In this work we study the performance of elevation estimators and lower bounds on the estimation error variance for a low angle target in a smooth sea scenario using an array antenna. The article is structured around some key assumptions on multipath knowledge, signal parameterization and noise covariance, giving the reader a framework in which Maximum-Likelihood estimators exploiting di erent a priori information can be found.The crucial factor that determines the estimator accuracy is the multipath modeling, and there are three alternative levels of knowledge that can be used: 1 two unknown target locations 2 the target and its corresponding sea-re ection are related via simple geometry 3 the sea re ection coe cient is known as a function of grazing angle.A compact expression for the Cram er-Rao lower bound is derived, including all special cases of the key assumptions. We prove that the Cram er-Rao bound is highly dependent on the multipath model, while it is the same for the di erent signal parameterizations and that it is independent of the noise covariance. However, the Cram er-Rao bound is sometimes too optimistic and not achievable. The tighter Barankin bound is derived to predict the threshold behavior seen at low SNR. At high SNR the Barankin bound coincides with the Cram er-Rao bound. Simulations show that the Maximum Likelihood methods are statistically e cient and achieve the theoretical lower bound on error variance, in case of high enough SNR.The bounds are also useful tools to design an improved array structure that can give better performance than the standard uniform linear array structure. The in uence of the number of sensors and the number of snapshots on the error variariance is also studied, showing the rate of improvement with more sensors or snapshots. Finally we discuss the use of multiple frequencies, which is mainly a tool for suppressing ambiguities. We show for which signal models it provides improved performance.

The short term thermal production planning problem is solved in two steps by integrating physical plant models into the standard approach. The first step aims at solving the discrete variables from the unit commitment sub-problem (UCP) using standard mixed integer linear models and optimization techniques. The second step focuses on the economic dispatch sub-problem (EDP) described by highfidelity, continuous time, physics-based Modelica models together with nonlinear optimization techniques from the JModelica.org platform. The output of the second step includes optimized power flows but also highly relevant variables such as supply temperature, supply flow rate, turbine bypass valve in the cogeneration plant. The optimization is formulated as a maximization of the benefit from heat and electricity sell over a finite time-horizon. fy flexible optimization problems including constraints on arbitrary process variables such as heat load of the production units, supply temperature and flow rate, pressures.