|
|
Control Strategy of Virtual Power Station in Distribution Transformer Area under Edge Computing Architecture |
Liu Dongqi1,2, Zeng Xiangjun1, Wang Yaonan2 |
1. School of Electrical and Information Engineering Changsha University of Science and Technology Changsha 410114 China; 2. National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha 410082 China |
|
|
Abstract In this paper, the operating mode and control method of distributed virtual power station in distribution network platform area under the framework of edge computing information network are presented. Firstly, the load, distributed energy and electric vehicles under the jurisdiction of each 380V distribution transformer station area are regarded as a virtual power station. Distribution transformer supervisory terminal unit (TTU) with edge computing capability is deployed on the low-voltage side of the Transformer in the Distribution station room, box Transformer or pole Transformer in each station area. Then, according to the physical quantity information of the distribution transformer monitored by the edge computing capacity of the distribution transformer terminal, the equivalent generalized load (source) of distributed generator (DG) and electric vehicles is perceived in each station area. On this basis, the local control of grid connection condition of electric vehicles in each station area is carried out by edge computing. For active power, the charging/idle working state of electric vehicles is regulated based on chaos optimization algorithm, which can not only absorb the DG power in the transformer area, but also take into account the peak shaving demand of large power grid. For reactive power, the idle electric vehicle is controlled as a virtual synchronous adjustment camera to support the local voltage and realize the local reactive power free. The method proposed in this paper provides a new solution and control scheme for the operation and control of distributed energy and electric vehicles widely connected distribution network, and promotes the expansion and development of the operation mode of distribution network from "centralized regulation" to "distributed autonomy".
|
Received: 12 July 2020
|
|
Fund:国家重点研发计划(2018YFB0904903)、国家自然科学基金(51807013)、湖南省自然科学基金(2019JJ50669)和湖南省教育厅(18B137)资助项目 |
|
|
|
[1] Mohanty S P, Choppali U, Kougianos E.Everything you wanted to know about smart cities: the internet of things is the backbone[J]. IEEE Consumer Electronics Magazine, 2016, 5(3): 60-70. [2] Zedadra O, Guerrieri A, Jouandeau N, et al.Swarm intelligence and IoT-based smart cities: a review: technology, communications and computing[M]. Cham: Springer, 2019. [3] Chen Xia, Shi Mengxuan, Zhou Jianyu, et al.Consensus-based distributed control for photovoltaic-battery units in a DC microgrid[J]. IEEE Transactions on Industrial Electronics, 2019, 66(10): 7778-7787. [4] Zhao Huiying, Hong Mingguo, Lin Wei, et al.Voltage and frequency regulation of microgrid with battery energy storage systems[J]. IEEE Transactions on Smart Grid, 2019, 10(1): 414-424. [5] 涂春鸣, 黄红, 兰征, 等. 微电网中电力电子变压器与储能的协调控制策略[J]. 电工技术学报, 2019, 34(12): 2627-2636. Tu Chunming, Huang Hong, Lan Zheng, et al.Coordinated control strategy of power electronic transformer and energy storage in microgrid[J]. Transactions of China Electrotechnical Society, 2019, 34(12): 2627-2636. [6] 刁涵彬, 李培强, 王继飞, 等. 