Meta-heuristic Search Algorithms for Solving the Economic Load Dispatch Problem

Abstract

Economic load dispatch (ELD) is a challenge optimization problem to minimize the total cost of the thermally generated power that satisfies a set of equality and inequality constraints. We need to maximize the power network load under several operational constraints to solve this problem. Meanwhile, we need to minimize the cost of power generation and minimize the loss in the network transmission. Traditional optimization methods were used to solve such problems as linear programming. Meta-heuristic search algorithms have shown encouraging performance in solving various real-life engineering problems. This paper attempts to provide a comprehensive comparison between nine meta-heuristic search algorithms, including Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Crow Search Algorithm (CSA), Differential Evolution (DE), Salp Swarm Algorithm (SSA), Harmony Search (HS), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), and Moth-Flame Optimization Algorithm (MFO) for solving the economic load dispatch problem. Our developed results demonstrated that meta-heuristics search algorithms (i.e., CSA and DE) offer the optimal power set for each power station. These computed power fulfill the supply needs and maintain both minimum power costs and power losses in power transmission.