IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i9p1812-d230640.html
   My bibliography  Save this article

Energy Storage Sizing Strategy for Grid-Tied PV Plants under Power Clipping Limitations

Author

Listed:
  • Nicolás Müller

    (Department of Electrical and Electronic Engineering, University of Nottingham, Nottingham NG7 2RD, UK
    Electronics Engineering Department, Universidad Técnica Federico Santa María, Valparaiso 2390123, Chile
    These authors contributed equally to this work.)

  • Samir Kouro

    (Electronics Engineering Department, Universidad Técnica Federico Santa María, Valparaiso 2390123, Chile
    These authors contributed equally to this work.)

  • Pericle Zanchetta

    (Department of Electrical and Electronic Engineering, University of Nottingham, Nottingham NG7 2RD, UK
    These authors contributed equally to this work.)

  • Patrick Wheeler

    (Department of Electrical and Electronic Engineering, University of Nottingham, Nottingham NG7 2RD, UK
    These authors contributed equally to this work.)

  • Gustavo Bittner

    (Electronics Engineering Department, Universidad Técnica Federico Santa María, Valparaiso 2390123, Chile
    These authors contributed equally to this work.)

  • Francesco Girardi

    (Bluefield Services, Bristol BS1 6DZ, UK
    These authors contributed equally to this work.)

Abstract

This paper presents an analyses of an Energy Storage System (ESS) for grid-tied photovoltaic (PV) systems, in order to harness the energy usually lost due to PV array oversizing. A real case of annual PV power generation analysis is presented to illustrate the existing problem and future solutions. Three PV modeling techniques have been applied to estimate non-measured non-harnessed PV power to provide an ESS energy and power sizing strategy. Moreover, a control strategy to store or release power from the DC-link, without modifying the Maximum Power Point Tracking (MPPT) strategy, is presented. The results show an estimation of the annual power loss caused by oversizing the PV array. The ESS sizing strategy gives insight into not only the energy requirements, but also the power requirements of the system. Simulation results show that the proposed ESS control strategy is capable of harnessing the extra power without modifying the existing power converter of the PV plant nor its control strategy.

Suggested Citation

  • Nicolás Müller & Samir Kouro & Pericle Zanchetta & Patrick Wheeler & Gustavo Bittner & Francesco Girardi, 2019. "Energy Storage Sizing Strategy for Grid-Tied PV Plants under Power Clipping Limitations," Energies, MDPI, vol. 12(9), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1812-:d:230640
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/9/1812/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/9/1812/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mellit, A. & Sağlam, S. & Kalogirou, S.A., 2013. "Artificial neural network-based model for estimating the produced power of a photovoltaic module," Renewable Energy, Elsevier, vol. 60(C), pages 71-78.
    2. Díaz-González, Francisco & Sumper, Andreas & Gomis-Bellmunt, Oriol & Villafáfila-Robles, Roberto, 2012. "A review of energy storage technologies for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 2154-2171.
    3. João Martins & Sergiu Spataru & Dezso Sera & Daniel-Ioan Stroe & Abderezak Lashab, 2019. "Comparative Study of Ramp-Rate Control Algorithms for PV with Energy Storage Systems," Energies, MDPI, vol. 12(7), pages 1-15, April.
    4. Good, Jeremy & Johnson, Jeremiah X., 2016. "Impact of inverter loading ratio on solar photovoltaic system performance," Applied Energy, Elsevier, vol. 177(C), pages 475-486.
    5. Luo, Xing & Wang, Jihong & Dooner, Mark & Clarke, Jonathan, 2015. "Overview of current development in electrical energy storage technologies and the application potential in power system operation," Applied Energy, Elsevier, vol. 137(C), pages 511-536.
    6. Ming-Hui Chang & Han-Pang Huang & Shu-Wei Chang, 2013. "A New State of Charge Estimation Method for LiFePO 4 Battery Packs Used in Robots," Energies, MDPI, vol. 6(4), pages 1-24, April.
    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. Francesco Lo Franco & Antonio Morandi & Pietro Raboni & Gabriele Grandi, 2021. "Efficiency Comparison of DC and AC Coupling Solutions for Large-Scale PV+BESS Power Plants," Energies, MDPI, vol. 14(16), pages 1-22, August.
    2. Xiaodong Yu & Xia Dong & Shaopeng Pang & Luanai Zhou & Hongzhi Zang, 2019. "Energy Storage Sizing Optimization and Sensitivity Analysis Based on Wind Power Forecast Error Compensation," Energies, MDPI, vol. 12(24), pages 1-21, December.
    3. Schleifer, Anna H. & Murphy, Caitlin A. & Cole, Wesley J. & Denholm, Paul, 2022. "Exploring the design space of PV-plus-battery system configurations under evolving grid conditions," Applied Energy, Elsevier, vol. 308(C).
    4. DiOrio, Nicholas & Denholm, Paul & Hobbs, William B., 2020. "A model for evaluating the configuration and dispatch of PV plus battery power plants," Applied Energy, Elsevier, vol. 262(C).
    5. Nissim Amar & Aaron Shmaryahu & Michael Coletti & Ilan Aharon, 2021. "Sizing Procedure for System Hybridization Based on Experimental Source Modeling in Grid Application," Energies, MDPI, vol. 14(15), pages 1-19, August.

