Levelized Cost of Energy: Page 6 of 10

It is clear that a PV system in Phoenix, Arizona, produces more power than a similar PV system in Portland, Oregon, but what does this difference mean with regard to the cost of energy from each system? What does it mean for the level of incentives that might be required to make solar financially viable in the two markets? What will PV need to cost before it makes sense in Massachusetts or New York without incentives? An LCOE calculation is essential to answering these questions.

While it is beyond the scope of this article to provide a comparison that would factor in all of the variables that change with location, it is a relatively straightforward task to evaluate how the LCOE of a PV system varies with the solar resources in different parts of the US. Changing the weather data used to simulate the production for the baseline fixedtilt system from Table 1 enabled us to create the graph shown in Figure 2. (The financial assumptions we used for these analyses are detailed in Table 2.) Among other things, Figure 2 shows that, all else being equal, the LCOE for a fixed-tilt system in Portland is 56% higher than the LCOE for a fixed-tilt system in Phoenix.


It is well known that single-axis tracking systems are more appropriate in some locations than in others since the production gain from a single-axis tracker over a fixed-tilt system is larger for sunny locations at southern latitudes compared to the gain at northern latitudes. How much better are single-axis trackers? Are there locations in the US where a fixed-tilt system provides better results than a single-axis tracker?

To provide insight into these questions, we ran LCOE calculations for our baseline fixed and tracking systems defined in Tables 1 and 2. Note that the installation cost, O&M cost and system downtime were increased for the single-axis tracker relative to the fixed-tilt system, and the weather data was varied. The other variables were held constant for the two project types. The results are provided in Figure 3 (below). This exercise did not account for changes in construction costs that may occur in different locations in the US; however, the trends observed are still relevant.

As can be seen in Figure 4 (below), the decrease in the LCOE for a single-axis tracker project in locations such as Phoenix and Sacramento is more than three times greater than the decrease in the LCOE seen in New York and more than two times greater than the decrease seen in Boston. Looking at these results, it is clear why single-axis trackers are a popular choice for projects in locations such as Phoenix or Sacramento. The LCOE for single-axis tracker projects is reduced by more than 12% in these locations compared to a fixed-tilt project.

It is also understandable why a fixed-tilt system might be chosen for a project constructed near Boston or New York. For these locations, the LCOE is only 5.8% and 3.5% less, respectively, with a single-axis tracker than with a fixed-tilt system. This decrease in LCOE could quickly be lost when you consider the additional weather-related issues that might occur in these locations, such as the effect of snow and ice on tracker accuracy and reliability, or the increased O&M costs that might be incurred due to snow damage.

The result for San Diego is both surprising and informative. While a single-axis tracker still provides a 7.2% lower LCOE than a fixed-tilt option, the difference is not nearly as pronounced as might be expected from the results in other locations. Without running this analysis, it would be easy to assume that the results in San Diego would be similar to those seen in Sacramento and Phoenix. However, when you compare the weather data for San Diego to the data for Sacramento or Phoenix, you see a higher percentage of diffuse irradiance in San Diego, which decreases the effectiveness of the tracker. The higher percentage of diffuse irradiance is likely due to San Diego’s proximity to the coast, and we would expect these results to change as a project site moved further inland.

Note that the production modeling portion of NREL’s System Advisor Model (SAM) tool does not currently model backtracking, which makes the production estimates for the tracker slightly higher than they should be.

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