Rollout Algorithm-Based Post-Disaster Distribution System Emergency Repair Optimization

doi:10.12204/j.issn.1000-7229.2023.12.007

Abstract

Abstract:

In order to cope with the loss from power outages caused by extreme disasters, it is necessary to construct a reasonable post-disaster recovery model and explore effective post-disaster emergency decision-making methods. In this study, a Markovian decision process model for post-disaster emergency recovery considering critical load failure events is constructed. It also considers the impact of outage duration on the functionality of different types of critical loads when calculating the losses. For post-disaster emergency decision-making, an online decision-making method based on a preview algorithm is proposed to quickly evaluate the action-state value function (i.e., Q function) by means of multi-scenario simulation and generate an online optimization strategy. It is demonstrated that the decision scheme based on the Q function generated by the rollout algorithm is better than that provided by the underlying strategy in the simulation. The proposed method does not rely on precise mathematical models and parameters and can update the policy in real time according to the system state, which is a more practical approach than that of the traditional offline optimization model.

Key words: resilience, distribution system, post-disaster restoration, online decision, rollout algorithm

CLC Number:

TM712

LI Mingxuan, WEI Wei, XU Yin, ZHANG Qiqi. Rollout Algorithm-Based Post-Disaster Distribution System Emergency Repair Optimization[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(12): 85-94.

