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Energy Management of Microgrids for Smart Cities: A Review

Author

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  • Muhammad Salman Sami

    (Department of Electrical Engineering, Bahauddin Zakariya University, Multan 66000, Pakistan)

  • Muhammad Abrar

    (Department of Electrical Engineering, Bahauddin Zakariya University, Multan 66000, Pakistan)

  • Rizwan Akram

    (Department of Electrical Engineering, College of Engineering, Qassim University, Qassim 51452, Saudi Arabia)

  • Muhammad Majid Hussain

    (Faculty of Computing, Engineering and Sciences, University of South Wales, Cardiff CF37 1DL, UK)

  • Mian Hammad Nazir

    (Faculty of Computing, Engineering and Sciences, University of South Wales, Cardiff CF37 1DL, UK)

  • Muhammad Saad Khan

    (Department of Electrical Engineering, Bahauddin Zakariya University, Multan 66000, Pakistan)

  • Safdar Raza

    (Department of Electrical Engineering, NFC Institute of Engineering and Technology (NFC-IET), Multan 60000, Pakistan)

Abstract

Electric power reliability is one of the most important factors in the social and economic evolution of a smart city, whereas the key factors to make a city smart are smart energy sources and intelligent electricity networks. The development of cost-effective microgrids with the added functionality of energy storage and backup generation plans has resulted from the combined impact of high energy demands from consumers and environmental concerns, which push for minimizing the energy imbalance, reducing energy losses and CO 2 emissions, and improving the overall security and reliability of a power system. It is now possible to tackle the problem of growing consumer load by utilizing the recent developments in modern types of renewable energy resources (RES) and current technology. These energy alternatives do not emit greenhouse gases (GHG) like fossil fuels do, and so help to mitigate climate change. They also have in socioeconomic advantages due to long-term sustainability. Variability and intermittency are the main drawbacks of renewable energy resources (RES), which affect the consistency of electric supply. Thus, utilizing multiple optimization approaches, the energy management system determines the optimum solution for renewable energy resources (RES) and transfers it to the microgrid. Microgrids maintain the continuity of power delivery, according to the energy management system settings. In a microgrid, an energy management system (EMS) is used to decrease the system’s expenses and adverse consequences. As a result, a variety of strategies and approaches are employed in the development of an efficient energy management system. This article is intended to provide a comprehensive overview of a range of technologies and techniques, and their solutions, for managing the drawbacks of renewable energy supplies, such as variability and load fluctuations, while still matching energy demands for their integration in the microgrids of smart cities.

Suggested Citation

  • Muhammad Salman Sami & Muhammad Abrar & Rizwan Akram & Muhammad Majid Hussain & Mian Hammad Nazir & Muhammad Saad Khan & Safdar Raza, 2021. "Energy Management of Microgrids for Smart Cities: A Review," Energies, MDPI, vol. 14(18), pages 1-18, September.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5976-:d:639572
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    References listed on IDEAS

