can reduce peak

  16. A Verified Hybrid Controller For Automated Vehicles

    E-Print Network [OSTI]

    Lygeros, J.; Godbole, D. N.; Sastry, S.

    1997-01-01T23:59:59.000Z

    con- trollers for vehicle automation," in American ControlTomizuka, Vehicle lateral control for highway automation,"

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

    SciTech Connect (OSTI)

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

    2011-10-10T23:59:59.000Z

    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.

  18. 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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOEThe Bonneville Power Administration wouldDECOMPOSITIONPortal DecisionRichlandDelegations,Demand

  19. Automated distribution scheme speeds service restoration

    SciTech Connect (OSTI)

    Atwell, E. [Lakeland Electric and Water, FL (United States)] [Lakeland Electric and Water, FL (United States); Gamvrelis, T. [Harris Canada, Inc., Calgary, Alberta (Canada). Control Div.] [Harris Canada, Inc., Calgary, Alberta (Canada). Control Div.; Kearns, D. [S and C Electric Co., Chicago, IL (United States)] [S and C Electric Co., Chicago, IL (United States); Landman, R. [H and L Instruments, North Hampton, NH (United States)] [H and L Instruments, North Hampton, NH (United States)

    1996-01-01T23:59:59.000Z

    This article describes an automated distribution scheme that met Lakeland Electric requirements for an automated scheme that would restore power to a major customer in less than 60 seconds. In January 1993, Lakeland Electric and Water (LEW) took on the design and construction of a new 12.47-kV automated distribution system for the Publix Supermarket Industrial complex. The industrial complex in Lakeland, Florida, totals 2 million square feet and houses a dairy processing plant, bakery, produce plant, deli plant, data processing facility for Publix`s entire retail network, purchasing department, as well as several maintenance facilities. The retail chain is LEW`s largest customer with a peak demand of 15.5 MW and a load factor of 81%. Publix`s rapid expansion plan has placed a great deal of pressure on this facility to perform at peak level with no interruptions of product flow. The task at hand was to provide Publix with a state-of-the-art, automated, distribution system built to withstand the inherent weather-related situations in central Florida, lightning and hurricanes.

  20. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01T23:59:59.000Z

    3-4 Table 4-1. How Building Automation Systems (i.e. , EMCS)infrastructure building automation system British thermalversions of a building automation system (BAS). 1 When

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

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01T23:59:59.000Z

    BEST PRACTICES AND RESULTS OF DR IMPLEMENTATION . 31 Encouraging End-User Participation: The Role of Incentives 16 Demand Response

  2. CALIFORNIA ENERGY DEMAND 2014-2024 PRELIMINARY

    E-Print Network [OSTI]

    CALIFORNIA ENERGY DEMAND 2014-2024 PRELIMINARY FORECAST Volume 1: Statewide Electricity Demand, End-User Natural Gas Demand, and Energy Efficiency The California Energy Demand 2014-2024 Preliminary Forecast, Volume 1: Statewide Electricity Demand

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

    SciTech Connect (OSTI)

    Lu, Shuai; Kintner-Meyer, Michael CW

    2008-06-06T23:59:59.000Z

    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.

  4. Electrical Demand Control

    E-Print Network [OSTI]

    Eppelheimer, D. M.

    1984-01-01T23:59:59.000Z

    to the reservoir. Util i ties have iiting for a number of years. d a rebate for reducing their When the utility needs to shed is sent to turn off one or mnre mer's electric water heater or equipment. wges have enticed more and more same strategies... an increased need for demand 1 imiting. As building zone size is reduced, total instal led tonnage increases due to inversfty. Each compressor is cycled by a space thermostat. There is no control system to limit the number of compressors running at any...

  5. Demand Response: Load Management Programs 

    E-Print Network [OSTI]

    Simon, J.

    2012-01-01T23:59:59.000Z

    CenterPoint Load 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...

  6. Demand Response: Load Management Programs

    E-Print Network [OSTI]

    Simon, J.

    2012-01-01T23:59:59.000Z

    CenterPoint Load 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...

  7. Cognitive Engineering Automation and Human

    E-Print Network [OSTI]

    Parasuraman, Raja

    · Home automation · Robotics · Unmanned vehicles (UAVs and UGVs) · Drug design/Molecular geneticsCognitive Engineering PSYC 530 Automation and Human Performance Raja Parasuraman #12;Overview Automation-Related Accidents Levels and Stages of Automation Information Acquisition and Analysis Decision

  8. 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

  9. 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

  10. 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.

    2014-10-29T23:59:59.000Z

    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.

  11. Automated Systematic Testing of Open Distributed Programs

    E-Print Network [OSTI]

    Sen, Koushik

    to alternate behaviors. At the same time, we use the concrete execution to determine, at runtime, the partial behaviors. At the same time, we use the concrete execution to guide the symbolic execution along a distinct, to explore all distinct behaviors that may result from a program's execution given different data inputs

  12. Automated Systematic Testing of Open Distributed Programs

    E-Print Network [OSTI]

    Sen, Koushik

    . At the same time, we use the concrete execution to determine, at runtime, the partial order of events. At the same time, we use the concrete execution to guide the symbolic execution along a distinct execution behaviors that may result from a program's execution given different data inputs and schedules. The key idea

  13. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty Edit withTianlinPapersWindey Wind Home Rmckeel's Homeguidance document Homeautomation

  14. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere IRaghuraji Agro Industries Pvt LtdShawangunk, New York:SiG26588°,SocorromercurySolaire Direct

  15. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, searchOfRoseConcernsCompany Oil and GasOff<div/0 en The Energybegun!

