Due to shortage of water resources and increasing water demands, the joint operation of multireservoir systems for balancing power generation, ecological protection, and the residential water supply has become a critical issue in hydropower management. However, the numerous constraints and nonlinearity of multiple reservoirs make solving this problem time-consuming. To address this challenge, a deep reinforcement learning approach that incorporates a transformer framework is proposed. The multihead attention mechanism of the encoder effectively extracts information from reservoirs and residential areas, and the multireservoir attention network of the decoder generates suitable operational decisions. The proposed method is applied to Lake Mead and Lake Powell in the Colorado River Basin. The experimental results demonstrate that the transformer-based deep reinforcement learning approach can produce appropriate operational outcomes. Compared to a state-of-the-art method, the operation strategies produced by the proposed approach generate 10.11% more electricity, reduce the amended annual proportional flow deviation by 39.69%, and increase water supply revenue by 4.10%. Consequently, the proposed approach offers an effective method for the multiobjective operation of multihydropower reservoir systems.

该研究提出了一种采用深度强化学习和Transformer框架的方法来解决在水力发电管理中应对多水库系统的电力生成、生态保护和居民用水等多方面协调而导致的时间成本水资源不足的问题，并在美国科罗拉多河流域的MeadLake和PowellLake上得到实际应用，表明这一方法能够有效提高水力发电系统的制电量和收益，同时还能减少对环境的影响。

基于Transformer的深度强化学习多目标水电站水库运行优化