    as
    1. Amponsah, Nana Yaw & Troldborg, Mads & Kington, Bethany & Aalders, Inge & Hough, Rupert Lloyd, 2014. "Greenhouse gas emissions from renewable energy sources: A review of lifecycle considerations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 461-475.
    2. Rui Yang & Yupeng Yuan & Rushun Ying & Boyang Shen & Teng Long, 2020. "A Novel Energy Management Strategy for a Ship’s Hybrid Solar Energy Generation System Using a Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 13(6), pages 1-14, March.
    3. Cagnano, A. & De Tuglie, E. & Mancarella, P., 2020. "Microgrids: Overview and guidelines for practical implementations and operation," Applied Energy, Elsevier, vol. 258(C).
    4. Onur Hınçal & A. Altan-Sakarya & A. Metin Ger, 2011. "Optimization of Multireservoir Systems by Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(5), pages 1465-1487, March.
    5. Marzband, Mousa & Ghadimi, Majid & Sumper, Andreas & Domínguez-García, José Luis, 2014. "Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode," Applied Energy, Elsevier, vol. 128(C), pages 164-174.
    6. Hossain, Md Alamgir & Pota, Hemanshu Roy & Squartini, Stefano & Abdou, Ahmed Fathi, 2019. "Modified PSO algorithm for real-time energy management in grid-connected microgrids," Renewable Energy, Elsevier, vol. 136(C), pages 746-757.
    7. Sen, Souvik & Ganguly, Sourav, 2017. "Opportunities, barriers and issues with renewable energy development – A discussion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1170-1181.
    8. Xuejie Wang & Yanchao Ji & Jianze Wang & Yuanjun Wang & Lei Qi, 2020. "Optimal energy management of microgrid based on multi-parameter dynamic programming," International Journal of Distributed Sensor Networks, , vol. 16(6), pages 15501477209, June.
    9. Zhao, Bo & Zhang, Xuesong & Li, Peng & Wang, Ke & Xue, Meidong & Wang, Caisheng, 2014. "Optimal sizing, operating strategy and operational experience of a stand-alone microgrid on Dongfushan Island," Applied Energy, Elsevier, vol. 113(C), pages 1656-1666.
    10. Sarshar, Javad & Moosapour, Seyyed Sajjad & Joorabian, Mahmood, 2017. "Multi-objective energy management of a micro-grid considering uncertainty in wind power forecasting," Energy, Elsevier, vol. 139(C), pages 680-693.
    11. Jafari, Mohammad & Malekjamshidi, Zahra, 2020. "Optimal energy management of a residential-based hybrid renewable energy system using rule-based real-time control and 2D dynamic programming optimization method," Renewable Energy, Elsevier, vol. 146(C), pages 254-266.
    12. Nemati, Mohsen & Braun, Martin & Tenbohlen, Stefan, 2018. "Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming," Applied Energy, Elsevier, vol. 210(C), pages 944-963.
    13. Panwar, N.L. & Kaushik, S.C. & Kothari, Surendra, 2011. "Role of renewable energy sources in environmental protection: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(3), pages 1513-1524, April.
    14. Ahmad, Jameel & Imran, Muhammad & Khalid, Abdullah & Iqbal, Waseem & Ashraf, Syed Rehan & Adnan, Muhammad & Ali, Syed Farooq & Khokhar, Khawar Siddique, 2018. "Techno economic analysis of a wind-photovoltaic-biomass hybrid renewable energy system for rural electrification: A case study of Kallar Kahar," Energy, Elsevier, vol. 148(C), pages 208-234.
    15. Keshtkar, Azim & Arzanpour, Siamak, 2017. "An adaptive fuzzy logic system for residential energy management in smart grid environments," Applied Energy, Elsevier, vol. 186(P1), pages 68-81.
    16. Roy, Kallol & Mandal, Kamal Krishna & Mandal, Atis Chandra, 2019. "Ant-Lion Optimizer algorithm and recurrent neural network for energy management of micro grid connected system," Energy, Elsevier, vol. 167(C), pages 402-416.
    17. Dufo-López, Rodolfo & Bernal-Agustín, José L. & Mendoza, Franklin, 2009. "Design and economical analysis of hybrid PV-wind systems connected to the grid for the intermittent production of hydrogen," Energy Policy, Elsevier, vol. 37(8), pages 3082-3095, August.
    18. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    19. Balderrama, Sergio & Lombardi, Francesco & Riva, Fabio & Canedo, Walter & Colombo, Emanuela & Quoilin, Sylvain, 2019. "A two-stage linear programming optimization framework for isolated hybrid microgrids in a rural context: The case study of the “El Espino” community," Energy, Elsevier, vol. 188(C).
    20. Chen, Yen-Haw & Lu, Su-Ying & Chang, Yung-Ruei & Lee, Ta-Tung & Hu, Ming-Che, 2013. "Economic analysis and optimal energy management models for microgrid systems: A case study in Taiwan," Applied Energy, Elsevier, vol. 103(C), pages 145-154.
    21. Tina, Giuseppe Marco & Gagliano, Salvina, 2011. "Probabilistic modelling of hybrid solar/wind power system with solar tracking system," Renewable Energy, Elsevier, vol. 36(6), pages 1719-1727.
    22. Caspary, Georg, 2009. "Gauging the future competitiveness of renewable energy in Colombia," Energy Economics, Elsevier, vol. 31(3), pages 443-449, May.
    23. Coelho, Vitor N. & Coelho, Igor M. & Coelho, Bruno N. & de Oliveira, Glauber C. & Barbosa, Alexandre C. & Pereira, Leo & de Freitas, Alan & Santos, Haroldo G. & Ochi, Luis S. & Guimarães, Frederico G., 2017. "A communitarian microgrid storage planning system inside the scope of a smart city," Applied Energy, Elsevier, vol. 201(C), pages 371-381.
    24. Dias, César Luiz de Azevedo & Castelo Branco, David Alves & Arouca, Maurício Cardoso & Loureiro Legey, Luiz Fernando, 2017. "Performance estimation of photovoltaic technologies in Brazil," Renewable Energy, Elsevier, vol. 114(PB), pages 367-375.
    25. Sukumar, Shivashankar & Mokhlis, Hazlie & Mekhilef, Saad & Naidu, Kanendra & Karimi, Mazaher, 2017. "Mix-mode energy management strategy and battery sizing for economic operation of grid-tied microgrid," Energy, Elsevier, vol. 118(C), pages 1322-1333.
    26. Du, Wen-Bo & Gao, Yang & Liu, Chen & Zheng, Zheng & Wang, Zhen, 2015. "Adequate is better: particle swarm optimization with limited-information," Applied Mathematics and Computation, Elsevier, vol. 268(C), pages 832-838.
    27. Liu, Nian & Tang, Qingfeng & Zhang, Jianhua & Fan, Wei & Liu, Jie, 2014. "A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids," Applied Energy, Elsevier, vol. 129(C), pages 336-345.
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    2. Jihed Hmad & Azeddine Houari & Allal El Moubarek Bouzid & Abdelhakim Saim & Hafedh Trabelsi, 2023. "A Review on Mode Transition Strategies between Grid-Connected and Standalone Operation of Voltage Source Inverters-Based Microgrids," Energies, MDPI, vol. 16(13), pages 1-41, June.
    3. Tatiana Tucunduva Philippi Cortese & Jairo Filho Sousa de Almeida & Giseli Quirino Batista & José Eduardo Storopoli & Aaron Liu & Tan Yigitcanlar, 2022. "Understanding Sustainable Energy in the Context of Smart Cities: A PRISMA Review," Energies, MDPI, vol. 15(7), pages 1-38, March.
    4. Nemanja Mišljenović & Matej Žnidarec & Goran Knežević & Damir Šljivac & Andreas Sumper, 2023. "A Review of Energy Management Systems and Organizational Structures of Prosumers," Energies, MDPI, vol. 16(7), pages 1-32, March.

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