Two-layer optimal scheduling of park integrated energy system considering the charging and discharging willingness of electric vehicles
冯野牧 1吕干云 1史明明 2朱志莹 1王浩宇 3陈光宇1
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作者信息
1. 南京工程学院电力工程学院,江苏 南京 211167
2. 国网江苏省电力有限公司电力科学研究院,江苏 南京 211103
3. 国网江苏省电力有限公司徐州供电分公司,江苏 徐州 221005
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摘要
随着电动汽车(electric vehicle,EV)普及度的不断提高,工业园区内的EV用户日益增多,其充放电行为给园区综合能源系统(park integrated energy system,PIES)的规划运行带来极大挑战.文中提出考虑EV充放电意愿的PIES双层优化调度.首先,基于动态实时电价、电池荷电量、电池损耗补偿、额外参与激励等因素建立充放电意愿模型,在此基础上得到改进的EV充放电模型;然后,以PIES总成本最小和EV充电费用最小为目标建立双层优化调度模型,通过Karush-Kuhn-Tucker(KKT)条件将内层模型转化为外层模型的约束条件,从而快速稳定地实现单层模型的求解;最后,进行仿真求解,设置 3 种不同场景,对比所提模型与一般充放电意愿模型,验证了文中所提引入EV充放电意愿模型的PIES双层优化调度的有效性和可行性.
Abstract
With the increasing popularity of electric vehicles,the number of electric vehicle users in industrial parks is increasing,and their charging and discharging behaviors pose great challenges to the planning and operation of park integrated energy system(PIES).A two-layer optimal scheduling of PIES considering the charging and discharging willingness of electric vehicles is proposed.Firstly,a charging and discharging willingness model is established based on factors such as dynamic real-time electricity price,battery charge capacity,battery loss compensation,and additional participation incentives.An improved electric vehicle charging and discharging model is obtained on this basis.A two-layer optimal scheduling model is established with the goal of minimizing the charging cost of the car,and the inner model is transformed into the constraints of the outer model through the Karush-Kuhn-Tucker(KKT)condition,so as to quickly and stably solve the single-layer model.Finally,the simulation solution is performed,and three different scenarios are set up.The proposed model is compared with the general charging and discharging willingness model.The effectiveness and feasibility of the two-layer optimal scheduling of PIES proposed in this paper are verified.
electric vehicle(EV)/charging and discharging willingness/park integrated energy system(PIES)/dynamic time-of-use electricity price/two-layer optimal scheduling/battery loss compensation
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