Efficiency Enhancements for Evolutionary Capacity Planning in Distribution Grids

N.H. Luong, M.O.W. Grond, J.A. Poutré, La, P.A.N. Bosman

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

In this paper, we tackle the distribution network expansion planning (DNEP) problem by employing two evolutionary algorithms (EAs): the classical Genetic Algorithm (GA) and a linkage-learning EA, specifically a Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA). We furthermore develop two efficiency-enhancement techniques for these two EAs for solving the DNEP problem: a restricted initialization mechanism to reduce the size of the explorable search space and a means to filter linkages (for GOMEA) to disregard linkage groups during genetic variation that are likely not useful. Experimental results on a benchmark network show that if we may assume that the optimal network will be very similar to the starting network, restricted initialization is generally useful for solving DNEP and moreover it becomes more beneficial to use the simple GA. However, in the more general setting where we cannot make the closeness assumption and the explorable search space becomes much larger, GOMEA outperforms the classical GA.
Original languageEnglish
Title of host publicationProceedings of the Green and Efficient Energy Applications of Genetic and Evolutionary Computation (GreenGEC) Workshop at the Genetic and Evolutionary Computation Conference (GECCO 2014), July 12–16 2014, Vancouver, British Columbia, Canada.
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages1189-1196
ISBN (Print)978-1-4503-2881-4
DOIs
Publication statusPublished - 2014

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    Luong, N. H., Grond, M. O. W., Poutré, La, J. A., & Bosman, P. A. N. (2014). Efficiency Enhancements for Evolutionary Capacity Planning in Distribution Grids. In Proceedings of the Green and Efficient Energy Applications of Genetic and Evolutionary Computation (GreenGEC) Workshop at the Genetic and Evolutionary Computation Conference (GECCO 2014), July 12–16 2014, Vancouver, British Columbia, Canada. (pp. 1189-1196). New York: Association for Computing Machinery, Inc. https://doi.org/10.1145/2598394.2605696