    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. Meng, Hui & Wang, Meihong & Olumayegun, Olumide & Luo, Xiaobo & Liu, Xiaoyan, 2019. "Process design, operation and economic evaluation of compressed air energy storage (CAES) for wind power through modelling and simulation," Renewable Energy, Elsevier, vol. 136(C), pages 923-936.
    2. He, Wei & Wang, Jihong, 2017. "Feasibility study of energy storage by concentrating/desalinating water: Concentrated Water Energy Storage," Applied Energy, Elsevier, vol. 185(P1), pages 872-884.
    3. Weitzel, Timm & Glock, Christoph H., 2018. "Energy management for stationary electric energy storage systems: A systematic literature review," European Journal of Operational Research, Elsevier, vol. 264(2), pages 582-606.
    4. Kebede, Abraham Alem & Kalogiannis, Theodoros & Van Mierlo, Joeri & Berecibar, Maitane, 2022. "A comprehensive review of stationary energy storage devices for large scale renewable energy sources grid integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    5. Fiammetta Rita Bianchi & Barbara Bosio, 2021. "Operating Principles, Performance and Technology Readiness Level of Reversible Solid Oxide Cells," Sustainability, MDPI, vol. 13(9), pages 1-23, April.
    6. Ren, Jingzheng, 2018. "Sustainability prioritization of energy storage technologies for promoting the development of renewable energy: A novel intuitionistic fuzzy combinative distance-based assessment approach," Renewable Energy, Elsevier, vol. 121(C), pages 666-676.
    7. Hinz, Juri & Yee, Jeremy, 2018. "Optimal forward trading and battery control under renewable electricity generation," Journal of Banking & Finance, Elsevier, vol. 95(C), pages 244-254.
    8. Dehghani-Sanij, A.R. & Tharumalingam, E. & Dusseault, M.B. & Fraser, R., 2019. "Study of energy storage systems and environmental challenges of batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 192-208.
    9. Leszczyński, Jacek S. & Gryboś, Dominik & Markowski, Jan, 2023. "Analysis of optimal expansion dynamics in a reciprocating drive for a micro-CAES production system," Applied Energy, Elsevier, vol. 350(C).
    10. Chowdhury, Jahedul Islam & Balta-Ozkan, Nazmiye & Goglio, Pietro & Hu, Yukun & Varga, Liz & McCabe, Leah, 2020. "Techno-environmental analysis of battery storage for grid level energy services," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    11. Parra, David & Swierczynski, Maciej & Stroe, Daniel I. & Norman, Stuart.A. & Abdon, Andreas & Worlitschek, Jörg & O’Doherty, Travis & Rodrigues, Lucelia & Gillott, Mark & Zhang, Xiaojin & Bauer, Chris, 2017. "An interdisciplinary review of energy storage for communities: Challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 730-749.
    12. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    13. Chen, Qi & Kuang, Zhonghong & Liu, Xiaohua & Zhang, Tao, 2022. "Energy storage to solve the diurnal, weekly, and seasonal mismatch and achieve zero-carbon electricity consumption in buildings," Applied Energy, Elsevier, vol. 312(C).
    14. Odukomaiya, Adewale & Abu-Heiba, Ahmad & Graham, Samuel & Momen, Ayyoub M., 2018. "Experimental and analytical evaluation of a hydro-pneumatic compressed-air Ground-Level Integrated Diverse Energy Storage (GLIDES) system," Applied Energy, Elsevier, vol. 221(C), pages 75-85.
    15. Ren, Guorui & Liu, Jinfu & Wan, Jie & Guo, Yufeng & Yu, Daren, 2017. "Overview of wind power intermittency: Impacts, measurements, and mitigation solutions," Applied Energy, Elsevier, vol. 204(C), pages 47-65.
    16. Caralis, George & Christakopoulos, Theofanis & Karellas, Sotirios & Gao, Zhiqiu, 2019. "Analysis of energy storage systems to exploit wind energy curtailment in Crete," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 122-139.
    17. Edison Banguero & Antonio Correcher & Ángel Pérez-Navarro & Francisco Morant & Andrés Aristizabal, 2018. "A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems," Energies, MDPI, vol. 11(4), pages 1-15, April.
    18. Paolo Falbo & Juri Hinz & Piyachat Leelasilapasart & Cristian Pelizzari, 2021. "A Computational Approach to Sequential Decision Optimization in Energy Storage and Trading," JRFM, MDPI, vol. 14(6), pages 1-22, May.
    19. Xiaotong Qie & Rui Zhang & Yanyong Hu & Xialing Sun & Xue Chen, 2021. "A Multi-Criteria Decision-Making Approach for Energy Storage Technology Selection Based on Demand," Energies, MDPI, vol. 14(20), pages 1-29, October.
    20. Venkataramani, Gayathri & Parankusam, Prasanna & Ramalingam, Velraj & Wang, Jihong, 2016. "A review on compressed air energy storage – A pathway for smart grid and polygeneration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 895-907.

    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:12:y:2019:i:9:p:1812-:d:230640. 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.