Dalle Smart Grids alle Smart Cities
Carlo Alberto Nucci
DEI – Guglielmo Marconi – Università di Bologna
Lunedì 28 ottobre 2015 Aula Magna, Palazzo del Bo
Padova
SMART GRIDSi sistemi elettrici del futuro
Smart City
1
Smart City: definizione?
Smart City: definizione?
Smart City: definizione?
11 Smart Energy
Renovation of
the built
environment
Smart grids
Smart lighting
Smart Energy
Renovation of
the built
environment
Smart grids
Smart lighting
22 Smart
Mobility
Pedestrian
and bicycle
mobility
Intermodality
Last mile
logistic
Smart
Mobility
Pedestrian
and bicycle
mobility
Intermodality
Last mile
logistic
33 Smart
environment
Safety
Waste
management
Water
Management
Smart
environment
Safety
Waste
management
Water
Management
44 Smart People
Inclusiveness
Smart devices
Social Innovation
Smart People
Inclusiveness
Smart devices
Social Innovation
55 Smart
Government
Digital
Agenda
Cloud
&Crowd
System
Cultural
Heritage
promotion
Smart
Government
Digital
Agenda
Cloud
&Crowd
System
Cultural
Heritage
promotion
66 Smart Living
Data
management
Business
Innovation
e.services
Smart Living
Data
management
Business
Innovation
e.services
Bologna Smart City: strategies overview
EU Smart Cities Stakeholder Platform:
11 priority areas
3 ‘vertical’ domains
8 ‘h
ori
zonta
l’ e
nabling
them
es
Smart Grid
2
Potenza elettrica installata in Italia
Potenza efficiente lorda degli impianti
elettrici di generazione in Italia al 31.12.14
Smart GridAnche la rete elettrica tradizionale è smart
Case description
� 80 MW power plant:
two aeroderivative gas turbine (GT) units and
a steam turbine unit (ST) in combined cycle;
� PP connected to a 132 kV substation feeding
a urban medium voltage (MV) distribution
network;
� PP substation is linked, by means of a cable
line, to the 132 kV substation that feeds 15
feeders of the local medium voltage (15 kV)
distribution network and provides also the
connection with the external transmission
network throughout circuit breaker BR1.
Intentional islanding/reconnection – PMU
- Operates BR1 for the disconnection of the
network from the external grid;
- Communicates the “Load Droop Anticipator
command” to the ST control system in case of
islanding maneuvers accomplished at rather
large power exported levels to the transmission
network;
- Disconnects MV feeders following a predefined
priority list in order to guarantee the load
balance;
- selects the operation control mode of the two
gas turbines (master and slave) for the
frequency regulation of the network in islanded
conditions;
� Islanding capabilities � tested with EMTP-RV.
� PP is equipped with a PMS that:
STGT1 GT2
Intentional islanding/reconnection – PMUProc. UPEC, Padua, Italy, Sept 2008
Islanding maneuver
Islanding of GT1: Distribution network voltage
phasors angles differences during the
1
23
Intentional islanding/reconnection – PMU
Reconnection maneuver
� A feedback of the PMU
measurements was given to the
PP operator.
� The synchro-check relay and the
synchronizing PMS action
permitted the smooth
reconnection maneuver.
� While the PMS controls the power
plant units in order to allow a
reliable reconnection maneuvers
of the network to the external grid.
Intentional islanding/reconnection – PMU
Reconnection of GT1:
Voltage phasors angle differences
0.16
0.17
0.18
0.19
0.2
0.21
-1600
-1400
-1200
-1000
-800
-600
-400
-200
0
200
0 10 20 30 40 50 60
An
gle
dif
fere
nce
be
twe
en
po
siti
ve
-se
qu
en
ce
com
po
ne
nts
of
PM
U1
an
d P
MU
2 p
ha
sors
(°)
An
gle
dif
fere
nce
be
twe
en
po
siti
ve
-se
qu
en
ce
com
po
ne
nts
of
PM
U2
an
d P
MU
3 p
ha
sors
(°)
Time (s)
PMU2-PMU3
PMU1-PMU2� Identification of
the correct phase
difference between
islanded and
external network to
trigger the
reconnection
maneuver.
