Application of Game Theory in Reliability-Centered Maintenance of Electric Power Systems

Pourahmadi, Farzaneh; Fotuhi‐Firuzabad, Mahmud; Dehghanian, Payman

doi:10.1109/tia.2016.2639454

Cited by 44 publications

(23 citation statements)

References 30 publications

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“…In today's electric power systems, with lower security margin and higher competition, properly planning out maintenance periods of generating units considering all possible states of the system such as random outages of units might be a tough decision. 7 To address the uncertain nature of GMS problem, two different methods, deterministic and probabilistic, have been proposed in the literature. Deterministic methods use simple practical ideas such as a fixed percentage of maximum load or the largest unit criteria to estimate reserve capacity, while probabilistic methods use probabilistic criteria, such as expected energy not supplied (EENS) or loss of load expectation (LOLE), taking into account stochastic processes in a power system.…”

Section: Uncertain Nature Of Gms Problem and The Associated State-of-mentioning

confidence: 99%

“…DC power flow equations for the no-contingency scenario and contingent scenarios are given in (5) and (6), respectively. The load shed of each bus, considered for contingent scenarios, is limited to the load of that bus based on (7). The power outputs of units are limited to their allowable ranges in (8) and (9) considering the maintenance status of units.…”

Section: Constraintsmentioning

confidence: 99%

“…In this paper, uncertainty sources or uncertain parameters are the uncertainties of units' forced outages. In today's electric power systems, with lower security margin and higher competition, properly planning out maintenance periods of generating units considering all possible states of the system such as random outages of units might be a tough decision …”

Section: Introductionmentioning

confidence: 99%

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Stochastic multiobjective generation maintenance scheduling using augmented normalized normal constraint method and stochastic decision maker

Bagheri

Amjady

2018

Int Trans Electr Energ Syst

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Summary This paper presents a stochastic multiobjective model for generation maintenance scheduling (GMS) problem and a solution method to solve it. The proposed model properly considers both competing objectives and uncertainty sources of GMS problem. Three competing objective functions including total cost, risk measure, and total emission are simultaneously minimized in the proposed model. Moreover, forced outages of generating units throughout the GMS horizon are characterized by externally generated scenarios. Stochastic programming as an efficient approach to model uncertainty sources has been used in the proposed stochastic multiobjective GMS. Augmented normalized normal constraint (A‐NNC) method is developed as an efficient multiobjective mathematical programming approach to obtain Pareto optimal solutions for the proposed model. Further, a stochastic decision maker based on out‐of‐sample analysis is suggested to find the most preferred solution for GMS problem. The IEEE 118‐bus test system is used to investigate the effectiveness of the proposed model and solution method.

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Section: Uncertain Nature Of Gms Problem and The Associated State-of-mentioning

confidence: 99%

Section: Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic multiobjective generation maintenance scheduling using augmented normalized normal constraint method and stochastic decision maker

Bagheri

Amjady

2018

Int Trans Electr Energ Syst

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“…Reliability aspects like increased complexity represented by the interconnection and interoperability between different types of systems are an additional aspect to consider in the smart grid context and motivates the exploration of new approaches to assess the smart grid operational reliability. In this context, Reliability Centered Maintenance (RCM) has been a solution proposed in recent years and applied to electric power systems [4][5][6][7][8][9][10][11]; RCM combines the reliability evaluation and maintenance planning [5,[12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Benefits of an RCM approach far exceed those of any type of maintenance program and it has been widely used by the aircraft, oil & gas, spacecraft or nuclear industry [17]. However, the use of RCM is relatively new in industries outside of these areas [4][5][6][7][8][9][10][11]. RCM strategies and their impact on electrical power systems are still undefined due to the lack of evidence and current applications.…”

Section: Introductionmentioning

confidence: 99%

Classical Failure Modes and Effects Analysis in the Context of Smart Grid Cyber-Physical Systems

Zúñiga¹,

Baleia²,

Fernandes³

et al. 2020

Preprint

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Reliability assessment in traditional power distribution systems has played a key role in power system planning, design, and operation. Recently, new information and communication technologies have been introduced in power systems automation and asset management, making the distribution network even more complex. In order to achieve efficient energy management, the distribution grid has to adopt a new configuration and operational conditions that are changing the paradigm of the actual electrical system. Therefore, the emergence of the cyber-physical systems concept to face future energetic needs requires alternative approaches for evaluating the reliability of modern distribution systems, especially in the smart grids environment. In this paper, a reliability approach that makes use of failure modes of power and cyber network main components is proposed to evaluate risk analysis in smart electrical distribution systems. We introduce the application of Failure Modes and Effects Analysis (FMEA) method in future smart grid systems in order to establish the impact of different failure modes on their performance. A smart grid test system is defined and failure modes and their effects for both power and the cyber components are presented. Preventive maintenance tasks are proposed and systematized to minimize the impact of high-risk failures and increase reliability.

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A mathematical framework for reliability-centered maintenance in microgrids

Moradi

Vahidinasab

Kia

et al. 2018

Int Trans Electr Energ Syst

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Summary In this paper, the central principles of reliability‐centered maintenance (RCM) and corresponding mathematical concepts for its implementation in microgrids are discussed. A systematic framework is suggested that exploits the fundamentals of multiattribute decision making to (i) evaluate the important features and failure attributes and (ii) effectively identify the microgrid critical components with their failure rates dynamically assessed at various time intervals. Lagrange multipliers of a budget‐constrained optimization formulation are suggested to be used to determine the desired values of system reliability indicators. The failure rates of critical components are efficiently determined over time through an exponential model that is empirically obtained via extracted condition scores of critical failure causes at multiple inspection times. A time‐varying benefit‐to‐cost ratio mechanism is designed via which the useful lifetime of critical components and the system economic benefits increase through timely maintenance in microgrids where an improved reliability performance can be realized. Finally, the proposed model is implemented in a real‐world scenario on a microgrid that is a part of the Tehran power distribution network. Extensive numerical results demonstrate the applicability and effectiveness of the suggested RCM model in microgrids.

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Application of Game Theory in Reliability-Centered Maintenance of Electric Power Systems

Cited by 44 publications

References 30 publications

Stochastic multiobjective generation maintenance scheduling using augmented normalized normal constraint method and stochastic decision maker

Stochastic multiobjective generation maintenance scheduling using augmented normalized normal constraint method and stochastic decision maker

Classical Failure Modes and Effects Analysis in the Context of Smart Grid Cyber-Physical Systems

A mathematical framework for reliability-centered maintenance in microgrids

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