click to edit master subtitle style an integrated architecture for demand response communications...
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An Integrated Architecture forDemand Response Communications and Control
Michael LeMay, Rajesh Nelli, Carl A. Gunter, and George GrossUniversity of Illinois at Urbana-Champaign (UIUC)
Demand Response (DR)
Demand response technologies enable changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized.
DoE 06
Wholesale Prices are Highly Variable
Real-time pricing for Midwest ISO, May 14 – June 10, 2007
300%
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Second Outline Level
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Fourth Outline Level
Fifth Outline Level
Sixth Outline Level
Seventh Outline Level
Eighth Outline Level
Ninth Outline LevelClick to edit Master text styles
Click to edit the outline text format
Second Outline Level
Third Outline Level Fourth Outline
Level Fifth Outline
Level Sixth
Outline Level
Seventh Outline Level
Eighth Outline Level
Ninth Outline LevelClick to edit Master text styles
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Focus
Price Based Time Of Use
(TOU) Critical Peak
Pricing (CPP) Real Time Pricing
(RTP)
Incentive Based Direct load control Interruptible/
Curtailable (I/C) service
Demand building/buyback programs
Capacity market programs
Ancillary services market programs
Approaches to DR
Example: Power Smart Pricing in Illinois Notifies customers of possible high prices
a day ahead by email and current hourly prices by radio broadcast.
Advanced in some ways; limited in others.
Implementation Challenges for DR How should prices be delivered? Email,
glowing orbs, web pages, tsunami warning sirens or something else?
Who should control the response of loads to this information? Manually by the end-user or automatically by independent real time aggregators, Building Automation Systems (BASs), or the loads themselves?
Meter Gateway Architecture (MGA) Uses “advanced meters” as the gateway
between loads, the prices from the utility, and the commands from real time energy aggregators.
Supports the integrated operation of diverse response control loci including real time aggregators, BASs, “intelligent” appliances, or end-user manual over-rides.
Advanced Metering
FERC 06
Advanced metering is a metering system that records customer consumption, and possibly other parameters, hourly or more frequently and that provides for daily or more frequent transmittal of measurements over a communication network to a central collection point.
Advanced Metering Infrastructure (AMI)
Example: Edison SmartConnect™What if your electric meter was much more than a
meter? What if your electric meter … gave you both an incentive to save energy and helped
you reduce energy costs without interrupting your life? could communicate with your appliances to produce big
energy and money savings? helped the whole electric system operate more
efficiently, reducing the chance of power emergencies, rolling blackouts and too-high wholesale electricity costs?
made it easier for you to keep track of your energy use so you could adjust your energy use, plan, budget and even pre-pay your electric bill?
MGA Description
The key architectural components are the advanced meter and the unified hub.
The unified hub is a system that enables the unified control of diverse loads.
Similar to BASs and Programmable Communicating Thermostats (PCTs).
But aims at multi-vendor support.
Loci of Control“appliance manufacturers [will] start to produce appliances designed to be able to exploit time varying prices”Spot Pricing of Electricity,Schweppe CaramanisTabors Bohn 1988
Independent direct load control (or similar) exploiting RTP
BAS and/or Smart Thermostat
Appliances designed for facility-based RTP control
Unified Hub Architecture
Global View
Our Experimental Study
Automate DR for “dumb” appliance: air conditioning wall unit.
Automate DR for “smart” appliance: power-price-aware laptop.
Integrate these in a single test bed and perform a rudimentary DR experiment.
Contribution: partial demonstration of feasibility of and challenges for MGA.
Non-contribution: proof that DR is beneficial.
Relation to MGA
Experimental Architecture
Software
The DR Algorithms
“Effective” price of energy: P = Pnow − (Pnow − Pnext) * 0.5
Laptop: Allow the battery to run 10% lower before starting to recharge for every 1 cent increase in effective price; continue recharge until 10% above threshold.
Air Conditioner: lower set point S by .5 deg C for every 2 cent decrease in effective price so S = 24.5 − Max(0, (0.12 − P) 25).
Experimental Conditions
Deployed in active single occupancy apartment with about 500 sq ft floor space in Urbana IL.
Used Ameren Price Smart prices from 24 May 2007 (high of 31.1°) for 11 June 2007 (high of 30.6°).
Average price per kWh on 24 May was 5.332¢.
Ran the experiment again on 12 June (also high of 30.6°) without using DR.
Price-Adaptive Air Conditioner Control Experimental Outcome
DR2.573 kW hCost 11.45¢Average Temp 23.13°
Fixed Set Points1.781 kW hCost 9.41¢Average Temp 23.99°
Fixed set points gave 31% decrease inenergy consumption but only reducedcosts by 17.8%
Price-Adaptive Laptop Charge Control Experimental Outcome
DR.783 kW hCost 3.768 ¢
Continuous Recharge.763 kW hCost 4.112 ¢
Cost reduction of 8.4% withno level less than 54% full.
Conclusions
Technology is ready for inexpensive implementation of MGA but there may be reliability concerns with equipment.
DR algorithms and their impact on applications (viz. humans), global behavior, and efficiency need research.
Larger experiments with more conditions, components and facilities are needed to prove DR benefits.
Vision
Demand response and AMI provide prospects for a dual to the vision of Samuel Insull at Commonwealth Edison in the early 20th Century: loads can now diversify themselves in response to the grid and other loads.
We should be much more aggressive about making this happen.
