Performance Loss Rates of a 1 MWp PV Plant with Various Tilt Angle, Orientation and Installed Environment in the Capital of Cyprus

Abstract

Evolution from fossil fuel sources to environmentally friendly and sustainable energy sources is one of the key aspects of the energy transition goal. On the other hand, Solar Photovoltaic systems are the most promising and improving technologies in the energy market However, it is well known that the performance of PV panels decreases in the process of time. This paper focuses on analyzing the performance loss rates (PLR) of mid-scale crystalline silicon (c-Si) PV systems of the same manufacturer with different orientations and tilt angles in the same region for the duration of four years of outdoor exposure. Three commonly used statistical methods are deployed to calculate PLRs; seasonal and trend decomposition using locally weighted scatterplot smoothing (STL), classical seasonal decomposition (CSD) and year on year (YoY) methods coupled with various performance metrics, namely Performance ratio (PR), temperature corrected performance ratio TCPR) and weather corrected performance ratio suggested by National Renewable Energy Laboratory (NRELPR). It is found that PLRs of analyzed venues, Arazi, Arena and Stonite, with the YOY approach are −1.2%/year, −0.73%/year and −2.65%/year, respectivelty., respectively. Furthermore, STL method demonstrated PLRs of −0.69%/year, −0.65%/year and −2.91%/year, respectively. On the other hand, the CSD method generated positive PLRs for some inverters while aforementioned statistical approaches proposed PLRs close to −2%/year, making the CSD method inappropriate for use under a heavy soiling environment. Within two km distance, variations up-to four-fold in PLRs are recorded between the venues during analyzed period. Furthermore, variations in PLRs are more dependent on the statistical approach rather than the performance metric.

Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks

“…These parameters are evaluated and compared with similar systems in different regions of the world [36]. Karahüseyin et al analyzed the performance of a mid-scale crystalline silicon (c-Si) PV system with different orientations and tilt angles in the same region for four years of outdoor exposure, using statistical methods to calculate PLRs; seasonal and trend decomposition using locally weighted scatterplot smoothing (STL); classical seasonal decomposition; and year-on-year methods coupled with PR, temperature-corrected PR, and weather-corrected PR [37]. Agyekum et al used the PR, degradation, energy-loss prediction, and the PVsyst simulation model to study the performance of solar photovoltaic (PV) modules under Russian weather conditions [38].…”

Assessing the Impact of Soiling, Tilt Angle, and Solar Radiation on the Performance of Solar PV Systems

“…This must be weighed against the additional costs associated with increasing the tilt angle of a solar PV module, such as additional costs for supporting structures, higher labour [11] and installation costs [12], and an increased risk of damage due to strong winds or snow loads [13]. Therefore, while it is important to consider the tilt angle and orientation of a solar PV module when selecting one for installation, this must be done in a balanced way to ensure that the costs are kept to a minimum [12] and that the efficiency of the module is maximized [11] while also ensuring that the risks of damage are minimized [14].…”

“…Numerous researchers have studied the effect of soiling and the optimum tilt angle on PV performance and found that soiling can reduce photovoltaic efficiency by up to 50% [11], while the optimum tilt angle can increase efficiency by up to 20% [12]. As such, maintaining clean solar panels and optimizing their tilt angle can have a significant positive impact on photovoltaic efficiency and should be taken into account when installing a solar PV system for maximum energy production.…”

A Novel Operating State Evaluation Method for Photovoltaic Strings Based on TOPSIS and Its Application

“…Qu et al proposed an hourly weather status pattern recognition model based on unsupervised clustering and the multiclass GBDT-LR classification process for the complex and variable weather status, which further improves the timeliness and accuracy of the PV array status evaluation [12]. Karahüseyin et al calculated the annual performance loss rate of PV plants for four-year outdoor exposure [13]. The above methods can be used to accurately analyze the overall operating state and efficiency of PV systems; however, there are few studies related to analyzing the operating state of PV strings based on conversion efficiency.…”