IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v215y2023ics0960148123008145.html
   My bibliography  Save this article

Analysis and prediction of the penetration of renewable energy in power systems using artificial neural network

Author

Listed:
  • Han, Yixiao
  • Liao, Yanfen
  • Ma, Xiaoqian
  • Guo, Xing
  • Li, Changxin
  • Liu, Xinyu

Abstract

The curtailment of renewable energy worsens with increasing penetration in power systems, so it is necessary to explore the upper limit value of the penetration of renewable energy (PRE). This paper uses artificial neural networks (ANN) to study the historical data of California independent system operator (CAISO), analyze the fluctuation balance strategy of wind and solar power, and predict the upper limit value of the PRE, which will peak at 40.5% in 2025. In addition, this paper also simulates the inclusion of energy storage unit (ESU) with an installed capacity of 3 GWh in the grid to reduce curtailments, and analyzes the grid operating conditions and the upper limit value of the PRE, showing that the storage units with an installed capacity of only 3% of the average daily power output of solar energy recover 58% of the annual curtailments (2021), and the maximum PRE is 41.2%.

Suggested Citation

  • Han, Yixiao & Liao, Yanfen & Ma, Xiaoqian & Guo, Xing & Li, Changxin & Liu, Xinyu, 2023. "Analysis and prediction of the penetration of renewable energy in power systems using artificial neural network," Renewable Energy, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:renene:v:215:y:2023:i:c:s0960148123008145
    DOI: 10.1016/j.renene.2023.118914
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123008145
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.118914?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
    2. Siddique, Muhammad Bilal & Thakur, Jagruti, 2020. "Assessment of curtailed wind energy potential for off-grid applications through mobile battery storage," Energy, Elsevier, vol. 201(C).
    3. Walter, Viktor & Göransson, Lisa, 2022. "Trade as a variation management strategy for wind and solar power integration," Energy, Elsevier, vol. 238(PA).
    4. Izadi, Ali & Shahafve, Masoomeh & Ahmadi, Pouria & Hanafizadeh, Pedram, 2023. "Design, and optimization of COVID-19 hospital wards to produce Oxygen and electricity through solar PV panels with hydrogen storage systems by neural network-genetic algorithm," Energy, Elsevier, vol. 263(PA).
    5. Hemmati, Reza & Saboori, Hedayat, 2016. "Emergence of hybrid energy storage systems in renewable energy and transport applications – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 11-23.
    6. Ogliari, Emanuele & Guilizzoni, Manfredo & Giglio, Alessandro & Pretto, Silvia, 2021. "Wind power 24-h ahead forecast by an artificial neural network and an hybrid model: Comparison of the predictive performance," Renewable Energy, Elsevier, vol. 178(C), pages 1466-1474.
    7. Rodríguez, Fermín & Florez-Tapia, Ane M. & Fontán, Luis & Galarza, Ainhoa, 2020. "Very short-term wind power density forecasting through artificial neural networks for microgrid control," Renewable Energy, Elsevier, vol. 145(C), pages 1517-1527.
    8. Ti, Zilong & Deng, Xiao Wei & Zhang, Mingming, 2021. "Artificial Neural Networks based wake model for power prediction of wind farm," Renewable Energy, Elsevier, vol. 172(C), pages 618-631.
    9. Wei, Hu & Hongxuan, Zhang & Yu, Dong & Yiting, Wang & Ling, Dong & Ming, Xiao, 2019. "Short-term optimal operation of hydro-wind-solar hybrid system with improved generative adversarial networks," Applied Energy, Elsevier, vol. 250(C), pages 389-403.
    10. Heydari, Azim & Astiaso Garcia, Davide & Keynia, Farshid & Bisegna, Fabio & De Santoli, Livio, 2019. "A novel composite neural network based method for wind and solar power forecasting in microgrids," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    11. Charrouf, O. & Betka, A. & Abdeddaim, S. & Ghamri, A., 2020. "Artificial Neural Network power manager for hybrid PV-wind desalination system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 167(C), pages 443-460.
