Simulation study models surplus photovoltaic absorption under variable electric vehicle charging patterns, highlighting the fleet scale and charging timing needed for reliable grid integration.
As the adoption of renewable energy accelerates, utilizing surplus photovoltaic (PV) power for electric vehicle (EV) charging has attracted growing attention. However, EV user charging behavior involves significant uncertainties—such as driving distance and plug-in probability—that cannot be accurately captured by average-based models, leading to potential misestimation of PV surplus absorption. This study constructs a probabilistic framework that models EV charging behavior using data and Monte Carlo simulations, evaluating the PV surplus mitigation rate as a probability density function (PDF). Furthermore, it analyzes how variations in charging timing and user behavior affect the stability of PV absorption, and derives indicators such as the required number of EVs and their standard deviation. These insights contribute to the strategic design of realistic and scalable solutions for social implementation.
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Sekiguchi et al. (2026) studied this question.
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