IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i24p10257-d458878.html
   My bibliography  Save this article

Optimizing Battery Energy Storage System Data in the Presence of Wind Power Plants: A Comparative Study on Evolutionary Algorithms

Author

Listed:
  • Ramin Sakipour

    (Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah 67144-14971, Iran)

  • Hamdi Abdi

    (Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah 67144-14971, Iran)

Abstract

This study deals with the optimization of battery energy storage system (BESS) data in terms of significant characteristics of life and efficiency, and their positive impacts on power system efficiency in the presence of wind power plants in a microgrid. To this end, a permanent magnet synchronous generator (PMSG) is used to convert the wind energy by connecting a three-phase dynamic load to the grid. The main novelty of the proposed method is designing a smart backup battery branch to improve the efficiency of the wind farm by maintaining the operating constraints even during the occurrence of harsh faults in the generation section. Additionally, for the first time, the characteristics of the BESS are optimized using nine evolutionary algorithms, including the genetic algorithm (GA), teaching–learning-based optimization (TLBO), particle swarm optimization (PSO), gravitational search algorithm (GSA), artificial bee colony (ABC), differential evolution (DE), grey wolf optimizer (GWO), moth–flame optimization algorithm (MFO), and sine cosine algorithm (SCA), and the results are compared with each other. The simulation results of a case study confirm the robustness of the proposed control strategy for the BESS.

Suggested Citation

  • Ramin Sakipour & Hamdi Abdi, 2020. "Optimizing Battery Energy Storage System Data in the Presence of Wind Power Plants: A Comparative Study on Evolutionary Algorithms," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:24:p:10257-:d:458878
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/24/10257/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/24/10257/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Simone Orcioni & Luca Buccolini & Adriana Ricci & Massimo Conti, 2017. "Lithium-ion Battery Electrothermal Model, Parameter Estimation, and Simulation Environment," Energies, MDPI, vol. 10(3), pages 1-20, March.
    2. Khalid, Muhammad & Aguilera, Ricardo P. & Savkin, Andrey V. & Agelidis, Vassilios G., 2018. "On maximizing profit of wind-battery supported power station based on wind power and energy price forecasting," Applied Energy, Elsevier, vol. 211(C), pages 764-773.
    3. Guan Wang & Zhongfu Tan & Qingkun Tan & Shenbo Yang & Hongyu Lin & Xionghua Ji & De Gejirifu & Xueying Song, 2019. "Multi-Objective Robust Scheduling Optimization Model of Wind, Photovoltaic Power, and BESS Based on the Pareto Principle," Sustainability, MDPI, vol. 11(2), pages 1-14, January.
    4. Hemmati, Reza & Azizi, Neda, 2017. "Advanced control strategy on battery storage system for energy management and bidirectional power control in electrical networks," Energy, Elsevier, vol. 138(C), pages 520-528.
    5. Mustafa Cagatay Kocer & Ceyhun Cengiz & Mehmet Gezer & Doruk Gunes & Mehmet Aytac Cinar & Bora Alboyaci & Ahmet Onen, 2019. "Assessment of Battery Storage Technologies for a Turkish Power Network," Sustainability, MDPI, vol. 11(13), pages 1-33, July.
    6. Simla, Tomasz & Stanek, Wojciech, 2020. "Reducing the impact of wind farms on the electric power system by the use of energy storage," Renewable Energy, Elsevier, vol. 145(C), pages 772-782.
    7. Ryuto Shigenobu & Ahmad Samim Noorzad & Cirio Muarapaz & Atsushi Yona & Tomonobu Senjyu, 2016. "Optimal Operation and Management of Smart Grid System with LPC and BESS in Fault Conditions," Sustainability, MDPI, vol. 8(12), pages 1-19, December.
    8. Jon Martinez-Rico & Ekaitz Zulueta & Unai Fernandez-Gamiz & Ismael Ruiz de Argandoña & Mikel Armendia, 2020. "Forecast Error Sensitivity Analysis for Bidding in Electricity Markets with a Hybrid Renewable Plant Using a Battery Energy Storage System," Sustainability, MDPI, vol. 12(9), pages 1-18, April.
    9. Chua, Kein Huat & Lim, Yun Seng & Morris, Stella, 2017. "A novel fuzzy control algorithm for reducing the peak demands using energy storage system," Energy, Elsevier, vol. 122(C), pages 265-273.
    10. Xu, Fangqiu & Liu, Jicheng & Lin, Shuaishuai & Dai, Qiongjie & Li, Cunbin, 2018. "A multi-objective optimization model of hybrid energy storage system for non-grid-connected wind power: A case study in China," Energy, Elsevier, vol. 163(C), pages 585-603.
    11. Liu, Ye & Wu, Xiaogang & Du, Jiuyu & Song, Ziyou & Wu, Guoliang, 2020. "Optimal sizing of a wind-energy storage system considering battery life," Renewable Energy, Elsevier, vol. 147(P1), pages 2470-2483.
    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. Carolina G. Marcelino & João V. C. Avancini & Carla A. D. M. Delgado & Elizabeth F. Wanner & Silvia Jiménez-Fernández & Sancho Salcedo-Sanz, 2021. "Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms," Sustainability, MDPI, vol. 13(21), pages 1-20, October.

