Development of New Algorithm for Power System Unit Commitment Considering Fuel Consumption Constraint
| Project/Area Number |
07455118
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
電力工学・電気機器工学
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| Research Institution | YOKOHAMA NATIONAL UNIVERSITY |
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Principal Investigator |
OYAMA Tsutomu Yokohama National University, Faculty of Engineering, Associate Professor, 工学部, 助教授 (40160642)
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| Project Period (FY) |
1995 – 1997
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| Project Status |
Completed (Fiscal Year 1997)
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| Budget Amount *help |
¥5,500,000 (Direct Cost: ¥5,500,000)
Fiscal Year 1997: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1996: ¥1,200,000 (Direct Cost: ¥1,200,000)
Fiscal Year 1995: ¥3,500,000 (Direct Cost: ¥3,500,000)
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| Keywords | Operation of Power System / Unit Commitment / Genetic Algorithm / 並列処理 |
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| Research Abstract |
The thermal plant unit commitmetn is a very important problem to improve the economics of power system without degrading the reliability. Since the number of power plant is very large in the real power system, however, it is very difficult to search the optimal solution. It is especially dificult to solve the problem if there are any long term constraint such as fuel consumption constraint.
In this study, the algorithm that can solve large size unit commitment problem including fuel consumption constraint is developed. In the developed algorithm, first, candidates of possible solution for each period are obtained. Using the obtained candidates, the size of the problem can be reduced. Therefore, a combinatorial problem involved in the unit commitment probelm can be solved by existing algorithms such as genetic algorithm. The most important point in the algorithm is to obtain a good and effective candidates for each period. In this study, the method based on the priority list method is used.
In order to prove the effectiveness of the developed algorithm, 5-machine 10-period model and 10-machine 20-period model, and 20-machine 24-period model is used. It is clarified that the result using the developed algorithm is better than that using the simple priority list method, genetic algorithm, or Lagrangian relaxation method.
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Report
(4 results)
Research Products
(4 results)
