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Timing of transmission line repairs considering the vulnerability of the power system

Number of pages: 147 File Format: word File Code: 32239
Year: 2016 University Degree: Master's degree Category: Electrical Engineering
Tags/Keywords: Dugan's theory - Multilevel planning - Power network - Power system - Transmission line repairs - Vulnerability of the power system
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  • Summary of Timing of transmission line repairs considering the vulnerability of the power system

    Master's Thesis in Electrical Engineering - Power

    Abstract

    The discussion of repairs in any system, including the power system, is of special importance. In the traditional structure of the electricity industry, repairs related to the generation and transmission sectors are both carried out by the network operator. With the restructuring of the electricity industry, the proposed repair time for different parts of the system is left to the owners of the parts, and the independent operator of the system is responsible for monitoring and coordinating the repair time. In the models presented for scheduling the transmission system repairs, it is generally tried to choose the best repair time in order to maintain the reliability of the system in a safe area, and the reliability of the system as the most important condition of this issue in terms of will be After 2001, the study of the effect of deliberate attacks on the power network has gained special importance; Because the application of classical standards to ensure the reliability of the system cannot sufficiently include the existing reality, that is, the issue of deliberate attack on the power grid. In this thesis, in the first step, a new model is presented that can examine the vulnerability of the power system in a time horizon. The "time dimension" of intentional attacks has not been considered in previous researches. The output of this step is a temporal model formulated as a two-level problem. This two-level model becomes a one-level programming problem using dual theory. In the second step, this model is used to present a new formulation for the scheduling of transmission line repairs. In the new formulation, the scheduling of transmission line repairs is considered as a multi-level planning problem, in which the vulnerability of the power system is considered along with the reliability of the system.

         The proposed models are implemented on Garver 6-Bus and IEEE-RTS 24-Bus standard networks and the capabilities of these methods are shown.

    Key words: system vulnerability Power, multi-level planning, dual theory, transmission line repair scheduling.

    Chapter One
    Introduction

    The power network is one of the most important infrastructures of a country, so that almost All other infrastructures depend on the proper functioning of this network. In any country, there is a close relationship between the economy and its electricity industry, and if the performance of the power grid is disturbed, there will be great economic losses for that country. For example, the damage caused by the blackout[1] that occurred in the United States of America in August 2003 was estimated between four and ten billion dollars. The blackout affected a population of about 50 million people, and in some areas, consumers were left without power for up to four days [1].  The largest blackout in history is the 2012 blackout in India, during which more than 600 million people lost access to electricity. Sometimes the consecutive outages of the transmission lines can prepare the ground for the occurrence of such destructive blackouts. For example, in the 2003 blackout in the United States of America, when a fault occurred on three transmission lines at the same time, these three lines went out of the circuit, and the outage of these three lines caused the rest of the network lines to be overloaded and quickly go out of the circuit, one after the other, and as a result, a load of about 61.8 gigawatts was lost. It is obvious that the importance of such a system makes it very necessary to ensure the correct operation of this system.

    The power network generally consists of four parts: production, transmission, distribution, and consumers, and in order to maintain the efficiency of this system, all four mentioned parts need maintenance and repairs. Increasing system reliability and increasing energy efficiency are among the most important results obtained from maintenance.

    In various books and standards, several definitions and meanings for "repairs" are mentioned; For example,
    IEEE Std 902-1998 considers maintenance to be the maintenance of conditions that are necessary and necessary for the proper operation of the equipment, with the purpose for which the equipment was used.

    In various books and standards, multiple definitions and meanings are mentioned for "repairs"; For example,
    IEEE Std 902-1998 considers maintenance and maintenance of conditions that are necessary and necessary for the correct operation of the equipment, with the same purpose for which the equipment was used [2]. Anyway, what is important is the significant dependence of the correct operation of the power system on the correct and timely repairs of its various parts.

    Since the repair period of various equipment of the power system varies from a few days to a few weeks, for this reason
    the timing of the repairs is also in several short-term time horizons (several weeks), Mid-term (about one year) and long-term (about three to four years) repairs are carried out, and these repairs fall into two categories: preventive repairs [2] and corrective repairs [3]. As it is clear from the names of these two categories, the first category of repairs is carried out in order to maintain the system in a suitable state that is favorable in terms of energy efficiency and reliability, and the second category is carried out to return the system to a normal and acceptable state as quickly as possible after an error or malfunction [3]. In addition to the length of time related to the timing of repairs, another issue that is raised in repairs is the coordinated repair of different parts, especially the production and transmission parts. A large number of papers have presented various methods for coordinated maintenance scheduling [4] of the production and transmission sector [4]–[6]. However, the repairs related to each part can be done separately. Among these, the repairs related to the transmission network are of special importance and short-term, medium-term and long-term repairs can be done for this part of the system. It performs the production and transmission parts of the system and transfers the schedule to the production units and transmission lines. With the restructuring of the electricity industry, the proposal of repair time related to different parts of the system is left to the owners of the parts, and the independent operator of the system is responsible for monitoring and coordinating the repair time.

