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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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Presentation on theme: "Overview of Communication Challenges in the Smart Grid: “Demand Response” David (Bong Jun) Choi Postdoctoral Fellow ECE, University of Waterloo 2011-11-10."— Presentation transcript:

1 Overview of Communication Challenges in the Smart Grid: “Demand Response” David (Bong Jun) Choi Postdoctoral Fellow ECE, University of Waterloo BBCR - SG Subgroup Meeting 1

2 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” 2

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

4 Overview Electricity Supply – “Non-renewable” (Nuclear, Fuel, etc.) Environmental problem, fuel cost – “Renewable” (Hydro, Wind, Solar, Tidal, etc.) Intermittent, low reliability, deployment cost 4 a) Ontario Power Generation by Type

5 System Architecture 5

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 6

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) 7 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 8

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 9 [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., [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, [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 10 [8] R. Urgaonkar et al., “Optimal Power Cost Management using Stored Energy in Data Centers,” Proc. SIGMETRICS, [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 11 [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., [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 12

13 “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: 13

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 14

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 15

16 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 16

17 Performance Evaluation 17

18 “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: 18

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 19

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 20

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 21

22 Problem Formulation OSR (Optimal Stopping Rule) – Objective: min cost – Constraints: energy allocation, capacity limit 22 [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 Full details:

23 DISCUSSION / QUESTION Thanks!!! 23


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