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The aim of the present study is to analyze the impact of the mooring system on the power extraction of a WEC (Wave Energy Converter). A comparison has been carried out between the scenarios of non moored hull and moored hull: for the latter case, the mooring system dynamics has been evaluated by means of a Deep Neural Network, which compares the time histories of two identical simulations (one with the mooring and the other without). This has allowed to include the influence of the mooring on the equations of motion of the hull. By coupling the equations of motion of the device with the equations of the Power Takeoff (PTO), it was possible to estimate the productivity of the WEC system for a given sea state. The results on productivity for various sea states were then compared in order to make some considerations on the influence of the mooring on the operational conditions of the system. In a future perspective, this methodology can be used as a tool for design and evaluation of mooring systems for WEC systems.

The paper presents a methodology to estimate the energy consumptions of a full electric bus in different traffic and operating conditions. Starting from driving cycles, evaluated through a simulation model developed in Aimsun, the Matlab/Simulink energy model of the bus calculates the main electrical and mechanical performance indicators such as the state of charge of the battery as well as power and torque provided by the electric motor. The methodology is applied to a real case study, that is an Italian town in the North of Italy, for which different traffic conditions and drivers' behaviors are analyzed.

The implementation of automated micro-irrigation in urban gardens using wireless sensor and actuator networks (WSAN) is being more popular. However, the use of WSAN in automated irrigation brings various technical challenges, for example, achieving low cost, long life battery, adequate communication range and compactness. In this paper, a WSAN is designed and implemented for controlling drip irrigation of urban garden that can achieve long battery life, low cost, compactness with the sufficient range of communication. The designed WSAN consists of end-devices and a coordinator, each of which is fitted with single-chip microcontroller along with wireless transceiver. Each end-device is equipped with a microcontroller, a water valve, a soil moisture sensor, a temperature sensor, a rain sensor, battery charger through solar panel and a ZigBee transceiver. The end-device reads the garden temperature, soil moisture level, battery charge status and rain presence and controls the water valve based on these data values. At the same time, the read data is transmitted to the coordinator station through ZigBee ad-hoc network. The coordinator receives the data from each end-device and presents to the interfaced user application. Due to being powered up by an energy limited resource, special techniques and algorithms are developed and only low-power electronic components are selected carefully. This paper includes implemented hardware and software results for the wireless nodes.

In nowadays high-performance sailing yacht design, a performance prediction tool capable of identifying the speed of the boat in numerous sailing conditions as a function of several design parameters is considered a crucial success factor. In the present work, a VPP is developed using validated results from CFD simulations for the hydrodynamic forces and an indirect approach to simulate the aerodynamic forces generated by the sail plan. Detailed performance results, together with a parametric study to highlight the influence of a few final design parameters on the boat speed, are presented for an R3 class SKIFF sailing yacht. The program takes as inputs the geometry and design of the boat, the sailor's mass and a range of true wind speed conditions and returns the boat speed, the displacement of the crew in order to maintain a zero roll angle, the leeway angle, the optimal equivalent sail lift coefficient as well as all the major forces acting in their relative center of pressure. The equilibrium equations are solved for each cycle on true wind angles and true wind speed, using a gradient based method that finds the minimum of a constrained nonlinear multivariable function. The solution found by the iterative method is the one that maximizes the velocity made good on course (VMC) of the boat under the specified conditions and constraints, for each true wind angle possible and low to medium wind speeds, accordingly to the average wind conditions of the regatta's location in which the boat will have to perform.

The solar energy source is characterized by unpredictability and an intermittent behaviour which must be faced using accurate forecasting tools. In the present paper an optimization algorithm is used to assess the possible impact of the vehicle to grid (V2G) technology to minimize solar photovoltaic (PV) uncertainties due to forecasting inaccuracies. In particular, forecasted values from the PVGIS database are compared with actual measurements from the Savona Campus facility and the exploitation of the V2G technology to mitigate discrepancies between the two datasets is investigated. Results show the impact of the initial state of charge (SOC) of the electrical vehicles (EVs) at the beginning of the day on the capability of balancing either the overestimation or the underestimation of the PV production. Moreover, a great influence is given by the number of involved EVs and the charging/discharging power limits of the charging stations.

The present paper describes an Energy Management System developed to operate a polygeneration microgrid where demand response strategies are applied. The proposed tool is characterized by a detailed representation of generation units and flexible loads, as well as electric/thermal networks and storage systems. Furthermore, the interaction between the microgrid and a smart building is modeled, taking into account the inner comfort level in the objective function of the optimization problem. The tool is applied to a real case study represented by the Savona Campus of the University of Genoa in Italy, where a polygeneration microgrid and a smart building are present.

In the latest times, power markets in Europe, including the Spanish one called MIBEL (Mercado Iberico de Electricidad), are being deregulated and coupled. As a result, electricity can be easily purchased and sold across further areas and countries. On the other hand, trying to guarantee renewable projects profitability, Power Purchase Agreements and Options contracts are arising as a feasible solution. The problem arises when the power plant owners have to negotiate the purchase electricity price in order to optimize risks and profits, as well as make future plans. Thus, several methods for Electricity Price Forecasting (EPF) have been developed and presented, showing different results, as market spot prices suffer from strong seasonality, spikes and high volatility. In this paper, three methods, based on Deep Learning Dynamic Neural Networks (NAR, NARX and LSTM) applied to forecast MIBEL electricity spot prices are discussed in order to evaluate their adequacy, accuracy and reliable horizon.