    Regarding the many researches that have been carried out in the field of power system repairs, we can refer to an article by Pai Konejo [5] [7], which, by presenting a repetitive process [6], tries to present It has a plan that in a restructured environment, production units can adjust their schedules in a round-trip process in such a way as to maximize their profits and maintain system reliability constraints with ISO supervision. Pandzik [7] [8] also presents a MILP model (which is actually a linearization of a two-level problem) to determine the best scheduling of transmission line repairs in a one-year horizon. In this model, the transmission system operator (TSO) [8] is placed in the high-level problem and sets his objective function to maximize the available transmission capacity during a year. The low-level problem also performs market settlement with the aim of maximizing social welfare [9]. Wu [10] [9], taking into account the uncertainties in the power system, determines the repair program of the production and transmission sector in a coordinated and security-bound manner [11]. Latifi [12] [10] also by presenting an iterative process, adds the limitations and uncertainties in the gas network to the discussion of repairs of production units in a restructured environment, and by establishing a relationship between the gas network operator (GNO) [13], the independent market operator (IMO) [14] and the independent system operator (ISO) [15], the mid-term planning of electricity and gas networks is carried out in a coordinated manner. In all the models that schedule power system repairs, system reliability is either the objective function itself or is added as a constraint to the problem. In the discussion of system reliability, more attention is paid to events that normally occur in the system itself without the intervention of external factors. Short circuit faults, load shedding, failure of a generator and sudden outage of transmission lines are examples of such incidents.

  • Contents & References of Timing of transmission line repairs considering the vulnerability of the power system

    Table of Contents Eight

    Abstract. 1

    Chapter One: Introduction

    Chapter Two: History of Work Done

    2-1. Introduction 8

    2-2. An overview of the research conducted in the field of power system repairs. 9

    2-3. An overview of the research conducted in the field of vulnerability of the power system. 25

    2-4. Chapter summary and conclusion. 43

    Chapter three: time model to investigate the vulnerability of the power system

    3-1. Motivation 44

    3-2. Approach 45

    3-3. Model innovations. 46

    3-4. Modeling the vulnerability problem considering the time dimension. 46

    3-4-1. Assumptions. 46

    3-4-2. Modeling the investigation of power system vulnerability in a time horizon. 47

    3-4-3. Converting the presented two-level model to a one-level model. 52

    3-4-4. Transforming MPEC into a MILP problem. 53

    3-5. The first numerical example. 54

    3-5-2. Study time horizon. 54

    3-5-3. Input data of the problem. 54

    3-5-4. Defined scenarios 56

    3-5-5. Presentation and analysis of results. 59

    3-5-6. Calculation load of problem 66

    3-6. The second numerical example. 67

    3-6-1. Study time horizon. 67

    3-6-2. Input data of the problem. 68

    3-6-3. Defining scenarios and presenting and analyzing the results. 69

    3-7. Chapter summary and conclusion. 73

    Chapter Four: A Model for Scheduling Transmission Line Repairs Considering Power System Vulnerability

    4-1. Introduction and approach 75

    4-1-1. Model innovations. 77

    4-2. Modeling the transmission line repair scheduling problem considering the vulnerability of the power grid. 78

    4-2-1. Assumptions. 78

    4-2-2. Modeling the timing of network transmission line repairs considering the vulnerability of the power system. 78

    4-3. MWAW model to investigate power system vulnerability in a time horizon. 87

    4-3-1. Formulation of MWaW model. 88

    4-3-2. MPEC related to the MWaW model. 94

    4-3-3. Converting the MPEC of the MWaW model into a MILP problem. 96

    4-3-4. The final MWaW model as a one-level MILP problem. 98

    4-4. The final model of VCTMS as a two-level MILP problem. 98

    4-5. Using genetic algorithm to solve VCTMS model. 98

    4-5-1. Selection of variables and objective function. 98

    4-5-2. Coding. 99

    4-5-3. primary population 100

    4-5-4. choice 100

    4-5-5. combination 101

    4-5-6. Mutation    101

     

    4-6. The first numerical example: implementation of the MWaW model on the Garver 101 six-bus network

    4-6-2. Study time horizon. 102

    4-6-3. Input data of the problem. 102

    4-6-4. Presentation and analysis of results. 104

    4-7. The second numerical example: implementation of the VCTMS model for scheduling routine repairs in the Garver 106 six-bus network

    4-7-1. Definition of scenarios 106

    4-7-2. solution method 107

    4-7-3. Presentation and analysis of the obtained results 109

    4-7-3-a. Presentation and analysis of results related to scenario number 1. 109

    4-7-3-b. Presentation and analysis of results related to scenario number 2. 113

    4-7-3-C. Presentation and analysis of results related to scenario number 3. 118

    4-7-3-d. Presentation and analysis of results related to scenario number 4. 121

    4-8. Chapter summary and conclusion. 125

    Chapter Five: Conclusion and Suggestions

    5-1. Summary of results. 127

    5-2. Suggestions and further research. 129

    Source:

    ADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY [1] “Final Report on the Implementation of the Task Force Recommendations,” 2006.

