Scalable and practical multi-objective distribution network expansion planning

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

4 Citations (Scopus)
1 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 2015 IEEE Power & Energy Society General Meeting, July 26–30 2015, Denver, Colorado
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages1-5
DOIs
Publication statusPublished - 2015

Fingerprint Dive into the research topics of 'Scalable and practical multi-objective distribution network expansion planning'. Together they form a unique fingerprint.

Cite this