Giorgio Graditi

A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module

The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models in order to predict accurately their electrical output behavior. The main aim of this paper is to investigate the application of an advanced neural network based model of a module to improve the accuracy of the predicted output I–V and P–V curves and to keep in account the change of all the parameters at different operating conditions. Radial basis function neural networks (RBFNN) are here utilized to predict the output characteristic of a commercial PV module, by reading only the data of solar irradiation and temperature. A lot of available experimental data were used for the training of the RBFNN, and a backpropagation algorithm was employed. Simulation and experimental validation is reported.

Heuristic-Based Shiftable Loads Optimal Management in Smart Micro-Grids

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.

A Generalized Framework for Optimal Sizing of Distributed Energy Resources in Micro-Grids Using an Indicator-Based Swarm Approach

In this paper, a generalized double-shell framework for the optimal design of systems managed optimally according to different criteria is developed. Optimal design is traditionally carried out by means of minimum capital and management cost formulations and does not typically consider optimized operation. In this paper, the optimized multiobjective management is explicitly considered into the design formulation. The quality of each design solution is indeed defined by the evaluation of operational costs and capital costs. Besides, the assessment of the operational costs term is deduced by means of the solution of a multiobjective optimization problem. Each design solution is evaluated using the outcomes of a multiobjective optimization run: a Pareto hyper-surface in the n-dimensional space of the operational objectives. In the literature, commonly the evaluation of each design solution is carried out based on an approximate evaluation of the operational costs, not considering the real multiobjective optimized management. In this paper, such assessment is carried out using a suitable convergence indicator typically used for multiobjective optimization algorithms. The application is devoted to the problem of optimal sizing of distributed energy resources in medium voltage or low voltage microgrids. For this problem, the identification of the multiple operational impacts comes along with the solution of the optimal unit commitment of distributed generators. After the introductory section, the problem formulation is presented and an interesting application of the considered approach to the design of distributed energy sources in a microgrid is shown.

Optimal Electrical Distribution Systems Reinforcement Planning Using Gas Micro Turbines by Dynamic Ant Colony Search Algorithm

Distribution systems management is becoming an increasingly complicated issue due to the introduction of new energy trading strategies and new technologies. In this paper, an optimal reinforcement strategy to provide reliable and economic service to customers in a given time frame is investigated. In the new deregulated energy market and considering the incentives coming from the political and economical fields, it is reasonable to consider distributed generation (DG) as a viable option for systems reinforcement. In the paper, the DG technology is considered as a possible solution for distribution systems capacity problems, along several years. Therefore, compound solutions comprising the installation of both feeders and substations reinforcement and DG integration at different times are considered in the formulation of a minimum cost distribution systems reinforcement strategy problem. An application on a medium size network, hypothesizing a scenario of reinforcement also using as DG gas micro-turbines, is carried out using a novel optimization technique allowing the identification of optimal paths in trees or graphs. The proposed technique is the Dynamic Ant Colony Search algorithm

Theoretical and experimental comparison of total harmonic distortion factors for the evaluation of harmonic and interharmonic pollution of grid-connected photovoltaic systems

Grid-connected photovoltaic systems are increasingly used in electrical distribution systems. However, they inject distorted currents. Therefore, special attention must be paid to harmonic and interharmonic measurements. The new edition of IEC 61000-4-7 introduces the concept of harmonic and interharmonic groups, which implies new expressions for total harmonic distortion (THD) factors. In this paper, a theoretical and experimental comparison is made between the different THD factors in order to show which of the currently defined distortion factors is best suited to detect harmonic and interharmonic pollution. Experimental tests were carried out first by means of a calibrator and subsequently in a single-phase grid-connected photovoltaic system. In both cases, measurements were carried out with a PC-based instrument developed by the authors and able to calculate the distortion factors according to IEC 61000-4-7.

Designing and testing decision support and energy management systems for smart homes

Most advantages that the smart grid will bring derive from its capability of improving reliability performance and customers’ responsiveness and encouraging greater efficiency decisions by the costumers. Demand side management is, therefore, considered as an integral part of the smart grid and one of the most important methods of energy saving. Accordingly, an innovative decision support and energy management system (DSEMS) for residential applications is proposed in this paper. The DSEMS is represented as a finite state machine and consists of a series of scenarios that may be selected according to the user preferences. The designing and testing methods are described and some simulations results are presented in order to verify its effectiveness both in terms of continuity of electricity supply and energy savings and economics.

An Innovative Conversion Device to the Grid Interface of Combined RES-Based Generators and Electric Storage Systems

This paper is focused on the development of an innovative device, which is based on a bidirectional converter, for the interface to the supply utility grid of combined renewable-energy-source-based generators and electric storage systems. The device can be controlled so as to ease the interface between the low-voltage grid and photovoltaic or wind generators combined with lithium-ion LiFePO4 batteries, taking into account the requirements of the reference technical standards for users connection and offering different ancillary services. The operational functioning of the device, the architecture, and its electronic components, as well as laboratory and field test activities and results are described. The conversion device has been developed, and the main results that have been achieved are detailed.

Transition of a distribution system towards an active network. Part I: Preliminary design and scenario perspectives

In an European perspective, the focus of Smart Grids initiatives (SET Plan - Strategic Energy Technology Plan) is strictly linked with the main commitment to achieve the goals of the Climate and Energy Package 20-20-20, at the light of the three main pillars of the European energy policy: competitiveness, sustainability and security of supply. Smart grid technologies will enable load levelling of the electrical grid, allowing a power company to run cleaner power sources - such as hydroelectric, wind, or solar - while reducing the need to use carbon-emitting gas, coal, or oil plants to meet peak demand. In this framework the proposed paper refers about the technical economical feasibility study and the preliminary design of a demonstrator of a distribution electrical system for the transition towards active networks. The study has been carried out by University of Palermo and ENEA (Italy), on a portion of real MV/LV distribution system of the research center ENEA of Casaccia (Rome, Italy).1

Transition of a distribution system towards an active network. Part II: Economical analysis of selected scenario

This paper outlines the economical issues related to the transition of the energy generation for a real MV/LV distribution system from a ‘fuel based’ one to a distributed and smart ‘renewables based’ one. It is the prosecution of a companion paper, which addressed the technical issues connected to such transition. The study has been carried out by University of Palermo and ENEA (Italy), on a portion of real MV/LV distribution system of the research center ENEA of Casaccia (Rome, Italy). The analysis is carried out for a specific scenario chosen among those proposed in the companion paper.

Multiobjective Optimal Design of Photovoltaic Synchronous Boost Converters Assessing Efficiency, Reliability, and Cost Savings

Optimal design of switching converters for the integration and optimal exploitation of renewable energy sources (RES) represents a crucial issue often debated in the recent power electronics literature. The design problem required to carry out a multiobjective optimization characterized by simultaneous conflicting objectives, such as efficiency, reliability, and price, where the best compromise solution should be found by the decision maker among Pareto-optimal solutions. In this paper, a novel design method for distributed maximum power point tracking (DMPPT) synchronous boost converter is proposed. The method is based on nondominated sorting genetic algorithm with the aim to obtain the best synchronous rectification (SR) boost topology while considering different targets such as converter efficiency and reliability maximization, as well as converter price minimization. New weighted indices are also proposed for a more realistic characterization of the devices.