2001 Fiscal Year Final Research Report Summary
A Study on Electric Power Leveling with Energy Storage Systems
| Project/Area Number |
12650279
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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 | SHIMANE UNIVERSITY |
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Principal Investigator |
FUNABIKI Shigeyuki SHIMANE UNIVERSITY, FACULTY OF INTERDISCIPLINARY OF SCIENCE AND ENGINEERING, PROFESSOR, 総合理工学部, 教授 (60108123)
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| Co-Investigator(Kenkyū-buntansha) |
FUJII Toshinori KURE INSTITUTE OF TECHNOLOGY, DEPERTMENT OF ELECTRICAL ENGINEERING, ASSISTANT RESEARCHER, 電気工学科, 助手 (80300614)
TANAKA Toshihiko SHIMANE UNIVERSITY, FACULTY OF INTERDISCIPLINARY OF SCIENCE AND ENGINEERING, ASSOCIATE PROFESSOR, 総合理工学部, 助教授 (00179772)
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| Project Period (FY) |
2000 – 2001
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| Keywords | superconducting energy storage / electric power leveling / neural networks / fuzzy / genetic algorithm |
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| Research Abstract |
Electric power in steel works and electric-railways, etc. irregularly fluctuates with protruded peak values. Therefore, much larger capacity of electric power facilities are required in comparison with the mean value of electric power consumed in the loads. If the electric power is leveled with a superconductive magnetic energy storage (SMES) system installed in the neighborhood of the consumer, the reduction in the rating and power loss of the electric power facilities can be achieved.
The fuzzy-based leveling control of power fluctuation in the substation of shinkansen-railway is examined and then its effectiveness on the abovementioned reduction is discussed. The procedure of deciding the energy capacity of SMES, degree of leveling electric power and scaling factors in the fuzzy control is introduced. They are decided by taking the reduction in the rating and power loss of electric power facilities as an index. The scaling factors in the fuzzy control are deduced for leveling the source current under 600 A with a 2.8 MWh-SMES. It is clarified by the computer simulation that the reduction of 56 % in the rating of the electric power facilities and 30 % in the power loss can be achieved.
Next, a new neuro-fuzzy leveling control method is proposed for the rolling mills in the steel works. Furtheremore, two new optimizing methods of SMES capacity are proposed for the fuzzy-based leveling control and neuro-fuzzy leveling control. The proposed methods are based on genetic algorithm (GA) with the evaluation function of the SMES capacity and the reduction in the rating and power loss of the electric power facilities. The scaling factors in the fuzzy control are extracted by minimizing the evaluation function. The learning coefficients in the neuro-fuzy control are extracted by the same minimization. Then, it is confirmed that the proposed method is available for minimizing the rating of the power leveling control system and maximizing the effect of power leveling.
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Research Products
(6 results)
