Energy Trading in the Smart Grid: From End-user’s Perspective Shengbo Chen Electrical and Computer Engineering & Computer Science and Engineering

2 The Smart Grid Next generation power grid: full visibility and pervasive control on both supplier and consumers  Smart meters Dynamic electricity prices according to demand  Shift demand from peak time Renewable energy  Reduce cost and greenhouse gas emission  Energy harvesting: highly dynamic  Battery: limited capacity With these new features and challenges, there is a need for comprehensive solutions for the smart grid

3 task schedule Model of Information Delivery Real-time communication between operator and consumers  Smart meters  Controller: operator/customer side Operator Smart Meter 1 Smart home appliances demand requests Smart Meter 2 Controller demand requests task schedule Controller electricity prices electricity prices

4 Energy Supply and Demand Attributes of energy supply  Unlike communication network — Storable  Renewable vs. Non-renewable  Micro-generation Energy Supply Energy Demand Energy Management Attributes of energy demand  Time-varying  Unpredictable vs predictable  Elastic vs. Non-elastic Random demand meets with possibly uncertain supply

5 Energy Trading Intuition: Dynamic electricity price combining an energy storage battery implies a trading opportunity (similar to stock) Objective: Maximize the profit by opportunistically selling energy to the grid Control variables  Amount of energy drawn/stored from/to the battery in each time slot Challenges  Uncertainty of incoming renewable energy, price of electricity and energy demand Energy selling price is always less than the energy buying price

6 System Model g(t) = l(t)-b(t)

7 Example Key factors:  Time-varying electricity price & Battery energy management

8 Problem Statement Models  Energy selling price is smaller by a factor of  Energy demand l(t) is exogenous process Profit of selling energy Cost of buying energy from the grid Energy drawn/stored from/to the battery Battery level Maximal output of the battery

9 Denote In each time slot, the energy allocation is given as follows  Case 1: If  Case 2: If  Case 3: If Algorithm Sketch Sell: Price is high or battery level is high Buy: Price is low and battery level is low Equal: Price and battery level are mild

10 Battery level is always bounded:  Only require finite battery capacity Asymptotically close to the optimum as T tends to infinity Main Results Diminish as V becomes large A tradeoff between the battery size and the performance

11 Simulation Results Compared to the greedy scheme: first use the renewable energy for the demand, and sell the extra if any Annual profit versus Beta (V=1000) Annual profit versus V (Beta=0.8) S. Chen, N. Shroff and P. Sinha, “Energy Trading in the Smart Grid: From End-user’s Perspective,” to appear in Asilomar Conference on Signals, Systems and Computers, (Invited paper)

12 Simulation Results (cont’) Real traces

13 Open Problems Game theory based schemes  The behavior of large number of customers can influence the market price Network Economics

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