A Comparison of Energy Management System for a DC Microgrid

Luis O. Polanco Vasquez; Victor M. Ramirez; Kary Thanapalan

doi:https://doi.org/10.3390/app10031071

A Comparison of Energy Management System for a DC Microgrid

Luis O. Polanco Vasquez, Victor M. Ramirez, Kary Thanapalan

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Abstract

This paper investigates the analysis of the energy management system for a DC microgrid. The microgrid consists of a photovoltaic panel and a batteries system that is connected to the microgrid through a bidirectional power converter. The optimization problem is solved by the hybrid internal point method with the genetic algorithms method and particle swarm optimization (PSO) method, by considering forecasting demand and generation for all the elements of the microgrid. The analysis includes a comparison of energy optimization of the microgrid for solar radiation data from two areas of the world and a comparison the efficiency and effectiveness of optimization methods. The efficiency of the algorithm for energy optimization is verified and analyzed through experimental data. The results obtained show that the optimization algorithm can intelligently handle the energy flows to store the largest amount in the batteries and thus have the least amount of charge and discharge cycles for the battery and prolong the useful life.

Original language	English
Article number	1071
Pages (from-to)	1071 - 1084
Number of pages	14
Journal	Applied Sciences
Volume	10
Issue number	3
DOIs	https://doi.org/10.3390/app10031071
Publication status	Published - 5 Feb 2020

Keywords

microgrid
genetic algorithms
energy management system

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

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title = "A Comparison of Energy Management System for a DC Microgrid",

abstract = "This paper investigates the analysis of the energy management system for a DC microgrid. The microgrid consists of a photovoltaic panel and a batteries system that is connected to the microgrid through a bidirectional power converter. The optimization problem is solved by the hybrid internal point method with the genetic algorithms method and particle swarm optimization (PSO) method, by considering forecasting demand and generation for all the elements of the microgrid. The analysis includes a comparison of energy optimization of the microgrid for solar radiation data from two areas of the world and a comparison the efficiency and effectiveness of optimization methods. The efficiency of the algorithm for energy optimization is verified and analyzed through experimental data. The results obtained show that the optimization algorithm can intelligently handle the energy flows to store the largest amount in the batteries and thus have the least amount of charge and discharge cycles for the battery and prolong the useful life. ",

keywords = "microgrid, genetic algorithms, energy management system",

author = "{Polanco Vasquez}, {Luis O.} and Ramirez, {Victor M.} and Kary Thanapalan",

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AU - Polanco Vasquez, Luis O.

AU - Ramirez, Victor M.

AU - Thanapalan, Kary

N1 - Funding Information: The research of authors has been supported by CONACYT-Mexico under the Project 2015-01-786 (Problemas Nacionales). Publisher Copyright: © 2020 by the authors. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/2/5

Y1 - 2020/2/5

N2 - This paper investigates the analysis of the energy management system for a DC microgrid. The microgrid consists of a photovoltaic panel and a batteries system that is connected to the microgrid through a bidirectional power converter. The optimization problem is solved by the hybrid internal point method with the genetic algorithms method and particle swarm optimization (PSO) method, by considering forecasting demand and generation for all the elements of the microgrid. The analysis includes a comparison of energy optimization of the microgrid for solar radiation data from two areas of the world and a comparison the efficiency and effectiveness of optimization methods. The efficiency of the algorithm for energy optimization is verified and analyzed through experimental data. The results obtained show that the optimization algorithm can intelligently handle the energy flows to store the largest amount in the batteries and thus have the least amount of charge and discharge cycles for the battery and prolong the useful life.

AB - This paper investigates the analysis of the energy management system for a DC microgrid. The microgrid consists of a photovoltaic panel and a batteries system that is connected to the microgrid through a bidirectional power converter. The optimization problem is solved by the hybrid internal point method with the genetic algorithms method and particle swarm optimization (PSO) method, by considering forecasting demand and generation for all the elements of the microgrid. The analysis includes a comparison of energy optimization of the microgrid for solar radiation data from two areas of the world and a comparison the efficiency and effectiveness of optimization methods. The efficiency of the algorithm for energy optimization is verified and analyzed through experimental data. The results obtained show that the optimization algorithm can intelligently handle the energy flows to store the largest amount in the batteries and thus have the least amount of charge and discharge cycles for the battery and prolong the useful life.

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KW - genetic algorithms

KW - energy management system

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ER -

A Comparison of Energy Management System for a DC Microgrid

Abstract

Keywords

UN SDGs

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