利用 CT 变压器深度学习进行并发光伏生产和消费负荷预测,以估算能源系统的灵活性

IF 2.6 4区 工程技术 Q3 ENERGY & FUELS
Mohammad Zarghami, Taher Niknam, Jamshid Aghaei, Azita Hatami Nezhad
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引用次数: 0

摘要

由于技术、经济和环境因素的影响,可再生能源(RES)在电力系统中的应用大幅增加,这就要求在管理可变消费负荷和可再生能源发电方面具有更大的灵活性。准确预测太阳能的生产和消费负荷对于提高电力系统的灵活性至关重要。本研究引入了一种新型深度学习模型,即具有独特功能和扩展内存容量的空间-时间混合卷积-变压器(CT-Transformer)网络。此外,还引入了灵活性指数(FI),以根据预测结果评估电力系统灵活性(PSF)。CT 变流器可预测未来 24 小时和 168 小时的 PSF,并使用 FI 根据预测结果评估 PSF。输入数据包括法国的气象、太阳能生产、负荷需求和定价数据,包括 2015 年和 2016 年的每小时数据(约 17520 个条目)。数据预处理包括纠正不完整和不相关的片段。与其他深度学习技术相比,CT-Transformer 的性能表现出众,预测误差最小(2.5%),最大误差为 10.1%(MAE)。它还实现了 0.08% 的系统灵活性预测误差,而最高误差为 0.96%。这项研究凸显了 CT 变压器在准确预测可再生能源和负荷以及 PSF 评估方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Concurrent PV production and consumption load forecasting using CT-Transformer deep learning to estimate energy system flexibility

Concurrent PV production and consumption load forecasting using CT-Transformer deep learning to estimate energy system flexibility

The integration of renewable energy sources (RESs) into power systems has increased significantly due to technical, economic, and environmental factors, necessitating greater flexibility to manage variable consumption loads and renewable energy generation. Accurate forecasting of solar energy production and consumption load is critical for enhancing power system flexibility. This study introduces a novel deep learning model, a spatial-temporal hybrid convolutional-transformer (CT-Transformer) network with unique features and extended memory capacity. Additionally, a flexibility index (FI) is introduced to evaluate power system flexibility (PSF) based on the forecasting results. The CT-Transformer forecasts PSF for the next 24 and 168 hours, using the FI to evaluate PSF based on forecasting results. The input data includes meteorological, solar energy production, load demand, and pricing data from France, comprising hourly data from 2015 and 2016 (about 17,520 entries). Data preprocessing involves correcting incomplete and irrelevant segments. The CT-Transformer's performance is compared to other deep learning techniques, showing superior results with the lowest prediction error (2.5%) and a maximum error of 10.1% (MAE). It also achieved a prediction error of 0.08% for system flexibility, compared to the highest error of 0.96%. This research highlights the CT-Transformer's potential for accurate RES and load forecasting and PSF evaluation.

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来源期刊
IET Renewable Power Generation
IET Renewable Power Generation 工程技术-工程:电子与电气
CiteScore
6.80
自引率
11.50%
发文量
268
审稿时长
6.6 months
期刊介绍: IET Renewable Power Generation (RPG) brings together the topics of renewable energy technology, power generation and systems integration, with techno-economic issues. All renewable energy generation technologies are within the scope of the journal. Specific technology areas covered by the journal include: Wind power technology and systems Photovoltaics Solar thermal power generation Geothermal energy Fuel cells Wave power Marine current energy Biomass conversion and power generation What differentiates RPG from technology specific journals is a concern with power generation and how the characteristics of the different renewable sources affect electrical power conversion, including power electronic design, integration in to power systems, and techno-economic issues. Other technologies that have a direct role in sustainable power generation such as fuel cells and energy storage are also covered, as are system control approaches such as demand side management, which facilitate the integration of renewable sources into power systems, both large and small. The journal provides a forum for the presentation of new research, development and applications of renewable power generation. Demonstrations and experimentally based research are particularly valued, and modelling studies should as far as possible be validated so as to give confidence that the models are representative of real-world behavior. Research that explores issues where the characteristics of the renewable energy source and their control impact on the power conversion is welcome. Papers covering the wider areas of power system control and operation, including scheduling and protection that are central to the challenge of renewable power integration are particularly encouraged. The journal is technology focused covering design, demonstration, modelling and analysis, but papers covering techno-economic issues are also of interest. Papers presenting new modelling and theory are welcome but this must be relevant to real power systems and power generation. Most papers are expected to include significant novelty of approach or application that has general applicability, and where appropriate include experimental results. Critical reviews of relevant topics are also invited and these would be expected to be comprehensive and fully referenced. Current Special Issue. Call for papers: Power Quality and Protection in Renewable Energy Systems and Microgrids - https://digital-library.theiet.org/files/IET_RPG_CFP_PQPRESM.pdf Energy and Rail/Road Transportation Integrated Development - https://digital-library.theiet.org/files/IET_RPG_CFP_ERTID.pdf
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