Short-term scheduling model for cascade diversion hydropower-wind-solar hybrid systems with Wasserstein-based distributionally robust chance constraints

Coordinating the operation of hydropower, wind, and solar energy improves renewable energy utilization but poses challenges in cascade serial diversion-type hydropower stations (CSDHS), including balancing upstream and downstream flows and managing renewable energy uncertainties. This paper proposes a short-term optimization scheduling model for CSDHS base hybrid system that incorporates the uncertainties of wind and solar power. First, a fuzzy uncertainty set based on the Wasserstein distance is formulated to model wind and solar power forecasting errors. Next, a detailed short-term hydro-wind-solar complementary scheduling model for CSDHS is developed, which integrates upstream and downstream flow matching and complex operation constraints. Finally, Conditional Value-at-Risk (CVaR) is employed to reformulate the distributionally robust chance constraints (DRCC), and linearization techniques are applied to transform the objective function and constraints, converting the model into a solvable mixed-integer linear programming (MILP) formulation. Case studies validate the effectiveness of the model in meeting flow-matching requirements and handling the complex operational constraints of CSDHS. Moreover, the model enables the complementary operation of the hybrid hydro-wind-solar system while fulfilling grid peak-shaving requirements. It strikes an optimal balance between robustness and economic efficiency, addressing both peak-shaving demands and diverse risk preferences.