An App-based Algorithmic Approach for Harvesting Local and

Renewable Energy using Electric Vehicles

Antoine Dubois, Antoine Wehenkel, Raphael Fonteneau, Fr

eric Olivier and Damien Ernst

Department of Electrical Engineering and Computer Science, University of Li

ege,

All

ee de la D

ecouverte, 10, 4000 Li

ege, Belgium

Keywords:

Multi-agent System, Electric Vehicles, Renewable Energy.

Abstract:

The emergence of electric vehicles (EVs), combined with the rise of renewable energy production capacities,

will strongly impact the way electricity is produced, distributed and consumed in the very near future. This

position paper focuses on the problem of optimizing charging strategies for a ﬂeet of EVs in the context where

a signiﬁcant amount of electricity is generated by (distributed) renewable energy. It exposes how a mobile

application may offer an efﬁcient solution for addressing this problem. This app can play two main roles.

Firstly, it would incite and help people to play a more active role in the energy sector by allowing photovoltaic

(PV) panel owners to sell their electrical production directly to consumers, here the EVs’ agents. Secondly, it

would help distribution system operators (DSOs) or transmission system operators (TSOs) to modulate more

efﬁciently the load by allowing them to inﬂuence EV charging behaviour in real time. Finally, the present

paper advocates for the introduction of a two-sided market-type model between EV drivers and electricity

producers.

1 INTRODUCTION

The past decade has seen a steep rise in the devel-

opment of renewable energy production capacities,

mainly driven by the willingness to (i) reduce pollu-

tion and greenhouse gas emissions and (ii) limit the

dependency on fossil fuels. In addition, electric ve-

hicles (EVs) are now emerging rapidly. The conjunc-

tion of these factors may be an opportunity to set the

basis of a joint optimization approach for charging

EVs with regard to the ﬂuctuation of renewable en-

ergy production. This has already been the topic of

academic research in the past decade (see (Palensky

et al., 2013) for a comprehensive review). In partic-

ular, it has been shown that EVs may be efﬁciently

used in order to balance the load within distribution

networks (Caramanis and Foster, 2009). Also, vehi-

cle charging can be inﬂuenced by dynamically adapt-

ing tariffs as shown in (O’Connell et al., 2011). Even

though these results are of signiﬁcant importance,

none of them propose a convenient way to apply them

to real situations.

In this paper, we focus on the problem of optimiz-

ing the charge of a ﬂeet of EVs in a context where it is

important to match, at best, local energy consumption

and renewable electricity production. We argue that a

mobile application would be an efﬁcient and elegant

support to deploy algorithms to dispatch EVs to elec-

tricity sources. In Section 2, we ﬁrst explain how this

app should be designed. In Section 3, we describe a

few problems that may be efﬁciently addressed by ad-

hoc algorithms integrated into the app. Section 4 lists

a few generic algorithmic techniques that could be

used for addressing decision-making problems within

the app. Finally, Section 5 concludes.

2 MOBILE APPLICATION

DESCRIPTION

The combination of (mobile) EVs and (static) elec-

tricity sources is seen as a multi-agent system, where

mobile agents (the EVs) should gather, at best, re-

newable energy under travel distances and electricity

production ﬂuctuations constraints. On the one hand,

electricity sources are static, but electricity production

is ﬂuctuating, depending on solar irradiation or wind

speed. On the other hand, EVs are mobile, but are

subject to travel constraints.

One main objective is to allocate electricity

sources to EVs based on geographical parameters

(starting point, destination, etc.). A second aspect

322

Dubois A., Wehenkel A., Fonteneau R., Olivier F. and Ernst D.

An App-based Algorithmic Approach for Harvesting Local and Renewable Energy using Electric Vehicles.

DOI: 10.5220/0006250803220327

In Proceedings of the 9th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2017), pages 322-327

ISBN: 978-989-758-219-6

is to take into account the fact that several EVs may

compete for the same electricity source. So, it ap-

pears that the ﬂeet of should be coordinated in order

to attain a consensus so that the amount of electricity

harvested by the whole ﬂeet of EVs is maximized. In

addition to this, one should take into account the fact

that a speciﬁc subclass of sources of electricity may

be used to rapidly charge EVs, thus offering the op-

portunity to make a stop when travelling, or even to

take a small detour, in order to get to a fast-charging

station.

