基于双重博弈的多综合能源系统低碳经济优化调度

Low-carbon Economic Optimal Dispatch of Multi-integrated Energy Systems Based on Dual-game Theory

  • 摘要: 随着能源系统向低碳化、清洁化转型,为促进可再生能源消纳与系统减碳,缓解多综合能源系统多主体利益冲突,提出一种基于双重博弈的多综合能源系统低碳经济优化策略。首先,考虑到多能源交互特性与低碳性,构建多综合能源系统低碳运行框架,采用奖惩阶梯型碳交易机制。其次,针对多综合能源系统间能源需求和碳配额需求的冲突,提出一种Stackelberg博弈-纳什谈判双重博弈的优化调度策略。在纵向传导碳交易方面,以综合能源系统运营商作为领导者,通过制定分时碳价,引导作为跟随者的综合能源系统优化碳配额购售计划。在横向协调能源共享方面,综合能源系统间通过纳什谈判的方式进行电热共享,以实现合作效益最大化。最后,采用自适应差分算法和交替方向乘子法对模型进行求解,算例结果验证了所提策略在提升综合能源系统经济性与低碳性方面的有效性。

     

    Abstract: With the transition of energy systems toward decarbonization and cleaner energy integration, this study proposes a low-carbon economic optimal strategy for multi-integrated energy systems based on a dual-game framework, aiming to promote renewable energy accommodation, reduce carbon emissions, and mitigate multi-agent interest conflicts in multi-integrated energy systems. First, considering the multi-energy interaction characteristics and low-carbon requirements, a low-carbon operation framework for integrated multi-energy systems is constructed, incorporating a tiered reward-punishment carbon trading mechanism. Second, to address conflicts in energy demands and carbon quota allocation among multiple integrated energy systems, a dual-game optimization strategy combining Stackelberg-Nash bargaining is proposed. In terms of vertical coordination for carbon trading, the integrated energy system operator acts as the leader, guiding follower integrated multi-energy systems in optimizing carbon quota trading plans by setting time-of-use carbon prices. While in terms of horizontal collaboration for energy sharing, integrated multi-energy systems engage in electricity-heat sharing through Nash bargaining to maximize cooperative benefits. Finally, the model is solved using an adaptive differential evolution algorithm and the alternating direction method of multipliers. Case studies demonstrate that the proposed strategy significantly enhances the economic efficiency and low-carbon performance of integrated energy systems.

     

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