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

Networks

The fast-growing popularity of electric vehicles
- a0 D6 g( B8 O+ O# ]* L(EVs) poses complex challenges for the existing power grid
infrastructure to meet the high demands at peak charging hours.
Discovering and transferring energy amongst EVs in mobile
( }2 ?) n7 {$ t8 _/ s' `; qvehicular edges and fogs is expected to be an effective solution for
; b# Z9 ~% V; D3 \% u9 x) Ybringing energy closer to where the demand is and improving the
scalability and flexibility compared to traditional charging
solutions. In this paper, we propose a fully-distributed energy
aware opportunistic charging approach which enables distributed
$ x' ?) i+ k5 C% wmulti-layer adaptive edge cloud platform for sustainable mobile
) P1 G; ]" Y$ Fautonomous vehicular edges which host dynamic on-demand
virtual edge containers of on-demand services. We introduce a
novel Reinforcement Learning (Q-learning) based SmartCharge
algorithm formulated as a finite Markov Decision Process. We
define multiple edge energy states, transitions and possible actions
of edge nodes in dynamic complex network environments which
are adaptively resolved by multilayer real-time multidimensional
predictive analytics. This allows SmartCharge edge nodes to more
( q' h2 x3 L; r1 Baccurately capture, predict and adapt to dynamic spatial-temporal
# B- G, s1 g% I! Denergy supply and demand as well as mobility patterns when
energy peaks are expected. More specifically, SmartCharge edge
nodes are able to autonomously and collaboratively understand
3 h, H+ R: A- _" T0 J- [4 gwhen (how soon) and where the geo-temporal peaks are expected
to happen, thus enable better local prediction and more accurate
' |; P6 U" [. _global distribution of energy resources. We provide multi-criteria
evaluation of SmartCharge against competitive protocols over
real-world San Francisco Cab mobility traces and in the presence
of real-world users’ energy interest traces driven by Foursquare
San Francisco dataset. We show that SmartCharge successfully
8 V6 ], d1 c6 _/ ?7 J, ?predicts and mitigates congestion in peak charging hours, reduces
, T% R" {/ n( u$ }9 J3 q5 Ythe waiting time between vehicles sending energy demand requests
% S4 \' i) f7 _" `and being successfully charged as well as significantly reduces the
' p+ S7 E% R! F% ~0 e9 {; S9 z4 {total number of vehicles in need of energy.



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