IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v243y2025ics0960148125002320.html
   My bibliography  Save this article

Impact of floating photovoltaic generation on distribution grids in rural areas of Ecuador. Case study the Esperanza

Author

Listed:
  • Valarezo Molina, Lucio A.
  • Balderramo Vélez, Ney R.
  • Cano Ortega, A.
  • Jurado, F.

Abstract

The integration of floating photovoltaic systems in electrical distribution networks enhances efficiency and sustainability in rural areas. This research analyses the impact of floating photovoltaic generation on electrical distribution systems in rural Ecuador, specifically at the La Esperanza hydroelectric dam. To achieve this, an artificial neural network model is developed to identify optimal outcomes. The objective function and constraints within the ANN model determine the optimal FPV capacity and placement to maximize active power injection at each node and minimise power losses. To simulate various scenarios and assess the effectiveness of FPV integration, a power flow model using the forward and backward sweep method was employed. The results for the electrical distribution systems indicate increased energy efficiency, improved voltage profiles, and reduced losses. Optimal FPV integration decreases grid dependency by up to 60 %, reduces losses by up to 20.2 %, and enhances the voltage profile by up to 5 %.

Suggested Citation

  • Valarezo Molina, Lucio A. & Balderramo Vélez, Ney R. & Cano Ortega, A. & Jurado, F., 2025. "Impact of floating photovoltaic generation on distribution grids in rural areas of Ecuador. Case study the Esperanza," Renewable Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:renene:v:243:y:2025:i:c:s0960148125002320
    DOI: 10.1016/j.renene.2025.122570
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125002320
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.122570?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Breban, Stefan & Saudemont, Christophe & Vieillard, Sébastien & Robyns, Benoît, 2013. "Experimental design and genetic algorithm optimization of a fuzzy-logic supervisor for embedded electrical power systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 91(C), pages 91-107.
    2. Pouran, Hamid & Padilha Campos Lopes, Mariana & Ziar, Hesan & Alves Castelo Branco, David & Sheng, Yong, 2022. "Evaluating floating photovoltaics (FPVs) potential in providing clean energy and supporting agricultural growth in Vietnam," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    3. Azim Heydari & Meysam Majidi Nezhad & Mehdi Neshat & Davide Astiaso Garcia & Farshid Keynia & Livio De Santoli & Lina Bertling Tjernberg, 2021. "A Combined Fuzzy GMDH Neural Network and Grey Wolf Optimization Application for Wind Turbine Power Production Forecasting Considering SCADA Data," Energies, MDPI, vol. 14(12), pages 1-13, June.
    4. Jinyoung Song & Yosoon Choi, 2016. "Analysis of the Potential for Use of Floating Photovoltaic Systems on Mine Pit Lakes: Case Study at the Ssangyong Open-Pit Limestone Mine in Korea," Energies, MDPI, vol. 9(2), pages 1-13, February.
    5. Hong, Ying-Yi & Rioflorido, Christian Lian Paulo P., 2019. "A hybrid deep learning-based neural network for 24-h ahead wind power forecasting," Applied Energy, Elsevier, vol. 250(C), pages 530-539.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kumar, Nitin & Pachauri, Rupendra Kumar & Kuchhal, Piyush & Nkenyereye, Lewis, 2025. "Floating photovoltaic system based electrical power generation study in Indian context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 212(C).
    2. Wei, Yujia & Khojasteh, Danial & Windt, Christian & Huang, Luofeng, 2025. "An interdisciplinary literature review of floating solar power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 209(C).
    3. Younes Zahraoui & Tarmo Korõtko & Argo Rosin & Saad Mekhilef & Mehdi Seyedmahmoudian & Alex Stojcevski & Ibrahim Alhamrouni, 2024. "AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review," Sustainability, MDPI, vol. 16(12), pages 1-35, June.
    4. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
    5. Kowsar, Abu & Hassan, Mahedi & Rana, Md Tasnim & Haque, Nawshad & Faruque, Md Hasan & Ahsan, Saifuddin & Alam, Firoz, 2023. "Optimization and techno-economic assessment of 50 MW floating solar power plant on Hakaluki marsh land in Bangladesh," Renewable Energy, Elsevier, vol. 216(C).
    6. Ma, Chao & Liu, Zhao, 2022. "Water-surface photovoltaics: Performance, utilization, and interactions with water eco-environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    7. Rômulo de Oliveira Azevêdo & Paulo Rotela Junior & Luiz Célio Souza Rocha & Gianfranco Chicco & Giancarlo Aquila & Rogério Santana Peruchi, 2020. "Identification and Analysis of Impact Factors on the Economic Feasibility of Photovoltaic Energy Investments," Sustainability, MDPI, vol. 12(17), pages 1-40, September.
    8. Maier, Rachel & Lütz, Luna & Risch, Stanley & Kullmann, Felix & Weinand, Jann & Stolten, Detlef, 2024. "Potential of floating, parking, and agri photovoltaics in Germany," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
    9. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    10. Zhao, Yincheng & Zhang, Guozhou & Hu, Weihao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2023. "Meta-learning based voltage control strategy for emergency faults of active distribution networks," Applied Energy, Elsevier, vol. 349(C).
    11. Kim, Daeyoung & Ryu, Geonhwa & Moon, Chaejoo & Kim, Bumsuk, 2024. "Accuracy of a short-term wind power forecasting model based on deep learning using LiDAR-SCADA integration: A case study of the 400-MW Anholt offshore wind farm," Applied Energy, Elsevier, vol. 373(C).
    12. Mokhinabonu Mardonova & Yosoon Choi, 2019. "Assessment of Photovoltaic Potential of Mining Sites in Uzbekistan," Sustainability, MDPI, vol. 11(10), pages 1-13, May.
    13. G. Ponkumar & S. Jayaprakash & Karthick Kanagarathinam, 2023. "Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis," Energies, MDPI, vol. 16(14), pages 1-24, July.
    14. Fachrizal Aksan & Yang Li & Vishnu Suresh & Przemysław Janik, 2023. "Multistep Forecasting of Power Flow Based on LSTM Autoencoder: A Study Case in Regional Grid Cluster Proposal," Energies, MDPI, vol. 16(13), pages 1-20, June.
    15. Choi, Yosoon & Song, Jinyoung, 2017. "Review of photovoltaic and wind power systems utilized in the mining industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1386-1391.
    16. Nebiyu Kedir & Phuong H. D. Nguyen & Citlaly Pérez & Pedro Ponce & Aminah Robinson Fayek, 2023. "Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation," Energies, MDPI, vol. 16(9), pages 1-38, April.
    17. Li, Tenghui & Yang, Jin & Ioannou, Anastasia, 2024. "Data-driven control of wind turbine under online power strategy via deep learning and reinforcement learning," Renewable Energy, Elsevier, vol. 234(C).
    18. Dong, Yunxuan & Wang, Jing & Xiao, Ling & Fu, Tonglin, 2021. "Short-term wind speed time series forecasting based on a hybrid method with multiple objective optimization for non-convex target," Energy, Elsevier, vol. 215(PB).
    19. Lin, Zi & Liu, Xiaolei, 2020. "Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network," Energy, Elsevier, vol. 201(C).
    20. Haspolat, Emre & Cicek, Doga Derman & Gokmener, Serkan & Melek, Abiddin Berhan & Deveci, Muhammet & Oguz, Elif, 2024. "Site selection of floating photovoltaic systems on hydropower reservoirs using fuzzy sine trigonometric decision-making model: Turkey as a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 206(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:243:y:2025:i:c:s0960148125002320. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.