SMART GRIDS WITH ELECTRIC VEHICLES:

THE INITIAL FINDINGS OF PROJECT REIVE

A Project Funded by the Portuguese Ministry of Economy, Innovation and

Development

F. J. Soares, C. Gouveia, P. N. Pereira Barbeiro, P. M. Rocha Almeida, C. Moreira

and J. A. Peças Lopes

INESC TEC – INESC Technology and Science (formerly INESC Porto) and FEUP – Faculty of Engineering, University of

Porto, Campus da FEUP, Rua Dr. Roberto Frias, 378, 4200 – 465, Porto, Portugal.

Keywords: Electric Vehicles, Micro-generation, Smart Charging, Smart Grids, Smart Metering.

Abstract: This paper provides a general overview of the initial developments in the REIVE project (Smart Grids with

Electric Vehicles). The main focus of the project is on smart grid infrastructures for large scale integration

of EV and micro-generation units. It is a natural evolution of the InovGrid project promoted by the EDP

Distribuição – the Portuguese Distribution Network Operator – and allows the development of seminal

concepts and enabling technological developments within the Smart Grid paradigm. This paper presents the

management and control architecture developed to allow electric vehicle integration in smart grid operation.

Additionally, it presents the major impacts in distribution grids of the simultaneous deployment of electric

vehicles, micro-generation and smart grid technologies.

1 INTRODUCTION

The integration of Electric Vehicles (EV) in

electricity grids presents numerous challenges in

terms of grid infrastructure, as well as in terms of

management and control capabilities of these entities

(Clement-Nyns et al., 2010, Galus et al., 2010). The

smart grids paradigm is possibly the only effective

way to cope with these new challenges. The

expected large scale deployment of intelligent

equipment’s on the grid in the near future is,

therefore, a unique opportunity to define innovative

features and functionalities (Lopes et al., 2006,

Lopes et al., 2011), which will allow a larger and

safer integration of micro-generation (μG) units and

EV, as well as the implementation of more

ambitious Demand Side Management (DSM)

solutions and control strategies (Callaway and

Hiskens, 2011, Xu et al., 2010).

The project REIVE – Smart Grids with Electric

Vehicles (Redes Inteligentes com Veículos

Eléctricos in the Portuguese designation) addresses

this problematic in a holistic manner. It exploits

synergies between all the elements that compose a

smart grid, especially focusing on μG units and EV.

The project can be regarded as an extension of an

on-going project led by the Portuguese Distribution

System Operator (DSO) – the InovGrid project

(Messias, 2009). InovGrid is focused on the

development of a fully active distribution network

introducing advanced DSM strategies where

common energy consumers are able to play an active

role in the consumption management and also be

micro-producers. The interaction between

consumers/micro-producers units and the network

operator is assured through the functionalities

provided by the strategically developed equipment,

such as “energy boxes” and “distribution

transformer controllers”, as well as a proper

communications infrastructure. The smart grids

framework envisaged in the InovGrid project allows

not only to optimize consumers energy consumption,

but also improving transmission and distribution

networks effectiveness, decreasing technical losses,

reducing metering costs, postponing network

investments, improving and monitoring quality of

service, among other benefits.

Following the findings of the InovGrid project, a

Soares F., Gouveia C., Barbeiro P., Almeida P., Moreira C. and Lopes J..

SMART GRIDS WITH ELECTRIC VEHICLES: THE INITIAL FINDINGS OF PROJECT REIVE - A Project Funded by the Portuguese Ministry of

Economy, Innovation and Development.

DOI: 10.5220/0003953500390048

In Proceedings of the 1st International Conference on Smart Grids and Green IT Systems (SMARTGREENS-2012), pages 39-48

ISBN: 978-989-8565-09-9

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

demonstration pilot was launched, named InovCity,

in Évora, which is a city in the south of Portugal

with c.a. 32000 customers. The main objective of

InovCity is to test the solutions developed in the

InovGrid project and to quantify the technical and

economic benefits yielding from the approaches

implemented (Giordano et al., 2011).

