### Abstract

Original language | English |
---|---|

Title of host publication | Proceedings of the 2015 IEEE Power & Energy Society General Meeting, July 26–30 2015, Denver, Colorado |

Place of Publication | Piscataway |

Publisher | Institute of Electrical and Electronics Engineers |

Pages | 1-5 |

DOIs | |

Publication status | Published - 2015 |

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### Cite this

*Proceedings of the 2015 IEEE Power & Energy Society General Meeting, July 26–30 2015, Denver, Colorado*(pp. 1-5). Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/PESGM.2015.7286113

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*Proceedings of the 2015 IEEE Power & Energy Society General Meeting, July 26–30 2015, Denver, Colorado.*Institute of Electrical and Electronics Engineers, Piscataway, pp. 1-5. https://doi.org/10.1109/PESGM.2015.7286113

**Scalable and practical multi-objective distribution network expansion planning.** / Luong, N.H.; Grond, M.O.W.; Poutré, La, J.A.; Bosman, P.A.N.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Scalable and practical multi-objective distribution network expansion planning

AU - Luong, N.H.

AU - Grond, M.O.W.

AU - Poutré, La, J.A.

AU - Bosman, P.A.N.

PY - 2015

Y1 - 2015

N2 - We formulate the distribution network expansion planning (DNEP) problem as a multi-objective optimization (MOO) problem with different objectives that distribution network operators (DNOs) would typically like to consider during decision making processes for expanding their networks. Objectives are investment cost, energy loss, total cost, and reliability in terms of the number of customer minutes lost per year. We consider two solvers: the widely-used Non-dominated Sorting Genetic Algorithm NSGA-II and the recently-developed Multiobjective Gene-pool Optimal Mixing Evolutionary Algorithm (MO-GOMEA). We also develop a scheme to get rid of the notoriously difficult-to-set population size parameter so that these solvers can more easily be used by non-specialists. Experiments are conducted on medium-voltage distribution networks constructed from real data. The results confirm that the MOGOMEA, with the scheme that removes the population size parameter, is a robust and user-friendly MOO solver that can be used by DNOs when solving DNEP.

AB - We formulate the distribution network expansion planning (DNEP) problem as a multi-objective optimization (MOO) problem with different objectives that distribution network operators (DNOs) would typically like to consider during decision making processes for expanding their networks. Objectives are investment cost, energy loss, total cost, and reliability in terms of the number of customer minutes lost per year. We consider two solvers: the widely-used Non-dominated Sorting Genetic Algorithm NSGA-II and the recently-developed Multiobjective Gene-pool Optimal Mixing Evolutionary Algorithm (MO-GOMEA). We also develop a scheme to get rid of the notoriously difficult-to-set population size parameter so that these solvers can more easily be used by non-specialists. Experiments are conducted on medium-voltage distribution networks constructed from real data. The results confirm that the MOGOMEA, with the scheme that removes the population size parameter, is a robust and user-friendly MOO solver that can be used by DNOs when solving DNEP.

U2 - 10.1109/PESGM.2015.7286113

DO - 10.1109/PESGM.2015.7286113

M3 - Conference contribution

SP - 1

EP - 5

BT - Proceedings of the 2015 IEEE Power & Energy Society General Meeting, July 26–30 2015, Denver, Colorado

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

ER -