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 to 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. Here 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.

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We present a new multi-agent model of generation expansion in electricity markets. The model simulates generation investment decisions of decentralized generating companies (GenCos) interacting in a complex, multidimensional environment. A probabilistic dispatch algorithm calculates prices and profits for new candidate units in different future states of the system. Uncertainties in future load, hydropower conditions, and competitors' actions are represented in a scenario tree, and decision analysis is used to identify the optimal expansion decision for each individual GenCo. We test the model using real data for the Korea power system under different assumptions about market design, market concentration, and GenCo's assumed expectations about their competitors' investment decisions.