IDEAS home Printed from https://ideas.repec.org/a/oup/ijlctc/v18y2023ip622-627..html
   My bibliography  Save this article

Capacity optimization strategy for energy storage system to ensure power supply

Author

Listed:
  • Huimin Fu
  • Ming Shi
  • Miaomiao Feng

Abstract

Photovoltaic (PV) and wind power generation are very promising renewable energy sources, reasonable capacity allocation of PV–wind complementary energy storage (ES) power generation system can improve the economy and reliability of system operation. In this paper, the goal is to ensure the power supply of the system and reduce the operation cost. The PV, wind and ES system models are analyzed. The differential evolutionary (DE) algorithm is adopted to optimize the particle swarm optimization (PSO) algorithm, and the parameters of the PSO algorithm are changed through the DE algorithm to obtain better performance. We use MATLAB to verify that when the system is composed of 100 kW PV and 100 kW wind power, the battery capacity obtained by PSO algorithm is 400 kWh, while the algorithm proposed in this paper only requires 330 kWh. Although the loss of load probability of the system is improved by about 0.12%, the cost is saved by 17.5%. To improve the system operation reliability, we recommend increasing PV, wind and ES capacity at the same time rather than increasing ES capacity separately.

Suggested Citation

  • Huimin Fu & Ming Shi & Miaomiao Feng, 2023. "Capacity optimization strategy for energy storage system to ensure power supply," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 18, pages 622-627.
    • Handle: RePEc:oup:ijlctc:v:18:y:2023:i::p:622-627.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/ijlct/ctad039
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Diaf, S. & Notton, G. & Belhamel, M. & Haddadi, M. & Louche, A., 2008. "Design and techno-economical optimization for hybrid PV/wind system under various meteorological conditions," Applied Energy, Elsevier, vol. 85(10), pages 968-987, October.
    2. Garg, Harish, 2016. "A hybrid PSO-GA algorithm for constrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 292-305.
    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. Hossain, Md. Faruque, 2017. "Green science: Independent building technology to mitigate energy, environment, and climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 695-705.
    2. Lan, Hai & Wen, Shuli & Hong, Ying-Yi & Yu, David C. & Zhang, Lijun, 2015. "Optimal sizing of hybrid PV/diesel/battery in ship power system," Applied Energy, Elsevier, vol. 158(C), pages 26-34.
    3. Jaszczur, Marek & Hassan, Qusay & Palej, Patryk & Abdulateef, Jasim, 2020. "Multi-Objective optimisation of a micro-grid hybrid power system for household application," Energy, Elsevier, vol. 202(C).
    4. Thopil, George Alex & Sachse, Christiaan Eddie & Lalk, Jörg & Thopil, Miriam Sara, 2020. "Techno-economic performance comparison of crystalline and thin film PV panels under varying meteorological conditions: A high solar resource southern hemisphere case," Applied Energy, Elsevier, vol. 275(C).
    5. Wang, Gang & Zhang, Zhen & Lin, Jianqing, 2024. "Multi-energy complementary power systems based on solar energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    6. Kirchner-Bossi, N. & Prieto, L. & García-Herrera, R. & Carro-Calvo, L. & Salcedo-Sanz, S., 2013. "Multi-decadal variability in a centennial reconstruction of daily wind," Applied Energy, Elsevier, vol. 105(C), pages 30-46.
    7. Gioutsos, Dean Marcus & Blok, Kornelis & van Velzen, Leonore & Moorman, Sjoerd, 2018. "Cost-optimal electricity systems with increasing renewable energy penetration for islands across the globe," Applied Energy, Elsevier, vol. 226(C), pages 437-449.
    8. Paliwal, Priyanka & Patidar, N.P. & Nema, R.K., 2014. "Determination of reliability constrained optimal resource mix for an autonomous hybrid power system using Particle Swarm Optimization," Renewable Energy, Elsevier, vol. 63(C), pages 194-204.
    9. Khatib, Tamer & Mohamed, Azah & Sopian, K., 2013. "A review of photovoltaic systems size optimization techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 454-465.
    10. Adane Abebaw Gessesse & Rajashree Mishra & Mitali Madhumita Acharya & Kedar Nath Das, 2020. "Genetic algorithm based fuzzy programming approach for multi-objective linear fractional stochastic transportation problem involving four-parameter Burr distribution," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(1), pages 93-109, February.
    11. Yassin Belkourchia & Mohamed Zeriab Es-Sadek & Lahcen Azrar, 2023. "New Hybrid Perturbed Projected Gradient and Simulated Annealing Algorithms for Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 197(2), pages 438-475, May.
    12. Kaabeche, A. & Belhamel, M. & Ibtiouen, R., 2011. "Sizing optimization of grid-independent hybrid photovoltaic/wind power generation system," Energy, Elsevier, vol. 36(2), pages 1214-1222.
    13. Abbes, Dhaker & Martinez, André & Champenois, Gérard, 2014. "Life cycle cost, embodied energy and loss of power supply probability for the optimal design of hybrid power systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 98(C), pages 46-62.
    14. Touqeer Ahmed Jumani & Mohd Wazir Mustafa & Nawaf N. Hamadneh & Samer H. Atawneh & Madihah Md. Rasid & Nayyar Hussain Mirjat & Muhammad Akram Bhayo & Ilyas Khan, 2020. "Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids," Energies, MDPI, vol. 13(16), pages 1-22, August.
    15. Bhandari, Binayak & Lee, Kyung-Tae & Lee, Caroline Sunyong & Song, Chul-Ki & Maskey, Ramesh K. & Ahn, Sung-Hoon, 2014. "A novel off-grid hybrid power system comprised of solar photovoltaic, wind, and hydro energy sources," Applied Energy, Elsevier, vol. 133(C), pages 236-242.
    16. Li, Chao & Zhai, Rongrong & Yang, Yongping & Patchigolla, Kumar & Oakey, John E. & Turner, Peter, 2019. "Annual performance analysis and optimization of a solar tower aided coal-fired power plant," Applied Energy, Elsevier, vol. 237(C), pages 440-456.
    17. Brayan A. Atoccsa & David W. Puma & Daygord Mendoza & Estefany Urday & Cristhian Ronceros & Modesto T. Palma, 2024. "Optimization of Ampacity in High-Voltage Underground Cables with Thermal Backfill Using Dynamic PSO and Adaptive Strategies," Energies, MDPI, vol. 17(5), pages 1-19, February.
    18. Mahesh, Aeidapu & Sandhu, Kanwarjit Singh, 2015. "Hybrid wind/photovoltaic energy system developments: Critical review and findings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1135-1147.
    19. Luo, Qifang & Yang, Xiao & Zhou, Yongquan, 2019. "Nature-inspired approach: An enhanced moth swarm algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 159(C), pages 57-92.
    20. Thirunavukkarasu, M. & Sawle, Yashwant & Lala, Himadri, 2023. "A comprehensive review on optimization of hybrid renewable energy systems using various optimization techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(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:oup:ijlctc:v:18:y:2023:i::p:622-627.. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/ijlct .

    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.