IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i22p14928-d969879.html
   My bibliography  Save this article

Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation

Author

Listed:
  • Ahmed M. Hussien

    (Electrical Engineering Department, Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Jonghoon Kim

    (Energy Storage and Conversion Laboratory, Department of Electrical Engineering, Chungnam National University, Daejeon 34134, Korea)

  • Abdulaziz Alkuhayli

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Mohammed Alharbi

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Hany M. Hasanien

    (Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

  • Marcos Tostado-Véliz

    (Department of Electrical Engineering, Superior Polytechnic School of Linares, University of Jaén, 23700 Linares, Spain)

  • Rania A. Turky

    (Electrical Engineering Department, Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Francisco Jurado

    (Department of Electrical Engineering, Superior Polytechnic School of Linares, University of Jaén, 23700 Linares, Spain)

Abstract

The present research produces a new technique for the optimum operation of an isolated microgrid (MGD) based on an enhanced block-sparse adaptive Bayesian algorithm (EBSABA). To update the proportional-integral (PI) controller gains online, the suggested approach considers the impact of the actuating error signal as well as its magnitude. To reach a compromise result between the various purposes, the Response Surface Methodology (RSMT) is combined with the sunflower optimization (SFO) and particle swarm optimization (PSO) algorithms. To demonstrate the success of the novel approach, a benchmark MGD is evaluated in three different Incidents: (1) removing the MGD from the utility (islanding mode); (2) load variations under islanding mode; and (3) a three-phase fault under islanding mode. Extensive simulations are run to test the new technique using the PSCAD/EMTDC program. The validity of the proposed optimizer is demonstrated by comparing its results with those obtained using the least mean and square root of exponential method (LMSRE) based adaptive control, SFO, and PSO methodologies. The study demonstrates the superiority of the proposed EBSABA over the LMSRE, SFO, and PSO approaches in the system’s transient reactions.

Suggested Citation

  • Ahmed M. Hussien & Jonghoon Kim & Abdulaziz Alkuhayli & Mohammed Alharbi & Hany M. Hasanien & Marcos Tostado-Véliz & Rania A. Turky & Francisco Jurado, 2022. "Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14928-:d:969879
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/22/14928/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/22/14928/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tabar, Vahid Sohrabi & Abbasi, Vahid, 2019. "Energy management in microgrid with considering high penetration of renewable resources and surplus power generation problem," Energy, Elsevier, vol. 189(C).
    2. Uddin, Moslem & Romlie, M.F. & Abdullah, M.F. & Tan, ChiaKwang & Shafiullah, GM & Bakar, A.H.A., 2020. "A novel peak shaving algorithm for islanded microgrid using battery energy storage system," Energy, Elsevier, vol. 196(C).
    3. Alghamdi, Baheej & Cañizares, Claudio, 2022. "Frequency and voltage coordinated control of a grid of AC/DC microgrids," Applied Energy, Elsevier, vol. 310(C).
    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. Mohammed Yousri Silaa & Oscar Barambones & José Antonio Cortajarena & Patxi Alkorta & Aissa Bencherif, 2023. "PEMFC Current Control Using a Novel Compound Controller Enhanced by the Black Widow Algorithm: A Comprehensive Simulation Study," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    2. Ahmed Sulaiman Alsafran, 2023. "A Feasibility Study of Implementing IEEE 1547 and IEEE 2030 Standards for Microgrid in the Kingdom of Saudi Arabia," Energies, MDPI, vol. 16(4), pages 1-15, February.
    3. Nour A. Mohamed & Hany M. Hasanien & Abdulaziz Alkuhayli & Tlenshiyeva Akmaral & Francisco Jurado & Ahmed O. Badr, 2023. "Hybrid Particle Swarm and Gravitational Search Algorithm-Based Optimal Fractional Order PID Control Scheme for Performance Enhancement of Offshore Wind Farms," Sustainability, MDPI, vol. 15(15), pages 1-25, August.
    4. Dina A. Zaki & Hany M. Hasanien & Mohammed Alharbi & Zia Ullah & Mariam A. Sameh, 2023. "Hybrid Driving Training and Particle Swarm Optimization Algorithm-Based Optimal Control for Performance Improvement of Microgrids," Energies, MDPI, vol. 16(11), pages 1-18, May.