The project REIVE aims at upgrading the

functionalities of the InovGrid’s “energy box” and

developing a technical platform, where innovative

features that allow the progressive integration of μG

and EV are developed and tested. Both technical and

commercial domains are addressed. The strategic

value and scope of the project covers contributions:

 For the industrialization of technologies and

products by industrial partners;

 To the mobility paradigm shift, by setting

technical conditions from the grids side for

increasing levels of EV deployment;

 To reduce CO

emissions, by allowing the

sustained usage of EV penetration combined

with further integration of intermittent

Renewable Energy Sources (RES).

The project REIVE also contemplates the

implementation of a smart grid in a laboratorial

environment, where it will be possible to test the

performance of new control and management

concepts for facilitating DER and EV integration in

LV networks. Additionaly, the laboratorial facilities

will also be used in order to develop new prototypes

for smart grid network controlers and power

electronic interfaces for EV and μG, which will

integrate the functional specifications undertaken

during the project development.

This paper presents the initial findings of the

project REIVE, with a special emphasis on the

control architecture developed, on the impacts

resulting from large scale depployment of μG units

and EV in distribution networks and on the smart

grid test bed that is being conceptualized for near

future implementation in laboratorial environment.

2 MANAGEMENT AND

CONTROL ARCHITECTURE

As referred in the previous section, the project

REIVE seeks to develop advanced management and

control mechanisms to facilitate the large scale

integration of EV and μG units in the power system,

using, as basis, the concepts and approaches

developed within the InovGrid project. The

InovGrid project was initiated in 2009 by the

Portuguese DSO, with support of INESC Porto,

Janz, Efacec and Logica.

The advanced infrastructures and functionalities

developed within InovGrid enable the

implementation of new commercial services,

allowing the active participation of the

consumers/micro-producers in both the electricity

market and system operation. From the system

operator point of view, the concept developed

promotes a more efficient renovation of the

distribution network infrastructures and management

systems, implementing investments that enhance

reliability and efficiency, and increasing the

capabilities of remote control and automation

systems.

The architecture of the system is represented in

Figure 1. It expands the utilities monitoring and

control capability downstream the MV network to

the consumers premises. This architecture is

constituted by 4 layers, defined according to the

identified players: the Energy Boxes (EB) installed

at the clients premises, the Distribution Transformer

Controllers (DTC) installed at the MV/LV

substations, the Smart Substation Controller (SSC)

installed at the HV/MV substations and the central

systems, namely the Distribution Management

System (DMS) and the commercial systems for

clients account managment purposes, which includes

only commercial information. As shown in Figure 1,

this architecture relies in communication network

allowing the communication within the same layer

and with the other layers.

2.1 Energy Box

The EB is a smart metering equipment to be

installed at the consumers/micro-producers premises

with bi-directional communication capability. The

EB functionalities include remote metering of

power, as well as other parameters related to the

number and duration of interruptions and also

voltage monitoring functionalities. In addition, the

EB also includes other functionalities for consumer

management purposes, such as: remote modification

of the contracted power, remote modification of

tariff regimes and remote interruption of fraudulent

clients. The EB was conceived modularly, thus

allowing the inclusion of a set of interface modules,

which enable the interaction with other home energy

management systems. Three different versions of the

EB were developed, which are capable of providing

different services, in order to face different

customers’ needs: an EB for simple consumers

(EB1), an EB for customers that own micro-

generation units (EB2) and an EB for customers that

SMARTGREENS2012-1stInternationalConferenceonSmartGridsandGreenITSystems

Figure 1: InovGrid advanced management and control system architecture.

own EV and wish to participate in controlled EV

charging schemes, such as smart charging (EB3).

Differentiating the EB according to their

functionalities enables the customers to acquire an

EB adapted to their specific needs, avoiding the

obligation of all customers acquiring equipment with

an increased cost and with useless advanced

processing capabilities.

The EB is prepared to receive control signals

from the entities in the upper hierarchical levels of

the control architecture developed within InovGrid

(see

Figure 1), which will vary according to the

version of the EB. EB1 include the base EB

functionalities such as remote metering and the

possibility of performing an on/off control over the

EV that are charging. This EB also allows the

remote change of commercial agreement conditions

such as change of tariffs or contracted power. EB2

and EB3 include several interface modules capable

of receiving specific control signals from the entities

in the upper hierarchical levels that will enable the

μG unit’s dispatch and the EV participation in

controlled charging schemes. In addition, EB3 also

enables the provision of several ancillary services by

EV, like reserves delivery.

