Analysis of Electrical Distribution Network Voltage Configuration with Mixed Integer Linear Programming Algorithm and Genetic Algorithm I Terms of Energy Cost


Akbulut L., TEZCAN S. S., ÇOŞGUN A.

ELECTRICA, cilt.20, sa.2, ss.124-132, 2020 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 20 Sayı: 2
  • Basım Tarihi: 2020
  • Doi Numarası: 10.5152/electrica.2020.20014
  • Dergi Adı: ELECTRICA
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.124-132
  • Anahtar Kelimeler: MATLAB GA, energy cost, voltage level configuration, MATLAB-mixed integer linear programming, electricity distribution network planning, optimization
  • Akdeniz Üniversitesi Adresli: Evet

Özet

Since natural and social resources are not evenly distributed over the earth's surface, socioeconomic developments differ in time and space. Although the most important causes of inequality are natural or geographical reasons, the lack of energy supply-demand balance specific to the region causes inequality to increase. Undoubtedly, eliminating the supply-demand imbalance as a result of the increase in energy demand by costing energy cheaply will play a major role in reducing these differences. To meet the energy demand, the existing electricity grid may need to be expanded or partially or completely replaced. The aim of the studies to design a new electricity network or to expand an existing network; to meet the needs of consumers by providing energy distribution with minimum cost and maximum quality. In this study; energy costs generated by re-planning in a network that distributes electricity at different voltage levels to meet the increasing energy needs were analysed. To obtain the optimum network design; a minimization function was established by determining the required transformer powers and their numbers considering the physical and electrical conditions. The generated function was analysed by using a mixed-integer programming algorithm and genetic algorithm in MATLAB.