Multiscale Modelling to Develop and Understand Future Photovoltaics

Abstract

In this thesis, multiscale modelling from the device to energy systems is used to identify and examine applications for emerging photovoltaics within our future climate and energy markets.
The concept of photovoltaics of enhanced capacity factors are introduced, and their benefits are demonstrated through a combination of plant dispatch modelling utilising experimental characteristics from collaborators. It is shown that possessing an increasing efficiency with reducing irradiance enables better alignment between demand and solar generation, increasing capacity factors. At most saving more than 3.5 times the greenhouse gas emissions than silicon photovoltaics.
A atmospheric composition model combining a radiative transfer model and single diode model is used to examine the effects wildfires, dust storms and pollution upon photovoltaics. Showing spectrally calculated losses of more than 100Wm^−2 can be accrued due to local effects. Paying further attention to spectral losses different photovoltaics are tested showing the benefits of broad and narrow absorption spectra.
Finally, genetic algorithms are used to design and optimise organic photovoltaics. Optimising for power conversion efficiency (PCE); PCE per unit cost, and levelised cost of energy. Resulting in different devices per objective function. Considering the cost of the active layer to be a variable the maximally acceptable commercial cost for the active layer material is shown to be £10 for P3HT:PCBM, and £100 for PM6:Y6.