Using stochastic models to determine financial indicators and technical objectives for organic solar cells

Organic photovoltaics (PVs) are a rapidly emerging technology that has the potential to provide low-cost power for many different applications. The TEEOS model had previously been developed to determine various financial indicators for this technology. This paper modifies TEEOS in order to better analyze the effects of variability in the inputs. This modified TEEOS incorporates a mean-reverting jump diffusion model to simulate synthetic electricity price and a modified rainfall occurrence model to simulate a weather series. As well, the external quantum efficiency (EQE) is used as an input in order to gain insight on using low band gap polymers or enhancing the existing EQE. The power production NPV of three example cells, a Si-based solar cell, dye-sensitized solar cell (DSSC) and an organic solar cell (OSC), are first examined using a 25-year time period where the DSSC and OSC have to be replaced five times due to low lifetimes. The DSSC and OSC are also compared using five years of actual data using a five-year horizon. The sensitivity analysis looks specifically at inherent increases in the electricity price, the correlation of electricity prices and weather, modeling electricity without jumps, as well as enhancing and expanding the EQE. The best option for increasing the economic feasbility for the OSCs is expanding the EQE, that is, reducing the band gap of the polymers used. A break-even target of $45/m2 for the OSC is stated, which is on the low range of current cost estimates; this break-even-target would increase for locales closer to the equator.