Cost-benefit analysis of an AI-driven operational digital platform for integrated electric mobility, renewable energy, and grid management

Abstract

Integrating electric mobility, including electric vehicles (EVs), electric trucks (ETs), and renewable energy sources (RES) with the power grid is paramount for decarbonization, efficiency, and stability. A critical gap remains, however: existing smart-grid and e-mobility cost-benefit analysis (CBA) approaches do not yet provide a unified framework for appraising AI-driven operational digital platforms (ODPs) that jointly coordinate EV/ET charging, renewable generation, and grid operations across sectoral and national boundaries. This paper develops a seven-step CBA framework tailored to this class of platform. The framework maps each layer of a multi-layered AI architecture to traceable, monetizable benefit streams-panning economic efficiency, grid reliability, and environmental externalities--while explicitly accounting for AI-specific capital and operational expenditures that conventional appraisals omit. Applied to a ten-year, three-country deployment across Austria, Hungary, and Slovenia, the analysis indicates a robust positive investment case under the modeled assumptions, confirmed through scenario sensitivity analysis, one-way parameter ranking, and probabilistic simulation. Benefit composition and country-level drivers differ systematically across national contexts, yet the economic rationale is preserved in each, reflecting the framework's adaptability to heterogeneous electrification trajectories. The findings indicate the economic viability of AI-driven digital platforms for cross-sectoral energy--mobility integration and highlight the critical role of ODPs in advancing decarbonization in the mobility--power nexus. To that end, they have direct implications for the design and appraisal of digital infrastructure investments under the EU's Fit for 55 and REPowerEU programmes.

Figures (13)

• Figure 1: Technological diagram of the core ODP architecture mapping to its five principal layers. Data flows bottom-to-top across five layers. The Perception layer collects telemetry from EV/ET, RES, grid, and weather sensors. The Data layer ingests, validates, and stores these streams. The Middleware layer provides standardized interoperability via API gateways and data-space connectors. The Business layer runs the AI engine: forecasting, smart-charging optimization, fleet routing, and demand-response dispatch. The Application layer exposes outputs through dashboards and mobile apps. Bidirectional arrows (right) show data exchange with external systems (energy markets, TSOs/DSOs, EU data spaces). An optional secure-logging sidebar indicates a transaction-record layer that is not modeled as a separate value driver in the CBA.
• Figure 2: Stakeholder-informed contextual assessment across regulatory, technical, governance, economic/financial, and relevance dimensions.
• Figure 3: Benefit analysis overview. Top: total discounted present value of each benefit stream over 2026--2035, aggregated for AT, HU, and SI (colour-coded by category: blue = economic, green = reliability, amber = environmental). Middle: year-on-year accumulation of all eight benefit streams, showing compound annual growth of approximately 7.9% as EV/ET penetration and RES capacity expand. Bottom panel: NO$_x$, SO$_x$, and PM$_{2.5}$ emission trajectories for the baseline and ODP-enhanced scenarios, indexed to EU-27 proxy (2005 = 100%), demonstrating the platform's contribution to meeting EU air-quality objectives alongside decarbonization targets. All values discounted at 4 % social rate to 2025; see \ref{['app_details']} for exact formulas.
• Figure 4: Cumulative discounted NPV trajectory and payback analysis (AT, HU, SI combined). Benefits are discounted at 4% and accumulated annually; the intersection with the x-axis identifies the approximate payback year. The growing positive surplus from 2031 onward confirms that the platform generates compounding net value over the analysis horizon.
• Figure 5: Indicative OPEX distribution by cost category in the first year of operation (2026), aggregated for AT, HU, SI. Major categories include cloud computing (€3.9M), AI inference & data processing (€3.9M), cybersecurity and threat monitoring (€3.6M), labor costs (€3.0M), grid & sensor maintenance (€2.8M), and AI model maintenance & retraining (€2.5M). Total discounted 10-year OPEX is €315.9 million.