IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i12p3707-d579096.html
   My bibliography  Save this article

Research on the Optimized Operation of Hybrid Wind and Battery Energy Storage System Based on Peak-Valley Electricity Price

Author

Listed:
  • Miao Miao

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Suhua Lou

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yuanxin Zhang

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
    China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Xing Chen

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
    China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

The combined operation of hybrid wind power and a battery energy storage system can be used to convert cheap valley energy to expensive peak energy, thus improving the economic benefits of wind farms. Considering the peak–valley electricity price, an optimization model of the economic benefits of a combined wind–storage system was developed. A charging/discharging strategy of the battery storage system was proposed to maximize the economic benefits of the combined wind–storage system based on the forecast wind power. The maximal economic benefits were obtained based on scenario analysis, taking into account the wind-power forecast error, and costs associated with the loss of battery life, battery operation, and maintenance. Case simulation results highlight the effectiveness of the proposed model. The results show that the hybrid wind–storage system is not only able to convert cheap electricity in the valley period into expensive electricity in the peak period, thus resulting in higher economic benefits, but can also balance the deviation between actual output and plans for the wind power generator to decrease the loss penalty. The analyzed examples show that, following an increase in the deviation of the forecast wind power, the profit of the combined wind–storage system can increase by up to 45% using the charging/discharging strategy, compared with a wind farm that does not utilize energy storage. In addition, the profit of the combined wind–storage system can increase by up to 16% compared with separate systems, following an increase in the deviation penalty deviation coefficient.

Suggested Citation

  • Miao Miao & Suhua Lou & Yuanxin Zhang & Xing Chen, 2021. "Research on the Optimized Operation of Hybrid Wind and Battery Energy Storage System Based on Peak-Valley Electricity Price," Energies, MDPI, vol. 14(12), pages 1-11, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3707-:d:579096
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/12/3707/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/12/3707/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Li, Sai & Fang, Huajing & Shi, Bing, 2021. "Remaining useful life estimation of Lithium-ion battery based on interacting multiple model particle filter and support vector regression," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    3. Li, Jianwei & Gee, Anthony M. & Zhang, Min & Yuan, Weijia, 2015. "Analysis of battery lifetime extension in a SMES-battery hybrid energy storage system using a novel battery lifetime model," Energy, Elsevier, vol. 86(C), pages 175-185.
    4. Loukatou, Angeliki & Johnson, Paul & Howell, Sydney & Duck, Peter, 2021. "Optimal valuation of wind energy projects co-located with battery storage," Applied Energy, Elsevier, vol. 283(C).
    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. Qi Huang & Aihua Jiang & Yu Zeng & Jianan Xu, 2022. "Community Flexible Load Dispatching Model Based on Herd Mentality," Energies, MDPI, vol. 15(13), pages 1-18, June.

    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. Harri Aaltonen & Seppo Sierla & Rakshith Subramanya & Valeriy Vyatkin, 2021. "A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage," Energies, MDPI, vol. 14(17), pages 1-20, September.
    2. Barelli, L. & Bidini, G. & Bonucci, F. & Castellini, L. & Fratini, A. & Gallorini, F. & Zuccari, A., 2019. "Flywheel hybridization to improve battery life in energy storage systems coupled to RES plants," Energy, Elsevier, vol. 173(C), pages 937-950.
    3. 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).
    4. Xu, Ying & Ren, Li & Zhang, Zhongping & Tang, Yuejin & Shi, Jing & Xu, Chen & Li, Jingdong & Pu, Dongsheng & Wang, Zhuang & Liu, Huajun & Chen, Lei, 2018. "Analysis of the loss and thermal characteristics of a SMES (Superconducting Magnetic Energy Storage) magnet with three practical operating conditions," Energy, Elsevier, vol. 143(C), pages 372-384.
    5. Cezar Antônio Rigo & Edemar Morsch Filho & Laio Oriel Seman & Luís Loures & Valderi Reis Quietinho Leithardt, 2023. "Instance and Data Generation for the Offline Nanosatellite Task Scheduling Problem," Data, MDPI, vol. 8(3), pages 1-14, March.
    6. 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.
    7. Bizon, Nicu, 2019. "Hybrid power sources (HPSs) for space applications: Analysis of PEMFC/Battery/SMES HPS under unknown load containing pulses," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 14-37.
    8. Zhang, Jiusi & Jiang, Yuchen & Li, Xiang & Huo, Mingyi & Luo, Hao & Yin, Shen, 2022. "An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. 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.
    10. Rigo, Cezar Antônio & Seman, Laio Oriel & Camponogara, Eduardo & Morsch Filho, Edemar & Bezerra, Eduardo Augusto & Munari, Pedro, 2022. "A branch-and-price algorithm for nanosatellite task scheduling to improve mission quality-of-service," European Journal of Operational Research, Elsevier, vol. 303(1), pages 168-183.
    11. Cao, Mengda & Zhang, Tao & Liu, Yajie & Zhang, Yajun & Wang, Yu & Li, Kaiwen, 2022. "An ensemble learning prognostic method for capacity estimation of lithium-ion batteries based on the V-IOWGA operator," Energy, Elsevier, vol. 257(C).
    12. Liu, Xingheng & Matias, José & Jäschke, Johannes & Vatn, Jørn, 2022. "Gibbs sampler for noisy Transformed Gamma process: Inference and remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    13. Wei, Yupeng & Wu, Dazhong, 2023. "Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    14. Li, Jianwei & Xiong, Rui & Mu, Hao & Cornélusse, Bertrand & Vanderbemden, Philippe & Ernst, Damien & Yuan, Weijia, 2018. "Design and real-time test of a hybrid energy storage system in the microgrid with the benefit of improving the battery lifetime," Applied Energy, Elsevier, vol. 218(C), pages 470-478.
    15. 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.
    16. 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.
    17. Song, Ziyou & Li, Jianqiu & Hou, Jun & Hofmann, Heath & Ouyang, Minggao & Du, Jiuyu, 2018. "The battery-supercapacitor hybrid energy storage system in electric vehicle applications: A case study," Energy, Elsevier, vol. 154(C), pages 433-441.
    18. Wang, Shuoqi & Guo, Dongxu & Han, Xuebing & Lu, Languang & Sun, Kai & Li, Weihan & Sauer, Dirk Uwe & Ouyang, Minggao, 2020. "Impact of battery degradation models on energy management of a grid-connected DC microgrid," Energy, Elsevier, vol. 207(C).
    19. Zhu, Yongmeng & Wu, Jiechang & Wu, Jun & Liu, Shuyong, 2022. "Dimensionality reduce-based for remaining useful life prediction of machining tools with multisensor fusion," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    20. Abdel-Raheem Youssef & Mohamad Mallah & Abdelfatah Ali & Mostafa F. Shaaban & Essam E. M. Mohamed, 2023. "Enhancement of Microgrid Frequency Stability Based on the Combined Power-to-Hydrogen-to-Power Technology under High Penetration Renewable Units," Energies, MDPI, vol. 16(8), pages 1-18, April.

    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:14:y:2021:i:12:p:3707-:d:579096. 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.