A decomposition approach to unit maintenance scheduling

Yellen, Jay; Alkhamis, Talal M.; Vemuri, S.; Lemonidis, L.

doi:10.1109/59.141779

Cited by 93 publications

(44 citation statements)

References 15 publications

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“…Figure 10 depicts the resulted maintenance schedules in various bidding scenarios. In scenario A, there is 19 weeks (1,2,5,(18)(19)(20)(23)(24)(25)(26), and 44-52) without scheduled maintenance outages. The numbers of weeks with no scheduled maintenance outages are equal to 15 (20,(23)(24)(25)30, and 44-52) and 13 (3)(4)(5)21, and 44-52) for scenarios of B and C respectively.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Transmission-constrained generation maintenance scheduling considering market-based demand response

Marini

Latify

2016

Int. Trans. Electr. Energ. Syst.

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SUMMARYIntroduction of smart grid has changed different paradigms in power industry. System-wide demand participation in power system operational planning is one of such influenced paradigms. To comply with these influenced paradigms, generation maintenance scheduling (GMS), as one of the operation planning processes, should be modified to adapt to the new changes. This paper proposes a new model to consider demand response (DR) in the GMS problem. The proposed GMS problem is modelled as a mixed integer linear programming problem and solved for the two known test cases, namely IEEE reliability test system and IEEE 118-bus test system. The results show that considering DR leads to more dispersed and uniformed generation unit maintenance schedules and lower overall system operation cost in comparison with the case in which DR is not considered.

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Section: Sensitivity Analysismentioning

confidence: 99%

Transmission-constrained generation maintenance scheduling considering market-based demand response

Marini

Latify

2016

Int. Trans. Electr. Energ. Syst.

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“…The solution methods can be categorized as follows: exact and heuristic approaches, which can then be further separated on a lower level into branch-and-bound decomposition, column generation, and techniques such as tabu search, simulated annealing, evolutionary algorithms and others including meta-heuristics. References treating MS for turbines include Dopazo and Merrill (1975), Zurn and Quintana (1975), Edwin and Curtius (1990), Satoh and Nara (1991) and Yellen et al (1992).…”

Section: Problem Background and Definitionmentioning

confidence: 99%

Preventive maintenance scheduling of multi-cogeneration plants using integer programming

Alardhi

Labib

2008

Journal of the Operational Research Society

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Maintenance scheduling of cogeneration plants, which produce both electric power and desalinated water, is a typical complex process with long-term operations and planning problems. The plants' maintenance scheduling process has to determine the appropriate schedule for preventive maintenance, while satisfying all the system constraints and maintaining adequate system availability. It is an optimization problem and the maintenance and system constraints include the crew constraint, maintenance window constraint and time limitation constraint. In this paper, an integer linear-programming model, which has been developed, is described which schedules the preventive maintenance tasks in a multi-cogeneration plant. Results of a test example of such a plant situated in Kuwait are presented to show the applicability of the approach.

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“…The average value of reliability index is calculated using equation (2). Then for each objective, the membership function is calculated using equations (10) (17) is considered as the fitness function to be maximized.…”

Section: Initializationmentioning

confidence: 99%

“…Using equations (13), (14), (15) and (16), the available generation is optimally dispatched with minimum production cost and hence generator limit and power balance constraints are satisfied. The average value of reliability index is calculated using (2). The membership function value for cost and reliability objective is calculated using (10) and (11) and the normalized membership function value for every individual population k, is calculated using (17).…”

Section: Crossovermentioning

confidence: 99%

“…The objective of minimizing the cost is considered and schedule is obtained for maintenance of generators using combination of logic programming, constraint satisfaction technique and Branch and Bound (B & B) algorithm [1], duality theory [2], Simulated Annealing (SA) [3], Genetic Algorithm (GA) [4], Tabu Search (TS) [5], Memetic Algorithm (MA) [6], Evolutionary Programming (EP) [7] and Particle Swarm Optimization (PSO) [8]. The reliability 2 objective of GMS is as important as cost objective. The reliability associated with a power system is a measure of its capability to provide an adequate supply of electrical energy for the period of time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy Clustered Multi Objective Differential Evolution for Thermal Generator Maintenance Scheduling

Balaji¹,

Balamurugan²,

Lakshminarasimman³

2016

IJIES

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Generator Maintenance Scheduling (GMS) problem in a vertically integrated electric utility system is considered in this paper. The cost and reliability objectives are included in the problem formulation. The GMS is formulated as a non linear, heavily constrained multi-objective optimization problem and the non dominated solutions are obtained using multi objective hybrid integer coded differential evolution (MOHDE) algorithm. To attain the best compromise non dominated solution between cost and reliability, fuzzy based mechanism has been used in MOHDE. In this paper, fuzzy clustered multi objective hybrid differential evolution (FC-MOHDE) is proposed for solving thermal generator maintenance scheduling. To validate the performance of the proposed algorithm, two systems having 4 and 22 generating units are considered. The test results confirm the superiority of the algorithm in determining the best compromise solution for the multi objective GMS problem.

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A decomposition approach to unit maintenance scheduling

Cited by 93 publications

References 15 publications

Transmission-constrained generation maintenance scheduling considering market-based demand response

Transmission-constrained generation maintenance scheduling considering market-based demand response

Preventive maintenance scheduling of multi-cogeneration plants using integer programming

Fuzzy Clustered Multi Objective Differential Evolution for Thermal Generator Maintenance Scheduling

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