IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p1786-d1065011.html
   My bibliography  Save this article

A Comprehensive Review: Study of Artificial Intelligence Optimization Technique Applications in a Hybrid Microgrid at Times of Fault Outbreaks

Author

Listed:
  • Musawenkosi Lethumcebo Thanduxolo Zulu

    (Discipline of Electrical, Electronic and Computer Engineering, University of KwaZulu-Natal, Durban 4001, KwaZulu-Natal, South Africa)

  • Rudiren Pillay Carpanen

    (Discipline of Electrical, Electronic and Computer Engineering, University of KwaZulu-Natal, Durban 4001, KwaZulu-Natal, South Africa)

  • Remy Tiako

    (Discipline of Electrical, Electronic and Computer Engineering, University of KwaZulu-Natal, Durban 4001, KwaZulu-Natal, South Africa)

Abstract

The use of fossil-fueled power stations to generate electricity has had a damaging effect over the years, necessitating the need for alternative energy sources. Microgrids consisting of renewable energy source concepts have gained a lot of consideration in recent years as an alternative because they use advances in information and communication technology (ICT) to increase the quality and efficiency of services and distributed energy resources (DERs), which are environmentally friendly. Nevertheless, microgrids are constrained by the outbreaks of faults, which have an impact on their performance and necessitate dynamic energy management and optimization strategies. The application of artificial intelligence (AI) is gaining momentum as a vital key at this point. This study focuses on a comprehensive review of applications of artificial intelligence strategies on hybrid renewable microgrids for optimization, power quality enhancement, and analyses of fault outbreaks in microgrids. The use of techniques such as machine learning (ML), genetic algorithms (GA), artificial neural networks (ANN), fuzzy logic (FL), particle swarm optimization (PSO), heuristic optimization, artificial bee colony (ABC), and others is reviewed for various microgrid strategies such as regression and classification in this study. Applications of AI in microgrids are reviewed together with their benefits, drawbacks, and prospects for the future. The coordination and maximum penetration of renewable energy, solar PV, and wind in a hybrid microgrid under fault outbreaks are furthermore reviewed.

