BBCR - SG Subgroup Meeting
Overview of Communication
Challenges in the Smart Grid:
“Demand Response”
David (Bong Jun) Choi
Postdoctoral Fellow
ECE, University of Waterloo
2011-11-10
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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 onpeak to off-peak
• Store: energy during off-peak and use during onpeak
• Induce/encourage: customers to use energy during
off peak
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Overview
• Energy Pricing
– Tiered (KWh/month
threshold)
• Lower-tier: inexpensive
• Higher-tier: expensive
– Time-of-Use (TOU)
– By Contract
– Market Price
a) TOU Pricing in Ontario
• Fluctuating price + fixed
price (global adjustment)
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 pricing 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 consumers
[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 Congestion,” 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.,
distributed power generation)
• Vehicle to Grid Systems (V2G)+
– Incorporation of PHEVs
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Literature Review 1:
“Challenges in Demand Load Control
for the Smart Grid”
Iordanis Koutsopoulos and Leandros Tassiulas,
University of Thessaly and Center for Research and Technology Hellas
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Overview
• Observation
– Cost of power increases as demand load
increases
• 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 onpeak 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 Policies
1.
Binary Control
• threshold value P
• If P(t) < P, activate
• Otherwise, postpone activation to the
deadline
2.
Controlled Release
• “Binary Control” + activate if deadline
or P(t) < P
• More flexible scheduling
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Performance Evaluation
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Literature Review 2:
“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
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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
(computing, sensing,
communication)
• Reduce energy cost
– Home Energy Controller
(HEC)
• Advanced Metering
Infrastructure (AMI)
– Bidirectional
– Wireless Technology
• GPRS, Wi-Fi, Mesh
network
• Neighbor Area Network
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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 appliances
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Problem Formulation
• OSR (Optimal Stopping Rule)
– Objective: min cost
– Constraints: energy allocation, capacity limit
Full details:
[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.
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Thanks!!!
DISCUSSION / QUESTION
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