Disaster Risk Mapping for Improving Power System Security Dispatch

doi:10.16183/j.cnki.jsjtu.2023.535

Abstract

Abstract:

Severe geological disasters can lead to major power outages, posing serious threats to the safe and stable operation of power grids. To address the impact of geologic disasters on power grids, a power system security scheduling strategy based on disaster risk mapping is proposed. First, a disaster risk mapping strategy is constructed based on a variety of disaster impact factors, and the hazardous situation of the transmission line is obtained by overlaying this map with the geographic location of the transmission line. Then, a power system operation risk model is established to quantitatively analyze the risk of the system using composite risk indexes. Finally, a particle swarm algorithm is used to construct the regulation strategy of generators and flexibility resources using the New England power system as a case study to validate the effectiveness and feasibility of the strategy. The results show that the proposed power system security scheduling strategy can effectively reduce the comprehensive risk indicators, including the risk of power system overrun/heavy load, enhancing the security and stability of power grid operations.

Key words: geohazards, risk mapping, overrun/heavy load risk, flexibility resources

CLC Number:

TM711

XIE Da, LI Ziyi, TIAN Zhou, WANG Kai. Disaster Risk Mapping for Improving Power System Security Dispatch[J]. Journal of Shanghai Jiao Tong University, 2025, 59(9): 1338-1347.