  16. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluating A Potential MicrohydroDistrict of Columbia: Energy Resources Jump to:Ditec

  17. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal Pwer Plant Jump to:LandownersLuther,Jemez PuebloManteca, California: EnergyChange |Manz

  18. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal Pwer Plant JumpMarysville, Ohio:Menomonee|MililaniMindanaoMinuanoIV Jump to: navigation,Mirle

  19. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup |JilinLu an GroupInformation Meier Solar Solutions GmbH

  20. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:EzfeedflagBiomass Conversions Inc Jump to:Auriga Energy JumpTexas:Texas:Alabama:Alley

  1. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual Siteof EnergyInnovation in CarbonofBiotinsBostonBridger Valley

  2. Development of Building Automation and Control Systems

    E-Print Network [OSTI]

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

    2012-01-01T23:59:59.000Z

    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

  3. Demonstration of Automated Heavy-Duty Vehicles

    E-Print Network [OSTI]

    2006-01-01T23:59:59.000Z

    a future in which vehicle automation technologies are ableto support the heavy vehicle automation including PrecisionCommittee on Vehicle-Highway Automation, and the attendees

  4. Development of Building Automation and Control Systems

    E-Print Network [OSTI]

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

    2012-01-01T23:59:59.000Z

    design flow for building automation systems that focuses onflow for building automation and control systems,’’ in Proc.Development of Building Automation and Control Systems Yang

  5. Automated Lattice Perturbation Theory

    SciTech Connect (OSTI)

    Monahan, Christopher

    2014-11-01T23:59:59.000Z

    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.

  6. Automated pavement crack detection

    E-Print Network [OSTI]

    Rao, Ashok Madhava

    1991-01-01T23:59:59.000Z

    : Electrical Engineering AUTOMATED PAVEMENT CRACK DETECTION A Thesis by ASHOK MADHAVA RAO Approved as to style and content by . c Norman C. Grisw d (Chair of Committ ) Nasser Kehtarnavaz (Member) g, J~, Karan Watson Robert L. Lytt (Member) Jo W.... Howze (Head of Department) December 1991 111 ABSTRACT Automated Pavement Crack Detection. (December 1991) Ashok Madhava, Rao, B. E. , Mysore University Chair of Advisory Committee: Norman. C. Griswold Due to load, environmental and structural...

  7. Demand Response Programs, 6. edition

    SciTech Connect (OSTI)

    NONE

    2007-10-15T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Aden, Nathaniel

    2010-01-01T23:59:59.000Z

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

  9. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

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

    2008-01-01T23:59:59.000Z

    gas demands are forecast for the four natural gas utilitythe 2006-2016 Forecast. Commercial natural gas demand isforecasts and demand scenarios. Electricity planning area Natural gas

  10. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01T23:59:59.000Z

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

  11. Retail Demand Response in Southwest Power Pool

    E-Print Network [OSTI]

    Bharvirkar, Ranjit

    2009-01-01T23:59:59.000Z

    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

  12. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01T23:59:59.000Z

    District Small Business Summer Solutions: Energy and DemandSummer Solutions: Energy and Demand Impacts Monthly Energy> B-2 Coordination of Energy Efficiency and Demand Response

  13. Coupling Renewable Energy Supply with Deferrable Demand

    E-Print Network [OSTI]

    Papavasiliou, Anthony

    2011-01-01T23:59:59.000Z

    World: Renewable Energy and Demand Response Proliferation intogether the renewable energy and demand response communityimpacts of renewable energy and demand response integration

  14. DEMAND CONTROLLED VENTILATION AND CLASSROOM VENTILATION

    E-Print Network [OSTI]

    Fisk, William J.

    2014-01-01T23:59:59.000Z

    of energy and environmental benefits of demand controlledindicate the energy and cost savings for demand controlled24) (California Energy Commission 2008), demand controlled

  15. Demand Controlled Ventilation and Classroom Ventilation

    E-Print Network [OSTI]

    Fisk, William J.

    2014-01-01T23:59:59.000Z

    of energy and environmental benefits of demand controlled indicate the energy and cost savings for  demand controlled 24) (California Energy  Commission 2008), demand controlled 

  16. Hawaiian Electric Company Demand Response Roadmap Project

    E-Print Network [OSTI]

    Levy, Roger

    2014-01-01T23:59:59.000Z

    integrating HECO and Hawaii Energy demand response relatedpotential. Energy efficiency and demand response efforts areBoth  energy  efficiency  and  demand  response  should  

  17. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01T23:59:59.000Z

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

  18. Demand Responsive Lighting: A Scoping Study

    E-Print Network [OSTI]

    Rubinstein, Francis; Kiliccote, Sila

    2007-01-01T23:59:59.000Z

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

  19. Hawaiian Electric Company Demand Response Roadmap Project

    E-Print Network [OSTI]

    Levy, Roger

    2014-01-01T23:59:59.000Z

    and best practices to guide HECO demand response developmentbest practices for DR renewable integration – Technically demand responseof best practices. This is partially because demand response

  20. Strategies for Demand Response in Commercial Buildings

    E-Print Network [OSTI]

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

    2006-01-01T23:59:59.000Z

    Strategies for Demand Response in Commercial Buildings DavidStrategies for Demand Response in Commercial Buildings Davidadjusted for demand response in commercial buildings. The

  1. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01T23:59:59.000Z

    In terms of demand response capability, building operatorsautomated demand response and improve building energy andand demand response features directly into building design

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

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01T23:59:59.000Z

    DEMAND RESPONSE .7 Wholesale Marketuse at times of high wholesale market prices or when systemenergy expenditure. In wholesale markets, spot energy prices

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

    SciTech Connect (OSTI)

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

    2012-01-18T23:59:59.000Z

    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.

  4. Demand Response and Energy Efficiency

    E-Print Network [OSTI]

    Demand Response & Energy Efficiency International Conference for Enhanced Building Operations ESL-IC-09-11-05 Proceedings of the Ninth International Conference for Enhanced Building Operations, Austin, Texas, November 17 - 19, 2009 2 ?Less than 5... for Enhanced Building Operations, Austin, Texas, November 17 - 19, 2009 5 What is Demand Response? ?The temporary reduction of electricity demanded from the grid by an end-user in response to capacity shortages, system reliability events, or high wholesale...

  5. 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

    2009-06-28T23:59:59.000Z

    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.

