Automated Demand Response Commercial Buildings

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Automated Demand Response Commercial Buildings

• David Watson
• Lawrence Berkeley National Lab
• phaves_at_lbl.gov
• Project team Mary Ann Piette (principle
  investigator),
• Osman Sezgen, Naoya Motegi, Christine Shockman

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Presentation Overview

• Demand Response Research Center
• 2003 Auto-DR Project Overview

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Demand Response Research Center

• Objective to develop, prioritize, conduct, and
  disseminate multi-institutional research to
  facilitate DR
• Scope technologies, policies, programs,
  strategies and practices, emphasizing a market
  connection
• Method Partners Planning Committee, Annual RD
  Plan
• Stakeholders

• State policy makers
• Researchers
• Information metering system developers
• Aggregators
• Program implementers

• Utilities
• Industry trade associations
• Building owners, engineers operators
• Building equipment manufacturers
• Other end-use customers

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2003 Auto-DR Project Overview

• Automated Demand Response (Auto-DR) test was
  conducted in fall of 2003
• Researchers published a fictional, variable price
  signal (/kWh) using XML/Web Services
• Commercial facilities automatically checked the
  price (every 1-5 minutes)
• Electric loads automatically shed upon a rise in
  price
• No human intervention

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Auto-DR System Definitions

• Price Server Common source of current price
  info
• Polling Client Software that polls the server
  to get price
• Business Logic Decides EMCS actions based on
  price

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Geographic Distribution

• All facilities were in California
• Auto-DR infrastructure nationwide

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Auto-DR System Communications Sequence

• LBNL defines the price vs. time schedule sends
  to Price Server.
• The current price is published on the server.
• Clients request the latest price from the server
  every few minutes.
• Business logic determines actions based on price.
• EMCS carries out commands based on logic.

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Recruited Sites

• Site A Supermarket, Oakland
• Site B Bank Office Building, Concord
• Site C Govt Office Building, Oakland
• Site D Pharm. Mfr. Cafeteria , Palo Alto
• Site E College Campus Library, SB

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Auto-DR SystemTest Sites - Circa 1999
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Auto-DR System - Circa 2002 (After State Program)
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Auto-DR System Nov. 2003 Auto-DR Test
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Auto-DR System Fictional Price Signal Nov. 19,
2003

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Results Site A Shed (lights and anti-sweat
heaters)
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Results Whole Building PowerAll Sites Combined
(Shed Savings in Yellow)
Site E
UCSB
Whole Building Power kW
Roche
Site D
GSA Oakland
Site C
Site B
BofA
Site A
Albertsons
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Auto-DR System Characterization Shed Control
Granularity
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Auto-DR System Findings What is
State-of-the-Art ?(1 of 5)

• The best system meets or exceeds the
  requirements at the lowest installed cost
•  Flexible Designs for the Future
• Auto-DR is an emerging field, subject to ongoing
  changes in programs, best practices, etc.
• Auto-DR systems must be as flexible and
  future-proof as possible
• Features
• Customer defined business logic
• Customer warnings, alerts local overrides
• Real-time, two-way verification of shed
• High security
• High (shed) availability

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Auto-DR System Findings What is
State-of-the-Art ?(2 of 5)

• Leverage Trends in Technology
•  Internet and private WANs are ideal platforms
  for Auto-DR controls and communications
•  Performance of IT equipment (e.g. routers,
  firewalls, etc.) continues to improve and prices
  continue to drop

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Auto-DR System Findings What is
State-of-the-Art ?(3 of 5)

• Enterprise, EMCS EIS Integration
•  To maximize system performance and functionality
  and minimize cost, EISs and EMCSs should share
  the same control network, database and HMI.
•  EMCS/EISs networks should be tightly integrated
  with enterprise networks
•  The most robust and least costly systems should
  have no more than 1 enterprise protocol and 1
  control protocol

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Auto-DR System Findings What is
State-of-the-Art ?(4 of 5)

• Open protocol standards should be used wherever
  possible
• Allows the greatest flexibility at the lowest
  cost
• Truly open systems are interoperable.
• XML alone is not a standard for building and
  energy data. OPC-XML, oBIX, BACnet-XML and others
  are developing XML standards of this type

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Auto-DR System Findings What is
State-of-the-Art ?(5 of 5)

• Shed Strategies
• Should be designed to minimize discomfort,
  inconvenience, and loss of revenue
• High granularity closed loop control are less
  likely to negatively impact building occupants
  for a given demand shed
• In addition to HVAC control strategies, lighting
  and other loads should be considered for sheds as
  well

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Further Information

• Contact Mary Ann Piette, mapiette_at_lbl.gov, 510
  486-6286
• Demand Response Research Center
• drrc.lbl.gov
• Current CEC Demand Response Sites
• www.energy.ca.gov/demandresponse
• Consortium for Electric Reliability Technology
  Solutions (CERTS)
• certs.lbl.gov
• Center for the Study of Energy Markets (CSEM)
• www.ucei.berkeley.edu/power.html
• Demand Response Enabling Technology Development
  (DRETD)
• ciee.ucop.edu/dretd
• Buildings Research
• Buildings.lbl.gov/hpcbs/Pubs.html