Intentional islanding/reconnection – PMU
PMU measured frequency transient
49.95
50
50.05
50.1
50.15
0 10 20 30 40 50 60
Fre
qu
en
cy (
Hz)
Time (s)
PMU1
PMU2
PMU3
� Monitoring of the
frequency difference
(positive) between the
islanded network
(PMU1 and PMU2)
and the external
network (PMU3).
Intentional islanding/reconnection – PMU
Smart Grid per Smart
Cities
3
Smart City: definizione?
The Smart Cities Council is a for-profit, Partner-led association for the advancement of the
smart city business sector. It promotes smart cities in general and our Partners in particular.
Allied Telesis � Alstom Grid � Bechtel � Cisco � Cubic Transportation Systems -
Enel � GE � IBM � Itron, Inc. � MasterCard � Mercedes-Benz � Microsoft �
Ooredoo � Qualcomm � S&C Electric Co. � Schneider Electric
Partendo dall’Utenza integrata[da R. Caldon]
Si dovrà arrivare ad una rete completamente
integrata [da R. Caldon]
http://esmig.eu/page/enabling-smarter-energy-world
http://iperbole2020.tumblr.com/smartcity
24
La Piattaforma Bologna Smart City
In sintonia con
Regione
CNR, ENEA, Lepida, imprese
locali e nazionali,..
Piano Strategico
Metropolitano
Per l’Università di Bologna
� pieno coinvolgimento del
Gruppo di Lavoro ‘Smart
City’ dell’Alma Mater
Studiorum
“Le smart city sono sistemi intelligenti e sostenibili, aree urbane
che pianificano coerentemente l’integrazione delle diverse
caratteristiche identitarie del proprio territorio - culturali,
economiche, produttive, ambientali - in un’ottica di innovazione.
Bologna ha scelto di percorrere questa strada nel solco della
propria tradizione civica, attraverso un’alleanza tra mondo della
ricerca ed Università, imprese e pubblica amministrazione per
sviluppare soluzioni utili ad affrontare problematiche urbane e
sociali, mettendo le tecnologie al servizio delle persone”
25
La Piattaforma Bologna Smart City 30 luglio 2012 - http://www.unibo.it/it/ricerca/progetti-e-iniziative/bologna-smart-city
In sintonia con
Regione
CNR, ENEA, Lepida, imprese locali
e nazionali,..
Piano Strategico Metropolitano
http://www.magazine.unibo.it/archivio/2012/bologna_smart_city
http://iperbole2020.tumblr.com/smartcity
http://www.aster.it/tiki-read_article.php?articleId=717
L’impegno dell’Alma Mater
all’interno della Piattaforma si
avvale del contributo del Gruppo
di lavoro “Bologna Smart City”
coordinato da C.A. Nucci
26Agenzia sanitaria e sociale regionale
MIUR
Smart cities and social innovationArt 1: …. The Ministry of Education, University and Research (MIUR henceforth), in line with
the European guidelines of "Horizon 2020," the guidelines the European Digital Agenda, the National Plan for E-Government, actions underway in the framework of the Digital Agenda Italian, attributes to interventions in the field of Smart Cities and Communities the value of a
strategic priority for the entire national research policy and innovation.