    [2] D. K. Neitzel, M. E. Simon, R. Widup, and R. J. Schuerger, “IEEE 3007 series: Operation and Management, Maintenance, and Safety of Industrial and Commercial Power Systems,” in Industrial & Commercial Power Systems Technical Conference (I&CPS), 2014 IEEE/IAS 50th, 2014, pp. 1-6. [3] L.a. Lightfoot, R. Billington, and R. N. Allan, Reliability evaluation of engineering systems, vol. 29. Pitman advanced publ. program Boston etc., 1983.

    [4]      A. M. Leite, L. A. F. Manso, and G. J. Anders, “Evaluation of Generation and Transmission Maintenance Strategies Based on Reliability Worth,” Electr. Power Syst. Res., vol. 71, pp. 99–107, 2004.

    [5]      T. Geetha and K. S. Swarup, “Coordinated Preventive Maintenance Scheduling of GENCO and TRANSCO in restructured Power Systems,” Int. J. Electr. Power Energy Syst., vol. 31, no. 10, pp. 626–638, 2009.

    [6] R. Billinton and R. Mo, “Composite System Maintenance Coordination in a Deregulated Environment,” IEEE Trans. Power Syst, vol. 20, no. 1, pp. 485–492, 2005.

    [7] A. J. Conejo, R. Garc?a-Bertrand, and M. D?az-Salazar, “Generation Maintenance Scheduling in Restructured Power Systems,” Power Syst. IEEE Trans., vol. 20, no. 2, pp. 984–992, 2005.

    [8]      H. Pand?i?, A. J. Conejo, I. Kuzle, and E. Caro, “Yearly Maintenance Scheduling of transmission Lines within a Market Environment,” Power Syst. IEEE Trans., vol. 27, no. 1, pp. 407–415, 2012.

    [9] Y. Fu, M. Shahidehpour, and Z. Li, “Security-Constrained Unit Commitment with Volatile Wind Power Generation,” Power Syst. IEEE Trans., vol. 23, no. 3, pp. 1319–1327, 2008.

    [10] M. A. Latify, H. Seifi, and H. Rajabi Mashhadi, “An Integrated Model for Generation Maintenance Coordination in a Restructured Power System Involving Gas Network Constraints and Uncertainties,” Int. J. Electr. Power Energy Syst., vol. 46, pp. 425–440, 2013.

    [11] R. Zimmerman, N. Dooskin, J. Miller, R. Hartwell, W. Remington, J. S. Simonoff, L. B. Lave, R. E. Schuler, and C. E. Restrepo, “Electricity Case: Main Report—Risk, Consequences, and Economic Accounting,” 2005.

    [12] J. S. Simonoff, C. E. Restrepo, and R. Zimmerman, “Risk-Management and Risk-Analysis-Based Decision Tools for Attacks on Electric Power,” Risk Anal., vol. 27, no. 3, pp. 547–570, 2007.

    [13] J. Salmeron, K. Wood, and R. Baldick, “Analysis of Electric Grid Security Under Terrorist Threat,” Power Syst. IEEE Trans., vol. 19, no. 2, pp. 905–912, 2004.

    [14] J. M. Arroyo and F. D. Galiana, “On the Solution of the Bilevel Programming Formulation of the Terrorist Threat Problem,” Power Syst. IEEE Trans., vol. 20, no. 2, pp. 789–797, 2005.

    [15] J. M. Arroyo, “Bilevel programming applied to power system vulnerability analysis under multiple contingencies,” IET Gener. Transm. Distrib., vol. 4, no. 2, pp. 178–190, 2010.

    [16]    A. L. Motto, J. M. Arroyo, and F. D. Galiana, “A Mixed-Integer LP Procedure for the Analysis of Electric Grid Security Under Disruptive Threat,” Power Syst. IEEE Trans., vol. 20, no. 3, pp. 1357–1365, 2005.

    [17] G. Chen, Z. Y. Dong, D. J. Hill, and Y. S. Xue, “Exploring Reliable Strategies for Defending Power Systems Against Targeted Attacks,” Power Syst. IEEE Trans., vol. 26, no. 3, pp. 1000–1009, 2011.

    [18] M. Bevilacqua and M. Braglia, “The Analytic Hierarchy Process Applied to Maintenance Strategy Selection,” Reliab. Eng. Syst. Saf., vol. 70, no. 1, pp. 71–83, 2000.

    [19] L. Wang, J. Chu, and J. Wu, “Selection of Optimum Maintenance Strategies Based on a Fuzzy Analytic Hierarchy Process,” Int. J. Prod. Econ., vol. 107, no. 1, pp. 151–163, 2007. [20] [Online]. Available: www.abb.com.

    [21] D. Yu, Q. Zhang, X. Han, and J. Zhao, “TS-based Generation and Transmission Maintenance Scheduling,” IEEE Congress on Evolutionary Computation, 2007, pp. 2936–2941.

    [22] M. K. C. Marwali and S. M. Shahidehpour, “A Deterministic Approach to Generation and Transmission Maintenance Scheduling with Network Constraints,” Electr. Power Syst. Res., vol. 47, no. 2, pp. 101–113, 1998. [23] E. L. Da Silva, M. Th. Schilling, M. C.

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