采用改进量子粒子群优化算法的虚拟电厂参与二次调频两阶段优化

Two-stage optimization of virtual power plant participating in secondary frequency regulation using improved quantum particle swarm optimization algorithm

虚拟电厂(virtual power plant,VPP)作为一种新型区域能源管理系统,可通过“源—荷—储”的协调优化调度,高效参与电网二次调频辅助服务。介绍虚拟电厂内部结构,建模分析新能源机组及可控负荷特性;搭建虚拟电厂参与二次调频两阶段调度模型,该模型能够兼顾二次调频净利润及调频效果;研究一种自适应权重的改进量子粒子群优化(quantum particle swarm optimization,QPSO)算法,通过引入自适应权重机制,在量子粒子更新过程中动态调整权重参数以提高算法的搜索能力和收敛速度;并将改进算法应用于两阶段优化过程中,使虚拟电厂获得更高的二次调频净利润及更好的调频效果;仿真结果表明,所提改进算法的收敛速度更快且全局寻优能力更强。

As a new type of regional energy management system,the virtual power plant(VPP)can efficiently participate in the secondary frequency regulation auxiliary services of the power grid through the coordinated optimal scheduling of"source-load-storage".This paper introduces the internal structure of the VPP,and models and analyzes the characteristics of new energy units and controllable loads.A two-stage scheduling model for the VPP participating in secondary frequency regulation is established,which can balance the net profit and frequency regulation effect of secondary frequency regulation.An improved quantum particle swarm optimization(QPSO)algorithm with adaptive weights is studied.By introducing an adaptive weighting mechanism,the weight parameters are dynamically adjusted during the quantum particle update process to improve the search ability and convergence speed of the algorithm.The improved algorithm is applied to the two-stage optimization process,enabling the VPP to achieve higher net profits from secondary frequency regulation and better frequency regulation effects.Simulation results demonstrate that the proposed improved algorithm has a faster convergence speed and stronger global optimization ability.