This paper presents a methodology to maximize the self-sufficiency or cost-effectiveness of grid-connected prosumers by optimizing the sizes of photovoltaic (PV) systems and electrochemical batteries. In the optimal sizing procedure, a limitation on the maximum injection in the grid can affect the energy flows, the economic effectiveness of the investments, and thus the sizing results. After the explanation of the procedure, a case study is presented, and a parametric analysis of the effect of possible injection limits is shown. The procedure is applied to size plants for an Italian domestic prosumer, whose electric load profile was measured for a year. A software program developed using the proposed methodology is also briefly presented. It is used for both research and educational purposes, both in laboratory classes and in remote lessons.

The fast growth of photovoltaic (PV) power generation requires dependable forecasting methods to support efficient integration of solar energy into power systems. This study conducts an up-to-date, systematized analysis of different models and methods used for photovoltaic power prediction. It begins with a new taxonomy, classifying PV forecasting models according to the time horizon, architecture, and selection criteria matched to certain application areas. An overview of the most popular heterogeneous forecasting techniques, including physical models, statistical methodologies, machine learning algorithms, and hybrid approaches, is provided; their respective advantages and disadvantages are put into perspective based on different forecasting tasks. This paper also explores advanced model optimization methodologies; achieving hyperparameter tuning; feature selection, and the use of evolutionary and swarm intelligence algorithms, which have shown promise in enhancing the accuracy and efficiency of PV power forecasting models. This review includes a detailed examination of performance metrics and frameworks, as well as the consequences of different weather conditions affecting renewable energy generation and the operational and economic implications of forecasting performance. This paper also highlights recent advancements in the field, including the use of deep learning architectures, the incorporation of diverse data sources, and the development of real-time and on-demand forecasting solutions. Finally, this paper identifies key challenges and future research directions, emphasizing the need for improved model adaptability, data quality, and computational efficiency to support the large-scale integration of PV power into future energy systems. By providing a holistic and critical assessment of the PV power forecasting landscape, this review aims to serve as a valuable resource for researchers, practitioners, and decision makers working towards the sustainable and reliable deployment of solar energy worldwide.

In general, Photovoltaic (PV) plants do not include components with moving parts and, as a consequence, they are wrongly considered maintenance-free. Thus, the present work presents a maintenance, reliability and availability analysis of ten PV systems, with different inverter configurations, in the context of the intermittent RES systems, including the wind farms. The first part of the analysis consists of the evaluation of reliability using failure rates from literature. In the second part, these results are compared with data obtained from industrial maintenance reports in the years 2016 - 2018. Finally, the yearly energy losses and the availability of each PV plant are assessed.

The present paper proposes a multidisciplinary procedure to correctly design a microgrid of all-electric nZEBs (nearly Zero Energy Buildings) from both electrical and thermal points of view. The procedure is suitable for new buildings supplied by local renewables, without the use of fossil fuel and with zero emissions. First, the thermal demand of each single nZEB is assessed, as a function of the installation site, building layout and physics, and material composing the envelope. Thanks to heat pumps, the thermal demand is transformed in electric load. Thus, the total electric consumption profiles, which include user's appliances and heating/cooling, are studied and compared with Photovoltaic (PV) generation supported by electrochemical storages. Both PV and batteries are simulated thanks to appropriate models. Regarding the PV production assessment, the present work proposes an improvement with respect to the use of traditional models, and it is based on experimental results on PV generators of recent production. The design methodology is applied to a real case of “energy community” composed of three nZEB units, that will be built in the campus of Politecnico di Torino, available to students and staff. The three units share PV production and storage capacity to reach the complete grid-independence.

The fast growth of photovoltaic power generation requires dependable forecasting methods to support efficient solar energy power integration into power systems. This study conducts an up-to-date systemized analysis of different models and methods used for photovoltaic power prediction. It begins with a new taxonomy, classifying PV forecasting models according to time horizon, architectures, and selection criteria matched to certain application areas. An overview of the most popular heterogeneous forecasting techniques, including physical models, statistical methodologies, machine learning algorithms, and hybrid approaches, is provided; their respective advantages and disadvantages are put into perspective based on different forecasting tasks. The paper also explores advanced model optimization methodologies, achieving hyperparameter tuning, feature selection, and the use of evolutionary and swarm intelligence algorithms, which have shown promise in enhancing the accuracy and efficiency of PV power forecasting models. The review includes a detailed examination of performance metrics and frameworks, as well as the consequences of different weather conditions affecting renewable energy generation and the operational as well as economic implications of forecasting performance. The paper also highlights recent advancements in the field, including the use of deep learning architectures, the incorporation of diverse data sources, and the development of real-time and on-demand forecasting solutions. Finally, the paper identifies key challenges and future research directions, emphasizing the need for improved model adaptability, data quality, and computational efficiency to support the large-scale integration of PV power into future energy systems. By providing a holistic and critical assessment of the PV power forecasting landscape, this review aims to serve as a valuable resource for researchers, practitioners, and decision-makers working towards the sustainable and reliable deployment of solar energy worldwide.

The reliability of photovoltaic (PV) generators is strongly affected by the performance of Direct Current/Alternating Current (DC/AC) converters, being the major source of PV underperformance. However, generally, their reliability is not investigated at component level: thus, the present work presents a reliability analysis and the repair activity for the components of full bridge DC/AC converters. In the first part of the paper, a reliability analysis using failure rates from literature is carried out for 132 inverters (AC rated power of 350 kW each) with global AC power of 46 MW in a large scale grid-connected PV plant. Then, in the second part of the work, results from literature are compared with data obtained by analyzing industrial maintenance reports in the years 2015–2017. In conclusion, the yearly energy losses involved in the downtime are quantified, as well as their availability.