  6. Components, Platforms and Possibilities: Towards Generic Automation for MDA

    E-Print Network [OSTI]

    Jackson, Daniel

    Components, Platforms and Possibilities: Towards Generic Automation for MDA Ethan K. Jackson Model-driven architecture (MDA) is a model-based approach for engineering complex software systems. MDA and software re- quirements evolve. However, efforts to apply MDA in in- dustrial settings expose several open

  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. General approach to automation of FLASH subsystems

    E-Print Network [OSTI]

    General approach to automation of FLASH subsystems Boguslaw Kosda #12;Agenda Motivation Nature of automation software for high energy experiments. Ultimate role of the automation software: Maximization of lasers availability. Automation of routine activities as startup, shutdown ... Continuous monitoring

  9. Demand Response Technology Roadmap A

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

    workshop agendas, presentation materials, and transcripts. For the background to the Demand Response Technology Roadmap and to make use of individual roadmaps, the reader is...

  10. ELECTRICITY DEMAND FORECAST COMPARISON REPORT

    E-Print Network [OSTI]

    CALIFORNIA ENERGY COMMISSION ELECTRICITY DEMAND FORECAST COMPARISON REPORT STAFFREPORT June 2005.................................................................................................................................3 PACIFIC GAS & ELECTRIC PLANNING AREA ........................................................................................9 Commercial Sector

  11. Driving Demand | Department of Energy

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

    strategies, results achieved to date, and advice for other programs. Driving Demand for Home Energy Improvements. This guide, developed by the Lawrence Berkeley National...

  12. Demand Response Technology Roadmap M

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

    between May 2014 and February 2015. The Bonneville Power Administration (BPA) Demand Response Executive Sponsor Team decided upon the scope of the project in May. Two subsequent...

  13. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

    Energy Commission's final forecasts for 2012­2022 electricity consumption, peak, and natural gas demand Electricity, demand, consumption, forecast, weather normalization, peak, natural gas, self generation CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 2: Electricity Demand

  14. Retail Demand Response in Southwest Power Pool

    E-Print Network [OSTI]

    Bharvirkar, Ranjit

    2009-01-01T23:59:59.000Z

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

  15. UBC STUDENT HOUSING DEMAND STUDY

    E-Print Network [OSTI]

    Ollivier-Gooch, Carl

    UBC STUDENT HOUSING DEMAND STUDY Presented by Nancy Knight and Andrew Parr FEBRUARY 5, 2010 #12;PURPOSE · To determine the need/demand for future on- campus student housing · To address requests from · A survey of students, and analysis of housing markets, and preparation of a forecast · The timeline

  16. Harnessing the power of demand

    SciTech Connect (OSTI)

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

    2008-03-15T23:59:59.000Z

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

  17. ERCOT Demand Response Paul Wattles

    E-Print Network [OSTI]

    Mohsenian-Rad, Hamed

    changes or incentives.' (FERC) · `Changes in electric use by demand-side resources from their normalERCOT Demand Response Paul Wattles Senior Analyst, Market Design & Development, ERCOT Whitacre thermostats -- Other DLC Possible triggers: Real-time prices, congestion management, 4CP response paid

  18. Demand Response for Ancillary Services

    SciTech Connect (OSTI)

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

    2013-01-01T23:59:59.000Z

    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.

  19. 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)

    1999-01-01T23:59:59.000Z

    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.

  20. Office Automation Document Preparation

    E-Print Network [OSTI]

    North Carolina at Chapel Hill, University of

    .2 Distinctions 1.3 Facilities 1.3.1 Document Preparation 1.3.2 Records Management 1.3.3 Communication 1 organizations contemplating the installation of document-preparation systems. * Administrative managersOffice Automation and Document Preparation for the v' University of North Carolina at Chapel Hill

  1. Automated Microbial Genome Annotation

    SciTech Connect (OSTI)

    Land, Miriam [DOE Joint Genome Institute at Oak Ridge National Laboratory

    2009-05-29T23:59:59.000Z

    Miriam Land of the DOE Joint Genome Institute at Oak Ridge National Laboratory gives a talk on the current state and future challenges of moving toward automated microbial genome annotation at the "Sequencing, Finishing, Analysis in the Future" meeting in Santa Fe, NM

  2. automated serum chemistry: Topics by E-print Network

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

    Summary: Test Automation Test Automation Mohammad Mousavi Eindhoven University of Technology, The Netherlands Software Testing 2013 Mousavi: Test Automation 12;Test Automation...

  3. Marketing Demand-Side Management

    E-Print Network [OSTI]

    O'Neill, M. L.

    1988-01-01T23:59:59.000Z

    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...

  4. Community Water Demand in Texas

    E-Print Network [OSTI]

    Griffin, Ronald C.; Chang, Chan

    Solutions to Texas water policy and planning problems will be easier to identify once the impact of price upon community water demand is better understood. Several important questions cannot be addressed in the absence of such information...

  5. Demand response enabling technology development

    E-Print Network [OSTI]

    2006-01-01T23:59:59.000Z

    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. Overview of Demand Side Response

    Broader source: Energy.gov [DOE]

    Presentation—given at the Federal Utility Partnership Working Group (FUPWG) Fall 2008 meeting—discusses the utility PJM's demand side response (DSR) capabilities, including emergency and economic responses.

  7. 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.

    2007-05-01T23:59:59.000Z

    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.

  8. CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST

    E-Print Network [OSTI]

    CALIFORNIA ENERGY DEMAND 2014­2024 REVISED FORECAST Volume 1: Statewide Electricity Demand, EndUser Natural Gas Demand, and Energy Efficiency SEPTEMBER 2013 CEC2002013004SDV1REV CALIFORNIA The California Energy Demand 2014 ­ 2024 Revised Forecast, Volume 1: Statewide Electricity Demand and Methods

  9. CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST

    E-Print Network [OSTI]

    CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST Volume 2: Electricity Demand The California Energy Demand 2014 ­ 2024 Revised Forecast, Volume 2: Electricity Demand by Utility Planning Area Energy Policy Report. The forecast includes three full scenarios: a high energy demand case, a low

  10. Falling MTBE demand bursts the methanol bubble

    SciTech Connect (OSTI)

    Wiesmann, G.; Cornitius, T.