Figures/Tables 10

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References 30

[1]	QIN P H. More than six billion people encountering more exposure to extremes with 1.5 ℃ and 2.0 ℃ warming[J]. Atmospheric Research, 2022, 273: 106165.
[2]	MASSON DELMOTTE V, ZHAI P, PIRANI A, et al. Climate change 2021—The physical science basis[J]. Chemistry International, 2021, 43(4): 22-23.
[3]	LI C J, LU T, FU B J, et al. Sustainable city development challenged by extreme weather in a warming world[J]. Geography and Sustainability, 2022, 3(2): 114-118.
[4]	刘书豪. 降雨条件下的输电线路滑坡风险评估与预警技术研究[D]. 武汉: 中国地质大学, 2021.
	LIU Shuhao. Research on risk assessment and early warning technology for landslides in power transmission lines under rainfall conditions[D]. Wuhan: China University of Geosciences, 2021.
[5]	ZHAO B R, DAI Q, ZHUO L, et al. Accounting for satellite rainfall uncertainty in rainfall-triggered landslide forecasting[J]. Geomorphology, 2022, 398: 108051.
[6]	SHOU K J, CHEN J R. On the rainfall induced deep-seated and shallow landslide hazard in Taiwan[J]. Engineering Geology, 2021, 288: 106156.
[7]	KIM H, LEE J H, PARK H J, et al. Assessment of temporal probability for rainfall-induced landslides based on nonstationary extreme value analysis[J]. Engineering Geology, 2021, 294: 106372.
[8]	HUANG F M, CHEN J W, LIU W P, et al. Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold[J]. Geomorphology, 2022, 408: 108236.
[9]	徐香香. 极端天气下电网故障在线预警及风险评估技术研究[D]. 南京: 东南大学, 2020.
	XU Xiangxiang. Research of risk assessment and online early warning of power grid fault under extreme weather[D]. Nanjing: Southeast University, 2020.
[10]	ZHANG H W, WANG Z Q. Human activities and natural geographical environment and their interactive effects on sudden geologic hazard: A perspective of macro-scale and spatial statistical analysis[J]. Applied Geography, 2022, 143: 102711.
[11]	LIN J H, CHEN W H, QI X H, et al. Risk assessment and its influencing factors analysis of geological hazards in typical mountain environment[J]. Journal of Cleaner Production, 2021, 309: 127077.
[12]	NIU Q F, LIU Y, XIE Y W, et al. Susceptibility assessment of secondary geological disaster based on GIS and Information value methodology for Yushu earthquake region[C]// The 2nd International Conference on Information Science and Engineering. Hangzhou, China: IEEE, 2010: 4098-4101.
[13]	LEE S. Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS[J]. Environmental Management, 2004, 34(2): 223-232. pmid: 15559946
[14]	LI Z W, TANG X L, LI L J, et al. GIS-based risk assessment of flood disaster in the Lijiang River Basin[J]. Scientific Reports, 2023, 13(1): 6160. doi: 10.1038/s41598-023-32829-5 pmid: 37061519
[15]	陈锐, 刘硕, 贺先强, 等. 计及源网不确定性的风险评估与优化调度[J]. 电力系统及其自动化学报, 2023, 35(5): 19-27.
	CHEN Rui, LIU Shuo, HE Xianqiang, et al. Risk assessment and optimal dispatching considering source-network uncertainties[J]. Proceedings of the CSU-EPSA, 2023, 35(5): 19-27.
[16]	颜文婷, 杨隆, 李长城, 等. 考虑地震攻击交通网影响的配电网韧性评估及提升策略[J]. 上海交通大学学报, 2023, 57(9): 1165-1175. doi: 10.16183/j.cnki.jsjtu.2022.152
	YAN Wenting, YANG Long, LI Changcheng, et al. Resilience evaluation and enhancement strategy of distribution network considering impact of seismic attack on transportation networks[J]. Journal of Shanghai Jiao Tong University, 2023, 57(9): 1165-1175.
[17]	崔伟, 李武璟, 牛拴保, 等. 自然灾害下高风险多重故障集快速生成方法[J]. 电力自动化设备, 2021, 41(4): 197-203.
	CUI Wei, LI Wujing, NIU Shuanbao, et al. Rapid generation method of high risk multiple fault set under natural disaster[J]. Electric Power Automation Equipment, 2021, 41(4): 197-203.
[18]	常康, 徐泰山, 郁琛, 等. 自然灾害下电网运行风险控制策略探讨[J]. 电力系统保护与控制, 2019, 47(10): 73-81.
	CHANG Kang, XU Taishan, YU Chen, et al. Discussion of power system operation risk control strategy in natural disasters[J]. Power System Protection and Control, 2019, 47(10): 73-81.
[19]	YU S W, ZHOU S S, QIN J P. Layout optimization of China’s power transmission lines for renewable power integration considering flexible resources and grid stability[J]. International Journal of Electrical Power & Energy Systems, 2022, 135: 107507.
[20]	王薪媛, 蔺红. 综合考虑多类型灵活性资源的主动配电网优化调度方法研究[J]. 可再生能源, 2023, 41(2): 227-235.
	WANG Xinyuan, LIN Hong. Research on optimal scheduling method of active distribution network considering multi-type flexible resources[J]. Renewable Energy Resources, 2023, 41(2): 227-235.
[21]	郭咏涛, 向月, 刘俊勇. 面向高比例清洁能源消纳的含灵活性资源电力系统规划方案优选[J]. 上海交通大学学报, 2023, 57(9): 1146-1155. doi: 10.16183/j.cnki.jsjtu.2022.269
	GUO Yongtao, XIANG Yue, LIU Junyong. Optimal planning of power systems with flexible resources for high penetrated renewable energy accommodation[J]. Journal of Shanghai Jiao Tong University, 2023, 57(9): 1146-1155.
[22]	UR REHMAN U. A robust vehicle to grid aggregation framework for electric vehicles charging cost minimization and for smart grid regulation[J]. International Journal of Electrical Power & Energy Systems, 2022, 140: 108090.
[23]	王精, 邢海军, 王华昕, 等. 考虑电动汽车及负荷聚合商参与的综合能源系统优化调度[J]. 上海交通大学学报, 2023, 57(7): 814-823. doi: 10.16183/j.cnki.jsjtu.2022.029
	WANG Jing, XING Haijun, WANG Huaxin, et al. Optimal scheduling of integrated energy system considering integration of electric vehicles and load aggregators[J]. Journal of Shanghai Jiao Tong University, 2023, 57(7): 814-823.
[24]	JIANG W G, RAO P Z, CAO R, et al. Comparative evaluation of geological disaster susceptibility using multi-regression methods and spatial accuracy validation[J]. Journal of Geographical Sciences, 2017, 27(4): 439-462. doi: 10.1007/s11442-017-1386-4
[25]	李怡飞, 刘延国, 梁丽萍, 等. 青藏高原高山峡谷地貌区地质灾害危险性评价: 以雅江县为例[J]. 水土保持研究, 2021, 28(3): 364-370.
	LI Yifei, LIU Yanguo, LIANG Liping, et al. Assessment on hazard of geological disasters in alpine and canyon landforms of Qinghai-Tibet Plateau—A case study of Yajiang County[J]. Research of Soil and Water Conservation, 2021, 28(3): 364-370.
[26]	梁丽萍, 刘延国, 唐自豪, 等. 基于加权信息量的地质灾害易发性评价: 以四川省泸定县为例[J]. 水土保持通报, 2019, 39(6): 176-182.
	LIANG Liping, LIU Yanguo, TANG Zihao, et al. Geologic hazards susceptibility assessment based on weighted information value—A case study in Luding County, Sichuan Province[J]. Bulletin of Soil and Water Conservation, 2019, 39(6): 176-182.
[27]	KENNEDY J, EBERHART R. Particle swarm optimization[C]// Proceedings of ICNN’95-International Conference on Neural Networks. Perth, WA, Australia: IEEE, 1995: 1942-1948.
[28]	徐正清, 肖艳炜, 李群山, 等. 基于灵敏度及粒子群算法的输电断面功率越限控制方法对比研究[J]. 电力系统保护与控制, 2020, 48(15): 177-186.
	XU Zhengqing, XIAO Yanwei, LI Qunshan, et al. Comparative study based on sensitivity and particle swarm optimization algorithm for power flow over-limit control method of transmission section[J]. Power System Protection and Control, 2020, 48(15): 177-186.
[29]	ABEGAZ B W. ASCPN-A security evaluation system for cyber power networks[C]// 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference. New York, NY, USA: IEEE, 2019: 809-816.
[30]	SU Q Y, CHEN C, SUN Z L, et al. Identification of critical nodes for cascade faults of grids based on electrical PageRank[J]. Global Energy Interconnection, 2021, 4(6): 587-595.

