基于协同免疫量子粒子群优化算法的虚拟电厂双层博弈模型

为了充分利用电动汽车(electric vehicle,EV)大规模的储能优势与代理聚合商在电力市场灵活购售电优势,以此弥补虚拟电厂(virtual power plant,VPP)内部供需不平衡情况,构建电动汽车参与的虚拟电厂双层博弈模型,对虚拟电厂同时进行内外部优化。首先,构建上层代理聚合商-虚拟电厂完全信息动态博弈模型进行虚拟电厂外部优化;其次,构建虚拟电厂-电动汽车聚合商合作博弈模型进行虚拟电厂内部优化,并利用改进的Shapley值分配虚拟电厂与电动汽车聚合商的合作收益;最后,以集成风电机组、可控负荷、储能电池、用户、电动汽车的虚拟电厂进行算例分析,采取协同免疫量子粒子群优化(coevolutionary immune quantum partical swarm optimization,CIQPSO)算法搜寻最优解。算例结果表明,电动汽车参与虚拟电厂能够同时提高两者的经济效益,提高虚拟电厂内部供需平衡能力。

Two-Layer Game Model of Virtual Power Plant Applying CIQPSO Algorithm

In order to make full use of the large-scale energy-storage advantages of electric vehicles （EVs） and the advantages of agent aggregators to flexibly purchase and sell electricity in the power market， to make up for the imbalance between supply and demand in virtual power plant （VPP）， this paper constructs a two-layer game model of a virtual power plant in which electric vehicles participate， to perform internal and external optimization of the virtual power plant at the same time. On this basis， firstly， a fully dynamic game model of the upper-level agent aggregator - virtual power plant is constructed to perform external optimization of the virtual power plant. Secondly， a virtual power plant - electric vehicle aggregator cooperation game model is built to optimize the virtual power plant internals， and the improved Shapley value is used to allocate the cooperation revenue between the virtual power plant and the electric vehicle aggregator. Finally， a virtual power plant with integrated wind turbines， controllable loads， energy storage batteries， users， and electric vehicles is used to analyze the example. The coevolutionary immune quantum partical swarm optimization （CIQPSO） algorithm is used to search for the optimal solution. The results of a numerical example show that the participation of electric vehicles in a virtual power plant can simultaneously improve the both economic benefits and increase the supply-demand balance capability of the virtual power plant.