Dynamic Electro-Thermal PV Temperature and Power Output Prediction Model for Any PV Geometries in Free-Standing and BIPV Systems Operating under Any Environmental Conditions

Abstract

PV temperature significantly affects the module’s power output and final system yield, and its accurate prediction can serve the forecasting of PV power output, smart grid operations, online PV diagnostics and dynamic predictive management of Building Integrated Photovoltaic (BIPV) systems. This paper presents a dynamic PV temperature prediction model based on transient Energy Balance Equations, incorporating theoretical expressions for all heat transfer processes, natural convection, forced convection, conduction and radiation exchanges between both module sides and the environment. The algorithmic approach predicts PV temperature at the centre of the cell, the back and the front glass cover with fast convergence and serves the PV power output prediction. The simulation model is robust, predicting PV temperature with high accuracy at any environmental conditions, PV inclination, orientation, wind speed and direction, and mounting configurations, free-standing and BIPV. These, alongside its theoretical basis, ensure the model’s wide applicability and clear advantage over existing PV temperature prediction models. The model is validated for a wide range of environmental conditions, PV geometries and mounting configurations with experimental data from a sun-tracking, a fixed angle PV and a BIPV system. The deviation between predicted and measured power output for the fixed-angle and the sun-tracking PV systems was estimated at −1.4% and 1.9%, respectively. The median of the temperature difference between predicted and measured values was as low as 0.5 °C for the sun-tracking system, and for all cases, the predicted temperature profiles were closely matching the measured profiles. The PV temperature and power output predicted by this model are compared to the results produced by other well-known PV temperature models, illustrating its high predictive capacity, accuracy and robustness.

PV temperature and performance prediction in free-standing, BIPV and BAPV incorporating the effect of temperature and inclination on the heat transfer coefficients and the impact of wind, efficiency and ageing

“…For the model validation, f and Tpv for the sun-tracking PV system, operating at a wide range of IT, vw,Τα and β, were predicted using eqs. (20)(21)(22)(23)(24)(25). The predicted Tpv values were compared with measured ones, using the 2 nd year monitoring data from the sun-tracking system and are presented in Fig.…”

“…ΔTf-b was experimentally measured between 0 and -3 o C. The value depends on the PV mounting mode, the cell material, construction and the vw strength and direction. Uf and Ub may be estimated through iterations [21]. However, this would not lead to a compact Tpv prediction tool as sought for here.…”

“…However, the heat convection and the radiated heat coefficients used do not cover sufficiently the whole range of the environmental conditions. More elaborated analysis was outlined in [3,17,21,22] and provided better Tc predictions. In a second group, the so called grey models, electric and heat transfer parameters associated to the operating PV module are introduced into an EBE and a regression analysis of recorded data from monitored quantities is applied for the development of semi-empirical models for Tc prediction.…”

“…hc,f and hc,b, are loosely dependent on Tpv and Ta. The most common Tc or Tpv prediction empirical formulas of explicit and implicit structure which appear in PV performance model comparisons [1,12,[19][20][21][22][23][24] take the forms summarized in Table 1.…”

“…A simpler Tpv implicit formula was developed [14] based on the EBE taking into consideration Tpv dependent PV coefficients and not the NOCT as it was done in [11,25]. In the above referenced models, the PV heat losses coefficient Upv(vw) was assumed to be linearly dependent on vw which does not hold for natural heat air flow as it was analytically presented in [17,21].…”