@Misc{Supelec428,
author = {Stephane Vialle and Xavier Warin and Constantinos Makassikis and Patrick Mercier},
title = {{Stochastic control optimization \& simulation applied to energy management: From 1-D to N-D problem distributions, on clusters, supercomputers and Grid}},
year = {2008},
booktitle = {{Grid@Mons conference}},
month = {may},
note = {(Invited Speaker)},
address = {Mons (Belgique)},
url = {http://www.metz.supelec.fr/metz/recherche/publis_pdf/Supelec428.pdf},
abstract = {Management of 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 for non convex optimization, 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 talk will introduce a collaboration between EDF (a French
electricity producer) and SUPELEC (a
French engineering school and research laboratory) that aimed to
distribute N-dimension stochastic
dynamic programming applications on large distributed
architectures, like PC clusters and IBM Blue
Gene supercomputers. This collaboration was initiated in a
French ANR project about Distributed and
Grid computing applied to financial mathematic problems
(the “GCPMF” ANR project).
From an applicative point of view, the goal of this research was
to be able to deal with at least three or
four uncertainty factors, and at least six or seven stocks in
optimization, while being able to efficiently
use in simulation the commands calculated. The simulations are
used after optimization in order to
generate gain estimations on different periods and in order to
estimate the associated risks. The
methodology developed in this research project will bring some
reference calculations that will help to
derive some simplified versions to use in production.
From a computer science point of view, three different
parallelization strategies have been carried out
in order to access input and output files from thousands of
processors, to distribute a N dimensional
cube of data used at each time step of an optimization
algorithm, and to compute independent
simulations requiring data spread in many separate files managed
by different processors. All
designed parallel algorithms have been experimented on a 7-
stocks problem (7-dimensions problem)
on different parallel architectures. We successfully used up to
256 processors of a PC cluster and up
to 8192 processors of a Blue Gene/L supercomputer, achieving
scalability with regular decrease of the
execution time.
We started distributing a 1-dimension stochastic control
algorithm (applied to a gas storage valuation)
in February 2007, and we extended our distribution to a N-
dimension algorithm in 2008 (applied to
electricity production management). In the next months this
industrial and large scale distributed
application will be used:
– by EDF to study and optimize its energetic stock management
and electricity production, using
its new Blue Gene/P supercomputer up to 32000 processors;
– by SUPELEC (IMS group) and INRIA (AlGorille and Reso teams) to
run large experiments on
Grid’5000, analyze communications and performances, and optimize
task distribution when
using several sites of Grid’5000.
A global collaboration between EDF, SUPELEC and INRIA will allow
comparing performances of this
real and not embarrassingly parallel application, on
supercomputers, different large PC-clusters and
one multi-site Grid.}
}