Abstract
High resolution global irradiance time series are needed for accurate simulations of photovoltaic (PV) systems, since the typical volatile PV power output induced by fast irradiance changes cannot be simulated properly with commonly available hourly averages of global irradiance. We present a two-step algorithm that is capable of synthesizing one-minute global irradiance time series based on hourly averaged datasets. The algorithm is initialized by deriving characteristic transition probability matrices (TPM) for different weather conditions (cloudless, broken clouds and overcast) from a large number of high resolution measurements. Once initialized, the algorithm is location-independent and capable of synthesizing one-minute values based on hourly averaged global irradiance of any desired location. The one-minute time series are derived by discrete-time Markov chains based on a TPM that matches the weather condition of the input dataset. One-minute time series generated with the presented algorithm are compared with measured high resolution data and show a better agreement compared to two existing synthesizing algorithms in terms of temporal variability and characteristic frequency distributions of global irradiance and clearness index values. A comparison based on measurements performed in Lindenberg, Germany, and Carpentras, France, shows a reduction of the frequency distribution root mean square errors of more than 60% compared to the two existing synthesizing algorithms.
Highlights
The efficiency of PV modules depends mainly on the irradiance, amongst other secondary effects such as module temperature [1, 2]
High resolution global irradiance time series are needed for accurate simulations of photovoltaic (PV) systems, since the typical volatile PV power output induced by fast irradiance changes cannot be simulated properly with commonly available hourly averages of global irradiance
The algorithm is initialized by deriving characteristic transition probability matrices (TPM) for different weather conditions from a large number of high resolution measurements
Summary
The efficiency of PV modules depends mainly on the irradiance, amongst other secondary effects such as module temperature [1, 2]. For the understanding of the dynamic interaction of PV generator, storage systems, loads, and grids on a worldwide scale, one-minute data series of high quality in terms of realistic variability and frequency distributions are a key factor Simulating those systems with hourly averaged values neglects significant behavior patterns like short time power enhancements [3]. While there exist several commercial providers and free sources of meteorological data in a resolution of one hour (e.g., Meteotest, SolarGIS, and TMY), covering nearly the whole earth, the availability of measured irradiance data with a resolution of less than an hour is very limited This limited availability leads to the necessity to synthesize one-minute time series from hourly averaged data. The contribution of Aguiar and Collares-Pereira was originally designed for the generation of hourly averaged time series with daily averages as input It is based on the modeling of probability densities as Gaussian functions that depend on the clearness index kt. We show that the improved algorithm exceeds the performance of the Aguiar and the Skartveit algorithm in terms of temporal variability and characteristic frequency distributions for the calculation of short-term global irradiance at two exemplary PV installation locations
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