Long-term complementary scheduling model of hydro-wind-solar under extreme drought weather conditions using an improved time-varying hedging rule

The frequent occurrence of extreme drought weather poses serious challenges to the complementary scheduling of renewable energy, including uncertain production processes, ineffective scheduling rules, and complex model solving, which further reduces the reliability of the power grid. To address these issues, a long-term complementary scheduling model of hydro-wind-solar (LCMHWS) considering hedging rules is proposed. First, to fully eliminate the uncertainty in monthly variables, Monte Carlo simulation and K-means clustering are sequentially absorbed into the model to process historical sequences and provide deterministic scenarios in which variable characteristics are conformed as input factors. Second, an improved time-varying hedging rule with strong drought resistance capability is proposed to optimize the complementary process of hydro-wind-solar and maximize the objectives of power generation and final energy storage. Last, NSGAII and membership function are adopted to search for the optimal Pareto solution for the uncertain multi-objective model. LCMHWS is validated for 7 hydropower and nearby wind and solar plants on the Lancang River in China. The results indicate that, while meeting the guaranteed output of the system, LCMHWS can increase the power generation and final energy storage by 14% and 3% under extreme drought conditions, respectively, and also smoothed the output process of power grid.