Review on population-based metaheuristic search techniques for optimal power flow

Muhammad Affiq Abd Rahman (Universiti Kuala Lumpur British Malaysian Institute)
Bazilah Ismail (Universiti Kuala Lumpur British Malaysian Institute)
Kanendra Naidu (Universiti Kuala Lumpur British Malaysian Institute)
Mohd Khairil Rahmat (Universiti Kuala Lumpur British Malaysian Institute)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 15, No 1: July 2019

Optimal power flow (OPF) is a non-linear solution which is significantly important in order to analyze the power system operation. The use of optimization algorithm is essential in order to solve OPF problems. The emergence of machine learning presents further techniques which capable to solve the non-linear problem. The performance and the key aspects which enhances the effectiveness of these optimization techniques are compared within several metaheuristic search techniques. This includes the operation of particle swarm optimization (PSO) algorithm, firefly algorithm (FA), artificial bee colony (ABC) algorithm, ant colony optimization (ACO) algorithm and differential evolution (DE) algorithm. This paper reviews on the key elements that need to be considered when selecting metaheuristic techniques to solve OPF problem in power system operation.