Ricerc@Sapienza

The present paper deals with storage resources optimization strategies to be adopted in the context of the European Electricity Markets by the Italian Transmission System Operator (TSO), in order to exploit, as much as possible, existing and future storage resources. The state-of-the-art of grid-scale storage technologies deployment in Italy is provided.

In this paper, a novel parameter determination technique is developed for mate-
rial models in continuum mechanics aimed at describing metamaterials. Owing
to their peculiar mechanical properties and behaviors, such as extreme elasticity
or high strength-to-weight ratio, metamaterials are of interest to be simulated by
reduced-order modeling by means of the generalized mechanics. Such models
incorporate constitutive parameters to be determined; we develop an automa-
tized optimization process specifically for obtaining metamaterials parameters.

The new concept of renewable energy communities introduced by the Revised European Directive on the promotion of renewable sources (2018/2001) has opened new possibilities for microgrids. In fact, it permits to enhance the value of the energy produced by renewable sources sharing it inside an 'energy community' and to increase the social welfare. In the present paper, the authors investigated about the actual legislation framework on energy communities at European and Italian level, highlighting regulatory problems and barriers that are delaying their constitutions.

This work presents a rigorous approach to simplify the design optimization process for Switched Reluctance Machines. First of all, the dimension of the Design Space is found to be equal to twelve, as the number of Independent Design Variables. Then, constraints and requirements in the design are represented as inequalities to determine the limit surfaces, which are nothing else than the boundaries of the Design Space.

This work aims to present a new statistical optimization approach of artificial neural network modified by multi objective genetic algorithm to improve the pipe flow hydrodynamic and thermal properties such as pressure drop and heat transfer coefficient for a non-Newtonian nanofluid composed of Fe3O4 nanoparticles dispersed in liquid paraffin. Hence the mixture pressure lose & convection coefficient are evaluated and then optimized so that to maximize the convection heat transfer and minimize the pressure drop.

A novel approach has been proposed for planning time-optimal maneuvers imposing a bang–bang external control. The optimizer was based on the particle swarm optimization and only required setting the maximum number of switches allowed for each axis. Two different test cases were analyzed and solved to validate the optimizer. In the first example, characterized by four state-space variables and no path constraints, the convergence toward the optimal solution has been demonstrated with different values of the maximum number of switches.

Buildings are heavily responsible for energy consumption and CO2 emissions. Increasing their energy efficiency is in the direction of the EU strategies; and this is consistent with EU Energy Performance of

The World energy demand is progressively growing, so that many different Renewable Energy Sources (RESs) are exploited to meet the user needs and to reduce the Global Warming. In this context, an emerging RES is the sea wave energy, because it can offer a better continuity in the energy production by taking advantage of the stationary nature of the waves. Research in the energy harvesting from waves has led to the development of Wave Energy Converters (WECs). The adoption of Computational Intelligence techniques become crucial for maximizing the WECs efficiency.

Metamaterial absorbers have recently attracted a lot of interest for applications spanning from microwave to terahertz, near infrared and optical frequencies, such as electromagnetic compatibility, thermal emitters, solar cells and micro-bolometers. In this paper, a procedure for the efficient design of metamaterial absorbers based on parametric macromodels is presented. These models are used to describe the frequency-domain behaviour of complex systems as a function of frequency and design parameters (e.g., layout features).

This paper investigates the advantages and design challenges of leveraging Unmanned Aerial Vehicles (UAVs) to deploy 4G/5G femto- and pico-cells to provide quality-aware user service and improve network performance. In order to do so, we combine UAVs dashing flight capabilities with Software-defined Radios (SDRs) flexibility and devise the concept of self-optimizing UAV Base Stations (UABSs). The proposed framework allows for on-the-fly drone repositioning based on rigorous optimization techniques using real-time network metrics to enhance users' service.