Bidding based generator maintenance scheduling with triple-objective optimization

zhang, Dabo; Li, Wenyuan; Xiong, Xiaofu

doi:10.1016/j.epsr.2012.07.014

Cited by 13 publications

(4 citation statements)

References 19 publications

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“…Deregulated electricity markets with insufficient generation capacity or less competitive structure usually fit into this category [21]. In the second category, generating companies (GENCOs) are actively incorporated into the GMS process [23,24]. In these methodologies, GENCOs usually schedule the maintenance periods by maximising their profits in the scheduling horizon [25] and then submit the obtained maintenance plans (with their maintenance bids [21]) to the ISO.…”

Section: Contributionsmentioning

confidence: 99%

Adaptive‐robust multi‐resolution generation maintenance scheduling with probabilistic reliability constraint

Bagheri

Amjady

2019

IET Generation, Transmission & Distribution

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Section: Contributionsmentioning

confidence: 99%

Adaptive‐robust multi‐resolution generation maintenance scheduling with probabilistic reliability constraint

Bagheri

Amjady

2019

IET Generation, Transmission & Distribution

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“…As for the maintenance urgency decision, Conejo [1] and Zhang [2] carried out a research on maintenance urgency decision and device supply issues of generating plant. Lu [3] and Feng [4] researched urgency of maintenance schedule making combining the electricity market environment.…”

Section: Introductionmentioning

confidence: 99%

A maintenance mode decision method for traction power supply system of high-speed railway

Wang

Feng

2015

2015 IEEE Conference on Prognostics and Health Management (PHM)

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The Traction Power Supply System (TPSS) as the only power source of High-Speed Railway (HSR), its maintenance management needs to be supported by a complete set of solutions. Maintenance mode decision is the primary and first part of the solutions. The most appropriate maintenance mode leads to the best maintenance effect, the least cost and the highest reliability. Because of the huge number of influence factors of maintenance mode decision and intricate relationship among those factors, the decision process is complicated. Therefore several alternative maintenance modes are summarized with their pros and cons. Factors Set of Maintenance Mode Decision (FSMMD) is established while the interdependence and the feedback relationships among factors are clarified and quantified. TheAnalytic Network Process (ANP) is applied and the ANP maintenance decision model is built to decide the most appropriate maintenance modes. The case study shows that the appropriate maintenance modes can be decided while the inappropriate maintenance modes of HSR TPSS can be avoid by this method.

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“…Aggregation methods are reasonably straightforward, and no modifications are required for the basic algorithm [16]. The most common intelligent methods that are used in the domain of multi-objective GMS are the Non-dominated Sorting Genetic Algorithm II (NSGAII), Strength Pareto Evolutionary Algorithm2 (SPEA2), Pareto Ant Colony Optimization, Multiobjective Simulated Annealing (MOSA), and Multiobjective Particle Swarm Optimization (MOPSO) [9,[18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Multi-objective Optimization Methods for Generator Maintenance Scheduling

2022

IJIES

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This study presents a comparison between multi-objective optimization methods used to obtain solution for generator maintenance scheduling (GMS) problem. The GMS problem with three objectives which include the total operation cost minimization, system's reliability (gross reserve) maximization and convenience is considered in this study. Convenience objective is represented by the minimization of the violation in the number of units' maintenance outage constraint. To solve the problem of GMS, there is a need for GMS model to represent the requirements of electrical power system and optimization method to implement the model and obtain solution for the GMS. The proposed Pareto ant colony system (PACS) algorithm is based on ant colony system algorithm and Pareto approach. Pareto approach is used to make trade-off between the obtained solutions based on the three objectives. In this study comparison is made based on the results of experiments using the IEEE RTS 32-and 36-unit systems while the demand for the 32-and 36-unit systems is based on IEEE RTS demand systems. In addition, four common multi-objective algorithms based on Pareto approach i.e., the Non-dominated Sorting Genetic Algorithm II, Strength Pareto Evolutionary Algorithm 2, Multi-objective Simulated Annealing, and Multi-objective Particle Swarm Optimization are used in the evaluation of the proposed PACS algorithm. The multi-objective GMS model is implemented by all the algorithms and five performance metrics i.e., the grey relational grade (GRG), coverage, distance to Pareto front, overall Pareto spread and the number of non-dominated solutions are used in the evaluation. The Friedman test is also used to evaluate the algorithms' performance statically, which is made based on GRG metric. The experimental results showed that the proposed PACS algorithm was able to obtain a robust solution by considering different initial operational hours of the units. In term of GRG metric, the PACS algorithm was able to obtain the best results for the 32-unit systems in all the maintenance windows. However, for the 36-unit system, the PACS algorithm secured the second-best results at the early stages of the operation time but outperformed other algorithms during other operation times. For other metrics, overall the PACS algorithm has the best performance in terms of coverage, distance to Pareto front and overall Pareto spread metrics while the NSGAII has the best result in terms of the number of obtained non-dominated solutions. Friedman test implies that the PACS algorithm is significantly better than the other comparative algorithms.

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Bidding based generator maintenance scheduling with triple-objective optimization

Cited by 13 publications

References 19 publications

Adaptive‐robust multi‐resolution generation maintenance scheduling with probabilistic reliability constraint

Adaptive‐robust multi‐resolution generation maintenance scheduling with probabilistic reliability constraint

A maintenance mode decision method for traction power supply system of high-speed railway

Comparison of Multi-objective Optimization Methods for Generator Maintenance Scheduling

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