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Title: Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration

Abstract

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operator can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.

Authors:
 [1];  [1];  [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE)
OSTI Identifier:
1378898
Report Number(s):
NREL/PO-5D00-68861
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2017 IEEE Power & Energy Society General Meeting, 16-20 July 2017, Chicago, Illinois
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; short-term; load forecasting; distribution; reconfiguration

Citation Formats

Jiang, Huaiguang, Ding, Fei, and Zhang, Yingchen. Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration. United States: N. p., 2017. Web.
Jiang, Huaiguang, Ding, Fei, & Zhang, Yingchen. Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration. United States.
Jiang, Huaiguang, Ding, Fei, and Zhang, Yingchen. 2017. "Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration". United States. doi:. https://www.osti.gov/servlets/purl/1378898.
@article{osti_1378898,
title = {Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration},
author = {Jiang, Huaiguang and Ding, Fei and Zhang, Yingchen},
abstractNote = {In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operator can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2017,
month = 8
}

Conference:
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