Summary: Automated Demand Response in Large FacilitiesMary Ann Piette, Dave Watson, Naoya Motegi,

Building Technologies Dept., LBNLOsman Sezgen, Energy Analysis Dept., LBNL

Christine Shockman, Shockman Consulting

Ron Hofmann, Project ManagerSponsored by the California Energy Commission

January 23, 2004

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Presentation OverviewPresentation Overview

Goal & Motivation Methodology Results Summary and Next Steps

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Goal, Motivation, & MethodGoal, 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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Methodology:Methodology:Energy Information SystemsEnergy 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 InformationSystems (EIS)

Utility EIS

EEM

DRS

Monitoringand Control

DemandResponse

Web-EMCS

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Methodology: Recruited SitesMethodology: Recruited SitesAlbertsons – 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 Methodology: Price Server System Architecture from InfotilityArchitecture from Infotility

WebServices

Database

WebMethods

Calls(HTTPS)

Participants

LBNL

Web Server

LBNL enters prices

Prices stored to the database

Prices

Monitoring datatransfer to participants

15-Minute Price

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Results: Summary of DR StrategiesResults: Summary of DR Strategies

Oakland Federal

UCSB Roche B of A Albertsons USPS

HVAC

Global zone set-point increase

x

Direct control of fans x x Reset duct static x x Direct control of chillers

x

Reset cooling and heating valves

x

Lighting Reduce ambient lighting

x

Other Reduce Anti-Sweat Heaters

x

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Time from 13:00 13:15 13:30 13:45 14:00 14:15 14:30 14:45 15:00 15:15 15:30 15:45 16:00 16:15to 13:15 13:30 13:45 14:00 14:15 14:30 14:45 15:00 15:15 15:30 15:45 16:00 16:15 16:30

Price signal $/kW 0.10 0.30 0.30 0.30 0.30 0.75 0.75 0.75 0.75 0.30 0.30 0.30 0.30 0.10

Albertsons Shed kW 23 27 27 27 40 42 42 42 31 27 27 27 10 0Contorollable load shed % 36% 41% 41% 42% 61% 64% 65% 65% 47% 42% 41% 42% 16% 0%WBP shed % 7% 8% 7% 7% 11% 11% 12% 12% 8% 7% 7% 7% 3% 0%Shed W/spft 0.46 0.54 0.54 0.54 0.79 0.83 0.85 0.85 0.61 0.54 0.54 0.54 0.20 0.00

Bank of Shed kW 0 6 11 7 1 8 8 0 0 9 12 0 0 0America Contorollable load shed % 0% 5% 9% 6% 1% 7% 7% 0% 0% 8% 10% 0% 0% 0%

WBP shed % 0% 1% 1% 1% 0% 1% 1% 0% 0% 1% 2% 0% 0% 0%Shed W/spft 0.00 0.03 0.05 0.03 0.00 0.04 0.04 0.00 0.00 0.04 0.06 0.00 0.00 0.00

GSA Shed kW 140 159 134 181 142 173 140 126 125 203 204 183 73 37Oakland Contorollable load shed % 27% 31% 26% 33% 26% 32% 26% 21% 21% 32% 33% 30% 12% 6%

WBP shed % 6% 7% 6% 8% 6% 8% 6% 6% 5% 8% 9% 8% 3% 2%Shed W/spft 0.14 0.16 0.14 0.19 0.14 0.18 0.14 0.13 0.13 0.21 0.21 0.19 0.07 0.04

Roche Shed kW 70 70 70 70 107 107 107 107 70 70 70 70 0 0Contorollable load shed % 36% 36% 36% 36% 55% 55% 55% 55% 36% 36% 36% 36% 0% 0%WBP shed % 11% 11% 11% 11% 18% 19% 20% 21% 13% 13% 13% 14% 0% 0%Shed W/spft 0.37 0.37 0.37 0.37 0.56 0.56 0.56 0.56 0.37 0.37 0.37 0.37 0.00 0.00

UCSB Shed kW 25 31 25 64 104 116 111 90 -33 -10 7 -14 -28 -22Contorollable load shed % 13% 16% 14% 35% 59% 67% 63% 53% -20% -6% 4% -9% -18% -14%WBP shed % 3% 4% 3% 8% 13% 14% 14% 12% -4% -1% 1% -2% -4% -3%Shed W/spft 0.08 0.11 0.09 0.22 0.36 0.40 0.38 0.31 -0.12 -0.04 0.02 -0.05 -0.10 -0.08

Total Shed kW 258 293 267 349 393 446 408 366 193 298 320 266 54 15WBP shed % 5% 6% 6% 8% 9% 9% 9% 5% 5% 6% 7% 5% 1% 0%Shed W/spft 0.15 0.17 0.16 0.20 0.23 0.26 0.24 0.21 0.11 0.17 0.19 0.15 0.03 0.01

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

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Results: Day-2 TestResults: Day-2 TestW

ho

le B

uild

ing

Po

wer

[kW

]

UCSB

GSA Oakland

BofA

Albertsons

Roche

0

1000

2000

3000

4000

5000

6000

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Results: AlbertsonsResults: Albertsons

0

50

100

150

200

250

300

350

400

Wh

ole

Bu

ildin

g P

ow

er [

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

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Results: AlbertsonsResults: Albertsons

Sales Lightings, Anti-Sweat Heater

0

5

10

15

20

25

30

35

40

45

50

Sales Lightings Anti-Sweat Door Heater

Po

wer

[kW

]

Anti-Sweat Heater

Sales Lightings

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0

50

100

150

200

250

300

350

400

450

Results: GSA OaklandResults: GSA Oakland

Component Analysis: Fans

Pow

er [k

W]

Regression Model

Actual

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Results: 3 Dimensions of DR CapabilityResults: 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 StepsSummary & 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 Future Directions: Dynamic Building TechnologyTechnology

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

HVAC

Lighting

EMCS &

Meter

Other

Windows

HVAC

Lighting

EMCS &

Meter

Other

Windows