    1995-03-01T23:59:59.000Z

    Methanol spot markets in Europe and the US have been hit hard by weakening demand from methyl tert-butyl ether (MTBE) producers. In Europe, spot prices for domestic T2 product have dropped to DM620-DM630/m.t. fob from early-January prices above DM800/m.t. and US spot prices have slipped to $1.05/gal fob from $1.35/gal. While chemical applications for methanol show sustained demand, sharp methanol hikes during 1994 have priced MTBE out of the gasoline-additive market. {open_quotes}We`ve learned an important lesson. We killed [MTBE] applications in the rest of the world,{close_quotes} says one European methanol producer. Even with methanol currently at DM620/m.t., another manufacturer points out, MTBE production costs still total $300/m.t., $30/m.t. more than MTBE spot prices. Since late 1994, Europe`s 3.3-million m.t./year MTBE production has been cut back 30%.

  11. Automated gas chromatography

    DOE Patents [OSTI]

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

    1999-07-13T23:59:59.000Z

    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.

  12. Theorie des langages Automates `a pile

    E-Print Network [OSTI]

    Bonzon, Elise

    Th´eorie des langages Automates `a pile Elise Bonzon http://web.mi.parisdescartes.fr/ bonzon/ elise.bonzon@parisdescartes.fr 1 / 62 Th´eorie des langages #12;Automates `a pile Automates `a pile Introduction Rappels sur les piles Automates `a pile : d´efinition Automates `a pile : configurations Les crit`eres d

  13. Methodology for Prototyping Increased Levels of Automation

    E-Print Network [OSTI]

    Valasek, John

    of automation than previous NASA vehicles, due to program requirements for automation, including Automated Ren into a human space flight vehicle, NASA has created the Function-specific Level of Autonomy and Automation Tool levels of automation than previous NASA vehicles. A key technology to the success of the CEV

  14. 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.

  15. Demand response-enabled residential thermostat controls.

    E-Print Network [OSTI]

    Chen, Xue; Jang, Jaehwi; Auslander, David M.; Peffer, Therese; Arens, Edward A

    2008-01-01T23:59:59.000Z

    human dimension of demand response technology from a caseArens, E. , et al. 2008. Demand Response Enabling TechnologyArens, E. , et al. 2006. Demand Response Enabling Technology

  16. Demand Response as a System Reliability Resource

    E-Print Network [OSTI]

    Joseph, Eto

    2014-01-01T23:59:59.000Z

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

  17. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

    the California Energy Commission staff's revised forecasts for 2012­2022 electricity consumption, peak Electricity, demand, consumption, forecast, weather normalization, peak, natural gas, self generation REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 1: Statewide Electricity Demand

  18. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

    Energy Commission staff's revised forecasts for 2012­2022 electricity consumption, peak, and natural Electricity, demand, consumption, forecast, weather normalization, peak, natural gas, self generation REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 2: Electricity Demand by Utility

  19. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

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

    2008-01-01T23:59:59.000Z

    California Energy Demand Scenario Projections to 2050 RyanCEC (2003a) California energy demand 2003-2013 forecast.CEC (2005a) California energy demand 2006-2016: Staff energy

  20. National Action Plan on Demand Response

    Broader source: Energy.gov [DOE]

    Presentation—given at the Federal Utility Partnership Working Group (FUPWG) Fall 2008 meeting—discusses the National Assessment of Demand Response study, the National Action Plan for Demand Response, and demand response as related to the energy outlook.

  1. MTBE: Capacity boosts on hold amid demand concerns

    SciTech Connect (OSTI)

    NONE

    1995-05-03T23:59:59.000Z

    Uncertainty reigns in the methyl tert-butyl ether (MTBE) market. {open_quotes}We have no choice but to put our expansion plans on the back burner,{close_quotes} says one producer. {open_quotes}Because of government actions, there are no MTBE plants being built or expanded.{close_quotes} Spot MTBE prices have risen ti 82 cts- 83 cts/gal from 76 cts-78 cts/gal earlier this month as the demand for octane enhancement increases for the summer driving season. Some observers say EPA may relax different oxygen requirements for gasoline in different seasons. That would simplify production and supply for MTBE makers.

  2. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01T23:59:59.000Z

    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

  3. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01T23:59:59.000Z

    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).

  4. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

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

    2008-01-01T23:59:59.000Z

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

  5. Demand Controlled Ventilation and Classroom Ventilation

    E-Print Network [OSTI]

    Fisk, William J.

    2014-01-01T23:59:59.000Z

    use of demand control ventilation systems in general officedemand controlled  ventilation systems, Dennis DiBartolomeo the demand controlled ventilation system increased the rate 

  6. Supply chain planning decisions under demand uncertainty

    E-Print Network [OSTI]

    Huang, Yanfeng Anna

    2008-01-01T23:59:59.000Z

    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 ...

  7. Demand Responsive Lighting: A Scoping Study

    E-Print Network [OSTI]

    Rubinstein, Francis; Kiliccote, Sila

    2007-01-01T23:59:59.000Z

    sector, the demand response potential of California buildinga demand response event prohibit a building’s participationdemand response strategies in California buildings are

  8. Sandia National Laboratories: demand response inverter

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

    demand response inverter ECIS-Princeton Power Systems, Inc.: Demand Response Inverter On March 19, 2013, in DETL, Distribution Grid Integration, Energy, Energy Surety, Facilities,...

  9. 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

    2009-10-08T23:59:59.000Z

    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.

  10. International Oil Supplies and Demands

    SciTech Connect (OSTI)

    Not Available

    1992-04-01T23:59:59.000Z

    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. Turkey's energy demand and supply

    SciTech Connect (OSTI)

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

    2009-07-01T23:59:59.000Z

    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.

