Gestion stratégique d'un système de ressources en eau : l'exemple du système NESTE

Abstract

L'article que nous proposons s'inscrit dans le cadre des problèmes d'optimisation bimensionnelle (irrigation & salubrité) des ressources en eau durant la période d'étiage. Sur le cas du système NESTE, la résolution est effectuée selon deux approches :- un modèle de programmation dynamique avec état de dimension deux (niveau des réserves, niveau dans la rivière) où, dans la solution numérique, les variables sont discrétisées;- un modèle « synthétique » où l'on calcule une probabilité de non dépassement caractérisant l'état hydrique des ressources du système. Une règle empirique permet d'associer à cette grandeur une décision de consigne à effectuer.Les résultats numériques sont comparés sur une série de chroniques historiques. Les avantages et les inconvénients de chacune des deux approches sont mis en lumière sur le cas réel du système NESTE.This paper deals with bicreteria (irrigation & water quality) weekly operation of a water resource system during dry period. Two ways of handling the problem are assessed and compared on a real case study :- a stochastic dynamic programming modal with a two dimensional state (reservoirs level, river level) that is numerically solved by discretization ;- a more « synthetic » model where the state is expressed in term of a tail aera probability related to the consumption of all the present water resources in the future. A practical decision rule is based upon the associated critical value.Numerical results are plotted on historical varies for both methods.From the present application to the NESTE system, the conclusions are :1) Both procedures allow the system manager to formulate operating strategies in a rational way :- An operating rule can be derived to allocate water so as to meet a combination of the various objectives. It is expressed as a feedback law linking what we know from the state of the system to how we control its evolution.- Both methods need a parameter to be set up by stochastic simulation.- They give close results on the basis of the past data and can be conveniently proposed to system managers.2) The system analysis approach is based on stochastic dynamic programming. If can be efficiently used to derive optimal feedback ruses of operation and can routinely deal with complex decisions such as limiting irrigation when a shortage is to occur or take the risk to keep going and decrease output targets for water quality management. At the same time, this procedure entails heavy computing time, uneasy interpretation of the weighting coefficient between irrigation and water quality objectives, and a rather artificial elicitation of the global compromise.Such an approach is very well fit for simulation because it is composed of elementary blocks that are gathered in a transition relationship to describe the system's dynamic evolution. This approach also provides a means to get an optimal policy as long as the system manager accepts the necessity to formulate an objective function consistently with dynamic programming (i. e. stages are separable and additive). Of co...