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.

中文翻译：

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用于频率调节的聚合电动汽车的状态空间模型

现有的具有众多用于集中式频率调节的电动汽车（EV）的模型以牺牲繁重的计算工作量和高实时通信需求为代价实现了高精度。本文开发了一种状态空间模型（SSM），它提供了一种可能性，可以以较高的准确度和计算效率实现对聚合电动汽车的实时功率控制，但对实时通信的要求较低。SSM是基于状态空间方法的简化模型，可准确描述具有不同连接状态和各种充电状态（SOC）状态的聚合EV。考虑到电动汽车的异构充电特性和随机行驶行为，SSM通过马尔可夫状态转移方法实现状态转移预测和调节能力估计，与现有模型相比，它具有更高的计算效率。SSM用于频率调节，实现SOC自适应系数以导出相同的控制信号并提高预测精度。SSM通过将某些实时进程替换为脱机进程来降低实时通信需求。同时，相同的控制信号更适合于实时调度，因为它会将控制信号全局广播到各个EV。仿真结果表明，SSM以较高的计算效率实现了较高的预测精度。进行比较结果以验证SSM在实时频率调节中的有效性。实现SOC自适应系数，以导出相同的控制信号，提高预测精度。SSM通过将某些实时进程替换为脱机进程来降低实时通信需求。同时，相同的控制信号更适合于实时调度，因为它会将控制信号全局广播到各个EV。仿真结果表明，SSM以较高的计算效率实现了较高的预测精度。进行比较结果以验证SSM在实时频率调节中的有效性。实现SOC自适应系数，以导出相同的控制信号，提高预测精度。SSM通过将某些实时进程替换为脱机进程来降低实时通信需求。同时，相同的控制信号更适合于实时调度，因为它会将控制信号全局广播到各个EV。仿真结果表明，SSM以较高的计算效率实现了较高的预测精度。进行比较结果以验证SSM在实时频率调节中的有效性。相同的控制信号更适合于实时调度，因为它将控制信号全局广播到各个EV。仿真结果表明，SSM以较高的计算效率实现了较高的预测精度。进行比较结果以验证SSM在实时频率调节中的有效性。相同的控制信号更适合于实时调度，因为它将控制信号全局广播到各个EV。仿真结果表明，SSM以较高的计算效率实现了较高的预测精度。进行比较结果以验证SSM在实时频率调节中的有效性。