Optimal Sizing of Remote Wind-Storage Power Plants Using Wasserstein-Based Distributionally Robust Optimization

The geographic mismatch between renewable energy generation and consumption centers necessitates long-distance power transmission, introducing complexities in planning and operation. This study addresses the planning of wind-storage hybrid power plants (WSPPs) in remote areas, considering the uncertainty of wind power and the fixed capacity of long-distance transmission lines. This paper develops a two-stage distributionally robust optimization (DRO) model with a Wasserstein metric to optimize WSPP investment under risk. The model is simplified into a linear programming problem by deriving a Lipschitz constant, facilitating practical application. The research offers a framework for balancing investment return and risk in the context of renewable energy integration, accounting for stakeholder interactions. The effectiveness of DRO is verified, and the planning results are compared with those of its stochastic and robust counterparts.