• Energy Research

While traditional methods for modelling the thermal and electrical behaviour of photovoltaic (PV) modules rely on analytical and empirical techniques, machine learning is gaining interest as a way to reduce the time, expertise, and tools required by designers or experts while maintaining high accuracy and reliability. This research presents a data-driven machine learning tool based on artificial neural networks (ANNs) that can forecast yearly PV electricity directly at the optimal PV inclination angle without geographic restrictions and is valid for a wide range of electrical characteristics of PV modules. Additionally, empirical correlations were developed to easily determine the optimal PV inclination angle worldwide. The ANN algorithm, developed in Matlab, systematically and quantitatively summarizes the behaviour of eight PV modules in 48 worldwide climatic conditions. The algorithm’s applicability and robustness were proven by considering two different PV modules in the same 48 locations. Yearly climatic variables and electrical/thermal PV module parameters serve as input training data. The yearly PV electricity is derived using dynamic simulations in the TRNSYS environment, which is a simulation program primarily and extensively used in the fields of renewable energy engineering and building simulation for passive as well as active solar design. Multiple performance metrics validate that the ANN-based machine learning tool demonstrates high reliability and accuracy in the PV energy production forecasting for all weather conditions and PV module characteristics. In particular, by using 20 neurons, the highest value of R-square of 0.9797 and the lowest values of the root mean square error and coefficient of variance of 14.67 kWh and 3.8%, respectively, were obtained in the training phase. This high accuracy was confirmed in the ANN validation phase considering other PV modules. An R-square of 0.9218 and values of the root mean square error and coefficient of variance of 31.95 kWh and 7.8%, respectively, were obtained.The results demonstrate the algorithm’s vast potential to enhance the worldwide diffusion and economic growth of solar energy, aligned with the seventh sustainable development goal.

Abstract The high uncertainty associated with renewable power production limits renewable energy penetration in off-grid systems. Advanced control strategies allow for a more effective exploitation of non-dispatchable sources. This paper presents a two-layer predictive management strategy for an off-grid hybrid microgrid featuring controllable and non-controllable generation units and a storage system. The upper layer deals with the unit commitment, while the second layer regulates real-time operation, applying a response filter to smooth out genset load variation. The algorithm is tested on data from a real rural microgrid in Somalia, performing minute-by-minute simulations. Results are compared to the currently deployed management strategy and to a new improved heuristic algorithm. The two new methods attain a fuel consumption reduction with respect to the previous management system of about 15%. Finally, a new configuration for the Somalian microgrid is evaluated, in the two cases where the predictive or heuristic management strategies are adopted. The comparison of the two optimal solutions demonstrates that the adoption of the proposed predictive strategy leads to a 6.5% cut of the overall system cost, ensuring at the same time a 24.1% fuel consumption reduction with respect to the best heuristic solution and attaining a renewable penetration as high as 65.1%.

As human activities are increasingly exploiting our planet’s scarce resources, managing them has become of primary importance. Specifically, this study examines the management of photovoltaic (PV) waste that is produced when PV modules reach end-of-life (EoL). PV modules contain precious and valuable materials, as well as toxic materials that may be harmful to human health and the environment if not disposed of properly. First, this study aims to review and analyze the current literature in order to gain a deeper understanding of the recycling of PV modules, particularly c-Si modules, which represent the largest market share. In the second part, an analysis is conducted of the energy consumption of these recycling processes using a proposed model based on the full recovery end-of-life photovoltaic (FRELP) process. PV modules manufactured from raw materials and PV modules manufactured from recycled materials are also compared in this section. In addition, improvements are suggested with respect to the design of PV modules (eco-design). According to this study, c-Si PV modules can be recycled with an energy consumption as low as 130 ÷ 300 kWh/ton of treated PV waste, estimating an overall recycling yield of about 84%.

Microgrids represent a flexible way to integrate renewable energy sources with programmable generators and storage systems. In this regard, a synergic integration of those sources is crucial to minimize the operating cost of the microgrid by efficient storage management and generation scheduling. The forecasts of renewable generation can be used to attain optimal management of the controllable units by predictive optimization algorithms. This paper introduces the implementation of a two-layer hierarchical energy management system for islanded photovoltaic microgrids. The first layer evaluates the optimal unit commitment, according to the photovoltaic forecasts, while the second layer deals with the power-sharing in real time, following as close as possible the daily schedule provided by the upper layer while balancing the forecast errors. The energy management system is experimentally tested at the Multi-Good MicroGrid Laboratory under three different photovoltaic forecast models: (i) day-ahead model, (ii) intraday corrections and (iii) nowcasting technique. The experimental study demonstrates the capability of the proposed management system to operate an islanded microgrid in safe conditions, even with inaccurate day-ahead photovoltaic forecasts.

The performance of bifacial photovoltaic (bPV) modules under various operating conditions needs to be analyzed with research and development activities. This research work performed an experimental comparison of the energy performance of a bPV module and a monofacial photovoltaic (mPV) module, based on experimental data measured at SolarTech(LAB), Department of Energy, Politecnico di Milano. For this purpose, the performance ratio (PR) and the temperature-corrected PR proposed by the IEC 61724-1 standard for bPV and mPV modules were evaluated. To calculate the energy improvement of the technology in terms of bifacial gain, measurements were recorded during the period from 8 June to 31 November, 2022, of PV modules facing south, ground-mounted and with a low albedo. It was found that the PR relative gain of the bPV compared with the mPV module is 10.8% higher in the whole period. Moreover, the presence of a commercial white plastic sheet with high reflectivity located beneath the bPV modules from 28 May to 5 June, 2022, showed an additional increase in the bPV PR of 0.0165, corresponding to a relative difference increased by 20%.

Organic photovoltaic (OPV) modules have significant advantages over conventional PV technologies drawing the attention of R&D activities. The OPV efficiency is increasing closing the gap against silicon-based modules. This article describes an experimental campaign performed at SolarTechLAB to assess the performance of six OPV modules in real environmental conditions (module nominal power 17.5 Wp). The first part of the activity was dedicated to the photoactivation process, which is a well-known phenomenon of this kind of modules. Measurements pointed out that the OPV modules reach stable conditions after collecting 10 kWh/m2 of solar radiation independently from the module conditions during the procedure. A second relevant result is about the reversibility of the photoactivation process: experiments showed that activated modules left in the dark for several days lose the activation indicating the reversibility of the process. Finally, in the second part, the performances of the six OPV modules have been analyzed and benchmarked against silicon (c-Si) and CIS photovoltaic technology. The measured electric efficiency of the six OPV modules under real environmental conditions was below 4%, which is significantly lower than 20% and 15% measured for c-Si and CIS modules under the same conditions.

This paper analyzes the impact of the snail trail phenomena on photovoltaic (PV) module performances and energy production. Several tests (visual inspection, maximum power determination, dielectric withstand, wet leakage current, and electroluminescence test) were carried out on 31 PV modules located in a PV plant in Italy. The electroluminescence test highlighted the strong correlation between the appearance of snail trails and presence of damaged cells in PV modules. The daily energy produced by four PV modules affected by snail trails ranged between 68% and 88% of the energy produced by a damage free commercial PV module over the same period.

The accurate forecasting of energy production from renewable sources represents an important topic also looking at different national authorities that are starting to stimulate a greater responsibility towards plants using non-programmable renewables. In this paper the authors use advanced hybrid evolutionary techniques of computational intelligence applied to photovoltaic systems forecasting, analyzing the predictions obtained by comparing different definitions of the forecasting error.