计及源荷不确定性及阶梯型碳交易的虚拟电厂优化调度方法
Optimal Scheduling of Virtual Power Plants Accounting for Source Load Uncertainty and Stepped Carbon Trading

在风光等清洁能源渗透率及能源低碳化需求不断提高的背景下，如何精确模拟新能源出力不确定性及引导负荷侧柔性资源参与需求响应显得尤为重要。针对上述问题，本文提出一种计及源荷不确定性及阶梯型碳交易的虚拟电厂优化调度模型。首先，源侧基于Frank-Copula函数建立风光出力联合概率分布模型，采样约简得风光出力典型场景。其次，在多能耦合虚拟电厂中引入碳捕集与封存(carbon capture and storage, CCS)设备，降低碳排放，荷侧建立考虑柔性用户用能满意度的需求响应模型，以提升风光消纳。同时，引入阶梯型碳交易机制，建立源荷协同优化及低碳性改造的VPP日前优化调度模型。然后，采用梯形隶属度函数将多目标优化问题模糊化为单目标优化问题，调用CPLEX求解器求解。最后，通过算例分析验证本文方法的有效性。
In the context of increasing penetration of clean energy sources such as wind and solar power and the demand for decarbonization of energy sources, it is particularly important to accurately simulate the uncertainty of new energy output and guide the load-side flexible resources to participate in demand response. To address the above issues, this paper proposes an optimal scheduling model for virtual power plants that takes into account the source-load uncertainty and stepwise carbon trading. First, the source side establishes a joint probability distribution model of wind and solar power based on the Frank-Copula function, and samples the typical scenarios of wind and solar power. Second, carbon capture and storage (CCS) equipment is introduced into the multi-energy coupled virtual power plant to reduce carbon emissions, and a demand response model is established on the load side that takes into account the energy satisfaction of the flexible users to enhance wind and solar power consumption. At the same time, a stepwise carbon trading mechanism is introduced to establish a VPP day-ahead optimal scheduling model for source-load cooperative optimization and low-carbon transformation. Then, the multi-objective optimization problem is blurred into a single-objective optimization problem by using the trapezoidal affiliation function, and the CPLEX solver is invoked to solve the problem. Finally, the effectiveness of the method in this paper is verified by case analysis.