Version 1
: Received: 5 April 2024 / Approved: 5 April 2024 / Online: 5 April 2024 (16:56:12 CEST)
How to cite:
Tahir, K.A.; Ordóñez, J.; Nieto, J. Exploring Evolution and Trends: A Bibliometric Analysis and Scientific Mapping of Multiobjective Optimization Applied to Hybrid Microgrid Systems. Preprints2024, 2024040448. https://doi.org/10.20944/preprints202404.0448.v1
Tahir, K.A.; Ordóñez, J.; Nieto, J. Exploring Evolution and Trends: A Bibliometric Analysis and Scientific Mapping of Multiobjective Optimization Applied to Hybrid Microgrid Systems. Preprints 2024, 2024040448. https://doi.org/10.20944/preprints202404.0448.v1
Tahir, K.A.; Ordóñez, J.; Nieto, J. Exploring Evolution and Trends: A Bibliometric Analysis and Scientific Mapping of Multiobjective Optimization Applied to Hybrid Microgrid Systems. Preprints2024, 2024040448. https://doi.org/10.20944/preprints202404.0448.v1
APA Style
Tahir, K.A., Ordóñez, J., & Nieto, J. (2024). Exploring Evolution and Trends: A Bibliometric Analysis and Scientific Mapping of Multiobjective Optimization Applied to Hybrid Microgrid Systems. Preprints. https://doi.org/10.20944/preprints202404.0448.v1
Chicago/Turabian Style
Tahir, K.A., Javier Ordóñez and Juanjo Nieto. 2024 "Exploring Evolution and Trends: A Bibliometric Analysis and Scientific Mapping of Multiobjective Optimization Applied to Hybrid Microgrid Systems" Preprints. https://doi.org/10.20944/preprints202404.0448.v1
Abstract
Hybrid Renewable Energy Systems (HRES) integrate renewable sources, storage, and optionally conventional energies, offering an eco-friendly solution to fossil fuels. Microgrids (MGs) bolster HRES integration, enhancing energy management, resilience, and reliability at various levels. This study, emphasizing the need for refined optimization methods, investigates three themes: renewable energy, microgrid, and multiobjective optimization (MOO), through a bibliometric analysis of 470 Scopus documents from 2010-2023, analyzed with SciMAT software. It segments the research into two periods, 2010-2019 and 2020-2023, revealing a surge in MOO focus, especially in the latter period, with a 35% increase in MOO-related research. This indicates a shift towards com-prehensive energy ecosystem management that balances environmental, technical, and economic elements. The initial focus on MOO, genetic algorithms, and energy management systems has expanded to include smart grids and electric power systems, with MOO remaining a primary theme in the second period. The increased application of Artificial Intelligence (AI) in optimizing HMGS within the MOO framework signals a move towards more sustainable, intelligent energy solutions. Despite progress, challenges remain, including high battery costs, the need for reliable MOO data, the intermittency of renewable energy sources, and HMGS network scalability issues, highlighting directions for future research.
Keywords
Renewable energy sources; Hybrid energy system; Microgrid; Multiobjective optimization; Bibliometric Analysis; SciMAT
Subject
Engineering, Energy and Fuel Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
