A framework has been developed based on coherent risk measures and multiparametric programming for the formulation and solution of multi-stage stochastic optimization problems that arise in the context of dynamic portfolio allocation. To address mean-risk trade-off we have used a mean-risk function based on CV@R. We show that this risk measure inherits coherence of CV@R. We have developed dynamic programming equations for the problem and have obtained the explicit feedback control law via solving a sequence of multiparametric linear programs.

Methodologies have been developed for managing large-scale energy systems and hybrid renewable energy systems, which are expected to become competitive in the near future and, thus, optimization of their operation is of particular interest. The methodologies are based on the rolling horizon philosophy. An important innovation for renewable energy systems is that the weather forecast is taken into account. We have also developed an MPC strategy for the real time control of fuel cell systems, which satisfies standard control targets (set-point tracking, disturbance rejection), but at the same time minimizes the hydrogen consumption.

Our laboratory has contributed in the development of a forest fire simulation tools based on two fold reasoning; a discrete contour propagation model for estimating fire consequences and a fuzzy / neural system for the estimation of fire spread as a function of influencing factors such as terrain characteristics, vegetation type and density and meteorological conditions.