PhD_Dissertation

ELETTRONICA PER L’ ENERGIA E L’AUTOMAZIONE
X I V C I C L O N . S .
ELECTRIC MOBILITY: SMART
TRANSPORTATION IN SMART CITIES
T U T O R : P h D C A N D I D A T E :
P R O F . V I N C E N Z O G A L D I G I U S E P P E G R A B E R
C O - T U T O R :
P R O F . P I E R L U I G I M A N C A R E L L A
UNIVERSITÀ DEGLI STUDI DI SALERNO
Scuola Dottorale di Ingegneria
Dottorato in Ingegneria dell’Informazione

Outline
 Transport Systems in Smart Cities of the Future
 Transport Systems for Sustainable Mobility
 Sustainable Transportation in Modern Power Systems
 Smart Grids: Power Systems in Smart Cities
 The Smart Grid Concept
 Generation Rescheduling and Load Shedding under Uncertainty Conditions
 EVs in Smart Grids: a Smart Charging Solution
 Prediction of Electric Vehicles Charging Demand
 Optimal Scheduling of Electric Vehicles Charging
 Energy Efficiency in Metro Systems
 Recovery of Braking Energy
 Eco-Drive Operation
2
GIUSEPPE GRABER - Electric Mobility: Smart Transportation in Smart Cities

Outline
3

4
Transport Systems in Smart Cities
of the Future
 Individual and collective transport demand grows because it is
increased the need to move and the average covered distances
ENERGY DEMAND IN TRANSPORT SECTOR
Source: IEA
(World Energy Outlook 2012)

5
In urban areas, the use of electrified
transport systems can improve the
air quality, traffic congestion and
noise pollution
 Transport Capacity
 Transition rates
 Cruising Speed
 Reliability
of the Future
TRAM, METRO AND LIGHT RAILWAY SYSTEMS
CO2 EMISSIONS
ENERGY CONSUMPTION
Source: IEA - Railway Handbook 2015

6
of the Future
RAILWAY TRANSPORT SYSTEM INTO MODERN POWER SYSTEMS
 Most electrical loads are spatio-
temporally varying load
 The load can vary significantly
in a few seconds
 Railway lines are often inter-
connected to heterogeneous
electrical grids
 Bidirectional power flows
between trains and the infrastructure
due to regenerative braking
RAILWAY ELECTRICAL
SMART GRIDS

7
 GHG Emission
 Energy Efficiency
 Performance
 Maintenance Cost
Electric vehicle ‘tank-to-wheels’
efficiency is a factor of about 3
higher than internal combustion
engine vehicles
of the Future
HYBRID AND BATTERY ELECTRIC VEHICLE
Source: IEA - Technology Roadmap 2011

8
of the Future
ELECTRIC VEHICLES INTO MODERN POWER SYSTEMS
Uncontrolled Charging can increase
average load in the existing power
systems, with problems in terms of
reliability and overloads
Controlled or Smart Charging:
 avoid local overload and allowing a
faster EVs penetration
 not require an improvement of the
electricity generating and grid capacity
SMART GRID TECHNOLOGIES

MODERN
POWER SYSTEMS
9
How to support the integration of SUSTAINABLE TRANSPORT
SYSTEMS into power systems of the smart cities?
INCREASE IN
ELECTRIFIED
TRANSPORT SYSTEMS
GROWING EVS
PENETRATION
of the Future
OBJECTIVE OF THE DISSERTATION
OPTIMIZED
MANAGEMENT OF EVS
CHARGING DEMAND
IMPROVING SAFETY
AND RELIABILITY IN
THE SMART GRIDS
INNOVATIVE
SOLUTIONS FOR
ENERGY EFFICIENCY

Outline
10

11
Smart Grids: Power Systems
in Smart Cities
GENERATION SCHEDULING & LOAD SHEDDING
Microgrid Energy Management
System MEMS is responsible for the
optimization of the microgrid operation
 It is necessary to balance the
power generation and demand
in microgrids
 The generation scheduling
consist in changing the generation
power to reduce the congestion
 If the congestion still prevails after
the generation scheduling, the load
shedding will be carried out