The app should be an efﬁcient tool for reach-

ing such a consensus among the static and dynamic

agents. To this end, we suggest building an app in the

form of a two-sided market platform.

2.1 Two-sided Market

One main role of the app is to create a convenient way

for PV panel owners to share (and sell) their electric-

ity to EV drivers. This is particularly important, since

the electrical network has not been designed to absorb

electricity production from private dwellings. For ex-

ample, during solar production peaks, PV panels may

have to be disconnected because of overvoltages on

the network (see e.g., (Olivier et al., 2016)). Neither

has the electric network been designed to supply a

sufﬁcient amount of power sufﬁcient to fully recharge

a large ﬂeet of EVs in a few hours within a neighbour-

hood.

Consequently, on one side we have PV panels

owners that may want to ﬁnd a way to proﬁtably ex-

ploit their installation, and on the other side we have

EV drivers who may be constrained to charge their

vehicles while they are occupied by other tasks. The

app is aimed at satisfying both requirements and to

make it easy for each sides’ to beneﬁt from the other

needs.

2.2 Driver Services

To be successful, the app must be attractive to EV

drivers. For this purpose, a booking service will be

provided to guarantee a charge level for the drivers.

Moreover, the app intelligence should be able to pro-

pose a ﬂexible and suitable choice of charging points

depending on the user needs.

One way for the app to output convenient charging

point suggestions would be to specify journeys using

a set of geographical points. The simplest case would

be to specify the starting point (A) and the destina-

tion (B). In this conﬁguration, the app would compute

the optimal stations at which the driver should stop as

they travel from A to B, knowing the level of charge,

consumption and battery autonomy of the vehicle. A

more-advanced case would be to specify three geo-

graphical points: the starting point (A), a stop (B)

and a ﬁnal destination (C), as well as the time dura-

tion spent at location B, where it may be possible to

recharge the EV. Taking all these parameters into ac-

count, plus eventually additional constraints that the

drivers may have, it would suggest the best stations

for drivers where they should stop during the jour-

ney. Obviously, suggestions should also be optimized

in order to minimize energy costs and/or curtailment.

As the app interacts with a ﬂeet of EVs and not just

one single EV, it may also be interesting to optimize

charging station suggestions globally rather than on a

single EV basis. This would lead to a better solution

at the level of an EV ﬂeet but may penalize some EV

owners due to a possible lack of fairness of the global

solution. This fairness issue could be addressed by

implementing a compensation mechanism. It could

consist in monetary terms, but also in other advan-

tages like free charge or booking priority.

2.3 Producer Services

Producers can be divided into two categories: compa-

nies and individuals. Companies can either be large

renewable energy source owners, DSOs or substantial

charging-point owners, whereas the individuals con-

sist mainly of private owners of PV units, with a nom-

inal power less than 10 kWp, and who also want to

sell the electricity they produce.

We may reasonably assume that, in many cases,

EV users may want to book a charging station,

through the app, that has a signiﬁcant charging power

(e.g., higher than 20 kW). This may penalize individ-

ual charging stations, which, if they want to sell their

green electricity, are limited to the power produced

by their PV installation. However, we can reason-

ably assume that in the coming years, with the rise

of batteries, these individual producers should be able

to store their excess of green electricity. In such a

context, during certain periods of the day, they could

offer a charging power close to the power of the PV

installation plus the power that their batteries can de-

liver. Indeed, with a 10 kWp PV installation, an indi-

vidual producer will not be able to sell many green

charges per week due to the rather limited amount

of energy that such an installation can produce. In-

deed, if we assume a load factor of 8.9% for the PV

installation, the average European load factor for PV

(Energy - Yearly statistics 2008 (Eurostat)), the 10

kWp PV installation would produce on a daily ba-

sis 10 × 0.089 × 24 = 21.36 kWh. So, at most, only

once every three days, the individual producer will be

An App-based Algorithmic Approach for Harvesting Local and Renewable Energy using Electric Vehicles

323

able to sell a full fast charge to an EV with a 60 kWh

battery. This illustrates why individual charging may

have a low level of availability.

