A Stochastic Transmission Planning Model with Dependent Load and Wind Forecasts

Heejung Park, Ross Baldick, David P. Morton

Research output: Contribution to journalArticlepeer-review

70 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 languageEnglish (US)
Article number7010062
Pages (from-to)3003-3011
Number of pages9
JournalIEEE Transactions on Power Systems
Volume30
Issue number6
DOIs
StatePublished - Nov 1 2015

Keywords

  • Decomposition
  • Gaussian copula
  • Wind power
  • stochastic optimization
  • transmission planning

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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