2.2 Distribution Transformer

Controller

The DTC is installed at the MV/LV distribution

network substations. It receives and processes the

data collected from all the EB downstream and

sends it to the higher control layers. At the same

time, it will also receive information from the central

systems and distribute the information or the

resulting control signals to the downstream EB. On

more advanced version of the InovGrid system, the

DTC will also include functionalities that enable

them to manage the operation of the local networks,

both in normal and emergency conditions.

2.3 Smart Substation Controller

The SSC is responsible for coordinating the active

players of the MV networks as well as the DTC

installed in the MV/LV substations. The SSC

functionalities include intelligent algorithms to

optimize energy flows and network topology, as

well as self-healing algorithms, in close coordination

with network operators via the SCADA/DMS. It

also has the capability of performing coordinated

voltage control and detecting faults in an effective

manner, contributing to smaller restoration times and

reducing the number of clients affected by the faults.

The SSC is in fact very similar to the Central

Autonomous Management Controller (CAMC)

entity, developed within the MORE-MICROGRIDS

Project, which main purpose is the management of

the Multi-Microgrid (Gil and Lopes, 2007).

2.4 Integration of EV in the InovGrid

Architecture

The large scale deployment of plug-in EV will

require new charging interface infrastructures, such

as fast charging stations, public and domestic

charging points and private charging stations

dedicated to EV fleets. With exception of the fast

SMARTGRIDSWITHELECTRICVEHICLES:THEINITIALFINDINGSOFPROJECTREIVE-AProjectFundedby

thePortugueseMinistryofEconomy,InnovationandDevelopment

charging stations, all the other infrastructures will

provide a slower charging, that can take up to 8

hours. For this reason, it is expected that EV will be

connected to the grid for large periods of time, being

potentially possible to exploit their storage capacity

in order to enable a better usage of the network

infrastructures.

Yet, the decision of the EV participating or not in

controlled charging schemes will always be a

decision taken by their owners. For this reason,

several possible charging schemes should be

available in order to fit the specific needs of the EV

owners.

In the non-controllable charging strategies, the

EV is envisioned as a conventional load, being the

EV owner free to charge the vehicle in any time of

the day. In the dumb charging mode the EV behave

has any other appliance, having no restrictions or

incentives to shift their charging to the lower

consumption periods. In order to provide load

shifting, a multiple tariff policy may be implemented

to incentivize the EV to charge the batteries in the

periods were the electricity price is lower. This

method is based on the dual tariff scheme

implemented in several countries, where during

valley hours the electricity price is lower. However,

as this is not an active management strategy, the

success of this method depends on the EV owner

willingness to take advantage of this policy, and thus

only part of the EV load would eventually shift

towards valley hours.

In the controllable strategies the EB3 will receive

specific control signals to control the EV battery

charging. The objectives may be commercial or to

ensure the secure operation of the system. The smart

charging strategy envisions an active management

system, where there are two hierarchical control

structures, one headed by an EV aggregating agent

and other by the DSO.

In normal operating conditions, the EV charging

will be managed and controlled exclusively by a

commercial aggregator, whose main functionality is

to cluster the EV, according to their owners’

willingness, and exploit business opportunities in the

electricity markets. In order to successfully respect

the agreements, both with the clients and with the

electricity market, the EV aggregator must be

capable of sending set-points to the EB3 related with

rates of charge or requests for provision of ancillary

services. Whenever the security of operation is

compromised, i.e. when the grid is being operated

near its technical limits, or in emergency operating

modes, e.g. islanded operation, the system operator

overrides the aggregator control signal, in order to

control the EV charging. This type of EV charging

management provides the most efficient usage of the

resources available at each moment, enabling

congestion prevention and voltage control, while

avoiding the need to invest largely in network

reinforcements.

In the V2G charging mode, the EV charging

interface admits bi-directional power flow, enabling

the EV to inject active power into the grid. From the

grid perspective, this is the most interesting way of

using EV capabilities, given that besides helping

managing branches’ congestion levels and voltage

related problems in some problematic spots of the

grid, EV have also the capability of providing peak

power in order to make the energy demand more

uniform along the day and to perform primary

frequency control. Nevertheless, there are also some

drawbacks related with the batteries degradation.