In this paper, we introduce an original Model Predictive Control (MPC) approach to optimize energy production of an inertial wave sea energy converter (ISWEC). In particular, within the MPC framework the performance tradeoff among different control objectives such as energy production, control effort and speed limitation are effectively handled. Mechanical and electrical physical constraints are taken into account to ensure proper working conditions to the ISWEC system. A linear model of the ISWEC device is employed to cast the underlying MPC optimization problem as a quadratic programme, to obtain a fast online implementation of the controller on the present commercial processor control unit platform by means of an efficient solver. Simulation tests performed on a detailed nonlinear ISWEC model show the effectiveness of the introduced approach.

EXTENDED ABSTRACT ENEA is the Italian National Agency for New Technologies, Energy and Sustainable Economic Development. ENEA operates in many sectors among which the most important are: energy technologies, materials physics, life sciences and climate. In the framework of its institutional mission, the ICT Division provides computing and storage resources integrated into ENEAGRID/CRESCO, an infrastructure distributed over 6 sites, whose main facilities are the HPC CRESCO clusters. The bulk of all storage is based on IBM Spectrum Scale (GPFS) since many years. The access to data, even over WAN, is managed by GPFS clusters. In May 2018 the new cluster CRSCO6 was inaugurated. CRSCO6, a 1.4 Pflops based on Intel Xeon X86-64 SkyLake CPU ranked at 420th of TOP 500 Nov.2018 list. While the interconnection of CRESCO6 is based on Intel Omni-Path (OPA) (100 Gbps), the previous CRESCO4 and CRESCO5 clusters have a network based on InfniBand QDR Truescale fabric (40 Gbps). Hence, in order to provides storage to all CRESCO clusters a GPFS multifabric layout has been implemented after dedicated tests. The work describes the ENEAGRID/CRESCO infrastructure and in particular the solution adopted to implement the GPFS multifabric.

The present paper is focused on the integration of renewable sources and storage systems in the household sector with a special attention to the adoption of home smart charging infrastructures for electric vehicles. The proposed mathematical model can be used to optimally design and daily manage a nanogrid composed of a photovoltaic plant, a storage system and an electric vehicle charging station installed at a single-family dwelling, with the goal of minimizing global costs and so reducing the annual energy bill. In the section of results, some different scenarios are reported and discussed in order to show the economic benefits consequent to the application of the aforesaid technologies. As far as e-mobility is concerned, the vehicle-to-home technology is investigated and suggestions on how to optimally manage the whole aforesaid energy infrastructure are reported.

The present contribution is devoted to describe in detail the Smart City Sustainable Energy Living Lab (LL) that the University of Genoa has conceived and positioned inside its Savona Campus research and teaching facilities. Such LL relies on the research and demonstration activities related to the use of special infrastructures as the Smart Polygeneration Microgrid (SPM) and Smart Energy Building (SEB), built with the fundamental contribution of the Italian Ministry of Education, University and Research and the Ministry for Environment, Land and Sea Protection. The LL is jointly operated with the Italian DSO Enel S.p.A. as an “open air” demo site for the testing of on grid/off grid electrical transitions (Sustainable Urban Energy Island) and of potentially new services strictly related to the wide deployment of “e-city” technological solutions (V2G, V2H, e-home and smart appliances). The present paper is mainly focused on the model developed to study a portion of the SPM in islanded configuration; the main results of the analysis are here reported and discussed in order to highlight stability and load sharing issues.

We introduce and discuss the principles of a new technology to increase the efficiency of oscillating wave energy converters: the Pitch Resonance Tuning Tanks (PRTT). This new technology is based on purposely sized U-tanks of naval derivation. The novelty of the proposed solution is the integration of the water U-shaped tanks on pitching/rocking Wave Energy Converter in order to tune the pitch resonance frequency as a function of the prevalent incoming wave period, hence increasing the energy harvesting efficiency. We demonstrate the capability of the proposed solution, in a case study, where the PRTT technology is integrated in the IOwec (gyroscopic PTO) pitching device. A numerical time domain wave-to-PTO model is developed and used to evaluate the energy performance enhancement of the integrated system with and without the PRTT technology.

While person tracking has made significant progress over the last few years, most of the existing approaches are of limited success in real-time applications using moving sensors. In particular, we emphasize in this paper the need for a visual tracker that enables autonomous navigation functionality to drones (UAVs), mainly to follow a specific target. To achieve this goal, a color-based detection framework is proposed. The approach includes as well the execution of control commands, which are essential to switch from the detection to automatically follow the detected target. Our proposed approach is evaluated on videos recorded by drones. The obtained results demonstrate the effectiveness of the proposed approach to accurately follow a target in real-time, and despite different challenges such as lighting changes, speed, and occlusions.

We summarize and explain the realization of witness particle injection into wakefields for the AWAKE experiment. In AWAKE, the plasma wakefields are driven by a self-modulating relativistic proton bunch. To demonstrate that these wakefields can accelerate charged particles, we inject a \unit[10-20]{MeV} electron bunch produced by a photo-injector. We summarize the experimental challenges of this injection process and present our plans for the near future., Comment: 4 pages, 3 figures