The project aims to develop model predictive co...the project aims to develop model predictive control (mpc) formulations for the optimal economic management of hybrid energy systems with renewable energy sources (mainly solar energy) and storage capabilities. the three basic objectives of the project are: 1. development of methodologies for obtaining models of hybrid generation systems (generation, storage and demand): the calculation of reduced models suited for analysis and design will be especially relevant. systems in which the energy comes from different sources (that should be combined for an optimal and safe exploitation) will be considered. renewable energy plants are characterized by the fact that the primary energy source (for example sun or wind) cannot be controlled and varies during the day. thus, the identification and prediction of these primary energy sources (that also act as disturbances from the control point of view) is of particular interest to optimize the process performance, minimize the use of auxiliary energy sources and planning to optimize the generation on the requested demand. both time series based models and artificial neural networks models will be studied. 2. development of model predictive control strategies for the optimal economic management of these heterogeneous energy systems: the main focus will be on the development of strategies with potential applicability in complex processes. different hierarchical levels will also be considered, characterized by the existence of different dynamical time scales, hybrid nature and changing operation modes. thus, both low-level (systems and equipment level) and high-level (setpoint optimization, process coordination, dynamic real-time optimization) modeling and control objectives arise in the scope. several of the high-level objectives that will be treated in this project are: adaptation of energy production to demand, planification of energy production, storage and use and integration of economical aspects in energy management. one of the low-level objectives will be related to the development of hybrid and nonlinear predictive control strategies to compensate for the intrinsic delays associated to solar plants. a methodology will be developed to use the possible degrees of freedom in the energy demand to regulate energy withdrawal. thus, one important objective is to develop algorithms based on adapting energy demand to energy production (and vice versa) through the use of predictive control algorithms. economic performance and optimal energy management will be taken explicitly into account in the derivation of the proposed predictive control technique. 3. implementation and validation of the strategies in hardware in the loop simulation scenarios in brazil and in the pilot plant at seville. this will facilitate the development of the different tasks of the project over quasi-realistic conditions.