    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. Luo, Zhaohui & Wang, Longyan & Xu, Jian & Wang, Zilu & Yuan, Jianping & Tan, Andy C.C., 2024. "A reduced order modeling-based machine learning approach for wind turbine wake flow estimation from sparse sensor measurements," Energy, Elsevier, vol. 294(C).
    2. Yin, Linfei & Zhang, Bin, 2021. "Time series generative adversarial network controller for long-term smart generation control of microgrids," Applied Energy, Elsevier, vol. 281(C).
    3. A. J. Jin & C. Li & J. Su & J. Tan, 2022. "Fundamental Studies of Smart Distributed Energy Resources along with Energy Blockchain," Energies, MDPI, vol. 15(21), pages 1-12, October.
    4. Al-Falahi, Monaaf D.A. & Jayasinghe, Shantha D.G. & Enshaei, Hossein, 2019. "Hybrid algorithm for optimal operation of hybrid energy systems in electric ferries," Energy, Elsevier, vol. 187(C).
    5. Zhou, Yuzhou & Zhao, Jiexing & Zhai, Qiaozhu, 2021. "100% renewable energy: A multi-stage robust scheduling approach for cascade hydropower system with wind and photovoltaic power," Applied Energy, Elsevier, vol. 301(C).
    6. Li, Siyi & Zhang, Mingrui & Piggott, Matthew D., 2023. "End-to-end wind turbine wake modelling with deep graph representation learning," Applied Energy, Elsevier, vol. 339(C).
    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. Das, Pronob & Das, Barun K. & Rahman, Mushfiqur & Hassan, Rakibul, 2022. "Evaluating the prospect of utilizing excess energy and creating employments from a hybrid energy system meeting electricity and freshwater demands using multi-objective evolutionary algorithms," Energy, Elsevier, vol. 238(PB).
    9. Fan, Ailong & Wang, Junteng & He, Yapeng & Perčić, Maja & Vladimir, Nikola & Yang, Liu, 2021. "Decarbonising inland ship power system: Alternative solution and assessment method," Energy, Elsevier, vol. 226(C).
    10. Akhlaque Ahmad Khan & Ahmad Faiz Minai & Rupendra Kumar Pachauri & Hasmat Malik, 2022. "Optimal Sizing, Control, and Management Strategies for Hybrid Renewable Energy Systems: A Comprehensive Review," Energies, MDPI, vol. 15(17), pages 1-29, August.
    11. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    12. Zhuoxin Lu & Xiaoyuan Xu & Zheng Yan & Dong Han & Shiwei Xia, 2024. "Mobile Energy-Storage Technology in Power Grid: A Review of Models and Applications," Sustainability, MDPI, vol. 16(16), pages 1-19, August.
    13. Aasadnia, Majid & Mehrpooya, Mehdi, 2018. "Large-scale liquid hydrogen production methods and approaches: A review," Applied Energy, Elsevier, vol. 212(C), pages 57-83.
    14. Anthony D Stephens & David R Walwyn, 2020. "Predicting the Performance of a Future United Kingdom Grid and Wind Fleet When Providing Power to a Fleet of Battery Electric Vehicles," Papers 2101.01065, arXiv.org.
    15. Katsanevakis, Markos & Stewart, Rodney A. & Lu, Junwei, 2017. "Aggregated applications and benefits of energy storage systems with application-specific control methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 719-741.
    16. Yuan, Ran & Wang, Bo & Mao, Zhixin & Watada, Junzo, 2021. "Multi-objective wind power scenario forecasting based on PG-GAN," Energy, Elsevier, vol. 226(C).
    17. Lu, Xin & Qiu, Jing & Lei, Gang & Zhu, Jianguo, 2022. "Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia," Applied Energy, Elsevier, vol. 308(C).
    18. Mohammadi, Zahra & Ahmadi, Pouria & Ashjaee, Mehdi, 2024. "Comparative transient assessment and optimization of battery and hydrogen energy storage systems for near-zero energy buildings," Renewable Energy, Elsevier, vol. 220(C).
    19. Castillejo-Cuberos, A. & Cardemil, J.M. & Escobar, R., 2023. "Techno-economic assessment of photovoltaic plants considering high temporal resolution and non-linear dynamics of battery storage," Applied Energy, Elsevier, vol. 334(C).
    20. Ai, Chunyu & He, Shan & Hu, Heng & Fan, Xiaochao & Wang, Weiqing, 2023. "Chaotic time series wind power interval prediction based on quadratic decomposition and intelligent optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 177(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:eee:renene:v:215:y:2023:i:c:s0960148123008145. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.