[en] We focus in this thesis, on the optimization process of large systems under uncertainty, and more specifically on solving the class of so-called deterministic equivalents with the help of splitting methods. The underlying application we have in mind is the electricity unit commitment problem under climate, market and energy consumption randomness, arising at EDF. We set the natural time-space-randomness couplings related to this application and we propose two new discretization schemes to tackle the randomness one, each of them based on non-parametric estimation of conditional expectations. This constitute an alternative to the usual scenario tree construction. We use the mathematical model consisting of the sum of two convex functions, a separable one and a coupling one. On the one hand, this simplified model offers a general framework to study decomposition-coordination algorithms by elapsing technicality due to a particular choice of subsystems. On the other hand, the convexity assumption allows to take advantage of monotone operators theory and to identify proximal methods as fixed point algorithms. We underlie the differential properties of the generalized reactions we are looking for a fixed point in order to derive bounds on the speed of convergence. Then we examine two families of decomposition-coordination algorithms resulting from operator splitting methods, namely Forward-Backward and Rachford methods. We suggest some practical method of acceleration of the Rachford class methods. To this end, we analyze the method from a theoretical point of view, furnishing as a byproduct explanations to some numerical observations. Then we propose as a response some improvements. Among them, an automatic updating strategy of scaling factors can correct a potential bad initial choice. The convergence proof is made easier thanks to stability results of some operator composition with respect to graphical convergence provided before. We also submit the idea of introducing 'jumps' in the method when applied to polyhedral problems based on the geometry shaped by the sequence of iterates. Finally, we prove it is possible to preserve global convergence while not taking into account all subproblems at every iteration but only a subset, via the addition of a control mechanism. The practical interest of theses propositions is corroborated by numerical experiments performed on the electric production management problem. (author)