Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement: Preprint

Yiyun Yao, Weijia Liu, Rishabh Jain, Santhosh Madasthu, Badrul Chowdhury, Robert Cox

Research output: Contribution to conferencePaper


Distribution system resilience enhancement is an important topic to ensure customers have access to the power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecasts. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.
Original languageAmerican English
Number of pages8
StatePublished - 2023

Bibliographical note

See NREL/CP-5D00-88288 for paper as published in proceedings

NREL Publication Number

  • NREL/CP-5D00-83982


  • distributed energy resources
  • forecast-based preventative scheduling
  • machine learning
  • outage prediction
  • power system resilience
  • responsive resource allocation


Dive into the research topics of 'Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement: Preprint'. Together they form a unique fingerprint.

Cite this