A Two-Stage Robust Capacity Optimal Configuration Model for Power Systems Considering Multi-Objective Optimization

To mitigate renewable energy curtailment and maintain long-term power balance, both planning and operational strategies must be addressed. However, most existing studies on power system capacity optimization focus on a single objective, such as economic efficiency or carbon reduction. To overcome this limitation, this paper proposes a two-stage robust capacity optimization and decision-making framework for power systems that incorporates multi-objective optimization. In the first stage, a bi-level robust capacity optimization model is developed, where the upper-level problem targets capacity expansion planning and the lower-level problem addresses chronological production simulation and operational optimization. The upper-level objectives include minimizing investment and operating costs, maximizing supply reliability, and maximizing renewable energy integration. Secondly, the NSGA-II algorithm is employed to solve the constructed bi-level multi-objective optimization model. Finally, a decision-making model based on the Best–Worst Method (BWM), entropy weighting, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is constructed to further evaluate and select among multiple Pareto-optimal solutions obtained in the first stage, thereby determining the final capacity configuration scheme. The case study demonstrates that the proposed two-stage framework maintains good stability under scenarios such as extreme weather, ensuring a power supply reliability of 98.78% and a new energy utilization rate of 98.5% under various conditions.