Accurate analysis of meteorological and pyranometric data for long-term prediction is the basis of the conception and development, so as decision-making for banks and investors, regarding solar energy conversion systems, either photovoltaic (PV) concentrated solar power (CSP) or concentrated photovoltaic (CPV). This has led to the development of methodologies for the generation of Typical Meteorological Years (TMY). A TMY is a customized weather dataset of one-year of meteorological data that aims at representing climatic conditions judged to be typical over a long-term period. A TMY dataset has 12 calendar months. The most representative block of monthly data for each calendar month is selected based on the smallest Filkenstein-Schafer distance measuring the difference of two cumulative distribution functions.
The “standard” method for solar energy conversion systems was proposed in 1978 by the Sandia Laboratory. In 2012, a new approach was proposed in the framework of the European project FP7 ENDORSE introducing the concept of “driver” time series defined by the user as a function of the pyranometric and meteorological relevant variables to improve the representativeness of the TMY datasets with respect the specific solar energy conversion system of interest.
Ana M. Realpe and colleagues from SOLAÏS, MINES ParisTech and NEOEN, in France benchmarked the classical Sandia method of TMY generation with innovative methods based on the driver concept, in the particular case of a given CPV system in a given site. The research work is now published in Energy Procedia.
According to the team, using 18-year data of the meteorological station Desert Rock (United States), five types of TMY were created using hourly and 1-minute data. The construction of the meteorological year is achieved by comparing the CDF of each block of data at a given month to the CDF of the concatenation of all blocks of data for this month over the long-term. The energy generation output of the concentrated photovoltaic system over the long-term 18-years period was the quantity used for the comparison between the different TMY datasets. The team used a simulation tool for each TMY as input to determine the yield of the concentrated photovoltaic system. Then two analyses were performed: one comparing the long-term average yield with the yield associated to the TMY datasets and another comparing the corresponding CDFs using the Kolmogorov-Smirnov test Integral parameter (KSI), in order to measure the distance between two cumulative distribution functions.
The team found out that the monthly results obtained from the drivers, with hourly and 1-min data, are significantly better than those obtained with the Sandia method. The maximum annual deviations obtained with the Sandia method and with the drivers have no consistent pattern. From the monthly KSI test, the team observed that the maximum monthly deviations from the Sandia method approach exceed twice the deviations obtained by the drivers. Considering the annual KSI, the simplified and filtered drivers provide relative KSI values systematically less than 45 % from 8-year data.
Researchers were able to compare the innovative method which is based on driver concept, by using classical Sandia method as reference point. They found the driver concept to be more promising and efficient than the classical Sandia method.
Ana M. Realpe1, Christophe Vernay1, Sébastien Pitaval1, Camille Lenoir2, Philippe Blanc3, Benchmarking of five typical Meteorological year Datasets dedicated to Concentrated-PV Systems, Energy Procedia 97 ( 2016 ) 108 – 115.Show Affiliations
- SOLAÏS, 400 avenue Roumanille, BP 309, F-06906 Sophia Antipolis Cedex, France.
- NEOEN, 4 rue Euler, 75008 Paris, France.
- MINES ParisTech, PSL Resear University, O.I.E. – Center of Observation, Impacts, Energy, 1 Rue Claude Daunesse, CS 10207, F-06904 Sophia Antipolis Cedex, France.
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