  12. International Oil Supplies and Demands

    SciTech Connect (OSTI)

    Not Available

    1991-09-01T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

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

  14. Automation of Painted Slate Inspection

    E-Print Network [OSTI]

    Whelan, Paul F.

    Automation of Painted Slate Inspection BY Tim Carew (B.Eng.) carewt@eeng.dcu.ie Submitted...........................................................................................................18 2.1 Prior research on inspection of slates

  15. Valliappa Lakshmanan Automating the Analysis of

    E-Print Network [OSTI]

    Lakshmanan, Valliappa

    Geospatial Images January 5, 2012 Springer #12;Contents 1 Automated Analysis of Spatial Grids: MotivationValliappa Lakshmanan Automating the Analysis of Spatial Grids A Practitioner's Guide to Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.5 Challenges in Automated Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1

  16. automation: Topics by E-print Network

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

    Websites Summary: of a partial automation since they act on the control part of the vehicle. This increasing automationABV- A Low Speed Automation Project to Study the...

  17. Integrated, Automated Distributed Generation Technologies Demonstration

    SciTech Connect (OSTI)

    Jensen, Kevin

    2014-09-30T23:59:59.000Z

    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.

  18. Laboratory Testing of Demand-Response Enabled Household Appliances

    SciTech Connect (OSTI)

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

    2013-10-01T23:59:59.000Z

    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.

  19. Integration of automation design information using XML technologies

    E-Print Network [OSTI]

    Integration of automation design information using XML technologies Master of Science Thesis Mika Degree Program Institute of Automation and Control Viinikkala, Mika: Integration of automation design Software Ltd., Metso Automation, and TEKES Department of Automation June 2002 Keywords: System integration

  20. 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

    2014-07-01T23:59:59.000Z

    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.

  1. Demand Response Programs for Oregon

    E-Print Network [OSTI]

    wholesale prices and looming shortages in Western power markets in 2000-01, Portland General Electric programs for large customers remain, though they are not active at current wholesale prices. Other programs demand response for the wholesale market -- by passing through real-time prices for usage above a set

  2. 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

  3. Water demand management in Kuwait

    E-Print Network [OSTI]

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

    2006-01-01T23:59:59.000Z

    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 ...

  4. obesity demands more than just

    E-Print Network [OSTI]

    Qian, Ning

    #12;The World That Makes Us Fat ***** ***** ***** Overcoming obesity demands more than just. By Melinda Wenner Moyer Illustrations by A. Richard Allen 27 #12;ON ONE LEVEL, of course, obesity has a sim to pollutants. Their research suggests that to solve the problem of obesity--and, ultimately, to prevent it from

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

    E-Print Network [OSTI]

    Benenson, P.

    2010-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    McKane, Aimee T.

    2009-01-01T23:59:59.000Z

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

  7. Automated fiber pigtailing machine

    DOE Patents [OSTI]

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

    1999-01-05T23:59:59.000Z

    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.

  8. Methods for Multisweep Automation

    SciTech Connect (OSTI)

    SHEPHERD,JASON F.; MITCHELL,SCOTT A.; KNUPP,PATRICK; WHITE,DAVID R.

    2000-09-14T23:59:59.000Z

    Sweeping has become the workhorse algorithm for creating conforming hexahedral meshes of complex models. This paper describes progress on the automatic, robust generation of MultiSwept meshes in CUBIT. MultiSweeping extends the class of volumes that may be swept to include those with multiple source and multiple target surfaces. While not yet perfect, CUBIT's MultiSweeping has recently become more reliable, and been extended to assemblies of volumes. Sweep Forging automates the process of making a volume (multi) sweepable: Sweep Verification takes the given source and target surfaces, and automatically classifies curve and vertex types so that sweep layers are well formed and progress from sources to targets.

  9. Automated fiber pigtailing machine

    DOE Patents [OSTI]

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

    1999-01-01T23:59:59.000Z

    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.

  10. 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

  11. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

    CALIFORNIA ENERGY DEMAND 2014­2024 FINAL FORECAST Volume 1: Statewide Electricity Demand, EndUser Natural Gas Demand, and Energy Efficiency DECEMBER 2013 CEC2002013004SFV1 CALIFORNIA and expertise of numerous California Energy Commission staff members in the Demand Analysis Office. In addition

  12. Demand Side Management in Rangan Banerjee

    E-Print Network [OSTI]

    Banerjee, Rangan

    Demand Side Management in Industry Rangan Banerjee Talk at Baroda in Birla Corporate Seminar August 31,2007 #12;Demand Side Management Indian utilities ­ energy shortage and peak power shortage. Supply for Options ­ Demand Side Management (DSM) & Load Management #12;DSM Concept Demand Side Management (DSM) - co

  13. Demand Response as a System Reliability Resource

    E-Print Network [OSTI]

    Joseph, Eto

    2014-01-01T23:59:59.000Z

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

  14. Near Optimal Demand-Side Energy Management Under Real-time Demand-Response Pricing

    E-Print Network [OSTI]

    Boutaba, Raouf

    Near Optimal Demand-Side Energy Management Under Real-time Demand-Response Pricing Jin Xiao, Jae--In this paper, we present demand-side energy manage- ment under real-time demand-response pricing as a task, demand-response, energy management I. INTRODUCTION The growing awareness of global climate change has

  15. Towards Automated Service Composition using Policy Ontology in Building Automation System

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Towards Automated Service Composition using Policy Ontology in Building Automation System Son N.crespi}@it-sudparis.eu Abstract--Automated service composition is critical for suc- cessfully implementing Building Automation-service composition; semantic web; policy; ontol- ogy; building automation system; I. INTRODUCTION In building

  16. L3 Informatique Automates et langages formels 4 mars 2009 TD 5 : Automates `a pile

    E-Print Network [OSTI]

    Schmitz, Sylvain

    L3 Informatique Automates et langages formels 4 mars 2009 TD 5 : Automates `a pile Exercice 1 (Exemples d'automates `a pile). Donner un automate `a pile A = Q, , Z, T, q0, z0, F pour chacun des trois pile. Montrer que l'on peut construire un automate `a pile A ´equivalent avec une relation de

  17. ABV-A Low Speed Automation Project to Study the Technical Feasibility of Fully Automated Driving

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    on vehicle automation since many years. From 1987 to 1995 the European Commission funded the 800 million concepts of vehicles designed as fully automated vehicles [1]. Beyond its fully automation ability Automated Highway System Consortium (NAHSC) that demonstrated about 20 automated vehicles in Demo'97 on I-15

  18. 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

  19. automated on-line separation-preconcentration: Topics by E-print...

