Area Constrained Optimal Planning Model of Renewable-Rich Hybrid Microgrid

Krishna, P. V. N. Mohan; Sekhar, P. C.

doi:10.1109/access.2023.3293732

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…MOO remained a significant topic, whereas MILP gained prominence and relevance, becoming a key theme in the research environment. The expanding relevance of electric power systems and smart grids, as shown by an emphasis on renewable-rich HMGSs [53], demonstrates the trend toward integrating intelligent technologies for optimal energy distribution while balancing cost, availability, and area limits. Emerging areas like Sustainable Development Goals, electric vehicles, and wind turbines gained focus, signaling a shift toward sustainable and renewable energy solutions.…”

Section: Strategic Diagramsmentioning

confidence: 99%

Exploring Evolution and Trends: A Bibliometric Analysis and Scientific Mapping of Multiobjective Optimization Applied to Hybrid Microgrid Systems

Tahir,

Ordóñez,

Nieto

2024

Sustainability

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Hybrid energy systems (HESs) integrate renewable sources, storage, and optionally conventional energies, offering a sustainable alternative to fossil fuels. Microgrids (MGs) bolster this integration, enhancing energy management, resilience, and reliability across different 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 to 2023, analyzed using SciMAT v1.1.04 software. It segments the research into two periods, 2010–2019 and 2020–2023, revealing a surge in MOO focus, particularly in the latter period, with a 35% increase in MOO-related research. This indicates a shift toward comprehensive 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 toward 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.

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Section: Strategic Diagramsmentioning

confidence: 99%