Modeling Effort Estimates Financial Uncertainties and Risks of Solar Generation | WPN World People News

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 commercial-scale solar power generation systems.

The 48 MW Copper Mountain solar facility uses First Solar thin film modules.

The researchers believe their approach will give utilities and investors a better understanding of a system's total costs and help them identify areas where financial risks can be minimized.

Argonne and Gartner base their model on the metric called the levelized cost of energy (LCOE), which is derived by dividing the lifetime cost of the system by the lifetime energy produced. In contrast, most solar projects today are evaluated based on a dollar-per-watt estimate. Argonne solar researcher Seth Darling, who leads the development of the new approach, said the LCOE metric provides a better way to determine the financial feasibility of one system over another.

LCOE 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. LCOE calculations also include other factors such as the amount of sunlight in a given location, conversion efficiency of the solar technology, solar panel degradation rate over time, and financial considerations including the discount rate and taxes.

Darling noted that there is great uncertainty in many of the parameters used in typical LCOE estimations, yet the calculated LCOE does not capture or reflect those uncertainties or the potential financial risks associated with them.

The Argonne and Gartner model tries to address these variations and uncertainties by factoring in the probability associated with each parameter value instead of a single value. For example, the researchers studied the 30-year history of solar insolation for Boston, Chicago and Sacramento 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 LCOE is calculated by picking values from each distribution randomly based on the probabilities. This process is repeated more than 1 million times.

This produces a range of LCOE values and estimates of the likelihood of that LCOE's occurrence. A simulation might find a particular project has an average LCOE of $0.15 per kilowatt-hour, with a 90 percent certainty that the rate could be between $0.10 and $0.18.

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, going with a vendor that has much more detailed degradation data 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, such as 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 LCOE calculations.

Source: Smarter Technology