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In this paper we consider the problem of controlling a service area hosting stations for the provisioning of the electric vehicles fast charging service, having the support of an electric energy storage system and local power production from renewables. Key aspects motivating the work are the hard temporal constraint imposed by drivers requiring the fast charging service and the impact high aggregated power withdrawal has on the economic viability of the investment for the service area operator; consequently key control requirements include a congestion level driven tracking of the charging power demand and the flattening of power flow at the point of connection of the service area to the electricity grid, while keeping stable ESS operation. These opposing control objectives, together with the uncertain nature of the power demand and production, brings to the formulation of a stochastic model predictive control problem, based on a continuous/finite-time optimal control problem, for which the explicit form of solution is determined. Simulations are presented to validate the proposed approach.

This paper presents a model predictive control approach for the control of a grid-connected service area equipped with an electric energy storage system, and with charging stations, delivering the fast charging service to plug-in electric vehicles. We consider two different possible configurations for this microgrid, implying different connection schemes among the storage, the charging stations, and the grid. The objective is to control the storage in order to lower and flatten the power profile at the point of connection of the service area with the grid, thus reducing the connection fees of the service area, and also the impact of charging on the grid. Simulation results are presented, to highlight the different performance of the two connection setups, with a focus on the uncertainty in the knowledge of the future charging demand.

This work deals with the problem of enabling plug-in electric vehicles (PEV) to the provisioning of ancillary services to the grid, for frequency regulation purposes. The paper presents and discusses a reference scenario for such a use case, describing the systems and actors involved. A system architecture for enabling the use case is proposed, detailing the relevant system components, technologies, and control algorithms. The paper also discusses the issue of the coexistence of the PEV-based frequency regulation services with PEV smart charging. Finally, it is discussed the crucial role that 5G technologies could play for making actually feasible the implementation of the proposed scheme for PEV-based frequency regulation service provisioning.

Radiometry, since its inception, has provided low-cost effective devices for the detection of thermal noise power in a given environment due to emission and reflection of thermal radiation. The idea to apply this as a sensor to detect and track targets is more recent. Therefore, in view of applications as a radar sensor, we investigate the theoretical performance of a passive millimeter-wave radiometric receiver expected to work as a seeker in missile-based applications. We show how the main parameters that determine the detection capability of this device are experimentally obtained and well consistent within the aimed application scope.

In this work a dynamic biasing circuit for current conveyor (CCII) applications is proposed and discussed with circuitry details. This solution, applied for the first time to current-mode circuits, allows to decrease the steady state power consumption of the considered circuit, minimally affecting its performance. The dynamic biasing solution here conceived is able to sense the input signal providing an extra current to the CCII only when an input voltage variation occurs. The proposed conveyor shows a good transient response and allows to combine the needs of both low-power and high slew-rate characteristics that are mandatory in many practical applications and wireless systems. A case-study is also illustrated with simulation results.

In the last few years, there has been a growing interest on the usage of passive WiFi signals for detecting and classifying human behavior activities. In most generality, the idea underlying most state of the art approaches is to monitor changes in the Channel State Information (CSI) or equivalent spectral metrics, caused by the different multi- path conditions (and their temporal variations) induced by human activity. The (implicit) underlying intuitive assumption is that, in a static environment, i.e. with no moving humans, the multi-path conditions do not vary, and hence the CSI gathered at the passive receiver side should remain substantially stable in time. Goal of this paper is to document, with an extensive set of experimental measurements, a perhaps surprising result: with modern multi-antenna 802.11n/ac access points, such an intuitive assumption does not seem to hold anymore. Rather, the measured CSI time pattern, which differs for different access point brands, appears to be affected by access points' implementation artifacts. Indeed, our measurements unveil frequent fluctuations of the CSI pattern among multiple ``modes". Albeit still preliminary and, at least in part, still requiring a more thorough understanding (that only the reverse engineering of the access points' implementation may ultimately provide), our measurements unveil a phenomenon which may play havoc with several CSI-based human behavior detection approaches proposed in past works, and especially with those based on CSI measured on data frames, opposed to those based on relatively more stable (but still to be reconsidered in sight of our results) Beacon frames.