工况	线路	信息量值	危险系数	极限功率/ MW	重载功率/ MW
1	2—3	7.2614	0.8309	500	400
2	1—2	6.6164	0.7494	600	480
3	2—25	5.0888	0.5869	500	400
4	3—4	5.0374	0.5821	500	400
5	4—14	5.0144	0.5800	500	400
6	28—29	4.7828	0.5589	600	480
7	16—21	4.1586	0.5058	600	480
8	22—23	4.1586	0.5058	600	480
9	17—18	4.0624	0.4980	600	480
10	2—30	3.9272	0.4874	900	720

节点	节点负荷	最大调节量	节点负荷调节范围	节点最优负荷	灵活性资源调节量
1	97.6	9.900	[87.7, 107.5]	91.82	-5.78
2	0	9.900	[-9.9, 9.9]	8.88	8.88
3	322.0	9.900	[312.1, 331.9]	313.12	-8.88
4	500.0	4.026	[496.0, 504.0]	498.32	-1.68
5	0	4.026	[-4.0, 4.0]	3.71	3.71

发电机序号	所接节点	优化前有功	优化后有功	出力调整量
1	30	250	400	150
2	31	677.87	495.46	-182.41
3	32	650	489	-161
4	33	632	580	-52
5	34	508	585	77
6	35	650	560	-90
7	36	560	564	4
8	37	540	410	-130
9	38	830	860	30
10	39	1000	1053.25	53.25

场景	越限风险指标	重载风险指标	网损指标	发电机运行成本指标	风险综合指标
1	8.65	27.76	192.22	45077.33	334.69
2	2.24	20.23	-0.43	41520.72	62.85
3	2.19	17.25	-0.53	44488.79	56.31
4	1.09	5.81	-3.64	42735.18	19.31