考虑物理-信息-交通网耦合的配电网多时段动态供电恢复策略

doi:10.19595/j.cnki.1000-6753.tces.220497

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Abstract The frequency and intensity of natural disasters have greatly increased over the past decades, and they are expected to further increase in the coming future. As a consequence, it becomes a critical issue worldwide to deal with the disasters and their impacts. More and more scholars carry out research on "grid resilience". And improving the fault recovery capability of distribution network(DN) is an important means to improve the resilience. At the same time, with the continuous deepening of the application of modern information communication technology in the power system, the power information physical system has been formed. Many blackouts indicate that the failure or attack of the information system will affect the information system and the safe and stable operation of the physical system. In this context, a multi-period dynamic power supply restoration strategy of DN considering the coupling of physical- cyber- traffic network is proposed. The impact of information network on power supply restoration is discussed in depth, and the flexible resources such as mobile energy storage systems (MESSs) are fully used for power supply restoration.
Firstly, consider the fluctuation of load power and the regulation ability of controllable load. Taking load recovery as the main recovery objective. A multi-period power supply topology reconfiguration model is established to realize dynamic optimization of DN topology. Secondly, A dynamic scheduling model of MESS is established, Give play to its multi-period power support capability in time, and play its mobile role in space to achieve spatio-temporal coordinated operation with other Distributed generations. The impact of traffic flow on travelling speed of MESS is taken into account and Floyd algorithm is used for the transmission route optimization. In addition, the coupling model of information-distribution network is established based on the information-physical network. The influence of different coupling degrees on load recovery is deeply studied.
In the recovery process, the first stage is the emergency response for load power supply restoration. By increasing the output power of power sources, optimizing the DN topological structure and the location of MESSs, most critical loads can be restored. The purpose of the second stage is to find the optimal path of MESS, so that MESS can reach the destination node in the shortest time. According to the start node and destination node obtained in the first stage, Floyd algorithm is used to solve the shortest scheduling path of MESSs. Combined with the traffic flow data in t period, the MESS running speed of each section is calculated according to the flow-velocity model. Finally, the scheduling time of the shortest path is obtained by using the path-time model.
The following conclusions can be drawn through case analysis.
(1) With the deepening of information-distribution network coupling, the more fragile the information network is, the less the load recovery of DN will be. Therefore, in order to ensure the reliability of load recovery and the effectiveness of recovery strategies, it is necessary to consider the impact of information networks in the actual recovery process.
(2) This paper considers the mobile energy supply capacity of MESS and the impact of traffic flow in different periods on MESS dispatching. The case results show that the restoration scheme of dynamically optimizing the location of MESS has certain advantages in the case of long-term power outage, and improves the restoration capability of DN.

Key words： Distribution network service restoration physical-cyber network mobile energy storage system

Received: 04 April 2022

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	Zhu Xiaorong
	Si Yu

Cite this article:

Zhu Xiaorong,Si Yu. Multi-Period Dynamic Power Supply Restoration Strategy Considering Physical-Cyber-Traffic Network Coupling[J]. Transactions of China Electrotechnical Society, 2023, 38(12): 3306-3320.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.220497 OR https://dgjsxb.ces-transaction.com/EN/Y2023/V38/I12/3306

