| Summary: | The large-scale deployment of Photovoltaic Storage Charging Station(PSCS) is a crucial factor for the rapid popularization of Electric Vehicles(EV). Effectively planning the operation of PSCS, dispatching multiple energy sources, optimizing the demand-supply chain, and maximizing operational efficiency are keys to the sustainable growth and development of PSCS. To address the uncertainty of the demand side and coordination problem of the supply side of the current optical storage charging station, this paper focusses on EV charging reservation scenario. For demand, factors, such as the car charging demand and remaining parking time, are considered to decide the charging mode of the corresponding EV. On the supply scheduling side, a genetic hybrid recursive algorithm, Hybrid Recursive(EGAHR), is designed based on the band elite strategy for energy optimization scheduling to minimize the grid withdrawal cost. Using EV charging time slots as the primary regulatory unit, the strategy coordinates both demand and supply information, ensuring energy from Photo Voltaics(PV), storage, and the grid is dispatched efficiently. This satisfies the EV charging requirements within the current time slot and optimizes overall electricity expenses. Experimental results reveal that the EGAHR strategy offers a charging cost reduction in the range of 2.1-21.9% when compared to strategies based on traditional algorithms such as the genetic algorithm, gray wolf algorithm, and particle swarm algorithm. Additionally, the EGAHR strategy can be applied to various EV charging models and differential tariff trends, offering a scientifically and economically sound blueprint for PSCS to adequately equip Energy Storage System(ESS) and PV.
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