Construction of Uncertainty Models for Renewable Energy Systems on Multiple Time Scales

Yuxuan Zheng; Shihua Luo; Weihao Hu; Zhenyuan Zhang; Qi Huang; Zhe Chen; Sayed Abulanwar

doi:10.1109/EI256261.2022.10116873

Construction of Uncertainty Models for Renewable Energy Systems on Multiple Time Scales

Yuxuan Zheng^*, Shihua Luo, Weihao Hu, Zhenyuan Zhang, Qi Huang, Zhe Chen, Sayed Abulanwar

^*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

Abstract

The output of the renewable energy system has strong randomness and volatility, and the construction of the uncertainty model of the system is of great significance to the power system planning. Scenario generation is a common method to analyze uncertainty problems by constructing deterministic scenarios. Since traditional scenario generation methods are sensitive to abnormal data, they cannot handle large-dimensional data well when the time scale is large. K-Medoids and Dynamic Time Warping (K-Medoids+DTW) methods can reduce the impact of abnormal data and generate scenarios for renewable energy output and load changes at multiple time scales. On this basis, the vector autoregressive model (VAR) is used to construct supply and demand matching patterns in different scenarios. This paper uses the historical data of renewable energy, adopts the improved clustering algorithm to generate typical scenarios, and builds a source-load supply-demand matching model, and finally verifies the validity of the model.

Original language	English
Title of host publication	EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration
Number of pages	7
Publisher	IEEE Signal Processing Society
Publication date	2022
Pages	561-567
ISBN (Electronic)	9798350347159
DOIs	https://doi.org/10.1109/EI256261.2022.10116873
Publication status	Published - 2022
Event	6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022 - Chengdu, China Duration: 11 Nov 2022 → 13 Nov 2022

Conference

Conference	6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022
Country/Territory	China
City	Chengdu
Period	11/11/2022 → 13/11/2022
Sponsor	Chengdu University of Technology, Chinese Society for Electrical Engineering, et al., IEEE Power and Energy Society, Tsinghua University Dept. of Electrical Engineering, University of Electronic Science and Technology of China

Series	EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

K-Mediods+DTW
Multiple Time Scales
Renewable Energy Systems
Scenario Generation
VAR

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10.1109/EI256261.2022.10116873

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Cite this

@inproceedings{f3a92a57079c4dbfadda7b2d3cdb002c,

title = "Construction of Uncertainty Models for Renewable Energy Systems on Multiple Time Scales",

abstract = "The output of the renewable energy system has strong randomness and volatility, and the construction of the uncertainty model of the system is of great significance to the power system planning. Scenario generation is a common method to analyze uncertainty problems by constructing deterministic scenarios. Since traditional scenario generation methods are sensitive to abnormal data, they cannot handle large-dimensional data well when the time scale is large. K-Medoids and Dynamic Time Warping (K-Medoids+DTW) methods can reduce the impact of abnormal data and generate scenarios for renewable energy output and load changes at multiple time scales. On this basis, the vector autoregressive model (VAR) is used to construct supply and demand matching patterns in different scenarios. This paper uses the historical data of renewable energy, adopts the improved clustering algorithm to generate typical scenarios, and builds a source-load supply-demand matching model, and finally verifies the validity of the model.",

keywords = "K-Mediods+DTW, Multiple Time Scales, Renewable Energy Systems, Scenario Generation, VAR",

author = "Yuxuan Zheng and Shihua Luo and Weihao Hu and Zhenyuan Zhang and Qi Huang and Zhe Chen and Sayed Abulanwar",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022 ; Conference date: 11-11-2022 Through 13-11-2022",

year = "2022",

doi = "10.1109/EI256261.2022.10116873",

language = "English",

series = "EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration",

pages = "561--567",

booktitle = "EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration",

publisher = "IEEE Signal Processing Society",

address = "United States",

}

Zheng, Y, Luo, S, Hu, W, Zhang, Z, Huang, Q, Chen, Z & Abulanwar, S 2022, Construction of Uncertainty Models for Renewable Energy Systems on Multiple Time Scales. in EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration. IEEE Signal Processing Society, EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration, pp. 561-567, 6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022, Chengdu, China, 11/11/2022. https://doi.org/10.1109/EI256261.2022.10116873

Construction of Uncertainty Models for Renewable Energy Systems on Multiple Time Scales. / Zheng, Yuxuan; Luo, Shihua; Hu, Weihao et al.
EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration. IEEE Signal Processing Society, 2022. p. 561-567 (EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Construction of Uncertainty Models for Renewable Energy Systems on Multiple Time Scales

AU - Zheng, Yuxuan

AU - Luo, Shihua

AU - Hu, Weihao

AU - Zhang, Zhenyuan

AU - Huang, Qi

AU - Chen, Zhe

AU - Abulanwar, Sayed

PY - 2022

Y1 - 2022

N2 - The output of the renewable energy system has strong randomness and volatility, and the construction of the uncertainty model of the system is of great significance to the power system planning. Scenario generation is a common method to analyze uncertainty problems by constructing deterministic scenarios. Since traditional scenario generation methods are sensitive to abnormal data, they cannot handle large-dimensional data well when the time scale is large. K-Medoids and Dynamic Time Warping (K-Medoids+DTW) methods can reduce the impact of abnormal data and generate scenarios for renewable energy output and load changes at multiple time scales. On this basis, the vector autoregressive model (VAR) is used to construct supply and demand matching patterns in different scenarios. This paper uses the historical data of renewable energy, adopts the improved clustering algorithm to generate typical scenarios, and builds a source-load supply-demand matching model, and finally verifies the validity of the model.

AB - The output of the renewable energy system has strong randomness and volatility, and the construction of the uncertainty model of the system is of great significance to the power system planning. Scenario generation is a common method to analyze uncertainty problems by constructing deterministic scenarios. Since traditional scenario generation methods are sensitive to abnormal data, they cannot handle large-dimensional data well when the time scale is large. K-Medoids and Dynamic Time Warping (K-Medoids+DTW) methods can reduce the impact of abnormal data and generate scenarios for renewable energy output and load changes at multiple time scales. On this basis, the vector autoregressive model (VAR) is used to construct supply and demand matching patterns in different scenarios. This paper uses the historical data of renewable energy, adopts the improved clustering algorithm to generate typical scenarios, and builds a source-load supply-demand matching model, and finally verifies the validity of the model.

KW - K-Mediods+DTW

KW - Multiple Time Scales

KW - Renewable Energy Systems

KW - Scenario Generation

KW - VAR

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DO - 10.1109/EI256261.2022.10116873

M3 - Article in proceeding

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T3 - EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration

SP - 561

EP - 567

BT - EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration

PB - IEEE Signal Processing Society

T2 - 6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022

Y2 - 11 November 2022 through 13 November 2022

ER -

Zheng Y, Luo S, Hu W, Zhang Z, Huang Q, Chen Z et al. Construction of Uncertainty Models for Renewable Energy Systems on Multiple Time Scales. In EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration. IEEE Signal Processing Society. 2022. p. 561-567. (EI2 2022 - 6th IEEE Conference on Energy Internet and Energy System Integration). doi: 10.1109/EI256261.2022.10116873

Construction of Uncertainty Models for Renewable Energy Systems on Multiple Time Scales

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Keywords

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