IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i3p729-d728600.html
   My bibliography  Save this article

Battery Sizing Optimization in Power Smoothing Applications

Author

Listed:
  • Asier Zulueta

    (Department of Nuclear and Fluid Mechanics, University of the Basque Country (UPV/EHU) Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

  • Decebal Aitor Ispas-Gil

    (System Engineering & Automation Control Department, University of the Basque Country (UPV/EHU) Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

  • Ekaitz Zulueta

    (System Engineering & Automation Control Department, University of the Basque Country (UPV/EHU) Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

  • Joseba Garcia-Ortega

    (System Engineering & Automation Control Department, University of the Basque Country (UPV/EHU) Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

  • Unai Fernandez-Gamiz

    (Department of Nuclear and Fluid Mechanics, University of the Basque Country (UPV/EHU) Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

Abstract

The main objective of this work was to determine the worth of installing an electrical battery in order to reduce peak power consumption. The importance of this question resides in the expensive terms of energy bills when using the maximum power level. If maximum power consumption decreases, it affects not only the revenues of maximum power level bills, but also results in important reductions at the source of the power. This way, the power of the transformer decreases, and other electrical elements can be removed from electrical installations. The authors studied the Spanish electrical system, and a particle swarm optimization (PSO) algorithm was used to model battery sizing in peak power smoothing applications for an electrical consumption point. This study proves that, despite not being entirely profitable at present due to current kWh prices, implanting a battery will definitely be an option to consider in the future when these prices come down.

