This thesis investigates how digital technologies shape business models in high-tech renewable energy firms. Using five firm cases and a systematic literature review, both coded independently with the Gioia methodology and then compared, the study maps the technologies discussed across sources, analyses their effects on value creation, delivery, and capture, and relates these component-level patterns to business model change types: adaptation (BMA), evolution (BME), and innovation (BMI). Technologies are organised into a functional grouping conceptualised as a layered stack from data capture to system-level orchestration, which clarifies roles and enables cross-dataset comparison. The thesis also develops digital integration depth, defined as the extent to which technologies are structurally embedded across components. Findings indicate that deeper and coherent embedding aligns with more extensive reconfiguration, whereas context-driven constraints commonly coincide with adaptation. Together, the functional grouping and integration-depth lens provide a testable framework for analysing and guiding digital transformation in renewable energy.