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- Creators: Tamizhmani, Govindasamy
Description
Thermal modeling and investigation into heat extraction methods for building-applied photovoltaic (BAPV) systems have become important for the industry in order to predict energy production and lower the cost per kilowatt-hour (kWh) of generating electricity from these types of systems. High operating temperatures have a direct impact on the performance of BAPV systems and can reduce power output by as much as 10 to 20%. The traditional method of minimizing the operating temperature of BAPV modules has been to include a suitable air gap for ventilation between the rooftop and the modules. There has been research done at Arizona State University (ASU) which investigates the optimum air gap spacing on sufficiently spaced (2-6 inch vertical; 2-inch lateral) modules of four columns. However, the thermal modeling of a large continuous array (with multiple modules of the same type and size and at the same air gap) had yet to be done at ASU prior to this project. In addition to the air gap effect analysis, the industry is exploring different ways of extracting the heat from PV modules including hybrid photovoltaic-thermal systems (PV/T). The goal of this project was to develop a thermal model for a small residential BAPV array consisting of 12 identical polycrystalline silicon modules at an air gap of 2.5 inches from the rooftop. The thermal model coefficients are empirically derived from a simulated field test setup at ASU and are presented in this thesis. Additionally, this project investigates the effects of cooling the array with a 40-Watt exhaust fan. The fan had negligible effect on power output or efficiency for this 2.5-inch air gap array, but provided slightly lower temperatures and better temperature uniformity across the array.
ContributorsHrica, Jonathan Kyler (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley (Committee member) / Macia, Narciso (Committee member) / Arizona State University (Publisher)
Created2010
Description
The operating temperature of photovoltaic (PV) modules has a strong impact on the expected performance of said modules in photovoltaic arrays. As the install capacity of PV arrays grows throughout the world, improved accuracy in modeling of the expected module temperature, particularly at finer time scales, requires improvements in the existing photovoltaic temperature models. This thesis work details the investigation, motivation, development, validation, and implementation of a transient photovoltaic module temperature model based on a weighted moving-average of steady-state temperature predictions.
This thesis work first details the literature review of steady-state and transient models that are commonly used by PV investigators in performance modeling. Attempts to develop models capable of accounting for the inherent transient thermal behavior of PV modules are shown to improve on the accuracy of the steady-state models while also significantly increasing the computational complexity and the number of input parameters needed to perform the model calculations.
The transient thermal model development presented in this thesis begins with an investigation of module thermal behavior performed through finite-element analysis (FEA) in a computer-aided design (CAD) software package. This FEA was used to discover trends in transient thermal behavior for a representative PV module in a timely manner. The FEA simulations were based on heat transfer principles and were validated against steady-state temperature model predictions. The dynamic thermal behavior of PV modules was determined to be exponential, with the shape of the exponential being dependent on the wind speed and mass per unit area of the module.
The results and subsequent discussion provided in this thesis link the thermal behavior observed in the FEA simulations to existing steady-state temperature models in order to create an exponential weighting function. This function can perform a weighted average of steady-state temperature predictions within 20 minutes of the time in question to generate a module temperature prediction that accounts for the inherent thermal mass of the module while requiring only simple input parameters. Validation of the modeling method presented here shows performance modeling accuracy improvement of 0.58%, or 1.45°C, over performance models relying on steady-state models at narrow data intervals.
This thesis work first details the literature review of steady-state and transient models that are commonly used by PV investigators in performance modeling. Attempts to develop models capable of accounting for the inherent transient thermal behavior of PV modules are shown to improve on the accuracy of the steady-state models while also significantly increasing the computational complexity and the number of input parameters needed to perform the model calculations.
The transient thermal model development presented in this thesis begins with an investigation of module thermal behavior performed through finite-element analysis (FEA) in a computer-aided design (CAD) software package. This FEA was used to discover trends in transient thermal behavior for a representative PV module in a timely manner. The FEA simulations were based on heat transfer principles and were validated against steady-state temperature model predictions. The dynamic thermal behavior of PV modules was determined to be exponential, with the shape of the exponential being dependent on the wind speed and mass per unit area of the module.
The results and subsequent discussion provided in this thesis link the thermal behavior observed in the FEA simulations to existing steady-state temperature models in order to create an exponential weighting function. This function can perform a weighted average of steady-state temperature predictions within 20 minutes of the time in question to generate a module temperature prediction that accounts for the inherent thermal mass of the module while requiring only simple input parameters. Validation of the modeling method presented here shows performance modeling accuracy improvement of 0.58%, or 1.45°C, over performance models relying on steady-state models at narrow data intervals.
ContributorsPrilliman, Matthew (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Phelan, Patrick (Thesis advisor) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2020