IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i11p2676-d1661687.html
   My bibliography  Save this article

Smart Technologies Applied in Microgrids of Renewable Energy Sources: A Systematic Review

Author

Listed:
  • Toni Alex Reis Borges

    (Departamento de Computação, Instituto Federal da Bahia, Feira de Santana 44096-486, Brazil
    Núcleo de Pesquisa Aplicada e Inovação—NPAI, Salvador 41741-020, Brazil)

  • Filipe Cardoso Brito

    (Núcleo de Pesquisa Aplicada e Inovação—NPAI, Salvador 41741-020, Brazil
    Departamento Stricto Sensu, Universidade SENAI CIMATEC, Salvador 41650-010, Brazil)

  • Rafael Guimarães Oliveira dos Santos

    (Núcleo de Pesquisa Aplicada e Inovação—NPAI, Salvador 41741-020, Brazil
    Departamento Stricto Sensu, Universidade SENAI CIMATEC, Salvador 41650-010, Brazil)

  • Paulo de Tarso Nascimento

    (Departamento Stricto Sensu, Universidade SENAI CIMATEC, Salvador 41650-010, Brazil)

  • Celso Barreto da Silva

    (Núcleo de Pesquisa Aplicada e Inovação—NPAI, Salvador 41741-020, Brazil
    Departamento Stricto Sensu, Universidade SENAI CIMATEC, Salvador 41650-010, Brazil)

  • Roberta Mota Panizio

    (Departamento de Tecnologias, Instituto Politécnico de Portalegre—IPP, 7300-110 Portalegre, Portugal
    VALORIZA—Research Centre for Endogenous Resources Valorisation, 7300-110 Portalegre, Portugal)

  • Hugo Saba

    (Núcleo de Pesquisa Aplicada e Inovação—NPAI, Salvador 41741-020, Brazil
    Departamento de Ciências Exatas e da Terra, Universidade do Estado da Bahia—UNEB, Salvador 41180-045, Brazil)

  • Aloísio Santos Nascimento Filho

    (Núcleo de Pesquisa Aplicada e Inovação—NPAI, Salvador 41741-020, Brazil
    Departamento Stricto Sensu, Universidade SENAI CIMATEC, Salvador 41650-010, Brazil)

Abstract

The production of electricity from renewable sources has become a global necessity due to concerns about reducing carbon dioxide (CO 2 ) emissions into Earth’s atmosphere. The use of microgrids has emerged as a potential scenario in this production process, especially with the application of smart technologies integrated into decision making. This article used the PRISMA method to identify the intelligent techniques employed in the context of microgrids from 2015 to 2023, totaling 156 articles. The results indicate progress in the use of artificial neural networks and multi-agent systems in environments of, respectively, predictability and management, and open space for discussions involving the incorporation of new techniques aimed for future implementations.

