A Two-Stage Stochastic Unit Commitment Model for Sustainable Large-Scale Power System Planning Under Renewable and EV Variability

The increasing integration of renewable energy sources and the widespread adoption of electric vehicles have introduced considerable uncertainty into the operation of large-scale power systems. Traditional deterministic unit commitment models are insufficient for managing such variability in a reliable and cost-effective manner. This study proposes a two-stage stochastic unit commitment model that captures uncertainties in solar photovoltaic generation, electric vehicle charging demand, and load fluctuations using a mixed-integer linear programming framework with recourse. The model is applied to Thailand’s national power system, comprising 171 generators across five regions, to assess its scalability for sustainable large-scale planning. Results indicate that the stochastic model significantly enhances system reliability across most demand profiles. Under the Winter Weekday group, the number of lacking scenarios decreases by 76.92 percent and the number of missing periods decreases by 78.57 percent, while the average and maximum lack percentages are reduced by 56.32 percent and 72.61 percent, respectively. Improvements are even greater under the Rainy Weekday group, where lacking scenarios and periods decline by more than 92 percent and the maximum lack percentage falls by over 98 percent, demonstrating the model’s robustness under volatile solar output and load conditions. Although minor anomalies are observed, such as slight increases in average and maximum lack percentages in the Summer Weekday group, these are minimal and likely attributable to randomness in scenario generation or boundary effects in optimization. Overall, the stochastic model provides substantial advantages in managing uncertainty, achieving notable improvements in reliability with only modest increases in operational cost and computational time. The findings confirm that the proposed approach offers a robust and practical framework for supporting sustainable and resilient power systems in regions with high variability in both generation and demand.