IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i22p14666-d966037.html
   My bibliography  Save this article

Optimal PI-Controller-Based Hybrid Energy Storage System in DC Microgrid

Author

Listed:
  • Maya Vijayan

    (Electrical and Electronics Engineering, SRM University-AP, Neerukonda 522502, India)

  • Ramanjaneya Reddy Udumula

    (Electrical and Electronics Engineering, SRM University-AP, Neerukonda 522502, India)

  • Tarkeshwar Mahto

    (Electrical and Electronics Engineering, SRM University-AP, Neerukonda 522502, India)

  • Bhamidi Lokeshgupta

    (Electrical and Electronics Engineering, SRM University-AP, Neerukonda 522502, India)

  • B Srikanth Goud

    (Electrical and Electronics Engineering, Anurag University, Hyderabad 500088, India)

  • Ch Naga Sai Kalyan

    (Electrical and Electronics Engineering, Vasireddy Venkatadri Institute of Technology, Guntur 522508, India)

  • Praveen Kumar Balachandran

    (Electrical and Electronics Engineering, Vardhaman College of Engineering, Hyderabad 501218, India)

  • Dhanamjayulu C

    (School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, India)

  • Sanjeevikumar Padmanaban

    (Department of Energy Engineering, Aalborg University, 9100 Aalborg, Denmark)

  • Bhekisipho Twala

    (Faculty of Engineering and Built Environment, Tshwane University of Technology (BTA), Pretoria 0001, South Africa)

Abstract

Power availability from renewable energy sources (RES) is unpredictable, and must be managed effectively for better utilization. The role that a hybrid energy storage system (HESS) plays is vital in this context. Renewable energy sources along with hybrid energy storage systems can provide better power management in a DC microgrid environment. In this paper, the optimal PI-controller-based hybrid energy storage system for a DC microgrid is proposed for the effective utilization of renewable power. In this model, the proposed optimal PI controller is developed using the particle swarm optimization (PSO) approach. A 72 W DC microgrid system is considered in order to validate the effectiveness of the proposed optimal PI controller. The proposed model is implemented using the MATLAB/SIMULINK platform. To show the effectiveness of the proposed model, the results are validated with a conventional PI-controller-based hybrid energy storage system.

