Variations in energy forecasts include: interannual variability, data uncertainty, modeling assumptions and methods. Several of these variables are discussed below.
1. year change
Interannual variation (lAV) refers to the natural variation of the weather in a place from year to year. Although IAV includes random elements, it still follows macroscopic long-term weather trends associated with multiple climate cycles. IAVs in resource analysis often exhibit a normal random distribution, so this assumption works in most cases and is simple. But this does not accurately represent the true lAV situation in a particular location. While IAV can be analyzed by a variety of techniques, it is critical to use long-term solar resource datasets that include at least one extreme meteorological event.
2. Data uncertainty
Uncertainty of resource data refers to the uncertainty of the dataset compiled for a project site. As discussed earlier, resource data uncertainty can be derived from a variety of factors, including the method used to obtain the data (satellites, ground measurements, ground modeling), the quality of the underlying instrumentation, or the data input to the model.
3. Modelling assumptions and methods
Modelling assumptions and methods may introduce additional uncertainty into the final generation and revenue assessment. Regardless of the specific technology and scale, hundreds of inputs are required to model PV and CSP systems at the system level. These inputs are (to some extent) based on site characteristics, system design, system layout, and manufacturer’s specifications. In most cases, only general guidelines for making appropriate or reasonable assumptions about most parameters are available.
In an ideal situation, warranties provided by the EPC contractor and/or O&M provider would fully constrain system performance, with interannual variability in solar resources being the primary consideration in hyperprobability assessments. In the absence of this definition, assumptions must be made.
One approach is to evaluate key input parameters and determine the expected value and potential range of volatility. In practical situations, the overall uncertainty in the amount of electricity produced can be calculated based on input assumptions (usually describing system-level power output as a normal distribution with a standard deviation between 2% and 3%). However, this still ignores the systematic bias that the simulation method may introduce. Another problem with this technique is that evaluating the uncertainty in the process of changing a single parameter introduces new uncertainty into the whole process.
Another approach is to evaluate input parameters under more conservative assumptions (for example, that module outputs are based on manufacturer’s minimum tolerances rather than average values) and consider certain events that could cause substantial changes in system performance as constraints . For debt financing purposes, this approach has the advantage of providing a reliable basis for planned plant performance and the ability to isolate specific events listed as constraints, which may be the most concerned.