Figures/Tables 15

Fig.1 Framework for emergency recovery problems in power distribution grids

Fig.2 Illustration of rollout algorithm

Fig.3 Implementation process of the base policy in rollout algorithm

Fig.4 The overall process of online decision-making

Fig.5 The 34-bus distribution system

Fig.6 The transportation network with failure locations

Table 1 Required repair time for fault lines

Table 2 Load at each bus

Table 3 Cost per hour of load loss at each bus

Table 4 Information of critical load

Table 5 Comparison of results

Fig.7 Accumulated power outage loss curve

Table 6 Order of line repair

Fig.8 System recovery status in various time periods for rollout policy

Fig.9 System recovery status in various time periods for base policy

References 26

[1]	WANG Y Z, CHEN C, WANG J H, et al. Research on resilience of power systems under natural disasters: a review[J]. IEEE Transactions on Power Systems, 2016, 31(2): 1604-1613. doi: 10.1109/TPWRS.2015.2429656 URL
[2]	HUSSAIN A, BUI V H, KIM H M. Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience[J]. Applied Energy, 2019, 240: 56-72. doi: 10.1016/j.apenergy.2019.02.055 URL
[3]	刘家妤, 沈冰, 周健, 等. 计及关键负荷功能恢复需求的韧性城市配电网恢复方法[J]. 电力建设, 2022, 43(8): 66-75. doi: 10.12204/j.issn.1000-7229.2022.08.007
	LIU Jiayu, SHEN Bing, ZHOU Jian, et al. Distribution system restoration method considering the function restoration requirements of critical loads[J]. Electric Power Construction, 2022, 43(8): 66-75. doi: 10.12204/j.issn.1000-7229.2022.08.007
[4]	中国日报. 郑州暴雨期间大范围停电原因公布[EB/OL].(2021-07-23)[2023-04-19]. .https://cn.chinadaily.com.cn/a/202107/23/WS60fa543ea3101e7ce975b20f.html
[5]	马晨霄, 戴则梅, 胡超凡, 等. 应对严重自然灾害的省级高压网架增强辅助策略[J]. 电力建设, 2022, 43(6): 119-127. doi: 10.12204/j.issn.1000-7229.2022.06.013
	MA Chenxiao, DAI Zemei, HU Chaofan, et al. Strengthening auxiliary strategy of provincial high-voltage power grid in response to severe natural disasters[J]. Electric Power Construction, 2022, 43(6): 119-127. doi: 10.12204/j.issn.1000-7229.2022.06.013
[6]	王伟胜, 林伟芳, 何国庆, 等. 美国得州2021年大停电事故对我国新能源发展的启示[J]. 中国电机工程学报, 2021, 41(12): 4033-4043.
	WANG Weisheng, LIN Weifang, HE Guoqing, et al. Enlightenment of 2021 texas blackout to the renewable energy development in China[J]. Proceedings of the CSEE, 2021, 41(12): 4033-4043.
[7]	李穆寅, 曹志远, 李佳旭, 等. 计及供电-供水系统耦合关系的配电网故障恢复优化决策方法[J]. 电力建设, 2021, 42(3): 54-60. doi: 10.12204/j.issn.1000-7229.2021.03.007
	LI Muyin, CAO Zhiyuan, LI Jiaxu, et al. Optimization decision-making method for distribution system restoration considering the interdependency between power and water distribution systems[J]. Electric Power Construction, 2021, 42(3): 54-60. doi: 10.12204/j.issn.1000-7229.2021.03.007
[8]	LI B D, CHEN Y, WEI W, et al. Enhancing resilience of emergency heat and power supply via deployment of LNG tube trailers: a mean-risk optimization approach[J]. Applied Energy, 2022, 318: 119204. doi: 10.1016/j.apenergy.2022.119204 URL
[9]	范馨予, 黄媛, 吴疆, 等. 考虑源网荷储协同优化的配电网韧性提升策略[J]. 电力建设, 2023, 44(4): 63-73. doi: 10.12204/j.issn.1000-7229.2023.04.008
	FAN Xinyu, HUANG Yuan, WU Jiang, et al. Resilience promotion strategy for distribution network considering source-network-load-storage coordination[J]. Electric Power Construction, 2023, 44(4): 63-73. doi: 10.12204/j.issn.1000-7229.2023.04.008
[10]	高天乐, 李佳旭, 王颖, 等. 计及负荷侧关键基础设施耦合性的配电网恢复优化决策方法[J]. 电力建设, 2019, 40(12): 38-44. doi: 10.3969/j.issn.1000-7229.2019.12.005
	GAO Tianle, LI Jiaxu, WANG Ying, et al. Optimization decision-making method for distribution network restoration considering the interdependency of critical infrastructure on load side[J]. Electric Power Construction, 2019, 40(12): 38-44. doi: 10.3969/j.issn.1000-7229.2019.12.005
[11]	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. Areview on distribution system restoration for resilience enhancement[J]. Transactions of China Electrotechnical Society, 2019, 34(16): 3416-3429.
[12]	张亚超, 丁志龙, 谢仕炜, 等. 面向能源互联网的配电网韧性提升研究综述及展望[J]. 电网技术, 2023, 47(5): 2054-2069.
	ZHANG Yachao, DING Zhilong, XIE Shiwei, et al. Review and prospect of power distribution network resilience enhancement for energy internet[J]. Power System Technology, 2023, 47(5): 2054-2069.
[13]	DING T, WANG Z K, JIA W H, et al. Multiperiod distribution system restoration with routing repair crews, mobile electric vehicles, and soft-open-point networked microgrids[J]. IEEE Transactions on Smart Grid, 2020, 11(6): 4795-4808. doi: 10.1109/TSG.5165411 URL
[14]	刘家妤, 马佳骏, 王颖, 等. 多源协同的智能配电网故障恢复次序优化决策方法[J]. 电力建设, 2020, 41(6): 100-106. doi: 10.12204/j.issn.1000-7229.2020.06.013
	LIU Jiayu, MA Jiajun, WANG Ying, et al. Restoration sequence decision-making for smart distribution system considering multiple sources coordination[J]. Electric Power Construction, 2020, 41(6): 100-106. doi: 10.12204/j.issn.1000-7229.2020.06.013
[15]	卢志刚, 王克胜. 基于多代理方法的配电网故障应急抢修调度[J]. 电网技术, 2013, 37(1): 137-143.
	LU Zhigang, WANG Kesheng. Multi-agent based rush repair scheduling for distribution networks[J]. Power System Technology, 2013, 37(1): 137-143.
[16]	郁琛, 李尚轩, 谢云云, 等. 考虑交通网与配电网信息融合的台风后配电网抢修策略优化[J]. 电力系统自动化, 2022, 46(4): 15-24.
	YU Chen, LI Shangxuan, XIE Yunyun, et al. Optimization of post-typhoon rush repair strategy for distribution network considering information integration of traffic network and distribution network[J]. Automation of Electric Power Systems, 2022, 46(4): 15-24.
[17]	ARIF A, MA S S, WANG Z Y, et al. Optimizing service restoration in distribution systems with uncertain repair time and demand[J]. IEEE Transactions on Power Systems, 2018, 33(6): 6828-6838. doi: 10.1109/TPWRS.2018.2855102 URL
[18]	梁海平, 石皓岩, 王岩, 等. 基于提升韧性的输电网灾后应急维修策略优化[J]. 中国电力, 2022, 55(3): 142-151.
	LIANG Haiping, SHI Haoyan, WANG Yan, et al. Resilience-improving based optimization of post-disaster emergency maintenance strategy for transmission networks[J]. Electric Power, 2022, 55(3): 142-151.
[19]	高玉昕, 王国庆, 朱建明, 等. 维修时间不确定性下配电网灾后维修队安排的鲁棒恢复研究[J]. 中国安全生产科学技术, 2020, 16(12): 44-49.
	GAO Yuxin, WANG Guoqing, ZHU Jianming, et al. Research on robust recovery of post-disaster maintenance team arrangement for power distribution network under uncertainty of emergency repair time[J]. Journal of Safety Science and Technology, 2020, 16(12): 44-49.
[20]	BHATTACHARYA S, KAILAS S, BADYAL S, et al. Multiagent rollout and policy iteration for POMDP with application to multi-robot repair problems[C]// Conference on Robot Learning. PMLR, 2021: 1814-1828.
[21]	AL-KANJ L, POWELL W B, BOUZAIENE-AYARI B. The information-collecting vehicle routing problem: stochastic optimization for emergency storm response[EB/OL]. [2023-04-19]. .https://arxiv.org/abs/1605.05711.pdf
[22]	SHUAI H, HE H B, WEN J Y. Post-storm vehicle routing for distribution grid restoration: an OLUCT based learning approach[C]// 2020 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2020: 1-5.
[23]	BERTSEKAS D. Rollout, policy iteration, and distributed reinforcement learning[M]. Athena Scientific, 2021.
[24]	FLOYD R W. Algorithm 97: shortest path[J]. Communications of the ACM, 1962, 5(6): 345.
[25]	KERSTING W. Radial distribution test feeders[J]. IEEE Transactions on Power Systems, 1991, 6(3): 975-985. doi: 10.1109/59.119237 URL
[26]	ERICSON S, LISELL L. A flexible framework for modeling customer damage functions for power outages[J]. Energy Systems, 2020, 11(1): 95-111. doi: 10.1007/s12667-018-0314-8