Total cost of ownership of electric vehicles using energy from a renewable-based microgrid

This work aims at analyzing the integration between electric mobility and renewable energy sources studying the case of the grid-connected microgrid under construction at the University of Trieste, Italy. A general model able to estimate the charging price and the resulting total cost of ownership per kilometer considering the match between the demand and the production of a photovoltaic generator is presented. The result is that the electric vehicle is mainly charged with the produced renewable energy (72%) and that the 60% of it flows through the storage unit. The study also presents a sensitivity analysis to show how the battery size and cost, together with the travelled distance, influence the charging price and the total cost of ownership per kilometer. Considering the current Italian prices and subsidies, results show that the use of an electric car is today feasibl

The effect of manufacturing mismatch on energy production for large-scale photovoltaic plants

In the literature, the effect of the mismatch due to manufacturing tolerances on PV plant productivity has been investigated under the hypothesis of plant operation in Standard Test Conditions (STC). In this paper, mismatch impacts are evaluated in more realistic terms taking into account various possible operating conditions. Results are illustrated through the study case of a 1 MWp solar park for which module datasheets as well as flash test data are available. The plant production is evaluated assuming operating conditions that comply with the European efficiency standards. It is shown how the effect of a given mismatch on the annual productivity estimation can significantly change depending on the operating conditions

Advanced Methods for Photovoltaic Output Power Forecasting: A Review

Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such as micro-grids (MGs), energy optimization and management, PV integrated in smart buildings, and electrical vehicle chartering. Over the last decade, a vast literature has been produced on this topic, investigating numerical and probabilistic methods, physical models, and artificial intelligence (AI) techniques. This paper aims at providing a complete and critical review on the recent applications of AI techniques; we will focus particularly on machine learning (ML), deep learning (DL), and hybrid methods, as these branches of AI are becoming increasingly attractive. Special attention will be paid to the recent development of the application of DL, as well as to the future trends in this topic

A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks

This work proposes a novel fault diagnostic technique for photovoltaic systems based on Artificial Neural Networks (ANN). For a given set of working conditions - solar irradiance and photovoltaic (PV) module's temperature - a number of attributes such as current, voltage, and number of peaks in the current-voltage (I-V) characteristics of the PV strings are calculated using a simulation model. The simulated attributes are then compared with the ones obtained from the field measurements, leading to the identification of possible faulty operating conditions. Two different algorithms are then developed in order to isolate and identify eight different types of faults. The method has been validated using an experimental database of climatic and electrical parameters from a PV string installed at the Renewable Energy Laboratory (REL) of the University of Jijel (Algeria). The obtained results show that the proposed technique can accurately detect and classify the different faults occurring in a PV array. This work also shows the implementation of the developed method into a Field Programmable Gate Array (FPGA) using a Xilinx System Generator (XSG) and an Integrated Software Environment (ISE)

Energy Scheduling and Performance Evaluation of an e-Vehicle Charging Station

This paper proposes an energy management system (EMS) for a photovoltaic (PV) grid-connected charging station with a battery energy storage system (BESS). The main objective of this EMS is to manage the energy delivered to the electric vehicle (EV), considering the price and (Formula presented.) emissions due to the grid’s connection. Thus, we present a multi-objective two-stage optimization to reduce the impact of the charging station on the environment, as well as the costs. The first stage of the optimization provides an energy schedule, taking into account the PV forecast, the hourly grid’s (Formula presented.) emissions factor, the electricity price, and the initial state of charge of the BESS. The output from this first stage corresponds to the maximum power permitted to be delivered to the EV by the grid. Then, the second stage of the optimization is based on model predictive control that looks to manage the energy flow from the grid, the PV, and the BESS. The proposed EMS is validated using an actual PV/BESS charging station located at the University of Trieste, Italy. Then, this paper presents an analysis of the performance of the charging station under the new EMS considering three main aspects, economic, environmental, and energy, for one month of data. The results show that due to the proposed optimization, the new energy profile guarantees a reduction of 32% in emissions and 29% in energy costs

Experimental evidence of PID effect on CIGS photovoltaic modules

As well known, potential induced degradation (PID) strongly decreases the performance of photovoltaic (PV) strings made of several crystalline silicon modules in hot and wet climates. In this paper, PID tests have been performed on commercial copper indium gallium selenide (CIGS) modules to investigate if this degradation may be remarkable also for CIGS technology. The tests have been conducted inside an environmental chamber where the temperature has been set to 85 \ub0C and the relative humidity to 85%. A negative potential of 1000 V has been applied to the PV modules in different configurations. The results demonstrate that there is a degradation affecting the maximum power point and the fill factor of the current\u2010voltage (I\u2010V) curves. In fact, the measurement of the I\u2010V curves at standard test condition show that all the parameters of the PV modules are influenced. This reveals that CIGS modules suffer PID under high negative voltage: this degradation occurs by different mechanisms, such as shunting, observed only in electroluminescence images of modules tested with negative bias. After the stress test, PID is partially recovered by applying a positive voltage of 1000 V and measuring the performance recovery of the degraded modules. The leakage currents flowing during the PID test in the chamber are measured with both positive and negative voltages; this analysis indicates a correlation between leakage current and power losses in case of negative potential

A hardware field simulator for photovoltaic materials applications.

This work is on a photovoltaic field simulator that is a power electronic device which produces direct voltages and currents. These simulate the behaviour of a photovoltaic field working in arbitrary conditions of solar irradiance and temperature. The most important application of the simulator is the testing of inverters. This work also introduces a new model describing the behaviour of photovoltaic modules of any generation. This is particularly useful to scientists and plant designers as it is based only on the electrical parameters that are always reported in a photovoltaic module\u2019s datasheet