[1] 王守相, 刘琪, 赵倩宇, 等. 配电网弹性内涵分析与研究展望[J]. 电力系统自动化, 2021, 45(9): 1-9.
Wang Shouxiang, Liu Qi, Zhao Qianyu, et al.Connotation analysis and prospect of distribution network elasticity[J]. Automation of Electric Power Systems, 2021, 45(9): 1-9.
[2] 许寅, 和敬涵, 王颖, 等. 韧性背景下的配网故障恢复研究综述及展望[J]. 电工技术学报, 2019, 34(16): 3416-3429.
Xu Yin, He Jinghan, Wang Ying, et al.A review on distribution system restoration for resilience enhancement[J]. Transactions of China Electrotechnical Society, 2019, 34(16): 3416-3429.
[3] Huang Gang, Wang Jianhui, Chen Chen, et al.Cyber-constrained optimal power flow model for smart grid resilience enhancement[J]. IEEE Transactions on Smart Grid, 2019, 10(5): 5547-5555.
[4] Ti Baozhong, Li Gengyin, Zhou Ming, et al.Resilience assessment and improvement for cyber-physical power systems under typhoon disasters[J]. IEEE Transactions on Smart Grid, 2022, 13(1): 783-794.
[5] 郭庆来, 辛蜀骏, 王剑辉, 等. 由乌克兰停电事件看信息能源系统综合安全评估[J]. 电力系统自动化, 2016, 40(5): 145-147.
Guo Qinglai, Xin Shujun, Wang Jianhui, et al.Comprehensive security assessment for a cyber physical energy system: a lesson from Ukraine's blackout[J]. Automation of Electric Power Systems, 2016, 40(5): 145-147.
[6] 文娟, 谭阳红, 何怡刚, 等. 含分布式电源的复杂配电网多阶段故障恢复方法[J]. 电工技术学报, 2018, 33(14): 3332-3341.
Wen Juan, Tan Yanghong, He Yigang, et al.A multi-stage service restoration method for complex distribution networks with distributed generators[J]. Transactions of China Electrotechnical Society, 2018, 33(14): 3332-3341.
[7] 陈厚合, 丛前, 姜涛, 等. 多能协同的配电网供电恢复策略[J]. 电工技术学报, 2022, 37(3): 610-622, 685.
Chen Houhe, Cong Qian, Jiang Tao, et al.Distribution systems restoration with multi-energy synergy[J]. Transactions of China Electrotechnical Society, 2022, 37(3): 610-622, 685.
[8] Demetriou P, Asprou M, Kyriakides E.A real-time controlled islanding and restoration scheme based on estimated states[J]. IEEE Transactions on Power Systems, 2019, 34(1): 606-615.
[9] Wang Ying, Xu Yin, He Jinghan, et al.Coordinating multiple sources for service restoration to enhance resilience of distribution systems[J]. IEEE Transactions on Smart Grid, 2019, 10(5): 5781-5793.
[10] Li Jiaxu, Xu Yin, Wang Ying, et al.Erratum to resilience-motivated distribution system restoration considering electricity-water-gas interdependency[J]. IEEE Transactions on Smart Grid, 2022, 13(3): 2495.
[11] 翁晓勇, 谭阳红. 考虑移动储能有功时空支撑的不对称配电网负荷恢复策略[J]. 电网技术, 2021, 45(4): 1463-1470.
Weng Xiaoyong, Tan Yanghong.Load restoration strategy for unbalanced distribution network considering active power temporal-spatial supporting of mobile energy storage[J]. Power System Technology, 2021, 45(4): 1463-1470.
[12] 杨丽君, 赵宇, 赵优, 等. 考虑交通路网应急电源车调度的有源配电网故障均衡恢复[J]. 电力系统自动化, 2021, 45(21): 170-180.
Yang Lijun, Zhao Yu, Zhao You, et al.Balanced fault recovery of active distribution network considering emergency power supply vehicle scheduling in traffic network[J]. Automation of Electric Power Systems, 2021, 45(21): 170-180.
[13] Lei Shunbo, Wang Jianhui, Chen Chen, et al.Mobile emergency generator pre-positioning and real-time allocation for resilient response to natural disasters[J]. IEEE Transactions on Smart Grid, 2018, 9(3): 2030-2041.
[14] Oyewole P A, Jayaweera D.Power system security with cyber-physical power system operation[J]. IEEE Access, 2020, 8: 179970-179982.
[15] Che Liang, Liu Xuan, Li Zuyi, et al.False data injection attacks induced sequential outages in power systems[J]. IEEE Transactions on Power Systems, 2019, 34(2): 1513-1523.
[16] Abianeh A J, Mardani M M, Ferdowsi F, et al.Cyber-resilient sliding-mode consensus secondary control scheme for islanded AC microgrids[J]. IEEE Transactions on Power Electronics, 2022, 37(5): 6074-6089.
[17] 陈健, 林咨良, 赵浩然, 等. 考虑信息耦合的电-气综合能源系统韧性优化方法[J]. 中国电机工程学报, 2020, 40(21): 6854-6864.
Chen Jian, Lin Ziliang, Zhao Haoran, et al.Optimization method for resilience of integrated electric-gas system with consideration of cyber coupling[J]. Proceedings of the CSEE, 2020, 40(21): 6854-6864.
[18] 陈彬, 于继来, 周霞, 等. 基于网格化的极端灾后配电网电力-通信协调恢复策略[J]. 电网技术, 2021, 45(5): 2009-2017.
Chen Bin, Yu Jilai, Zhou Xia, et al.Power-communication coordination recovery strategy based on gridding method after disasters[J]. Power System Technology, 2021, 45(5): 2009-2017.