14.5M€
***
**
Partners:- 4 large companies- 4 SME- 5 research institutions
Formal commitment from:- Ministry of Health- regional governments and health agencies (4), - healthcare local institution (4) - municipalities of major impacted towns (3) - associatations (GPs, municipalities & healthcare institutions)
Line of intervention: ageing of the society
OplonProf. Danilo Montesi
OPLON: costo 773.558,99
(contributo 618.847,19)
Watertech
Progetto
SMART WATERTECH
Monitoraggio
SCARICATORI DI PIENA
Monitoraggio ACQUE
PARASSITESMART WATER METERING
(modelli di previsione
domanda e tariffazione)
Sistemi di CONTROLLO ed
ATTUAZIONE in TEMPO REALE
(pressione, qualità delle acque)
Prelocalizzazione
PERDITE IDRICHE
Rilevamento inquinanti mediante
IMMAGINI SATELLITARI
Proff. Armando Brath
e Sandro Artina
WATERTECH: costo 620.000
(contributo 496.000)
RIGERS – RIGENERAZIONE DELLA CITTÀ: EDIFICI E RETI INTELLIGE NTIICIE - CPL - SACMI - CMC - SATA - CNR ICT - UNIBO (DA - DE I – DICAM)Decreto Direttoriale 13 febbraio 2014 n.428 - BANDO SMART CITIES NAZIONALE
Ambito di interesse primario: ARCHITETTURA SOSTENIBILE E MATERIALIAmbito di interesse secondario: SICUREZZA DEL TERRITORIOAmbito di interesse secondario: SMART GRIDS
Prof. Andrea Boeri
Rigers
RIGERS: costo 799.096,24
(contributo 639.276,99)
RIQUALIFICAZIONE, RIGENERAZIONE E VALORIZZAZIONE DE GLI INSEDIAMENTI DI EDILIZIA SOCIALE AD ALTA INTENS ITÀ ABITATIVA NELLE PERIFERIE URBANE NELLA SECONDA METÀ DEL ‘900 – PRIN 2008 DA UNIBO
IPOTESI RIQUALIFICAZIONE DELL’EDIFICIO VIRGOLONE: QUARTIERE PILASTRO BOLOGNA
UNIVERSITÀ DI BOLOGNA – DIPARTIMENTO DI ARCHITETTURA – A. Boeri – E. Antonini – D. Longo – R. Roversi – G. ChieregatoCASO STUDIO: COMPLESSO RESIDENZIALE “PILASTRO” – BOLOGNA
24,86kWh/m 2y
RIDUZIONE DEL CONSUMO DI ENERGIA
118,54 kWh/m 2yFABBISOGNO ENERGETICO ATTUALE
E2SGEnergy to Smart Grid
E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu
Luca Bedogni ([email protected]), Luciano Bononi ([email protected]), Alberto Borghetti (IUNET – [email protected]), Riccardo Bottura (IUNET - [email protected]), Alfredo D’Elia ([email protected]), Federico Montori ([email protected]), Carlo Alberto Nucci (IUNET – [email protected]), Elisa Pitti ([email protected]), Tullio Salmon Cinotti ([email protected])
Final Review E2SGOctober 21/22 2015, Berlin
T4.11 Demonstration of storage optimization by exploiting electric vehiclesD4.11 Simulator of storage optimizing policies
p. 31E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu
T4.11 – Simulator of storage policies of EVsOverview
WP3 Grid Topology & ControlWP4 Demonstrators
Objectives:- Development of a co-simulation platform that integrates a mobility simulator of the electric vehicles and their charging requests with a dynamic simulator of the power distribution network. - Implementation and tests of control functions able to limit the impact of electric vehicle charging on power distribution.
Deliverable (M36 available)
3.4 Advanced storage management policies 4.11 Demonstration of storage optimization by exploiting electric vehicles
p. 32E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu
T4.11 – Simulator of storage policies of EVsArchitecture of the co-simulation environment
p. 33E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu
Mobility simulator: OMNET++ - SUMO - Veins - Openstreetmap
Simulator for analysis of traffic including EV and EVSE entities in realistic scenarios (including support for ext. services and apps).� Based on Omnet++, SUMO, Veins
and Openstreetmap� Accurate modeling of city scenarios
and multiple eMobility entities� Modeling and control of traffic
(realistic data vs. model assumptions)� Modeling EV and EVSE parameters,
their distribution, use and location� Integrated with Storage Information
Broker (SIB) -based service platform for integration support of external apps and services
Traffic Simulation Framework
p. 34E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu
Power Distribution simulator: EMTP-rv
� Model of the aggregated unbalanced loads (constant impedance / current / power) that includes the EVSE profiles : the amplitude of a triplet of current sources is controlled by a feed-back regulator in order to inject or absorb the requested value per-phase of active and reactive power.
