Scholarlink

<p>The integration of distributed renewable energy (DRE) introduces significant uncertainty into distribution networks. Research on the secure operation of modern distribution systems is crucial for ensuring reliable electricity supply. Typically, such studies rely on well-defined grid topologies along with matched load and DRE generation profiles. However, a critical challenge in this field is the lack of publicly available, distribution-tailored DRE scenario datasets. To address this gap, we present DDRE-33, a <b>D</b>ataset of <b>D</b>istributed <b>R</b>enewable <b>E</b>nergy for standardized IEEE-<b>33</b> distribution networks. Unlike existing centralized generation datasets, DDRE-33 captures distributed DRE output characteristics. The dataset provides single-node profiles and spatiotemporally correlated scenarios. Each scenario is meticulously labeled to facilitate customized research setups, enhancing its utility for diverse distribution network analyses. DDRE-33 represents a valuable resource for advancing studies on renewable-rich distribution systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Large-Scale Dataset of Distributed Renewable Energy Scenarios on the IEEE-33 Bus Network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月19日

查看全文
  • Yuxuan Chen,
  • Haipeng Xie,
  • Wenqi Huang,
  • Peng Li

摘要

The integration of distributed renewable energy (DRE) introduces significant uncertainty into distribution networks. Research on the secure operation of modern distribution systems is crucial for ensuring reliable electricity supply. Typically, such studies rely on well-defined grid topologies along with matched load and DRE generation profiles. However, a critical challenge in this field is the lack of publicly available, distribution-tailored DRE scenario datasets. To address this gap, we present DDRE-33, a Dataset of Distributed Renewable Energy for standardized IEEE-33 distribution networks. Unlike existing centralized generation datasets, DDRE-33 captures distributed DRE output characteristics. The dataset provides single-node profiles and spatiotemporally correlated scenarios. Each scenario is meticulously labeled to facilitate customized research setups, enhancing its utility for diverse distribution network analyses. DDRE-33 represents a valuable resource for advancing studies on renewable-rich distribution systems.