A memory-based gravitational search algorithm for solving economic dispatch problem in micro-grid

Abstract

In recent years, the integration of renewable generation into micro-grid has been growing. Therefore, it is essential to optimize the power generation from multiple sources with minimal cost. This paper presents a Memory-Based Gravitational Search Algorithm (MBGSA) for solving the economic load dispatch in a micro-grid. The problem with current metaheuristic optimization techniques and the conventional gravitational search algorithm (GSA) are largely associated with slow gathering rate, less memory to save the best agent position of the optimal solution and poor performance in solving the complex optimization problems. The MBGSA is based on the concept of saving the best solution of the agent from the last iteration to calculate the new agent based on Newton's laws of gravitation. In this work, the MBGSA has been utilized to optimize power generation from multiple generation sources such as Photovoltaic (PV) systems, combined heat power (CHP) systems, and diesel generators. The results have been compared to classic methods such as Quadratic Programming (QP) and other metaheuristics techniques such as the GSA, Artificial Bee Colony (ABC), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The results illustrate that the proposed method has higher performance in solving the optimal power generation problem compared to other methods.