Batteries have a finite number of charge/discharge

cycles and its usage in a V2G mode might represent

an aggressive operation regime due to frequent shifts

from injecting to absorbing modes. Thus the

economic incentive to be provided to EV owners

must be even higher than in the smart charging

approach, so that they cover the eventual battery

damage owed to its extensive use.

The additional storage capacity provided by EV

has also the potential to enhance grids’ resilience,

namely regarding isolated systems, improving the

frequency response and increasing the amount of

renweable-based μG that can be safely integrated in

the system (Lopes et al., 2009). The V2G control

strategies for frequency regulation adopted in project

REIVE are based on the ω-P characteristics

represented in Figure 2. The EV will change its

power output based on the isolated grid frequency in

order to reduce the imbalance between generation

and load. For frequencies around 50 Hz the EV

charge its battery at its nominal power. A dead-band

is considered in order to avoid the degradation of EV

batteries resulting from frequent solicitations for

small frequency deviations. When the frequency

drops below the dead-band minimum, the EV

reduces its power consumption and if the frequency

drops further bellow the zero-crossing frequency

), the EV starts to inject power into the grid.

When the grid frequency increases to values superior

to the dead-band maximum, the EV increases

gradually its power consumption until the maximum

possible power consumption is reached.

The parameters of the frequency-droop

characteristic will depend on the EV charger

characteristics and on the willingness of EV owners

to participate in such services. These parameters

SMARTGREENS2012-1stInternationalConferenceonSmartGridsandGreenITSystems

may differ from grid to grid and can be remotely

changed by the DTC, in order to coordinate the EV

participation with the other grid frequency

regulation mechanisms (load shedding schemes and

availability of energy storage devices).

Figure 2: EV frequency-droop characteristic.

3 IMPACTS OF MICRO-

GENERATION AND EV IN

DISTRIBUTION NETWORKS

The large deployment of EV is very likely to

provoke changes in the power demand patterns,

causing changes in the grids’ voltage profiles,

branches’ congestion levels and energy losses,

namely at the distribution level, where the EV will

connect for charging purposes (Lopes et al., 2011).

The identification of the control and management

strategies described in section 2 of this paper was

complemented by an exhaustive evaluation, in a

steady-state framework, of the impacts that EV

charging and µG will have in the system operation.

The evaluation performed, required the development

of an innovative methodology to assess the referred

impacts in the Portuguese distribution system (LV +

MV networks). Different EV charging strategies

were also considered, as well as several future μG

and EV penetration scenarios.

3.1 Methodology

The main objective of the steady-state studies

performed under the REIVE project was to assess

the impact of the µG and EV in the Portuguese

distribution system. Due to the large extension of the

network and consequent simulation complexity,

some general assumptions were made, namely:

 Separate analysis of LV and MV networks;

 Only the LV and MV networks with the

highest degree of representativeness of the

Portuguese distribution system were

selected;

 Load and generation diagrams are

represented by a discrete time step of half an

hour, being conducted one power flow for

each time step.

Under these assumptions, the impact assessment

studies were conducted for each selected network,

using a simulation tool that provides a reliable and

detailed characterization of the grid operating

conditions. The outputs of this tool are voltage

profiles, branches' loadings, grid peak power, energy

losses and the identification of networks components

that will possibly be operated near, or above,

technical limits. The general methodology adopted is

shown in Figure 3.

To obtain a detailed evaluation, three studies

were performed considering: a) Only the connection

of EV; b) only µG; c) both EV and µG connections.

The first two studies, which address separately the

EV and μG integration in the system, were

performed in order to assess more accurately the

individual impacts of each technology.

In order to assess the importance of controlling

EV load, the dumb charging, multiple tariff scheme

and smart charging strategies were considered in the

studies a) and c). The V2G concept was not included

in the steady-state simulations, since its main

contribution is for the transient stability of the

system, requiring dynamic studies that are currently

on-going. As these studies were not concluded yet,

their results will not be presented in this paper.

The developed tool combines a stochastic model

based on a Monte Carlo method with the PSS/E tool

for electric grid simulation purposes, according to

the principles described in (Rosa et al., 2011). The

algorithm associated with this evaluation suite is

represented in Figure 4.

