Investigating the Combined Effect of Probabilistic Generation Forecasts and Dynamic Line Rating on Optimal Power Flow

Cheng Lyu, Sara Eftekharnejad, David Larson

Research output: Chapter in Book/Entry/PoemConference contribution

Abstract

Current literature predominantly analyzes the effects of Variable Renewable Energy (VRE) forecasts on Optimal Power Flow (OPF) by presuming static line capacities, neglecting their variability with respect to temperature and wind speed. This study investigates the effect of probabilistic VRE forecasts on OPF, considering dynamic line rating (DLR) under scenarios of high VRE integration and variable weather conditions. Using various machine learning methods, VRE forecasts with different accuracy and prediction intervals (PIs) are generated. These forecasts are incorporated into a Monte Carlo-based probabilistic OPF (POPF) framework to evaluate the impact of probabilistic VRE forecasts on POPF, in contrast to OPF using actual generation data. Case studies using a modified IEEE 118-bus system demonstrate that while improvements in VRE forecast accuracy yield limited improvements to POPF reliability, DLR implementation significantly enhances OPF reliability.

Original languageEnglish (US)
Title of host publication2024 IEEE Power and Energy Society General Meeting, PESGM 2024
PublisherIEEE Computer Society
ISBN (Electronic)9798350381832
DOIs
StatePublished - 2024
Event2024 IEEE Power and Energy Society General Meeting, PESGM 2024 - Seattle, United States
Duration: Jul 21 2024Jul 25 2024

Publication series

NameIEEE Power and Energy Society General Meeting
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2024 IEEE Power and Energy Society General Meeting, PESGM 2024
Country/TerritoryUnited States
CitySeattle
Period7/21/247/25/24

Keywords

  • Dynamic Line Rating
  • probabilistic optimal power flow
  • probabilistic renewable generation forecasts

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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