12
in Smart Cities
A linguistic declaration as ”power load may occur between Pa and Pc MW
but it is likely Pb” can be modeled by the fuzzy number πP(x, Pa, Pb, Pc)
POSSIBILITY THEORY & FUZZY NUMBERS
A fuzzy variable with its membership function is related to a possibility
distribution (probability distribution  random variable)
  UxxxA A  |)(,
 πA(x)=0 means that A=x is definitely impossible
 πA(x)=1 means that absolutely nothing prevents
that A=x

13
Rescheduling problem is formulated as an optimization fuzzy
problem based on an AC power flow, subject to operating constraints
PROBLEM FORMULATION: Rescheduling
XPxts
PF
G
G
P gN
G



)
~
(~..
]1,0[)
~
(
~
min 1
F
~



gN
i
L
TOTGi PPF
1
1
~~~

























gGiGiGi
gGiGiGi
gGiGiGi
lhh
jkkjjk
N
k
kj
SP
i
ijjiij
N
i
ji
SP
i
NiVVV
NiQQQ
NiPPP
NhII
nQjYVVQ
nPiYVVP
b
b
...1
~
...1
~
...1
~
...1
~
)
~~
sin(
~~~
)
~~
sin(
~~~
maxmin
maxmin
maxmin
max
1
1


ELECTRICAL CONSTRAINTS
Active power of the
DER at the bus i
Overall load
active power
in Smart Cities

14
 
 
 










 
 

 
 
bg
g b
g b
N
j
H
Lj
N
i Gi
N
i
N
j
M
Lj
H
LjGi
N
i
N
j
L
Lj
M
Lj
H
LjGi
PPF
PPPF
PPPPF
113
1 12
1 11
~~~
~~~~
~~~~~
In the load-shedding problem, each bus is associated with an
aggregate load with a priority degree
PROBLEM FORMULATION: Load Shedding
XPPPxts
PPPF
L
L
M
L
H
L
L
L
M
L
H
L
P BNL
L



)
~
,
~
,
~
(~..
]1,0[)
~
,
~
,
~
(
~
min 1
F
~ 
XPPxts
PPF
M
L
H
L
M
L
H
L
P BNM
L



)
~
,
~
(~..
]1,0[)
~
,
~
(
~
min 2
F
~ 
XPxts
PF
H
L
H
L
P BNH
L



)
~
(~..
]1,0[)
~
(
~
min 3
F
~ 
M
LP
~
H
LP
~
Multilevel Optimization Problem



















DfPPDfP
NhiIIVVV
nQjYVVQ
nPiYVVP
sh
LL
sh
L
lhhiii
jkkjjk
N
k
kj
SP
i
ijjiij
N
i
ji
SP
i
b
b
)(
~~
)(
~
...1,
~
;
~
)
~~
sin(
~~~
)
~~
sin(
~~~
minmin
maxmaxmin
1
1


in Smart Cities

15
SOLUTION ALGORITHM
in Smart Cities
 Translate the deterministic solution to
a fuzzy number solution by applying the
same maximum uncertainty width interval
 Define an interval for all load flow
input variables
 Carry out Monte Carlo simulations by
using input variables values selected in the
interval
 Check that the power flow solutions are
feasible according to technical constraints
 Repeat the last two steps in order to
obtain a fuzzy number solution

16
 The tests are performed on a 69 branch:
an outage occurs at the bus 3
 Total load is 3802.19 kW
 Not-programmable and programmable
DER units are added in correspondence of
the buses 4, 14, 35, 38, 46, 47, 52, 58 and
65, (3950 kW)
 Each DER unit is characterized by using
symmetrical triangular fuzzy
numbers
in Smart Cities
NUMERICAL RESULTS

17
 It is not always possible to obtain a new network configuration for
each α value but it is possible to evaluate feasible solutions with a
risk factor αcrit
OPTIMAL
LOAD SHEDDING + RESCHEDULING
OPTIMAL RESCHEDULING
per α >0.2
in Smart Cities
V. Calderaro, V. Galdi, G. Graber, A. Piccolo - “Optimal Generation Rescheduling in Microgrids under Uncertainty”

18
 Comparison between the proposed fuzzy numbers based approach and
the classical stochastic optimization
V. Calderaro, V. Galdi, G. Graber, A. Piccolo - “Generation Rescheduling and Load Shedding in Distribution Systems under Imprecise Information”
in Smart Cities
 Computationally more efficient
due to fuzzy arithmetic
 More conservative estimation
of the solution
For each possible event A=x its
possibility πA(x) is the greatest
possible probability of the event

