A Stochastic Transmission Planning Model with Dependent Load and Wind Forecasts

Heejung Park; Ross Baldick; David P. Morton

doi:10.1109/TPWRS.2014.2385861

A Stochastic Transmission Planning Model with Dependent Load and Wind Forecasts

Heejung Park, Ross Baldick, David P. Morton

University of Texas at Austin

Research output: Contribution to journal › Article › peer-review

102 Scopus citations

Abstract

This paper introduces a two-stage stochastic program for transmission planning. The model has two dependent random variables, namely, total electric load and available wind power. Given univariate marginal distributions for these two random variables and their correlation coefficient, the joint distribution is modeled using a Gaussian copula. The optimal power flow (OPF) problem is solved based on the linearized direct current (DC) power flow. The Electric Reliability Council of Texas (ERCOT) network model and its load and wind data are used for a test case. A 95% confidence interval is formed on the optimality gap of candidate solutions obtained using a sample average approximation with 200 and 300 samples from the joint distribution of load and wind.

Original language	English
Article number	7010062
Pages (from-to)	3003-3011
Number of pages	9
Journal	IEEE Transactions on Power Systems
Volume	30
Issue number	6
DOIs	https://doi.org/10.1109/TPWRS.2014.2385861
State	Published - 1 Nov 2015

Keywords

Decomposition
Gaussian copula
Wind power
stochastic optimization
transmission planning

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abstract = "This paper introduces a two-stage stochastic program for transmission planning. The model has two dependent random variables, namely, total electric load and available wind power. Given univariate marginal distributions for these two random variables and their correlation coefficient, the joint distribution is modeled using a Gaussian copula. The optimal power flow (OPF) problem is solved based on the linearized direct current (DC) power flow. The Electric Reliability Council of Texas (ERCOT) network model and its load and wind data are used for a test case. A 95% confidence interval is formed on the optimality gap of candidate solutions obtained using a sample average approximation with 200 and 300 samples from the joint distribution of load and wind.",

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N2 - This paper introduces a two-stage stochastic program for transmission planning. The model has two dependent random variables, namely, total electric load and available wind power. Given univariate marginal distributions for these two random variables and their correlation coefficient, the joint distribution is modeled using a Gaussian copula. The optimal power flow (OPF) problem is solved based on the linearized direct current (DC) power flow. The Electric Reliability Council of Texas (ERCOT) network model and its load and wind data are used for a test case. A 95% confidence interval is formed on the optimality gap of candidate solutions obtained using a sample average approximation with 200 and 300 samples from the joint distribution of load and wind.

AB - This paper introduces a two-stage stochastic program for transmission planning. The model has two dependent random variables, namely, total electric load and available wind power. Given univariate marginal distributions for these two random variables and their correlation coefficient, the joint distribution is modeled using a Gaussian copula. The optimal power flow (OPF) problem is solved based on the linearized direct current (DC) power flow. The Electric Reliability Council of Texas (ERCOT) network model and its load and wind data are used for a test case. A 95% confidence interval is formed on the optimality gap of candidate solutions obtained using a sample average approximation with 200 and 300 samples from the joint distribution of load and wind.

KW - Decomposition

KW - Gaussian copula

KW - Wind power

KW - stochastic optimization

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A Stochastic Transmission Planning Model with Dependent Load and Wind Forecasts

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Keywords

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