The current work focuses on two specific issues concerning grid-connected solar PV units, i.e. the fault ride-through capability, also called low voltage ride-through capability, and the voltage support function through reactive power injection during faults. With the first one the PV unit can actually provide some limited grid support, whereas with a defined reactive power characteristic it can give a complete dynamic grid support. These two requirements, already known for wind power generation but new for the PV, have been recently introduced in the German technical guidelines for connection to the MV grid. Scope of the paper is to implement these requirements in a large solar PV plant, modeled in DIgSILENT PowerFactory, in order to understand its operation, and to evaluate its behavior and impact on the grid, in terms of stability and voltage support during grid fault.

This paper presents the problem of power penetration of photovoltaic (PV) systems in urban networks. After a qualitative problem description, two different environments for PV systems have been analyzed: rural and urban networks. Concerning the rural network, a single line has been simulated in order to evaluate the relation between the number of PV systems installed along the line and the maximum PV power installable. In the second part of this paper, possible problems due to power penetration have been examined concerning a clustered PV system that will be installed in the city of Torino (Italy) within the ambit of the EU project called POLYCITY.

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.