An Enhanced Decomposition Algorithm for Multistage Stochastic Hydroelectric Scheduling
Morton, David P.
MetadataShow full item record
Handling uncertainty in natural inflow is an important part of a hydroelectric scheduling model. In a stochastic programming formulation, natural inflow may be modeled as a random vector with known distribution, but the size of the resulting mathematical program can be formidable. Decomposition-based algorithms take advantage of special structure and provide an attractive approach to such problems. We develop an enhanced Benders decomposition algorithm for solving multistage stochastic linear programs. The enhancements include warm start basis selection, preliminary cut generation, the multicut procedure, and decision tree traversing strategies. Computational results are presented for a collection of stochastic hydroelectric scheduling problems. Stochastic programming, Hydroelectric scheduling, Large-scale Systems
NPS Report NumberNPS-OR-94-001
Showing items related by title, author, creator and subject.
Humara, Michael Jesus (Monterey California. Naval Postgraduate School, 2020-05);Developing accurate and computationally efficient models for ocean acoustics is inherently challenging due to several factors including the complex physical processes and the need to provide results on a large range of ...
Copley, David C. (Monterey, California. Naval Postgraduate School, 1984-06);The stochastic forcing theory of Frankignoul and Hasselmann, 1977 is modified to include a mixed layer model. This enables the examination of the interaction between stochastic heat flux or wind stress components and the ...
Goggins, David A. (Monterey, California. Naval Postgraduate School, 1995-09);This thesis is a continuation of optimization modeling research conducted at the Naval Postgraduate School for the U.S. Air Force Studies and Analyses Agency. That work resulted in Throughput II, a multi-period model for ...