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

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The fast-growing popularity of electric vehicles
(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
vehicular edges and fogs is expected to be an effective solution for
bringing 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
: x; n' v' T1 Saware opportunistic charging approach which enables distributed
multi-layer adaptive edge cloud platform for sustainable mobile
autonomous vehicular edges which host dynamic on-demand
7 K7 e: C/ U( r4 h" t+ F3 J9 S: Tvirtual edge containers of on-demand services. We introduce a
8 d7 b! ?2 s- U0 O6 Mnovel Reinforcement Learning (Q-learning) based SmartCharge
9 L' U% G4 [- Z- u4 c; Q' Calgorithm formulated as a finite Markov Decision Process. We
. ]2 ^  E+ ?+ wdefine multiple edge energy states, transitions and possible actions
- K' G. ~0 p4 \) _, _8 r2 r# z3 oof 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
1 B7 y% g4 A0 o/ S1 M6 y, H" qaccurately capture, predict and adapt to dynamic spatial-temporal
8 D, i4 J- O5 h- f/ l' }energy 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
when (how soon) and where the geo-temporal peaks are expected
5 H; t/ e, x/ h; u: i  @to happen, thus enable better local prediction and more accurate
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
predicts and mitigates congestion in peak charging hours, reduces
( }* j. [- x0 k4 ]% x2 b! n! i3 u+ hthe waiting time between vehicles sending energy demand requests
# l- P  r( b9 Q4 t- F3 V: J% z) c$ Tand being successfully charged as well as significantly reduces the
- X; T% f7 B" `+ A, ktotal number of vehicles in need of energy.


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