GraphSAGE-Based Probabilistic Optimal Power Flow in Distribution System

Yibo Ding; Huayi Wu; Zhao Xu; Hongming Yang

doi:10.1109/PowerCon58120.2023.10331518

GraphSAGE-Based Probabilistic Optimal Power Flow in Distribution System

Yibo Ding
, Huayi Wu
, Zhao Xu
, Hongming Yang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

4 Citations (Scopus)

Abstract

The large-scale penetration of wind and photovoltaic (PV) power generation brings significant uncertainties to the distribution system power flows. In this paper, a data-driven Graph Sample Aggregate (GraphSAGE) - based deep learning method is proposed to solve the probabilistic optimal power flow (POPF) problems for distribution systems, whose key concept is to flexibly aggregate and transform the nodal and branch features of the distribution system. By fully extracting the features via aggregation, the proposed method is capable of directly learning the implicit correlation among renewable and load uncertainties to improve the calculation accuracy as well as reducing the time and efforts on mathematical modelling. Cases studies on IEEE 33-node system are conducted to compare the effectiveness of the GraphSAGE-based method with other classical deep learning network based methods. Numerical simulation results further prove the effectiveness and accuracy of the GraphSAGE-based method on POPF problems.

Original language	English
Title of host publication	Proceedings - 2023 International Conference on Power System Technology
Subtitle of host publication	Technological Advancements for the Construction of New Power System, PowerCon 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350300222
DOIs	https://doi.org/10.1109/PowerCon58120.2023.10331518
Publication status	Published - Dec 2023
Event	2023 International Conference on Power System Technology, PowerCon 2023 - Jinan, China Duration: 21 Sept 2023 → 22 Sept 2023

Publication series

Name	Proceedings - 2023 International Conference on Power System Technology: Technological Advancements for the Construction of New Power System, PowerCon 2023

Conference

Conference	2023 International Conference on Power System Technology, PowerCon 2023
Country/Territory	China
City	Jinan
Period	21/09/23 → 22/09/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

deep learning
distribution system
GraphSAGE network
Probabilistic optimal power flow

ASJC Scopus subject areas

Computer Networks and Communications
Renewable Energy, Sustainability and the Environment
Energy Engineering and Power Technology
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality
Control and Optimization

More information

10.1109/PowerCon58120.2023.10331518

Cite this

Ding, Y., Wu, H., Xu, Z., & Yang, H. (2023). GraphSAGE-Based Probabilistic Optimal Power Flow in Distribution System. In Proceedings - 2023 International Conference on Power System Technology: Technological Advancements for the Construction of New Power System, PowerCon 2023 (Proceedings - 2023 International Conference on Power System Technology: Technological Advancements for the Construction of New Power System, PowerCon 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PowerCon58120.2023.10331518

Ding, Yibo ; Wu, Huayi ; Xu, Zhao et al. / GraphSAGE-Based Probabilistic Optimal Power Flow in Distribution System. Proceedings - 2023 International Conference on Power System Technology: Technological Advancements for the Construction of New Power System, PowerCon 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings - 2023 International Conference on Power System Technology: Technological Advancements for the Construction of New Power System, PowerCon 2023).

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title = "GraphSAGE-Based Probabilistic Optimal Power Flow in Distribution System",

abstract = "The large-scale penetration of wind and photovoltaic (PV) power generation brings significant uncertainties to the distribution system power flows. In this paper, a data-driven Graph Sample Aggregate (GraphSAGE) - based deep learning method is proposed to solve the probabilistic optimal power flow (POPF) problems for distribution systems, whose key concept is to flexibly aggregate and transform the nodal and branch features of the distribution system. By fully extracting the features via aggregation, the proposed method is capable of directly learning the implicit correlation among renewable and load uncertainties to improve the calculation accuracy as well as reducing the time and efforts on mathematical modelling. Cases studies on IEEE 33-node system are conducted to compare the effectiveness of the GraphSAGE-based method with other classical deep learning network based methods. Numerical simulation results further prove the effectiveness and accuracy of the GraphSAGE-based method on POPF problems.",

keywords = "deep learning, distribution system, GraphSAGE network, Probabilistic optimal power flow",

author = "Yibo Ding and Huayi Wu and Zhao Xu and Hongming Yang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Power System Technology, PowerCon 2023 ; Conference date: 21-09-2023 Through 22-09-2023",

year = "2023",

month = dec,

doi = "10.1109/PowerCon58120.2023.10331518",

language = "English",

series = "Proceedings - 2023 International Conference on Power System Technology: Technological Advancements for the Construction of New Power System, PowerCon 2023",

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Ding, Y, Wu, H, Xu, Z & Yang, H 2023, GraphSAGE-Based Probabilistic Optimal Power Flow in Distribution System. in Proceedings - 2023 International Conference on Power System Technology: Technological Advancements for the Construction of New Power System, PowerCon 2023. Proceedings - 2023 International Conference on Power System Technology: Technological Advancements for the Construction of New Power System, PowerCon 2023, Institute of Electrical and Electronics Engineers Inc., 2023 International Conference on Power System Technology, PowerCon 2023, Jinan, China, 21/09/23. https://doi.org/10.1109/PowerCon58120.2023.10331518

GraphSAGE-Based Probabilistic Optimal Power Flow in Distribution System. / Ding, Yibo; Wu, Huayi; Xu, Zhao et al.
Proceedings - 2023 International Conference on Power System Technology: Technological Advancements for the Construction of New Power System, PowerCon 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings - 2023 International Conference on Power System Technology: Technological Advancements for the Construction of New Power System, PowerCon 2023).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - GraphSAGE-Based Probabilistic Optimal Power Flow in Distribution System

AU - Ding, Yibo

AU - Wu, Huayi

AU - Xu, Zhao

AU - Yang, Hongming

PY - 2023/12

Y1 - 2023/12

N2 - The large-scale penetration of wind and photovoltaic (PV) power generation brings significant uncertainties to the distribution system power flows. In this paper, a data-driven Graph Sample Aggregate (GraphSAGE) - based deep learning method is proposed to solve the probabilistic optimal power flow (POPF) problems for distribution systems, whose key concept is to flexibly aggregate and transform the nodal and branch features of the distribution system. By fully extracting the features via aggregation, the proposed method is capable of directly learning the implicit correlation among renewable and load uncertainties to improve the calculation accuracy as well as reducing the time and efforts on mathematical modelling. Cases studies on IEEE 33-node system are conducted to compare the effectiveness of the GraphSAGE-based method with other classical deep learning network based methods. Numerical simulation results further prove the effectiveness and accuracy of the GraphSAGE-based method on POPF problems.

AB - The large-scale penetration of wind and photovoltaic (PV) power generation brings significant uncertainties to the distribution system power flows. In this paper, a data-driven Graph Sample Aggregate (GraphSAGE) - based deep learning method is proposed to solve the probabilistic optimal power flow (POPF) problems for distribution systems, whose key concept is to flexibly aggregate and transform the nodal and branch features of the distribution system. By fully extracting the features via aggregation, the proposed method is capable of directly learning the implicit correlation among renewable and load uncertainties to improve the calculation accuracy as well as reducing the time and efforts on mathematical modelling. Cases studies on IEEE 33-node system are conducted to compare the effectiveness of the GraphSAGE-based method with other classical deep learning network based methods. Numerical simulation results further prove the effectiveness and accuracy of the GraphSAGE-based method on POPF problems.

KW - deep learning

KW - distribution system

KW - GraphSAGE network

KW - Probabilistic optimal power flow

UR - https://www.scopus.com/pages/publications/85180404369

U2 - 10.1109/PowerCon58120.2023.10331518

DO - 10.1109/PowerCon58120.2023.10331518

M3 - Conference article published in proceeding or book

AN - SCOPUS:85180404369

T3 - Proceedings - 2023 International Conference on Power System Technology: Technological Advancements for the Construction of New Power System, PowerCon 2023

BT - Proceedings - 2023 International Conference on Power System Technology

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 International Conference on Power System Technology, PowerCon 2023

Y2 - 21 September 2023 through 22 September 2023

ER -

Ding Y, Wu H, Xu Z, Yang H. GraphSAGE-Based Probabilistic Optimal Power Flow in Distribution System. In Proceedings - 2023 International Conference on Power System Technology: Technological Advancements for the Construction of New Power System, PowerCon 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings - 2023 International Conference on Power System Technology: Technological Advancements for the Construction of New Power System, PowerCon 2023). doi: 10.1109/PowerCon58120.2023.10331518

GraphSAGE-Based Probabilistic Optimal Power Flow in Distribution System

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

More information

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