PHASE 1 PHASE 2 PHASE 3
P
Q
Scheme for the interface between the model of urban traffic and the power distribution system simulator
Model of the electric power
distribution system
Initial power flow
Transient 3ph simulation
(Δt=1ms)
Model of nodal aggregated
(1ph and 3ph) EV charging
systems
Check of network
constrains by using IEDs
information
Networked control EV
charging systems
Model of the communication network
Model of the urban traffic
Initial occupation of EV
charging systems
Traffic simulation
(Δt=100ms)
Start and end of charging of
individual EV at a specific
charging station
Update of charging profile
and duration due to electric
network limitations
ID of active EV charging
systems and power
ID of active EV charging
systems and maximum
power profile
Limitation of the power for
each specific active/idle
EV charging system
Synchronization between the model of urban traffic and the power distribution system simulator
EMTP-rv
External SW 1
OMNET++ & SUMO
SIB
(Storage
Information)
+
WS
synchronizer
Reservation event
EVSE plugLoad change
Network operating
conditionEVSE charging
EVSE profileEVSE profile
EVSE unplugload change
External SW 2 External SW n…
e.g.: OPNET,
CityService,
Smartphone App,
…
*EVSE: electric vehicles supply equipment
LinuxWindows Linux
SocketSocket
Description of test caseSB: MV/LV Substation equipped with a 15 kV/ 400 V transformer every that feeds the LV distribution lines.
10 EVSEs at each location.
Locations of EVSEs in Bologna
SUMO generates a new car every 2 s.
MV line
HV/MV SB
Top view of the map of Bologna with the indication of parking lots with EVSE clusters (orange bullets), HV/MV substations (blue rectangles), and the two 15kV feeders from substation SB_A that connects the EVSE clusters denoted as EVSE_1, EVSE_2, EVSE_3, and EVSE_4
Test caseSB: MV/LV Substation equipped with a 15 kV/ 400 V transformer every that feeds the LV distribution lines.
10 EVSEs at each location.
Present locations of EVSEs in Bologna
SUMO generates a new car every 2 s.
MV line
HV/MV SB
Top view of the map of Bologna with the indication of parking lots with EVSE clusters (red round), HV/MV substations (blue rectangles), and the two 15kV feeders from substation SB_A that connects the EVSE clusters denoted as EVSE_1, EVSE_2, EVSE_3, and EVSE_4
p. 39E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu
Multi agent system for the distributed control of charging stations
Hypotheses:• One agent (i.e. a control system connected to the shared communication network) to each
MV/LV transformer that feeds a cluster of EVSE units. • The intelligent electronic devices (IEDs) are installed at the HV/MV substation and at the
feeder branches that may reach their maximum current rate during the operation of the distribution network.
• One IED at the beginning of each feeder connected to the secondary side of the HV/MV transformer.
• This IED measures the current and compares the value with the maximum operation current value of the line.
Each IED calculates and broadcasts, over UMTS cellular network, the variation of a congestion index:
Each agent associated to an EVSE cluster updates its own congestion index and fixes the maximum power that could be absorbed from the MV network:
,
,
,
i t
i t
EVSE
EVSEi t
PP
pr=
)
, , 1 ,max(1, )i t i t j tpr pr pr−= + ∆
maximum requested charging power at time t
, , 1, , , 1( )
2j t j t
j t c j t j tI
e etpr K e e
τ−
−
+ ∆∆ = − +
Could you explain the physical meaning of the constants Kc & t1? Why 0.2 and 0.1?
, ,max,
,max
j t jj t
j
i ie
i
−=where
p. 40E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu
Test case
The simulations are repeated for two different BLER values: 1E-5 (case indicated as BLER0) and 0.1 (BLER1), which is a typical reference performance value (e.g. [21]).
The same simulations are repeated with the BT (case indicated as BT1) and without the BT (BT0). BT is generated by the UEs associated to the agents and by seven additional UEs and two traffic receivers. The two adopted mobile users application models are characterized by two different gamma distributions of the inter-arrival packet time in s (with parameters 0.0068, 5 and 0.2, 0.5, respectively) and exponential distributions of packet size in bytes (with parameters 41.03 and 62.97, respectively).
