SMART GRIDS Dalle Smart Grids alle Smart Cities - i sistemi elettrici del futuro - Levi Cases
←
→
Trascrizione del contenuto della pagina
Se il tuo browser non visualizza correttamente la pagina, ti preghiamo di leggere il contenuto della pagina quaggiù
SMART GRIDS
i sistemi elettrici del futuro
Dalle Smart Grids alle Smart Cities
Carlo Alberto Nucci
DEI – Guglielmo Marconi – Università di Bologna
carloalberto.nucci@unibo.it
Lunedì 28 ottobre 2015
Aula Magna, Palazzo del Bo
Padova1 Smart City
Smart City: definizione?
Smart City: definizione?
Smart City: definizione?
1
Bologna Smart City: strategies overview
2
3
4
5
6
Smart Energy Smart Smart Smart People Smart Smart Living
Mobility environment Government
Renovation of Inclusiveness Data
the built Pedestrian Safety Digital management
and bicycle Agenda
Smart devices
mobility
environment Waste Business
management Cloud Innovation
Intermodality &Crowd
Smart grids
Water System
e.services
Social Innovation
Last mile Management
Smart lighting
logistic Cultural
Heritage
promotionEU Smart Cities Stakeholder Platform:
11 priority areas
3 ‘vertical’ domains
8 ‘horizontal’ enabling
themes2 Smart Grid
Potenza elettrica installata in Italia
Potenza efficiente lorda degli impianti
elettrici di generazione in Italia al 31.12.14Smart Grid Anche la rete elettrica tradizionale è smart
Intentional islanding/reconnection – PMU 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.
Proc. UPEC, Padua, Italy, Sept 2008
Intentional islanding/reconnection – PMU
Islanding maneuver
PP is equipped with a PMS that:
- 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; GT1 GT2
Islanding capabilities tested with EMTP-RV. STIntentional islanding/reconnection – PMU
Islanding of GT1: Distribution network voltage
phasors angles differences during the
3
2
1Intentional 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
200 0.21
Angle difference between positive-sequence
Angle difference between positive-sequence
components of PMU2 and PMU3 phasors (°)
components of PMU1 and PMU2 phasors (°)
PMU2-PMU3
0 Identification of
PMU1-PMU2
-200 0.2 the correct phase
-400 difference between
0.19
-600 islanded and
-800 external network to
0.18
-1000 trigger the
-1200 0.17
reconnection
-1400 maneuver.
-1600 0.16
0 10 20 30 40 50 60
Time (s)Intentional islanding/reconnection – PMU
PMU measured frequency transient
50.15
PMU1
Monitoring of the
frequency difference
PMU2
50.1 (positive) between the
PMU3
Frequency (Hz)
islanded network
50.05 (PMU1 and PMU2)
and the external
50
network (PMU3).
49.95
0 10 20 30 40 50 60
Time (s)3
Smart Grid per Smart
CitiesSmart 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
La Piattaforma Bologna Smart City
“Le smart city sono sistemi intelligenti e sostenibili, aree urbane
In sintonia con
che pianificano coerentemente l’integrazione delle diverse
Regione
caratteristiche identitarie del proprio territorio - culturali,
economiche, produttive, ambientali CNR,- inENEA, Lepida,
un’ottica imprese
di innovazione.
locali estrada
Bologna ha scelto di percorrere questa nazionali,..
