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

杨利霞

Energy-Aware Opportunistic Charging and Energy

Distribution for Sustainable Vehicular Edge and Fog

Networks

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The fast-growing popularity of electric vehicles
(EVs) poses complex challenges for the existing power grid
* |& a5 G: S3 J3 }3 G! E; [infrastructure to meet the high demands at peak charging hours.
7 ^. C7 h6 f  o3 l5 X3 gDiscovering 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
) U; [/ m% j' [0 Q! Escalability and flexibility compared to traditional charging
solutions. In this paper, we propose a fully-distributed energy
, ?8 ^2 K# N+ J6 _! Gaware opportunistic charging approach which enables distributed
multi-layer adaptive edge cloud platform for sustainable mobile
+ z6 D: O8 z4 w# ~( p+ g( _% Qautonomous vehicular edges which host dynamic on-demand
2 \2 t! U) f6 r* ]) wvirtual edge containers of on-demand services. We introduce a
% `+ g% j8 H" Fnovel Reinforcement Learning (Q-learning) based SmartCharge
algorithm formulated as a finite Markov Decision Process. We
# ]) R* ?, O, O% {define multiple edge energy states, transitions and possible actions
$ K6 Z) \: E9 A: Y" s- a5 pof edge nodes in dynamic complex network environments which
" x3 g# [8 Z. x+ L! E. Hare adaptively resolved by multilayer real-time multidimensional
0 y4 \4 g* c& j  zpredictive analytics. This allows SmartCharge edge nodes to more
accurately capture, predict and adapt to dynamic spatial-temporal
energy supply and demand as well as mobility patterns when
' {8 ~0 \; ^( O4 E6 ]& j4 z* @/ D) {energy peaks are expected. More specifically, SmartCharge edge
nodes are able to autonomously and collaboratively understand
5 x; r* f  s9 G1 w) ~/ z' [- Q$ iwhen (how soon) and where the geo-temporal peaks are expected
to happen, thus enable better local prediction and more accurate
- d7 A" v( B  M- b3 D( cglobal distribution of energy resources. We provide multi-criteria
evaluation of SmartCharge against competitive protocols over
0 R+ d- u& z' oreal-world San Francisco Cab mobility traces and in the presence
, p0 y, E: Z0 u/ Kof real-world users’ energy interest traces driven by Foursquare
San Francisco dataset. We show that SmartCharge successfully
predicts and mitigates congestion in peak charging hours, reduces
8 i5 L0 i( c" c) `the waiting time between vehicles sending energy demand requests
" z* e# T  J# b( P  L; b5 S$ nand being successfully charged as well as significantly reduces the
total number of vehicles in need of energy.



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