日本のグリーン移行を推進する：電力系統安定性と再生可能エネルギー統合のための先進的モデリングフレームワーク

抄録

The increasing integration of photovoltaic (PV) systems into Japan's power grid presents significant challenges for grid stability and energy forecasting, primarily due to the intermittent nature of solar power. A key impediment to accurate power output prediction is the difficulty in precisely estimating PV cell parameters from the limited information available on manufacturer datasheets. This study proposes and validates a robust methodology to extract comprehensive PV parameters using advanced metaheuristic optimization for both single-diode and double-diode models. We apply and compare several algorithms, including Best–Worst–Random(BWR), Best-Mean-Random (BMR), and JAYA, utilizing data from three distinct module types: polycrystalline, monocrystalline, and thin-film. The optimization process minimizes the error between the model's calculated IV characteristics and the empirical data at the open-circuit, short-circuit, and maximum power points. The results demonstrate that the proposed metaheuristic approach successfully identifies optimal parameter sets, achieving a precise match with experimental IV and PV curves. This enhanced modeling accuracy provides a reliable foundation for predicting energy production, facilitating more effective grid management and operational planning for Japanese power utilities and supporting the nation's transition to a renewable-centric power system.