This paper presents a real time control strategy for energy storage systems integration in electric vehicles fast charging applications combined with generation from intermittent renewable energy sources. A two steps approach taking advantage of the model predictive control methodology is designed on purpose to optimally allocate the reference charging power while managing the priority among the plugged vehicles and then control the storage for efficiently sustaining the charging process. Two different use cases are considered: in the former the charging area is disconnected from the grid, so that the objective is to minimize the deviation of electric vehicles charging power from the nominal value; in the latter the focus is on the point of connection to the grid and the need of mitigating the related power flow. In both cases the fundamental requirement for feasible control system operation is to guarantee stability of the storage's state of charge over the time. Simulation results are provided and discussed in detail, showing the effectiveness of the proposed approach., 6 pages

This paper presents an easily implementable load control strategy aimed at managing shiftable loads in residential buildings, possibly equipped also with centralized storage and generation facilities. The algorithm is compliant with the KNX standard for communication of smart home devices, and ensures the controlled reduction of power peak profiles. Simulation results are discussed in details, comparing the achieved performances with the ones attainable by more complicated benchmark algorithms present in literature, and also giving a first assessment of the return on the investments needed to implement the proposed load control architecture.

The aim of this work is to provide the elements to design an inter-subjective augmented reality in order to make the augmented objects part of our everyday world. This work will analyse intersubjectivity from a phenomenological point of view using the works by Husserl and Schutz. Thanks to these two authors it will be possible to understand how our in-tersubjective world is constituted by highlighting its constitutive elements. Therefore, it will be possible to make the inverse process and to create an intersubjective augmented world. While phenomenology starts from an intersubjective world and it analyses how such an intersubjectivity it is possible, this work will start from the constitutive elements of intersubjectivity in order to apply them in augmented reality and to create an intersubjective augmented world. This work will be divided in two main parts. The first part of the work will focus on intersubjectivity from a phenomenological point of view and the second part will focus on the application of these elements on augmented reality. It will show how it is mandatory to create objects which could make us scream aldquo;Ouch!ardquo; while bumping into them and augmented presences which resist to us.

This paper deals with the design of a lightweight sensor scheduling algorithm in clustered wireless sensor networks (WSNs) for environmental observation. The aim is that of achieving energy savings and a fair distribution of the sensing task among WSN nodes, while ensuring that critical network tasks, such as data fusion, are not impaired. The proposed scheduling policy takes into account feedback on nodes' health status and the quality of the information provided by the nodes to be scheduled. As a result, dynamically variable schedules are produced, adapted to the current status of the WSN. Simulation results are presented to highlight the relevant features of the algorithm.

This paper presents a real time strategy for optimal power flow in presence of storage devices and wind turbine driven by Doubly Fed Induction Generators. These elements work in cooperation defining a dynamic bus where the generated power is subject to temporal constraints, which establish a coupling between traditional power flow problems related to consecutive time periods; further the uncertainty in wind power generation forecasts requires a continuous update of the planned power profiles, in order to guarantee a dynamic equilibrium among demand and supply. Model predictive control is used for this purpose, considering the dynamic equations of the storage and the wind turbine rotor as prediction models. A proper target function is introduced in order to find a trade-off between the need of minimizing generation costs and the excursions of the storage state of charge and the wind turbine angular speed from reference states. In the case study under consideration storage, wind turbines and a traditional synchronous generator are operated by the Transmission System Operator in the form of a Virtual Power Plant working as slack bus to cover network losses. The proposed approach is validated on simulation basis.

