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Overview of Communication Chal-lenges in the Smart Grid: “Demand
Response”
David (Bong Jun) ChoiPostdoctoral Fellow
ECE, University of Waterloo2011-11-10
BBCR - SG Subgroup Meeting
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Table of Contents• Overview of Demand and Response in
SG– Demand and Supply?
• Literature Review: “IEEE Networks: Communication Infrastructure for SG”① “Challenges in Demand Load Control for the
Smart Grid”② “Knowing When to Act: An Optimal Stopping
Method for Smart Grid Demand Response”
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Overview• Electricity Demand– Large variations– Some patterns
a) Individual Household b) Ontario Aggregated
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Overview• Electricity Supply– “Non-renewable” (Nuclear, Fuel, etc.)• Environmental problem, fuel cost
– “Renewable” (Hydro, Wind, Solar, Tidal, etc.)• Intermittent, low reliability, deployment cost
a) Ontario Power Generation by Type
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System Architecture
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Overview• Demand Response– Goal• Electricity Demand = Electricity Supply
– Basic Methodology• Transfer: non-emergent power demand
from on-peak to off-peak • Store: energy during off-peak and use dur-
ing on-peak• Induce/encourage: customers to use en-
ergy during off peak
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Overview• Energy Pricing– Tiered (KWh/month
threshold)• Lower-tier: inexpen-
sive• Higher-tier: expensive
– Time-of-Use (TOU)– By Contract– Market Price
• Fluctuating price + fixed price (global ad-justment)
a) TOU Pricing in Ontario
b) Real-Time Pricing in Ontario
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Overview• Expected Gain– Supplier (Utilities)• Lower operation cost (a.k.a. “peak shaving”)
– Consumer (Customers)• Lower real-time electricity price• Due to being aware of quick real-time pric-
ing and response
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Current Development• Demand Task Scheduling– Satisfy future power demand request
within some bound• Various threshold based schemes • Load shifting to off-peak periods by con-
sumers[5] M. J. Neely, A. Saber Tehrani, and A.G. Dimakis, “Efficient Algorithms forRenewable Energy Allocation to Delay Tolerant Consumers,” Proc. IEEE Int’l. Conf. Smart Grid Commun., 2010.[6] I. Koutsopoulos and L. Tassiulas, “Control and Optimization Meet the Smart Power Grid: Scheduling of Power Demands for Optimal Energy Management,” Proc. Int’l. Conf. Energy Efficient Computing and Networking, 2011.[7] A.-H. Mohsenian-Rad and A. Leon-Garcia, “Optimal Residential Load Control with Price Prediction in Real-time Electricity Pricing Environments,” IEEE Trans. Smart Grid, vol. 1, no. 2, Sept. 2010, pp. 120–33.
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Current Development• Use of Stored Energy– Store at off-peak + Use at on-peak• Online algorithms• Considering PHEVs
[8] R. Urgaonkar et al., “Optimal Power Cost Management using Stored Energy in Data Centers,” Proc. SIGMETRICS, 2011.[9] M. C. Caramanis and J. Foster “Management of Electric Vehicle Charging to Mitigate Renewable Generation Intermittency and Distribution Network Conges-tion,” Proc. 48th IEEE Conf. Dec. Control, 2009.
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Current Development• Real-Time Pricing– Encourage consumers to shift their
power demand to off-peak periods• Incentive based algorithms• Group based algorithms
[10] A.-H. Mohsenian-Rad et al., “Optimal and Autonomous Incentive-based Energy Consumption Scheduling Algorithm for Smart Grid,” Proc. IEEE PES Conf. Innovative Smart Grid Tech., 2009.[11] L. Chen et al., “Two Market Models for Demand Response in Power Networks,” Proc. IEEE Int’l. Conf. Smart Grid Commun., 2010.
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Research Challenges• Energy Storage+– Battery management
• Communication– Which technology to use?
• Distributed Generation+– Fixed (not so adaptive) electricity supply– Diversifying power generation options (i.e., dis-
tributed power generation)• Vehicle to Grid Systems (V2G)+– Incorporation of PHEVs
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“Challenges in Demand Load Control for the Smart Grid”Iordanis Koutsopoulos and Leandros Tassiulas,University of Thessaly and Center for Research and Technology Hellas
Literature Review 1:
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Overview• Observation– Cost of power increases as demand load in-
creases• Solution– Online scheduling, – Threshold-based policy that (1) activate demand
when the demand is low or (2) postpone demand when the demand is high
• Battery for demand shading– i.e., Increase off-peak demand load, decrease on-
peak demand load
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Online Dynamic Demand Scheduling• Goal: Minimize long run average cost
– Steady state• exponential dist. (request arrival, deadline)
– P(t): total instantaneous consumed power in the grid– d: deadline by which request to be activated
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Online Dynamic Demand Scheduling• No Control:
– Activate upon demand request• Threshold-based Control
Policies1. Binary Control
• threshold value P• If P(t) < P, activate• Otherwise, postpone activation to the
deadline2. Controlled Release
• “Binary Control” + activate if dead-line or P(t) < P
• More flexible scheduling
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Performance Evaluation
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“Knowing When to Act: An Optimal Stopping Method for Smart Grid Demand Response” Abiodun Iwayemi, Peizhong Yi, Xihua Dong, and Chi Zhou, Illinois Institute of Technology
Literature Review 2:
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Overview• Motivation
– Real time pricing– Operate electrical appliances when the energy price
is low– Tradeoff
• Energy Saving vs. Delaying Device Usage• Goal
– Home automation– “Decide when to start appliances”
• Solution Approach– Optimal Stopping Approach to optimize the tradeoff
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System Model• Home Area Networks
– Smart appliances (comput-ing, sensing, communica-tion) • Reduce energy cost
– Home Energy Controller (HEC)
• Advanced Metering In-frastructure (AMI)– Bidirectional– Wireless Technology
• GPRS, Wi-Fi, Mesh network• Neighbor Area Net-
work
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Solution Approach• “Marriage Problem” (Secretary
Problem)– 100 brides– Interview in random order and take
score– Choose one bride from interviewed
brides• Solution– interview 37 (=100/e) and then select
one– Prob(select best choice) = 0.37
• Extended to scheduling appli-ances
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Problem Formulation• OSR (Optimal Stopping Rule)– Objective: min cost
– Constraints: energy allocation, capacity limit
[14] P. Yi, X. Dong, and C. Zhou, “Optimal Energy Management for Smart Grid Systems - An Optimal Stopping Rule Approach,” accepted for publication at the IFAC World Congress Invited Session on Smart Grids-2011.
Full details:
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DISCUSSION / QUESTIONThanks!!!