A Comparative Study of Optimization Techniques for Minimizing Fuel Cost in the Smart Grid

Abstract

The design of a smart electric power grid is a challenge. One of the famous problems in this field is the Economic load dispatch (ELD) problem. 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. To solve this problem, we need to maximize the power network load under several operational constraints. Meanwhile, we need to minimize the cost of power generation and minimizing 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 complex problems. This thesis 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). Our developed results demonstrated that meta-heuristics search algorithms (i.e., CSA and DE) can offer the optimal set of power for each power station. These are computed power fulfill the supply needs and maintain both minimum power cost and minimum power losses in power transmission. In the future, we hope to continue to solving the power generation problem area like unit commitment problems by apply on Meta-heuristics algorithm and explores the best minimums fuel cost.