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Summary: Automated Demand Response in Large Facilities

Mary Ann Piette , Dave Watson, Naoya Motegi, Building Technologies Dept., LBNL Osman Sezgen, Energy Analysis Dept., LBNL Christine Shockman, Shockman Consulting Ron Hofmann, Project Manager Sponsored by the California Energy Commission January 23, 2004.

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Summary: Automated Demand Response in Large Facilities

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  1. Mary Ann Piette, Dave Watson, Naoya Motegi, Building Technologies Dept., LBNL Osman Sezgen, Energy Analysis Dept., LBNL Christine Shockman, Shockman Consulting Ron Hofmann, Project Manager Sponsored by the California Energy Commission January 23, 2004 Summary: Automated Demand Response in Large Facilities

  2. Presentation Overview • Goal & Motivation • Methodology • Results • Summary and Next Steps

  3. Goal, Motivation, & Method • Primary Goal • Evaluate the technological performance of automated DR hardware and software systems in large buildings • Motivations for Demand Response • Improve grid reliability • Flatter system load shape • Lower wholesale and retail electricity costs • Method • Provide fictitious dynamic XML-based electric prices with 15-minute notification • Program building EMCS & EIS to receive signals & respond • Document building shed using EMCS & metered data

  4. Energy Information Systems (EIS) EEM Utility EIS Web-EMCS DRS Demand Response Monitoring and Control Methodology:Energy Information Systems • Utility Energy Information Systems (Utility EIS) • Demand Response Systems (DRS) • Enterprise Energy Management (EEM) • Web-base Energy Management & Control System (Web-EMCS)

  5. Methodology: Recruited Sites Albertsons – East 9th St. Oakland Engage/eLutions Bank of America – Concord Technology Center Webgen General Services Admin - Oakland Fed. Building BACnet Reader Roche Palo Alto – Office and Cafeteria Tridium Univ. of Calif. Santa Barbara – Library Itron

  6. Methodology: Price Server System Architecture from Infotility 15-Minute Price Participants Database Prices Web Services Prices stored to the database Web Methods Calls (HTTPS) Web Server Monitoring data transfer to participants LBNL enters prices LBNL

  7. Results: Summary of DR Strategies

  8. Results: Day-2 Test, November 19Bottom Up Savings Estimate

  9. Results: Day-2 Test UCSB Roche Whole Building Power [kW] GSA Oakland BofA Albertsons

  10. Results: Albertsons • Saving Estimation Method • Sales Lightings - Activation: $0.30/kW • Baseline - Previous days average • Anti-Sweat Door Heaters - Activation: $0.75/kW • Baseline Previous 15-minute load DR Savings Whole Building Power [kW]

  11. Results: Albertsons • Sales Lightings, Anti-Sweat Heater Sales Lightings Power [kW] Anti-Sweat Heater

  12. Results: GSA Oakland • Component Analysis: Fans Regression Model Power [kW] Actual

  13. Results: 3 Dimensions of DR Capability • Automation Reduces Costs of DR • Response time • Cost of initiating & running DR event • Customer constraints that involve the timing, pattern and frequency of DR • Automated DR facilitates participation in more ISO markets • Day-ahead electricity • Emergency • Ancillary services • Balancing markets

  14. Summary & Next Steps • Findings (forthcoming report: dr.lbl.gov) • Demonstrated feasibility of fully automated shedding • XML and related technology effective • Minimal sheddingduring initial test/Minimal loss of service • Next Steps: Performance of Current Test Sites • In hot weather • Participation in DR programs • Annual benefits at each site & through enterprise • Beyond Test Sites • What other strategies offer kW savings & minimal impact? • How could automation be scaled up? • What are costs for such technology? • What is statewide savings potential? • What is value of fully automated vs manual DR?

  15. Future Directions: Dynamic Building Technology • Underlying technology to support DR • Shell & Lights: Dimmable ballasts & Electro-chromic windows • HVAC: Real-time-models for optimization and diagnostics • System: Connectivity to grid & cost minimization models

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