Abstract: Large-scale integration of wind power and solar power has led to highly fluctuating residual loads on the power systems, posing a great challenge to the short-term operation of pumped storage stations. This study focuses on a real case of the Heimifeng pumped storage station that connects to the Hunan power grid in Central China. Based on previous studies of residual load forecasting, we develop a short-term model for optimal operation of the station, incorporating multi-period operational constraints. This model considers dual objectives－maximizing power generation benefit and minimizing residual load fluctuations; it is equipped with an improved genetic algorithm for efficient solution, applicable to the cases of complicated constraints－including operational mode transitions and reservoir water balance. Results demonstrate that with the forecasted typical residual load sequences used as input, the optimized operation achieves an average daily power generation benefit of 1.5254 million Yuan, or an increase of 36% relative to the existing real operation, while reducing residual load fluctuations by 34%. This study is practically useful for economical operation of a pumped storage station, and helps enhance grid flexibility under high penetration of renewable energy integration.

Key words: pumped storage power station, residual load, forecast scheduling, renewable energy consumption, improved genetic algorithm