Alessandro Massi Pavan

Photovoltaics in Italy: Toward grid parity in the residential electricity market

The Italian photovoltaic market, since 2011 the worlds largest, represents a success story having attained grid parity for the residential market of electricity - thus setting the basis for surviving without subsidies. The Levelized Cost Of Energy (LCOE) is calculated for three representative locations in Northern, Central, and Southern Italy, and compared with the residential end-user electricity price. It is shown that grid parity is attained under most conditions for the Italian residential market, thus laying the basis for its survival without subsidies.

Grid parity in the Italian commercial and industrial electricity market

The Italian photovoltaic market is since 2011 the worlds largest and represents a success story having attained grid parity for the commercial and industrial (C&I) market of electricity. In this paper, the Levelized Cost Of Energy (LCOE) is calculated for three representative locations in Northern, Central, and Southern Italy, and compared with the C&I end-user electricity price. The grid parity is shown under certain conditions showing that the photovoltaic market is already ready to survive without the feed-in tariff mechanism.

On the impact of photovoltaic module characterization on the prediction of PV plant productivity

A key factor in the design of photovoltaic fields is their yearly productivity and normally the nominal power is the only module parameter taken into account in the productivity predictions. The present paper intends to demonstrate how this approach disregards an important parameter, the nominal fill factor, that may significantly affect module efficiency when working in realistic environmental conditions. A complete calculation method is presented to compute a module productivity in any operating condition, using data sheets information. As an example two commercial module characteristics with different fill factors are compared to illustrate the concept in quantitative terms.

Application of Artificial Neural Networks for the Prediction of a 20-kWp Grid-Connected Photovoltaic Plant Power Output

Due to various seasonal, hourly and daily changes in climate, it is relatively difficult to find a suitable analytic model for predicting the output power of Grid-Connected Photovoltaic (GCPV) plants. In this chapter, a simplified artificial neural network configuration is used for estimating the power produced by a 20kWp GCPV plant installed at Trieste, Italy. A database of experimentally measured climate (irradiance and air temperature) and electrical data (power delivered to the grid) for nine months is used. Four Multilayer-perceptron (MLP) models have been investigated in order to estimate the energy produced by the GCPV plant in question. The best MLP model has as inputs the solar irradiance and module temperature. The results show that good effectiveness is obtained between the measured and predicted power produced by the 20kWp GCPV plant. The developed model has been compared with different existing regression polynomial models in order to show its effectiveness. Three performance parameters that define the overall system performance with respect to the energy production, solar resource, and overall effect of system losses are the final PV system yield, reference yield and performance ratio.

Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid

In this paper, the behavior of a grid-connected hybrid ac/dc microgrid has been investigated. Different renewable energy sources—photovoltaics modules and a wind turbine generator—have been considered together with a solid oxide fuel cell and a battery energy storage system. The main contribution of this paper is the design and the validation of an innovative online-trained artificial neural network-based control system for a hybrid microgrid. Adaptive neural networks are used to track the maximum power point of renewable energy generators and to control the power exchanged between the front-end converter and the electrical grid. Moreover, a fuzzy logic-based power management system is proposed in order to minimize the energy purchased from the electrical grid. The operation of the hybrid microgrid has been tested in the MATLAB/Simulink environment under different operating conditions. The obtained results demonstrate the effectiveness, the high robustness and the self-adaptation ability of the proposed control system.

Evolution of the main economic parameters for photovoltaic plants installed in Italy

The Italian photovoltaic market is today the third worlds largest and represents a success story having attained the grid parity for certain type of residential and Commercial/Industrial (C&I) customers. In this paper, the main economic parameters involved in the installation of a photovoltaic plant (i.e. the Payback Time, the Internal Rate of Return and the Levelized Cost Of Energy) are calculated for three representative locations in Northern, Central, and Southern Italy. The parameters are calculated at different times between 2003 and 2014 considering different regime of governmental incentives and the grid parity scenario. The result of such analysis shows how the dramatic reduction of installation in the Italian market after 2011 cannot be directly related to a reduction of the economic convenience for the enduser. Moreover, the opportunities given by the attainment of the grid parity seem to have the potential to pave the way to a new impulse in the market of this solar technology.

The Photovoltaic Laboratory at the University of Trieste, Italy

This work deals with the description of the Photovoltaic Laboratory at the Department of Engineering and Architecture of the University of Trieste. The description of the main facilities involved, such as three grid-connected photovoltaic plants and a test facility for the characterization of photovoltaic modules, is being proposed. Moreover, an overview of the recent and future research activities is given.

Assessment of photovoltaic systems for electric power generation using EROEI (energy return on energy investment)

The ratio between the energy returned from an energy production process and the energy invested in such process is a numerical indicator of the benefit involved in the exploitation of that source of energy. This paper is a first report of an ongoing project at the University of Trieste, focused on the calculation of the EROEI (Energy Return on Energy Investment) for two different photovoltaic systems.