基于主从博弈和IGDT的含电动汽车需求响应光伏园区储能优化配置  

Optimal Configuration of Energy Storage in Photovoltaic Park with Electric Vehicle Demand Response Based on Stackelberg Game and Information Gap Decision Theory

作　　者：李晨朝 陈佳佳[1] 王敬华[1] LI Chenzhao;CHEN Jiajia;WANG Jinghua(College of Electrical and Electronic Engineering,Shandong University of Technology,Zibo 255000,Shandong Province,China)

机构地区：[1]山东理工大学电气与电子工程学院,山东省淄博市255000

出　　处：《电力建设》2025年第4期126-136,共11页Electric Power Construction

基　　金：国家自然科学基金项目(52377110)。

摘　　要：【目的】随着光伏渗透率增加,光伏的波动性和随机性导致用户净负荷峰谷波动加剧,从而引起需量电费增加。储能可以利用削峰填谷的特性降低需量电费,但储能高昂的初始投资限制了其在用户侧的大规模应用。【方法】为此,提出了一种基于主从博弈定价和信息间隙决策理论(information gap decision theory,IGDT)的含电动汽车需求响应光伏园区储能最优配置方法。首先,综合考虑上网电价、分时电价、需量电价、电动汽车的购售电价及光伏出力的不确定性,构建了基于IGDT的储能配置模型和电动汽车集群优化运行模型。其次,将园区作为领导者,电动汽车作为跟随者,构建了园区和电动汽车成本最小化的主从博弈模型。然后,通过KKT(Karush-Kuhn-Tucker)条件和线性规划对偶定理将主从博弈模型转化为混合整数线性规划问题进行求解。最后,以某一地区光伏园区为研究对象进行分析。【结果】结果表明,所提策略在光伏出力不确定环境下使园区年综合成本降低了12.06%,电动汽车用户的充放电成本降低了54.88%,由于电动汽车参与园区调度,储能配置容量和储能配置功率减少了62.80%,上网电量减少了1.32%,提高了光伏的就地消纳率,鲁棒优化模型与所提IGDT模型相比,园区成本高出了1.97%,证明IGDT模型的经济性更佳。【结论】所提策略在降低园区综合成本的同时满足了电动汽车的充电需求,降低了车主的充电成本,实现了博弈双方互利共赢。[Objective]As the penetration rate of photovoltaics(PVs)increases,their volatility and randomness lead to intensified peak and valley fluctuations in the user net load,resulting in an increase in electricity demand.Energy storage can reduce the demand for electricity by utilizing the characteristics of peak shaving and valley filling;however,the high initial investment in energy storage limits its large-scale application on the user side.[Methods]A photovoltaic park energy storage optimal configuration method based on Stackelberg game pricing and information gap decision theory(IGDT)with electric vehicle(EV)demand response is proposed.First,considering the uncertainty of the grid,time-of-use,demand,and purchase and sale electricity price of EVs and PV output,an energy storage configuration model based on IGDT and an optimized operation model for EV clusters were constructed.Second,with the park as the leader and EVs as followers,a Stackelberg game model is constructed to minimize the costs of the park and EVs.Then,the Stackelberg game model is transformed into a mixed-integer linear programming problem for a solution using Karush-Kuhn-Tucker(KKT)conditions and the dual theorem of linear programming.Finally,we analyzed a PV park in a certain region as the research object.[Results]The results show that the proposed strategy reduced the annual comprehensive cost of the park by 12.06%and the charging and discharging costs of EV users by 54.88%.Owing to the participation of EVs in park scheduling,the storage configuration capacity and power were reduced by 62.80%,and the on-grid power by 1.32%,which improved the local consumption rate of PV.Compared with the IGDT model proposed in this study,the park cost of the robust optimization model is 1.97%higher,which proves that the IGDT model is more economical.[Conclusions]The comparison shows that the strategy proposed in this study meets the charging demand of EVs while reducing the comprehensive cost of the park,reduces the charging cost of the owners,and realizes a mutua

关 键 词：光伏园区 主从博弈 电动汽车 储能配置 两部制电价 信息间隙决策理论(IGDT) 

分 类 号：TM73[电气工程—电力系统及自动化]