The app could offer many services to the owners

of the charging stations. It would include a booking

system, but also other services based, for example, on

data to manage prices and attractiveness of the charg-

ing points. More speciﬁcally, the app could allow

for the efﬁcient retrieval of data about those stations

(such as price, power, etc.). From these data, it would

be able to carry out an analysis in order to establish

how pricing can inﬂuence the attractiveness of charg-

ing points. Based on such an analysis, the app could

also help the producers to better manage their charg-

ing stations, by having, for example, dynamic pricing

to adapt demand to production. It could also help in-

dividual users to manage their batteries and the ﬂex-

ible loads that they may have at home (e.g., washing

machine, heat pump, etc.).

3 SUPPORTED UNDERLYING

PROBLEMS

In addition to creating a two-sided market, a mo-

bile application could also provide a set of services

for addressing underlying problems associated with

EV charging and network management. We believe

that the opportunity to solve these problems via the

app may be a game changer in the electricity sector,

mainly due to the app’s ability to enable producers

and consumers react quickly and in a very ﬂexible

way.

3.1 Booking Service

In the short-term, congestion problems are going to

occur close to well-located charging points as a full

recharge typically takes at least 30 minutes with su-

perchargers, and more generally many hours with

classic chargers. As a result, we can expect that know-

ing in advance whether a charging station will be

available or not may probably become one of the main

problems for EV drivers. For now, no suitable solu-

tion to address this problem has emerged, although

most EV drivers have already experienced this incon-

venience.

The app’s booking service would be a solution to

this problem, and it should work as follow: Firstly,

stations will only be bookable a short period in ad-

vance (say, for example, 24 hours), and this for sev-

eral reasons. For example, if someone could book a

station for an entire year, this would lead to a lack

of attractiveness of the app for new users. As an-

other example: the app intelligence will be respon-

sible for organizing a fair distribution of charging sta-

tions among users to avoid the same type of problem.

Indeed, we want to avoid situations where far-sighted

users would always book the most attractive stations,

at the expense of other users. Actually, two main rea-

sons motivate this proposal: on the one hand, the app

must remain attractive by providing each type of user

with a good suggestion, while on the other hand, one

of the main goals of the app is to retain and maintain

as much ﬂexibility as possible in the demand for elec-

tricity.

We propose two solutions to maintain ﬂexibility of

the demand while allowing charging station booking:

• The ﬁrst solution is to adapt electricity pricing.

By booking a charging station suggested by the

app just prior to stopping to charge their EV, the

driver is more likely to offer a valuable ﬂexibil-

ity service to the network (provided that the app

is smart enough) that should be rewarded, for in-

stance, with a reduction in the cost of the charge.

On the other side of the coin, the early booking

driver puts advance constraints in advance on the

network that may not match renewables produc-

tion (corresponding to the situation where the bid

is too early compared, for example, to the the time

horizon at which it is possible to get accurate pre-

dictions of renewable production). Thus such a

user would have to pay an additional fee for an

early booking. It is important to note that the fron-

tier between early and late booking is not clearly

deﬁned a priori. However, one could imagine de-

signing learning algorithms that are able to clas-

sify users’ behaviour according to the service they

are actually offering to the network.

• In the second solution, the booking transactions

could be stored in blocks, which would only

be accepted after a certain time, thus allowing

more ﬂexibility in terms of charging station al-

location. This solution could be based on dis-

tributed ledgers (such as blockchain technology

(Nakamoto, 2008)) associated with smart con-

tracts

which could be used to determine the con-

Smart contracts (also called self-executing contracts,

blockchain contracts, or digital contracts) are simply com-

puter programs that act as agreements where the terms of

the agreement can be coded in advance with the ability to

self-execute and self-enforce themselves. This code deﬁnes

the rules and consequences in the same way that a tradi-

tional legal document would, stating the obligations, bene-

ﬁts and penalties which may be due to either party in various

different circumstances. This code can then be automati-

cally executed by a distributed ledger system.

