• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.137-140
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• 1996

This paper proposes a unit commitment scheduling method based on Parallel Genetic Algorithm(PGA). Due to a variety of constraints to be satisfied, such as the minimum up and down time constraints, the search space of the UC problem is highly nonconvex. So, we used transputer which is one of the practical parallel processors. It can give us fastness and effectiveness features of the proposed method for solving the problem. To show the effectiveness of the PGA based unit commitment scheduling, we tested results for system of 5 units and we can get desirable results.

• Water Engineering Research
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• v.1 no.4
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• pp.279-291
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• 2000

A mathematical model using dynamic programming approach is applied to an optimal unit commitment problem. In this study, the units are treated as stages instead of as state dimension, and the time dimension corresponds to the state dimension instead of stages. A considerable amount of computer time is saved as compared to the normal approach if there are many units in the basin. A case study on the Lower Colorado River Basin System is presented to demonstrate the capabilities of the optimal scheduling of hydropower units.

• Journal of Energy Engineering
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• v.12 no.4
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• pp.274-280
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• 2003

This paper presents a Case-Sort method to solve the unit commitment problem using database in electric power systems. The formulation of the unit commitment nay be described as nonlinear mixed integer programming. However, it is hard to optimize a problem with discrete and continuous variables in a large-scale system at the same time. The Case-Sort method is based on the unit［MW］generation cost considered drive hour. Then, this paper shows effectiveness and economical efficiency of the proposed algorithm.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.161-163
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• 2001

This paper presents a Case-Sort method to solve the unit commitment problem using database in electric power systems. The formulation of the unit commitment may be described as nonlinear mixed integer programming. However, it is hard to optimize a problem with discrete and continuous variables in a large-scale system at the same time. The Case-Sort method is based on the unit [MW] generation cost considered drive hour. Then, this paper shows effectiveness and economical efficiency of the proposed algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1057-1064
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• 2009

This paper introduces a new approach based on a quantum-inspired evolutionary algorithm (QEA) to solve unit commitment (UC) problems. The UC problem is a complicated nonlinear and mixed-integer combinatorial optimization problem with heavy constraints. This paper proposes a bounded quantum evolutionary algorithm (BQEA) to effectively solve the UC problems. The proposed BQEA adopts both the bounded rotation gate, which is simplified and improved to prevent premature convergence and increase the global search ability, and the increasing rotation angle approach to improve the search performance of the conventional QEA. Furthermore, it includes heuristic-based constraint treatment techniques to deal with the minimum up/down time and spinning reserve constraints in the UC problems. Since the excessive spinning reserve can incur high operation costs, the unit de-commitment strategy is also introduced to improve the solution quality. To demonstrate the performance of the proposed BQEA, it is applied to the large-scale power systems of up to 100-unit with 24-hour demand.

• Journal of KIISE:Software and Applications
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• v.29 no.9
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• pp.645-653
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• 2002

The unit commitment problem in a power system involves determining the start-up and shut-down schedules of many dynamos for a day or a week while satisfying the power demands and diverse constraints of the individual units in the system. It is very difficult to derive an economically optimal schedule due to its huge search space when the number of dynamos involved is large. Tabu search is a popular solution method used for various optimization problems because it is equipped with effective means of searching beyond local optima and also it can naturally incorporate and exploit domain knowledge specific to the target problem. When given a large-scaled problem with a number of complicated constraints, however, tabu search cannot easily find a good solution within a reasonable time. This paper shows that a large- scaled optimization problem such as the unit commitment problem can be solved efficiently by using a parallel tabu search. The parallel tabu search not only reduces the search time significantly but also finds a solution of better quality.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.959-968
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• 2013

This paper presents a new approach with artificial immune system algorithm to solve the profit based unit commitment problem. The objective of this work is to find the optimal generation scheduling and to maximize the profit of generation companies (Gencos) when subjected to various constraints such as power balance, spinning reserve, minimum up/down time and ramp rate limits. The proposed hybrid method is developed through adaptive search which is inspired from artificial immune system and genetic algorithm to carry out profit maximization of generation companies. The effectiveness of the proposed approach has been tested for different Gencos consists of 3, 10 and 36 generating units and the results are compared with the existing methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.6
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• pp.708-714
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• 2018

Islanded operation of a microgrid can ensure the reliable operation of the system when a large accident occurs in the main grid. However, because the generation capability of a microgrid is typically limited, a microgrid operator should take islanded operation risk into account in scheduling its generation resources. To address this problem, in this paper we have proposed two unit commitment formulations based on the islanding scenario that reflect the expected and worst-case values of the islanded operation risk. An optimal resource scheduling strategy is obtained for the microgrid operator by solving these optimization problem, and the effectiveness of the proposed method is investigated by numerical simulations.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.4
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• pp.234-239
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• 2003

In recent decades, many countries have introduced competition in the electricity industry. Now, unit commitment becomes not a problem to be solved by a monopoly company but the one to be tackled by each generation company(GENCO). Its aim has been altered from the global cost minimization to the each GENCO's profit maximization. In this paper, the author proposes the scheme of unit commitment of a GENCO to maximize profit considering the uncertainty of market clearing price. The type of the assumed market is a uniform price market. A genetic algorithm is used for the maximization of the profit.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.117-119
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• 1993

This paper presents a new method for solving unit commitment problem including hydro and pumped storage hydro units in a large scale power system. The proposed method makes it possible to get variable power of hydro and pumped storage hydro units and results in the better unit commitment with good convergency. Moreover this paper proposes an unit commitment algorithm to consider variable power of these units effectively by Lagrangian Relaxation method. By applying the proposed method to the test system, it is verified the usefulness of this method.