Metodologia para análise da complementaridade entre energia solar e eólica em usinas híbridas.

Abstract

This work presents a methodology to determine the optimal capacity of a photovoltaic plant (UFV) to be integrated with a wind plant (EOL), maximizing the complementarity between the sources and minimizing losses due to curtailment or generation reduction. The proposed methodology uses real capacity factor data from operational solar and wind plants, provided by the National Electric System Operator (ONS), enabling an evaluation based on hourly data over an entire year. To develop the data processing, the Python environment was used. Initially, the preprocessing of the hourly data provided by the ONS included cleaning, standardization, and filtering of the records to ensure the consistency and quality of the information. Next, through the proposed methodology, the Modified Brent Method was employed, an optimization method that, without relying on derivatives, efficiently determines the value of the installed photovoltaic capacity that minimizes generation curtailment. Statistical metrics such as the Pearson coefficient, MSE (Mean Squared Error), RMSE RMSE (Root Mean-Square Error), and R² were applied to assess the accuracy and reliability of the data used in the methodology. This methodological framework enabled a detailed analysis grounded in the operational reality of the hybrid plants studied, ensuring greater precision in defining the optimal integration conditions between solar and wind sources. Ten pairs of hybrid plants were analyzed, considering statistical metrics to quantify the complementarity between the sources and define the optimal sizing of the UFV. The results demonstrated that hybridization can significantly increase the capacity factor of the plants, reducing generation intermittency and optimizing the use of existing infrastructure. For certain configurations, it was possible to double the installed capacity of the EOL without generation curtailment exceeding 10%, making hybridization energetically viable. Furthermore, the results showed that pairs of plants with a higher negative correlation between the sources exhibited better performance, reinforcing the importance of strategic selection of hybrid plants. The proposed methodology can be applied in the planning of electric sector expansion, optimizing resource allocation and the use of transmission infrastructure, as well as serving for future research on economic viability, integration of energy storage, and regulatory impacts of hybrid generation.