© 2018 JETIR December 2018, Volume 5, Issue 12 www.jetir.org (ISSN-2349-5162) JETIR1812A19 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 139 Review of Metaheuristic Optimization Methods for Unit Commitment in Power System Operation and Planning Abdulhamid Musa Training Officer Electrical and Electronic Engineering Department, Petroleum Training Institute, Effurun, Delta state, Nigeria Abstract : Unit commitment (UC) is considered one of the essential activities in power system planning and operation. The complexity and non-linearity nature of the UC problem make metaheuristic optimizations techniques more relevant for their solutions. Apart from minimizing the operational cost, the increased public awareness regarding the harmful effects of atmospheric pollutants on the environment and other environmental regulations have led researchers to focus on environmental effect as another unit commitment objective function. This paper reviews some published research papers based on metaheuristic techniques considering their single and multi-objective functions. However, the practical, technical and economic importance of the unit commitment problem is proven by the enormous amount of the available literature for attaining their solutions. Keywords : Cost of Production, Hybrid Optimization, Metaheuristic Technique, Optimization, Unit Commitment. I. INTRODUCTION Unit commitment (UC) problem is an important optimizing task for scheduling the on/off states of generating units with objective function usually of minimizing cost [1, 2]. The increased public awareness regarding the harmful effects of atmospheric pollutants on the environment, as well as the tightening of environmental regulations has led to another objective function of UC in minimizing the environmental effect. The carbon emissions produced by fossil-fueled thermal power plants need also to be minimized. It is necessary to consider these emissions as another objective [3]. A small improvement in the optimization like 0.5% would bring millions of dollars cost reduction per year for a large utility grid [4]. The unit commitment problem is considered as one of the key aspects in power system operation [5]. UC is a traditional mixed-integer non-convex problem and remains a key optimization task in power system scheduling [4]. The UC problem (UCP) consists of deciding which power generator units must be committed or decommitted in order to satisfy demand over a planning horizon. A short-term planning is generally split into periods of one hour each in [6, 7] with varying loads and generations under different generational, environmental and technical constraints [7]. The production levels at which units operate (pre-dispatch) must also be determined, and the committed units must generally satisfy the forecasted system load and reserve requirements, as well as a large set of technological constraints [8]. Therefore, UC plays a major role in the operation planning of power systems [6] including environmental concerns [3]. II. PREVIOUS RESEARCHES Since increasing the number of generating units makes it difficult to solve in practice, many approaches have been introduced to solve the UC problem [1]. For the purpose of this writeup, metaheuristic optimization approach is reviewed. The approach of dealing with UC problems include the Binary Whale Optimization Algorithm (BWOA) [9], Binary Grey Wolf Optimizer (BGWO) algorithm [10], Grey Wolf Optimization (GWO) Algorithm [11], a Quantum Inspired Binary Grey Wolf Optimizer (QI-BGWO) [12], HYBRID-BAT search algorithm 10-unit system [13], an improved version of the Binary Quantum-Inspired Gravitational Search Algorithm (BQIGSA) and proposes a new approach to solve the UC problem based on the improved BQIGSA, called QGSA-UC. [1], a Hybrid Particle Swarm Optimization Approach With Small Population size (HPSO-SP) in [2], ‘‘local branching’’ and an hybridization of Particle Swarm Optimization (PSO) with a Mixed Integer Programming Solver [8], Binary Coded Modified Moth Flame Optimization Algorithm (BMMFOA) [5], Particle Swarm Optimization and Tabu Search algorithm [14], using Lagrangian Relaxation (LR) and PSO [15], deterministic method named Cut-And-Branch [6], Combination of proposed Weighted-Improved Crazy Particle Swarm Optimization with a Pseudo Code Based Algorithm and scenario analysis method [16], Genetic Algorithm (GA) or Dynamic Programming (DP) to solve UC and then Shuffled BAT (BAT) technique as an evolutionary based approach to solve the constrained Economic Load Dispatch (ELD) problem [17], an improved real-coded genetic algorithm and an enhanced Mixed Integer Linear Programming (MILP) [18], combinations of three algorithms including Charged Search System (CSS), PSO and Ants Colony Search (ACS) [19] and hybrid metaheuristic DEEPSO, which is combination of Differential Evolution (DE), Evolutionary Programming (EP) and PSO [20] metaheuristic approach combined with a non-dominated sorting known as Biased Random Key Genetic Algorithm [3, 21].