### 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 20 → 1995 三月 24 |

### Other

Other | Proceedings of the 1995 IEEE International Conference on Fuzzy Systems. Part 1 (of 5) |
---|---|

城市 | Yokohama, Jpn |

期間 | 95-03-20 → 95-03-24 |

### 指紋

### All Science Journal Classification (ASJC) codes

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

### 引用此文

*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, .

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

研究成果: Paper

TY - CONF

T1 - Applications of the genetic algorithm to the unit commitment problem in power generation industry

AU - Yang, Hong Tzer

AU - Yang, Pai Chuan

AU - Huang, Ching Lien

PY - 1995/1/1

Y1 - 1995/1/1

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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M3 - Paper

AN - SCOPUS:0029213524

SP - 267

EP - 274

ER -