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

被引:84
作者
Wang, Mingshen [1 ,2 ]
Mu, Yunfei [1 ,2 ]
Shi, Qingxin [3 ]
Jia, Hongjie [1 ,2 ]
Li, Fangxing [3 ]
机构
[1] Tianjin Univ, Key Lab Smart Grid, Minist Educ, Tianjin 300072, Peoples R China
[2] Tianjin Univ, Key Lab Smart Energy & Informat Technol Tianjin M, Tianjin 300072, Peoples R China
[3] Univ Tennessee Knoxville, Dept Elect Engn & Comp Sci, Knoxville, TN 37996 USA
基金
中国国家自然科学基金;
关键词
Frequency control; Computational modeling; Regulation; Batteries; Power systems; Switches; Probabilistic logic; Electric vehicle (EV); electric vehicle aggregator (EVA); state-space method; state recovery; frequency regulation; DYNAMIC DEMAND CONTROL; REGULATION SERVICES; CONTROL STRATEGY; SYSTEM;
D O I
10.1109/TSG.2020.2981843
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];
摘要
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.
引用
收藏
页码:4176 / 4189
页数:14
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