IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i9p2079-d1134442.html
   My bibliography  Save this article

An Enhanced Multi-Objective Optimizer for Stochastic Generation Optimization in Islanded Renewable Energy Microgrids

Author

Listed:
  • Upasana Lakhina

    (Department of Electrical and Electronics Engineering, Institute of Health and Analytics, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia)

  • Nasreen Badruddin

    (Department of Electrical and Electronics Engineering, Institute of Health and Analytics, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia)

  • Irraivan Elamvazuthi

    (Department of Electrical and Electronics Engineering, Institute of Health and Analytics, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia)

  • Ajay Jangra

    (Department of Computer Science and Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra 136119, India)

  • Truong Hoang Bao Huy

    (Department of Future Convergence Technology, Soonchunhyang University, Asan-si 31538, Republic of Korea)

  • Josep M. Guerrero

    (Centre of Research on Microgrids, Department of Energy Technology, Aalborg University, P.O. Box 159 Aalborg, Denmark)

Abstract

A microgrid is an autonomous electrical system that consists of renewable energy and efficiently achieves power balance in a network. The complexity in the distribution network arises due to the intermittent nature of renewable generation units and varying power. One of the important objectives of a microgrid is to perform energy management based on situational awareness and solve an optimization problem. This paper proposes an enhanced multi-objective multi-verse optimizer algorithm (MOMVO) for stochastic generation power optimization in a renewable energy-based islanded microgrid framework. The proposed algorithm is utilized for optimum power scheduling among various available generation sources to minimize the microgrid’s generation costs and power losses. The performance of MOMVO is assessed on a 6-unit and 10-unit test system. Simulation results show that the proposed algorithm outperforms other metaheuristic algorithms for multi-objective optimization.