After evaluating the initial conditions of the grid

under study, the stochastic model based on a Monte

Carlo method is used to characterize the EV

regarding different EV drivers’ behaviours, EV

charging strategies, battery capacities, charging rates

and energy consumptions per distance travelled

(kWh/km). Then, it simulates EV movement and

charging for each ½ h period of a day.

Regarding the modelling of the µG units, the

power produced by each µG unit is subtracted to the

existing load at each bus for each time step of the

simulation, as in (Barbeiro et al., 2010). µG units are

allocated to the buses proportionally to the

residential load installed in each bus and it is

considered that they have unity power factor. EV are

distributed through the grid according to a set of

Dead

Band

EV consumption

max

min

ection

Consum

tion

neg

SMARTGRIDSWITHELECTRICVEHICLES:THEINITIALFINDINGSOFPROJECTREIVE-AProjectFundedby

thePortugueseMinistryofEconomy,InnovationandDevelopment

probabilities, proportional to the residential load

installed in each bus. Therefore, the buses with

higher residential loads installed will have larger µG

units connections and a higher probability of having

EV parked.

Figure 3: Methodology adopted for the µG and EV impact

evaluation.

Figure 4: Algorithm to evaluate EV impacts.

3.2 Simulation Scenarios

The steady-state simulations required the definition

of the EV and μG integration scenarios, as well as a

detailed characterization of the distribution

networks.

3.2.1 μG Integration Scenarios

In the analysed scenarios, 80% of all the μG units

considered were assumed to be photovoltaic, while

the remaining 20% were assumed to be micro-wind

turbines. All the μG units are considered to operate

at unity power factor and can be generally

characterized by specific generation patterns, which

depend on the technology specificities. The Decree-

Law No. 363/2007 establishes the legal regime

applicable to electricity production through μG

units, stating for the year of 2008 a maximum power

capacity of 10 MW that could be subsidized. This

value is successively increased at a rate of 20% per

year up to 2015. Based on this legal frame and as in

(Barbeiro et al., 2010), two μG integration scenarios

were considered:

 Scenario A – μG installed capacity at

national level grows at a rate of 20% until

2015 and 3% from 2016 to 2030, reaching

250MW of installed capacity in 2030.

 Scenario B – μG installed capacity at

national level grows at a rate of 65% until

2015 and 6% from 2016 to 2030, reaching

2000MW of installed capacity in 2030.

3.2.2 EV Integration Scenarios

Since the aim of this study is to assess the impact of

the EV in the distribution grids, it is of utmost

importance to define a reasonable set of hypothesis

for EV integration until 2030. In this paper two

scenarios of EV deployment were defined,

considering two different automobile replacing rates

and attending to the social and political

circumstances.

Table 1: Nr. of BEV and PHEV (thousands of units).

PHEV BEV

2020 2030 2020 2030

High variant (103)

168 444 84 1035

Low variant (103)

114 422 76 281

Additionally, the scenarios defined differentiate

the growth of the two main types of vehicles that are

expected to be deployed: the battery EV (BEV) and

the plug-in hybrid EV (PHEV). The number of EV

expected to be integrated in the Portuguese fleet by

2020 and 2030 is presented in Table 1.



SMARTGREENS2012-1stInternationalConferenceonSmartGridsandGreenITSystems

3.2.3 Portuguese MV and LV Distribution

Networks

As it was previously referred, the presented studies

were performed on a set of LV and MV Portuguese

distribution networks, based on real data. For the LV

simulations, five typical LV networks were used,

classified according to their MV/LV transformer

rated power, which is a satisfactory approximation

of load density. Regarding the MV networks

existing in Portugal, six typical MV networks were

identified. The load and topological characteristic of

these networks are assumed to be representative of

different parts of the overall MV distribution grids in

Portugal. In terms of distributed energy, the six MV

grids approximately represent a geographical area

totalizing approximately half the total energy

consumption registered in the whole Portuguese MV

distribution network. A detailed description of these

networks can be found in (Barbeiro et al., 2010).

In 2010, the annual consumption in the entire

Portuguese distribution network was approximately

44.7 TWh. Considering the evolution of the

Portuguese electricity consumption in recent years

and the current economic and financial situation, a

load growth rate of 1.5% per year was considered

for a time horizon from 2010 to 2030.

