Abstract:

As China's power market reforms advance, pilot regions across the country have encountered imbalance funds of different scales during actual settlement processes in their spot power markets. This significantly impacts the profitability of each market participant and reduces market efficiency. Therefore, this study proposes an optimization method for allocating power market imbalance funds based on multi-agent games. Considering significant differences among provinces in medium-to-long-term trading rules, energy structures, and spot market development, an Agent-Based Model (ABM) is employed to characterize market participants such as system operators and power generators. Reinforcement learning is used to explore the behavioral decisions of these participants under different allocation methods. Second, within the Stackelberg game framework, we uncover the endogenous evolutionary mechanisms of market participants under different allocation methods, evaluating the impact of multiple imbalance fund allocation approaches on market efficiency and other metrics. By leveraging ABM and reinforcement learning to capture the interactive characteristics among multiple market participants and constructing endogenous evolutionary mechanisms to simulate risk-averse behaviors, this approach facilitates the identification of province-specific, optimized imbalance fund allocation proposed methods, thereby preventing resource misallocation. The results of the calculation examples show that the ABM algorithm and endogenous evolution mechanism fully reflect the behavioral strategies of market entities. When the imbalance fund allocation proposed method causes partial capital outflow, the incentive compatibility indicator reaches 0.22, and all market entities exhibit a strong conservative stance. When the imbalance fund allocation proposed method guides market entities through spread penalties, all market entities adopt proactive strategies, effectively improving market operational efficiency.

Key words: electricity market, imbalance funds, spot market