Research on collaborative planning strategy of source network load and storage based on deep learning

Yukun Ma, Kun Yang, Jia Zhao, Hai Zeng
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Abstract

In the construction and development of modern cities, creating a new power system with new energy as the main body combined with the dual-carbon strategic goal is the main topic of comprehensive exploration by government departments and power enterprises in the new era. Especially after entering the era of big data, how to meet the proportion of new energy that has risen significantly has brought new opportunities and challenges to the power system. At present, some scholars have carried out relevant research on renewable energy access to power system, and mainly put forward the collaborative planning strategy of source and network load and storage. On the basis of understanding the current situation of power grid system construction and development in the new era, this paper mainly explores the main direction of the future construction and development of the power industry according to the deep learning-centered collaborative planning strategy of source and network load and storage, in order to solve the problems existing in the current power system construction planning.
基于深度学习的源网络负载与存储协同规划策略研究
在现代化城市的建设和发展中,结合双碳战略目标,打造以新能源为主体的新型电力系统,是新时期政府部门和电力企业全面探索的主要课题。特别是进入大数据时代后,如何满足新能源比重大幅上升的需求,给电力系统带来了新的机遇和挑战。目前,已有学者针对可再生能源接入电力系统开展了相关研究,主要提出了源网荷储协同规划策略。本文在了解新时期电网系统建设发展现状的基础上,主要根据以深度学习为核心的源网荷储协同规划策略,探索未来电力行业建设发展的主要方向,以解决当前电力系统建设规划中存在的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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