Outline
19

20
EVs in Smart Grids: a Smart Charging
Solution
Prediction of Electric Vehicles charging demand is fundamental:
• to identify the bottlenecks and benefits of their future integration
scenarios
• to evaluate the impact on smart grids of a deep EVs penetration
 overloading
 voltage profile changes
 power losses
 electricity markets
 power system reliability

21
CO.S.MO. (Cooperative Systems for Sustainable
Mobility and Energy Efficiency) was a pilot project
co-founded by the European Commission developed
by UniSA with CRF
Solution
3 PARKING AREAS
2 COGENERATION UNITS
(580 KW EACH ONE)
8 PHOTOVOLTAIC POWER PLANTS
(1076 KW TOTAL POWER)

22
By analyzing parking data,
50 CLUSTERS are derived,
each one with a different residual
SoC value at the UniSA arrival
Nissan Leaf Route Type
Value
[kWh/km]
Urban 0.160
Extra urban 0.126
Highway 0.185
Mixed 0.169
Batt
HHEEUU
da
C
dcdcdc
SoCSoC
)()()( 

Solution
AVERAGE SoC 72%
V. Calderaro, V. Galdi, G. Graber, G. Massa, A. Piccolo - “Plug-in EV Charging Impact on Grid Based on Vehicles Usage Data”

23
 Parking areas occupancy is taken into
account by splitting the observation period
into different clusters
 The clustering function is implemented
by using k-means algorithm
Solution

24
Daily charging profiles are obtained by considering the charging
power absorption in Monte Carlo simulation (step-time 15 minutes) for
each parking area, for each EVs incoming cluster and for each charging
station type
AC1 (230 V, 3 kW) for domestic use
AC2 (230 V, 6 kW) for public use
Solution
1.5MW

25
The need for OPTIMAL SCHEDULING ALGORITHMS able to
allow EVs charging while avoiding negative drawbacks on
distribution networks is becoming a relevant issue to face with future
power system planning and management actions
Solution

26
The aim is to jointly minimize the electrical peak power and the
charging cost in compliance with the expected parking times
 






  


CS
CHARGE
i
START
i
START
i
START
N
i
Tt
t
START
i
CHARGE
iBASE
START
i
t
dtttptpttMJ
J
1
)()()(
min















),()(0
)1()(
)(
tSoCPtp
CSoCdttp
TTtNt
tNt
i
BATTERY
i
CHARGE
i
Tt
t
Batt
i
START
i
CHARGE
i
CHARGE
i
PARKING
i
START
i
START
i
CHARGE
i
START
i
START
i
 
 



















bus
bus
CS
N
j
jkkjjkkjjkk
N
j
jkkjjkkjjkk
BASE
N
i
CHARGE
i
BGVVQ
BGVVP
tpPtp
1
1
max
1
)cos()sin(
)sin()cos(
)()(


MAIN CONSTRAINTS
PROBLEM FORMULATION
Base Load
EV Charging Cost
Solution

27
The CC-CV charging model of the EV battery is taken into account
in the proposed optimization problem
Solution
Batt
i
ii
ii
C
dTIV
tSoCdTtSoC



3600
)()(
 
 




























SoC
i
cv
i
SoC
icv
i
cv
ii
SoC
icc
i
cc
SoC
i
cc
ii
uVu
T
t
VV
u
T
t
IuII
1exp1
1exp
 
 






%100,800
%80,01
SoCu
SoCu
SoC
i
SoC
i
NISSAN LEAF BATTERY PACK
courteously by Nissan-Renault Italy
CC charging CV charging

28
G. Graber, G. Massa, V. Galdi, V. Calderaro, A. Piccolo - “Performance Comparison between Scheduling Strategies for PEVs Charging in Smart Grids”
Solution
NUMERICAL RESULTS
Conventional Scheduling Strategy Proposed Scheduling Strategy
Scheduled charging shows a flattened load profile and a significant
reduction in the active power absorption peak in the hours between 8:00 a.m.
and 12:00 p.m. compared to the uncontrolled charging

