Summary: Automated Demand Response in Large Facilities

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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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Presentation Overview

• Goal Motivation
• Methodology
• Results
• Summary and Next Steps

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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

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MethodologyEnergy Information Systems

• Utility Energy Information Systems (Utility EIS)
• Demand Response Systems (DRS)
• Enterprise Energy Management (EEM)
• Web-base Energy Management Control System
  (Web-EMCS)

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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

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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
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Results Summary of DR Strategies
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Results Day-2 Test, November 19Bottom Up
Savings Estimate
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Results Day-2 Test
UCSB
Roche
Whole Building Power kW
GSA Oakland
BofA
Albertsons
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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
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Results Albertsons

• Sales Lightings, Anti-Sweat Heater

Sales Lightings
Power kW
Anti-Sweat Heater
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Results GSA Oakland

• Component Analysis Fans

Regression Model
Power kW
Actual
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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

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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?

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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