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Abstract An accurate estimation of schedulable capacity (SC) is especially crucial given the rapid growth of electric vehicles, their new energy charging stations, and the promotion of vehicle‐to‐grid (V2G) technology. In this study, an evaluation approach for a photovoltaic (PV) and storage‐integrated fast charging station is established. The energy relationship between the SC of electric vehicles (EVs), the SC of centralized energy storage, and the PV power generation is constructed to solve for the upward SC and downward SC of the entire charging station based on the detailed explanation of the electrical structure of the PV and storage integrated fast charging station. To facilitate the grid to improve the dispatching efficiency, a strategy for solving the daytime SC is developed. This work uses an actual charging station as the research object for case analysis, and fine‐grained modeling of its component characteristics is finished in order to test the validity and robustness of the model. Four views are used to examine the variable properties and affecting elements of the schedulable capacity: light circumstances, EV load typical scenarios, dispatching interval length, and centralized energy storage configuration.

With the increase in the power receiving proportion and an insufficient peak regulation capacity of the local units, the receiving-end power grid struggles to achieve peak regulation in valley time. To solve this problem while considering the potential of the large-scale charge load of electric vehicles (EVs), an aggregator-based demand response (DR) mechanism for EVs that are participating in the peak regulation in valley time is proposed in this study. In this aggregator-based DR mechanism, the profits for the power grid’s operation and the participation willingness of the EV owners are considered. Based on the characteristics of the EV charging process and the day-ahead unit generation scheduling, a rolling unit commitment model with the DR is established to maximize the social welfare. In addition, to improve the efficiency of the optimization problem solving process and to achieve communication between the independent system operator (ISO) and the aggregators, the clustering algorithm is utilized to extract typical EV charging patterns. Finally, the feasibility and benefits of the aggregator-based DR mechanism for saving the costs and reducing the peak-valley difference of the receiving-end power grid are verified through case studies.

Uncontrolled charging of large-scale electric vehicles (EVs) can affect the safe and economic operation of power systems, especially at the distribution level. The centralized EVs charging optimization methods require complete information of physical appliances and using habits, which will cause problems of high dimensionality and communication block. Given this, an ant-based swarm algorithm (ASA) is proposed to realize the EVs charging coordination at the transformer level, which can overcome the drawbacks of centralized control method. First, the EV charging load model is developed, and the charging management structure based on swarm intelligence is presented. Second, basic data of the EV using habit is sampled by the Monte Carlo method, and the ASA is applied to realize the load valley filling. The load fluctuation and the transformer capacity are also considered in the algorithm. Finally, the charging coordination of 500 EVs under a 12.47 KV transformer is simulated to demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]