Short-term multi-objective scheduling optimization model for a pumping-integrated hybrid renewable energy system considering ecological flow constrain

The increasing penetration of wind and photovoltaic (PV) power has led to insufficient flexibility in hybrid renewable energy systems (HRES). Integrating pumping installations (PI) into cascade hydropower plants (CHP) offers a promising solution to enhance system regulation capability. This study establishes a short-term multi-objective optimization model for a pumping-integrated hydro-wind-PV hybrid system, incorporating ecological flow constraints. The model aims to minimize the peak-valley difference of residual load and maximize renewable energy grid-connected power. By treating generating units as the minimum scheduling entities, granular dispatch optimization is achieved. The model is formulated as a mixed-integer linear programming (MILP) problem, and the Pareto frontier is generated using the ε-constraint method. Simulation studies based on an actual CHP in South China validate the proposed approach, leading to the following main contributions: (1) quantifying the contribution of PI integration to peak-shaving performance in a hydro-wind-PV system; (2) achieving unit-level granularity in dispatch optimization to enable refined scheduling strategies; (3) quantifying the inherent trade-off between minimizing residual load fluctuations and maximizing renewable energy integration; and (4) revealing the mechanism by which ecological flow constraints dynamically reshape target trade-offs. The proposed methodology offers a valuable decision-support tool, especially in regions with rich renewable energy resources.