Optimal energy management of smart households with electric vehicles, energy storage and distributed generation

In this thesis, the optimal operation of a neighborhood of smart households in terms of minimizing the total energy cost is analyzed. Each household may comprise several assets such as electric vehicles, controllable appliances, energy storage and distributed generation. Bi-directional power flow is considered for each household . Apart from the distributed generation unit, technological options such as vehicle-to-home and vehicle-to-grid are available to provide energy to cover self-consumption needs and to export excessive energy to other households, respectively.

Simulation of URLLC slicing with resource reconfiguration in 5G networks

This thesis seeks to analyse the performance of dynamic slice provisioning in a 5G metro network with the low latency and reliability guaranties. This elaborate highlight the comparison in terms of performance of two versions of a simulator developed in Python based on different models: the Exhaustive research model and Shortest Path First Fit (SPFF) model. It further presents the differences between the dedicated path protection and the shared path protection. This analysis is made through several simulations at different network conditions by varying networks resources and observing the network performances while comparing the 2 models mentioned above. A reconfiguration procedure was implemented on backup resources in the shortest path first fit in order to improve its performance with respect to the exhaustive research which is more optimised. Subsequently, several triggering events was implemented, for the reconfiguration. And a comparison is made between these different triggering events in terms blocking probability, bandwidth at link, capacity at each node, primary and backup bandwidth per slice and backup capacity per slice.

Non-line-of-sight localization with frequency-selective metasurfaces

An emerging technology, that Smart Radio Environments rely on to improve wireless link quality, are Reconfigurable Intelligent Surfaces (RISs). A RIS, in general, can be understood as a thin layer of EM composite material, typically mounted on the walls or ceilings of buildings, which can be reconfigured even after its deployment in the network. RISs made by composing artificial materials in an engineered way, in order to obtain unconventional characteristics, are called metasurfaces. Through the programming of the RIS, it is possible to control and/or modify the radio waves that affect it, thus shaping the radio environment. To overcome the limitations of RISs, the metaprism represents an alternative: it is a passive and non-reconfigurable frequency-selective metasurface that acts as a metamirror to improve the efficiency of the wireless link. In particular, using an OFDM (Orthogonal Frequency-Division Multiplexing) signaling it is possible to control the reflection of the signal, suitably selecting the sub-carrier assigned to each user, without having to interact with the metaprism or having to estimate the CSI. This thesis investigates how OFDM signaling and metaprism can be used for localization purposes, especially to extend the coverage area at low cost, in a scenario where the user is in NLoS (Non-line-of-sight) conditions with respect to the base station, both single antenna. In particular, the paper concerns the design of the analytical model and the corresponding Matlab implementation of a Maximum Likelihood (ML) estimator able to estimate the unknown position, behind an obstacle, from which a generic user transmits to a base station, exploiting the metaprism.