不确定性环境下考虑弃风的电力系统日前调度

为尽可能减少弃风,将弃风电量期望作为最小化优化目标加入日前调度模型。该优化目标与原有模型中的发电成本最小互相矛盾,为协调这两个子优化目标,通过隶属度函数分别将它们模糊化,构建了基于最大满意度的单目标优化模型,并采用遗传算进行了求解。风电并网增加了系统的不确定性,模型采用基于风险指标失负荷概率的机会约束代替传统的旋转备用约束。失负荷概率与弃风电量期望计算过程中考虑了负荷、风功率随机预测误差以及常规机组随机强迫停运。基于IEEE 118节点系统的仿真实验表明,该调度模型可给出兼顾发电成本与风电接纳水平的日前调度计划,为系统调度人员提供参考。

Day-ahead dispatching in consideration of wind power curtailments in uncertain environments

In order to reduce wind power curtailments as far as possible, the expected wind power curtailments is incorporated into day-ahead dispatching formulation as a newly added minimum optimization object. The newly added optimization object is contradicted with generation costs that are intrinsic optimization object in formulation. In order to coordinate them, two optimization objects are handled respectively by fuzzy method, and a single-object optimization formulation based on maximum satisfaction extent is presented subsequently. Genetic algorithm is utilized to solve the proposed model. Large-scale integration of wind power increased uncertainties with respect to day-ahead scheduling, a constraint on risk index lost of load probability (LOLP) is proposed to replace conventional constraint on spinning reserve. Random errors on load / wind power predictions and random outages of generator units are considered in calculations of index LOLP and the expected wind power curtailments. Simulation results based on IEEE 118 test systems indicate that day-ahead dispatching formulation can provide a dispatching schedule that considers wind power curtailment and generation costs at the same time, and provide supports for system operators.