Monte Carlo (Importance) Sampling within a Benders' Decomposition Algorithm for Stochastic Linear Programs

Abstract

A method employing decomposition techniques and Monte Carlo sampling (importance sampling) to solve stochastic linear programs is described and applied to capacity expansion planning problems of electric utilities. We consider uncertain availability of generators and transmission lines and uncertain demand. Numerical results are presented.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1989
Accession Number
ADA212854

Entities

People

  • Gerd Infanger

Organizations

  • Stanford University

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Computational Science
  • Computations
  • Computer Programming
  • Estimators
  • Generators
  • Integrals
  • Linear Programming
  • Mathematical Programming
  • Operations Research
  • Optimization
  • Parallel Processing
  • Probability
  • Random Variables
  • Sampling
  • Transmission Lines

Readers

  • Computational Modeling and Simulation
  • Operations Research
  • Regression Analysis.