Machine learning-driven PV forecasting and coordinated PV–BESS dispatch for optimizing distribution grid performance under high EV penetration

• Seasonally adaptive PV–BESS dispatch framework for high EV penetration grids. • CNN–BiLSTM model achieves >92 % accuracy in short-term PV forecasting. • GA optimizes multi-objective PV-EV-BESS scheduling. • Reduces feeder losses by 63.55 % and Voltage Unbalance Factor by 55 %. • Enhances PV utilization, power quality, and grid resilience under stress. The rapid proliferation of photovoltaic (PV) systems and electric vehicles (EVs) poses significant challenges for distribution networks, including voltage unbalance, high feeder losses, and deteriorating power quality, further complicated by seasonal variability in solar output and demand. This paper proposes a seasonally adaptive PV–battery energy storage system (BESS) dispatch strategy that integrates short-term PV forecasting with multi-objective optimization. The primary novelty of this work lies in the tight integration of a seasonally adaptive, forecasting-driven dispatch strategy that synergistically combines a hybrid CNN-BiLSTM model for high-accuracy PV prediction with a Genetic Algorithm for multi-objective optimization in unbalanced distribution grids. A hybrid CNN–BiLSTM model, trained on distinct seasonal datasets (summer and winter) from NASA irradiance data, enables accurate real-time PV prediction, achieving an RMSE of 13.99 kWh, MAE of 12.94 kWh, and MAPE of 7.45 %, corresponding to a forecasting accuracy of 92.55 %. Meanwhile, a genetic algorithm (GA) optimizes coordinated PV–BESS scheduling under diverse seasonal scenarios. The approach is validated on a modified IEEE 13-bus system, demonstrating robustness across a wide range of EV penetration levels (40–70 %) and both summer and winter conditions, and showing reductions of up to 55 % in Voltage Unbalance Factor (VUF) and 63.55 % in feeder losses, alongside enhanced PV utilization. Compared to uncoordinated dispatch, the proposed framework delivers superior power quality, higher grid resilience, and practical scalability. The framework is designed for real-world application, with the forecasting model providing high-frequency predictions and the GA-based optimization operating on a computationally feasible hourly rolling horizon, making it suitable for deployment in larger distribution networks. These findings highlight the potential of forecast-driven, seasonally aware coordinated dispatch as a practical pathway toward reliable and sustainable smart grid operation.