Suggested Citation

  • Musawenkosi Lethumcebo Thanduxolo Zulu & Rudiren Pillay Carpanen & Remy Tiako, 2023. "A Comprehensive Review: Study of Artificial Intelligence Optimization Technique Applications in a Hybrid Microgrid at Times of Fault Outbreaks," Energies, MDPI, vol. 16(4), pages 1-32, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1786-:d:1065011
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/1786/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/4/1786/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhaoxuan Li & SM Mahbobur Rahman & Rolando Vega & Bing Dong, 2016. "A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting," Energies, MDPI, vol. 9(1), pages 1-12, January.
    2. Kalogirou, Soteris A., 2004. "Optimization of solar systems using artificial neural-networks and genetic algorithms," Applied Energy, Elsevier, vol. 77(4), pages 383-405, April.
    3. Emami, Alireza & Noghreh, Pirooz, 2010. "New approach on optimization in placement of wind turbines within wind farm by genetic algorithms," Renewable Energy, Elsevier, vol. 35(7), pages 1559-1564.
    4. Mohamed Faroug, M. Alfaki & Rao, Dasari Narasimha & Samikannu, Ravi & Venkatachary, Sampath Kumar & Senthilnathan, Karthikrajan, 2020. "Comparative analysis of controllers for stability enhancement for wind energy system with STATCOM in the grid connected environment," Renewable Energy, Elsevier, vol. 162(C), pages 2408-2442.
    5. Lu, Ning & Qin, Jun & Yang, Kun & Sun, Jiulin, 2011. "A simple and efficient algorithm to estimate daily global solar radiation from geostationary satellite data," Energy, Elsevier, vol. 36(5), pages 3179-3188.
    6. Li, Weili & Su, Ying & Li, Dong & Li, Yong & Hu, Lei & Wang, Purui, 2019. "Multi-physical fields of rotor windings with axial-radial ventilation system for 1100 MW nuclear half-speed turbine generator," Energy, Elsevier, vol. 188(C).
    7. Dagar, Annu & Gupta, Pankaj & Niranjan, Vandana, 2021. "Microgrid protection: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    8. Md Mainul Islam & Mahmood Nagrial & Jamal Rizk & Ali Hellany, 2021. "General Aspects, Islanding Detection, and Energy Management in Microgrids: A Review," Sustainability, MDPI, vol. 13(16), pages 1-45, August.
    9. Lagorse, Jeremy & Paire, Damien & Miraoui, Abdellatif, 2009. "Sizing optimization of a stand-alone street lighting system powered by a hybrid system using fuel cell, PV and battery," Renewable Energy, Elsevier, vol. 34(3), pages 683-691.
    10. Linares-Rodriguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2013. "An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images," Energy, Elsevier, vol. 61(C), pages 636-645.
    11. Ould Bilal, B. & Sambou, V. & Ndiaye, P.A. & Kébé, C.M.F. & Ndongo, M., 2010. "Optimal design of a hybrid solar–wind-battery system using the minimization of the annualized cost system and the minimization of the loss of power supply probability (LPSP)," Renewable Energy, Elsevier, vol. 35(10), pages 2388-2390.
    12. Kalantar, M. & Mousavi G., S.M., 2010. "Dynamic behavior of a stand-alone hybrid power generation system of wind turbine, microturbine, solar array and battery storage," Applied Energy, Elsevier, vol. 87(10), pages 3051-3064, October.
    13. Jinjiang Wang & Lunkuan Ye & Robert X. Gao & Chen Li & Laibin Zhang, 2019. "Digital Twin for rotating machinery fault diagnosis in smart manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3920-3934, June.
    14. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
    15. Yang, Hongxing & Wei, Zhou & Chengzhi, Lou, 2009. "Optimal design and techno-economic analysis of a hybrid solar-wind power generation system," Applied Energy, Elsevier, vol. 86(2), pages 163-169, February.
    16. Llaria, Alvaro & Curea, Octavian & Jiménez, Jaime & Camblong, Haritza, 2011. "Survey on microgrids: Unplanned islanding and related inverter control techniques," Renewable Energy, Elsevier, vol. 36(8), pages 2052-2061.
    17. Yu, Dongmin & Zhu, Haoming & Han, Wenqi & Holburn, Daniel, 2019. "Dynamic multi agent-based management and load frequency control of PV/Fuel cell/ wind turbine/ CHP in autonomous microgrid system," Energy, Elsevier, vol. 173(C), pages 554-568.
    18. Xia, Lei & Ma, Zhenjun & Kokogiannakis, Georgios & Wang, Shugang & Gong, Xuemei, 2018. "A model-based optimal control strategy for ground source heat pump systems with integrated solar photovoltaic thermal collectors," Applied Energy, Elsevier, vol. 228(C), pages 1399-1412.
    19. Shi, Zhongtuo & Yao, Wei & Li, Zhouping & Zeng, Lingkang & Zhao, Yifan & Zhang, Runfeng & Tang, Yong & Wen, Jinyu, 2020. "Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions," Applied Energy, Elsevier, vol. 278(C).
    20. Leva, S. & Dolara, A. & Grimaccia, F. & Mussetta, M. & Ogliari, E., 2017. "Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 88-100.
    21. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    22. Dettori, S. & Iannino, V. & Colla, V. & Signorini, A., 2018. "An adaptive Fuzzy logic-based approach to PID control of steam turbines in solar applications," Applied Energy, Elsevier, vol. 227(C), pages 655-664.
    23. Grady, S.A. & Hussaini, M.Y. & Abdullah, M.M., 2005. "Placement of wind turbines using genetic algorithms," Renewable Energy, Elsevier, vol. 30(2), pages 259-270.
    24. Li, Xue & Li, Wenming & Zhang, Rufeng & Jiang, Tao & Chen, Houhe & Li, Guoqing, 2020. "Collaborative scheduling and flexibility assessment of integrated electricity and district heating systems utilizing thermal inertia of district heating network and aggregated buildings," Applied Energy, Elsevier, vol. 258(C).
    25. Koohi-Kamali, Sam & Tyagi, V.V. & Rahim, N.A. & Panwar, N.L. & Mokhlis, H., 2013. "Emergence of energy storage technologies as the solution for reliable operation of smart power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 135-165.
    26. Chine, W. & Mellit, A. & Lughi, V. & Malek, A. & Sulligoi, G. & Massi Pavan, A., 2016. "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, Elsevier, vol. 90(C), pages 501-512.
    27. Vaz, A.G.R. & Elsinga, B. & van Sark, W.G.J.H.M. & Brito, M.C., 2016. "An artificial neural network to assess the impact of neighbouring photovoltaic systems in power forecasting in Utrecht, the Netherlands," Renewable Energy, Elsevier, vol. 85(C), pages 631-641.
    28. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    29. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2009. "Performance prediction of a direct expansion solar assisted heat pump using artificial neural networks," Applied Energy, Elsevier, vol. 86(9), pages 1442-1449, September.
    30. Xia, Lei & Ma, Zhenjun & Kokogiannakis, Georgios & Wang, Zhihua & Wang, Shugang, 2018. "A model-based design optimization strategy for ground source heat pump systems with integrated photovoltaic thermal collectors," Applied Energy, Elsevier, vol. 214(C), pages 178-190.
    31. Ruhnau, Oliver & Hennig, Patrick & Madlener, Reinhard, 2020. "Economic implications of forecasting electricity generation from variable renewable energy sources," Renewable Energy, Elsevier, vol. 161(C), pages 1318-1327.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gergo Varhegyi & Mutasim Nour, 2024. "Advancing Fast Frequency Response Ancillary Services in Renewable-Heavy Grids: A Global Review of Energy Storage-Based Solutions and Market Dynamics," Energies, MDPI, vol. 17(15), pages 1-29, July.
    2. Dagui Liu & Weiqing Wang & Huie Zhang & Wei Shi & Caiqing Bai & Huimin Zhang, 2023. "Day-Ahead and Intra-Day Optimal Scheduling Considering Wind Power Forecasting Errors," Sustainability, MDPI, vol. 15(14), pages 1-17, July.
    3. José R. Ortiz-Castrillón & Sergio D. Saldarriaga-Zuluaga & Nicolás Muñoz-Galeano & Jesús M. López-Lezama & Santiago Benavides-Córdoba & Juan B. Cano-Quintero, 2023. "Optimal Sliding-Mode Control of Semi-Bridgeless Boost Converters Considering Power Factor Corrections," Energies, MDPI, vol. 16(17), pages 1-24, August.
    4. 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.
    5. Izabela Rojek & Dariusz Mikołajewski & Adam Mroziński & Marek Macko, 2023. "Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage," Energies, MDPI, vol. 16(18), pages 1-26, September.