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

    Automation 12;Test Automation Outline Test Automation Mousavi: Test Automation 12;Test Automation Why? Challenges of Manual Testing Test-case design: Choosing inputs Mousavi,...

  20. automated on-line solvent: Topics by E-print Network

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

    Automation 12;Test Automation Outline Test Automation Mousavi: Test Automation 12;Test Automation Why? Challenges of Manual Testing Test-case design: Choosing inputs Mousavi,...

  1. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag JumpID-fTriWildcat 1 Wind Projectsource History ViewZAPZinccellranking of utilities

  2. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation,PillarPublicationType JumpDOEInvolve Jump to: navigation,DayQuantity

  3. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdfGetecGtel JumpCounty, Texas: EnergyHy9 CorporationHydraA) Jump to:

  4. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, searchOfRoseConcerns Jump to:Neppel Wind Power Project Jump to:Nestle

  5. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model,DOEHazel Crest,EnergySerranopolis JumpESL Jump to:CostaEnergyGridEnergySolve

  6. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model,DOE Facility DatabaseMichigan: Energy Resources Jump to:Delta, Ohio:Charges

  7. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual Siteof EnergyInnovation in Carbon Capture andsoftwareAsianEnergy Information Assisting

  8. Real-time Pricing Demand Response in Operations

    SciTech Connect (OSTI)

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

    2012-07-26T23:59:59.000Z

    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.

  9. 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

    2011-10-01T23:59:59.000Z

    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.

  10. Quantification of latent travel demand on new urban facilities in the state of Texas

    E-Print Network [OSTI]

    Henk, Russell H

    1989-01-01T23:59:59.000Z

    transportation facility was opened for use. The comparison of the photographs allowed for any changes in land use to be easily recognized. 15 Preliminar Anal sis of Variables Identification of Latent Travel Oemand The first step in this analysis of latent... facilities (3). Therefore, this highway characteristic was also included as a possible latent demand indicator. Detailed Anal sis of Variables In order to examine their relationships with latent travel demand on new urban transportation facilities...

  11. Demand Response Providing Ancillary Services

    E-Print Network [OSTI]

    Wholesale Markets Presentation by Jason MacDonald Grid Integration Group, Lawrence Berkeley National;Contents · Introduction ­ Wholesale Markets and AS ­ Why DR for AS? ­ Market Clearing Price · Value/RTOs · ISO/RTOs are balancing authorities that run open wholesale markets for both energy and Ancillary

  12. Open Access: From Myth to Paradox

    ScienceCinema (OSTI)

    Paul Ginsparg

    2010-01-08T23:59:59.000Z

    True open access to scientific publications not only gives readers the possibility to read articles without paying subscription, but also makes the material available for automated ingestion and harvesting by 3rd parties. Once articles and associated data become universally treatable as computable objects, openly available to 3rd party aggregators and value-added services, what new services can we expect, and how will they change the way that researchers interact with their scholarly communications infrastructure? I will discuss straightforward applications of existing ideas and services, including citation analysis, collaborative filtering, external database linkages, interoperability, and other forms of automated markup, and speculate on the sociology of the next generation of users.

  13. Automation of Termination: Abstracting CCG through MWG Automation of Termination: Abstracting Calling

    E-Print Network [OSTI]

    Ayala-Rincón, Mauricio

    Automation of Termination: Abstracting CCG through MWG Automation of Termination: Abstracting of Termination: Abstracting CCG through MWG Motivation Termination analysis is a fundamental topic in computer science. While classical results state the undecidability of various termination problems, automated

  14. Physically-based demand modeling 

    E-Print Network [OSTI]

    Calloway, Terry Marshall

    1980-01-01T23:59:59.000Z

    Transactions on Automatic Control, vol. AC-19, December 1974, pp. 887-893. L3] |4] LS] [6] [7] LB] C. W. Brice and S. K. Jones, MPhysically-Based Demand Modeling, d EC-77-5-01-5057, RF 3673, Electric Power Institute, Texas A&M University, October 1978.... C. W. Br ice and 5, K, Jones, MStochastically-Based Physical Load Models Topical Report, " EC-77-5-01-5057, RF 3673, Electric Power Institute, Texas A&M University, May 1979. S. K. Jones and C. W. Brice, "Point Process Models for Power System...

  15. Aspects of automation mode confusion

    E-Print Network [OSTI]

    Wheeler, Paul H. (Paul Harrison)

    2007-01-01T23:59:59.000Z

    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 ...

  16. A Survey of Automated Deduction 

    E-Print Network [OSTI]

    Bundy, Alan

    We survey research in the automation of deductive inference, from its beginnings in the early history of computing to the present day. We identify and describe the major areas of research interest and their applications. ...

  17. Automated Assembly Using Feature Localization

    E-Print Network [OSTI]

    Gordon, Steven Jeffrey

    1986-12-01T23:59:59.000Z

    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 ...

  18. Facilities Automation and Energy Management

    E-Print Network [OSTI]

    Jen, D. P.

    1983-01-01T23:59:59.000Z

    Computerized facilities automation and energy management systems can be used to maintain high levels of facilities operations efficiencies. The monitoring capabilities provides the current equipment and process status, and the analysis...

