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Evaluation of solar PV-based microgrids viability utilizing single and multi-criteria decision analysis

Author

Listed:
  • Abo-Zahhad, Essam M.
  • Rashwan, Ahmed
  • Salameh, Tareq
  • Hamid, Abdul Kadir
  • Faragalla, Asmaa
  • El-Dein, Adel Z.
  • Chen, Yong
  • Abdelhameed, Esam H.

Abstract

Feasibility analysis is crucial for solar projects to minimize risk and ensure goal achievement. Essential factors include the PV system's location, elevation, land slope, energy yield, soil texture, mean annual cloud frequency map, and distance from urban areas. This study examines the performance of a 3 MW PV system across five remote Egyptian regions characterized by desert weather conditions. The influence of available weather data sources and different yield estimation methods on the feasibility of the PV system is treated as a Single-Criteria Decision Making (SCDM) scheme. Meteorological data from Meteonorm 8.1, PVGIS, and NASA-SSE satellite data are utilized as input data for the designed PVsyst and ANSYS-based energy yield estimation models and for empirical formulas. Significant variations in estimated energy yield are observed, depending on the weather data source. The study also underscores the importance of diligently evaluating and comparing the accuracy and reliability of different weather data sources and estimation methods for simulating PV-based microgrid system performance. The yield estimation-based SCDM indicates that the New Valley PV system consistently outperformed the other systems. However, a Multi-Criteria Decision Making (MCDM) indicates that the Red Sea performs well in technical criteria evaluation, while alternative Aswan City excels in practical and feasibility criteria assessment. The study employs the MCDM approach to provide a more detailed evaluation. Specifically, the technical criteria focus on energy yield, system efficiency, and reliability.

Suggested Citation

  • Abo-Zahhad, Essam M. & Rashwan, Ahmed & Salameh, Tareq & Hamid, Abdul Kadir & Faragalla, Asmaa & El-Dein, Adel Z. & Chen, Yong & Abdelhameed, Esam H., 2024. "Evaluation of solar PV-based microgrids viability utilizing single and multi-criteria decision analysis," Renewable Energy, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:renene:v:221:y:2024:i:c:s0960148123016282
    DOI: 10.1016/j.renene.2023.119713
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    2. Hosseini Dehshiri, Seyyed Jalaladdin & Amiri, Maghsoud & Mostafaeipour, Ali & Le, Ttu, 2024. "Evaluation of renewable energy projects based on sustainability goals using a hybrid pythagorean fuzzy-based decision approach," Energy, Elsevier, vol. 297(C).
    3. Anshuman Satpathy & Rahimi Bin Baharom & Naeem M. S. Hannon & Niranjan Nayak & Snehamoy Dhar, 2024. "Assessing Stability in Renewable Microgrid Using a Novel-Optimized Controller for PVBattery Based Micro Grid with Opal-RT-Based Real-Time Validation," Energies, MDPI, vol. 17(20), pages 1-23, October.
    4. Dwivedi, Pankaj Prasad & Sharma, Dilip Kumar, 2025. "Quantitative assessment of photovoltaic thermal collectors to enhance energy efficiency in the power system by SWEI MCDM method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 217(C).
    5. Muhammad Nadeem Akram & Walid Abdul-Kader, 2025. "Supporting Sustainable Development Goals with Second-Life Electric Vehicle Battery: A Case Study," Sustainability, MDPI, vol. 17(14), pages 1-36, July.
    6. Al-Dahidi, Sameer & Rinchi, Bilal & Dababseh, Raghad & Ayadi, Osama & Alrbai, Mohammad, 2024. "A geographic multi-scale machine learning framework for predicting solar irradiation on tilted surfaces," Energy, Elsevier, vol. 313(C).
    7. Fernando Venâncio Mucomole & Carlos Augusto Santos Silva & Lourenço Lázaro Magaia, 2025. "Mapping of the Literal Regressive and Geospatial–Temporal Distribution of Solar Energy on a Short-Scale Measurement in Mozambique Using Machine Learning Techniques," Energies, MDPI, vol. 18(13), pages 1-55, June.
    8. Ghaffarpour, Zahra & Fakhroleslam, Mohammad & Amidpour, Majid, 2024. "Calculation of energy consumption, tomato yield, and electricity generation in a PV-integrated greenhouse with different solar panels configuration," Renewable Energy, Elsevier, vol. 229(C).

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