In recent decades, advances in the development of solar tracking systems (STSs) have led to concentrating solar technologies to increase their energy conversion efficiency. These systems, however, still have areas of opportunity or improving their performance and reducing their manufacturing costs. This paper presents the design, construction and evaluation of a high-precision dual-axis solar tracking system with a technology readiness level of 7–8. The system is controlled by a low-cost Arduino board in a closed-loop control using a micro-electromechanical solar sensor. Real-time tracking experiments were performed under a clear sky as well as during partly and mostly cloudy days. Solar tracking accuracy was evaluated in an operational environment using test procedures adapted from the International Electrotechnical Commission (IEC) 62817 standard. The total mean instantaneous solar tracking error on a clear day measured with a calibrated digital solar sensor was 0.37${}^{°}$ and 0.52${}^{°}$ with a developed pinhole projection system. Similarly, the total mean reported solar tracking accuracy achieved was 0.390${}^{°}$ on a sunny day and 0.536${}^{°}$ on a partially cloudy day. An annual power generation analysis considering a conventional photovoltaic (PV) panel system and a typical concentrator photovoltaic (CPV) module as payloads was also presented. Simulations showed an increase in the generation of up to 37.5% for a flat panel with dual-axis tracking versus a fixed panel. In the case of the CPV system, first, a ray tracing study was implemented to determine the misalignment coefficient, then the annual power generation was estimated. The developed STS allowed the CPV modules to reach at least 90% of their nominal energy conversion efficiency.

Tracking systems with stringent performance are required to take advantage of solar energy produced through the use of High Concentration Photovolatics (HCPV) and Concentrating Solar Power (CSP) technologies. Closed-loop tracking systems use measurements provided by a sun sensor, which picks up direct and diffuse sunlight. High levels of diffuse sunlight produce increased levels of measurement noise at the output of the sensor, which in turn limits the maximal achievable gains in the closed-loop controller and consequently the tracking performance deteriorates. The goal of this work is to present the advantages of equipping a four-quadrant photodiode sun sensor with infrared and linear polarizing optical filters. Experimental results show that these filters reduce the effect of diffuse radiation and allow increasing the performance of sun tracking systems regardless of the type of controller used in the solar tracker. Tracking experiments are presented under indoor and outdoor conditions employing algorithms currently used in solar trackers including Proportional-Integral (PI), and Proportional-Integral-Derivative (PID) controllers, and a recently proposed cascade control law. In all the cases, the best performance is obtained with the sun sensor equipped with an infrared filter. Experiments show that an infrared optical filter reduces the Filtered Mean Squared Tracking Error up to 75under artificial light conditions and 90% under direct sunlight.

Solar resource assessment is fundamental to reduce the risk in selecting the solar power-plants’ location; also for designing the appropriate solar-energy conversion technology and operating new sources of solar-power generation. Having a reliable methodology for solar irradiance forecasting allows accurately identifying variations in the plant energy production and, as a consequence, determining improvements in energy supply strategies. A new trend for solar resource assessment is based on the analysis of the sky dynamics by processing a set of images of the sky dome. In this paper, a methodology for processing the sky dome images to obtain the position of the Sun is presented; this parameter is relevant to compute the solar irradiance implemented in solar resource assessment. This methodology is based on the implementation of several techniques in order to achieve a combined, fast, and robust detection system for the Sun position regardless of the conditions of the sky, which is a complex task due to the variability of the sky dynamics. Identifying the correct position of the Sun is a critical parameter to project whether, in the presence of clouds, the occlusion of the Sun is occurring, which is essential in short-term solar resource assessment, the so-called irradiance nowcasting. The experimental results confirm that the proposed methodology performs well in the detection of the position of the Sun not only in a clear-sky day, but also in a cloudy one. The proposed methodology is also a reliable tool to cover the dynamics of the sky.