nel solco della
Piano
propria tradizione civica, attraverso Strategico
un’alleanza tra mondo della
Metropolitano
ricerca ed Università, imprese e pubblica amministrazione per
sviluppare soluzioni utili ad affrontare problematiche urbane e
sociali, mettendo le tecnologie al Per l’Università
servizio di Bologna
delle persone”
pieno coinvolgimento del
Gruppo di Lavoro ‘Smart
City’ dell’Alma Mater
Studiorum
24La 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
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
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
25Prof. Danilo Montesi
Oplon
MIUR
Smart cities and social innovation
Art 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€
Line of intervention: ageing of the society
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)
OPLON: costo 773.558,99
(contributo 618.847,19)
Agenzia sanitaria e sociale regionale 26Proff. Armando Brath
e Sandro Artina
Watertech
WATERTECH: costo 620.000
(contributo 496.000)
Prelocalizzazione
Progetto PERDITE IDRICHE
SMART WATERTECH
Monitoraggio ACQUE
SMART WATER METERING PARASSITE
(modelli di previsione
domanda e tariffazione)
Sistemi di CONTROLLO ed
ATTUAZIONE in TEMPO REALE
(pressione, qualità delle acque)
Monitoraggio
SCARICATORI DI PIENA
Rilevamento inquinanti mediante
IMMAGINI SATELLITARIRIGERS – RIGENERAZIONE DELLA CITTÀ: EDIFICI E RETI INTELLIGENTI
ICIE - CPL - SACMI - CMC - SATA - CNR ICT - UNIBO (DA - DEI – DICAM) Prof. Andrea Boeri
Decreto Direttoriale 13 febbraio 2014 n.428 - BANDO SMART CITIES NAZIONALE
Ambito di interesse primario:
Ambito di interesse secondario:
ARCHITETTURA SOSTENIBILE E MATERIALI
SICUREZZA DEL TERRITORIO
Rigers
Ambito di interesse secondario: SMART GRIDS
RIGERS: costo 799.096,24
(contributo 639.276,99)RIQUALIFICAZIONE, RIGENERAZIONE E VALORIZZAZIONE DEGLI INSEDIAMENTI DI EDILIZIA SOCIALE AD ALTA INTENSITÀ
ABITATIVA NELLE PERIFERIE URBANE NELLA SECONDA METÀ DEL ‘900 – PRIN 2008 DA UNIBO
IPOTESI RIQUALIFICAZIONE DELL’EDIFICIO VIRGOLONE: QUARTIERE PILASTRO BOLOGNA
FABBISOGNO ENERGETICO ATTUALE
118,54 kWh/m2y
RIDUZIONE DEL CONSUMO DI ENERGIA
24,86
kWh/m2y
UNIVERSITÀ DI BOLOGNA – DIPARTIMENTO DI ARCHITETTURA – A. Boeri – E. Antonini – D. Longo – R. Roversi – G. Chieregato
CASO STUDIO: COMPLESSO RESIDENZIALE “PILASTRO” – BOLOGNAE2SG
Energy to Smart Grid
T4.11 Demonstration of storage optimization by
exploiting electric vehicles
D4.11 Simulator of storage optimizing policies
Luca Bedogni (IUNET-luca.bedogni4@unibo.it), Luciano Bononi (IUNET-
luciano.bononi@unibo.it), Alberto Borghetti (IUNET – alberto.borghetti@unibo.it),
Riccardo Bottura (IUNET - riccardo.bottura2@unibo.it), Alfredo D’Elia (IUNET-
alfredo.delia4@unibo.it), Federico Montori (IUNET-federico.montori2@unibo.it), Carlo
Alberto Nucci (IUNET – carloalberto.nucci@unibo.it), Elisa Pitti (HERA-
Elisa.Pitti@gruppohera.it), Tullio Salmon Cinotti (IUNET-tullio.salmoncinotti@unibo.it)
Final Review E2SG
October 21/22 2015, Berlin
E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.euT4.11 – Simulator of storage policies of EVs
Overview
WP3 Grid Topology & Control
WP4 Demonstrators
3.4 Advanced storage management policies 4.11 Demonstration of
storage optimization by
exploiting electric vehicles
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)
E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu p. 31T4.11 – Simulator of storage policies of EVs
Architecture of the co-simulation environment
E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu p. 32Mobility simulator: OMNET++ - SUMO - Veins - Openstreetmap
Traffic Simulation Framework
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
E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu p. 33Power 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
E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu p. 34Scheme for the interface between the model of urban traffic
and the power distribution system simulator
Model of the electric power Model of the urban traffic
distribution system ID of active EV charging
systems and power Initial occupation of EV
Initial power flow charging systems
Transient 3ph simulation Traffic simulation
(Δt=1ms) ID of active EV charging (Δt=100ms)
systems and maximum
Model of nodal aggregated power profile Start and end of charging of
(1ph and 3ph) EV charging individual EV at a specific
systems charging station
Check of network
constrains by using IEDs
Update of charging profile
information Limitation of the power for
each specific active/idle and duration due to electric
EV charging system
Networked control EV network limitations
charging systems
Model of the communication networkSynchronization between the model of urban traffic and the
power distribution system simulator
Windows Linux Linux
EMTP-rv OMNET++ & SUMO
Socket Socket
Reservation event
Load change SIB EVSE plug
(Storage
Network operating Information) EVSE charging
condition +
WS
EVSE profile synchronizer EVSE profile
load change EVSE unplug
*EVSE: electric vehicles supply equipment
e.g.: OPNET,
External SW 1 External SW 2 External SW n CityService,
… Smartphone App,
…Description of test case
SB: MV/LV Substation equipped with a 15 kV/ 400 V transformer every that feeds the
LV distribution lines.