Test case results
Number of charging EVs in each cluster:
Power requested by each cluster:
Current values (in p.u. of the maximum operating current) measured by the IEDs associated to the first branch of the two considered feeders:
Congestion index variations calculated by the IEDs:
Congestion indexes calculated by each agent:
3
4
5
6
7
8
9
10
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
N°
of E
lect
ric
Veh
icle
s co
nnec
ted
Time (s)
EVSE_1EVSE_2EVSE_3EVSE_4
280
300
320
340
360
380
400
420
440
460
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
Pow
er (k
W)
Time (s)
EVSE_1 - BT0 BLER0 EVSE_1 - BT1 BLER1EVSE_2 - BT0 BLER0 EVSE_2 - BT1 BLER1EVSE_3 - BT0 BLER0 EVSE_3 - BT1 BLER1EVSE_4 - BT0 BLER0 EVSE_4 - BT1 BLER1
0.998
1
1.002
1.004
1.006
1.008
1.01
1.012
1.014
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
Cur
rent
(p.
u.)
Time (s)
feeder 1 (EVSE_1, EVSE_2) - BT0 BLER0feeder 1 (EVSE_1, EVSE_2) - BT1 BLER1feeder 2 (EVSE_3, EVSE_4) - BT0 BLER0feeder 2 (EVSE_3, EVSE_4) - BT1 BLER1
0.E+00
5.E-03
1.E-02
2.E-02
2.E-02
3.E-02
3.E-02
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
∆pr
Time (s)
feeder 1 (EVSE_1, EVSE_2) - BT0 BLER0feeder 1 (EVSE_1, EVSE_2) - BT1 BLER1feeder 2 (EVSE_3, EVSE_4) - BT0 BLER0feeder 2 (EVSE_3, EVSE_4) - BT1 BLER1
11.051.1
1.151.2
1.251.3
1.351.4
1.451.5
1.551.6
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
Con
gest
ion
Inde
x
Time (s)
EVSE_1 - BT0 BLER0EVSE_2 - BT0 BLER0EVSE_3 - BT0 BLER0EVSE_4 - BT0 BLER0EVSE_1 - BT1 BLER1EVSE_2 - BT1 BLER1EVSE_3 - BT1 BLER1EVSE_4 - BT1 BLER1
∆t = 1 s
Test case resultsNumber of charging EVs in each cluster:
Power requested by each cluster:
Current values (in p.u. of the maximum operating current) measured by the IEDs associated to the first branch of the two considered feeders:
Congestion index variations calculated by the IEDs:
Congestion indexes calculated by each agent:
3
4
5
6
7
8
9
10
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
N°
of E
lect
ric
Veh
icle
s co
nnec
ted
Time (s)
EVSE_1EVSE_2
EVSE_3EVSE_4
0.990.9920.9940.9960.998
11.0021.0041.0061.0081.01
1.0121.014
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
Cur
rent
(p.
u.)
Time (s)
feeder 1 (EVSE_1, EVSE_2) - BT0 BLER0
feeder 1 (EVSE_1, EVSE_2) - BT1 BLER1
feeder 2 (EVSE_3, EVSE_4) - BT0 BLER0
feeder 2 (EVSE_3, EVSE_4) - BT1 BLER1
0.E+00
2.E-02
4.E-02
6.E-02
8.E-02
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
∆pr
Time (s)
feeder 1 (EVSE_1, EVSE_2) - BT0 BLER0
feeder 1 (EVSE_1, EVSE_2) - BT1 BLER1
feeder 2 (EVSE_3, EVSE_4) - BT0 BLER0
feeder 2 (EVSE_3, EVSE_4) - BT1 BLER1
1
1.1
1.2
1.3
1.4
1.5
1.6
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
Con
gest
ion
Inde
x
Time (s)
EVSE_1 - BT0 BLER0EVSE_2 - BT0 BLER0EVSE_3 - BT0 BLER0EVSE_4 - BT0 BLER0EVSE_1 - BT1 BLER1EVSE_2 - BT1 BLER1EVSE_3 - BT1 BLER1EVSE_4 - BT1 BLER1
BT BLER Time
I1>1pu (s)Time
I2>1pu (s)I1 max (p.u.)
I2 max (p.u.)
