Monte Carlo (importance) sampling within a benders' decomposition algorithm for stochastic linear programs. Technical report

Technical Report · Fri Sep 01 00:00:00 EDT 1989

OSTI ID:5054101

Infanger, G

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. The author considers uncertain availability of generators and transmission lines and uncertain demand. Numerical results are presented.

• Research Organization: Stanford Univ., CA (USA). Systems Optimization Lab.
• OSTI ID: 5054101
• Report Number(s): AD-A-212854/4/XAB; SOL-89-13
• Country of Publication: United States
• Language: English

Related Subjects

29 ENERGY PLANNING
POLICY AND ECONOMY
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
CAPACITY
NUMERICAL ANALYSIS
ELECTRIC UTILITIES
MONTE CARLO METHOD
ALGORITHMS
AVAILABILITY
LINEAR PROGRAMMING
PLANNING
POWER DEMAND
POWER TRANSMISSION
PROGRESS REPORT
SAMPLING
STOCHASTIC PROCESSES
DOCUMENT TYPES
MATHEMATICAL LOGIC
MATHEMATICS
PROGRAMMING
PUBLIC UTILITIES
296000* - Energy Planning & Policy- Electric Power
990200 - Mathematics & Computers