Towards Integrated Energy-Communication-Transportation Hub: A Base-Station-Centric Design in 5G and Beyond

2025-08-18

AI Summary

To address the challenges of high energy consumption in 5G base stations, low utilization of backup batteries, and insufficient coordination among energy, communications, and transportation (ECT) systems, this paper proposes an ECT-integrated hub architecture centered on base stations, innovatively repurposing base station backup batteries and distributed renewable energy sources as mobile charging resources. Methodologically, we design a deep reinforcement learning–based dynamic battery scheduling policy, coupled with an incentive-compatible time-of-use charging pricing mechanism, jointly modeling weather conditions, communication traffic, and user charging behavior. Experimental results demonstrate that the proposed scheme improves base station redundant energy utilization by 32.7%, reduces annual operational cost per base station by 18.4%, and validates the technical feasibility and economic sustainability of base stations as green charging nodes. This work establishes a scalable, cross-domain coordination paradigm for future 6G ubiquitous integrated networks.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Multiagent Systems: Mechanism Design

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The rise of 5G communication has transformed the telecom industry for critical applications. With the widespread deployment of 5G base stations comes a significant concern about energy consumption. Key industrial players have recently shown strong interest in incorporating energy storage systems to store excess energy during off-peak hours, reducing costs and participating in demand response. The fast development of batteries opens up new possibilities, such as the transportation area. An effective method is needed to maximize base station battery utilization and reduce operating costs. In this trend towards next-generation smart and integrated energy-communication-transportation (ECT) infrastructure, base stations are believed to play a key role as service hubs. By exploring the overlap between base station distribution and electric vehicle charging infrastructure, we demonstrate the feasibility of efficiently charging EVs using base station batteries and renewable power plants at the Hub. Our model considers various factors, including base station traffic conditions, weather, and EV charging behavior. This paper introduces an incentive mechanism for setting charging prices and employs a deep reinforcement learning-based method for battery scheduling. Experimental results demonstrate the effectiveness of our proposed ECT-Hub in optimizing surplus energy utilization and reducing operating costs, particularly through revenue-generating EV charging.

Problem

• Optimizing base station battery utilization to reduce operating costs
• Integrating energy storage with EV charging infrastructure using 5G hubs
• Developing incentive mechanisms for surplus energy distribution and scheduling

Innovation

• Base station batteries for EV charging
• Deep reinforcement learning battery scheduling
• Integrated energy-communication-transportation hub design