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. 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. 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. 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
Web
Methods
Calls
(HTTPS)
Prices stored to the database
Web
Server
Draw infotility boundard
Monitoring data
transfer to participants
LBNL enters prices
LBNL

7. Results: Summary of DR Strategies
Change case lights if incorrect

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

9. Results: Day-2 Test UCSB Roche GSA Oakland BofA Albertsons
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
The DR savings were sum of sales lighting and A/S saving. Just added onto the actual WBP.
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 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. 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