A Multi‐Agent Bidding Strategy in Day‐Ahead Joint Energy and Reserve Markets Considering Risk Preferences

In day‐ahead joint energy and reserve markets, power generators face intricate challenges in formulating their bidding strategies due to the coupled effects of source‐load uncertainty and multi‐agent game behaviours. To address this issue, this paper proposes a deep reinforcement learning–driven bidding model for multi‐agent generators operating in an incomplete information market environment. First, a risk preference decision mechanism based on forecast information is designed, in which an auxiliary decision network outputs an aggressiveness coefficient, providing a quantitative basis for bidding strategy formulation. Second, the Kolmogorov–Arnold Network (KAN) is adopted to improve the decision network, thereby enhancing the intrinsic interpretability of the model. Third, to solve the multi‐agent game problem, we employ the prioritised experience replay multi‐agent twin delayed deep deterministic policy gradient (PER‐MATD3) algorithm, where sampling probabilities are assigned based on the temporal difference error of samples. Simulation results verify the effectiveness of the proposed method: Compared with the PER‐MADDPG and PER‐MATD3 algorithms, the total profit of the proposed method increased by 38.72% and 13.43%, respectively, while demonstrating strong interpretability and robust training performance.