29
The proposed scheduling algorithm
ensures satisfaction of user needs
and a reduction of charging cost
EV Charging
Charging Cost [€]
AC1 Mode AC2 Mode
UNCONTROLLED 1,78 1,62
SCHEDULED 1,49 1.32
G. Graber, G. Massa, V. Galdi, V. Calderaro, A. Piccolo - “Performance Comparison between Scheduling Strategies for PEVs Charging in Smart Grids”
Solution
Residual SoC [%]
Average extra time
[min]
AC1 Mode AC2 Mode
SoC ≥ 80 45 18
65 ≤ SoC < 80 74 30
50 ≤ SoC < 65 99 46
30 ≤ SoC < 50 121 52
AC1 MODE
AC2 MODE
- 16.3% - 18.5%

Outline
30

31
 Energy consumption in railway systems can be split:
 more efficient drive train technologies and designs
 recovering vehicles braking energy
 drive cycle minimizing the energy consumption
 traction systems
 infrastructure service utilities
 signalling systems
80%
5%
15%
FS Data
Energy Efficiency in Metro Systems
SUSTAINABILITY MEANS ENERGY EFFICIENCY
S.F.E.R.E. Research
Project - Sistemi
Ferroviari: Eco-
sostenibilità e
Risparmio Energetico
jointly with Hitachi
Rail Italy

32
STATIONARY ENERGY STORAGE SYSTEMS
 Batteries
 Flywheels
 SUPERCAPACITORS

33
PROBLEM FORMULATION
CONSTRAINTS
METRO
NETWORK
SIMULATOR
 
 







)(),(
min
CECPEJ
J
SCSS
C
P
N
N



   

N
n
nSCSC
N
n
nSC cVVcCE
1
2
max
2
max
1 2
1
2
1

















NntSoCTtSoC
NnTtSocSoCSoC
NnTtVVV
TtPPP
Tt
V
P
I
TtVVV
nn
n
SCSCnSC
TRACTIONTrainBRAKE
LINE
SSE
SSE
LINELINELINE
,...,1)0()(
,...,1,
,...,1,
0
maxmin
maxmin
maxmax
min
max
maxmin
The problem to solve consists in finding optimal number, sizing and
positioning on the track of supercapacitor-based energy storage systems
V. Calderaro, V. Galdi, G. Graber, A. Piccolo - “Optimal siting and sizing of stationary supercapacitors in a metro network using PSO”

34
SOLUTION ALGORITHM & METRO SYSTEM ELECTRICAL MODEL
ENERGY STORAGE
SYSTEM
METRO VEHICLE
CONTACT WIRE
ELECTRIC
SUBSTATION

35
SUPERCAPACITOR-BASED ENERGY STORAGE SYSTEM ELECTRICAL MODEL
 sc
SCs
T
SCSCSC
VCC
tIRdI
C
VtV



 
1
)()(
1
)0()(
0

36
CASE STUDY & NUMERICAL RESULTS
NETWORK PARAMETERS VALUE
CASAZZA-MOMPIANO LENGTH 1045 m
MOMPIANO-EUROPA LENGTH 592 m
RAIL ELECTRIC RESISTANCE 0.0133 Ω/km
SUBSTATION DC VOLTAGE 750 V
SUBSTATION INTERNAL RESISTANCE 0.0125 Ω
MAXIMUM LINE VOLTAGE +20 %
MINIMUM LINE VOLTAGE -33 %
VEHICLE PARAMETERS VALUE
EMPTY MASS 59357 kg
LOADED MASS 88170 kg
MAX. TRACTION POWER 630 kW
ACCESSORIES POWER 90 kW
GEAR BOX EFFICIENCY 0.98
MOTOR EFFICIENCY 0.85
INVERTER EFFICIENCY 0.90
BRESCIA METRO NETWORK:
Prealpino - S. Eufemia

37
 Reduction of the energy supplied by
the substation (≥12.7% )
 Reduction in substation peak current
(≈15%)
 Better stabilization of the line voltage
NUMERICAL RESULTS
Siting
[F]
Theoretical
Sizing
[F]
Substation
Energy
[kWh]
Real
Sizing
[F]
Substation
Energy
[kWh]
200 10
6.392
0
6.505
300 150 155.7
800 20 0
1200 160 173.2
1500 20 0
V. Calderaro, V. Galdi, G. Graber, A. Piccolo - “Optimal siting and sizing of stationary supercapacitors in a metro network using PSO”