  19. Justice and the demands of realism

    E-Print Network [OSTI]

    Munro, Daniel K., 1972-

    2006-01-01T23:59:59.000Z

    The dissertation examines how concerns about the demands of realism should be addressed in political theories of justice. It asks whether the demands of realism should affect the construction of principles of justice and, ...

  20. Industrial Equipment Demand and Duty Factors

    E-Print Network [OSTI]

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

    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 compressors were near 100...

  1. automation simulation system: Topics by E-print Network

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

    and the future expectations and challenges in process automation and power system automation. Anannya Mukherjee 9 Emergency Vehicle Maneuvers and Control Laws for Automated...

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

    Energy Savers [EERE]

    drivingdemandsocialmedia010611.pdf More Documents & Publications Marketing & Driving Demand: Social Media Tools & Strategies - January 16, 2011 Social Media for Natural...

  3. Hawaiian Electric Company Demand Response Roadmap Project

    E-Print Network [OSTI]

    Levy, Roger

    2014-01-01T23:59:59.000Z

    renewable integration capability. Coordinating and integrating HECO and Hawaii Energy demand response related activities has the potential

  4. Wireless Demand Response Controls for HVAC Systems

    E-Print Network [OSTI]

    Federspiel, Clifford

    2010-01-01T23:59:59.000Z

    temperature-based demand response in buildings that havedemand response advantages of global zone temperature setup in buildings

  5. Demand Responsive Lighting: A Scoping Study

    E-Print Network [OSTI]

    Rubinstein, Francis; Kiliccote, Sila

    2007-01-01T23:59:59.000Z

    demand-side management (DSM) framework presented in Table x provides three major areas for changing electric loads in buildings:

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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 are new actors in the energy scenario: they gather a group of energy consumers and implement a demand

  13. Transportation Energy: Supply, Demand and the Future

    E-Print Network [OSTI]

    Saldin, Dilano

    Transportation Energy: Supply, Demand and the Future http://www.uwm.edu/Dept/CUTS//2050/energy05 as a source of energy. Global supply and demand trends will have a profound impact on the ability to use our) Transportation energy demand in the U.S. has increased because of the greater use of less fuel efficient vehicles

  14. Demand Side Bidding. Final Report

    SciTech Connect (OSTI)

    Spahn, Andrew

    2003-12-31T23:59:59.000Z

    This document sets forth the final report for a financial assistance award for the National Association of Regulatory Utility Commissioners (NARUC) to enhance coordination between the building operators and power system operators in terms of demand-side responses to Location Based Marginal Pricing (LBMP). Potential benefits of this project include improved power system reliability, enhanced environmental quality, mitigation of high locational prices within congested areas, and the reduction of market barriers for demand-side market participants. NARUC, led by its Committee on Energy Resources and the Environment (ERE), actively works to promote the development and use of energy efficiency and clean distributive energy policies within the framework of a dynamic regulatory environment. Electric industry restructuring, energy shortages in California, and energy market transformation intensifies the need for reliable information and strategies regarding electric reliability policy and practice. NARUC promotes clean distributive generation and increased energy efficiency in the context of the energy sector restructuring process. NARUC, through ERE's Subcommittee on Energy Efficiency, strives to improve energy efficiency by creating working markets. Market transformation seeks opportunities where small amounts of investment can create sustainable markets for more efficient products, services, and design practices.

  15. Demand Response Valuation Frameworks Paper

    SciTech Connect (OSTI)

    Heffner, Grayson

    2009-02-01T23:59:59.000Z

    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.

  16. Secure Demand Shaping for Smart Grid On constructing probabilistic demand response schemes

    E-Print Network [OSTI]

    Sastry, S. Shankar

    Secure Demand Shaping for Smart Grid On constructing probabilistic demand response schemes. Developing novel schemes for demand response in smart electric gird is an increasingly active research area/SCADA for demand response in smart infrastructures face the following dilemma: On one hand, in order to increase

  17. Patterns of crude demand: Future patterns of demand for crude oil as a func-

    E-Print Network [OSTI]

    Langendoen, Koen

    #12;2 #12;Patterns of crude demand: Future patterns of demand for crude oil as a func- tion schemes, and/or change quality of the feedstock (crude). Demand for crude oil is growing, especially perspective. This thesis aims pre- cisely at understanding the quality of oil from a demand side perspective

  18. Using Temporal Information in an Automated Classification of Summer, Marginal Ice Zone Imagery*

    E-Print Network [OSTI]

    Kansas, University of

    Using Temporal Information in an Automated Classification of Summer, Marginal Ice Zone Imagery, even with the human eye. BackScatter instability causu the intensities of the fiistyear ice, multiyear ice, and open water classes to intermix, thus making an intensity-based classification invalid

  19. Hochtief | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup | Open EnergyInformation HessHirschmann Automation and

  20. Automation in image cytometry : continuous HCS and kinetic image cytometry

    E-Print Network [OSTI]

    Charlot, David J.

    2012-01-01T23:59:59.000Z

    OF CALIFORNIA, SAN DIEGO Automation in Image Cytometry:xiv Abstract of Dissertation Automation in Image Cytometry:

  1. Demand Response in U.S. Electricity Markets: Empirical Evidence

    E-Print Network [OSTI]

    Cappers, Peter

    2009-01-01T23:59:59.000Z

    Reliability Corporation. Demand response data task force:Energy. Benefits of demand response in electricity marketsAssessment of demand response & advanced metering, staff

  2. Modeling, Analysis, and Control of Demand Response Resources

    E-Print Network [OSTI]

    Mathieu, Johanna L.

