Solving the Multi-Objective Optimal Power Flow Problem Using the Multi-Objective Firefly Algorithm with a Constraints-Prior Pareto-Domination Approach

• Published in: Energies (Basel) Vol. 11; no. 12; p. 3438
• Main Authors: Chen, Gonggui, Yi, Xingting, Zhang, Zhizhong, Lei, Hangtian
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2018
• Subjects: Algorithms; Communications networks; Computer engineering; Constraint modelling; constraints-prior Pareto-domination approach; Cost control; Dependent variables; Electricity distribution; Emission analysis; Generators; Genetic algorithms; Heuristic methods; Incompatibility; International conferences; Laboratories; Mathematical models; multi-objective firefly algorithm; multi-objective optimal power flow problem; Multiple objective analysis; Optimization; Optimization algorithms; Population number; Power flow; Power loss; quality indicator; Smart grid technology; Thailand; China
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en11123438

Known as a multi-objective, large-scale, and complicated optimization problem, the multi-objective optimal power flow (MOOPF) problem tends to be introduced with many constraints. In this paper, compared with the frequently-used penalty function-based method (PFA), a novel constraint processing approach named the constraints-prior Pareto-domination approach (CPA) is proposed for ensuring non-violation of various inequality constraints on dependent variables by introducing the Pareto-domination principle based on the sum of constraint violations. Moreover, for solving the constrained MOOPF problem, the multi-objective firefly algorithm with CPA (MOFA-CPA) is proposed and some optimization strategies, such as the crowding distance calculation and non-dominated sorting based on the presented CPA, are utilized to sustain well-distributed Pareto front (PF). Finally, in order to demonstrate the feasible and effective improvement of MOFA-CPA, a comparison study between MOFA-CPA and MOFA-PFA is performed on two test systems, including three bi-objective optimization cases and three tri-objective optimization cases. The simulation results demonstrate the capability of the MOFA-CPA for obtaining PF with good distribution and superiority of the proposed CPA for dealing with inequality constraints on dependent variables. In addition, some quality indicators are used to evaluate the convergence, distribution, and uniformity of the PFs found by the MOFA-CPA and MOFA-PFA.