1. 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

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

3. Page 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. Page 4 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) Energy Information Systems (EIS) Utility EIS EEM DRS Monitoring and Control Demand Response Web- EMCS

5. Page 5 Methodology: Recruited Sites Albertsons – East 9 th 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. Page 6 Methodology: Price Server System Architecture from Infotility Web Services Database Web Methods Calls (HTTPS) Participants LBNL Web Server LBNL enters prices Prices stored to the database Prices Monitoring data transfer to participants 15-Minute Price

7. Page 7 Results: Summary of DR Strategies

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

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

10. Page 10 Results: Albertsons Whole Building Power [kW] DR Savings 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

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

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

13. Page 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. Page 14 Summary & Next Steps Findings (forthcoming report: dr.lbl.gov)  Demonstrated feasibility of fully automated shedding  XML and related technology effective  Minimal shedding during 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. Page 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