Mobile Edge Computing Assisted Green Scheduling of On-Move Electric Vehicles
نویسندگان
چکیده
Mobile edge computing (MEC) has been proposed as a promising solution, which enables the content processing at edges of network helping to significantly improve quality experience (QoE) end users. In this article, we aim utilize MEC facilities integrated with time-varying renewable energy resources for charging/discharging scheduling known green on-move electric vehicles (EVs) in geographical wide area comprising multiple charging stations (CSs). system, demands and contextual information EVs are first transmitted nearby servers. With instantaneous electricity load/pricing availability CSs collected by aggregators, weighted social-welfare maximization problem is then solved using greedy-based algorithms choose best CS EV’s service. From system point view, our results reveal that compared cloud-based scheme, MEC-assisted improves complexity burden, boosts satisfaction EVs’ drivers localizing traffic CSs, further helps efficiently across CSs. Furthermore, algorithm, utilizes internal updating heuristics, outperforms some baseline solutions terms social welfare power grid ancillary services.
منابع مشابه
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ژورنال
عنوان ژورنال: IEEE Systems Journal
سال: 2022
ISSN: ['1932-8184', '1937-9234', '2373-7816']
DOI: https://doi.org/10.1109/jsyst.2021.3084746