Abstract:In order to cope with the environmental crisis, the popularization of electric vehicles will become a trend, but the random charging/discharging behavior of a large number of electric vehicles may cause harm to the power grid. Taking the microgrid (MG) as the research object, this paper studies the behavior characteristics of household electric vehicles. Aiming at the matching problem between the electric vehicle discharge process and the supply and demand balance of the MG system, a reservation-default discharge mechanism for electric vehicles taking into account the uncertainty of default is proposed. The charging behavior and reservation discharge mechanism of electric vehicles (EVs), the uncertainty of default of electric vehicles, reservation discharge flow and discharge flow of electric vehicles are described. On this basis, considering the large number of EVs, the large difference of user behavior and the uncertainty of EVs default, a two-stage scheduling including "MG -EV aggregator" and "EV aggregator -EV" is formulated. The first stage of scheduling is dominated by the MG, which publishes the discharge reservation demand of each period to users through EV aggregation, and the EV users make reservations according to their own conditions. The optimization strategy aiming at minimizing the operation cost of the MG is adopted. The subject of the second stage of scheduling is the EV aggregator. According to the quantity of electricity purchased by the micro-grid and the state of EV to pile, the aggregator proposes an allocation strategy considering the state of charge and aiming at minimizing the comprehensive income difference of user discharge. Furthermore, the default uncertainty of electric vehicles is considered, and countermeasures are taken in the aspects of scheduled discharge plan and scheduling strategy. Finally, a small MG system including fan, photovoltaic, energy storage and electric vehicle was established on the MATLAB platform for simulation, and the scheduled discharge information of electric vehicle and the optimal scheduling model of the MG taking electric vehicle discharge into account were obtained. The power distribution results of different EV to pile scenarios and MG default were obtained. The following conclusions can be drawn from the simulation analysis: (1) The scheduling strategy under the reservation-default discharge mechanism proposed focuses on the information interaction between MG and EV users, and users participate in scheduling in the form of autonomous reservation, which effectively highlights the initiative of users. At the same time, considering the uncertainty of breach of contract reduces the adverse impact of user behavior stochasticity on scheduling, makes the EV to pile discharge situation in each period more matching with the power shortage in each period of the system, and can effectively alleviate the MG peak power supply pressure. (2) The MG-EV two-stage scheduling strategy based on EV aggregator proposed in this paper can make MG purchase less power from the grid to meet the power balance of the system, reduce its electricity cost to a certain extent, and contribute to the development of clean energy in the long run. The EV discharge power allocation strategy takes into account the EV state of charge and the balance of user income. (3) A relatively loose default mechanism is designed on the basis of meeting the normal operation. By providing a reasonable scheduling interval for MG side and measures such as judging default based on the overall power generation and settling default penalty by individual EV side, the flexibility of MG scheduling and user behavior is guaranteed, which is conducive to the development of this model. (4) A series of simulation experiments are carried out to verify the effectiveness of the scheduling strategy under the proposed reservation-default mechanism. In the subsequent research, it will be further improved from the aspects of technical scheme and engineering application based on the actual situation.