ICAART 2017 - 9th International Conference on Agents and Artiﬁcial Intelligence

324

ditions of the transactions without passing by a

third party, so adding more dynamism and auton-

omy to the app.

Finally, a problem which rapidly comes to mind

when proposing the idea of a global booking service

is the difﬁculty to control whether bookings are re-

spected. Indeed, unless you own every single station

that you propose to drivers, you cannot guarantee that

a station booked by one driver will not be occupied by

another driver that is not using the app. The solution

we propose amounts to creating a global booking ser-

vice, which would not be part of the one single app,

but should also be available for other app creators. It

would retain all the booking data about each station.

Station owners would just have to rely on this tool to

manage the booking around their stations. The tool

could probably have some speciﬁc features and pro-

pose some additional services but this is not the sub-

ject we want to explore here.

3.2 Demand Management and Dynamic

Pricing

Managing the demand is probably one of the best as-

sets of the app. By maintaining a certain degree of

ﬂexibility in booking, the app allows to better cor-

relate local consumption with production by making

EVs charge close to energy sources. As shown in

(O’Connell et al., 2011), a dynamic pricing system

could allow the app to boost the demand in order to

follow, more closely, the production. Moreover, with

smart chargers (i.e. chargers that may automatically

change the amount of power they output) and accu-

rate weather forecasting, we can hope to ﬁt, exactly,

the (over)production to the EV charging by allocating

each vehicle to an appropriate station for a precise du-

ration of time.

To allow for such a load management, a conve-

nient dynamic pricing system must be implemented.

It therefore has to allow producers to keep a hand

on the demand simply by raising or lowering charg-

ing prices. For instance, they could lower prices at

overproduction times to increase demand and do the

opposite when domestic consumption is peaking, or

when the level of renewable energy production is low.

This will all be done through the app that should help

charging point owners to understand the inﬂuence of

these pricing choices. For instance, the app could pro-

vide estimations of the demand as a function of the

price chosen for one charging point.

3.3 Data Mining

Another strength of the app is the ﬂow of data passing

through it and that can be stored. These data con-

sist mainly of information about drivers trips (travel

time, distance, ...) or the performance of EVs (mean

consumption, charge level variations, ...). More gen-

erally, we can summarize this information as when,

where, and at which frequency EV drivers charge

their cars and how it inﬂuences the vehicles’ perfor-

mances, the vehicles’ ﬂeet dynamics, and the network

load. This information may be of interest to a num-

ber of actors in the automotive and energy industries.

Here is a short list of such interested actors, as well as

of their potential use of the app’s data:

• Drivers: The drivers themselves would be the

ﬁrst to beneﬁt from these data through a self-

learning improvement of the app’s dispatching

strategy based on previous results.

• Car manufacturers: More practically, EV

real-time performances could help manufacturers

identify key points for future improvements.

• Producers: Forecast of the density of trafﬁc at

different locations, at different times could help

producers apply an appropriate dynamic pricing

strategy.

• Electricity Networks: These same forecasts,

combined to renewable energy production pre-

dictions, can be used by TSOs/DSOs to ﬁnd

methods to balance the load and prevent over-

loads/overvoltages. Dynamic pricing can be used

in two different schemes: (i) it can inﬂuence the

time at which EV users decide to charge their cars,

or (ii) it can be used to orientate the EV users to

the right charging station where they would not

cause any problems, or even be beneﬁcial to the

network.

3.4 Shared Economy Model

EV drivers can already use a panel of apps help-

ing them to ﬁnd information about charging sta-

tions that they could possibly use (PlugShare

,

ChargeBump

, NextCharge

). However, all of

them work with a one-view driver-oriented system.

An advantage of our app would be its multi-view or-

ganization aiming to be proﬁtable for every actors in-

volved. This approach, supported by a shared econ-

omy model, would beneﬁt drivers by optimizing their

travel and charging time, and also the producers by al-

lowing them to implement dynamic pricing methods

to modulate the attractiveness of their products.

