Optimal Control and Optimization of Grid-Connected PV and Wind Turbine Hybrid Systems Using Electric Eel Foraging Optimization Algorithms Article Swipe
Saad A. Mohamed Abdelwahab
,
Ali M. El‐Rifaie
,
Hossam Youssef Hegazy
,
Mohamed A. Tolba
,
Wael I. Mohamed
,
Moayed Mohamed
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24072354
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24072354
This paper presents a comprehensive exploration of a hybrid energy system that integrates wind turbines with photovoltaics (PVs) to address the intermittent nature of electricity production from these sources. The necessity for such technology arises from the sporadic nature of electricity generated by PV cells and wind turbines. The envisioned outcome is an emissions-free, more efficient alternative to traditional energy sources. A variety of optimization techniques are utilized, specifically the Particle Swarm Optimization (PSO) algorithm and Electric Eel Foraging Optimization (EEFO), to achieve optimal power regulation and seamless integration with the public grid, as well as to mitigate anticipated loading issues. The employed mathematical modeling and simulation techniques are used to assess the effectiveness of EEFO in optimizing the operation of grid-connected PV and wind turbine hybrid systems. In this paper, the optimization methods applied to the system’s architecture are described in detail, providing a clear understanding of the intricate nature of the approach. The efficacy of these optimization strategies is rigorously evaluated through simulations of diverse operating scenarios using MATLAB/SIMULINK. The results demonstrate that the proposed optimization strategies are not only capable of precisely and swiftly compensating for linked loads, but also effectively controlling the energy supply to maintain the load’s power at the desired level. The findings underscore the potential of this hybrid energy system to offer a sustainable and reliable solution for meeting power demands, contributing to the advancement of clean and efficient energy technologies. The results demonstrate the capability of the proposed approach to improve system performance, maximize energy yield, and enhance grid integration, thereby contributing to the advancement of renewable energy technologies and sustainable energy systems.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s24072354
- https://www.mdpi.com/1424-8220/24/7/2354/pdf?version=1712570419
- OA Status
- gold
- Cited By
- 21
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394577552
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394577552Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s24072354Digital Object Identifier
- Title
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Optimal Control and Optimization of Grid-Connected PV and Wind Turbine Hybrid Systems Using Electric Eel Foraging Optimization AlgorithmsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-07Full publication date if available
- Authors
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Saad A. Mohamed Abdelwahab, Ali M. El‐Rifaie, Hossam Youssef Hegazy, Mohamed A. Tolba, Wael I. Mohamed, Moayed MohamedList of authors in order
- Landing page
-
https://doi.org/10.3390/s24072354Publisher landing page
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https://www.mdpi.com/1424-8220/24/7/2354/pdf?version=1712570419Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1424-8220/24/7/2354/pdf?version=1712570419Direct OA link when available
- Concepts
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Photovoltaic system, Wind power, Grid, Particle swarm optimization, Computer science, Hybrid system, Turbine, Control engineering, Engineering, Automotive engineering, Electrical engineering, Algorithm, Mathematics, Machine learning, Mechanical engineering, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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21Total citation count in OpenAlex
- Citations by year (recent)
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2025: 15, 2024: 6Per-year citation counts (last 5 years)
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30Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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