[1] 中华人民共和国国务院新闻办公室. 新时代的中国能源发展: (2020年12月)[N]. 人民日报, 2020-12-22(10). [2] 程春田. 碳中和下的水电角色重塑及其关键问题[J]. 电力系统自动化, 2021, 45(16): 29-36. Cheng Chuntian.Function remolding of hydropower systems for carbon neutral and its key problems[J]. Automation of Electric Power Systems, 2021, 45(16): 29-36. [3] 杜祥琬, 冯丽妃. 碳达峰与碳中和引领能源革命[N]. 中国科学报, 2020-12-22(1). [4] 刘东奇, 曾祥君, 王耀南. 边缘计算架构下配电台区虚拟电站控制策略[J]. 电工技术学报, 2021, 36(13): 2852-2860, 2870. Liu Dongqi, Zeng Xiangjun, Wang Yaonan.Control strategy of virtual power station in distribution transformer area under edge computing architecture[J]. Transactions of China Electrotechnical Society, 2021, 36(13): 2852-2860, 2870. [5] 孙惠, 翟海保, 吴鑫. 源网荷储多元协调控制系统的研究及应用[J]. 电工技术学报, 2021, 36(15): 3264-3271. Sun Hui, Zhai Haibao, Wu Xin.Research and application of multi-energy coordinated control of generation, network, load and storage[J]. Transactions of China Electrotechnical Society, 2021, 36(15): 3264-3271. [6] 陈丽娟, 秦萌, 顾少平, 等. 计及电池损耗的电动公交车参与V2G的优化调度策略[J]. 电力系统自动化, 2020, 44(11): 52-60. Chen Lijuan, Qin Meng, Gu Shaoping, et al.Optimal dispatching strategy of electric bus participating in vehicle-to-grid considering battery loss[J]. Automation of Electric Power Systems, 2020, 44(11): 52-60. [7] Infante W, Ma Jin, Han Xiaoqing, et al.Optimal recourse strategy for battery swapping stations considering electric vehicle uncertainty[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(4): 1369-1379. [8] 崔杨, 刘柏岩, 仲悟之, 等. 考虑积压惩罚机制的含BSS微网联合系统优化调度策略[J]. 电网技术, 2020, 44(10): 3787-3793. Cui Yang, Liu Baiyan, Zhong Wuzhi, et al.Optimal scheduling strategy for joint system with micro-grid containing BSS considering overstock punishment mechanism[J]. Power System Technology, 2020, 44(10): 3787-3793. [9] 杨晓东, 张有兵, 赵波, 等. 供需两侧协同优化的电动汽车充放电自动需求响应方法[J]. 中国电机工程学报, 2017, 37(1): 120-129. Yang Xiaodong, Zhang Youbing, Zhao Bo, et al.Automated demand response method for electric vehicles charging and discharging to achieve supply-demand coordinated optimization[J]. Proceedings of the CSEE, 2017, 37(1): 120-129. [10] 孔顺飞, 胡志坚, 谢仕炜, 等. 含电动汽车充电站的主动配电网二阶段鲁棒规划模型及其求解方法[J]. 电工技术学报, 2020, 35(5): 1093-1105. Kong Shunfei, Hu Zhijian, Xie Shiwei, et al.Two-stage robust planning model and its solution algorithm of active distribution network containing electric vehicle charging stations[J]. Transactions of China Electrotechnical Society, 2020, 35(5): 1093-1105. [11] Liu Wenjie, Chen Shibo, Hou Yunhe, et al.Optimal reserve management of electric vehicle aggregator: discrete bilevel optimization model and exact algorithm[J]. IEEE Transactions on Smart Grid, 2021, 12(5): 4003-4015. [12] 吴洲洋, 艾欣, 胡俊杰. 电动汽车聚合商参与调频备用的调度方法与收益分成机制[J]. 电网技术, 2021, 45(3): 1041-1049. Wu Zhouyang, Ai Xin, Hu Junjie.Dispatching and income distributing of electric vehicle aggregators' participation in frequency regulation[J]. Power System Technology, 2021, 45(3): 1041-1049. [13] 魏震波, 田轲, 罗筱均, 等. 电动汽车聚合商参与下的主辅联合市场均衡分析[J]. 电力建设, 2021, 42(2): 50-57. Wei Zhenbo, Tian Ke, Luo Xiaojun, et al.Analysis on equilibrium of the main and auxiliary joint markets considering the aggregators of electric vehicles[J]. Electric Power Construction, 2021, 42(2): 50-57. [14] Said D, Mouftah H T.Novel communication protocol for the EV charging/discharging service based on VANETs[J]. IEEE Transactions on Intelligent Vehicles, 2017, 2(1): 25-37. [15] Kaur K, Dua A, Jindal A, et al.A novel resource reservation scheme for mobile PHEVs in V2G environment using game theoretical approach[J]. IEEE Transactions on Vehicular Technology, 2015, 64(12): 5653-5666. [16] 许刚, 张丙旭, 张广超. 