一种计及风光储的虚拟电厂多目标调度控制模型

A Multi-Objective Scheduling Control Model for Virtual Power Plant Considering Wind and Solar Energy

针对数量巨大、地域分散且容量较小的分布式电源的调度优化问题,考虑风场和光伏电源出力的间歇性与不确定性,采用虚拟电厂的方式,建立基于虚拟电厂的多目标调度模型,提出了电力系统消纳分布式电源的新方法,实现多分布式电源并网协调优化运行。首先,根据光伏电源(PV)、风力电源(WPP)、燃气机(CGT)以及储能装置(ESS)的出力特性,构建虚拟电厂,并以虚拟电厂的收益最大作为模型的目标函数;其次,考虑到光伏、风能的出力不确定性,基于极端学习机算法(ELM算法)建立可再生能源惩罚成本与其出力的关系,用于优化虚拟电厂的调度模型,采用粒子群优化算法求解模型。案例验证表明,惩罚成本公式具有可行性,通过对比得出,粒子优化算法在求解虚拟电厂模型时具有更好的收敛性。

For the scheduling optimization problem of large-scale,geographically dispersed and small-capacity distributed power sources,considering the intermittent and uncertainties of wind farm and photovoltaic power output,a virtual power plant-based multi-objective scheduling model was established by means of virtual power plants.A new method for power system to dissipate distributed power was proposed,which realizes the coordinated operation of multi-distributed power grids.Firstly,according to the output characteristics of photovoltaic power,wind power,gas engine power supply and energy storage device,the virtual power plant is constructed,and the maximum benefit of the virtual power plant is taken as the objective function of the model.Secondly,considering the uncertainty of the output of photovoltaic and wind energy,based on ELM algorithm,the relationship between the penalty cost of renewable energy and its output is established.It is used to optimize the scheduling model of virtual power plant,and the improved PSO algorithm is used to solve the model.The case study shows that the penalty cost formula is feasible.By comparison,the particle optimization algorithm has better convergence when solving the virtual power plant model.