摘要：Many studies have considered the solution of Unit Commitment problems for the management of energy networks. In this field, earlier work addressed the problem in determinist cases and in cases dealing with demand uncertainties. In this paper, the authors develop a method to deal with uncertainties related to the cost function. Indeed, such uncertainties often occur in energy networks (waste incinerator with a priori unknown waste amounts, cogeneration plant with uncertainty of the sold electricity price...). The corresponding optimization problems are large scale stochastic non-linear mixed integer problems. The developed solution method is a recourse based programming one. The main idea is to consider that amounts of energy to produce can be slightly adapted in real time, whereas the on/off statuses of units have to be decided very early in the management procedure. Results show that the proposed approach remains compatible with existing Unit Commitment programming methods and presents an obvious interest with reasonable computing loads.

摘要：The design of H∞ reduced order controllers is known to be a non-convex optimization problem for which no generic solution exists. In this paper, the use of Particle swarm Optimization (PsO) for the computation of H∞ static output feedbacks is investigated. Two approaches are tested. In a first part, a probabilistic-type PsO algorithm is defined for the computation of discrete sets of stabilizing static output feedback controllers. This method relies on a technique for random sample generation in a given domain. It is therefore used for computing a suboptimal H∞ static output feedback solution. In a second part, the initial optimization problem is solved by PsO, the decision variables being the feedback gains. Results are compared with standard reduced order problem solvers using the COMPleib (Constraint Matrix-optimization Problem Library) benchmark examples and appear to be much than satisfactory, proving the great potential of PsO techniques.