This paper presents an innovative design of a photovoltaic panel system for agricultural applications, particularly in regions prone to drought and extreme temperatures, known as Agri-PV. The proposed solution utilizes optical elements of divergent lens types to illuminate the ground beneath photovoltaic panels in greenhouse or indoor controlled cultivation areas. The Agri-PV solution improves the ratio between the area occupied by the photovoltaic panels and the total cultivated area therefore the land under the photovoltaic panels is fully cultivable, produces clean electricity that can be used in the agricultural process, reduces solar energy at the ground level up to 16 times, reducing water evaporation from the ground diminishing the summer-extreme temperatures effect on crops. With an optimal vertical layout of the optical lens PV system, areas of minimum illumination can be overlapped to provide a more uniform and consistent light intensity at ground level. The overall illumination uniformity is important for maximizing energy efficiency and maintaining optimal growing conditions in agrivoltaics applications.

This study aims to improve power law extrapolation accuracy by proposing a new methodology for wind shear coefficient calculation. Real wind speed profiles, measured with a LIDAR remote sensor at two different sites, were extrapolated according to the power law at heights up to 200 m. Two different definitions of the wind shear coefficient are proposed considering wind speed profiles measured at three, four, and five different heights using either the first height as a reference or all of the heights successively. These two definitions were tested, along with the generally accepted constant values of 1/7, 0.25 at the first site, and 0.2 at the second site, to identify the most efficient one. The extrapolated wind profiles were compared with those measured using LIDAR. The results show that the computed wind shear coefficients provide more accurate results compared with constant wind shear coefficients, as the mean absolute errors at 200 m were reduced from 1.396 m/s to 0.437 m/s at the first site and from 1.203 m/s to 0.411 m/s at the second site.

At complex industrial sites, the high number of large consumers that make the technological process chain requires direct supply from the main high-voltage grid. Often, for operational flexibility and redundancy, the main external supply is complemented with small local generation units. When a contingency occurs in the grid and the main supply is cut off, the local generators are used to keep in operation the critical consumers until the safe shutdown of the entire process can be achieved. In these scenarios, in order to keep the balance between local generation and consumption, the classic approach is to use under-frequency load-shedding schemes. This paper proposes a new load-shedding algorithm that uses particle swarm optimization and forecasted load data to provide a low-cost alternative to under-frequency methods. The algorithm is built using the requirements and input data provided by a real industrial site from Romania. The results show that local generation and critical consumption can be kept in stable operation for the time interval required for the safe shutdown of the running processes.