An App-based Algorithmic Approach for Harvesting Local and Renewable Energy using Electric Vehicles

325

In addition, shared economy models are becoming

more and more popular and are reaching every branch

of capitalism, as stated in (Rifkin, 2014). The success

of Uber

 or Airbnb

 prove that people are eager

for a change of economic model. Adequacy of supply

and demand, efﬁciency, affordability and scalability

are all different reasons why these models are so suc-

cessful and are all properties that would be beneﬁcial

for an EV driver app. As a matter of fact, it would

seem more convenient nowadays to directly book a

station to its owner than having to solicit the interven-

tion of an external organization.

3.5 Reactivity

To work properly, all the solutions proposed have to

be extremely reactive. Nowadays, as the number of

EVs and charging stations increases, there is a grow-

ing need for a real-time information platform. Mo-

bile applications currently offer one of the most reac-

tive platforms and a true answer to this kind of prob-

lems. In particular, the booking can be easily and dy-

namically managed by connecting together a series of

drivers’ and producers’s phones. More than connect-

ing people, it would also offer a direct connection to

smart electric cars, instantly collecting immediately

all the data needed to predict when and where the next

charge should be done. Moreover, the app would open

the door to more connected payment methods like vir-

tual money or phone payment which would add some

more ﬂuidity to the system.

Finally, a mobile application adds a layer of re-

activity over all these features due to its proximity

to people. More than 2 billion people have a smart-

phone

. Therefore, apps are always with us allow-

ing them to inform us of any important update like

price modiﬁcations, overproduction risks or reserva-

tion problems directly when they happen so that we

can react in a minimum amount of time.

4 ALGORITHMIC SOLUTIONS

As showed in previous sections, a mobile application

could work as a good support to implement efﬁcient

EV charging, while favouring the integration of re-

newables into electricity networks. Nevertheless, as

pointed out previously, a series of algorithms is neces-

sary to achieve this implementation. In the following,

we describe a few possibilities for designing them.

According to the website statista.com:

http://www.statista.com/statistics/330695/number-of-

smartphone-users-worldwide/

4.1 Discrete Optimization and Machine

Learning

Dispatching EVs to a set of charging points while

minimizing certain constraints for the user (e.g. costs,

detour time) and/or for the network (e.g. amount of

energy curtailed) can be modelled as a discrete op-

timization problem. A possible solution is therefore

to use graph theory and algorithms, such as the mini-

mum spanning tree-type algorithms, where the nodes

represent either a charging point or a driver destina-

tion, while the weights of the edges are equivalent to

the constraints of the problem. Meta-heuristics, such

as genetic algorithms, have also proven to be useful

in the resolution of this kind of problems.

Pricing decisions is another important aspect of

the problem these should take into account not only

the variability of renewable energy but also the be-

haviour of the different actors. This could typically

be solved with some machine learning algorithms.

4.2 Multi-agent Systems

A last generic domain that is also closely related to

our problematic is, of course, the one of multi-agents

systems. Multi-agent technology has already been

used in power networks, leading, for example, to

ways to reduce imbalances in distribution networks

(Kok et al., 2008) or to manage congestions caused

by electric vehicles in a distribution grid (Hu et al.,

2015). Matching this technology with our mobile ap-

plication could probably enhance its performances.

5 CONCLUSION AND FUTURE

WORKS

This article shows how a mobile application (app)

may be an appropriate support for tackling several

upcoming obstacles associated with the rise of EVs

and renewable energy production capacities, com-

bined with the management of the electrical network

load. This app may be seen as an interface between

drivers and producers, providing services to both of

them. This paper also lists a series of algorithms that

could be used in this app to solve several decision-

making issues related to this app.

The main purpose of this paper is to make the

public aware that the upcoming issues regarding EV

charging and network management could be solved

by using an app-based strategy. Several research di-

rections should be taken for accelerating the develop-

ment of such an app. They should encompass algo-

rithmic research for tackling all the decision-making

ICAART 2017 - 9th International Conference on Agents and Artiﬁcial Intelligence

326

issues related to this app. They should also relate to

all the more “practical aspects” to be put in place for

such an app to be successful (e.g., design of the right

user interface, data management issues, etc.).

ACKNOWLEDGMENT

This research was carried out thanks to the support

of ENGIE - Electrabel. We also thank Powerdale for

many valuable discussions.

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