Ayaz, Ferheen, Nekovee, Maziar, Sheng, Zhengguo and Saeed, Nagham ORCID: https://orcid.org/0000-0002-5124-7973 (2025) Digital Twin based reinforcement learning for energy exchange among electric vehicles and base stations in a disaster-affected region. IEEE Transactions on Intelligent Transportation Systems. ISSN 1524-9050 (In Press)

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Abstract

The cellular base stations (BSs) have backup batteries to maintain uninterrupted power supply. Recent studies have shown that a backup battery may have some spare energy to act as a flexible resource in the power system. Similarly, electric vehicles (EVs) are also capable to give surplus energy stored in their batteries to other consumers or back to the grid. Therefore, both BSs and EVs can also effectively share energy among themselves through Telecom-to-Vehicle (T2V) and Vehicleto-Telecom (V2T) exchange. However, it is difficult for BSs and EVs to exchange their energies in a disaster-affected region as they may encounter challenges such as connectivity failures, power disruption and damaged routes. This paper proposes an energy exchange solution among BSs and EVs in a post disaster situation. We propose a digital-twin (DT) based solution which utilizes Artificial Intelligence (AI) algorithms to estimate energy consumption of BSs and EVs and identifies their role as energy buyers or sellers. It also models power disruption and disaster-affected blocked routes as Markov processes with parameters derived from real historic data of floods. Then, a reinforcement learning (RL) algorithm is proposed to match BSs and EVs which can feasibly take part in either T2V or V2T exchange. Performance of the proposed solution is compared with independent RL without DT and assisted by federated learning. Simulations show that the DT-based RL results in averagely twice the amount of energy being exchanged as compared to the only RL algorithm run by EVs.

Item Type:	Article
Additional Information:	© 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Keywords:	EV, base station, backup batteries, disaster
Subjects:	Construction and engineering
Depositing User:	Nagham Saeed
Date Deposited:	10 Sep 2025 09:53
Last Modified:	10 Sep 2025 10:00
URI:	https://repository.uwl.ac.uk/id/eprint/14058

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