In order to promote the grid-connecting and generation of distributed energy generation, represented by wind and photovoltaic power,the paper introduces a two-stage optimization model to solve the uncertainty of virtual power plant (VPP) operation,and set up a bi-level stochastic scheduling optimization model for virtual power plant. The paper describes VPP components including wind power plant (WPP), photovoltaic generators (PV), convention gas turbine (CGT), energy storage systems (ESSs)and IBDR. Firstly,the basic structure of VPP is introduced,and the power output model and the demand response model are established. Secondly,a bi-level stochastic scheduling model of virtual power plant is established. In upper layer model, day-ahead dispatching Scheduling plan is made according to the day-ahead prediction results of wind power and photovoltaic power generation;in lower layer model,the day-ahead dispatching scheduling plan is updated according to the actual output of WPP and PV,and the final VPP scheduling scheme is form. Finally, the case analysis shows that the proposed model could link the day-ahead dispatch and time-ahead dispatch,and reduce penalty cost in unbalance time, and improve the operating income of VPP. The energy storage system could make use of the self-charging and self-discharging characteristics to cooperate with the wind power and photovoltaic power generation inside VPP. The ESS discharges in peak time and charges in valley time,which helps to smooth the net load curve of the system. Price-based demand response (PBDR)could guide the user side to cooperate with VPP power generation scheduling. The peak shaving effect of PBDR is weaker than that of IBDR, but the valley filling effect is stronger. After introducing ESSs and DR,the operating income of VPP reached the peak,indicating that ESSs and DR have synergy optimization effect.%为促进以风光为代表的分布式能源发电并网,文章集成了风电、光伏发电、燃气轮机、储能系统和激励型需求响应为虚拟电厂,引入两阶段优化理论克服风光不确定性,建立VPP双层随机调度优化模型.首先,介绍了VPP的基本结构,建立了电源出力模型和需求响应模型.然后,建立了虚拟电厂双层调度优化模型:在上层模型中,根据风电和光伏发电日前预测结果,制定日前调度计划;在下层模型中,根据WPP和PV的实际输出,修正日前调度计划,形成最终VPP调度方案.最后算例分析表明:所提模型能够衔接日前调度和时前调度,降低系统缺电惩罚成本,提升VPP运营收益.储能系统能够利用自身充放电特性配合VPP内部风电和光伏发电出力,有利于平缓系统净负荷曲线.PBDR能够引导用户侧配合VPP发电调度,削峰作用弱于IBDR,而填谷作用强于IBDR.同时引入ESSs和DR后,VPP运营收益达到最高,表明ESSs和DR具有协同优化效应.
展开▼
