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

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  • 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
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    References listed on IDEAS

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    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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    Full references (including those not matched with items on IDEAS)

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