Study on Unit Commitment Problems for Large Scaled Distributed Generators
| Project/Area Number |
15560250
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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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 | University of the Ryukyus |
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Principal Investigator |
UEZATO Katsumi UNIVERSITY OF THE RYUKYUS, FACULTY OF ENGINEERING, PROFESSOR, 工学部, 教授 (70045029)
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| Co-Investigator(Kenkyū-buntansha) |
SENJYU Tomonobu UNIVERSITY OF THE RYUKYUS, FACULTY OF ENGINEERING, PROFESSOR, 工学部, 教授 (40206660)
URASAKI Naomitsu UNIVERSITY OF THE RYUKYUS, FACULTY OF ENGINEERING, RESEARCH ASSOCIATE, 工学部, 助手 (70305184)
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| Project Period (FY) |
2003 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥3,200,000 (Direct Cost: ¥3,200,000)
Fiscal Year 2004: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2003: ¥2,200,000 (Direct Cost: ¥2,200,000)
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| Keywords | UNITCOMMITMENT / OPTIMIZATION / EXTENDED PRIORITY LIST METHOD / MIXED OPTIMIZATION / TRANSMISSION RESTRICTION / RAMP RESTRICTION / LARGE SCALE OPTIMIZATION PROBLEM / ECONOMIC LOAD DISPATCH |
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| Research Abstract |
This research project presents the genetic algorithm solution to the thermal unit commitment problem. Unit commitment problem is one of the most difficult optimization problems in power systems as the search space is vast. To reduce search space, unit integration technique is proposed. The initial population is often generated randomly. However, it is difficult to generate feasible solutions. To obtain feasible initial solutions, initial population is generated based on load data. Therefore, feasible initial solutions can be obtained. Constraints, output range and operation cost varies with each unit. Units are classified into several groups based on minimum up/down times constraint. The operation schedule of small units is determined by numerical calculation based on cost characteristic. Other unit schedule is determined by genetic algorithm. To obtain more optimal operation schedule, new genetic operators are introduced. The intelligent mutation performs local hill-climbing optimization technique. From simulation results, the proposed method can be determined satisfactory commitment schedule in reasonable computation time.
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Report
(3 results)
Research Products
(7 results)
