Capacity planning for hydro-wind-photovoltaic-storage systems considering high-dimensional uncertainties

The rapid development of renewable energy has made hydropower’s role as a flexible resource increasingly important in power systems. However, hydropower generation capability highly depends on water inflows, particularly during dry seasons, making it difficult to independently meet growing load demands. The application of hydro-wind-photovoltaic-storage systems offers a promising solution, yet faces challenges from the high-dimensional uncertainties in natural conditions. This paper proposes a capacity planning method that considers high-dimensional uncertainties characterized by spatiotemporal correlations of natural factors. Firstly, a scenario generation method based on the transition probability matrix and C-Vine Copula model is developed. The constructed scenario sets capture the temporal correlations of natural conditions and spatial correlations between different parameters. Secondly, a bi-level optimization model for capacity planning is established. The upper level minimizes the deviation of operational cost and grid supply revenue to determine optimal capacity allocation, while the lower level optimizes both economic and safe objectives for operational dispatch. The normal boundary intersection method is employed to obtain Pareto front solutions that balance economy and safety. Different case studies are conducted to validate the effectiveness of the proposed method. Compared with the fixed ratio and variable ratio capacity allocation strategies without uncertainty, the optimal total system cost is reduced by 2.90% and 3.88%, respectively.