风光等新能源出力的不确定性使得电源侧的碳排放难以准确追踪量化，可调度负荷的参与将进一步增加碳排放优化的复杂度。为此，该文提出一种源荷互动下的随机碳流优化方法。首先，结合风电、光伏以及可调度负荷的工作特性，从隶属度函数以及距离函数的角度出发改进模糊 C 均值聚类法，高效合理聚合出随机环境下电网的随机出力场景集;然后，基于碳排放流理论，建立考虑网损的增量碳流网络模型，实现在源荷出力随机波动过程中及时快速地追踪碳流的变化;最后，兼顾经济性、环保性和用户满意度，建立多目标的阶梯式节点边际碳排放模型，提出边际碳参数代替平均碳排放因子，精细量化负荷侧的碳减排潜力。通过算例仿真验证该文所提源荷互动优化方法的优越性。相较于传统碳流理论调度方法，其综合成本降低 2。50%，碳排放量减少7。11%。
Stochastic Carbon Flow Optimization of Power System Considering Source-load Interaction
The uncertainty of new energy output such as wind and solar makes it difficult to accurately track and quantify carbon emissions on the power supply side,and the participation of dispatchable loads will further increase the complexity of carbon emission optimization.Therefore,a stochastic carbon flow optimization method under source-load interaction is proposed.First,combined with the working characteristics of wind power,photovoltaic and dispatchable load,the fuzzy C-means clustering method is improved from the perspective of membership function and distance function,and the stochastic output scenario set of the power grid in stochastic environment is efficiently and reasonably aggregated.Then,based on the carbon emission flow theory,an incremental carbon flow network model considering network loss is established to track the changes of carbon flow in a timely and rapid manner during the stochastic fluctuation of source load output.Finally,taking into account the economy,environmental protection and user satisfaction,a multi-objective stepped node marginal carbon emission model is established,and marginal carbon parameters are proposed instead of average carbon emission factors,and the carbon emission reduction potential of the load side is finely quantified.The superiority of the source-load interaction optimization method proposed in this paper is verified by example simulation.Compared with the traditional carbon flow theory scheduling method,the comprehensive cost is reduced by 2.50%and the carbon emission is reduced by 7.11%.
万方数据
维普
