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The electric energy matrix in Brazil mostly comprises energy from hydroelectric power plants, approximately 60.8%. This large participation presents some vulnerability in periods of drought and often the country's electric energy matrix is complemented by the use of other energy sources, such as thermoelectric. Even though it is currently among the largest onshore wind power producers in the world, Brazil has not yet started developing this energy source in the offshore segment. However, this study shows that offshore wind energy can be a complementary energy source for the country's electric energy matrix, and thus replace other energy sources which have more impact on the environment. Therefore, this study sought to develop a methodology that can be easily replicated using free access tools, aiming to contribute to the development of offshore wind technology and future research in the country. The results show a large production of offshore wind energy in the southeastern region of Brazil. The "Cabo Frio 2″ data collection point presented the highest annual production of offshore wind energy. However the study opted for the "Arraial do Cabo-A606″ collection point located in the state of Rio de Janeiro and at ocean depths that can be reached by fixed foundations. • A simplified methodology is presented to estimate offshore wind energy potential. • The energy potential is calculated in different sites in the southeast of Brazil. • The developed methodology is user-friendly and has free access tools. • The offshore wind energy can contribute to complement the energy matrix of Brazil. • This paper expands the scarce literature about offshore wind energy in Brazil. [ABSTRACT FROM AUTHOR]

Wind energy is one of the sources which is still in development in Brazil. However, it already represents 17% of the National Interconnected System. Due to the high level of uncertainty and fluctuations in wind speed, predicting wind energy with high accuracy is challenging. In this context, this paper proposes a novel decomposition-ensemble learning approach that combines Complete Ensemble Empirical Mode Decomposition (CEEMD) and Stacking-ensemble learning (STACK) based on Machine Learning algorithms to forecast the wind energy of a turbine in a wind farm at Parazinho city, Brazil, using multi-step-ahead forecasting strategy. The approached forecasting models were k -Nearest Neighbors, Partial Least Squares Regression, Ridge Regression, Support Vector Regression, and Cubist Regression. Additionally, Box-Cox transformation, correlation matrix, and principal component analysis were used to pre-process the data. The performance of the proposed forecasting models was evaluated by using three performance metrics: mean absolute error, mean absolute percentage error, and root mean square error, and the Diebold-Mariano statistical test to evaluate the forecasting error signals. The proposed models outperform the CEEMD, STACK, and single models in all forecasting horizons, with a performance improvement that ranges 0.06%–97.53%. Indeed, the decomposition-ensemble learning model is an efficient and accurate model for wind energy forecasting. Image 1 • A novel decomposition-ensemble learning model is proposed for wind energy forecasting. • CEEMD method deal with the non-linearity and non-stationarity of the time series. • Different preprocessing approaches are employed to deal with the high-correlation of the system's inputs. • STACK approach by divide-and-conquer scheme takes advantages of the models. • Proposed model improves the accuracy of wind energy forecasting multi-step ahead. [ABSTRACT FROM AUTHOR]

The profitability of a wind power project is evaluated during its development phase through a complete site assessment. At this stage, the LCoE (Levelized Cost of Energy) is determined considering many variables, such as wind turbine model, project layout, energy production, CAPEX (Capital Expenditure), OPEX (Operational Expenditure) and financial costs. However, in recent years, the seasonality and variability of the wind farms energy production have been gaining importance in this process in some markets, due to a migration of the new wind power projects from the regulated market to the free market. In the free market, the PPAs (Power Purchase Agreements) have short-term balance payments, different from the regulated market. In Brazil, payments in the regulated market are performed monthly based on the long-term AEP (Annual Energy Production) expected values, with annual and quadrennial balance payments. This paper focuses on an extensive case study of Brazil, where the energy spot prices vary constantly between 10 USD/MWh and 100 USD/MWh, exposing the wind farms owners to very relevant financial risk when trading energy based on long-term bilateral contracts. The assumed risks can become critical in cases of periods with extreme meteorological anomalies (wind speed variations below the respective long-term expected values), especially in situations where a simultaneous drought period occurs, eventually raising the market spot price to its ceiling value. A SPE (Specific Purpose Entity) account must be properly dimensioned and maintained, similar to a working capital, in order to the wind farm to be able to operate through these low wind speed periods without any capital call to the wind farm's controllers. This represents additional financial costs in the form of trapped cash. This study analyses the wind speed variability in NE-Brazil, where most of the wind farms in the country are concentrated, and analyses how the portfolio effect contributes to the reduction of this variability. This has been performed making use of the data provided by 4 met masts that surround the NE Brazilian territory (with distances between them ranging from 459 km to 724 km) and combining the obtained values to theoretical turbine power curves of different MW platforms (2, 3, 4 and 6 MW). The total period of available measurement sums more than 29 years of data. The results showed that the variability was higher in periods of lower average wind speed and that the analyzed location with the highest variability was the one close to the coast and with higher altitude. Additionally, the combinations that provided the best portfolio effect were those that included the location in the SW, furthest from the coast and with higher altitude. The different turbine platforms did not present a relevant difference in terms of resulting variability on the energy production nor portfolio effect, but when considering the cumulative impact of the meteorological anomalies (larger periods of wind speeds above or below the long-term expected values), the larger the wind turbine, the larger was the resulting exposure. • Results cover Brazilian NE territory, the windiest in the country. • Variability and portfolio effect results analyzed in terms of wind and energy. • Variability is higher in periods of lower average wind speed. • Variability is higher in region closest to the coast and with high altitude. • Best portfolio effect was not the one with all regions included. [ABSTRACT FROM AUTHOR]

This paper analyzes the main impacts of high penetration levels of wind power in the power system of Southern Brazil. For this, an hourly operation of the Brazilian Southern power system is simulated using an Economic Dispatch Model. Two main scenarios are explored: a baseline and a wind penetration case, both for 2030 and 2050. To create these scenarios, results from an Integrated Energy Model are used as inputs for the dispatch model. The economic dispatch modeling led to some expected outcomes, such as a reduction in the system generation cost, a reduction in greenhouse-gas emissions, and a greater need for transmission expansion in the wind-power scenarios. Results also show that, even for a power system as flexible as the one in Southern Brazil, wind power penetration impacts can be observed. Moreover, results show that setting thermal power plants to provide secondary reserve can potentially improve the balancing management in hydropower-based systems. Finally, results from linking the Economic Dispatch Model and the Integrated Energy Model showed the need for better combining energy expansion models with power dispatch models that consider both the operation and the transmission system restrictions, in order to have stable and feasible unit commitment schedules. • Different levels of wind power penetration impact hydropower systems differently. • High wind penetration systems require detailed power transmission models. • Greater insights can be gained by combining the strengths of various modeling tools. • Secondary reserve provided by thermal plants can improve balancing management. [ABSTRACT FROM AUTHOR]