The load diagrams adopted for this study were

based in the diagrams used in (Barbeiro et al., 2010).

The study considers two different periods in a year –

winter and summer. For the MV, it is possible to

distinguish three types of consumers: residential,

commercial (both at the aggregate level of the

MV/LV substation) and industrial (consumers fed

directly at MV level). In LV grids only residential

and commercial consumers were considered, since

the number of industrial consumers does not have

significant relevance.

3.3 Results Analysis

Following the methodology previously described,

this section presents the results obtained regarding

the different EV charging strategies as well as the

future integration scenarios of EV and μG.

The daily load curve presented in Figure 5

illustrates the consumption pattern for a typical

winter day for the year of 2030 in a LV network

with a 630kVA transformer capacity. In this

scenario this network supplies approximately 63

plugged-in EV. From Figure 5 it is also possible to

compare the impact of the considered charging

strategies on the daily load curve.

As shown, adopting a dumb charging strategy

will increase the peak power approximately 62 kW,

since EV are likely to be charged at the end of the

day. Adopting a multiple tariff scheme will result in

a new peak at 22:00h, when the period with a lower

tariff begins. When adopting a smart charging

strategy, the EV charge occurs preferentially on the

valley hours, contributing to keep the peak load

almost unchanged.

Regarding the contribution of the μG units, in

Figure 6 it is possible to verify that since the

majority of μG units are photovoltaic systems,

during the hours with greater sunlight exposure there

is only a small reduction in the peak power. It is

important to state that Figure 6 is referred to the

scenarios b), where only the μG was considered.

Figure 5: 2030 load diagram at the MV/LV substation of a

LV network with 630 kVA transformer adopting different

charging strategies.

Figure 6: 2030 load diagram at the MV/LV substation of a

LV network with 630 kVA transformer for the different

scenarios of μG.

The evaluation of the branches overloading is

also an important measurement of the adequacy of

the Portuguese networks for future deployment of

EV. In general, the branches’ congestion levels

increase in the scenarios without direct control of the

EV charging.

Figure 7 shows an example obtained for a LV

network with a 400kVA MV/LV transformer. The

100

150

200

250

300

350

400

450

048121620

Power (kW)

Without EV Dumb Charging

Multi-tariff Smart Charging

Time

100

150

200

250

300

350

400

450

1357911131517192123

Power(kW)

Time

2030‐CEN0

2030‐CEN1

2030‐CEN4

2008

2030

–

noµG

2030

–

µG scenarioA

2030

–

µG scenarioB

2008

SMARTGRIDSWITHELECTRICVEHICLES:THEINITIALFINDINGSOFPROJECTREIVE-AProjectFundedby

thePortugueseMinistryofEconomy,InnovationandDevelopment

figures provide a comparison of branches loading in

the peak hour of the scenarios without EV (upper-

left picture) and with 14 EV, in order to provide a

general overview of the three charging methods’

impacts in this matter. In this case the maximum

branch loading detected increased 45% with the

dumb charging, 56% with the dual tariff and only

11% with the smart charging.

Adopting different charging strategies is also

expected to increase the networks active power

losses, since the power consumption increases.

As shown before, uncontrolled charging

strategies increase the peak power consumption,

increasing also the current and consequently the

active power losses. However, controlling the EV

load though smart charging strategies,

complemented by local generation from the µG

units, contributes to avoid significant increases in

the network losses. The value of energy losses in the

Portuguese distribution network (MV and LV) was

1782 GWh in the year 2008. This value represents

approximately 4.14% of the total consumption in the

entire Portuguese distribution network.

From the results obtained due to conventional

load growth, in 2030, energy losses will reach the

value of 4394 GWh. When considering the future

deployment of EV, losses could reach 5209 GWh if

EV charging is not controlled. In this case, the

integration of μG could reduce losses to 4698 GWh

in scenario A and to 4457 GWh in scenario B. This

value can be further reduced if the smart charging is

adopted, as shown in Figure 8.

As expected, the smart charging provides better

results since it makes the load distribution along the

day more uniform, consequently reducing the grid’s

peak demand.

a) Without EV

b) Dumb Charging

c) Multiple tariff scheme

d) Smart charging

Figure 7: Lines loading for a LV network with a 400 kVA transformer (low EV scenario, Summer day, without μG and for

the year 2030). Grading between light grey and black, stand for increasing values of congestion, from 0 to 100%.

