• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.408-409
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• 2006

In this paper, we surveyed researches on the generation maintenance scheduling in competitive electricity markets. Maintenance scheduling can be one of the strategies submitted by market participants in electricity market. Many researches on the maintenance scheduling were preceded with or without transmission system. They have their own specific algorithm for ISO to operate, and control the maintenance schedules. This paper is focused on the survey on the generator and transmission system maintenance scheduling in competitive electricity markets.

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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• pp.331-338
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• 2005

In this paper, an improved maintenance scheduling approach suitable for the competitive environment is proposed by taking account of profits and costs of generation companies and the formulated combinatorial optimization problem is solved by using Reactive Tabu search (RTS). In competitive power markets, electricity prices are determined by the balance between demand and supply through electric power exchanges or by bilateral contracts. Therefore, in decision makings, it is essential for system operation planners and market participants to take the volatility of electricity price into consideration. In the proposed maintenance scheduling approach, firstly, electricity prices over the targeted period are forecasted based on Artificial Neural Network (ANN) and also a newly proposed aggregated bidding curve. Secondary, the maintenance scheduling is formulated as a combinatorial optimization problem with a novel objective function by which the most profitable maintenance schedule would be attained. As an objective function, Opportunity Loss by Maintenance (OLM) is adopted to maximize the profit of generation companies (GENCOS). Thirdly, the combinatorial optimization maintenance scheduling problem is solved by using Reactive Tabu Search in the light of the objective functions and forecasted electricity prices. Finally, the proposed maintenance scheduling is applied to a practical test power system to verify the advantages and practicability of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.545-552
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• 1999

The Maintenance scheduling is one of the mid-term scheduling problems systems. There have been many methods for this problem, but there is no effective way to treat all the generators simultaneously. In this paper, we apply a genetic algorithm(GA) to the maintenance scheduling problem. We proposed new crossover operators(BOX type crossover) to improve searching ability of GA. Satisfactory results are obtained by GA with the proposed corssover operators.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.131-138
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• 1995

A new technique using integer programming based on fuzzy multi-criteria function is proposed for generator maintenance scheduling. Minimization maintenance delay cost and maximization reserve power are considered for fuzzy multi-criteria function. To obtain an optimal solution for generator maintenance scheduling under fuzzy environment, fuzzy multi-criteria integer programming is used. In the maintenance scheduling, a characteristic feature of the presented approach is that the crisp constraints with uncertainty can be taken into account by using fuzzy set theory and so more flexible solution can be obtained. The effectiveness of the proposed approach is demonstrated by the simulation results.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.701-703
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• 1996

Maintenance scheduling plays an important role in evaluating the supply reliability of power systems. Since generating units must be maintained and inspected, the generation planner must schedule planned outages during the year. Several factors entering into this scheduling analysis include: seasonal load-demand profile, amount of maintenance to bo done on all generating units, size of the units, elapsed time from last maintenance, and availability of maintenance crews. This paper proposes a new algorithm to decide the multi-year maintenance scheduling with considering the total cost. We adjust the maintenance scheduling to levelize the reliability over all period. The proposed algorithm is applied to a real size power system and the developed reliability results are obtained.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.3
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• pp.318-327
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• 2008

This paper considers a single-machine scheduling problem with availability constraints. In many realistic situations, machines may not be always available due to various reasons such as maintenance, breakdown and repair. However, most literature on scheduling assume that the machines are available at all times. This paper deals with a single-machine scheduling problem with periodic maintenance. If the maintenance decision is made jointly with the job scheduling, the system will be more effective. The objective is to minimize the total completion time of jobs. This problem is proved to be NP-hard in the strong sense. The proposed breaking heuristic(BH) algorithm rule is established by some theorems and conditions. Our computational results show that the BH algorithm is much more efficient than existing heuristic.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.8
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• pp.1382-1389
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• 2007

This paper presents a new approach for solving the problem of maintenance scheduling of generating units using a binary particle swarm optimization (BPSO). In this paper, we find the optimal solution of the maintenance scheduling of generating units within a specific time horizon using a binary particle swarm optimization algorithm, which is the discrete version of a conventional particle swarm optimization. It is shown that the BPSO method proposed in this paper is effective in obtaining feasible solutions in the maintenance scheduling of generating unit. IEEE reliability test systems(1996) including 32-generators are selected as a sample system for the application of the proposed algorithm. From the result, we can conclude that the BPSO can find the optimal solution of the maintenance scheduling of the generating unit with the desirable degree of accuracy and computation time, compared to other heuristic search algorithm such as genetic algorithms. It is also envisaged that BPSO can be easily implemented for similar optimizations and scheduling problems in power system problems to obtain better solutions and improve convergence performance.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.829-831
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• 1996

This paper is concerning on a study on the object-oriented programming and its application to maintenance scheduling. The concept of object-oriented programming enables us to modify and reuse software with much ease. By introducing object-oriented programming to maintenance scheduling, we can develop a hierarchical and reusable software in maintenance scheduling. The maintenance scheduling problem becoming more and more large and complex can be dealt with the concept of object-oriented technique and we hope this concept will give a reasonable solution. And evolutionary computation will be developed as a optimization technique.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.826-828
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• 1996

Genetic Algorithm(GA) is a searching or optimizing algorithm based on natural evolution principle. GA has demonstrated considerable success in providing good solutions to many nonlinear, multi-dimensional optimization problems. The preventative maintenance scheduling is a kind of dynamic optimization problem with constraints. This paper applies GA to the preventative maintenance scheduling problem. In the case study, we can get the preventative maintenance scheduling of 3-generators during 8 weeks using GA. It is shown that GA can be available to the preventative maintenance scheduling problem.

• Journal of Energy Engineering
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• v.7 no.1
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• pp.89-95
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• 1998

Maintenance scheduling of generators plays an important role in evaluating supply reliability of power systems. Since generators must be maintained and inspected, the generation planner must schedule planned outages during the year. Several factors entering into this scheduling analysis include: seasonal load-demand profile, amount of maintenance, size of the units, elapsed time from last maintenance, and availability of maintenance crew. This paper proposes a new maintenance scheduling algorithm for the alternatives of long-term generation expansion planning by using LOLP levelization method which is known as an effective method for the generator's maintenance scheduling. To get the best supply reliability of power systems, we change the maintenance period to levelize the reliability over all period. The proposed algorithm is applied to a real size power system and the better reliability results are obtained.