Packet delay (ms)mean (stdev)
Number of packets TX
RXBT0
BLER0∆t = 1 s
93 79 1.0109 1.0143 142.6 (10.0)378378
BT1 BLER1∆t = 1 s
101 81 1.0113 1.0147 231.3 (189.0)406363
BT0 BLER0∆t = 3 s
71 69 1.0107 1.0149149.7(12.7)
122122
BT1 BLER1∆t = 3 s
81 69 1.0107 1.0149235.3
(180.5)128120
∆t = 3 s
Test case results
5
6
7
8
9
10
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
N°
of E
lect
ric
Veh
icle
s co
nnec
ted
Time (s)
EVSE_1
EVSE_2
EVSE_3
EVSE_4
240
260
280
300
320
340
360
380
400
420
440
460
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
Pow
er (
kW)
Time (s)
EVSE_1
EVSE_2
EVSE_3
EVSE_4
0.965
0.97
0.975
0.98
0.985
0.99
0.995
1
1.005
1.01
1.015
1.02
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
Cur
rent
(pu)
Time (s)
feeder 1 (EVSE_1, EVSE_2)
feeder 2 (EVSE_3, EVSE_4)
-1.5E-05-1.0E-05-5.0E-060.0E+005.0E-061.0E-051.5E-052.0E-052.5E-053.0E-053.5E-054.0E-054.5E-055.0E-05
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
∆pr
Time (s)
feeder 1 (EVSE_1, EVSE_2)
feeder 2 (EVSE_3, EVSE_4)
11.021.041.061.081.1
1.121.141.161.181.2
1.221.241.261.281.3
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
Con
gest
ion
Inde
x
Time (s)
feeder 1 (EVSE_1, EVSE_2)feeder 2 (EVSE_3, EVSE_4)
Number of charging EVs in each cluster:
Power requested by each cluster:
Current values (in p.u. of the maximum operating current) measured by the IEDs associated to the first branch of the two considered feeders:
Congestion index variations calculated by the IEDs:
Congestion indexes calculated by each agent:
p. 44E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu
Analysis of the power flow profiles
Ideal congestion management strategy
- book the EVSE for the minimum time required to get the battery fully recharged assuming to get the maximum power (e.g. a driver in a hurry);- book the EVSE for a long time (some hours) to enable slow recharge when the demand is high (e.g. a driver that parks the car close to his or her workplace).
Electric vehicles drivers can behave basically in two different ways
What if EVs are considered as energy sources as well?
p. 45E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu
Analysis of the power flow profiles
Ideal congestion management strategy
Allocation of power cuts among all the EVSE fed by the same GCP
- maximize the number of vehicles fully recharged;- maximize the energy given to each vehicle connected to an EVSE.
p. 46E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu
Analysis of the power flow profiles
Power load profile in a typical weekday
p. 47E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu
Analysis of the power flow profiles
Analysis of the occupancy of charging stations
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
av
era
ge
po
we
r a
bso
rbe
d b
y e
ach
EV
SE
(in
kW
)
number of occupied EVSEs
Average power absorbed by each EVSE downstream to IED 1 around 6 PM
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
av
era
ge
po
we
r a
bso
rbe
d b
y e
ach
EV
SE
(in
kw
)
number of occupied EVSEs
Average power absorbed by each EVSE downstream to IED 1 around 10 AM
p. 48E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu
Analysis of the power flow profiles
0
1
2
3
4
5
6
7
8
12:00
AM
1:00
AM
2:00
AM
3:00
AM
4:00
AM
5:00
AM
6:00
AM
7:00
AM
8:00
AM
9:00
AM
10:00
AM
11:00
AM
12:00
PM
1:00
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2:00
PM
3:00
PM
4:00
PM
5:00
PM
6:00
PM
7:00
PM
8:00
PM
9:00
PM
10:00
PM
11:00
PM
Av
era
ge n
um
be
r o
f ru
nn
ing
ve
hic
les
(pe
rce
nta
ge o
f th
e t
ota
l nu
mb
er)
hour of the day
Profile of vehicle traffic during the day
p. 49E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu
Analysis of the power flow profiles
0
1
2
3
4
5
16:00 16:30 17:00 17:30 0
10
20
30
40
50
# E
VSE
Pow
er
(Kw
)
HourOccupied EVSEs Average Power
Num
ber
of E
VS
Es
Pow
er (
kW)
Portion of a simulation run showing the average pow er absorbed by the EVSEs downstream to IED 2 and the number of occupied EVSEs
Coinvolgimento internazionale Coinvolgimento internazionale
