Day-ahead optimal bidding and scheduling strategies for DER aggregator considering responsive uncertainty under real-time pricing

This paper addresses the optimal decision problem of a distributed energy resources (DER) aggregator who manages wind turbines, solar PV systems and battery energy storage (BES) units while implementing real-time pricing (RTP) demand response program. The DER aggregator can procure electricity by bidding in the electricity market and scheduling its DER to meet the load demand of its customers. In the bidding and scheduling processes, the intrinsic uncertainties of distributed renewable generations and customer's responsiveness to RTP program have brought economical risks to the DER aggregator, which will lower the DER aggregator's profit. However, most of the current researches only consider the uncertainty of renewable generations while neglecting the uncertainty of customer's responsiveness. To this end, a robust optimization-based day-ahead optimal bidding and scheduling model is proposed for DER aggregator by jointly considering these two uncertainties. The objective of the proposed model is to maximize the aggregator's profit via optimally determining the hourly bidding quantities in the day-ahead market and the scheduled output power of distributed renewable generations and BES units. Case studies demonstrate that the proposed robust optimization model can help DER aggregator reduce the bidding and scheduling costs to obtain a higher expected profit.