Suggested Citation

  • Toni Alex Reis Borges & Filipe Cardoso Brito & Rafael Guimarães Oliveira dos Santos & Paulo de Tarso Nascimento & Celso Barreto da Silva & Roberta Mota Panizio & Hugo Saba & Aloísio Santos Nascimento , 2025. "Smart Technologies Applied in Microgrids of Renewable Energy Sources: A Systematic Review," Energies, MDPI, vol. 18(11), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:11:p:2676-:d:1661687
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/11/2676/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/11/2676/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yan, Xingyu & Abbes, Dhaker & Francois, Bruno, 2017. "Uncertainty analysis for day ahead power reserve quantification in an urban microgrid including PV generators," Renewable Energy, Elsevier, vol. 106(C), pages 288-297.
    2. Morsali, Roozbeh & Thirunavukkarasu, Gokul Sidarth & Seyedmahmoudian, Mehdi & Stojcevski, Alex & Kowalczyk, Ryszard, 2020. "A relaxed constrained decentralised demand side management system of a community-based residential microgrid with realistic appliance models," Applied Energy, Elsevier, vol. 277(C).
    3. Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 318(C).
    4. Hua, Weiqi & Jiang, Jing & Sun, Hongjian & Tonello, Andrea M. & Qadrdan, Meysam & Wu, Jianzhong, 2022. "Data-driven prosumer-centric energy scheduling using convolutional neural networks," Applied Energy, Elsevier, vol. 308(C).
    5. Troy Malatesta & Jessica K. Breadsell, 2022. "Identifying Home System of Practices for Energy Use with K-Means Clustering Techniques," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    6. Christos-Spyridon Karavas & Konstantinos Arvanitis & George Papadakis, 2017. "A Game Theory Approach to Multi-Agent Decentralized Energy Management of Autonomous Polygeneration Microgrids," Energies, MDPI, vol. 10(11), pages 1-22, November.
    7. Filipe Martins Rodrigues & Carlos Cardeira & João M. F. Calado & Rui Melicio, 2022. "Home Energy Forecast Performance Tool for Smart Living Services Suppliers under an Energy 4.0 and CPS Framework," Energies, MDPI, vol. 15(3), pages 1-22, January.
    8. Kapil Deshpande & Philipp Möhl & Alexander Hämmerle & Georg Weichhart & Helmut Zörrer & Andreas Pichler, 2022. "Energy Management Simulation with Multi-Agent Reinforcement Learning: An Approach to Achieve Reliability and Resilience," Energies, MDPI, vol. 15(19), pages 1-35, October.
    9. Madler, Jochen & Harding, Sebastian & Weibelzahl, Martin, 2023. "A multi-agent model of urban microgrids: Assessing the effects of energy-market shocks using real-world data," Applied Energy, Elsevier, vol. 343(C).
    10. Jaber Alshehri & Muhammad Khalid & Ahmed Alzahrani, 2019. "An Intelligent Battery Energy Storage-Based Controller for Power Quality Improvement in Microgrids," Energies, MDPI, vol. 12(11), pages 1-21, June.
    11. Ahmed Maged Abdelhamid & Nahla E. Zakzouk & Samah El Safty, 2022. "A Multi-Agent Approach for Self-Healing and RES-Penetration in Smart Distribution Networks," Mathematics, MDPI, vol. 10(13), pages 1-26, June.
    12. Ogliari, Emanuele & Dolara, Alberto & Manzolini, Giampaolo & Leva, Sonia, 2017. "Physical and hybrid methods comparison for the day ahead PV output power forecast," Renewable Energy, Elsevier, vol. 113(C), pages 11-21.
    13. Rostislav Krč & Martina Kratochvílová & Jan Podroužek & Tomáš Apeltauer & Václav Stupka & Tomáš Pitner, 2021. "Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
    14. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    15. Khaizaran Abdulhussein Al Sumarmad & Nasri Sulaiman & Noor Izzri Abdul Wahab & Hashim Hizam, 2022. "Energy Management and Voltage Control in Microgrids Using Artificial Neural Networks, PID, and Fuzzy Logic Controllers," Energies, MDPI, vol. 15(1), pages 1-22, January.
    16. Carlos M. Paredes & Andrés F. Bayona & Diego Martínez & Alfons Crespo & Apolinar González & José Simo, 2021. "Approach to an Emulation Model to Evaluate the Behavior and Impact of Microgrids in Isolated Communities," Energies, MDPI, vol. 14(17), pages 1-34, August.
    17. Xiong, Linyun & Li, Penghan & Wang, Ziqiang & Wang, Jie, 2020. "Multi-agent based multi objective renewable energy management for diversified community power consumers," Applied Energy, Elsevier, vol. 259(C).
    18. Rosero, D.G. & Díaz, N.L. & Trujillo, C.L., 2021. "Cloud and machine learning experiments applied to the energy management in a microgrid cluster," Applied Energy, Elsevier, vol. 304(C).
    19. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    20. Yang, Jun & Su, Changqi, 2021. "Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty," Energy, Elsevier, vol. 223(C).
    21. Mansouri, Seyed Amir & Rezaee Jordehi, Ahmad & Marzband, Mousa & Tostado-Véliz, Marcos & Jurado, Francisco & Aguado, José A., 2023. "An IoT-enabled hierarchical decentralized framework for multi-energy microgrids market management in the presence of smart prosumers using a deep learning-based forecaster," Applied Energy, Elsevier, vol. 333(C).
    22. Aiman J. Albarakati & Younes Boujoudar & Mohamed Azeroual & Reda Jabeur & Ayman Aljarbouh & Hassan El Moussaoui & Tijani Lamhamdi & Najat Ouaaline, 2021. "Real-Time Energy Management for DC Microgrids Using Artificial Intelligence," Energies, MDPI, vol. 14(17), pages 1-16, August.
