Energy-Aware Opportunistic Charging and Energy Distribution for Sustainable ...

杨利霞

Energy-Aware Opportunistic Charging and Energy

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Distribution for Sustainable Vehicular Edge and Fog

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Networks

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6 ^. r3 d* M1 {& \/ DThe fast-growing popularity of electric vehicles
(EVs) poses complex challenges for the existing power grid
0 T( A( r% z. Y' P* h7 Zinfrastructure to meet the high demands at peak charging hours.
Discovering and transferring energy amongst EVs in mobile
vehicular edges and fogs is expected to be an effective solution for
bringing energy closer to where the demand is and improving the
; P9 [+ V; ~" d$ ^$ r7 F6 zscalability and flexibility compared to traditional charging
solutions. In this paper, we propose a fully-distributed energy
! M" ^% Y3 ^1 @$ c( u  S/ ?aware opportunistic charging approach which enables distributed
$ R( h  E, O8 b9 \4 B8 amulti-layer adaptive edge cloud platform for sustainable mobile
autonomous vehicular edges which host dynamic on-demand
virtual edge containers of on-demand services. We introduce a
novel Reinforcement Learning (Q-learning) based SmartCharge
; G, }( {, g# M4 q5 A5 p' J5 F, jalgorithm formulated as a finite Markov Decision Process. We
4 ?$ K9 U. M7 \/ u/ q# U( P: Ydefine multiple edge energy states, transitions and possible actions
8 ]( ^) u8 ^" i8 Z" ~of edge nodes in dynamic complex network environments which
/ [4 [$ H% K' F6 A, D0 {are adaptively resolved by multilayer real-time multidimensional
# _1 B0 v# `9 b3 b5 K$ O# Epredictive analytics. This allows SmartCharge edge nodes to more
accurately capture, predict and adapt to dynamic spatial-temporal
& s9 l& h+ {, C/ Qenergy supply and demand as well as mobility patterns when
- z( n- w9 p2 @* \energy peaks are expected. More specifically, SmartCharge edge
2 z$ U3 ]5 q* e6 P# ^+ @4 m5 @0 Xnodes are able to autonomously and collaboratively understand
when (how soon) and where the geo-temporal peaks are expected
! c) H) H- X- u% M7 P3 Uto happen, thus enable better local prediction and more accurate
' P/ m  ?$ U* x5 [* _1 hglobal distribution of energy resources. We provide multi-criteria
evaluation of SmartCharge against competitive protocols over
7 M9 j, l# g; J" A& ^6 h+ N/ p  Dreal-world San Francisco Cab mobility traces and in the presence
3 D3 [! @; u( h; A8 Oof real-world users’ energy interest traces driven by Foursquare
San Francisco dataset. We show that SmartCharge successfully
) _- J* y  r1 Z" }+ _( Y0 g6 _predicts and mitigates congestion in peak charging hours, reduces
the waiting time between vehicles sending energy demand requests
9 P* T* l" R1 |, Kand being successfully charged as well as significantly reduces the
  p9 e9 \" L4 W) f" ?/ \, ?total number of vehicles in need of energy.
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