A Reinforcement-learning-based Bidding Strategy for Power Suppliers with Limited Information
Qiangang Jia, Yiyan Li, Zheng Yan, Chengke Xu, Sijie Chen
Abstract
The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors. Most existing studies assume that a power supplier is accessible to the sufficient market information to derive an optimal bidding strategy. However, this assumption may not be true in reality, particularly when a power market is newly launched. To help power suppliers bid with the limited information, a modified continuous action reinforcement learning automata algorithm is proposed. This algorithm introduces the discretization and Dyna structure into continuous action reinforcement learning automata algorithm for easy implementation in a repeated game. Simulation results verify the effectiveness of the proposed learning algorithm.