Reinforcement Learning Model and Its Application for Balancing Ecological and Power Generation Objectives of Cascaded Reservoirs
To better leverage economic and ecological benefits,a reinforced learning algorithm-based model for opti-mal scheduling of cascaded reservoirs was developed to explore the trade-off between power generation and ecological ob-jectives based on the natural inlet flow process of Xiaolangdi at the weekly scale.The model was applied to the Xiaolang-di-Xiaoxiyuan cascaded reservoirs,and the optimal scheduling strategies of power generation optimization,ecological opti-mization and power generation-ecological trade-off under different scheduling schemes were explored respectively.The re-sults show that with the goal of optimal power generation,the average annual power generation of cascade reservoirs has increased by 3.62% -7.92% compared to conventional scheduling;When targeting ecological optimization,the average ec-ological guarantee rate increases by 31.68% -33.66% compared to conventional scheduling.Thus,it provides a feasible method for multi-objective optimal scheduling of cascaded reservoirs.
万方数据
维普
