Enhancing energy planning with copula-based dependency models

The growing global demand for sustainable energy solutions has highlighted the importance of renewable energy sources, especially in regions with abundant natural resources. This study proposes a copula-based approach to model the dependency between hydroelectric and wind energy sources, focusing on improving energy planning. The research utilizes copula theory to identify and model the dependency structure between hydroelectric reservoirs’ Natural Affluent Energy (NAE) and the Capacity Factor (CF) of wind complexes. Using historical data from 1980 to 2023, the analysis applies conditional copulas to simulate scenarios and predict CF for 2024, conditioned on NAE values simulated by the current model. The results demonstrate that copulas effectively capture the non-linear dependencies between the energy sources, providing realistic simulations that closely align with historical data distributions. The findings emphasize the potential of copula-based approaches to enhance the accuracy of energy planning models, supporting the optimized integration of renewable resources and contributing to more resilient and sustainable energy systems. The proposed methodology can be applied in various contexts, supporting the development of more robust energy planning strategies.