    2012-01-01T23:59:59.000Z

    advanced metering and demand response in electricityGoldman, and D. Kathan. “Demand response in U.S. electricity29] DOE. Benefits of demand response in electricity markets

  3. Coordination of Retail Demand Response with Midwest ISO Markets

    E-Print Network [OSTI]

    Bharvirkar, Ranjit

    2008-01-01T23:59:59.000Z

    Robinson, Michael, 2008, "Demand Response in Midwest ISOPresentation at MISO Demand Response Working Group Meeting,Coordination of Retail Demand Response with Midwest ISO

  4. India Energy Outlook: End Use Demand in India to 2020

    E-Print Network [OSTI]

    de la Rue du Can, Stephane

    2009-01-01T23:59:59.000Z

    Institute, “Curbing Global Energy Demand Growth: The Energyup Assessment of Energy Demand in India Transportationa profound effect on energy demand. Policy analysts wishing

  5. Sandia National Laboratories: How a Grid Manager Meets Demand...

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

    Demand (Load) How a Grid Manager Meets Demand (Load) In the "historical" electric grid, power-generating plants fell into three categories: No daily electrical demand data plot...

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

    E-Print Network [OSTI]

    McKane, Aimee T.

    2009-01-01T23:59:59.000Z

    13 Table 2. Demand Side Management Framework for IndustrialDR Strategies The demand-side management (DSM) frameworkpresented in Table 2. Demand Side Management Framework for

  7. SAN ANTONIO SPURS DEMAND FOR ENERGY EFFICIENCY | Department of...

    Energy Savers [EERE]

    SAN ANTONIO SPURS DEMAND FOR ENERGY EFFICIENCY SAN ANTONIO SPURS DEMAND FOR ENERGY EFFICIENCY SAN ANTONIO SPURS DEMAND FOR ENERGY EFFICIENCY As a city that experiences seasonal...

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

    E-Print Network [OSTI]

    Kiliccote, Sila

    2014-01-01T23:59:59.000Z

    in California. DEMAND RESPONSE AND COMMERCIAL BUILDINGSload and demand response against other buildings and alsoDemand Response and Energy Efficiency in Commercial Buildings",

  9. Technical Meeting: Data/Communication Standards and Interoperability...

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

    & ISOIEC 14908 (LonMark International) Presentation: Introducing Open Automated Demand Response (OpenADR) (Honeywell) Presentation: Consortium for SEP 2 Interoperability...

  10. Transport Research Arena Europe 2010, Brussels Towards Highly Automated Driving

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    of HAVEit is to develop and investigate vehicle automation beyond ADAS systems, especially highly automated automated vehicles In 2010, two lines of research and development exist in the domain of ground vehicle automation: Either the automation is driving the vehicle fully automated without a human driver

  11. Disciplined agility for process control & automation

    E-Print Network [OSTI]

    Tibazarwa, Augustine

    2009-01-01T23:59:59.000Z

    Process automation vendors must consider agility as a basis to gain a competitive edge in innovation. Process Automation systems can impact the operating cost of manufacturing equipment, the safe control of large quantities ...

  12. Technical University of Denmark rsted DTU Automation

    E-Print Network [OSTI]

    Technical University of Denmark �rsted · DTU Automation Project: SICAM - SIngle Conversion stage based SICAM using an LC-network Petar Ljusev, MSc., Ph.D. student, �rsted · DTU Automation e-mail: pl

  13. The Automation Of Proof By Mathematical Induction 

    E-Print Network [OSTI]

    Bundy, Alan

    This paper is a chapter of the Handbook of Automated Reasoning edited by Voronkov and Robinson. It describes techniques for automated reasoning in theories containing rules of mathematical induction. Firstly, inductive reasoning is defined and its...

  14. Technical University of Denmark rsted DTU Automation

    E-Print Network [OSTI]

    Technical University of Denmark �rsted · DTU Automation Project: SICAM - SIngle Conversion stage, �rsted · DTU Automation e-mail: pl@oersted.dtu.dk Abstract In this report an isolated PWM DC-AC SICAM

  15. INTRODUCTION Sophisticated automation is becoming ubiq-

    E-Print Network [OSTI]

    Lee, John D.

    INTRODUCTION Sophisticated automation is becoming ubiq- uitous, appearing in work environments as di- verse as aviation, maritime operations, process control, motor vehicle operation, and informa- tion retrieval. Automation is technology that actively selects data, transforms information, makes

  16. TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 1 Automated Guiding Task of a Flexible Micropart

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 1 Automated Guiding Task of a Flexible Micropart Lutz, Member, IEEE Abstract--This paper studies automated tasks based on hybrid force/position control of automated guiding task are presented. Note to Practitioners -- This article's motivation is the need of very

  17. An Ontology-based Model to Determine the Automation Level of an Automated Vehicle for

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    An Ontology-based Model to Determine the Automation Level of an Automated Vehicle for Co). In addition, an automated vehicle should also self-assess its own perception abilities, and not only perceive this idea, cybercars were designed as fully automated vehicles [3], thought since its inception as a new

  18. On Demand Surveillance Service in Vehicular Cloud

    E-Print Network [OSTI]

    Weng, Jui-Ting

    2013-01-01T23:59:59.000Z

    Toward Vehicular Service Cloud . . . . . . . . . . . . . . .4.2 Open Mobile Cloud Requirement . . . . .3.1 Mobile Cloud

  19. Scalable Distributed Automation System: Scalable Real-time Decentralized Volt/VAR Control

    SciTech Connect (OSTI)

    None

    2012-03-01T23:59:59.000Z

    GENI Project: Caltech is developing a distributed automation system that allows distributed generators—solar panels, wind farms, thermal co-generation systems—to effectively manage their own power. To date, the main stumbling block for distributed automation systems has been the inability to develop software that can handle more than 100,000 distributed generators and be implemented in real time. Caltech’s software could allow millions of generators to self-manage through local sensing, computation, and communication. Taken together, localized algorithms can support certain global objectives, such as maintaining the balance of energy supply and demand, regulating voltage and frequency, and minimizing cost. An automated, grid-wide power control system would ease the integration of renewable energy sources like solar power into the grid by quickly transmitting power when it is created, eliminating the energy loss associated with the lack of renewable energy storage capacity of the grid.

  20. Automated Immobilized Metal Affinity Chromatography System for...

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

    Immobilized Metal Affinity Chromatography System for Enrichment of Escherichia coli Phosphoproteome. Automated Immobilized Metal Affinity Chromatography System for Enrichment of...