State Space Model of Aggregated Electric Vehicles for Frequency Regulation

Existing models featuring numerous electric vehicles (EVs) for centralized frequency regulation achieved high accuracy at the expense of heavy computational workloads and a high real-time communication requirement. This paper develops a state space model (SSM) that provides a probability to realize the real-time power control of aggregated EVs with high accuracy and computational efficiency but a low real-time communication requirement. The SSM, a reduced model based on the state space method, accurately describes aggregated EVs with different connecting states and various state-of-charge (SOC) states. Considering heterogeneous charging characteristics and random traveling behaviors of EVs, the SSM realizes the state transition prediction and the regulation capacity estimation with the Markov state transition method, which has a much higher computational efficiency than the existing models. The SSM is used for the frequency regulation, and the SOC adaptive coefficient is implemented to derive the identical control signal and improve the prediction accuracy. The SSM lowers the real-time communication requirement by replacing some real-time processes with offline processes. Meanwhile, the identical control signal is more suitable for real-time dispatching because it broadcasts the control signal to individual EVs globally. Simulation results indicate that the SSM achieves the high prediction accuracy with much higher computational efficiency. Comparison results are conducted to validate the effectiveness of SSM for real-time frequency regulation.