【摘要】 为提高虚拟电厂调度计划的精确性，充分挖掘虚拟电厂内灵活性资源的调节潜力，该文提出一种日前–日内多时间尺度优化调度方法，对风光出力和负荷分别进行日前预测和日内预测。采用储能电池对日内风光出力波动进行平抑。利用光热电站对热电机组解耦，降低热电机组最小出力，考虑不同响应速度的需求响应资源，建立含光热电站的虚拟电厂多时间尺度热电联合优化调度模型，采用自适应遗传算法求解。算例仿真结果表明：相较于传统日前调度，多时间尺度优化调度能够获得更加精细的调度计划，进一步促进风光消纳，降低虚拟电厂出力偏差，提高虚拟电厂经济性。更多还原

【Abstract】 To enhance the accuracy of the virtual power plant scheduling plan and fully exploit the adjustment potential of flexible resources in the virtual power plant, in this paper we propose a day-ahead and intraday multi-time scale optimization scheduling method that makes both day-ahead and intraday forecast of wind and solar output as well as load. Additionally,we utilize energy storage batteries to smooth the fluctuations of intraday wind and solar output, employ solar-thermal power station to decouple the thermoelectric units with the aim of mitigating their minimum output, and consider demand response resources with varying response speeds. A multi-time scale thermoelectric joint optimization scheduling model for virtual power plants containing solar-thermal power stations is established, and the adaptive genetic algorithm is employed to solve it. The simulation results of the example show that,compared to traditional day-ahead scheduling, the multi-time scale optimal dispatching enables a more refined dispatching plan, thereby further promoting wind and solar consumption,reducing the output deviation of the virtual power plant, and improving the economy of the virtual power plant.更多还原