Un contrôleur centralisé en temps-réel pour la gestion des réservoirs utilisant les prévisions météorologiques d'ensemble : application sur la Seine

[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAU [Encadrant_IRSTEA]Dorchies, D.The management of multi-objective reservoirs will face increasing difficulties in the future due to climate change and its impact on future hydrologic conditions. Climate models agree in predicting an increase in frequency and intensity of extreme events for the future. Thus, the actual management of reservoirs, designed under current climate conditions and socio-economic environment, might be inadequate in the future. Adaptation strategies are necessary to continue to fulfill the objectives of water systems management, as floods prevention and water supply. This work is part of the Climaware European project (2010-2013) set up to develop and to assess measures for sustainable water resources management regarding adaptation to climate change. Our purpose is to investigate a possible adaptation strategy for the reservoirs management on the Seine river basin, upstream of Paris. These reservoirs are regulated with the objective of reducing floods and augmenting low flows. Their current management is empirical, reactive, and decentralized, mainly based on objective filling curves, constructed from an analysis of historical floods and low flows. Therefore, the efficiency of this management strategy could be enhanced when inflows are significantly different from their seasonal average. To improve such management strategy and better adapt to climate changes, we investigate the use of Model Predictive Control (MPC) and Tree-Based MPC (TB-MPC). These control methods are two proactive and centralized methods that use all the information available in real-time, including respectively deterministic and ensemble weather forecasting. This information is then used in the model predictive control framework, to optimize an objective function over a finite receding horizon, using a model to predict the evolution of the system in response to the forecasted inputs. The MPC and TB-MPC controllers are implemented in combination with the integrated model of the Seine river basin, including a semi-distributed hydrologic model of the watershed, a simplified hydraulic model of the river network, and the four reservoir models. The controller optimizes a global cost function that takes into account the costs associated to high and low flows, based on critical thresholds at some key downstream stations, and a penalty cost based on the final reservoir storages, to guarantee a sustainable management in the long term. The reservoir's management using MPC and TB-MPC will be tested using different climate forecasting models. We will assume a perfect predictor, using weather observations as forecasts, real deterministic and ensemble forecasts