Optimization of Operation Strategy and Economic Benefit Evaluation of Urban New Energy Vehicle Charging Stations Based on Dynamic Demand

Abstract

With the promotion of environmental awareness and new energy policies, the electric vehicle market is expanding rapidly, and the demand for urban charging facilities is increasing, making them a key infrastructure for sustainable urban development. Existing charging station operation strategies are often unable to effectively adapt to the dynamically changing market demand, leading to uneven resource allocation and operational inefficiencies. In this study, a dynamic optimization model is constructed to combine real-time traffic flow data and grid load data to achieve real-time adjustment of charging capacity and operation strategies of charging stations. Specific methods include using mixed-integer linear programming to optimize the daily operation decisions of charging stations, and applying Monte Carlo simulation to analyze the economic benefits under different operation strategies. Optimization models and strategies not only improve the service efficiency and economic returns of charging stations, but also provide scientific decision support for policy makers and promote the healthy development of the new energy vehicle industry. On February 5, during the midday to evening hours, although the predicted charging demand reached 60 vehicles, the actual charging demand was as high as 65 vehicles, and the user response rate was as high as 108%. Through this approach, it can effectively balance the supply and demand relationship, reduce operating costs, while enhancing the reliability and flexibility of the charging network, and provide strong support for the popularization of new energy vehicles in the city and the construction of a sustainable transportation system.