Optimal scheduling and operation of load aggregators with electric energy storage facing price and demand uncertainties

Abstract

In competitive power markets, with increasing penetration of variable renewable energy resources such as wind power, electricity price becomes more uncertain. In distribution systems, adoption of renewable distributed generation technologies adds another dimension of uncertainty in load forecast. Facing these higher price and load uncertainties, it becomes more challenging for load aggregators to manage their electricity cost. Within this context, this paper presents a Model Predictive Control (MPC)-based scheduling and operation strategy for the load aggregator with electric energy storage (EES) to manage electricity cost in day-ahead and real-time power markets with different levels of price and load uncertainties. Price and load forecasts are actively integrated into the scheduling and operation decision making process to determine the optimal operation. Two other strategies are also discussed and studied for comparison. Case studies demonstrate better performance of the proposed MPC-based strategy compared to the other two strategies facing different levels of price and load uncertainties. The MPC-based strategy is also shown to be robust with the increase of price and load uncertainties. The benefit of energy arbitrage with MPC-based strategy is also illustrated. Index Terms--Electricitycost management, Electric energy storage, Power market, Load aggregator, Model Predictive Control, Price and load uncertainties. Yixing Xu (S'08) is currently pursuing Ph.D. degree at Texas A&M University. He received his B.E. in Electrical Engineering in 2007 from Tsinghua University, Beijing, China. His research interest includes power systems reliability evaluation, integration of energy storage systems and renewable energy in the power systems, smart grid and electricity market. His industry experience includes an internship (

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