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Energy Trading in the Smart Grid: From End-user’s Perspective

Energy Trading in the Smart Grid: From End-user’s Perspective. Shengbo Chen Electrical and Computer Engineering & Computer Science and Engineering. The Smart Grid. Next generation power grid: full visibility and pervasive control on both supplier and consumers Smart meters

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Energy Trading in the Smart Grid: From End-user’s Perspective

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

  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. 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 Trading • Energy selling price is always less than the energy buying price

  6. System Model • g(t) = l(t)-b(t)

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

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

  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 A tradeoff between the battery size and the performance Diminish as V becomes large

  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, 2013. (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

  14. Thank you

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