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Schmidt, Johannes
Cancella, Rafael
Junior, Amaro Olímpio Pereira
Year of Publication: 
Series/Report no.: 
Diskussionspapier No. DP-56-2014
A high share of Brazilian power production comes from hydropower sources. A further expansion of power generation is necessary due to high growth rates in electricity demand. As an alternative to carbon intensive thermal power production and the expansion of hydropower in the ecologically and socially sensitive North of Brazil, windpower production could help to cover increasing levels of demand. Variability of wind is however often considered a major obstacle for further expansion. We assess the variability of potential windpower production in the four most important windpower producing states Ceará (CE), Rio Grande do Norte (RN), Bahia (BA) and Rio Grande do Sul (RS). Instead of focusing on hourly or sub-hourly variability, we assess the seasonality and inter-annual variability. This is important as hydropower production shows strong seasonality in Brazil and as inter-annual variation of hydropower production is high. We generate and validate time series of windpower production from wind speeds derived from measurements and two global climate models (NCAR reanalysis and ECMWF reanalysis). Our results show that seasonal variability of windpower generation in the North-Eastern states is anticyclical to hydrological seasonality in the South-East, North-East, and North of Brazil. Inter-annual variability is lower for windpower production than for hydro inflows. No consistent inter-annual relationship between the two sources of renewable energy can be found with the exemption of the state of Ceará which shows low positive correlation with hydro inflows. This indicates that although integrating windpower into the system may cause electrical problems due to very short term variability, seasonal and inter-annual variability is considerably decreased if windpower expansion is favoured instead of hydropower. Our results also show that ECMWF data may be the best source of long-term wind timeseries as it is better able to reproduce ground measurements than NCAR.
Inter-annual variability
Document Type: 
Working Paper

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