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

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  • Arega Getaneh Abate
  • Xiaobing Zhang
  • Xiufeng Liu
  • Dogan Keles

Abstract

Integrating electric mobility, such as electric vehicles (EVs), electric trucks (ETs), and renewable energy sources (RES) with the power grid is paramount for decarbonization, efficiency, and stability. Two gaps remain: the need for digital platforms that coordinate operations across sectors and borders, and the lack of a rigorous framework on their cost-benefit profile. This paper presents a comprehensive cost-benefit analysis (CBA) of an AI-driven operational digital platform (ODP) designed for holistic, cross-sectoral and cross-border optimization. The ODP targets improvements in energy efficiency, grid reliability, and environmental sustainability. We develop a seven-step CBA framework that links each benefit stream to the platform's architecture and quantifies incremental gains relative to the existing system, while accounting for investment and operating costs. The framework is demonstrated with case studies for Austria, Hungary, and Slovenia. Results indicate a benefit-cost ratio (BCR) of about 1.41 and a net present value (NPV) exceeding euro 356 million over 2026-2035. Robustness is examined through extensive sensitivity analyses that vary discount rates, cost components, and adoption trajectories, as well as through Monte Carlo simulations that capture uncertainty in BCR, NPV, data availability, and AI accuracy. The findings support the viability of investing in digital platforms for cross-sectoral and cross-border integration, and highlight the role of ODPs in advancing decarbonization and efficiency in the mobility-power nexus.

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  • Arega Getaneh Abate & Xiaobing Zhang & Xiufeng Liu & Dogan Keles, 2025. "Cost-benefit analysis of an AI-driven operational digital platform for integrated electric mobility, renewable energy, and grid management," Papers 2506.20631, arXiv.org, revised Oct 2025.
    • Handle: RePEc:arx:papers:2506.20631
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