Deciphering uncertainties in the cost of solar energy
Photovoltaic electricity is a rapidly growing renewable energy source and will ultimately assume a major role in global energy production. The cost of solar-generated electricity is typically compared with electricity produced by traditional sources with a levelized cost of energy (LCOE) calculation. Generally, LCOE is treated as a definite number, and the assumptions lying beneath that result are rarely reported or even understood. We shed light on some of the key assumptions and offer a new approach to calculating LCOE for photovoltaics based on input parameter distributions feeding a Monte Carlo simulation. In this framework, the influence of assumptions and confidence intervals becomes clear.
In this example figure, Monte Carlo simulations produce an LCOE distribution for a modeled 20-MW utility-scale one-axis tracking array at three diverse locations. The focus is on assumptions revolving around (decoupled) sunlight variation, panel performance, operating costs, and inflation. The distributions are approximations with no interdependence used to demonstrate the Monte Carlo approach to LCOE. Even within this limited scope, it is clear that the LCOE output can vary substantially from a single value, giving enhanced guidance to all stakeholders in the solar energy arena. Users adopting this approach will require more rigorous and coupled input distributions based on the best available geography-specific data, which in many cases is not currently available. Responsibility for collecting and distributing these data lies with partnerships between industry and national laboratories. With such data in hand, this Monte Carlo approach is a means to generate reliable statistical projections for photovoltaic LCOE.
S.B. Darling et al., “Assumptions and the levelized cost of energy for photovoltaics,” Energy Environ. Sci. (in press). DOI: 10.1039/c0ee00698j