电动汽车集群并网的分布式鲁棒优化调度模型[J]. 电工技术学报, 2021, 36(3): 565-578. Xu Gang, Zhang Bingxu, Zhang Guangchao.Distributed and robust optimal scheduling model for large-scale electric vehicles connected to grid[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 565-578. [17] 杨晓东, 张有兵, 蒋杨昌, 等. 微电网下考虑分布式电源消纳的电动汽车互动响应控制策略[J]. 电工技术学报, 2018, 33(2): 390-400. Yang Xiaodong, Zhang Youbing, Jiang Yangchang, et al.Renewable energy accommodation-based strategy for electric vehicle considering dynamic interaction in microgrid[J]. Transactions of China Electrotechnical Society, 2018, 33(2): 390-400. [18] 崔金栋, 罗文达, 周念成. 基于多视角的电动汽车有序充放电定价模型与策略研究[J]. 中国电机工程学报, 2018, 38(15): 4438-4450, 4644. Cui Jindong, Luo Wenda, Zhou Niancheng.Research on pricing model and strategy of electric vehicle charging and discharging based on multi view[J]. Proceedings of the CSEE, 2018, 38(15): 4438-4450, 4644. [19] 李霞林, 郭力, 王成山, 等. 直流微电网关键技术研究综述[J]. 中国电机工程学报, 2016, 36(1): 2-17. Li Xialin, Guo Li, Wang Chengshan, et al.Key technologies of DC microgrids: an overview[J]. Proceedings of the CSEE, 2016, 36(1): 2-17. [20] 范智伟, 乔丹, 崔海港. 锂离子电池充放电倍率对容量衰减影响研究[J]. 电源技术, 2020, 44(3): 325-329. Fan Zhiwei, Qiao Dan, Cui Haigang.Influence of charge and discharge rate on capacity fade of lithium ion battery[J]. Chinese Journal of Power Sources, 2020, 44(3): 325-329. [21] Carmichael R, Gross R, Hanna R, et al.The demand response technology cluster: accelerating UK residential consumer engagement with time-of-use tariffs, electric vehicles and smart meters via digital comparison tools[J]. Renewable and Sustainable Energy Reviews, 2021, 139: 110701. [22] 吴赋章, 杨军, 林洋佳, 等. 考虑用户有限理性的电动汽车时空行为特性[J]. 电工技术学报, 2020, 35(7): 1563-1574. Wu Fuzhang, Yang Jun, Lin Yangjia, et al.Research on spatiotemporal behavior of electric vehicles considering the users' bounded rationality[J]. Transactions of China Electrotechnical Society, 2020, 35(7): 1563-1574. [23] 罗凡, 陈渊, 王伟, 等. 西北地区电动汽车发展策略探讨[J]. 电力需求侧管理, 2017, 19(5): 48-51. Luo Fan, Chen Yuan, Wang Wei, et al.Discussion on electric vehicles development in northwest area[J]. Power Demand Side Management, 2017, 19(5): 48-51. [24] 胡鹏, 艾欣, 张朔, 等. 基于需求响应的分时电价主从博弈建模与仿真研究[J]. 电网技术, 2020, 44(2): 585-592. Hu Peng, Ai Xin, Zhang Shuo, et al.Modelling and simulation study of TOU stackelberg game based on demand response[J]. Power System Technology, 2020, 44(2): 585-592. [25] 毛玉荣. 电动汽车充放电与微电网运行的协调优化[D]. 长沙: 湖南大学, 2016. [26] 赖纪东, 谢天月, 苏建徽, 等. 基于粒子群优化算法的孤岛微电网电压不平衡补偿协调控制[J]. 电力系统自动化, 2020, 44(16): 121-129. Lai Jidong, Xie Tianyue, Su Jianhui, et al.Coordinated control of voltage unbalance compensation in islanded microgrid based on particle swarm optimization algorithm[J]. Automation of Electric Power Systems, 2020, 44(16): 121-129. [27] Giacomuzzi S, Langwasser M, de Carne G, et al. Smart transformer-based medium voltage grid support by means of active power control[J]. CES Transactions on Electrical Machines and Systems, 2020, 4(4): 285-294. [28] 苏粟, 蒋小超, 王玮, 等. 计及电动汽车和光伏—储能的微网能量优化管理[J]. 电力系统自动化, 2015, 39(9): 164-171. Su Su, Jiang Xiaochao, Wang Wei, et al.Optimal energy management for microgrids considering electric vehicles and photovoltaic-energy storage[J]. Automation of Electric Power Systems, 2015, 39(9): 164-171. [29] 王凌云, 安晓, 杨波, 等. 考虑负荷聚合商参与下的微网双层两阶段优化调度[J]. 三峡大学学报(自然科学版), 2021, 43(2): 86-92. Wang Lingyun, An Xiao, Yang Bo, et al.Double level two-stage optimal scheduling of microgrid with the participation of load aggregator[J]. Journal of China Three Gorges University (Natural Sciences), 2021, 43(2): 86-92. [30] 刘敦楠, 徐尔丰, 刘明光, 等. 面向分布式电源就地消纳的园区分时电价定价方法[J]. 电力系统自动化, 2020, 44(20): 19-28. Liu Dunnan, Xu Erfeng, Liu Mingguang, et al.TOU pricing method for park considering local consumption of distributed generator[J]. Automation of Electric Power Systems, 2020, 44(20): 19-28.