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基于碳交易和主从博弈的园区综合能源系统多主体双层优化调度方法 被引量:2

Multi-agent Bi-level Optimal Scheduling Method for Park Integrated Energy Systems Based on Carbon Trading and Leader-Follower Game Theory
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摘要 为实现“碳达峰”和“碳中和”的战略目标,园区综合能源系统的清洁低碳利用显得尤为迫切,本文提出了一种基于碳交易机制和主从博弈的多主体双层优化调度方法。通过引入碳交易模型和需求响应机制,提升系统运行经济性和效率,降低碳排放量。首先,介绍了园区内部能量转换设备以及阶梯形碳交易机制。然后,建立了一个以园区综合能源系统运营商和负荷聚合商为博弈双方的多主体博弈均衡模型,该模型融合了综合需求响应机制。最后,通过算例对比仿真分析,验证了所提方法的有效性和合理性,碳交易机制和综合需求响应具有叠加改善碳排放的效果。 To achieve the strategic goals of“carbon peaking”and“carbon neutrality”,the clean and low-carbon utilization of integrated energy systems in parks is particularly urgent.A multi-agent bi-level optimal scheduling method based on the carbon trading mechanism and leader-follower game is proposed.By introducing the carbon trading model and demand response mechanism,the economic efficiency and operational efficiency of the system are enhanced,and carbon emissions are reduced.First,the energy conversion equipment within the park and the tiered carbon trading mechanism are introduced.Then,a multi-agent game equilibrium model is established,where the park’s integrated energy system operator and the load aggregator are the two players.This model incorporates an integrated demand response mechanism.Finally,through comparative simulation analysis of case studies,the effectiveness and rationality of the proposed method are verified,showing that the carbon trading mechanism and integrated demand response have a cumulative effect in improving carbon emission reduction.
作者 吴迪凡 徐婷婷 张楠 高亦凌 韩钰 郑成 王晨杰 WU Difan;XU Tingting;ZHANG Nan;GAO Yiling;HAN Yu;ZHENG Cheng;WANG Chenjie(State Grid Shibei Power Supply Company,SMEPC,Shanghai 201900,China;State Grid Shanghai Electric Power Research Institute,State Grid Shanghai Municipal Electric Power Company,Shanghai 200437,China)
出处 《电力大数据》 2024年第5期9-18,共10页 Power Systems and Big Data
基金 国家电网有限公司总部科技项目(52094023001N)。
关键词 园区综合能源系统 碳排放 碳交易 双层模型 综合需求响应 主从博弈 优化调度 park integrated energy system carbon emissions carbon trading bi-level model integrated demand response leader-follower game optimal scheduling
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