计及源-荷多灵活备用资源的随机优化调度

大规模新能源并网给电力系统的调度运行带来了新的挑战。为缓解系统的备用压力,提出一种计及源-荷多灵活备用资源的随机优化调度方法。首先,基于场景生成方法建立可变场景模型,考虑了风电并网容量和光伏并网面积对新能源出力不确定性的影响。其次,建立电力系统中多种灵活资源的备用模型:在源侧,分别建立常规机组和风电/光伏的备用模型,并考虑了风电/光伏备用的不确定性;在负荷侧,引入激励型需求响应,对需求侧备用进行建模。然后,基于两阶段随机优化方法建立备用调度模型。该模型考虑了日前的运行和备用决策以及日内不确定场景下的弃风、弃光以及切负荷风险。最后,基于改进的IEEE RTS-24测试系统验证了所提模型的有效性。

Stochastic Optimal Scheduling Considering Multiple Flexible Reserve Resources on Both Source and Load Sides

High proportion of new energy grid-connected brings new challenges to the dispatch and operation of power systems. In order to alleviate the reserve pressure of power systems, a stochastic optimal scheduling model considering multiple flexible reserve resources on both source and load sides is proposed. Firstly, a model of variable scenario is established on the basis of scenario generation method, which considers the influence of the capacity of grid-connected wind power and the area of grid-connected photovoltaic power on the uncertainty of new energy generation. Then, the reserve model of various flexible resources in the system is established. On the source side, the reserve models of conventional units, photovoltaic and wind power are established, respectively, and the uncertainty of wind power and photovoltaic reserve is considered; On the load side, the reserve model is established on the basis of the incentive demand response. Then, the reserve dispatching model is established on the basis of the two-stage stochastic optimization model. The model considers the day-ahead operation and reserve decisions, as well as wind power curtailment, photovoltaic power curtailment, and load shedding risks in uncertain scenarios within the day. Finally, the case studies based on the modified IEEE RTS-24 system verify the effectiveness of the proposed model.