5/17/2023 0 Comments Solarfire vs timepassagesThis can be easily observed for all parameters used as inputs in the simulations in our case Global Horizontal Irradiation (GHI), Diffuse Horizontal Irradiation (DIF), Air temperature (TEMP) and Wind Speed (WS). There are differences between the maximum values registered in the data, but also in the statistical occurrence of hourly values above or below a certain threshold. Selection of optimum design and components without making higher risk assumptions on expected weather conditions becomes an impossible task when using synthetic data.įor the sample site used in this article, such differences can be directly observed when comparing both datasets, real hourly TMY and synthetically generated year. This means that in a synthetically-generated hourly time series the typical and extreme values are not fully captured and the synthetic data typically show systematic deviations. Understand range and extremes in weather data for optimum PV designĮven though monthly sums of both real and synthetic data are the same, the hour-by-hour analysis will always show critical differences. Table 2 : Interannual variability (standard deviation) for a sample location in Almería, Spain Second, as explained in a previous blog article, annual variability of GHI does not equal annual variability of PV output. First, the annual variability can vary significantly at different locations that have similar climate conditions and solar radiation. For example, this approach is used as default when calculating P90 in PVsyst software (see ). In absence of time series data, a substitute approach is to make use of GHI annual variability values from sites with similar irradiation levels and climate conditions. In absence of multiple year time series, we can only guess the expected annual variability of annual energy production, which is very difficult, as year-by-year variability has very complex geographical patterns. This can be estimated accurately if we know the annual sums for at least a 10-year period, from recent history. P50 and P90 energy values from two different simulations with different uncertaintiesĪccurate estimate of interannual variabilityįor estimation of total uncertainty of the annual energy estimates we need to know the annual variability of weather conditions and expected energy output. Once all uncertainties are accounted (more information about how to calculate P90 in a previous article), we can compare the results of PV energy simulation for our sample site in Table 1, below.įig. For sites with more complex patterns of solar radiation and temperature the additional uncertainty is expected to be even higher. A similar exercise for other locations showed higher differences but mostly within ± 2%, which can be considered as a reasonable estimate of additional uncertainty when using synthetically generated data. In a sample simulation in the software PVsyst for a default 15 kWp system located in Southeast Spain ( Plataforma Solar de Almería, sample files available here), we see a difference of 1.2% in annual energy output when using real hourly data compared to the use of synthetically generated hourly data. This effect should be accounted as an additional factor to the uncertainty analysis when using synthetic data. Otherwise, power plant output and relevant energy losses can be over- or under-estimated. The main solar and weather parameters needed for PV energy output simulation are the incident solar radiation and the PV cell temperature (calculated from air temperature).įor a reliable energy simulation, it is important to have the correct match between solar radiation and temperature for each time step. We then compared the results with the same simulation using synthetically generated hourly data (derived from monthly averages of original hourly time series). In this article, we discuss the benefits of using real hourly or sub-hourly time series instead of hourly data generated synthetically from monthly averages.įor this purpose, we have run a PV simulation using real data obtained from Solargis model in hourly resolution. The use of real hourly data time series, as an output of the weather data models.Įven though use of synthetically-generated, artificial hourly values has been a common practice, this approach is not recommended.The use of artificial hourly data profiles, generated from monthly averages by synthetic generation.In these regards, we have seen two main approaches when running PV simulations: Together with quality and accuracy, it is also important to look at the original time granularity of the input solar and weather data. Most solar companies understand this and are carefully evaluating uncertainty of solar resource data used for feasibility purposes. This well-known phrase is very relevant in a context of technical design and energy simulation of photovoltaic (PV) power plants.
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