Applications of the genetic algorithm to the unit commitment problem in power generation industry

Hong-Tzer Yang, Pai Chuan Yang, Ching Lien Huang

研究成果: Paper

6 引文 (Scopus)

摘要

This paper proposes an innovative genetic algorithm (GA) approach to solve the thermal unit commitment (UC) problem in power generation industry through a constraint satisfaction technique. Due to a large variety of constraints to be satisfied, the solution space of the UC problem is highly nonconvex, and therefore the UC problem can not be solved efficiently by the standard GA. To effectively deal with the constraints of the problem and greatly reduce the search space of the GA, the minimum up- and down-time constraints are embedded in the binary strings that are coded to represent the on-off states of the generating units. The violations of the other constraints are handled by integrating penalty factors into the cost function. Numerical results on the practical Taiwan Power (Taipower) system of 38 thermal units over a 24-hour period show that the features of easy implementation, fast convergence, and highly near-optimal solution in solving the UC problem can be achieved by the proposed GA approach.

原文English
頁面267-274
頁數8
出版狀態Published - 1995 一月 1
事件Proceedings of the 1995 IEEE International Conference on Fuzzy Systems. Part 1 (of 5) - Yokohama, Jpn
持續時間: 1995 三月 201995 三月 24

Other

OtherProceedings of the 1995 IEEE International Conference on Fuzzy Systems. Part 1 (of 5)
城市Yokohama, Jpn
期間95-03-2095-03-24

指紋

Unit Commitment
Power generation
Genetic algorithms
Genetic Algorithm
Industry
Unit
Constraint Satisfaction
Cost functions
Taiwan
Power System
Search Space
Cost Function
Penalty
Optimal Solution
Strings
Binary
Numerical Results
Hot Temperature

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

引用此文

Yang, H-T., Yang, P. C., & Huang, C. L. (1995). Applications of the genetic algorithm to the unit commitment problem in power generation industry. 267-274. 論文發表於 Proceedings of the 1995 IEEE International Conference on Fuzzy Systems. Part 1 (of 5), Yokohama, Jpn, .
Yang, Hong-Tzer ; Yang, Pai Chuan ; Huang, Ching Lien. / Applications of the genetic algorithm to the unit commitment problem in power generation industry. 論文發表於 Proceedings of the 1995 IEEE International Conference on Fuzzy Systems. Part 1 (of 5), Yokohama, Jpn, .8 p.
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abstract = "This paper proposes an innovative genetic algorithm (GA) approach to solve the thermal unit commitment (UC) problem in power generation industry through a constraint satisfaction technique. Due to a large variety of constraints to be satisfied, the solution space of the UC problem is highly nonconvex, and therefore the UC problem can not be solved efficiently by the standard GA. To effectively deal with the constraints of the problem and greatly reduce the search space of the GA, the minimum up- and down-time constraints are embedded in the binary strings that are coded to represent the on-off states of the generating units. The violations of the other constraints are handled by integrating penalty factors into the cost function. Numerical results on the practical Taiwan Power (Taipower) system of 38 thermal units over a 24-hour period show that the features of easy implementation, fast convergence, and highly near-optimal solution in solving the UC problem can be achieved by the proposed GA approach.",
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Yang, H-T, Yang, PC & Huang, CL 1995, 'Applications of the genetic algorithm to the unit commitment problem in power generation industry' 論文發表於 Proceedings of the 1995 IEEE International Conference on Fuzzy Systems. Part 1 (of 5), Yokohama, Jpn, 95-03-20 - 95-03-24, 頁 267-274.

Applications of the genetic algorithm to the unit commitment problem in power generation industry. / Yang, Hong-Tzer; Yang, Pai Chuan; Huang, Ching Lien.

1995. 267-274 論文發表於 Proceedings of the 1995 IEEE International Conference on Fuzzy Systems. Part 1 (of 5), Yokohama, Jpn, .

研究成果: Paper

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N2 - This paper proposes an innovative genetic algorithm (GA) approach to solve the thermal unit commitment (UC) problem in power generation industry through a constraint satisfaction technique. Due to a large variety of constraints to be satisfied, the solution space of the UC problem is highly nonconvex, and therefore the UC problem can not be solved efficiently by the standard GA. To effectively deal with the constraints of the problem and greatly reduce the search space of the GA, the minimum up- and down-time constraints are embedded in the binary strings that are coded to represent the on-off states of the generating units. The violations of the other constraints are handled by integrating penalty factors into the cost function. Numerical results on the practical Taiwan Power (Taipower) system of 38 thermal units over a 24-hour period show that the features of easy implementation, fast convergence, and highly near-optimal solution in solving the UC problem can be achieved by the proposed GA approach.

AB - This paper proposes an innovative genetic algorithm (GA) approach to solve the thermal unit commitment (UC) problem in power generation industry through a constraint satisfaction technique. Due to a large variety of constraints to be satisfied, the solution space of the UC problem is highly nonconvex, and therefore the UC problem can not be solved efficiently by the standard GA. To effectively deal with the constraints of the problem and greatly reduce the search space of the GA, the minimum up- and down-time constraints are embedded in the binary strings that are coded to represent the on-off states of the generating units. The violations of the other constraints are handled by integrating penalty factors into the cost function. Numerical results on the practical Taiwan Power (Taipower) system of 38 thermal units over a 24-hour period show that the features of easy implementation, fast convergence, and highly near-optimal solution in solving the UC problem can be achieved by the proposed GA approach.

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Yang H-T, Yang PC, Huang CL. Applications of the genetic algorithm to the unit commitment problem in power generation industry. 1995. 論文發表於 Proceedings of the 1995 IEEE International Conference on Fuzzy Systems. Part 1 (of 5), Yokohama, Jpn, .