Multiobjective Hydropower Reservoir Operation Optimization with Transformer-Based Deep Reinforcement Learning

TL;DR

This paper introduces a transformer-based deep reinforcement learning method for optimizing multireservoir hydropower operations, balancing power, ecological, and water supply needs more effectively than existing approaches.

Contribution

It develops a novel transformer-augmented deep reinforcement learning framework for multireservoir management, improving decision-making efficiency and operational outcomes.

Findings

Achieves 10.11% more electricity generation

Reduces flow deviation by 39.69%

Increases water supply revenue by 4.10%

Abstract

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.