During the wet season

the substantial influx of water surpasses the adaptive capacity of cascade hydropower stations

resulting in considerable conflicts between hydropower generation and the integration of renewable energy.This leads to an increase in the curtailment of both hydropower and renewable energy.During the dry season

the reduction in incoming water requires hydropower reservoirs to conserve adequate water for emergency power supply to the grid

hence diminishing the capacity of hydropower stations to mitigate fluctuations in renewable energy output.In the wet season

a multi objective optimization scheduling model is designed to maximize power generation and reduce residual net load fluctuations at cascade hydropower units.In the dry season

a multi objective optimization scheduling model is developed to maximize storage capacity and minimize residual net load variability.The normal boundary intersection (NBI) method is applied to solve the multi objective optimization problem

generating a Pareto optimal solution set.A compromise solution is selected from the Pareto set using fuzzy membership degree and entropy weighting methods

serving as the optimal scheduling plan for both the wet and dry seasons.The optimization results for a real world power grid demonstrate the efficacy of the proposed methodology

offering significant insights for actual dispatching operations.