### Abstract

A fuel cell has been expected to be a very energy efficient and clean device and has been technologically matured to a stage of practical use. In consideration of its relatively small generation capacity, a fuel cell may be installed in a distribution network. This paper presents the framework of a method of introducing fuel cells into a radial distribution system. According to this framework, an optimal deployment problem of fuel cells is formulated as a combinatorial optimization problem. Since this problem has nonlinear objective function to be minimized, the optimal solution could be obtains only through an exhaustive search, which is susceptible to combinatorial explosion for large scale systems. Hence, a genetic algorithm (GA) must be an adequate method to obtain a solution within reasonable computation time. Further, several kinds of techniques to improve GA performance have been introduced in this paper. The proposed algorithm has been applied to test distribution systems of 69 and 111 nodes with successful results.

Original language | English |
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Title of host publication | Proceedings of the IEEE Conference on Evolutionary Computation |

Place of Publication | Piscataway, NJ, United States |

Publisher | IEEE |

Pages | 479-484 |

Number of pages | 6 |

Volume | 1 |

Publication status | Published - 1995 |

Event | Proceedings of the 1995 IEEE International Conference on Evolutionary Computation. Part 1 (of 2) - Perth, Aust Duration: 1995 Nov 29 → 1995 Dec 1 |

### Other

Other | Proceedings of the 1995 IEEE International Conference on Evolutionary Computation. Part 1 (of 2) |
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City | Perth, Aust |

Period | 95/11/29 → 95/12/1 |

### Fingerprint

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*Proceedings of the IEEE Conference on Evolutionary Computation*(Vol. 1, pp. 479-484). Piscataway, NJ, United States: IEEE.

**Optimal deployment of fuel cells in distribution systems by using genetic algorithms.** / Zoka, Y.; Sasaki, H.; Kubokawa, J.; Yokoyama, R.; Tanaka, H.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the IEEE Conference on Evolutionary Computation.*vol. 1, IEEE, Piscataway, NJ, United States, pp. 479-484, Proceedings of the 1995 IEEE International Conference on Evolutionary Computation. Part 1 (of 2), Perth, Aust, 95/11/29.

}

TY - GEN

T1 - Optimal deployment of fuel cells in distribution systems by using genetic algorithms

AU - Zoka, Y.

AU - Sasaki, H.

AU - Kubokawa, J.

AU - Yokoyama, R.

AU - Tanaka, H.

PY - 1995

Y1 - 1995

N2 - A fuel cell has been expected to be a very energy efficient and clean device and has been technologically matured to a stage of practical use. In consideration of its relatively small generation capacity, a fuel cell may be installed in a distribution network. This paper presents the framework of a method of introducing fuel cells into a radial distribution system. According to this framework, an optimal deployment problem of fuel cells is formulated as a combinatorial optimization problem. Since this problem has nonlinear objective function to be minimized, the optimal solution could be obtains only through an exhaustive search, which is susceptible to combinatorial explosion for large scale systems. Hence, a genetic algorithm (GA) must be an adequate method to obtain a solution within reasonable computation time. Further, several kinds of techniques to improve GA performance have been introduced in this paper. The proposed algorithm has been applied to test distribution systems of 69 and 111 nodes with successful results.

AB - A fuel cell has been expected to be a very energy efficient and clean device and has been technologically matured to a stage of practical use. In consideration of its relatively small generation capacity, a fuel cell may be installed in a distribution network. This paper presents the framework of a method of introducing fuel cells into a radial distribution system. According to this framework, an optimal deployment problem of fuel cells is formulated as a combinatorial optimization problem. Since this problem has nonlinear objective function to be minimized, the optimal solution could be obtains only through an exhaustive search, which is susceptible to combinatorial explosion for large scale systems. Hence, a genetic algorithm (GA) must be an adequate method to obtain a solution within reasonable computation time. Further, several kinds of techniques to improve GA performance have been introduced in this paper. The proposed algorithm has been applied to test distribution systems of 69 and 111 nodes with successful results.

UR - http://www.scopus.com/inward/record.url?scp=0029492418&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029492418&partnerID=8YFLogxK

M3 - Conference contribution

VL - 1

SP - 479

EP - 484

BT - Proceedings of the IEEE Conference on Evolutionary Computation

PB - IEEE

CY - Piscataway, NJ, United States

ER -