A memory-based gravitational search algorithm for solving economic dispatch problem in micro-grid
Document Type
Article
Publication Date
6-1-2021
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. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.
Keywords
Micro-grid, Optimal economic load, Memory based Gravitational Search, Algorithm
Divisions
fac_eng
Funders
Universiti Kuala Lumpur[UniKL/CoRI/UER20003]
Publication Title
Ain Shams Engineering Journal
Volume
12
Issue
2
Publisher
Elsevier
Publisher Location
RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS