GraphSAGE-Based Probabilistic Optimal Power Flow in Distribution System

Yibo Ding, Huayi Wu, Zhao Xu, Hongming Yang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

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 languageEnglish
Title of host publicationProceedings - 2023 International Conference on Power System Technology
Subtitle of host publicationTechnological Advancements for the Construction of New Power System, PowerCon 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350300222
DOIs
Publication statusPublished - Dec 2023
Event2023 International Conference on Power System Technology, PowerCon 2023 - Jinan, China
Duration: 21 Sept 202322 Sept 2023

Publication series

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

Conference

Conference2023 International Conference on Power System Technology, PowerCon 2023
Country/TerritoryChina
CityJinan
Period21/09/2322/09/23

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

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