Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Reliability improvement of distribution system through distribution system planning: MILP vs. GA
KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering. (QED)ORCID iD: 0000-0002-6779-4082
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.ORCID iD: 0000-0002-5263-1950
KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering. (QED)
KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering. (QED)ORCID iD: 0000-0002-2964-7233
2019 (English)In: 2019 IEEE Milan PowerTech, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Distribution system planning (DSP) is very important because it can result in reliability enhancement and large cost savings for both utilities and consumers. DSP is a complex nonlinear problem, which can be solved with different optimization methods. This paper compares two such optimization methods, conventional (mixed-integer linear programming - MILP) and meta-heuristic (genetic algorithm - GA), applied to the DSP problem: construction of feeders in distribution power system from scratch. The main objective of DSP is to minimize the total cost, where both the investment and operational outage costs are considered, while the reliability of the whole system is maximized. DSP problem is applied to an actual distribution system. Solution methods are outlined, and computational results show that even though GA gives reasonably good results in faster computation time, MILP provides a better optimal solution with simpler implementation.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Distribution system, distribution system planning, edge-sets, genetic algorithm, mixed-integer programming, power system reliability
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-259601DOI: 10.1109/PTC.2019.8810515Scopus ID: 2-s2.0-85072341947OAI: oai:DiVA.org:kth-259601DiVA, id: diva2:1352451
Conference
2019 IEEE Milan PowerTech
Funder
SweGRIDS - Swedish Centre for Smart Grids and Energy Storage, CP26
Note

QC 20190930

Available from: 2019-09-18 Created: 2019-09-18 Last updated: 2019-09-30Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopushttps://ieeexplore.ieee.org/abstract/document/8810515

Authority records BETA

Stankovic, StefanShayesteh, EbrahimHilber, Patrik

Search in DiVA

By author/editor
Duvnjak Zarkovic, SanjaStankovic, StefanShayesteh, EbrahimHilber, Patrik
By organisation
Electromagnetic EngineeringElectric Power and Energy Systems
Other Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 20 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf