Model Predictive Control for EV Aggregators Participating in System Frequency Regulation Market

Abstract

The penetration rate of renewable energy source generation in power systems continues to increase in recent years. However, due to the intermittent nature of renewable energy source (RES) generation, more frequency regulation resources with faster response time and larger capacities are required in the system in order to maintain the system frequency stability. Electric vehicles (EVs) can provide frequency regulation capacities to the system with their batteries when idle, but first they need to be aggregated to enter the ancillary service market for frequency regulation. In this paper, a model predictive control scheme is proposed for the EV aggregators. With the proposed control scheme, an EV aggregator can receive more payment through participation in system frequency regulation while not violating the EV users' convenience. A prediction method based on a seasonal-autoregressive-integral-moving-average (SARIMA) model on the regulation capacity price is also implemented to further boost the EV aggregator's payment. Simulation based on actual frequency regulation market price is conducted to examine the performance of the proposed control scheme. Compared with the simple prediction method on the regulation capacity price used in the existing literature, the proposed MPC scheme with SARIMA prediction increases the payments from the ancillary service market by 4.3% for the EV aggregator.