Incorporating Attention Mechanism into Long Short-Term Memory Reinforcement Learning for Renewable Energy Bidding and Battery Control

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Abstract
Various renewable energy sources, along with corresponding large-scale batteries, have been integrated into power grids, making renewable energy bidding and battery control critical in the real-time energy market. However, most bidding and control problems have been studied separately despite their accompanying impact on the total profit of renewable energy producers. Recently, a Reinforcement Learning (RL) strategy has been proposed to investigate renewable energy bidding and battery control jointly. It determines bidding values based on the battery’s error compensability and then applies additional battery control to the energy arbitrage process. Based on the same experimental scenarios, we present a method that incorporates the attention mechanism into long short-term memory reinforcement learning to increase total profits. We also consider various settings for our models to conduct a comprehensive survey. According to the experimental results, our method achieves significant performance gains over existing strategies, producing cumulative profits of over 400 k for solar energy and more than 200 k for wind energy. These results highlight the superior ability to balance real-time bidding precision and battery utilization efficiency, leading to higher profitability and stability in renewable energy market participation.
Funding Information
  • National Science and Technology Council (112-2221-E-035-062-MY3)

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