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Overview of Communication Challenges in the Smart Grid: “Demand Response”

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. Table of Contents. Overview of Demand and Response in SG Demand and Supply?

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Overview of Communication Challenges in the Smart Grid: “Demand Response”

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  1. 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

  2. Table of Contents • Overview of Demand and Response in SG • Demand and Supply? • Literature Review: “IEEE Networks: Communication Infrastructure for SG” • “Challenges in Demand Load Control for the Smart Grid” • “Knowing When to Act: An Optimal Stopping Method for Smart Grid Demand Response”

  3. Overview • Electricity Demand • Large variations • Some patterns b) Ontario Aggregated a) Individual Household

  4. 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

  5. System Architecture

  6. 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 during on-peak • Induce/encourage: customers to use energy during off peak

  7. Overview • Energy Pricing • Tiered (KWh/month threshold) • Lower-tier: inexpensive • Higher-tier: expensive • Time-of-Use (TOU) • By Contract • Market Price • Fluctuating price + fixed price (global adjustment) a) TOU Pricing in Ontario b) Real-Time Pricing in Ontario

  8. 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

  9. 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. SaberTehrani, 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.

  10. 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.

  11. 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.

  12. 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

  13. Literature Review 1: “Challenges in Demand Load Control for the Smart Grid”IordanisKoutsopoulos and LeandrosTassiulas,University of Thessaly and Center for Research and Technology Hellas

  14. 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 on-peak demand load

  15. 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

  16. Online Dynamic Demand Scheduling • No Control: • Activate upon demand request • Threshold-based Control Policies • Binary Control • threshold value P • If P(t) < P, activate • Otherwise, postpone activation to the deadline • Controlled Release • “Binary Control” + activate if deadline or P(t) < P • More flexible scheduling

  17. Performance Evaluation

  18. Literature Review 2: “Knowing When to Act: An Optimal Stopping Method for Smart Grid Demand Response”AbiodunIwayemi, Peizhong Yi, Xihua Dong, and Chi Zhou, Illinois Institute of Technology

  19. 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

  20. 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

  21. 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

  22. 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.

  23. Thanks!!! Discussion / Question

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