Locations of EVSEs in
Bologna
10 EVSEs at each
location.
SUMO generates a new car every 2 s.
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
MV line
HV/MV SBTest case
SB: MV/LV Substation equipped with a 15 kV/ 400 V transformer every that feeds the
LV distribution lines.
Present locations of
EVSEs in Bologna
10 EVSEs at each
location.
SUMO generates a new car every 2 s.
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
MV line
HV/MV SBMulti 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.
∆t e + e j ,t −1
Each IED calculates and broadcasts, over ∆prj ,t = Kc ( e j ,t − e j ,t −1 ) + j ,t
UMTS cellular network, the variation of a τ I 2
congestion index: i j ,t − i j ,max
where e j ,t =
Each agent associated to an EVSE cluster i j ,max
updates its own congestion index and fixes
the maximum power that could be
pri ,t = max(1, pri ,t −1 + ∆prj ,t )
absorbed from the MV network:
Could you explain the physical )
PEVSEi ,t maximum requested
meaning of the constants Kc & PEVSEi ,t = charging power at
t1? Why 0.2 and 0.1? pri ,t time t
E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu p. 39Test 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).
E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu p. 40Current values (in p.u. of the maximum operating
Test case results ∆t = 1 s current) measured by the IEDs associated to the first
branch of the two considered feeders:
1.014
feeder 1 (EVSE_1, EVSE_2) - BT0 BLER0
Number of charging EVs in each cluster: 1.012 feeder 1 (EVSE_1, EVSE_2) - BT1 BLER1
feeder 2 (EVSE_3, EVSE_4) - BT0 BLER0
1.01 feeder 2 (EVSE_3, EVSE_4) - BT1 BLER1
10
Current (p.u.)
N° of Electric Vehicles connected
1.008
9 1.006
8 1.004
EVSE_1 1.002
7
EVSE_2 1
6 EVSE_3 0.998
EVSE_4 400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
5 Time (s)
4
Congestion index variations calculated by the IEDs:
3.E-02
3 feeder 1 (EVSE_1, EVSE_2) - BT0 BLER0
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700 3.E-02 feeder 1 (EVSE_1, EVSE_2) - BT1 BLER1
Time (s) feeder 2 (EVSE_3, EVSE_4) - BT0 BLER0
2.E-02 feeder 2 (EVSE_3, EVSE_4) - BT1 BLER1
∆pr
2.E-02
Power requested by each cluster: 1.E-02
460 EVSE_1 - BT0 BLER0 EVSE_1 - BT1 BLER1
EVSE_2 - BT0 BLER0 EVSE_2 - BT1 BLER1 5.E-03
440
EVSE_3 - BT0 BLER0 EVSE_3 - BT1 BLER1
420 EVSE_4 - BT0 BLER0 EVSE_4 - BT1 BLER1 0.E+00
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
Power (kW)
400 Time (s)
380 Congestion indexes calculated by each agent:
360 1.6
1.55
340 1.5
320 1.45
Congestion Index
1.4
300 1.35
280 1.3 EVSE_1 - BT0 BLER0
1.25 EVSE_2 - BT0 BLER0
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700 1.2 EVSE_3 - BT0 BLER0
EVSE_4 - BT0 BLER0
Time (s) 1.15 EVSE_1 - BT1 BLER1
1.1 EVSE_2 - BT1 BLER1
1.05 EVSE_3 - BT1 BLER1
EVSE_4 - BT1 BLER1
1
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
Time (s)Current values (in p.u. of the maximum operating
Test case results ∆t = 3 s current) measured by the IEDs associated to the first
branch of the two considered feeders:
feeder 1 (EVSE_1, EVSE_2) - BT0 BLER0
Number of charging EVs in each cluster: 1.014
feeder 1 (EVSE_1, EVSE_2) - BT1 BLER1
1.012
feeder 2 (EVSE_3, EVSE_4) - BT0 BLER0
10 1.01
N° of Electric Vehicles connected
1.008 feeder 2 (EVSE_3, EVSE_4) - BT1 BLER1
9 1.006
Current (p.u.)