Suggested Citation

  • Maya Vijayan & Ramanjaneya Reddy Udumula & Tarkeshwar Mahto & Bhamidi Lokeshgupta & B Srikanth Goud & Ch Naga Sai Kalyan & Praveen Kumar Balachandran & Dhanamjayulu C & Sanjeevikumar Padmanaban & Bhek, 2022. "Optimal PI-Controller-Based Hybrid Energy Storage System in DC Microgrid," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14666-:d:966037
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/22/14666/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/22/14666/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lu, D. & Fakham, H. & Zhou, T. & François, B., 2010. "Application of Petri nets for the energy management of a photovoltaic based power station including storage units," Renewable Energy, Elsevier, vol. 35(6), pages 1117-1124.
    2. Karimi, Hamid & Jadid, Shahram, 2020. "Optimal energy management for multi-microgrid considering demand response programs: A stochastic multi-objective framework," Energy, Elsevier, vol. 195(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. Matija Kostelac & Lin Herenčić & Tomislav Capuder, 2022. "Planning and Operational Aspects of Individual and Clustered Multi-Energy Microgrid Options," Energies, MDPI, vol. 15(4), pages 1-17, February.
    2. 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).
    3. Mansour-Saatloo, Amin & Pezhmani, Yasin & Mirzaei, Mohammad Amin & Mohammadi-Ivatloo, Behnam & Zare, Kazem & Marzband, Mousa & Anvari-Moghaddam, Amjad, 2021. "Robust decentralized optimization of Multi-Microgrids integrated with Power-to-X technologies," Applied Energy, Elsevier, vol. 304(C).
    4. Hasankhani, Arezoo & Hakimi, Seyed Mehdi, 2021. "Stochastic energy management of smart microgrid with intermittent renewable energy resources in electricity market," Energy, Elsevier, vol. 219(C).
    5. Shahid Nawaz Khan & Syed Ali Abbas Kazmi & Abdullah Altamimi & Zafar A. Khan & Mohammed A. Alghassab, 2022. "Smart Distribution Mechanisms—Part I: From the Perspectives of Planning," Sustainability, MDPI, vol. 14(23), pages 1-109, December.
    6. 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.
    7. Mahmoud H. Elkholy & Tomonobu Senjyu & Mohammed Elsayed Lotfy & Abdelrahman Elgarhy & Nehad S. Ali & Tamer S. Gaafar, 2022. "Design and Implementation of a Real-Time Smart Home Management System Considering Energy Saving," Sustainability, MDPI, vol. 14(21), pages 1-22, October.
    8. Fathy, Ahmed, 2023. "Bald eagle search optimizer-based energy management strategy for microgrid with renewable sources and electric vehicles," Applied Energy, Elsevier, vol. 334(C).
    9. Zhou, Xu & Ma, Zhongjing & Zou, Suli & Zhang, Jinhui, 2022. "Consensus-based distributed economic dispatch for Multi Micro Energy Grid systems under coupled carbon emissions," Applied Energy, Elsevier, vol. 324(C).
    10. Wu, Chuantao & Zhou, Dezhi & Lin, Xiangning & Sui, Quan & Wei, Fanrong & Li, Zhengtian, 2022. "A novel energy cooperation framework for multi-island microgrids based on marine mobile energy storage systems," Energy, Elsevier, vol. 252(C).
    11. 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.
    12. Wang, B.C. & Sechilariu, M. & Locment, F., 2013. "Power flow Petri Net modelling for building integrated multi-source power system with smart grid interaction," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 91(C), pages 119-133.
    13. Morteza Zare Oskouei & Ayşe Aybike Şeker & Süleyman Tunçel & Emin Demirbaş & Tuba Gözel & Mehmet Hakan Hocaoğlu & Mehdi Abapour & Behnam Mohammadi-Ivatloo, 2022. "A Critical Review on the Impacts of Energy Storage Systems and Demand-Side Management Strategies in the Economic Operation of Renewable-Based Distribution Network," Sustainability, MDPI, vol. 14(4), pages 1-34, February.
    14. Seshu Kumar, R. & Phani Raghav, L. & Koteswara Raju, D. & Singh, Arvind R., 2021. "Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids," Applied Energy, Elsevier, vol. 301(C).
    15. Ghanbari, Ali & Karimi, Hamid & Jadid, Shahram, 2020. "Optimal planning and operation of multi-carrier networked microgrids considering multi-energy hubs in distribution networks," Energy, Elsevier, vol. 204(C).
    16. Wu, Kunming & Li, Qiang & Chen, Ziyu & Lin, Jiayang & Yi, Yongli & Chen, Minyou, 2021. "Distributed optimization method with weighted gradients for economic dispatch problem of multi-microgrid systems," Energy, Elsevier, vol. 222(C).
    17. Shanmugarajah Vinothine & Lidula N. Widanagama Arachchige & Athula D. Rajapakse & Roshani Kaluthanthrige, 2022. "Microgrid Energy Management and Methods for Managing Forecast Uncertainties," Energies, MDPI, vol. 15(22), pages 1-22, November.
    18. Ben Arab, Marwa & Rekik, Mouna & Krichen, Lotfi, 2023. "A priority-based seven-layer strategy for energy management cooperation in a smart city integrated green technology," Applied Energy, Elsevier, vol. 335(C).
    19. Courtecuisse, Vincent & Sprooten, Jonathan & Robyns, Benoît & Petit, Marc & Francois, Bruno & Deuse, Jacques, 2010. "A methodology to design a fuzzy logic based supervision of Hybrid Renewable Energy Systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(2), pages 208-224.
    20. Jianying Li & Minsheng Yang & Yuexing Zhang & Jianqi Li & Jianquan Lu, 2023. "Micro-Grid Day-Ahead Stochastic Optimal Dispatch Considering Multiple Demand Response and Electric Vehicles," Energies, MDPI, vol. 16(8), pages 1-15, April.

    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:14666-:d:966037. 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.