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Title: Load Forecasting Based Distribution System Network Reconfiguration -- A Distributed Data-Driven Approach

Abstract

In this paper, a short-term load forecasting approach based network reconfiguration is proposed in a parallel manner. Specifically, a support vector regression (SVR) based short-term load forecasting approach is designed to provide an accurate load prediction and benefit the network reconfiguration. Because of the nonconvexity of the three-phase balanced optimal power flow, a second-order cone program (SOCP) based approach is used to relax the optimal power flow problem. Then, the alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the limited number of the switches and the increasing computation capability, the proposed network reconfiguration is solved in a parallel way. The numerical results demonstrate the feasible and effectiveness of the proposed approach.

Authors:
 [1];  [1]; ORCiD logo [1];  [2];  [2];  [3]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. University of Denver
  3. Arizona State University
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1440308
Report Number(s):
NREL/CP-5D00-71665
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2017 51st Asilomar Conference on Signals, Systems, and Computers, 29 October - 1 November 2017, Pacific Grove, California
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; electrical distribution system; network reconfiguration; alternating direction method of multipliers; support vector regression; semidefinite relaxation programming; convex optimization; optimal power flow; short-term load forecasting

Citation Formats

Jiang, Huaiguang, Zhang, Yingchen, Muljadi, Eduard, Gu, Yi, Zhang, Jun Jason, and Solis, Francisco J. Load Forecasting Based Distribution System Network Reconfiguration -- A Distributed Data-Driven Approach. United States: N. p., 2018. Web. doi:10.1109/ACSSC.2017.8335576.
Jiang, Huaiguang, Zhang, Yingchen, Muljadi, Eduard, Gu, Yi, Zhang, Jun Jason, & Solis, Francisco J. Load Forecasting Based Distribution System Network Reconfiguration -- A Distributed Data-Driven Approach. United States. doi:10.1109/ACSSC.2017.8335576.
Jiang, Huaiguang, Zhang, Yingchen, Muljadi, Eduard, Gu, Yi, Zhang, Jun Jason, and Solis, Francisco J. Mon . "Load Forecasting Based Distribution System Network Reconfiguration -- A Distributed Data-Driven Approach". United States. doi:10.1109/ACSSC.2017.8335576.
@article{osti_1440308,
title = {Load Forecasting Based Distribution System Network Reconfiguration -- A Distributed Data-Driven Approach},
author = {Jiang, Huaiguang and Zhang, Yingchen and Muljadi, Eduard and Gu, Yi and Zhang, Jun Jason and Solis, Francisco J.},
abstractNote = {In this paper, a short-term load forecasting approach based network reconfiguration is proposed in a parallel manner. Specifically, a support vector regression (SVR) based short-term load forecasting approach is designed to provide an accurate load prediction and benefit the network reconfiguration. Because of the nonconvexity of the three-phase balanced optimal power flow, a second-order cone program (SOCP) based approach is used to relax the optimal power flow problem. Then, the alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the limited number of the switches and the increasing computation capability, the proposed network reconfiguration is solved in a parallel way. The numerical results demonstrate the feasible and effectiveness of the proposed approach.},
doi = {10.1109/ACSSC.2017.8335576},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Apr 16 00:00:00 EDT 2018},
month = {Mon Apr 16 00:00:00 EDT 2018}
}

Conference:
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