Renewable energy is crucial for addressing the growing energy demands of modern society while mitigating the adverse effects of climate change. Unlike fossil fuels, renewable energy sources such as solar, wind, hydro, geothermal, and biomass are abundant, sustainable, and environmentally friendly. This study focuses on addressing a critical challenge in renewable energy decision-making by developing a novel framework for optimal solar panel selection, a key component of sustainable energy solutions. Solar panel selection involves evaluating multiple interdependent criteria, such as efficiency, cost, durability, and environmental impact. Traditional multi-criteria decision-making (MCDM) methods often fail to account for the interdependencies among these criteria, leading to suboptimal outcomes. To overcome this limitation, the study introduces the Choquet Aggregated Sum Product Assessment (CASPAS) method, a Choquet integral-based MCDM approach that incorporates fuzzy measures to model interactions among criteria. CASPAS generalizes the Weighted Aggregated Sum Product Assessment (WASPAS) method, thereby enhancing decision-making accuracy and reliability. This study also introduces the concept of disc intuitionistic fuzzy set (D-IFS), a generalization of the concept of circular intuitionistic fuzzy set, which employ a radius function capable of assigning varying values to individual elements instead of relying on a fixed radius. Recognizing that traditional weighted aggregation operators neglect the interaction among criteria, this study proposes disc intuitionistic fuzzy Choquet integral operators by incorporating the concept of fuzzy measures, which are effective in modeling such interactions. The proposed method is applied to a renewable energy problem on selecting optimal solar panels.