MOPF solution methodology

• Published in: IET generation, transmission & distribution Vol. 11; no. 2; pp. 570 - 581
• Main Authors: Shaheen, Abdullah M, Farrag, Sobhy M, El-Sehiemy, Ragab A
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 26.01.2017
• Subjects: Algorithms; bi‐objective MOPF problem; convergence speed; economical objective function; Evolution; evolutionary computation; fuzzy set theory; fuzzy‐based best compromise solution; load flow; Mathematical models; Methodology; mono‐objective MOPF problem; MOPF solution methodology; multiobjective optimal power flow problem; Multiple objective analysis; mutation operator; novel MDE algorithm; novel multiobjective differential evolution solution methodology; Operators; Pareto ranking; power system economics; quad‐objective MOPF problem; search capability enhancement; search problems; Searching; selection operator; standard IEEE 118‐bus test system; standard IEEE 57‐bus power system; technical objective function; tri‐objective MOPF problem; selection operator; search capability enhancement; technical objective function; standard IEEE 57-bus power system; mutation operator; Pareto ranking; fuzzy set theory; economical objective function; convergence speed; mono-objective MOPF problem; novel MDE algorithm; power system economics; fuzzy-based best compromise solution; evolutionary computation; multiobjective optimal power flow problem; bi-objective MOPF problem; standard IEEE 118-bus test system; tri-objective MOPF problem; MOPF solution methodology; novel multiobjective differential evolution solution methodology; load flow; search problems; quad-objective MOPF problem
• ISSN: 1751-8687; 1751-8695; 1751-8695
• DOI: 10.1049/iet-gtd.2016.1379

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.