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Geographical Impacts of Natural Disaster on Power System Reliability

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Geographical Impacts of Natural Disaster on Power System Reliability Geographical Impacts of Natural Disaster on Power System Reliability Jingkun Liu, Ning Zhang, Jianhua Bai, Jian Tan, Zhenjian Xie, Chongqing Kang Lu Cheng Junhui Huang Department of Electrical Engineering State Grid Energy Research Institute Grid Planning Center Tsinghua University Beijing, P. R. China State Grid Jiangsu Power Economic Beijing, P. R. China Research Institute [email protected] Nanjing, P. R. China Abstract--The natural disasters would strongly increase the well as the significance level, determine how the power probabilities of transmission lines outage and thus would rise system is influenced by natural disasters. It is of great interest the risks of power system. The interaction between the electrical to investigate how the electrical topology of the power system topology of the power system and the physical geography of the and the physical geography of the disasters interact with each disasters determines how much the impact is on power system other and jointly determine the reliability level of the power reliability. This paper focuses on the influence of natural system. disasters on power system in a reliability geography point of view. We propose an index of disaster zone importance index The issue of natural environmental impacts on power (DZII) to quantify the influence of disaster zones on the system system risk has been widely studied with the introduction of and to identify the critical disaster zones. A non-sequential operational reliability assessment. The methodology and Monte Carlo method considering model of disasters are carried techniques of operational reliability have been widely studied out to implement the operational reliability assessment. A real [1]. It evaluates the short-term and real-time reliability of the world case study based on Jiangxi provincial power system in power system conditional to the operational status and the China is carried out to demonstrate the geographical impacts of external environment, while regarding components of power ice disasters on power system reliability. system as irreparable [5]. The methods for operational reliability assessment is similar with the long-term reliability Index Terms—conditional outage rate, disaster, ice storm, assessment, which can be classified into three categories: operational reliability, reliability evaluation analytical method [6]-[8], Monte Carlo simulation method [9] and intelligent search method [10]. The impact of natural I. INTRODUCTION environment on power system has been widely discussed in the study of operational reliability. The impact of natural Natural disasters have a strong influence on the reliability of the power system. Disasters like ice storm would environment is usually modeled using the condition-dependent significantly increase the risks of transmission line outages, failure rate (CDFR) model in order to reflect the increase of and in turn, further increase the risks of blackouts. The outage risk under extreme conditions [11]. The functions of reliability of the power system largely depends on its failure rate, repair rate of transmission line with respect of the topology, which is mainly determined by where the power weather states are proposed in [9]. Multi-state weather resources and electric demands are located. Generally the modeling is introduced in [12] to reflect the effect of node with a stronger connection to the power sources has a continuously varying impact of the weather on the higher reliability. In other words, the reliability is mainly transmission facilities. A conditions-dependent model is driven by the power system geography which is defined on proposed in [13] forming a hybrid model considering electrical topology point of view. On the other hand natural component aging, weather and current. The influences of wind disasters are mainly driven by natural factors such as weather speed, ice load and power flow on failure rate are studied in or terrain. Therefore the involved area would have a natural [14]. geographic distribution. The overlap between the area of one The contribution of this paper is to explore the influence of disaster and the transmission lines determines how much the natural disaster on power system reliability from a geography branches are affected by the disaster. Therefore the relative point of view. The Monte Carlo based method is employed to allocation of transmission lines and of the disaster regions, as perform the operational reliability assessment. A disaster zone importance index (DZII) is proposed to quantify the influence This work was supported jointly by National Natural Science Foundation of China (No.51307092, 51325702), and science and technical project of State Grid. of disasters located in different regions on the power system reliability. A real world power system assessment is carried 978-1-4673-8040-9/15/$31.00 ©2015 IEEE out to provide a better understanding on how the blackout risk The outages of transmission lines are regarded as is