Electric Vehicle Aggregator Modeling and Control for Frequency Regulation Considering Progressive State Recovery

Abstract

High penetration of renewable energy may cause considerable system frequency deviations. Electric vehicles (EVs) offer alternative potentials for frequency regulation with rapid responding speed. However, for many EVs under centralized control, existing modeling methods may achieve accurate control results at the cost of high computational complexity, a high real-time communication requirement due to many individual control signals, and possible disturbances to charging preferences. This paper focuses on solving these crucial issues. At the system level, we use the state-space method to further simplify the model for an EV aggregator (EVA) with limited data measurements from EVs. During frequency regulation, the reduced EVA model predicts the EVA's regulation capacity and generates a simplified global control signal for the probabilistic control of individual EVs. In cooperation with the ramp rate of conventional generation, the EVA implements a progressive state recovery strategy to reduce the disturbance of regulation service to EVs' charging preferences. The EVA decentralizes partial control authorities to individual EVs by applying response functions at the user side. Based on operating states and laxities, an individual EV adjusts the response process to ensure its charging preference under probabilistic control. Comparative simulations validate the effectiveness of this EVA modeling and control method.