Research on Improvement Calculation Method of Grid Power Losses Based on New Energy Access Model

Jun Zhang, Huakun Que, Xiashan Feng, Xiaofeng Feng, Xiling Tang
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Abstract

This research presents an improved calculation method for grid power losses, particularly focusing on the challenges posed by new energy access models. With the integration of electric vehicles and the rise of data centers, the demand for electrical energy has surged, leading to increased strain on grid stations and subsequent power losses. The proposed model aimed at reducing these power losses, while also examining existing systems to mitigate and analyze such issues. A significant contribution of this work is the application of the Random Forest machine learning algorithm, which enables efficient and accurate power flow calculations essential for optimizing grid performance. The proposed method is expected to enhance the grid’s ability to handle future energy demands and contribute to the sustainable development of electrical energy systems.
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基于新能源接入模型的电网电力损耗改进计算方法研究
本研究提出了一种改进的电网电力损耗计算方法,尤其关注新能源接入模式带来的挑战。随着电动汽车的集成和数据中心的兴起,对电能的需求激增,导致电网站的压力增大,电能损耗随之增加。所提出的模型旨在减少这些电力损耗,同时还研究了现有系统,以缓解和分析此类问题。这项工作的一个重要贡献是应用了随机森林机器学习算法,从而实现了对优化电网性能至关重要的高效、准确的功率流计算。所提出的方法有望提高电网处理未来能源需求的能力,并为电力能源系统的可持续发展做出贡献。
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