Bi-level multi-objective configuration and operation optimization for photovoltaic-energy storage systems considering the demand increment effect

Industrial and commercial user-side photovoltaic-energy storage systems face challenges in coordinating multiple objectives such as economic and technical performance during the planning and configuration phase. Traditional methods often fail to accurately account for the impact of demand charges on system benefits. This paper proposes a bi-level multi-objective configuration and operation optimization method for photovoltaic-energy storage systems that considers the demand increment effect. A bi-level optimization framework is constructed, combining an upper-level multi-objective genetic algorithm with a lower-level mixed-integer linear programming model. The upper level optimizes the photovoltaic and storage capacity, while the lower level solves for the optimal operation strategy and quantifies the demand increment effect. An accelerated evaluation strategy based on “typical months” is designed to align with the monthly settlement characteristics of demand charges. Simulation verification using data from a typical industrial and commercial park shows that the method yields a set of Pareto optimal solutions revealing the trade-off relationship among objectives such as return on investment and photovoltaic self-consumption rate. The proposed method achieves coordinated optimization of planning and operation for photovoltaic-energy storage systems, providing decision-making support for users to develop configuration schemes that balance both economic and technical objectives under multiple goals.