An Optimized Dynamic Benefit Evaluation Method for Pumped Storage Projects in the Context of the “Dual Carbon” Goal

With the rapid development of a new power system under the “dual carbon” goal, pumped storage has gained increasing attention for its role in integrating renewable energy and enhancing power system flexibility and security. This study proposes a dynamic benefit evaluation method for pumped storage projects, addressing the limitations of static analyses in capturing the evolving benefit trends. In this paper, the multi-stage dynamic benefit evaluation model was constructed by introducing time-of-use tariffs, periodic capacity pricing mechanism, and ancillary service revenue prediction based on machine learning and the multiple regression method. Sensitivity analysis was applied to explore the impact of key parameter variations on economic indicators. The results show that the benefit structure differs significantly across stages, and with electricity market development, a diversified pattern supported by electricity, capacity, and ancillary service revenues will emerge. The application of the model to an actual operating pumped storage power station yielded an internal rate of return of 8.18%, a payback period of 16.4 years, and a 26% increase in net present value compared with traditional methods. The proposed model expands the theoretical framework for pumped storage benefit evaluation and provides strong support for investment decisions, policy design, and operational strategy optimization.