Abstract：In view of the development direction from the traditional "source following load" scheduling mode to the "Generation Network Load Storage multiple coordination ubiquitous scheduling control" mode, this paper designs the source network load storage multiple coordination control system. Through the access layer, network layer, platform layer, application layer four layer system structure to build the entire system, using huge amounts of data unified management technology, multi-source data fusion technology, multi-source data unified service technology, resource holographic perception and decision making in this paper, the source of net charge to store each application scenarios to support key technologies, eventually fall to the ground pilot east China control sub-center through source network integrated resource management platform access storage of all kinds of adjusting resources, selection during the National Day in east China new energy given the most severe situation in anhui power grid as an experiment object. Through the comprehensive resource management platform of charge storage and storage in the source network, it releases peak demand for adjusting, and makes use of the adjustable capacity of electric vehicle companies, Tongli regional comprehensive energy body and other resources to support the consumption of new energy in the noon of holidays in Anhui, so as to reduce the peak and valley difference of the power grid and reduce the operation risk of the power grid.
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. Transactions of China Electrotechnical Society, 2021, 36(15): 3264-3271.
[1] 舒印彪, 张智刚, 郭剑波, 等. 新能源消纳关键因素分析及解决措施研究[J]. 中国电机工程学报, 2017, 37(1): 1-9. Shu Yinbiao, Zhang Zhigang, Guo Jianbo, et al.Study on key factors and solution of renewable energy accommodation[J]. Proceedings of the CSEE, 2017, 37(1): 1-9. [2] 许洪强. 调控云架构及应用展望[J]. 电网技术, 2017, 41(10): 3104-3111. Xu Hongqiang.Architecture of dispatching and control cloud and its application prospect[J]. Power System Technology, 2017, 41(10): 3104-3111. [3] 杨东升, 王道浩, 周博文, 等. 泛在电力物联网的关键技术与应用前景[J]. 发电技术, 2019, 40(2): 107-114. Yang Dongsheng, Wang Daohao, Zhou Bowen, et al.Technologies and application prospects of ubiquitous power internet of things[J]. Power Generation Technology. 2019, 40(2): 107-114. [4] 赵云山, 刘焕焕. 大数据技术在电力行业的应用研究[J]. 电信科学, 2014, 30(1): 57-62. Zhao Yunshan, Liu Huanhuan.Research on application of big data technique in electricity power industry[J]. Telecommunications Science, 2014, 30(1): 57-62. [5] 许洪强. 面向调控云的电力调度通用数据对象结构化设计及应用[J]. 电网技术, 2018, 42(7): 2248-2254. Xu Hongqiang.Structured design and application of power dispatching universal data object for dispatching and control cloud[J]. Power System Technology 2018, 42(7): 2248-2254. [6] 郝然, 艾芊, 肖斐. 基于多元大数据平台的用电行为分析构架研究[J]. 电力自动化设备, 2017, 37(8): 20-27. Hao Ran, Ai Qian, Xiao Fei.Architecture based on multivariate big data platform for analyzing electricity consumption behavior[J]. Electric Power Automation Equipment, 2017, 37(8): 20-27. [7] 冷喜武, 陈国平, 白静洁, 等. 智能电网监控运行大数据分析系统总体设计[J]. 电力系统自动化, 2018, 42(12): 160-166. Leng Xiwu, Chen Guoping, Bai Jingjie, et al.Design of smart grid monitoring operation big data analysis system[J]. Automation of Electric Power Systems, 2018, 42(12): 160-166. [8] 冷喜武, 陈国平, 蒋宇, 等. 智能电网监控运行大数据分析系统的数据规范和数据处理[J]. 电力系统自动化, 2018, 42(20): 169-176. Leng Xiwu, Chen Guoping, Jiang Yu, et al.Data specification and processing in big-data analysis system for monitoring and operation of smart grid[J]. Automation of Electric Power Systems, 2018, 42(19): 169-176. [9] 冷喜武, 陈国平, 蒋宇, 等. 智能电网监控运行大数据应用模型构建方法[J]. 电力系统自动化, 2018, 42(12): 115-122. Leng Xiwu, Chen Guoping, Jiang Yu, et al.Model construction method of big data application for monitoring and control of smart grid[J]. Automation of Electric Power Systems, 2018, 42(20): 115-122. [10] 赖昌伟, 黎静华, 陈博, 等. 光伏发电出力预测技术研究综述[J]. 电工技术学报, 2019, 34(6): 1201-1217. Lai Changwei, Li Jinghua, Chen Bo, et al.Review of photovoltaic power output prediction technology[J]. Transactions of China Electrotechnical Society, 2019, 34(6): 1201-1217. [11] 方勇杰, 王胜明. 适应多级调度安全稳定分析资源共享的分布式计算管理平台[J]. 电力系统自动化, 2016, 40(23): 1-8. Fang Yongjie, Wang Shengming.Distributed computing management platform for sharing resource of multi-level dispatch security and stability analysis[J]. Automation of Electric Power Systems, 2016, 40(23): 1-8. [12] 杨佩, 蔡皓, 裘洪彬. 面向能源互联网的大数据关键技术研究[J]. 电力信息与通信技术, 2016, 14(4): 9-12. Yang Pei, Cai Hao, Qiu Hongbin.Research on key technologies of big data for energy internet[J]. Electric Power Information and Communication Technology, 2016, 14(4): 9-12. [13] 王德文, 宋亚奇, 朱永利. 基于云计算的智能电网信息平台[J]. 电力系统自动化, 2010, 34(22): 7-12. Wang Dewen, Song Yaqi, Zhu Yongli.Information platform of smart grid based on cloud computing[J]. Automation of Electric Power Systems, 2010, 34(22): 7-12. [14] 彭小圣, 邓迪元, 程时杰, 等. 面向智能电网应用的电力大数据关键技术[J]. 中国电机工程学报, 2015, 35(3): 503-511. Peng Xiaosheng, Deng Diyuan, Cheng Shijie, et al.Key technologies of electric power big data and its application prospects in smart grid[J]. Proceedings of the CSEE, 2015, 35(3): 503-511. [15] 李建林, 郭斌琪, 牛萌, 等. 风光储系统储能容量优化配置策略[J]. 电工技术学报, 2018, 33(6): 1189-1196. Li Jianlin, Guo Binqi, Niu Meng, et al.Optimal configuration strategy of energy storage capacity in wind/PV/storage hybrid system[J]. Transactions of China Electrotechnical Society, 2018, 33(6): 1189-1196.
2021, Vol. 36 
