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

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

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

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

System Architecture 5

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

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

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

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.

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.

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.

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

“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

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

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

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

Performance Evaluation 17

“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

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

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

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

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:

DISCUSSION / QUESTION Thanks!!! 23