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

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.

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Publication Details

Journal
Scientific Data
Published
2025-12-19
DOI
https://doi.org/10.1038/s41597-025-06464-w
Primary Topic
Optimal Power Flow Distribution
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Haipeng Xie, Peng Li, Wenqi Huang, Y. F. Chen
Scientific Data
Optimal Power Flow Distribution
article

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

Haipeng Xie, Peng Li, Wenqi Huang, Y. F. Chen
article en

Abstract

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.

Scientific DataVol. 13(1)
China Southern Power Grid (China) (CN), Xi'an Jiaotong University (CN)
National Natural Science Foundation of China, Beijing Nova Program
Openalex Percentile: Top 58%
Optimal Power Flow Distribution
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