distributed geographically and how it interacts with irreparable ones. The trip-off probability of each segment of differently distributed disasters. transmission lines can be calculated by Section II presents the methodology employed for Plt=−1exp1 − −ηλ (2) operational reliability evaluation considering disaster, ij,,,()() i imij including the modeling and sampling method of disaster zones, where j is the index of segments of each transmission line, the framework of the reliability assessment, and the definition of DZII. Section III carries out a real world case study of the ηi is the probability of successful reclosing of line i , lij, is Jiangxi provincial power system of China. Section IV draws the length of segment j of line i and t is the duration of the conclusions. time for reliability evaluation. The state of the entire transmission line is determined by all its segments, obeying II. METHODOLOGY the serial connection model. A. Reliability Model of Disaster Zones For more detailed model of transmission lines in disaster We define disasters as the extreme natural events that zones and sampling method considering outage correlation, would largely increase the outage risks of the power system readers can refer to [17]. components, such as ice storm, lightning storm, air pollution and bird migration. The geographical overlap between the B. Reliability Assessment corridor of transmission lines and the disaster determines how A non-sequential Monte-Carlo based operational reliability the transmission lines are affected by the disaster. There are assessment framework shown in Fig.2 is employed in this many types of relative positions between disaster zones and study. The assessment belongs to hierarchical level II (HLII) transmission lines. One disaster zone may cover several assessment which considers the fluctuation of load and the transmission lines. The corridor of transmission line may go random outages of transmission lines, transformers and through several disaster zones. If a transmission line is not generating units[18]. It should be noted that since the entirely covered by a disaster zone, it can be divided into 3 happening frequency of the disaster is very low, the variation segments, as illustrated in Figure 1. Segment 2 is in the disaster of reliability due to the disaster would also be small. To zone, however, Segment 1 and Segment 3 are out of the highlight the impact of disasters, the conditional system disaster zone. When the disaster happens, the outage rate of reliability was carried out to separately evaluate the cases per unit length of Segment 2 would be greater than that of where the disaster does not happen and the one where the Segment 1 and Segment 3, other the outage rate of each disaster happens with 100% probability. The impact of the segment stays the same. disasters is then evaluated by comparing the results between the cases. Figure 1. Schematic diagram of the geographical relationship between a transmission line and a disaster zone The disaster-dependent random failure model of transmission lines is as follows: ⎧Qim, ⎪ λi , disaster in zone m happens ⎪ Sm λ = ⎨ (1) im, 1− Q ⎪ im, , disaster in zone m dose not happen ⎪ λi ⎩ 1− Sm where i is the index of transmission lines, m is the index of disaster zones, λim, is the conditional outage rate of the segment of line i in disaster zone m , λi is the overall outage rate of line i , Qim, is the ratio of outage events caused by disaster zone m of line i over all outage events of line i and Figure 2. Flowchart of reliability assesment S is the ratio of time disaster zone m happens over a year. m The procedure of the reliability assessment is as follows. Firstly, form the segments of transmission lines and calculate the forced outage probability of each segment using the model presented in Section II.A. Secondly, choose the outage rate of In this paper we chose ice storm to illustrate the the segments of transmission lines according to the failure rate geographic influence of the disasters. The map of ice storms conditional to the happening of disasters. When sample the of Jiangxi province is shown in Fig.3. In this study we select segments in the same disaster zone, dependent sampling is only 6 typical zones, which are labeled in Fig.3. The employed to account for the common mode failure under information of the disaster zones proposed is shown in Table I. disaster. The state of each transmission line is decided Although Disaster Zone 6 occupies a relatively small area, it according to all its state using logical multiplication. Then includes the most line segments with the highest total length. sample the bus load and the states of transformers and The reason is that Disaster Zone 6 is located in Nanchang, the generating units. Thirdly, calculate the minimum load province capital of Jiangxi, of which the transmission lines shedding for the current system state using DC optimal power intensity is relatively high. flow (DCOPF) model. Update the reliability indices using the results of the DCOPF model, such as loss of load probability The value of Qim, (0.9) and the Sm (0.08082) are set for (LOLP) and expected energy not supplied (EENS).
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