Model-based control for hybrid and uncertain smart energy systems

Energy systems influence many aspects of society, from the residential sector to the commercial one. Improving the performance and efficiency of energy systems and guaranteeing their stability is a fundamental task of control engineers. In this regard, this thesis presents modeling and control solutions for energy systems, with a focus on both electric and thermal ones. The thesis is divided in three parts. Firstly, we consider an online partitioning and stability problem of a network applied to frequency regulation. Secondly, we present algorithms for energy management system of an electrical microgrid. In particular, we focus on providing a trade-off between computational complexity and performance of the obtained solution. Lastly, we focus on thermal energy systems by designing an algorithm for room temperature control in commercial buildings. In the first part of the thesis, we consider a linear switching large-scale system and we focus on the problem of partitioning the system into smaller subsystems. We assume that the different modes of the switching system are not known a priori, but they can be detected. We propose an online scheme that can partition the system when the mode switches, adapting therefore the partition to the mode of the switching system. The goal of the partitioning algorithmis on the one hand to minimize the coupling between subsystems, in order to facilitate the task of a distributed/decentralized controller, and on the other hand to obtain subsystems with similar sizes, in order to distribute the control effort equally. Moreover, after the system has been partitioned, we apply a decentralized state-feedback control scheme to stabilize the overall system. In order to prove stability, we apply a dwell time stability scheme such that the closed-loop system remains stable even after both the mode and partition changes. The online partitioning method, together with the control algorithm, is applied to an automatic generation control problem of frequency regulation in a large-scale power network. In the second part of the thesis,we consider the energy management system problem in a microgrid. We present several Model Predictive Control (MPC) approaches for optimally managing the power flows in the microgrid, from an economical point of view. The microgrid is modeled using the Mixed Logical Dynamical (MLD) framework. We provide three different strategies that yield a trade-off between computational complexity and performance by parameterizing the inputs to the system. First, we propose a parametric MPC approach, in which the continuous inputs are expressed as parametric functions and the binary variables are heuristically parameterized. Next, we propose an if-then-else parametrization of the binary variables in the MLD model, so that they are assigned a value before the optimization takes place, yielding therefore a real-valued optimization instead of a mixed-integer one. Finally, we use past optimization results obtained from simulations to develop two machine learning methods, i.e. decision trees and random forests, that can provide a binary variable configuration so as to, once again, remove the binary variables from the optimization problem. The results obtained show that the methods can provide a very large decrease in computation time while having almost no loss in performance. Simulation results show how the developed methods are able to provide a large reduction in computation time while having a very little performance loss. Lastly, in the third part we focus on thermal networks. We propose a scenario-based MPC approach to control the temperature room in office buildings. The building is modeled using the tool Modelica that yields a better model description compared to linearized models. The adopted scenario generation method improves upon the current literature by considering that the marginal distributions depend both the prediction time steps and on time itself and that the distributions of the disturbances are not stationary. By combining scenario-based MPC together with Modelica, we can improve the performance of the controller of the building and we show this by comparing our method against a deterministic method using a Modelica model description, but also against the same controllers with a linearized model.