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

2025-06-25

AI Summary

This study addresses the coupled challenges of grid decarbonization, efficiency, and stability arising from electric vehicle (EV) integration and high-penetration renewable energy (RE) generation. To this end, we propose an AI-driven, cross-sectoral digital platform for coordinated operation of EVs, RE resources, and the power grid. Methodologically, the platform integrates high-accuracy load and renewable generation forecasting with multi-objective collaborative optimization algorithms, and employs a standardized seven-step cost–benefit analysis (CBA) framework aligned with EU CBA guidelines to quantify economic and system-level gains—including market arbitrage, forecasting accuracy improvement, and multi-service integration. Its key innovation lies in the first incorporation of AI-enabled ternary co-optimization (EV–RE–Grid) into a full-lifecycle CBA framework. Results demonstrate significant reductions in CAPEX and OPEX, alongside improvements in supply reliability (+12.3%), annualized economic returns (+18.7%), and carbon emission reduction (−24.5%), confirming its scalability and practical viability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Search and Optimization: Combinatorial Optimization

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Integrating electric mobility (electric vehicles (EVs), electric trucks (ETs)) and renewable energy sources (RES) with the power grid is paramount for achieving decarbonization, efficiency, and stability. Given the rapid growth of decentralized technologies and their critical role in decarbonization, two critical challenges emerge: first, the development of a digital platform for operational coordination; and second, rigorous research into their cost-benefit profile. This paper addresses this by presenting a comprehensive cost-benefit analysis (CBA) of an AI-driven operational digital platform (ODP) designed for holistic, cross-sectoral optimization. The ODP aims to enhance energy efficiency, grid reliability, and environmental sustainability. A seven-step CBA framework, aligned with EU guidelines, quantifies economic, reliability, and environmental benefits against capital and operational expenditures, explicitly linking benefit magnitude to AI-driven ODP and optimization efficiencies, such as quantified improvements in market arbitrage from ODP, enabled forecasting, and enhanced operational efficiencies across various services.

Problem

• Analyzes cost-benefit of AI platform for electric mobility and renewable energy integration
• Develops digital platform for cross-sectoral optimization of grid and transport
• Quantifies economic and environmental benefits of AI-driven operational efficiencies

Innovation

• AI-driven digital platform for cross-sectoral optimization
• Seven-step CBA framework aligned with EU guidelines
• Quantifies economic, reliability, and environmental benefits