考虑电/热储能互补协调的综合能源系统优化调度[J]. 电工技术学报, 2020, 35(21): 4532-4543. Diao Hanbin, Li Peiqiang, Wang Jifei, et al.Optimal dispatch of integrated energy system considering complementary coordination of electric/thermal energy storage[J]. Transactions of China Electrotech-nical Society, 2020, 35(21): 4532-4543. [7] 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. [8] Liu Hui, Qi Junjian, Wang Jianhui, et al.EV dispatch control for supplementary frequency regulation considering the expectation of EV owners[J]. IEEE Transactions on Smart Grid, 2018, 9(4): 3763-3772. [9] 宁佳, 汤奕, 高丙团. 基于需求响应潜力时变性的风火荷协同控制方法[J]. 电工技术学报, 2019, 34(8): 1728-1738. Ning Jia, Tang Yi, Gao Bingtuan.Coordinated control method of wind farm-AGC unit-load based on time-varying characteristics of demand response potential[J]. Transactions of China Electrotechnical Society, 2019, 34(8): 1728-1738. [10] 李彬, 贾滨诚, 曹望璋, 等. 边缘计算在电力需求响应业务中的应用展望[J]. 电网技术, 2018, 42(1): 79-87. Li Bin, Jia Bincheng, Cao Wangzhang, et al.Application prospect of edge computing in power demand response business[J]. Power System Technology, 2018, 42(1): 79-87. [11] Liu Dongqi, Zeng Xiangjun, Wang Yaonan.Edge-computing-driven autonomous ubiquitous internet of things in electricity: architecture and challenges[C]// 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), Changsha, China, 2019: 456-461. [12] 孙浩洋, 张冀川, 王鹏, 等. 面向配电物联网的边缘计算技术[J]. 电网技术, 2019, 43(12): 4314-4321. Sun Haoyang, Zhang Yichuan, Wang Peng, et al.Edge computation technology based on distribution internet of things[J]. Power System Technology, 2019, 43(12): 4314-4321. [13] Shi Weisong, Dustdar S.The promise of edge computing[J]. Computer, 2016, 49(5): 78-81. [14] 施巍松, 孙辉, 曹杰, 等. 边缘计算:万物互联时代新型计算模型[J]. 计算机研究与发展, 2017, 54(5): 907-924. Shi Weisong, Sun Hui, Cao Jie, et al.Edge computing: an emerging computing model for the internet of everything era[J]. Journal of Computer Research and Development, 2017, 54(5): 907-924. [15] 张冀川, 陈蕾, 张明宇, 等. 配电物联网智能终端的概念及应用[J]. 高电压技术, 2019, 45(6): 1729-1736. Zhang Jichuan, Chen Lei, Zhang Mingyu, et al.Conception and application of smart terminal for distribution internet of things[J]. High Voltage Engineering, 2019, 45(6): 1729-1736. [16] 刘日亮, 刘海涛, 夏圣峰, 等. 物联网技术在配电台区中的应用与思考[J]. 高电压技术, 2019, 45(6): 1707-1714. Liu Riliang, Liu Haitao, Xia Shengfeng, et al.Internet of things technology application and prospects in distribution transformer service area management[J]. High Voltage Engineering, 2019, 45(6): 1707-1714. [17] Aybar A, Iftar A.Deadlock avoidance controller design for timed petri nets using stretching[J]. IEEE Systems Journal, 2008, 2(2): 178-188. [18] 钟庆昌. 虚拟同步机与自主电力系统[J]. 中国电机工程学报, 2017, 37(2): 336-348. Zhong Qingchang.Virtual synchronous machines and autonomous power systems[J]. Proceedings of the CSEE, 2017, 37(2): 336-348. [19] 刘东奇, 钟庆昌, 王耀南, 等. 基于同步逆变器的电动汽车V2G 智能充放电控制技术[J]. 中国电机工程学报, 2017, 37(2): 544-556. Liu Dongqi, Zhong Qingchang, Wang Yaonan, et al.A synchronverter-based V2G smart charging and discharging control strategy for electric vehicles[J]. Proceedings of the CSEE, 2017, 37(2): 544-556. [20] 姜静雅, 王玮, 吴学智, 等. 基于自适应无功功率补偿的虚拟同步机功率解耦策略[J]. 电工技术学报, 2020, 35(13): 2747-2756. Jiang Jinya, Wang Wei, Wu Xuezhi, et al.Power decoupling strategy in virtual synchronous generator based on adaptive reactive power compensation[J]. Transactions of China Electrotechnical Society, 2020, 35(13): 2747-2756. |
|
|
|