    23. Tan, Bifei & Lin, Zhenjia & Zheng, Xiaodong & Xiao, Fu & Wu, Qiuwei & Yan, Jinyue, 2023. "Distributionally robust energy management for multi-microgrids with grid-interactive EVs considering the multi-period coupling effect of user behaviors," Applied Energy, Elsevier, vol. 350(C).
    24. Alberto Dolara & Francesco Grimaccia & Sonia Leva & Marco Mussetta & Emanuele Ogliari, 2015. "A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output," Energies, MDPI, vol. 8(2), pages 1-16, February.
    25. Chreim, Bashar & Esseghir, Moez & Merghem-Boulahia, Leila, 2022. "LOSISH—LOad Scheduling In Smart Homes based on demand response: Application to smart grids," Applied Energy, Elsevier, vol. 323(C).
    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. Sarmas, Elissaios & Spiliotis, Evangelos & Stamatopoulos, Efstathios & Marinakis, Vangelis & Doukas, Haris, 2023. "Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent Long Short-Term Memory models," Renewable Energy, Elsevier, vol. 216(C).
    2. Sana Qaiyum & Martin Margala & Pravin R. Kshirsagar & Prasun Chakrabarti & Kashif Irshad, 2023. "Energy Performance Analysis of Photovoltaic Integrated with Microgrid Data Analysis Using Deep Learning Feature Selection and Classification Techniques," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    3. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
    4. Khaizaran Abdulhussein Al Sumarmad & Nasri Sulaiman & Noor Izzri Abdul Wahab & Hashim Hizam, 2022. "Microgrid Energy Management System Based on Fuzzy Logic and Monitoring Platform for Data Analysis," Energies, MDPI, vol. 15(11), pages 1-19, June.
    5. Limouni, Tariq & Yaagoubi, Reda & Bouziane, Khalid & Guissi, Khalid & Baali, El Houssain, 2023. "Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model," Renewable Energy, Elsevier, vol. 205(C), pages 1010-1024.
    6. Hassan, Muhammed A. & Bailek, Nadjem & Bouchouicha, Kada & Nwokolo, Samuel Chukwujindu, 2021. "Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks," Renewable Energy, Elsevier, vol. 171(C), pages 191-209.
    7. Trinadh Pamulapati & Muhammed Cavus & Ishioma Odigwe & Adib Allahham & Sara Walker & Damian Giaouris, 2022. "A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective," Energies, MDPI, vol. 16(1), pages 1-34, December.
    8. Brester, Christina & Kallio-Myers, Viivi & Lindfors, Anders V. & Kolehmainen, Mikko & Niska, Harri, 2023. "Evaluating neural network models in site-specific solar PV forecasting using numerical weather prediction data and weather observations," Renewable Energy, Elsevier, vol. 207(C), pages 266-274.
    9. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    10. Noman Khan & Fath U Min Ullah & Ijaz Ul Haq & Samee Ullah Khan & Mi Young Lee & Sung Wook Baik, 2021. "AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting," Mathematics, MDPI, vol. 9(19), pages 1-18, October.
    11. Emanuele Ogliari & Alessandro Niccolai & Sonia Leva & Riccardo E. Zich, 2018. "Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed," Energies, MDPI, vol. 11(6), pages 1-16, June.
    12. Romain Mannini & Julien Eynard & Stéphane Grieu, 2022. "A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-37, September.
    13. Karol Bot & Samira Santos & Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano, 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems," Energies, MDPI, vol. 14(22), pages 1-37, November.
    14. Inês F. G. Reis & Ivo Gonçalves & Marta A. R. Lopes & Carlos Henggeler Antunes, 2021. "Assessing the Influence of Different Goals in Energy Communities’ Self-Sufficiency—An Optimized Multiagent Approach," Energies, MDPI, vol. 14(4), pages 1-32, February.
    15. Zhang, Pan & Mansouri, Seyed Amir & Rezaee Jordehi, Ahmad & Tostado-Véliz, Marcos & Alharthi, Yahya Z. & Safaraliev, Murodbek, 2024. "An ADMM-enabled robust optimization framework for self-healing scheduling of smart grids integrated with smart prosumers," Applied Energy, Elsevier, vol. 363(C).
    16. Erdal Irmak & Ersan Kabalci & Yasin Kabalci, 2023. "Digital Transformation of Microgrids: A Review of Design, Operation, Optimization, and Cybersecurity," Energies, MDPI, vol. 16(12), pages 1-58, June.
    17. Erdener, Burcin Cakir & Feng, Cong & Doubleday, Kate & Florita, Anthony & Hodge, Bri-Mathias, 2022. "A review of behind-the-meter solar forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    18. Arafat, M.Y. & Hossain, M.J. & Alam, Md Morshed, 2024. "Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    19. Emrani-Rahaghi, Pouria & Hashemi-Dezaki, Hamed & Ketabi, Abbas, 2023. "Efficient voltage control of low voltage distribution networks using integrated optimized energy management of networked residential multi-energy microgrids," Applied Energy, Elsevier, vol. 349(C).
    20. Federica Cucchiella & Idiano D’Adamo & Paolo Rosa, 2015. "Industrial Photovoltaic Systems: An Economic Analysis in Non-Subsidized Electricity Markets," Energies, MDPI, vol. 8(11), pages 1-16, November.

    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:gam:jeners:v:18:y:2025:i:11:p:2676-:d:1661687. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.