An Analysis of the Reliability of AC Optimal Power Flow Deep Learning Proxies

My H. Dinh, Ferdinando Fioretto, Mostafa Mohammadian, Kyri Baker

Research output: Chapter in Book/Entry/PoemConference contribution

Abstract

Optimal Power Flow (OPF) is a challenging problem in power systems, and recent research has explored the use of Deep Neural Networks (DNNs) to approximate OPF solutions with reduced computational times. While these approaches show promising accuracy and efficiency, there is a lack of analysis of their robustness. This paper addresses this gap by investigating the factors that lead to both successful and suboptimal predictions in DNN-based OPF solvers. It identifies power system features and DNN characteristics that contribute to higher prediction errors and offers insights on mitigating these challenges when designing deep learning models for OPF.

Original languageEnglish (US)
Title of host publication2023 IEEE PES Innovative Smart Grid Technologies Latin America, ISGT-LA 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages170-174
Number of pages5
ISBN (Electronic)9798350336962
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 IEEE PES Innovative Smart Grid Technologies Latin America, ISGT-LA 2023 - San Juan, United States
Duration: Nov 6 2023Nov 9 2023

Publication series

Name2023 IEEE PES Innovative Smart Grid Technologies Latin America, ISGT-LA 2023

Conference

Conference2023 IEEE PES Innovative Smart Grid Technologies Latin America, ISGT-LA 2023
Country/TerritoryUnited States
CitySan Juan
Period11/6/2311/9/23

ASJC Scopus subject areas

  • Control and Optimization
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment

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