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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!!!