| 引用本文: | 万思宇,程 杉,刘炜炜,卢渊涛,程 颖.基于多主体博弈的配电网-多综合能源系统分布式优化调度[J].电力系统保护与控制,2025,53(22):111-122.[点击复制] |
| WAN Siyu,CHENG Shan,LIU Weiwei,LU Yuantao,CHENG Ying.Distributed optimal scheduling of distribution network-multi-integrated energy systems based on multi-agent game theory[J].Power System Protection and Control,2025,53(22):111-122[点击复制] |
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| 摘要: |
| 针对多综合能源系统(multi-integrated energy system, MIES)与配电网(distribution network, DN)协同优化中存在的源荷不确定性、多主体利益分配失衡及求解缓慢难题，提出一种基于混合博弈与改进目标级联分析的分布式优化调度策略。首先，采用区间数表征分布式电源与负荷的波动特性，建立具有鲁棒性的多主体博弈模型。其次，构建DN-MIES双层协同架构，通过融合纳什议价模型，建立动态电价交易机制，解决多主体利益均衡与市场激励相容问题。进一步地，设计基于最大变差分析的目标级联(maximum variation analysis based analytical target cascading, MVA-ATC)算法，在分布式求解过程中同步处理不确定性与高效求解需求。仿真结果表明本方法在提升系统经济性、保障主体收益均衡性及降低计算复杂度方面具有显著优势，为新型电力系统下多能源主体协同优化提供了理论支撑与技术路径。 |
| 关键词: 多综合能源系统 配电网 分布式优化 最大变差分析 目标级联分析法 |
| DOI：10.19783/j.cnki.pspc.250051 |
| 投稿时间：2025-01-15修订日期：2025-04-09 |
| 基金项目:国家自然科学基金项目资助(52407118) |
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| Distributed optimal scheduling of distribution network-multi-integrated energy systems based on multi-agent game theory |
| WAN Siyu1,CHENG Shan1,LIU Weiwei1,LU Yuantao1,CHENG Ying2 |
| (1. Hubei Engineering Research Center of New Energy Microgrid (China Three Gorges University), Yichang 443000, China;
2. State Grid Changshou Power Supply Company, Chongqing 401220, China) |
| Abstract: |
| To address the challenges of source-load uncertainty, multi-agent benefit distribution imbalance, and slow convergence in the collaborative optimization of multi-integrated energy systems (MIES) and distribution networks (DN), a distributed optimal scheduling strategy based on hybrid game theory and improved analytical target cascading (ATC) method is proposed. First, interval numbers are used to characterize the fluctuation characteristics of distributed generation and loads, establishing a robust multi-agent game model. Second, the double-layer DN-MIES collaborative framework is constructed. By integrating the Nash bargaining model, a dynamic electricity price trading mechanism is established to achieve multi-agent benefit balance and market incentive compatibility among multiple agents. Furthermore, a maximum variation analysis based ATC (MVA-ATC) algorithm is designed to simultaneously handle uncertainty and computation efficiency during the distributed optimization process. Simulation results show that the proposed method significantly improves system economy, ensures fair benefit distribution among agents, and reduces computational complexity. The work provides both theoretical support and a technical pathway for collaborative optimization of multi-energy subjects in new power systems. |
| Key words: multi-integrated energy system distribution network distributed optimization maximum variation analysis analytical target cascading |
