A Stochastic Transmission Planning Model With Dependent Load and Wind Forecasts

Author

Heejung Park ; Baldick, Ross ; Morton, David P.

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Texas at Austin, Austin, TX, USA

Volume

30

Issue

6

fYear

2015

fDate

Nov. 2015

Firstpage

3003

Lastpage

3011

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.

Keywords

Gaussian distribution; load flow; load forecasting; power transmission planning; wind power; ERCOT network model; Electric Reliability Council of Texas network model; Gaussian copula; OPF problem; available wind power; correlation coefficient; dependent load forecasts; joint distribution; linearized DC power flow; linearized direct current power flow; optimal power flow problem; stochastic transmission planning; total electric load; two-stage stochastic program; univariate marginal distributions; wind data; wind forecasts; Load modeling; Power system planning; Stochastic processes; Wind forecasting; Wind power generation; Decomposition; Gaussian copula; stochastic optimization; transmission planning; wind power;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/TPWRS.2014.2385861

Filename

7010062