Giorgio Graditi

Time-of-use (TOU) energy cost management involves the use of energy storage systems (ESSs) by customers to reduce their electricity bills. The ESS is charged during off-peak time periods, when electricity energy prices are low, and discharged during times when on-peak energy prices are applied. This article addresses the question whether it is economically viable to install medium-scale distributed ESSs designed to lower the electricity cost for a customer-side application, assuming flexible electricity tariffs. The technical/economical evaluation is carried out referring to lithium-ion (Li-ion), sodium sulfur (NaS) and vanadium redox battery (VRB) technologies, performing a parametric analysis by changing the capital cost of the batteries and the difference between the maximum and minimum electricity price. A case study is performed to show the advantages/disadvantages of the proposed approach. The analysis reveals that, at the current costs of ESSs, the use of batteries for TOU applications is economically advantageous for a public institution facility in Italy only if there is a significant difference between the maximum and the minimum electricity price. The decrease in the cost of storage, stimulated by the implementation of support policies, will make ESS even more convenient for load shifting applications.

The power grid consists of various electrical components and of multiple levels: transmission HV (High Voltage), distribution in MV (Medium Voltage) and distribution in LV (Low Voltage). In this framework, the MGs (Micro Grids) are classified as a distribution grid, usually in LV, able to provide services both in autonomous (island mode) and in grid connected mode. MGs are composed by traditional and renewable energy power plants, storages and loads and, due to their limited capacity, generally the main applications are on residential level (e.g., campus, hospitals, hotels, sport centers, commercial location). Different components, design and rules are defined by the manager of MG: in this work, there is a prosumer which aggregates the capacity of different components and buys or sells, for each hour, power from/to the grid with upper level voltage. In this paper, a decision making model to formulate the optimal bidding in the Day-Ahead energy market and to evaluate the risk management for a LV grid-connected residential MG, taking into account the uncertainty of renewable power production, i.e., PV (photovoltaic), is proposed. Several investigators have analyzed the role played by MGs into the deregulated electricity market, their contribution to energy price reduction and to the reliability system increase, as well as their impact on the best strategy devising to minimize operating costs. Although in literature it is possible to find similar decision support models, the use of uncertainty evaluation to makedecisionsandtoparticipateinaderegulatedenergymarketisatthepresentanimportantopenresearchissue. The uncertainty can be expressed in many different ways, either qualitative or quantitative, and it is possible to generate a reasonable measure of uncertainty by various methods. In this work an original approach based on AnEn (Analog Ensemble) method to estimate the uncertainty linked to the energy provided by PV plant own to the MG is presented. The AnEn is able to estimate the pdf (probability density function) of forecasts solutions by sampling the uncertainty in the analysis and running a number of forecast from perturbed analysis. The analogs generated become the input of our optimization model. Based on a genetic algorithm, the economic model is applied to a heterogeneous residential MG with traditional different power plants and RES (Renewable Energy Sources), i.e., PV, evaluating different prosumer risk tolerances (adverse, neutral and incline). Developed methodology can aid the decision maker to understand the potential impact of a wrong decision throughout information included in a forecast concerning renewable power production. The effectiveness of the proposed methodology is assessed through the analysis of a case study consisting of a grid connected residential MG. The obtained results show different optimal bids depending on the risk adversity with respect to the uncertainty of PV power production, and how PV energy production can be integrated with optimal results in a MG if the prosumer's strategy takes into account the uncertainty linked to the energy output.

With the growing demand of energy on a worldwide scale, improving the efficiency of energy resource use has become one of the key challenges. Application of exergy principles in the context of building energy supply systems can achieve rational use of energy resources by taking into account the different quality levels of energy resources as well as those of building demands. This paper is on the operation optimization of a Distributed Energy System (DES). The model involves multiple energy devices that convert a set of primary energy carriers with different energy quality levels to meet given time-varying user demands at different energy quality levels. By promoting the usage of low-temperature energy sources to satisfy low-quality thermal energy demands, the waste of high-quality energy resources can be reduced, thereby improving the overall exergy efficiency. To consider the economic factor as well, a multi-objective linear programming problem is formulated. The Pareto frontier, including the best possible trade-offs between the economic and exergetic objectives, is obtained by minimizing a weighted sum of the total energy cost and total primary exergy input using branch-and-cut. The operation strategies of the DES under different weights for the two objectives are discussed. The operators of DESs can choose the operation strategy from the Pareto frontier based on costs, essential in the short run, and sustainability, crucial in the long run. The contribution of each energy device in reducing energy costs and the total exergy input is also analyzed. In addition, results show that the energy cost can be much reduced and the overall exergy efficiency can be significantly improved by the optimized operation of the DES as compared with the conventional energy supply system using the grid power only.