    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. Zhou, Dengji & Yan, Siyun & Huang, Dawen & Shao, Tiemin & Xiao, Wang & Hao, Jiarui & Wang, Chen & Yu, Tianqi, 2022. "Modeling and simulation of the hydrogen blended gas-electricity integrated energy system and influence analysis of hydrogen blending modes," Energy, Elsevier, vol. 239(PA).
    2. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).
    3. Konečná, Eva & Teng, Sin Yong & Máša, Vítězslav, 2020. "New insights into the potential of the gas microturbine in microgrids and industrial applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    4. Constantino Dário Justo & José Eduardo Tafula & Pedro Moura, 2022. "Planning Sustainable Energy Systems in the Southern African Development Community: A Review of Power Systems Planning Approaches," Energies, MDPI, vol. 15(21), pages 1-28, October.
    5. Zhou, Yuekuan & Cao, Sunliang & Hensen, Jan L.M., 2021. "An energy paradigm transition framework from negative towards positive district energy sharing networks—Battery cycling aging, advanced battery management strategies, flexible vehicles-to-buildings in," Applied Energy, Elsevier, vol. 288(C).
    6. Zhang, Bin & Hu, Weihao & Ghias, Amer M.Y.M. & Xu, Xiao & Chen, Zhe, 2022. "Multi-agent deep reinforcement learning-based coordination control for grid-aware multi-buildings," Applied Energy, Elsevier, vol. 328(C).
    7. Zejun Tong & Chun Zhang & Xiaotai Wu & Pengcheng Gao & Shuang Wu & Haoyu Li, 2023. "Economic Optimization Control Method of Grid-Connected Microgrid Based on Improved Pinning Consensus," Energies, MDPI, vol. 16(3), pages 1-31, January.
    8. Mousavizade, Mirsaeed & Bai, Feifei & Garmabdari, Rasoul & Sanjari, Mohammad & Taghizadeh, Foad & Mahmoudian, Ali & Lu, Junwei, 2023. "Adaptive control of V2Gs in islanded microgrids incorporating EV owner expectations," Applied Energy, Elsevier, vol. 341(C).
    9. Gerald Jones & Xueping Li & Yulin Sun, 2024. "Robust Energy Management Policies for Solar Microgrids via Reinforcement Learning," Energies, MDPI, vol. 17(12), pages 1-22, June.
    10. Md Masud Rana & Mohamed Atef & Md Rasel Sarkar & Moslem Uddin & GM Shafiullah, 2022. "A Review on Peak Load Shaving in Microgrid—Potential Benefits, Challenges, and Future Trend," Energies, MDPI, vol. 15(6), pages 1-17, March.
    11. Lu, Xiaoxing & Li, Kangping & Xu, Hanchen & Wang, Fei & Zhou, Zhenyu & Zhang, Yagang, 2020. "Fundamentals and business model for resource aggregator of demand response in electricity markets," Energy, Elsevier, vol. 204(C).
    12. Jiao, P.H. & Chen, J.J. & Peng, K. & Zhao, Y.L. & Xin, K.F., 2020. "Multi-objective mean-semi-entropy model for optimal standalone micro-grid planning with uncertain renewable energy resources," Energy, Elsevier, vol. 191(C).
    13. Md Masud Rana & Akhlaqur Rahman & Moslem Uddin & Md Rasel Sarkar & SK. A. Shezan & C M F S Reza & Md. Fatin Ishraque & Mohammad Belayet Hossain, 2022. "Efficient Energy Distribution for Smart Household Applications," Energies, MDPI, vol. 15(6), pages 1-19, March.
    14. Panyawoot Boonluk & Sirote Khunkitti & Pradit Fuangfoo & Apirat Siritaratiwat, 2021. "Optimal Siting and Sizing of Battery Energy Storage: Case Study Seventh Feeder at Nakhon Phanom Substation in Thailand," Energies, MDPI, vol. 14(5), pages 1-20, March.
    15. 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.
    16. Ng, Rong Wang & Begam, K.M. & Rajkumar, Rajprasad Kumar & Wong, Yee Wan & Chong, Lee Wai, 2022. "A novel dynamic two-stage controller of battery energy storage system for maximum demand reductions," Energy, Elsevier, vol. 248(C).
    17. Harsh, Pratik & Das, Debapriya, 2022. "Optimal coordination strategy of demand response and electric vehicle aggregators for the energy management of reconfigured grid-connected microgrid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    18. Mukhopadhyay, Bineeta & Das, Debapriya, 2021. "Optimal multi-objective expansion planning of a droop-regulated islanded microgrid," Energy, Elsevier, vol. 218(C).
    19. Ding, Xiaoyi & Sun, Wei & Harrison, Gareth P. & Lv, Xiaojing & Weng, Yiwu, 2020. "Multi-objective optimization for an integrated renewable, power-to-gas and solid oxide fuel cell/gas turbine hybrid system in microgrid," Energy, Elsevier, vol. 213(C).
    20. Zhang, Jingrui & Zhu, Xiaoqing & Chen, Tengpeng & Yu, Yanlin & Xue, Wendong, 2020. "Improved MOEA/D approach to many-objective day-ahead scheduling with consideration of adjustable outputs of renewable units and load reduction in active distribution networks," Energy, Elsevier, vol. 210(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:jsusta:v:14:y:2022:i:22:p:14928-:d:969879. 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.