    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. Zahraee, S.M. & Khalaji Assadi, M. & Saidur, R., 2016. "Application of Artificial Intelligence Methods for Hybrid Energy System Optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 617-630.
    2. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    3. Erdinc, O. & Uzunoglu, M., 2012. "Optimum design of hybrid renewable energy systems: Overview of different approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1412-1425.
    4. Tezer, Tuba & Yaman, Ramazan & Yaman, Gülşen, 2017. "Evaluation of approaches used for optimization of stand-alone hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 840-853.
    5. Md Tahmid Hussain & Adil Sarwar & Mohd Tariq & Shabana Urooj & Amal BaQais & Md. Alamgir Hossain, 2023. "An Evaluation of ANN Algorithm Performance for MPPT Energy Harvesting in Solar PV Systems," Sustainability, MDPI, vol. 15(14), pages 1-36, July.
    6. Maria Manzoor & Usman Rauf Kamboh & Sumaira Gulshan & Sven Tomforde & Iram Gul & Alighazi Siddiqui & Muhammad Arshad, 2023. "Optimizing Sustainable Phytoextraction of Lead from Contaminated Soil Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)," Sustainability, MDPI, vol. 15(14), pages 1-17, July.
    7. Dimri, Neha & Tiwari, Arvind & Tiwari, G.N., 2019. "Comparative study of photovoltaic thermal (PVT) integrated thermoelectric cooler (TEC) fluid collectors," Renewable Energy, Elsevier, vol. 134(C), pages 343-356.
    8. Larbi Chrifi-Alaoui & Saïd Drid & Mohammed Ouriagli & Driss Mehdi, 2023. "Overview of Photovoltaic and Wind Electrical Power Hybrid Systems," Energies, MDPI, vol. 16(12), pages 1-35, June.
    9. Khan, Muhammad Waseem & Wang, Jie, 2017. "The research on multi-agent system for microgrid control and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1399-1411.
    10. Fadaee, M. & Radzi, M.A.M., 2012. "Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3364-3369.
    11. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
    12. Song, Mengxuan & Wen, Yi & Duan, Bin & Wang, Jun & Gong, Qi, 2017. "Micro-siting optimization of a wind farm built in multiple phases," Energy, Elsevier, vol. 137(C), pages 95-103.
    13. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    14. Mehrabankhomartash, Mahmoud & Rayati, Mohammad & Sheikhi, Aras & Ranjbar, Ali Mohammad, 2017. "Practical battery size optimization of a PV system by considering individual customer damage function," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 36-50.
    15. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    16. Dalibor Petković & Siti Hafizah Ab Hamid & Žarko Ćojbašić & Nenad T. Pavlović, 2014. "RETRACTED ARTICLE: Adapting project management method and ANFIS strategy for variables selection and analyzing wind turbine wake effect," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 463-475, November.
    17. Mittal, Prateek & Kulkarni, Kedar & Mitra, Kishalay, 2016. "A novel hybrid optimization methodology to optimize the total number and placement of wind turbines," Renewable Energy, Elsevier, vol. 86(C), pages 133-147.
    18. Jiang, Hou & Lu, Ning & Huang, Guanghui & Yao, Ling & Qin, Jun & Liu, Hengzi, 2020. "Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data," Applied Energy, Elsevier, vol. 270(C).
    19. Lo Brutto, Ottavio A. & Nguyen, Van Thinh & Guillou, Sylvain S. & Thiébot, Jérôme & Gualous, Hamid, 2016. "Tidal farm analysis using an analytical model for the flow velocity prediction in the wake of a tidal turbine with small diameter to depth ratio," Renewable Energy, Elsevier, vol. 99(C), pages 347-359.
    20. Bansal, Jagdish Chand & Farswan, Pushpa, 2017. "Wind farm layout using biogeography based optimization," Renewable Energy, Elsevier, vol. 107(C), pages 386-402.

    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:16:y:2023:i:4:p:1786-:d:1065011. 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.