Nowadays the estimation of power production yield by stand-alone and grid-connected Photovoltaic (PV) plants is crucial for technical and economic feasibility design analyses. The main goal is to overcome renewables unpredictability by properly estimating the power production and by suitably balancing generation and consumption. In this context, many methods can be applied to forecast renewables energy production. The scope of this paper is a comparative analysis of three different methods to estimate the power production of a preexisting PV plant. It is installed at ENEA Research Centre located in Portici (South Italy) and it is integrated in a Micro Grid (MG) configuration. In detail a phenomenological model proposed by Sandia National Laboratories and two statistical learning models, a Multi-Layer Perceptron (MLP) Neural Network and a Regression approach, are compared. These models are deeply different also in terms of required input data and parameters. In detail, phenomenological model application requires the availability of design parameters and technical devices specifications. Statistical machine learning models need, however, input variable previously acquired datasets. The a-Si/mc-Si PV plant, installed at Portici, represents an adequate case study for the three models comparison, as both design and acquired data are available. In fact, the plant was designed at the ENEA Research Centre so this makes possible the knowledge of the design parameters and, being a part of the MG, its data are continuously acquired and transmitted to other network devices. Obtained results demonstrate more accurate power predictions can be reached by statistical machine learning approaches. The main novelty of the paper consists in the optimization of the considered models by the appropriate identification of the minimum and more representative training dataset. Authors underline the unnecessary use of thousands samples by suitably selecting the dataset size and samples by means of a Genetic Algorithm. The optimization strategy effectiveness is verified comparing the prediction performances obtained employing the optimal dataset with those obtained with a randomly chosen dataset. In this scenario, Genetic Algorithm strategy represents a successful approach to the suitable identification of statistical models datasets.

—In this paper, an optimal power dispatch problem on a 24-h basis for distribution systems with distributed energy resources (DER) also including directly controlled shiftable loads is presented. In the literature, the optimal energy management problems in smart grids (SGs) where such types of loads exist are formulated using integer or mixed integer variables. In this paper, a new formulation of shiftable loads is employed. Such formulation allows reduction in the number of optimization variables and the adoption of real valued optimization methods such as the one proposed in this paper. The method applied is a novel nature-inspired multiobjective optimization algorithm based on an original extension of a glowworm swarm particles optimization algorithm, with algorithmic enhancements to treat multiple objective formulations. The performance of the algorithm is compared to the NSGA-II on the considered power systems application.

The light collection properties of PhoCUS C-Module photovoltaic concentration units have been investigated by realizing a rugged “Mock-up” containing the primary refractive optics, a secondary optical element (SOE) and a receiver. To independently investigate the sole collection efficiency of the optical unit, the receiver was realized by an integrating sphere equipped with a photodetector, able to collect, with known efficiency, all the radiation reaching the receiver area. To investigate the optical efficiency of the whole C-Module photovoltaic concentration unit, a concentration silicon cell, pre-viously tested in the PhoCUS C-Modules, was used as receiver. Two methods were applied for the optical measurements, the conventional “direct” method using a parallel beam with solar divergence to irradiate the front side of concentrator, and the “inverse” method using a lambertian source applied in place of the concentrating cell in order to operate the concentrator in the reverse way.

A regenerative hybrid PV-PEMFC low power system is a suitable solution to replace batteries and to supply small electric devices placed in remote areas with no grid connection. Such a system was designed and built including a PV array, electrolyzer, PEMFC stack (6We) and hydrogen storage tank. As well known a hybrid PV-PEMFC energy system may represent a reliable solution

ABSTRACT BIPV components provide added values to the building and for this application they should be designed to perform functions as defined in the REGULATION (EU) No 305/2011 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 9 March 2011. So the need for new BIPV products strongly exists and specific standards and testing must be appointed. At the moment the prEN 50583:2012 is just in preparation at European level. It contains links to existing Standards already applied for traditional building components. This paper gives an overview of the general requirements imposed by this Standard, introduces the European project Bfirst, aimed to realize specific BIPV products, and present preliminary results made at ENEA on traditional modules for optical and thermal requirements that could be applied to new BIPV products.

ABSTRACT The reduction of energy consumption in electric trains is a big challenge of the next years. Lower consumptions means a more convenient design of the electric power supply network of the trains; in particular increasing the margin between peak demand and maximum power limit of the network improves the overall reliability. To this aim, the energy recovered from braking of one train may be used to aid the acceleration of the same or another train. Recovering energy from braking is a well-known idea, and the possibility to exchange energy between breaking trains and departing trains is very attractive. With this goal in mind the train vectors need to exchange information about their instantaneous power consumption and the data should be available for comparisons in a common time scale. In this paper will be presented an Android based measurement system for electric trains able to make synchronous measurements. The measurement system presented is composed of wireless sensors making it easy to install on modern train vectors.

ABSTRACT The high penetration of Distributed Generation (DG) into Distribution Networks (DN) has highlighted the necessity to develop proper control techniques to supply customers with voltage levels contained within regulatory limits and to offer ancillary services to the Distributed Network Operators (DNOs). This paper supposes unbalanced DNs in which the importance to apply voltage control strategies is largely justified in literature. In particular, we present a strategy that offers the mandatory voltage control ancillary service, able to avoid active power production curtailments, using the minimum reactive power for the regulation. Validation of the proposed control has been conducted on the IEEE 13-bus distribution system and the obtained results are encouraging.

Electric distribution systems have been undergoing substantial changes in the recent years, aiming more efficient and cost effective operation. In this paper, new operation philosophies and systems in the literature have been analyzed in parallel with these changes. There are various applications of Distribution automation systems performing automatic fault detection, isolation and restoration. However, these systems fail to identify fault

ABSTRACT Today the number of phones in the world are more than five billion. In the last generation of phone there are a subset called smartphone, the use of this subset is growing. These devices are more and more sophisticated, capable of measuring many parameters from environment. In order to do that this smartphone is equipped with a several sensors and by means of an application (APP) that pick up the information from each sensor, it is possible to send, share, analyze, and display the measured data. Nevertheless, people could use this data without real information on accuracy of measured parameters. Thanks to these devices in the next few years there will be the opportunity to have a big wireless sensor network. With this paper the authors try to tackle the uncertainty question related to sensors suited to smart phone or any other android devices. The main goal has been to address this new idea implementing the first application that analyze a method of calibration with the aim of evaluating the uncertainty of sensors data.