Prognóza vlivu elektromobility na spotřebu elektřiny v ČR do 2050: Integrovaný model kvantitativní analýzy a competitive intelligence

The aim of this thesis is to design and validate a scenario-based forecasting approach for quantifying the impact of electric mobility on electricity demand in the Czech Republic up to 2050 within the context of a transmission system operator. The proposed solution combines a quantitative technology diffusion model (logistic S-curve) with a structured competitive intelligence framework that supports the continuous updating of assumptions in a dynamic legislative and technological environment. Based on current market data and three defined scenarios (conservative, reference, accelerated), the thesis derives electric vehicle stock trajectories, annual electricity consumption of electric mobility, and indicative implications for peak load. A key practical output is a prototype web application (MVP) that implements a unified scenario data module, a calculation engine, interactive visualizations, and standardized exports for downstream internal processes, especially load profiling and compatibility with DFT-type tools. The application also includes a scenario module for fiscal measures, allowing the testing of impact ranges of selected tax instruments on the adoption trajectory. The development process was iterative and supported by AI assistance to accelerate prototyping, while methodological choices, consistency checks, and interpretation of results remained the responsibility of the author. The thesis contributes to transparent uncertainty management, strengthens internal forecasting capacity, and reduces dependence on external predictive studies in long-term planning.