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Title: Chance-Constrained Day-Ahead Hourly Scheduling in Distribution System Operation

Abstract

This paper aims to propose a two-step approach for day-ahead hourly scheduling in a distribution system operation, which contains two operation costs, the operation cost at substation level and feeder level. In the first step, the objective is to minimize the electric power purchase from the day-ahead market with the stochastic optimization. The historical data of day-ahead hourly electric power consumption is used to provide the forecast results with the forecasting error, which is presented by a chance constraint and formulated into a deterministic form by Gaussian mixture model (GMM). In the second step, the objective is to minimize the system loss. Considering the nonconvexity of the three-phase balanced AC optimal power flow problem in distribution systems, the second-order cone program (SOCP) is used to relax the problem. Then, a distributed optimization approach is built based on the alternating direction method of multiplier (ADMM). The results shows that the validity and effectiveness method.

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:
1440309
Report Number(s):
NREL/CP-5D00-71666
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; renewable energy integration; second-order cone program; Gaussian mixture model; optimal power flow; stochastic optimization; alternating direction method of multiplier

Citation Formats

Jiang, Huaiguang, Zhang, Yingchen, Muljadi, Eduard, Gu, Yi, Zhang, Jun Jason, and Solis, Francisco J. Chance-Constrained Day-Ahead Hourly Scheduling in Distribution System Operation. United States: N. p., 2018. Web. doi:10.1109/ACSSC.2017.8335577.
Jiang, Huaiguang, Zhang, Yingchen, Muljadi, Eduard, Gu, Yi, Zhang, Jun Jason, & Solis, Francisco J. Chance-Constrained Day-Ahead Hourly Scheduling in Distribution System Operation. United States. doi:10.1109/ACSSC.2017.8335577.
Jiang, Huaiguang, Zhang, Yingchen, Muljadi, Eduard, Gu, Yi, Zhang, Jun Jason, and Solis, Francisco J. Mon . "Chance-Constrained Day-Ahead Hourly Scheduling in Distribution System Operation". United States. doi:10.1109/ACSSC.2017.8335577.
@article{osti_1440309,
title = {Chance-Constrained Day-Ahead Hourly Scheduling in Distribution System Operation},
author = {Jiang, Huaiguang and Zhang, Yingchen and Muljadi, Eduard and Gu, Yi and Zhang, Jun Jason and Solis, Francisco J.},
abstractNote = {This paper aims to propose a two-step approach for day-ahead hourly scheduling in a distribution system operation, which contains two operation costs, the operation cost at substation level and feeder level. In the first step, the objective is to minimize the electric power purchase from the day-ahead market with the stochastic optimization. The historical data of day-ahead hourly electric power consumption is used to provide the forecast results with the forecasting error, which is presented by a chance constraint and formulated into a deterministic form by Gaussian mixture model (GMM). In the second step, the objective is to minimize the system loss. Considering the nonconvexity of the three-phase balanced AC optimal power flow problem in distribution systems, the second-order cone program (SOCP) is used to relax the problem. Then, a distributed optimization approach is built based on the alternating direction method of multiplier (ADMM). The results shows that the validity and effectiveness method.},
doi = {10.1109/ACSSC.2017.8335577},
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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