【摘要】 在“双碳”目标驱动下，综合能源系统(integrated energy system，IES)参与电-碳市场协同优化面临自愿减排激励不足与跨主体利益分配失衡的双重挑战，亟须构建兼顾经济性、低碳性与公平性的市场交易机制。提出一种综合能源系统自愿碳减排的电-碳市场投标竞价与利益分配机制。基于核密度估计与Wasserstein距离生成风光不确定性场景，考虑风电及光伏出力预测误差对电-碳市场投标的影响，建立了三阶段电-碳市场投标竞价与利益分配方法。第一阶段为日前电-碳预投标阶段，基于风光出力的预测值进行预投标，获得预期投标量与预计碳排放。第二阶段为日内调度阶段，考虑IES间的合作博弈与能源共享，确定模糊集内最坏情况分布中最小化调度成本。第三阶段为利益分配阶段，采用Myerson值与Shapley值分配方法分配收益，同时考虑碳转移、碳抵消、碳补偿等多种碳减排机制，激励IES减少系统碳排放，减少投标成本。最后以某工业园区为例，验证了自愿碳减排的电-碳市场投标竞价与利益分配方法的有效性。更多还原

【Abstract】 Driven by the “dual carbon” strategy,integrated energy system (IES) face dual challenges in participating in coordinated electricity–carbon markets:insufficient incentives for voluntary emission reduction and imbalanced benefit distribution among multiple stakeholders.To address these issues,this paper proposes a bidding and benefit allocation mechanism for electricity–carbon markets based on voluntary carbon reduction in IES.A three-stage optimization framework is constructed,including day-ahead bidding,real-time scheduling,and benefit allocation.Wind and photovoltaic uncertainties are modeled using kernel density estimation and Wasserstein distance,enabling robust scenario generation that accounts for forecast errors in renewable outputs.In the day-ahead stage,each IES submits electricity and carbon bids based on forecasted renewable generation and estimated emissions.In the real-time stage,cooperative game theory and energy sharing are incorporated to minimize dispatch costs under the worst-case distribution within a fuzzy uncertainty set.In the allocation stage,the Myerson value and Shapley value are employed to ensure fair distribution of joint bidding profits,considering mechanisms such as carbon transfer,offsetting,and compensation to further incentivize carbon reduction and cost savings.Finally,a case study of an industrial park validates the effectiveness and applicability of the proposed mechanism.更多还原