Optimal Management In Island Microgrids Using D-FACTS Devices And Electric Vehicles: LSTPA Approach

Amidst the increasing complexity of optimization problems, characterized by a surge in decision variables and intricate non-linear relationships, the demand for highly efficient algorithms has become imperative. In response to this challenge, this paper introduces a cutting-edge algorithm and a tail...

Full description

Saved in:
Bibliographic Details
Published inIEEE access p. 1
Main Authors Khademi, Mohamad Mehdi, Moghaddam, Mahmoud Samiei, Davarzani, Reza, Azarfar, Azita, Hoseini, Mohamad Mehdi
Format Journal Article
LanguageEnglish
Published IEEE 2023
Subjects
Batteries
Costs
Electric vehicles
Evolutionary computation
evolutionary method
Microgrid
Microgrids
Renewable energy sources
renewable resources
Resilience
Stochastic processes
Online AccessGet full text
ISSN2169-3536
2169-3536
DOI10.1109/ACCESS.2023.3332516

Cover

More Information
Summary:Amidst the increasing complexity of optimization problems, characterized by a surge in decision variables and intricate non-linear relationships, the demand for highly efficient algorithms has become imperative. In response to this challenge, this paper introduces a cutting-edge algorithm and a tailored Mixed Integer Nonlinear Programming (MINLP)-based model, specifically designed for the optimal operation of microgrids. The primary objective of this model is to minimize a multi-objective function, optimizing the charging and discharging schedules of electric vehicles (EVs) and energy storage systems (ESSs), while simultaneously incorporating the implementation of Distributed Flexible AC Transmission System (D-FACTS) devices. To tackle this formidable task, we propose a novel approach built upon the foundation of the Large-Scale Two-Population Algorithm (LSTPA). This algorithm has proven its exceptional prowess in resolving intricate optimization problems, making it particularly adept at handling large-scale scenarios. In our study, we subject the proposed algorithm and model to comprehensive analysis, utilizing a 33-node microgrid across various test cases. Through rigorous evaluation, we showcase its remarkable performance, highlighting the superior outcomes achieved in microgrid operations when compared to existing methodologies. This innovative solution holds significant promise for advancing the efficiency and effectiveness of microgrid management in the face of increasing complexities.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3332516