The first chapter provides some basic definitions and results from the theory of convex analysis and nonlinear mappings related to our work. Some sufficient conditions for the existence of a solution of problem (GVIP) are also recalled. In the second chapter, we first illustrate the scope of the auxiliary problem procedure designed to solve problems like (GVIP) by examining some well-known methods included in that framework. Then, we review the most representative convergence results for that class of methods that can be found in the literature in the case where F is singlevalued as well as in the multivalued case. Finally, we somewhat discuss the particular case of projection methods to solve affine variational inequalities. The third chapter introduces the variational convergence notion of Mosco and combines it with the auxiliary problem principle. Then, we recall the convergence conditions existing for the resulting perturbed scheme before our own contribution and we comment them. Finally, we introduce and illustrate the rate of convergence condition that we impose on the perturbations to obtain better convergence results. Chapter 4 presents global and local convergence results for the family of perturbed methods in the case where F is singlevalued. We also discuss how our results extend or improve the previous ones. Chapter 5 studies the multivalued case. First, we present convergence results generalizing those obtained when there is no perturbations. Then, we relax the scheme by means of a notion of enlargement of an operator and we provide convergence conditions for this inexact scheme. In Chapter 6, we build a bundle algorithm to solve problem (GVIP) and we study its convergence.

This operations research thesis should be situated in the field of the power generation industry. The general objective of this work is to efficiently solve the Generalized Unit Commitment (GUC) problem by means of specialized software. The GUC problem generalizes the Unit Commitment (UC) problem by simultane-ously solving the associated Optimal Power Flow (OPF) problem. There are many approaches to solve the UC and OPF problems separately, but approaches to solve them jointly, i.e. to solve the GUC problem, are quite scarce. One of these GUC solving approaches is due to professors Batut and Renaud, whose methodology has been taken as a starting point for the methodology presented herein.This thesis report is structured as follows. Chapter 1 describes the state of the art of the UC and GUC problems. The formulation of the classical short-term power planning problems related to the GUC problem, namely the economic dispatching problem, the OPF problem, and the UC problem, are reviewed. Special attention is paid to the UC literature and to the traditional methods for solving the UC problem. In chapter 2 we extend the OPF model developed by professors Heredia and Nabona to obtain our GUC model. The variables used and the modelling of the thermal, hydraulic and transmission systems are introduced, as is the objective function. Chapter 3 deals with the Variable Duplication (VD) method, which is used to decompose the GUC problem as an alternative to the Classical Lagrangian Relaxation (CLR) method. Furthermore, in chapter 3 dual bounds provided by the VDmethod or by the CLR methods are theoretically compared.Throughout chapters 4, 5, and 6 our solution methodology, the Radar Multiplier (RM) method, is designed and tested. Three independent matters are studied: first, the auxiliary problem principle method, used by Batut and Renaud to treat the inseparable augmented Lagrangian, is compared with the block coordinate descent method from both theoretical and practical points of view. Second, the Radar Sub- gradient (RS) method, a new Lagrange multiplier updating method, is proposed and computationally compared with the classical subgradient method. And third, we study the local character of the optimizers computed by the Augmented Lagrangian Relaxation (ALR) method when solving the GUC problem. A heuristic to improve the local ALR optimizers is designed and tested.Chapter 7 is devoted to our computational implementation of the RM method, the MACH code. First, the design of MACH is reviewed brie y and then its performance is tested by solving real-life large-scale UC and GUC instances. Solutions computed using our VD formulation of the GUC problem are partially primal feasible since they do not necessarily fulfill the spinning reserve constraints. In chapter 8 we study how to modify this GUC formulation with the aim of obtaining full primal feasible solutions. A successful test based on a simple UC problem is reported. The conclusions, contributions of the thesis, and proposed further research can be found in chapter 9.