Steps to be taken for estimate of P90 annual PV energy yield when using three different data steps are described below. For the sample considered in this article, the results of applying the uncertainties for each dataset are presented in the Table 5. These deviations are related to the assumptions taken when calculating the interannual variability on the one hand, and the loss of information related to TMY generation on the other hand. This exercise was done as an example, and the obtained results may not show the same trend for other locations.
Table 5: How to calculate PV energy yield value for P90 using different data sets for the sample site considered. Caballero, G. Srinivasan, M. Factors of uncertainty considered in photovoltaic energy calculation The calculation of Pxx scenarios from the P50 estimate takes into account the total uncertainty that summarizes all factors involved in the PV energy yield modelling.
The following sources of uncertainty are to be considered in evaluating a total uncertainty: Uncertainty of models. The standard data deliveries include information about the model uncertainty referring to yearly GHI estimates. The general uncertainty information is provided in PDF data reports, and on request it can be more accurately specified with regard to the region of interest. The model uncertainty already includes the uncertainties related to the measurements used for the model validation.
Interannual variability. Weather changes year-by-year, in longer-term cycles and has also stochastic nature. Therefore, solar radiation, air temperature and PV energy yield in each year can deviate from the long-term average to some extent, and this is called interannual variability. It can be calculated from the historical time series as a standard deviation of the series of annual values.
If the interannual variability for a period of N years is being considered, then the STDEV is to be divided by the square root of N typically one year, 10 years, or the total expected lifetime of the solar energy asset. For single year this uncertainty is highest, and it decreases with number of years. In P90 energy calculation, the case of variability that can be expected at any single year is typically assumed.
According to the latest forecasting trends and best practices, we can identify losses as the following:. When evaluating the Net Yield, each step of the applied methodology is subject to uncertainties. The identification and quantification of uncertainties are crucial to determine the correct exceedance probabilities. The first category refers to the variability of the wind resource over time.
Experience shows that we can have significant variations from one year to another. Unfortunately, no model can forecast those changes. In this context, an uncertainty is calculated to account for inter-annual variation over the wind farm lifetime. The longer the lifespan of the project is, the more likely it is to reach the P50 in cumulated production.
Correction of Hourly values. This is not correct, as the behavior of your system will be exactly the same for clear conditions. The eventual P90 "correction" would affect the distribution and frequency of bad weather conditions, not the absolute yield of each hour. Some meteo data providers propose Meteo Time series corresponding to P90 or other Pxx.
We don't know how these data are elaborated, and we don't know the significance of such data. P50 - P90 evaluations. Probability law This approach supposes that over several years of operation, the distribution of the annual yields will follow a statistical law, which is assumed to be the Gaussian or "normal" distribution.
Uncertainties on Meteo data Commonly available meteo climatic data have usually some uncertainties, of different kinds, which may produce very significant differences between sources, or years in a same source. These may be: - The yearly variability, which is supposed to have a gaussian distribution, - The quality of the data recording, care of the operators, positioning, calibration and drift of the sensors, perturbations like shadings, dirt or snow on the sensors, etc. Use of the PP90 tool in PVsyst P50 determination The simulation result is closely related to the Meteo input used for the simulation.
This may be of different kinds: - If the data are representative of an average over several years like monthly averages or TMY , the result should be considered as an average, and corresponds to P50 mean value of the Gaussian. Variability determination The annual variability sigma value will be dominated by the meteo year-to-year variability. PVsyst proposes default values according to these data.
If you avail of such meteo data for your site, you can calculate the RMS of the annual GlobInc distribution. Therefore, TMYs need to cover a full year and shall provide the most relevant meteorological parameters for the technology to be evaluated.
Also ambient air temperature, humidity and wind are relevant for CSP. A 60 min time resolution for TMY and performance model is currently the standard, but higher time resolution can lead to more realistic simulation results.
The goal of a TMY is to represent the long-term average meteorological conditions at a site. This is prerequisite for reliable simulation of plant performance. The TMY shall be based on as many years as possible — ideally using up to 30 years of site-specific observations.
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