Kumar Rohit, Mishra Sudhansu Kumar, Sahoo Amit Kumar, Swain Subrat Kumar, Bajpai Ram Sharan, Flah Aymen, Almalki Mishari Metab, Kraiem Habib, Elnaggar Mohamed F
Birla Institute of Technology, Mesra, Ranchi, 835215, India.
Department of EEE, Centurion University of Technology and Management, Bhubaneswar, Odisha, India.
Sci Rep. 2025 Mar 18;15(1):9335. doi: 10.1038/s41598-025-94106-x.
In the era of renewable energy integration, precise solar energy modeling in power systems is crucial for optimized generation planning and facilitating sustainable energy transitions. The present research proposes a comprehensive framework for assessing the operational reliability of solar integrated systems, validated using the IEEE RTS 96 test system. A robust uncertainty model has been developed to characterize variations in solar irradiance to address the uncertainties in solar panel output, followed by a multi-state modeling approach to account for the dynamic nature of solar panel output. The research introduces a time series-based 'non-linear autoregressive neural network' (NAR-Net) to forecast the solar irradiance levels five days ahead to optimize solar power efficiency. A comparative analysis has been conducted of three other state-of-the-art approaches, such as auto-regressive (AR), auto-regressive with moving average, and multi-layer perceptron, for predicting solar irradiance. Performance metrics, including mean square error, regression, and computational time, were evaluated to demonstrate the efficacy of the NAR-Net. The proposed prediction-based approach enhances the reliability of power generation planning by integrating modeling, which is based on forecasting. It is found that the proposed method achieves an accuracy of 98% w.r.t its counterpart. Moreover, the assessment to optimize the operational reliability of solar-integrated systems and improve generation planning for a sustainable energy future is achieved.
在可再生能源整合的时代，电力系统中的精确太阳能建模对于优化发电规划和促进可持续能源转型至关重要。本研究提出了一个用于评估太阳能集成系统运行可靠性的综合框架，并使用IEEE RTS 96测试系统进行了验证。开发了一个强大的不确定性模型来表征太阳辐照度的变化，以解决太阳能电池板输出中的不确定性，随后采用多状态建模方法来考虑太阳能电池板输出的动态特性。该研究引入了一种基于时间序列的“非线性自回归神经网络”（NAR-Net）来提前五天预测太阳辐照度水平，以优化太阳能发电效率。对其他三种先进方法进行了比较分析，如自回归（AR）、自回归移动平均和多层感知器，用于预测太阳辐照度。评估了包括均方误差、回归和计算时间在内的性能指标，以证明NAR-Net的有效性。所提出的基于预测的方法通过集成基于预测的建模提高了发电规划的可靠性。结果发现，所提出的方法相对于其对应方法实现了98%的准确率。此外，实现了对优化太阳能集成系统运行可靠性和改进可持续能源未来发电规划的评估。
