City Research Online

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.

Publication Type:	Article
Additional Information:	For the purpose of open access, the author(s) has applied a Creative Commons Attribution (CC BY) license to any Accepted Manuscript version arising.
Publisher Keywords:	EV, base station, backup batteries, disaster
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TL Motor vehicles. Aeronautics. Astronautics
Departments:	School of Science & Technology School of Science & Technology > Department of Computer Science
SWORD Depositor:	Symplectic Administrator

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