[19] 秦清, 韩蓓, 李国杰, 等. 含智能软开关的配电网多阶段弹性力学映射与评估[J]. 电工技术学报, 2021, 36(21): 4444-4458.
Qin Qing, Han Bei, Li Guojie, et al.Multi-stage elastic mechanical modelling and evaluation of distribution networks with soft open point[J]. Transactions of China Electrotechnical Society, 2021, 36(21): 4444-4458.
[20] 刘炜, 谢文君, 孙名刚, 等. 基于分层优化的分散式城轨供电系统网络化支援供电[J]. 电工技术学报, 2021, 36(11): 2306-2314.
Liu Wei, Xie Wenjun, Sun Minggang, et al.Research on networked support power supply of urban rail power supply system based on hierarchical optimization[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2306-2314.
[21] 唐晓健, 林济铿, 郭其一. 基于网损估计的系统恢复新方法[J]. 电气技术, 2020, 21(10): 15-20.
Tang Xiaojian, Lin Jikeng, Guo Qiyi.A new system recovery algorithm based on network loss estimation[J]. Electrical Engineering, 2020, 21(10): 15-20.
[22] 遆宝中, 李庚银, 王剑晓, 等. 计及监测与控制功能的电力信息物理系统关键输电线路辨识方法[J]. 中国电机工程学报, 2022, 42(7): 2556-2566.
Ti Baozhong, Li Gengyin, Wang Jianxiao, et al.Identification of critical transmission lines in cyber-physical power system considering monitoring function and control function[J]. Proceedings of the CSEE, 2022, 42(7): 2556-2566.
[23] 周湶, 解慧力, 郑柏林, 等. 基于混合算法的配电网故障重构与孤岛运行配合[J]. 电网技术, 2015, 39(1): 136-142.
Zhou Quan, Xie Huili, Zheng Bolin, et al.Hybrid algorithm based coordination between distribution network fault reconfiguration and islanding operation[J]. Power System Technology, 2015, 39(1): 136-142.
[24] 李丝雨, 张彼德, 彭丽维, 等. 考虑柔性负荷调节能力的主动配电网动态孤岛划分策略[J]. 高电压技术, 2019, 45(6): 1835-1842.
Li Siyu, Zhang Bide, Peng Liwei, et al.Dynamic Island partitioning strategy for active distribution network considering adjustment ability of flexible load[J]. High Voltage Engineering, 2019, 45(6): 1835-1842.
[25] 王颖, 许寅, 和敬涵, 等. 基于断线解环思想的配电网辐射状拓扑约束建模方法[J]. 中国电机工程学报, 2021, 41(7): 2395-2404.
Wang Ying, Xu Yin, He Jinghan, et al.Radiality constraint modelling method in distribution network based on cutting-line and opening-loop idea[J]. Proceedings of the CSEE, 2021, 41(7): 2395-2404.
[26] 王颖. 大面积停电场景下配电网多源协同故障恢复方法研究[D]. 北京: 北京交通大学, 2020.
[27] Lavorato M, Franco J F, Rider M J, et al.Imposing radiality constraints in distribution system optimization problems[J]. IEEE Transactions on Power Systems, 2012, 27(1): 172-180.
[28] 邵尹池, 穆云飞, 余晓丹, 等. “车-路-网”模式下电动汽车充电负荷时空预测及其对配电网潮流的影响[J]. 中国电机工程学报, 2017, 37(18): 5207-5219, 5519.
Shao Yinchi, Mu Yunfei, Yu Xiaodan, et al.A spatial-temporal charging load forecast and impact analysis method for distribution network using EVs-traffic-distribution model[J]. Proceedings of the CSEE, 2017, 37(18): 5207-5219, 5519.
[29] 张英敏, 蒋容, 刘凯, 等. 含柔性直流电网的交直流系统潮流转移搜索与量化分析[J]. 高电压技术, 2019, 45(8): 2553-2561.
Zhang Yingmin, Jiang Rong, Liu Kai, et al.Path search and analysis on power flow transferring of power system with VSC-HVDC grid[J]. High Voltage Engineering, 2019, 45(8): 2553-2561.
[30] 宋雨浓, 林舜江, 唐智强, 等. 基于动态车流的电动汽车充电负荷时空分布概率建模[J]. 电力系统自动化, 2020, 44(23): 47-56.
Song Yunong, Lin Shunjiang, Tang Zhiqiang, et al.Spatial-temporal distribution probabilistic modeling of electric vehicle charging load based on dynamic traffic flow[J]. Automation of Electric Power Systems, 2020, 44(23): 47-56.
[31] 马钰, 韦钢, 李扬, 等. 考虑孤岛源-荷不确定性的直流配电网可靠性评估[J]. 电工技术学报, 2021, 36(22): 4726-4738.
Ma Yu, Wei Gang, Li Yang, et al.Reliability evaluation of DC distribution network considering islanding source-load uncertainty[J]. Transactions of China Electrotechnical Society, 2021, 36(22): 4726-4738.
[32] 黄登一, 刘灏, 毕天姝, 等. 基于扰动传播时空相关性的电网扰动事件快速定位方法[J]. 中国电机工程学报, 2022, 42(6): 2045-2060.
Huang Dengyi, Liu Hao, Bi Tianshu, et al.A novel method for fast event location based on the temporal and spatial relevance of disturbance propagation[J]. Proceedings of the CSEE, 2022, 42(6): 2045-2060.
[33] 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.
[34] 李婧祺, 王丹, 樊华, 等. 含移动式储能的主动配电网分层优化控制方法[J]. 电力系统自动化, 2022, 46(10): 189-198.
Li Jingqi, Wang Dan, Fan Hua, et al.Hierarchical optimal control method for active distribution network with mobile energy storage[J]. Automation of Electric Power Systems, 2022, 46(10): 189-198.
[35] Zhang Zhu, Liu Yunfan, Wang Jingyuan.Optimal design of multi-channel water cooled radiator for motor controller of new energy vehicle[J]. CES Transactions on Electrical Machines and Systems, 2022, 6(1): 87-94.