含光热电站的虚拟电厂多时间尺度热电联合优化调度OA

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.