【摘要】 【目的】综合能源系统（integrated energy system, IES）在碳交易与绿证交易市场下面临多重不确定性挑战，现有调度模型难以兼顾经济收益与低碳目标的动态平衡，且缺乏对非结构化不确定性的有效量化方法。提出一种计及绿证与碳交易不确定性的IES区间优化调度策略，并结合主从博弈机制协调多主体利益冲突，提升系统经济性与低碳性。【方法】首先，构建基于区间数学的碳交易与绿证不确定性量化模型，以区间数表征绿证交易额与碳排放量的波动范围，避免对概率分布的依赖；其次，建立IES运营商与能源供应商的主从博弈框架，设计双层区间优化模型：上层以IES运营商收益最大化为目标动态优化能源定价策略，下层以能源供应商收益最大为目标优化机组出力与交易决策；进而，采用粒子群算法嵌套CPLEX求解器实现博弈均衡解的高效搜索，并通过多场景对比分析验证模型性能。【结果】仿真结果表明，所提双层区间优化模型下，综合能源系统运营商与能源供应商收益分别提升2.3%与5.1%，碳排放成本降低12.7%，且窄区间优化结合动态定价策略显著缓解了绿证-碳交易波动对系统的冲击，验证了模型的经济性与低碳协同优化能力。【结论】所提模型通过区间优化与主从博弈的结合，在考虑碳交易与绿证不确定性情况下，有效平衡了IES多主体利益冲突，提升了系统低碳性与经济性。更多还原

【Abstract】 [Objective] Integrated energy systems(IESs) face multiple challenges in carbon and green certificate trading markets. Existing scheduling models have difficulty in balancing the dynamics of economic returns with low-carbon objectives and lack effective quantification methods for unstructured uncertainty. An optimal scheduling strategy for IES intervals that takes into account the uncertainties of green certificates and carbon trading is proposed, and a master-slave game mechanism is combined to coordinate conflicts of interest with multiple actors to enhance economic outcomes and minimize the overall carbon footprint of the system. [Methods] First, a quantitative model of uncertainty in carbon trading and green certificate markets is constructed based on interval mathematics to characterize the range of fluctuation of green certificate trades and carbon emissions in terms of the number of intervals to avoid relying on a probability distribution. Second, a master-slave game framework is established for an IES operator and an energy supplier, and a two-layer interval optimization model is designed. The upper layer dynamically optimizes an energy pricing strategy to maximize the revenue of the IES operator, and the lower layer optimizes the unit output to maximize the revenue of the energy supplier. Furthermore, the particle swarm algorithm was applied to nest the CPLEX solver to efficiently search for an equilibrium solution to the game. The performance of the model was verified through a multi-scenario comparative analysis. [Results] Simulation results show that the proposed two-layer interval optimization model increased the revenue of the integrated energy system operator and energy supplier by 2. 3 % and 5. 1 %, respectively, and reduced the cost of carbon emissions by 12. 7 %. Narrow interval optimization combined with the dynamic pricing strategy significantly mitigated the impact of fluctuations in green certificates and carbon trading on the system, which verifies the model′s economic efficacy and synergistic capability to optimize for minimal carbon consumption. [Conclusions] By integrating interval optimization and the master-slave game, the proposed model balances the conflicting interests of IESs effectively and improves the low-carbon economics of the system by taking the uncertainty of carbon trading and green certificate markets into consideration.更多还原