Optimal scheduling and operation of load aggregator with electric energy storage in power markets

This paper presents an optimization framework for a load aggregator with electric energy storage (EES) to determine its net imported power in electricity markets. The EES is operated by the load aggregator. The imported power from both day-ahead and real-time markets is a combination of the load and the EES power charging and discharging. The load aggregator´s objective is to minimize its energy cost by scheduling the imported power in the day-ahead market and determining the imported power during operation in the real-time balancing market. The flexible operation of the EES is the tool for achieving this goal. Forecasted price and load are used to determine the optimal scheduling and operation to minimize the energy cost. In the day-ahead market, a load aggregator uses the proposed method to determine the schedule of the imported power in each period with the day-ahead forecasted price and load. During real-time operation, the discrepancies caused by the forecast errors are settled in the real-time balancing market. Model Predictive Control (MPC)-based algorithm is used to determine its imported power in balancing market by using the most updated price and load forecast over a receding horizon. Results from two case studies with stationary EES and plug-in hybrid electric vehicles (PHEV)´ batteries respectively using the proposed method are presented to demonstrate the energy cost savings.