Open AccessMOPF solution methodology
Author(s) -
Shaheen Abdullah M.,
Farrag Sobhy M.,
ElSehiemy Ragab A.
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.1379
Subject(s) - mathematical optimization , electric power system , power flow , computer science , differential evolution , pareto principle , operator (biology) , evolutionary algorithm , convergence (economics) , fuzzy logic , power (physics) , mathematics , artificial intelligence , biochemistry , physics , chemistry , repressor , quantum mechanics , transcription factor , economics , gene , economic growth
This study investigates a novel multi‐objective differential evolution (MDE) solution methodology for multi‐objective optimal power flow (MOPF) problem. The MOPF problem is modelled with various technical and economical objective functions. These objectives are handled as mono, bi, tri, and quad‐objective MOPF problems. For solving these MOPF formulations, a novel MDE algorithm is proposed. The novel MDE algorithm modifies the DE variant (DE/best/1) with Pareto ranking in the selection operator and develops a fuzzy‐based best compromise solution for each generation to feed the mutation operator. This modification guarantees high convergence speed and enhances the search capability via exploring the neighbourhood of the best compromise solution in successive generations. The standard IEEE 57‐bus power system is emulated to prove the effectiveness and competence solutions of the mono, bi, tri, and quad‐objective MOPF at acceptable techno‐economic benefits compared with other evolutionary methods. Similarly, the standard IEEE 118‐bus test system is used to show the effectiveness of the proposed algorithm for solving the OPF problem in a large‐scale power system.