IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i3p1060-d739509.html
   My bibliography  Save this article

A Grid-Connected Microgrid Model and Optimal Scheduling Strategy Based on Hybrid Energy Storage System and Demand-Side Response

Author

Listed:
  • Yaqian Jing

    (School of Management, Guizhou University, Guiyang 550025, China)

  • Honglei Wang

    (School of Management, Guizhou University, Guiyang 550025, China
    Key Laboratory of “Internet+” Collaborative Intelligent Manufacturing in Guizhou Province, Guiyang 550025, China)

  • Yujie Hu

    (School of Management, Guizhou University, Guiyang 550025, China
    Key Laboratory of “Internet+” Collaborative Intelligent Manufacturing in Guizhou Province, Guiyang 550025, China)

  • Chengjiang Li

    (School of Management, Guizhou University, Guiyang 550025, China
    Key Laboratory of “Internet+” Collaborative Intelligent Manufacturing in Guizhou Province, Guiyang 550025, China)

Abstract

The power gap between supply and demand in the microgrid caused by the uncertainty of wind and solar output and users’ electricity consumption needs to be absorbed by the hybrid energy storage devices and the demand-side electricity price response. To maximize the service life of the lithium battery pack, this paper optimizes a reasonable ratio of the supercapacitor pack’s daily charge and discharge times to the daily cycle times of the lithium battery pack. The model construction includes two parts: power prediction and multi-objective optimization modeling. In the case study, a microgrid district under the Guizhou Power Grid is analyzed and discussed. Based on the predicted wind output, solar output, and load demand on a certain day, the optimal scheduling results have been obtained. On the one hand, a reasonable ratio regarding the daily charge and discharge times of hybrid energy storage devices has been obtained under the optimized parameter k in the model. Correspondingly, the daily operation and maintenance of the lithium battery pack is minimum. On the other hand, when the hybrid energy storage devices and demand-side electricity price response are included and not, the changes on the supply and demand sides (a) and of three evaluation indicators (b) are compared, respectively. Thus, the effectiveness of the model in this paper is verified.

Suggested Citation

  • Yaqian Jing & Honglei Wang & Yujie Hu & Chengjiang Li, 2022. "A Grid-Connected Microgrid Model and Optimal Scheduling Strategy Based on Hybrid Energy Storage System and Demand-Side Response," Energies, MDPI, vol. 15(3), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1060-:d:739509
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/3/1060/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/3/1060/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shaozhen Jin & Zhizhong Mao & Hongru Li & Wenhai Qi, 2018. "Dynamic Operation Management of a Renewable Microgrid including Battery Energy Storage," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-19, September.
    2. Byeong-Cheol Jeong & Dong-Hwan Shin & Jae-Beom Im & Jae-Young Park & Young-Jin Kim, 2019. "Implementation of Optimal Two-Stage Scheduling of Energy Storage System Based on Big-Data-Driven Forecasting—An Actual Case Study in a Campus Microgrid," Energies, MDPI, vol. 12(6), pages 1-20, March.
    3. Tiezhou Wu & Fanchao Ye & Yuehong Su & Yubo Wang & Saffa Riffat, 2020. "Coordinated control strategy of DC microgrid with hybrid energy storage system to smooth power output fluctuation," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 15(1), pages 46-54.
    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. Ji, Jie & Zhou, Mengxiong & Guo, Renwei & Tang, Jiankang & Su, Jiaoyue & Huang, Hui & Sun, Na & Nazir, Muhammad Shahzad & Wang, Yaodong, 2023. "A electric power optimal scheduling study of hybrid energy storage system integrated load prediction technology considering ageing mechanism," Renewable Energy, Elsevier, vol. 215(C).
    2. Paweł Dworak & Andrzej Mrozik & Agata Korzelecka-Orkisz & Adam Tański & Krzysztof Formicki, 2023. "Energy Self-Sufficiency of a Salmonids Breeding Facility in the Recirculating Aquaculture System," Energies, MDPI, vol. 16(6), pages 1-22, March.

    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. Ammar Armghan & Muhammad Kashif Azeem & Hammad Armghan & Ming Yang & Fayadh Alenezi & Mudasser Hassan, 2021. "Dynamical Operation Based Robust Nonlinear Control of DC Microgrid Considering Renewable Energy Integration," Energies, MDPI, vol. 14(13), pages 1-23, July.
    2. Amrutha Raju Battula & Sandeep Vuddanti & Surender Reddy Salkuti, 2021. "Review of Energy Management System Approaches in Microgrids," Energies, MDPI, vol. 14(17), pages 1-32, September.
    3. Shuang Lei & Yu He & Jing Zhang & Kun Deng, 2023. "Optimal Configuration of Hybrid Energy Storage Capacity in a Microgrid Based on Variational Mode Decomposition," Energies, MDPI, vol. 16(11), pages 1-19, May.
    4. Senthilkumar, S. & Balachander, K & Mohamed Mansoor, V.M., 2024. "A hybrid technique for impact of hybrid renewable energy systems on reliability of distribution power system," Energy, Elsevier, vol. 306(C).
    5. Zhu, Junjie & Huang, Shengjun & Liu, Yajie & Lei, Hongtao & Sang, Bo, 2021. "Optimal energy management for grid-connected microgrids via expected-scenario-oriented robust optimization," Energy, Elsevier, vol. 216(C).
    6. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    7. 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.
    8. Gi-Ho Lee & Jae-Young Park & Seung-Jun Ham & Young-Jin Kim, 2020. "Comparative Study on Optimization Solvers for Implementation of a Two-Stage Economic Dispatch Strategy in a Microgrid Energy Management System," Energies, MDPI, vol. 13(5), pages 1-21, March.
    9. Hamidreza Mirtaheri & Piero Macaluso & Maurizio Fantino & Marily Efstratiadi & Sotiris Tsakanikas & Panagiotis Papadopoulos & Andrea Mazza, 2021. "Hybrid Forecast and Control Chain for Operation of Flexibility Assets in Micro-Grids," Energies, MDPI, vol. 14(21), pages 1-22, November.
    10. Mahmoud Elkazaz & Mark Sumner & David Thomas, 2019. "Real-Time Energy Management for a Small Scale PV-Battery Microgrid: Modeling, Design, and Experimental Verification," Energies, MDPI, vol. 12(14), pages 1-26, July.
    11. Kim, Jangkyum & Oh, Hyeontaek & Choi, Jun Kyun, 2022. "Learning based cost optimal energy management model for campus microgrid systems," Applied Energy, Elsevier, vol. 311(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:jeners:v:15:y:2022:i:3:p:1060-:d:739509. 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.