SMARTGREENS2012-1stInternationalConferenceonSmartGridsandGreenITSystems

Figure 8: Total distribution network losses (MV and LV)

for 2020 and 2030, High EV integration scenarios.

From the results obtained it was also possible to

conclude that the µG and controlled EV charging

also have a positive impact in the CO2 emissions

and voltage profiles. When comparing the EV

charging schemes (µG not considered), the results

obtained show that the the smart charging can avoid

~110 ktons of CO2emissions when compared to the

dumb charging. The μG, in turn, can avoid 70 ktons

when comparing Scenario B with the case without

μG (EV not considered). Contrary to the EV

charging strategy adopted, the μG has little influence

in the grids’ voltage profiles. Although not presented

in the paper, the results obtained showed that the

smart charging can avoid possible voltage violations

that are likely to occur if other charging schemes are

adopted.

4 SMART GRID IN

LABORATORIAL

ENVIRONMENT

As previously stated, the REIVE project aims the

conceptualization of innovative control and

management algorithms for smart grid applications,

where EV and micro-generation units present one of

the most relevant roles. Additionally, the smart grid

paradigm is necessarily supported by an adequate

communication infrastructure, which will

conditioned the possibility of developing more or

less ambitious control and management schemes.

Additionally, the deployment of EV and micro

generation units in distribution grids introduce

important impacts in terms of voltage profiles and

branches congestion levels, thus requiring specific

systems allowing interaction with these elements in

order to control and manage their power

interchanges with the grid. Therefore, the

laboratorial infrastructure under development is the

physical space that will enable pre-prototyping new

power electronic interfaces for EV and micro

generation units, which incorporate the capability of

active interaction with the smart grid control

infrastructure. The laboratorial infrastructure will

allow the individual and integrated testing

procedures for new concepts, control algorithms to

be housed at the different smart grid hierarchical

layers, communication architectures, technologies

and protocols that will allow feasibility

demonstration regarding functional and technical

specifications developed within the project. In this

sense, the main objectives of the REIVE laboratorial

infrastructure are:

1) Development of applied research activities

regarding the development of the microgrid

concept as the base power system active cell for

smart grids.

2) Development of advanced research activities

regarding active integration of EV in the smart

distribution grid control architecture.

3) Consolidation key competences regarding the

natural moving from the actual distribution grid

operational paradigm to a more active one,

where the smart grid concept is fully

envisioned.

4) Development of software modules for pre-

prototypes of the smart grid key controllers for

its different operational layers (such as MGCC,

SSC, etc) and perform a preliminary evaluation

of its performance in close collaboration with

simulation results

5) Development of pre-prototypes of advanced

interfaces for EV and micro-generation units, in

accordance with the project on-going functional

and technical specifications.

6) Actively support national and international

standardization activities in different domains,

according to the laboratorial developments

obtained in the project

7) Technology transfer to the industry regarding

innovative concepts for smart grid controllers

and for EV and microgeneration interfaces

under development within the project.

5 CONCLUSIONS

The REIVE project is dedicated to develop and test

technical solutions and pre-industrial prototypes for

the active and intelligent management of electricity

grids with large scale integration of μG and EV.

1000

2000

3000

4000

5000

6000

Without μG Scenario A Scenario B Without μG Scenario A Scenario B

2020 2030

Losses (GWh)

Without EV Dumb Ch arg ing Multiple Tariff Smart Ch arging

SMARTGRIDSWITHELECTRICVEHICLES:THEINITIALFINDINGSOFPROJECTREIVE-AProjectFundedby

thePortugueseMinistryofEconomy,InnovationandDevelopment

Within the framework of this project, innovative

tools were developed to identify the impacts of the

μG and of the different strategies adopted for the

management and control of EV. The steady-state

results presented proved that µG and EV integration

might bring important technical benefits to

distribution grids, namely if the EV deployment is

accompanied with the implementation of controlled

EV charging schemes. The major benefits are related

with the reduction of branches overloading, grids’

peak power, energy losses and CO

emissions.