The aim of this work is to provide the elements to design an intersubjective augmented reality in order to make the augmented objects part of our everyday world. This work will analyse intersubjectivity from a phenomenological point of view using the works by Husserl and Schutz. Thanks to these two authors it will be possible to understand how our intersubjective world is constituted by highlighting its constitutive elements. Therefore, it will be possible to make the inverse process and to create an intersubjective augmented world. While phenomenology starts from an intersubjective world and it analyses how such an intersubjectivity it is possible, this work will start from the constitutive elements of intersubjectivity in order to apply them in augmented reality and to create an intersubjective augmented world. This work will be divided in two main parts. The first part of the work will focus on intersubjectivity from a phenomenological point of view and the second part will focus on the application of these elements on augmented reality. It will show how it is mandatory to create objects which could make us scream “Ouch!” while bumping into them and augmented presences which resist to us.

The SPectral IMaging (SPIM) facility is a laboratory imaging VIS-IR spectrometer, operative in the INAF/IAPS laboratory in Rome. The facility is used as a laboratory support for the DAWN mission (to the asteroids Vesta and Ceres) and for the 2018 ExoMars mission (to Mars). This imaging spectrometer, which is the spare of the VIR spectrometer [1,2] on-board the DAWN spacecraft, is operative in the 0.22-5.05 μm spectral range. It is characterized by high spatial (38 μm) and spectral (2 nm in the VIS channel, 12 nm in the IR channel) resolution. The high spectral performances, combined with the high spatial resolution imaging capability of this instrument allow a very accurate laboratory investigation and characterization of numerous types of mineral and rock samples, both in powder and in slab form, and also of extra-terrestrial samples, down to a few tens of micrometers in size. © 2014 IEEE.

This paper deals with the robust regulation of reactive power and rotor angular speed in a wind turbine driven Doubly Fed Induction Generator, which constitutes one of the key functionalities for the implementation of the future Smart Grids. The focus of the work is the application of a recent development in the theory of robust control of nonlinear systems, which combines robust feedback linearization and H∞ linear control. The derived robust control system is compared to a traditional one making use of classical feedback linearization and PI controllers. Simulations show the effectiveness of the new approach in extending the performances of the classical feedback linearization based regulator from nominal parameters condition to the perturbed one.

This paper deals with the problem of controlling electric vehicles charging in a smart grid. We present an event driven model predictive control (MPC) approach, which aims to find a proper trade-off between the needs of minimizing the cost of energy withdrawal and the error in the tracking of a reference aggregated charging power profile. All in respect of drivers' preferences, technical bounds on the control action (in compliance with the IEC 61851 standard) and grid constraints. The proposed control approach allows “flexible” EV users to participate in demand side management programs, which will play a crucial role for stability and efficiency of future smart grids. Simulation results are provided and discussed in details, showing the relevance of our contribution.

The SPectral IMaging (SPIM) facility is a laboratory imaging VIS-IR spectrometer, operative in the INAF/IAPS laboratory in Rome. The facility is used as a laboratory support for the DAWN mission (to the asteroids Vesta and Cerere) and for the 2018 ExoMars mission (to Mars). This imaging spectrometer, which is the spare of the VIR spectrometer [1] on-board the DAWN spacecraft, is operative in the 0.22-5.1 spectral range. It is characterized by high spatial (38 μm) and spectral (2 nm in the VIS channel, 12 nm in the IR channel) resolution. The high spectral performances, combined with the high spatial resolution imaging capability of this instrument allow a very accurate laboratory investigation and characterization of numerous types of mineral and rock samples, both in powder and in slab form, and also of extraterrestrial samples down to a few tens of micrometers in size (such as for example Interplanetary Dust Particles). © 2013 IEEE.

