Modeling Solar Costs

Evaluating Financial Risk

Published In: EnergyBiz Magazine May/June 2011


DO YOU BUILD YOUR NEW SOLAR INSTALLATION USING wafers of crystalline silicon or thin-film cadmium telluride? Do you start now or wait another year or two for a more favorable discount rate? Do you locate it outside of Boston or Seattle?

Each choice has risks and uncertainties and all have financial implications. Until now, utilities and investors have had little help in making these decisions when developing new solar power generation systems.

Argonne National Laboratory, teaming with analyst firm Gartner, and using sophisticated simulation techniques, has developed a methodology for evaluating the lifetime cost, cost uncertainties and associated financial risks of building and operating a commercial-scale solar power generation system.

Today, the common metric to assess a solar facility is the dollar per watt. This metric is derived by adding the price of the solar panels, installation, wiring, subsystems and other components and dividing that total by the maximum electricity output of the system.

Argonne and Gartner claim a better metric is the levelized cost of energy, which is derived by dividing the lifetime cost of the system by the lifetime energy produced. Levelized cost can be thought of as the price at which energy must be sold to break even over the system's lifetime. It is measured in cents per kilowatt-hour and takes into account the project costs and operating costs. Cost calculations also factor in other parameters like the amount of sunlight in a given location, conversion efficiency of the solar technology, degradation rate and financial considerations including the discount rate and taxes.

Although levelized cost calculations has been around for many years, it has been misused when applied to solar. "In typical projections for solar energy, many assumptions are swept under the rug, and we wanted to make a small step toward lifting up that rug and showing how you can truly get a handle on those assumptions to develop a more accurate picture of the potential costs," said Argonne solar researcher Seth Darling, who leads the development of the new approach.

Many factors used in cost calculations have a range of values, but are typically entered as a single value. The Argonne and Gartner model tries to address these variations and uncertainties by factoring in the probability associated with each parameter's value. For example, the researchers studied the 30-year history of solar insolation for Boston, Chicago and Sacramento, Calif., and created a probability distribution for each city.

They also developed probability distributions for degradation rates, discount rates and operational costs. Essentially, these distributions specify a range of values for each parameter and the probability that each value will occur. Darling noted that the distributions used may not be the best; people should focus on the methodology, not the specific values used in their model.

Employing a commonly used mathematical technique called Monte Carlo simulation, the cost is calculated by picking values from each distribution randomly based on the probabilities. This process is repeated over 1 million times.

This produces a range of levelized cost values and estimates of the likelihood of that cost's occurrence. A simulation might find a particular project has an average cost break-even price for electricity of 15 cents per kilowatt-hour, with a 90 percent certainty that the rate would be between 10 cents and 18 cents.

An investor could then look at the factors that produce that price variability and look for ways to reduce financial risks. For instance, the choice of a lower-cost panel certainly cuts the initial project cost, but a more expensive product might offer a better degradation rate. Similarly, choosing a product from a vendor that has much more data about degradation could limit the uncertainties and thus reduce the investment risk. If the discount rate is a source of great uncertainty, a utility might seek other funding or lock in a slightly higher fixed rate early on to reduce the chance of greater variation over time. Other factors, like the amount of sunlight in a given location, obviously cannot be controlled.

Darling noted that the biggest challenge with using the methodology is that much of the required data is lacking. In particular, what's needed is a publicly available real-world database across the country of solar insolation, degradation of materials in specific locations, and other information used in levelized cost calculations.

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