The future work of this project is focused on the

development of laboratorial prototypes of the

software modules that will constitute the EB2, EB3,

DTC and SSC controllers, together with the required

interfaces in order to interact with the devices

installed in the field (loads, EV, μG, storage

devices). The usage of laboratorial facilities will

allow extensive validation of the developed concepts

and control strategies for facilitating μG and EV

integration.

ACKNOWLEDGEMENTS

This work was supported in part by Fundo de Apoio

à Inovação (Ministério da Economia, da Inovação e

do Desenvolvimento), within the framework of the

Project REIVE – Redes Eléctricas Inteligentes com

Veículos Eléctricos, and by Fundação para a Ciência

e Tecnologia under Grants SFRH/BD/48491/2008

and SFRH/BD/47973/2008 and within the

framework of the Project Microgrids+EV:

Identification of Control and Management Strategies

for Microgrids with Plugged-in Electric Vehicles –

ref. PTDC/EEA-EEL/103546/2008.

REFERENCES

Barbeiro, P. N. P., Moreira, C. L., Soares, F. J. &

Almeida, P. M. R. Year. Evaluation Of The Impact Of

Large Scale Integration Of Micro-Generation Units In

Low And Medium Voltage Distribution Networks. In:

Innovative Technologies For An Efficient And

Reliable Electricity Supply (Citres), 2010 Ieee

Conference On, 2010.

Callaway, D. S. & Hiskens, I. A. 2011. Achieving

Controllability Of Electric Loads. Proceedings Of The

Ieee, 99, 184-199.

Clement-Nyns, K., Haesen, E. & Driesen, J. 2010. The

Impact Of Charging Plug-In Hybrid Electric Vehicles

On A Residential Distribution Grid. Power Systems,

Ieee Transactions On, 25, 371-380.

Galus, M. D., Zima, M. & Andersson, G. 2010. On

Integration Of Plug-In Hybrid Electric Vehicles Into

Existing Power System Structures. Energy Policy, 38,

6736-6745.

Gil, N. J. & Lopes, J. A. P. Year. Hierarchical Frequency

Control Scheme For Islanded Multi-Microgrids

Operation. In: Power Tech, 2007 Ieee Lausanne, 1-5

July 2007 2007. 473-478.

Giordano, V., Gangale, F., Fulli, G. & Jiménez, M. S.

2011. Smart Grid Projects In Europe: Lessons Learned

And Current Developments. Joint Research Centre

Institute For Energy.

Lopes, J. A. P., Moreira, C. L. & Madureira, A. G. 2006.

Defining Control Strategies For Microgrids Islanded

Operation. Power Systems, Ieee Transactions On, 21,

916-924.

Lopes, J. A. P., Rocha Almeida, P. M. & Soares, F. J.

Year. Using Vehicle-To-Grid To Maximize The

Integration Of Intermittent Renewable Energy

Resources In Islanded Electric Grids. In: Clean

Electrical Power, 2009 International Conference On,

9-11 June 2009 2009. 290-295.

Lopes, J. A. P., Soares, F. J. & Almeida, P. M. R. 2011.

Integration Of Electric Vehicles In The Electric Power

System. Proceedings Of The Ieee, 99, 168-183.

Messias, A. A. 2009. The Inovgrid Project: Distribution

Network Evolution As A Decisive Answer To The

New Challenges In The Electrical Sector. Edp

Distribuição.

Rosa, M., Issicaba, D., Gil, N., Soares, F. J., Almeida, P.

M. R., Moreira, C., Ribeiro, P., Heleno, M., Ferreira,

R., Sumaili, J., Meirinhos, J., Seca, L., Lopes, J. A. P.,

Matos, M., Soultanis, N., Anestis, A., Karfopoulos, E.,

Mu, Y., Wu, J., Ekanayake, J. & Narayana, M. 2011.

Functional Specification For Tools To Assess Steady

State And Dynamic Behaviour Impacts, Impact On

Electricity Markets And Impact Of High Penetration

Of Ev On The Reserve Levels. Deliverable D2.2 Of

The European Project Merge, .

Xu, Z., Ostergaard, J. & Togeby, M. 2010. Demand As

Frequency Controlled Reserve. Power Systems, Ieee

Transactions On, Pp, 1-10.

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