European energetic policy aims to complete the liberalization market process and to improve a rational use of energy promoting strongly `Nearly Zero-Energy Buildings' NZEB and the use of highly efficient cogeneration CHP also for residential/office/commercial buildings. NZEB means a building that has a very high energy performance, and the very low amount of energy required should be covered to a very significant extent by energy from renewable sources, including CHP. The actual promotion of renewable energies and distributed generation obliges to review the traditional “load-driven” “top-down” power system. Electric demand side management (DSM) focuses on changing the electricity consumption patterns of end-use customers through improving energy efficiency and optimizing allocation of power. Demand response (DR) is a DSM solution that targets residential and commercial customers, and is developed for demand reduction or demand shifting at a specific time for a specific duration. Renewable power and CHP systems can be sized and managed more efficiently for loads of tens of kVA, more than an individual consumer. So, it is necessary for small and medium consumers, to aggregate their load profile in order to reach a threshold value of some ten of kW and a more demand flexibility like the Heating System where a unique boiler supplies more efficiently the building. The actual regulatory rules don't permit the aggregation of consumers in a unique Point of Delivery POD. The paper presents a case study of a microgrid arranged for a complex of two commercial/residential buildings in order to overcome the regulatory barrier mentioned and propose a load management strategy aimed at controlling the power withdrawal at the POD. The authors suggest the ecodesign of the residential and commercial low voltage distribution like a microgrid allows to guarantee a reduced impact as ever net load on the net supply at least in a first evolution.

The aim of this paper is to focus attention on how it is possible to better the embodiment of the AR technologies.

Hellinger distance provides a way to evaluate how far is a given probability distribution from another one. This kind of tool is well-suited e.g. for target recognition in a radar system. The aim of this paper is to show that, given a couple of probability distributions with a single estimator, the evaluation of their Hellinger distance provides a metric for a Riemann manifold that, being conformal, implies that an estimator can always be found that makes the distance minimal. So, in this case, the choice of the best distribution reduces simply to the computation of this estimator. Finally, applications in the area of target recognition can be devised.

In this paper we outline a novel approach for the design of an electric vehicle (EV) aggregator, a controller whose objective is to optimally manage the charging operations of an EV fleet. The control strategy we derive is based on model predictive control and allows to achieve costs minimization, also enabling the aggregator (hence, the EV fleet) to participate to the provisioning of active demand services to upper level market players. Explicative simulations are presented and discussed in order to show the effectiveness of the approach and also to investigate the role of vehicle to grid power.

Mobile-Ad-Hoc-Networks (MANET) are self-configuring networks of mobile nodes, which communicate through wireless links. One of the main issues in MANETs is the mobility of the network nodes: routing protocols should explicitly consider network changes into the algorithm design. MANETs are particularly suited to guarantee connectivity in disaster relief scenarios, which are often impaired by the absence of network infrastructures. This work proposes a proactive routing protocol, developed via Reinforcement Learning (RL) techniques, to dynamically choose the most stable path, basing on GPS information, among the feasible ones and to consequently increase resiliency to link failures. Simulations show the effectiveness of the proposed protocol, through comparison with the Optimized Link State Routing (OLSR) protocol.

This paper deals with the load shifting problem in a household equipped with smart appliances and an energy storage unit with conversion losses. The problem is faced by establishing an event driven Model Predictive Control framework aiming to meet the real life dynamics of a household and to keep low the impact of the control system on the total electric energy consumption. The proposed approach allows the consumer to minimize the daily energy cost in scenarios characterized by Time of Use tariffs and Demand Side Management, by dynamically evaluating the best time to run of the appliances and the optimal evolution of the battery level of charge. A proper set of realistic simulations validates the proposed approach, showing the relevance of the energy storage unit in the domestic load shifting architecture.

This paper presents a novel approach to the critical infrastructure (CI) interdependencies analysis, based on the Dynamic Bayesian Network (DBN) formalism. Our original modeling procedure divides the DBN in three levels: an atomic events level, which models the adverse events impacting on the analyzed CIs, a propagation level, which captures CI interdependencies, and a services level, which allows to monitor the state of provided services. Three types of analyses can be performed: a reliability study, an adverse events propagation study, and a failure identification analysis. A case study provided by Israel Electric Corporation is considered, and explicative simulations are presented and discussed in detail.