EN基于多主体博弈的电力市场不平衡资金分摊优化方法
An optimization method for imbalance funds in a multi-agent game-theoretic electricity market
- 中国电力 2026年59卷第1期 页码：153-162
- 作者机构：
1. 广州电力交易中心有限责任公司,广东,广州,510000
2. 北京清大科越股份有限公司,北京,100102
- 作者简介：
- 基金信息：广州电力交易中心科技项目（180000KC23080001）。
DOI：10.11930/j.issn.1004-9649.202409028
中图分类号：-
网络出版：2026-01-13，
纸质出版：2026-01-13
- 稿件说明：
移动端阅览
王奖, 陈晓东, 许喆, 王景亮, 王立鹏. 基于多主体博弈的电力市场不平衡资金分摊优化方法[J]. 中国电力, 2026, 59(1): 153-162.
王奖, 陈晓东, 许喆, et al. An optimization method for imbalance funds in a multi-agent game-theoretic electricity market[J]. 2026, 59(1): 153-162.
王奖, 陈晓东, 许喆, 王景亮, 王立鹏. 基于多主体博弈的电力市场不平衡资金分摊优化方法[J]. 中国电力, 2026, 59(1): 153-162. DOI： 10.11930/j.issn.1004-9649.202409028.
王奖, 陈晓东, 许喆, et al. An optimization method for imbalance funds in a multi-agent game-theoretic electricity market[J]. 2026, 59(1): 153-162. DOI： 10.11930/j.issn.1004-9649.202409028.
摘要
随着中国电力市场改革的推进，中国电力现货市场各个试点地区在实际结算过程中均出现了不同规模的不平衡资金，这极大地影响了每个市场主体的效益，降低了市场效率。因此，提出基于多主体博弈的电力市场不平衡资金分摊优化方法，考虑到各省在中长期交易规则、能源结构、现货市场建设等方面存在显著差异，使用基于主体（agent-based model，ABM）的建模方法，刻画系统运营商、发电商等各个市场主体，基于强化学习探索不同分摊方法下各个市场主体的行为决策。基于Stackel-berg博弈框架，挖掘各个市场主体在不同分摊方法下的内生演化机制，评估多种不平衡资金分摊方法对市场效率等方面的影响。基于ABM和强化学习刻画了多市场主体之间的互动特征，构建内生演化机制模拟市场主体的风险规避特征，有利于挖掘各省差异化的不平衡资金分摊优化方法，避免资源错配。仿真结果表明：ABM算法与内生演化机制充分反映了市场主体的行为策略。当不平衡资金分摊方法造成部分资金外流时，激励相容性指标达到0.22，各个市场主体呈现出强烈保守态势；当不平衡资金分摊方法通过价差惩罚引导市场主体时，各个市场主体均采取积极策略，有效提升了市场运行效率。
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.
关键词
Keywords
references
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