Suggested Citation

  • Asier Zulueta & Decebal Aitor Ispas-Gil & Ekaitz Zulueta & Joseba Garcia-Ortega & Unai Fernandez-Gamiz, 2022. "Battery Sizing Optimization in Power Smoothing Applications," Energies, MDPI, vol. 15(3), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:729-:d:728600
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/3/729/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/3/729/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Jianxiao & Zhong, Haiwang & Tang, Wenyuan & Rajagopal, Ram & Xia, Qing & Kang, Chongqing & Wang, Yi, 2017. "Optimal bidding strategy for microgrids in joint energy and ancillary service markets considering flexible ramping products," Applied Energy, Elsevier, vol. 205(C), pages 294-303.
    2. Jinbao Jian & Lin Yang & Xianzhen Jiang & Pengjie Liu & Meixing Liu, 2020. "A Spectral Conjugate Gradient Method with Descent Property," Mathematics, MDPI, vol. 8(2), pages 1-13, February.
    3. Jingcheng Zhou & Wei Wei & Ruizhi Zhang & Zhiming Zheng, 2021. "Damped Newton Stochastic Gradient Descent Method for Neural Networks Training," Mathematics, MDPI, vol. 9(13), pages 1-12, June.
    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. Jiang, Tao & Wu, Chenghao & Huang, Tao & Zhang, Rufeng & Li, Xue, 2024. "Optimal market participation of VPPs in TSO-DSO coordinated energy and flexibility markets," Applied Energy, Elsevier, vol. 360(C).
    2. Hong, Bowen & Zhang, Weitong & Zhou, Yue & Chen, Jian & Xiang, Yue & Mu, Yunfei, 2018. "Energy-Internet-oriented microgrid energy management system architecture and its application in China," Applied Energy, Elsevier, vol. 228(C), pages 2153-2164.
    3. Cong Liu & Jingyang Zhou & Yi Pan & Zhiyi Li & Yifei Wang & Dan Xu & Qiang Ding & Zhiqiang Luo & Mohammad Shahidehpour, 2019. "Multi-period Market Operation of Transmission-Distribution Systems Based on Heterogeneous Decomposition and Coordination," Energies, MDPI, vol. 12(16), pages 1-20, August.
    4. Erika Kaempf & Bernhard Ernst & Martin Braun, 2019. "Competitive cross‐voltage level procurement of reactive power considering reliable capacity from wind and photovoltaics," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(4), July.
    5. Rezaei, Navid & Pezhmani, Yasin & Khazali, Amirhossein, 2022. "Economic-environmental risk-averse optimal heat and power energy management of a grid-connected multi microgrid system considering demand response and bidding strategy," Energy, Elsevier, vol. 240(C).
    6. Gupta, Aparna & Palepu, Sai, 2024. "Designing risk-free service for renewable wind and solar resources," European Journal of Operational Research, Elsevier, vol. 315(2), pages 715-728.
    7. 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.
    8. Correa-Florez, Carlos Adrian & Michiorri, Andrea & Kariniotakis, Georges, 2018. "Robust optimization for day-ahead market participation of smart-home aggregators," Applied Energy, Elsevier, vol. 229(C), pages 433-445.
    9. Dranka, Géremi Gilson & Ferreira, Paula & Vaz, A. Ismael F., 2021. "A review of co-optimization approaches for operational and planning problems in the energy sector," Applied Energy, Elsevier, vol. 304(C).
    10. Mehdizadeh, Ali & Taghizadegan, Navid & Salehi, Javad, 2018. "Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management," Applied Energy, Elsevier, vol. 211(C), pages 617-630.
    11. Das, Saborni & Basu, Mousumi, 2020. "Day-ahead optimal bidding strategy of microgrid with demand response program considering uncertainties and outages of renewable energy resources," Energy, Elsevier, vol. 190(C).
    12. Tang, Hong & Wang, Shengwei, 2022. "Multi-level optimal dispatch strategy and profit-sharing mechanism for unlocking energy flexibilities of non-residential building clusters in electricity markets of multiple flexibility services," Renewable Energy, Elsevier, vol. 201(P1), pages 35-45.
    13. Harold Doran, 2023. "A Collection of Numerical Recipes Useful for Building Scalable Psychometric Applications," Journal of Educational and Behavioral Statistics, , vol. 48(1), pages 37-69, February.
    14. Schwidtal, Jan Marc & Agostini, Marco & Coppo, Massimiliano & Bignucolo, Fabio & Lorenzoni, Arturo, 2023. "Optimized operation of distributed energy resources: The opportunities of value stacking for Power-to-Gas aggregated with PV," Applied Energy, Elsevier, vol. 334(C).
    15. Cui, Mingjian & Zhang, Jie, 2018. "Estimating ramping requirements with solar-friendly flexible ramping product in multi-timescale power system operations," Applied Energy, Elsevier, vol. 225(C), pages 27-41.
    16. Greve, Thomas & Teng, Fei & Pollitt, Michael G. & Strbac, Goran, 2018. "A system operator’s utility function for the frequency response market," Applied Energy, Elsevier, vol. 231(C), pages 562-569.
    17. Yeh, Wei-Chang & He, Min-Fan & Huang, Chia-Ling & Tan, Shi-Yi & Zhang, Xianyong & Huang, Yaohong & Li, Li, 2020. "New genetic algorithm for economic dispatch of stand-alone three-modular microgrid in DongAo Island," Applied Energy, Elsevier, vol. 263(C).
    18. Wang, Yubin & Yang, Qiang & Zhou, Yue & Zheng, Yanchong, 2024. "A risk-averse day-ahead bidding strategy of transactive energy sharing microgrids with data-driven chance constraints," Applied Energy, Elsevier, vol. 353(PB).
    19. Nikpour, Ahmad & Nateghi, Abolfazl & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Day-ahead optimal bidding of microgrids considering uncertainties of price and renewable energy resources," Energy, Elsevier, vol. 227(C).
    20. Carlos Adrian Correa-Florez & Andrea Michiorri & Georges Kariniotakis, 2019. "Comparative Analysis of Adjustable Robust Optimization Alternatives for the Participation of Aggregated Residential Prosumers in Electricity Markets," Energies, MDPI, vol. 12(6), pages 1-27, March.

    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:729-:d:728600. 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.