A N-dimensional Stochastic Control Algorithm for Electricity Asset Management on PC cluster and Blue Gene Supercomputer
Identifieur interne : 004024 ( Main/Exploration ); précédent : 004023; suivant : 004025A N-dimensional Stochastic Control Algorithm for Electricity Asset Management on PC cluster and Blue Gene Supercomputer
Auteurs : Stéphane Vialle [France] ; Xavier Warin [France] ; Patrick Mercier [France]Source :
Abstract
Management of French electricity production to control cost while satisfying demand, leads to solve a stochastic optimization problem where the main sources of uncertainty are the demand load, the electricity and fuel market prices, the hydraulicity, and the availability of the thermal production assets. A stochastic dynamic programming method is an interesting solution, but is both CPU and memory consuming. It requires parallelization to achieve speedup and size up, and to deal with a big number of stocks (N) and a big number of uncertainty factors. This paper introduces a distribution of a N-dimension stochastic dynamic programming application, on PC clusters and IBM Blue Gene/L super-computer. It has needed to parallelize input and output file accesses from thousands of processors, to load balance a N-dimension cube of data and computation evolving at each time step, and to compute Monte-Carlo simulations requiring data spread in many separate files managed by different processors. Finally, a successful experiment of a 7-stock problem using up to 8192 processors validates this distribution strategy.
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A stochastic dynamic programming method is an interesting solution, but is both CPU and memory consuming. It requires parallelization to achieve speedup and size up, and to deal with a big number of stocks (N) and a big number of uncertainty factors. This paper introduces a distribution of a N-dimension stochastic dynamic programming application, on PC clusters and IBM Blue Gene/L super-computer. It has needed to parallelize input and output file accesses from thousands of processors, to load balance a N-dimension cube of data and computation evolving at each time step, and to compute Monte-Carlo simulations requiring data spread in many separate files managed by different processors. 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