An Image Processing-Based Method to Assess the Monthly Energetic Complementarity of Solar and Wind Energy in Colombia

Abstract

Solar and wind energy systems, without storage, cannot satisfy variable load demands, but their combined use can help to solve the problem of the balance between generation and consumption. Energetic complementarity studies are useful to evaluate the viability of the use of two or more renewable energy sources with high variability in a specific interval of time in a determined region. In this paper, the monthly energetic complementarity study of solar and wind resources of Colombia is carried out. A novel approach to conduct the study is proposed. A dataset with the average monthly solar radiation and wind speed values is obtained from high-resolution images of renewable resources maps, using image processing algorithms. Then, the dataset is used to calculate the energetic complementarity of the sources employing the negative of the Pearson correlation coefficient. The obtained values are transformed to energetic complementarity maps, previously eliminating the protected areas. The obtained results show that there is a good energetic complementarity in the north and northeastern regions of the country throughout the year. The results indicate that projects related to the joint use of solar and wind generation systems could be developed in these regions.