基于计算热点转移的5G车联网能量实时协同管理策略

doi:10.19595/j.cnki.1000-6753.tces.231896

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Abstract

With the development of smart vehicle network promoted by China, the number of electric vehicle (EV) and 5G base station (BS) is increasing rapidly, while also bringing huge operation pressure and energy consumption to power grid and network operator, respectively. Therefore, how to coordinate the flexible resource of EV and BS to reduce electric load peak for power grid are important issues. At the same time, as EV equipping with powerful chip, BS can transfer some tasks to idle parked EV for cut down energy consumption of BS. However, due to the communication of EV is coupling with 5G BS, additional communication delay caused by BS energy management will affect the ordered charging scheduling signal between power grid and EV, increasing charging cost of EV. In addition, if 5G BS provided low delay communication for EV, it will bring more energy consumption. Moreover, the optimization of air condition load of BS is still lack of attention, which energy consumption is related with computing hotspot. To this end, this paper establishes a Stackelberg game based online energy management strategy faced to 5G vehicle network, aiming at minimizing the time average battery charging cost of EV and electricity purchasing cost of BS.
First of all, considering vehicle computing characteristics, and the coupling between hot load of air condition and computing tasks, a task migration model between 5G BS and EV is established to transfer the hot load and electric load of BS. Secondly, according to the analysis of communication delay to EV ordered charging, this paper proposes a calculation method of EV real charging capacity considering 5G wireless communication delay, and a communication delay penalty function for 5G network operator. Thirdly, based on above model, considering the stochastic events including task arrival and renewable energy, this paper investigates a long-term average electricity purchasing cost of BS and EV charging cost minimizing problem. Finally, an improved Lyapunov optimization method is proposed to transform the problem to a real time optimization problem. The real time problem can be divided into one upper layer subproblem and two lower layer subproblems, including computing electricity and battery (dis)charging problem, EV charging and communication resource allocation problem, and Stackelberg game-based computing hotspot transferring and BS air condition load control problem, respectively. By introducing an incentive function for EV accepting tasks of BS, the existence of the Nash equilibrium is proved, and this paper gives a close form solution on computing hotspot transferring. Meanwhile, other subproblems can be solved by successive convex approximation method.
In this paper, four scenarios are designed for verifying the performance of our proposed strategy, and analyze the impact of network scale, random task traffic, control parameter, and EV computing capacity on EV charging and BS electricity cost optimization. The simulation results show that proposed method can reduce electricity purchasing cost for both EV and BS without prior knowledge, and the hot load of BS is cut down without adding extra charging cost of parked EV. The following conclusions can be drawn from the simulation analysis: 1) The cooperation between EV and 5G BS can optimize the distribution of energy and information flow, reduce the energy consumption caused by cooling computing hotspot of BS. 2) By introducing improved Lyapunov optimization method, the network resource and energy scheduling of 5G BS and EV can be made online, and the resource scheduling result will not break the backup energy constraint and communication quality.

Key words： 5G edge computing electric vehicle energy management Lyapunov optimization hotspot transfer Stackelberg game

Received: 04 November 2023

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TM93

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	Chen Kai
	Fu Yu
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	Yang Hongyue

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Chen Kai,Fu Yu,Sun Yi等. Online Energy Management Strategy for 5G-Vehicle Network based on Computing Hotspot Transfer[J]. Transactions of China Electrotechnical Society, 0, (): 231896-231896.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.231896 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/231896

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