Suggested Citation

  • Upasana Lakhina & Nasreen Badruddin & Irraivan Elamvazuthi & Ajay Jangra & Truong Hoang Bao Huy & Josep M. Guerrero, 2023. "An Enhanced Multi-Objective Optimizer for Stochastic Generation Optimization in Islanded Renewable Energy Microgrids," Mathematics, MDPI, vol. 11(9), pages 1-24, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2079-:d:1134442
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/9/2079/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/9/2079/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nadeem Javaid & Sakeena Javaid & Wadood Abdul & Imran Ahmed & Ahmad Almogren & Atif Alamri & Iftikhar Azim Niaz, 2017. "A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid," Energies, MDPI, vol. 10(3), pages 1-27, March.
    2. Kaiye Gao & Tianshi Wang & Chenjing Han & Jinhao Xie & Ye Ma & Rui Peng, 2021. "A Review of Optimization of Microgrid Operation," Energies, MDPI, vol. 14(10), pages 1-39, May.
    3. Kalim Ullah & Quanyuan Jiang & Guangchao Geng & Sahar Rahim & Rehan Ali Khan, 2022. "Optimal Power Sharing in Microgrids Using the Artificial Bee Colony Algorithm," Energies, MDPI, vol. 15(3), pages 1-22, January.
    4. Roslan, M.F. & Hannan, M.A. & Jern Ker, Pin & Begum, R.A. & Indra Mahlia, TM & Dong, Z.Y., 2021. "Scheduling controller for microgrids energy management system using optimization algorithm in achieving cost saving and emission reduction," Applied Energy, Elsevier, vol. 292(C).
    5. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Tang, Yong, 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges," Applied Energy, Elsevier, vol. 301(C).
    6. Sadeghi, Delnia & Hesami Naghshbandy, Ali & Bahramara, Salah, 2020. "Optimal sizing of hybrid renewable energy systems in presence of electric vehicles using multi-objective particle swarm optimization," Energy, Elsevier, vol. 209(C).
    7. Maen Z. Kreishan & Ahmed F. Zobaa, 2021. "Optimal Allocation and Operation of Droop-Controlled Islanded Microgrids: A Review," Energies, MDPI, vol. 14(15), pages 1-45, July.
    8. Raya-Armenta, Jose Maurilio & Bazmohammadi, Najmeh & Avina-Cervantes, Juan Gabriel & Sáez, Doris & Vasquez, Juan C. & Guerrero, Josep M., 2021. "Energy management system optimization in islanded microgrids: An overview and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    9. El-Sattar, Hoda Abd & Kamel, Salah & Hassan, Mohamed H. & Jurado, Francisco, 2022. "An effective optimization strategy for design of standalone hybrid renewable energy systems," Energy, Elsevier, vol. 260(C).
    10. Yang, Qiangda & Dong, Ning & Zhang, Jie, 2021. "An enhanced adaptive bat algorithm for microgrid energy scheduling," Energy, Elsevier, vol. 232(C).
    11. Makbul A.M. Ramli & H.R.E.H. Bouchekara & Abdulsalam S. Alghamdi, 2019. "Efficient Energy Management in a Microgrid with Intermittent Renewable Energy and Storage Sources," Sustainability, MDPI, vol. 11(14), pages 1-28, July.
    12. Tayab, Usman Bashir & Lu, Junwei & Yang, Fuwen & AlGarni, Tahani Saad & Kashif, Muhammad, 2021. "Energy management system for microgrids using weighted salp swarm algorithm and hybrid forecasting approach," Renewable Energy, Elsevier, vol. 180(C), pages 467-481.
    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. Upasana Lakhina & Irraivan Elamvazuthi & Nasreen Badruddin & Ajay Jangra & Bao-Huy Truong & Joseph M. Guerrero, 2023. "A Cost-Effective Multi-Verse Optimization Algorithm for Efficient Power Generation in a Microgrid," Sustainability, MDPI, vol. 15(8), pages 1-25, April.
    2. Chen, Weidong & Wang, Junnan & Yu, Guanyi & Chen, Jiajia & Hu, Yumeng, 2022. "Research on day-ahead transactions between multi-microgrid based on cooperative game model," Applied Energy, Elsevier, vol. 316(C).
    3. Yang, Peiwen & Fang, Debin & Wang, Shuyi, 2022. "Optimal trading mechanism for prosumer-centric local energy markets considering deviation assessment," Applied Energy, Elsevier, vol. 325(C).
    4. 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.
    5. Saif Jamal & Jagadeesh Pasupuleti & Nur Azzammudin Rahmat & Nadia M. L. Tan, 2022. "Energy Management System for Grid-Connected Nanogrid during COVID-19," Energies, MDPI, vol. 15(20), pages 1-20, October.
    6. Yin, Sihua & Yang, Haidong & Xu, Kangkang & Zhu, Chengjiu & Zhang, Shaqing & Liu, Guosheng, 2022. "Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty," Applied Energy, Elsevier, vol. 307(C).
    7. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
    8. Ahmadi, Seyed Ehsan & Sadeghi, Delnia & Marzband, Mousa & Abusorrah, Abdullah & Sedraoui, Khaled, 2022. "Decentralized bi-level stochastic optimization approach for multi-agent multi-energy networked micro-grids with multi-energy storage technologies," Energy, Elsevier, vol. 245(C).
    9. Sulman Shahzad & Muhammad Abbas Abbasi & Hassan Ali & Muhammad Iqbal & Rania Munir & Heybet Kilic, 2023. "Possibilities, Challenges, and Future Opportunities of Microgrids: A Review," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    10. Mahsa Dehghan Manshadi & Milad Mousavi & M. Soltani & Amir Mosavi & Levente Kovacs, 2022. "Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System," Energies, MDPI, vol. 15(24), pages 1-16, December.
    11. Giovanni Pau & Mario Collotta & Antonio Ruano & Jiahu Qin, 2017. "Smart Home Energy Management," Energies, MDPI, vol. 10(3), pages 1-5, March.
    12. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    13. Mohammad Javad Bordbari & Fuzhan Nasiri, 2024. "Networked Microgrids: A Review on Configuration, Operation, and Control Strategies," Energies, MDPI, vol. 17(3), pages 1-28, February.
    14. Chen, Fuhao & Yan, Jie & Liu, Yongqian & Yan, Yamin & Tjernberg, Lina Bertling, 2024. "A novel meta-learning approach for few-shot short-term wind power forecasting," Applied Energy, Elsevier, vol. 362(C).
    15. Herc, Luka & Pfeifer, Antun & Duić, Neven & Wang, Fei, 2022. "Economic viability of flexibility options for smart energy systems with high penetration of renewable energy," Energy, Elsevier, vol. 252(C).
    16. Zilong, Ti & Yubing, Song & Xiaowei, Deng, 2022. "Spatial-temporal wave height forecast using deep learning and public reanalysis dataset," Applied Energy, Elsevier, vol. 326(C).
    17. Tan, Hong & Yan, Wei & Ren, Zhouyang & Wang, Qiujie & Mohamed, Mohamed A., 2022. "Distributionally robust operation for integrated rural energy systems with broiler houses," Energy, Elsevier, vol. 254(PC).
    18. Abbas Rabiee & Ali Abdali & Seyed Masoud Mohseni-Bonab & Mohsen Hazrati, 2021. "Risk-Averse Scheduling of Combined Heat and Power-Based Microgrids in Presence of Uncertain Distributed Energy Resources," Sustainability, MDPI, vol. 13(13), pages 1-24, June.
    19. Xiao, Yulong & Zou, Chongzhe & Chi, Hetian & Fang, Rengcun, 2023. "Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis," Energy, Elsevier, vol. 267(C).
    20. Capone, Martina & Guelpa, Elisa & Verda, Vittorio, 2021. "Multi-objective optimization of district energy systems with demand response," Energy, Elsevier, vol. 227(C).

    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:jmathe:v:11:y:2023:i:9:p:2079-:d:1134442. 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.