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

The fast-growing popularity of electric vehicles
(EVs) poses complex challenges for the existing power grid
% [8 x* N$ y- N; Y4 linfrastructure to meet the high demands at peak charging hours.
Discovering and transferring energy amongst EVs in mobile
9 @+ ^5 ?, N. s' C# svehicular edges and fogs is expected to be an effective solution for
2 ]. I' C$ [! {2 |# D3 T  j4 [bringing energy closer to where the demand is and improving the
scalability and flexibility compared to traditional charging
- e& }" e2 ^+ I9 I0 y% P; e# |+ z$ Msolutions. In this paper, we propose a fully-distributed energy
aware opportunistic charging approach which enables distributed
) c' m: j- U+ B* L- ?/ Pmulti-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
algorithm formulated as a finite Markov Decision Process. We
$ D2 w$ T; I; mdefine 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
/ O9 h$ S9 b: q4 W4 }predictive analytics. This allows SmartCharge edge nodes to more
. D! p0 H6 n" C2 r- g9 q3 jaccurately capture, predict and adapt to dynamic spatial-temporal
' H. g$ C7 h; C" Ienergy supply and demand as well as mobility patterns when
energy peaks are expected. More specifically, SmartCharge edge
+ i# H* A+ J7 [; |) ^, Tnodes are able to autonomously and collaboratively understand
when (how soon) and where the geo-temporal peaks are expected
4 a0 C4 n: \4 [+ F8 t; Mto happen, thus enable better local prediction and more accurate
global distribution of energy resources. We provide multi-criteria
9 W9 A8 x8 ?3 `& g: }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
1 j, G! Q2 s  R& O: P" F  m0 F  n2 @9 opredicts and mitigates congestion in peak charging hours, reduces
3 g4 n  ]" h& K9 t2 pthe waiting time between vehicles sending energy demand requests
8 O9 Z  N0 I, eand being successfully charged as well as significantly reduces the
- x8 {  E' j8 V2 C5 Gtotal number of vehicles in need of energy.

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