1.004
8
1.002
EVSE_1 1
7
EVSE_2 0.998
6 EVSE_3 0.996
EVSE_4 0.994
5 0.992
0.99
4
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
3 Time (s)
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
Time (s) Congestion index variations calculated by the IEDs:
Power requested by each cluster: feeder 1 (EVSE_1, EVSE_2) - BT0 BLER0
8.E-02
feeder 1 (EVSE_1, EVSE_2) - BT1 BLER1
feeder 2 (EVSE_3, EVSE_4) - BT0 BLER0
6.E-02 feeder 2 (EVSE_3, EVSE_4) - BT1 BLER1
∆pr
4.E-02
2.E-02
0.E+00
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
Time (s)
Congestion indexes calculated by each agent:
1.6
Number of
Time Time I1 max I2 max Packet delay (ms)
BT BLER packets TX
I1>1pu (s) I2>1pu (s) (p.u.) (p.u.) mean (stdev) 1.5
RX
BT0
378 1.4
BLER0 93 79 1.0109 1.0143 142.6 (10.0)
Congestion Index
∆t = 1 s 378
BT1 1.3 EVSE_1 - BT0 BLER0
406 EVSE_2 - BT0 BLER0
BLER1 101 81 1.0113 1.0147 231.3 (189.0) EVSE_3 - BT0 BLER0
∆t = 1 s 363 1.2 EVSE_4 - BT0 BLER0
BT0 EVSE_1 - BT1 BLER1
149.7 122
BLER0 71 69 1.0107 1.0149 1.1 EVSE_2 - BT1 BLER1
∆t = 3 s (12.7) 122 EVSE_3 - BT1 BLER1
BT1 EVSE_4 - BT1 BLER1
235.3 128 1
BLER1 81 69 1.0107 1.0149 400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
∆t = 3 s (180.5) 120
Time (s)Current values (in p.u. of the maximum operating
Test case results current) measured by the IEDs associated to the first
branch of the two considered feeders:
Number of charging EVs in each cluster: 1.02
10 1.015
N° of Electric Vehicles connected
1.01
9 1.005
Current (pu)
1
8 0.995
EVSE_1
0.99
EVSE_2 feeder 1 (EVSE_1, EVSE_2)
0.985
7 EVSE_3 feeder 2 (EVSE_3, EVSE_4)
0.98
EVSE_4
0.975
6 0.97
0.965
5 400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700 Time (s)
Time (s)
Congestion index variations calculated by the IEDs:
Power requested by each cluster: 5.0E-05 feeder 1 (EVSE_1, EVSE_2)
460 4.5E-05 feeder 2 (EVSE_3, EVSE_4)
440 4.0E-05
3.5E-05
420
3.0E-05
400 2.5E-05
380
∆pr
2.0E-05
Power (kW)
360 1.5E-05
340 EVSE_1 1.0E-05
5.0E-06
320 EVSE_2
0.0E+00
300 EVSE_3 -5.0E-06
280 EVSE_4 -1.0E-05
260 -1.5E-05
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
240 Time (s)
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
Time (s) Congestion indexes calculated by each agent:
1.3
1.28
feeder 1 (EVSE_1, EVSE_2)
1.26
1.24 feeder 2 (EVSE_3, EVSE_4)
1.22
Congestion Index 1.2
1.18
1.16
1.14
1.12
1.1
1.08
1.06
1.04
1.02
1
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
Time (s)Analysis of the power flow profiles
Ideal congestion management strategy
Electric vehicles drivers can behave basically in two different ways
- 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).
What if EVs are
considered as energy
sources as well?
E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu p. 44Analysis 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.
E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu p. 45Analysis of the power flow profiles
Power load profile in a typical weekday
E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu p. 46Analysis of the power flow profiles
Analysis of the occupancy of charging stations
60
average power absorbed by each EVSE
50
40 Average power absorbed by each
EVSE downstream to IED 1
(in kW)
30
20
around 6 PM
10
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
number of occupied EVSEs
60
average power absorbed by each EVSE
50
40
Average power absorbed by
each EVSE downstream to IED
(in kw)
30
1 around 10 AM
20
10
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
number of occupied EVSEs
E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu p. 47Analysis of the power flow profiles
Profile of vehicle traffic during the day
8
7
Average number of running vehicles
6
(percentage of the total number)
5
4
3
2
1
0
12:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00
AM AM AM AM AM AM AM AM AM AM AM AM PM PM PM PM PM PM PM PM PM PM PM PM
hour of the day
E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu p. 48Analysis of the power flow profiles
Portion of a simulation run showing the average power
absorbed by the EVSEs downstream to IED 2 and the
number of occupied EVSEs
5 50
4 40
of EVSEs
(Kw)
3 30
(kW)
# EVSE
Power
Power
Number
2 20
1 10
0 0
16:00 16:30 17:00 17:30
Hour
Occupied EVSEs Average Power
E2SG INTERNAL/CONFIDENTIAL Copyright @ E2SG Consortium www.e2sg-project.eu p. 49Coinvolgimento internazionale
Puoi anche leggere