    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. Oh, Eunsung & Son, Sung-Yong, 2020. "Theoretical energy storage system sizing method and performance analysis for wind power forecast uncertainty management," Renewable Energy, Elsevier, vol. 155(C), pages 1060-1069.
    2. Badrinarayanan, Rajagopalan & Tseng, King Jet & Soong, Boon Hee & Wei, Zhongbao, 2017. "Modelling and control of vanadium redox flow battery for profile based charging applications," Energy, Elsevier, vol. 141(C), pages 1479-1488.
    3. Chao Ma & Sen Dong & Jijian Lian & Xiulan Pang, 2019. "Multi-Objective Sizing of Hybrid Energy Storage System for Large-Scale Photovoltaic Power Generation System," Sustainability, MDPI, vol. 11(19), pages 1-15, October.
    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. Liu, Hailiang & Brown, Tom & Andresen, Gorm Bruun & Schlachtberger, David P. & Greiner, Martin, 2019. "The role of hydro power, storage and transmission in the decarbonization of the Chinese power system," Applied Energy, Elsevier, vol. 239(C), pages 1308-1321.
    6. Jinwoo Jeong & Heewon Shin & Hwachang Song & Byongjun Lee, 2018. "A Countermeasure for Preventing Flexibility Deficit under High-Level Penetration of Renewable Energies: A Robust Optimization Approach," Sustainability, MDPI, vol. 10(11), pages 1-16, November.
    7. Muhammad Khalid, 2019. "A Review on the Selected Applications of Battery-Supercapacitor Hybrid Energy Storage Systems for Microgrids," Energies, MDPI, vol. 12(23), pages 1-34, November.
    8. Hau, Lee Cheun & Lim, Yun Seng & Liew, Serena Miao San, 2020. "A novel spontaneous self-adjusting controller of energy storage system for maximum demand reductions under penetration of photovoltaic system," Applied Energy, Elsevier, vol. 260(C).
    9. Mohseni, Soheil & Brent, Alan C. & Burmester, Daniel, 2020. "A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid," Applied Energy, Elsevier, vol. 259(C).
    10. Lange, Christopher & Rueß, Alexandra & Nuß, Andreas & Öchsner, Richard & März, Martin, 2020. "Dimensioning battery energy storage systems for peak shaving based on a real-time control algorithm," Applied Energy, Elsevier, vol. 280(C).
    11. Zhong, Shengyuan & Zhao, Jun & Li, Wenjia & Li, Hao & Deng, Shuai & Li, Yang & Hussain, Sajjad & Wang, Xiaoyuan & Zhu, Jiebei, 2021. "Quantitative analysis of information interaction in building energy systems based on mutual information," Energy, Elsevier, vol. 214(C).
    12. Sun, Jie & Zheng, Menglian & Yang, Zhongshu & Yu, Zitao, 2019. "Flow field design pathways from lab-scale toward large-scale flow batteries," Energy, Elsevier, vol. 173(C), pages 637-646.
    13. Li, Yang & Vilathgamuwa, Mahinda & Choi, San Shing & Farrell, Troy W. & Tran, Ngoc Tham & Teague, Joseph, 2019. "Development of a degradation-conscious physics-based lithium-ion battery model for use in power system planning studies," Applied Energy, Elsevier, vol. 248(C), pages 512-525.
    14. Abdul Rauf & Mahmoud Kassas & Muhammad Khalid, 2022. "Data-Driven Optimal Battery Storage Sizing for Grid-Connected Hybrid Distributed Generations Considering Solar and Wind Uncertainty," Sustainability, MDPI, vol. 14(17), pages 1-27, September.
    15. Bandara, T.G. Thusitha Asela & Viera, J.C. & González, M., 2022. "The next generation of fast charging methods for Lithium-ion batteries: The natural current-absorption methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    16. Roman Niestrój & Tomasz Rogala & Wojciech Skarka, 2020. "An Energy Consumption Model for Designing an AGV Energy Storage System with a PEMFC Stack," Energies, MDPI, vol. 13(13), pages 1-31, July.
    17. Diankai Wang & Inna Gryshova & Anush Balian & Mykola Kyzym & Tetiana Salashenko & Viktoriia Khaustova & Olexandr Davidyuk, 2022. "Assessment of Power System Sustainability and Compromises between the Development Goals," Sustainability, MDPI, vol. 14(4), pages 1-23, February.
    18. Li, Yang & Vilathgamuwa, Mahinda & Choi, San Shing & Xiong, Binyu & Tang, Jinrui & Su, Yixin & Wang, Yu, 2020. "Design of minimum cost degradation-conscious lithium-ion battery energy storage system to achieve renewable power dispatchability," Applied Energy, Elsevier, vol. 260(C).
    19. Wenhao Zhuo & Andrey V. Savkin, 2019. "Profit Maximizing Control of a Microgrid with Renewable Generation and BESS Based on a Battery Cycle Life Model and Energy Price Forecasting," Energies, MDPI, vol. 12(15), pages 1-17, July.
    20. Fan, Feilong & Huang, Wentao & Tai, Nengling & Zheng, Xiaodong & Hu, Yan & Ma, Zhoujun, 2018. "A conditional depreciation balancing strategy for the equitable operation of extended hybrid energy storage systems," Applied Energy, Elsevier, vol. 228(C), pages 1937-1952.

    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:12:y:2020:i:24:p:10257-:d:458878. 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.