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

Yixiao Han, Yanfen Liao, Xiaoqian Ma, Xing Guo, Changxin Li and Xinyu Liu

Renewable Energy, 2023, vol. 215, issue C

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%.

Keywords: Artificial neural network; Wind and solar penetration; Prediction; Energy storage; Renewable energy curtailments (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:215:y:2023:i:c:s0960148123008145

DOI: 10.1016/j.renene.2023.118914

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