A N-dimensional Stochastic Control Algorithm for Electricity Asset Management on PC cluster and Blue Gene Supercomputer

Abstract

International audienceManagement 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