38
 ACCELERATION
 CRUISING
 COASTING
 BRAKING
An ECO-DRIVE CYCLE ensures:
• same trip distance
• equivalent stops
• same final time
• lower energy consumption

39
Computing of the minimal energy consumption speed cycle according to the
vehicle's kinematics and the time-table
   
  0)(,00
,00


Tvv
LTss
BOUNDARY CONDITIONS
 
   xVtv
utuu
max
maxmin
0 

KINEMATIC CONSTRAINTS
  
  bxr
a
mgxmgR
vmR
line
base





sin
)( 2
21










TtPPP
Tt
V
P
I
TtVVV
TRACTIONTrainBRAKE
LINE
SSE
SSE
LINELINELINE
maxmax
min
max
maxmin
0
PROBLEM FORMULATION
   












dt
dx
v
t
dt
dv
tvxx
xRvRtu
dt
dv
m LINEBASE
2
00
2
1
)(
 





 
T
SS
tu
dt
dt
tdu
tuPJ
J
0
)(
)(
))((
min

METRO
NETWORK
SIMULATOR

40
HEURISTIC VS. DETERMINISTIC SOLUTION ALGORITHMS
V. Calderaro, V. Galdi, G. Graber, A. Piccolo - “Deterministic vs Heuristic Algorithms for Eco-Driving Application in Metro Network”

41
REFERENCE PSO DPO
Computing
Time [s]
Substation
Energy [kWh]
REF. -- 6.935
DPO 64853 6.491
PSO 25362 6.640
- 6.4%
- 4.2%
NUMERICAL RESULTS

Publication List
Conference Papers
 V. Calderaro, V. Galdi, G. Graber, A. Piccolo - “Siting and Sizing of Stationary Supercapacitors in a
Metro Network”, in proc. of AEIT Annual Conference, 2013, pp. 1-5
 V. Calderaro, V. Galdi, G. Graber, G. Graditi, F. Lamberti - “Impact assessment of energy storage and
electric vehicles on smart grids”, in proc. of Electric Power Quality and Supply Reliability Conference
(PQ), 2014, pp. 15-18
 V. Calderaro, D. Cogliano, V. Galdi, G. Graber, A. Piccolo - “An algorithm to optimize speed profiles of
the metro vehicles for minimizing energy consumption”, in proc. of Power Electronics, Electrical
Drives, Automation and Motion (SPEEDAM), 2014 International Symposium on, pp. 813-819
 V. Calderaro, V. Galdi, G. Graber, G. Massa, A. Piccolo - “Plug-in EV Charging Impact on Grid Based
on Vehicles Usage Data”, in proc. of Electric Vehicle Conference (IEVC), 2014 IEEE International, pp. 1-7
 V. Calderaro, V. Galdi, G. Graber, A. Piccolo - “Deterministic vs Heuristic Algorithms for Eco-
Driving Application in Metro Network”, in proc. of Electrical Systems for Aircraft, Railway, Ship
Propulsion and Road Vehicles (ESARS), 2015 International Conference on, pp. 1-6
 V. Calderaro, V. Galdi, G. Graber, A. Piccolo - “Optimal siting and sizing of stationary
supercapacitors in a metro network using PSO”, in proc. of Industrial Technology (ICIT), 2015 IEEE
International Conference on, pp. 2680-2685
 R. Lamedica, A. Ruvio, V. Galdi, G. Graber, P. Sforza, G. G. Buffarini, C. Spalvieri - “Application of Battery
Auxiliary Substations in 3kV Railway Systems” in proc of. AEIT Annual Conference, 2015, pp. 1-6
42

Publication List
 V. Calderaro, V. Galdi, G. Graber, A. Capasso, R. Lamedica, A. Ruvio - “Energy Management of
Auxiliary Battery Substation Supporting High-Speed Train on 3 kV DC Systems”, in proc. of
Renewable Energy Research and Applications (ICRERA), 2015 IEEE International Conference on, pp. 1-6
 G. Graber, G. Massa, V. Galdi, V. Calderaro, A. Piccolo - “Performance Comparison between
Scheduling Strategies for PEVs Charging in Smart Grids”, in proc. of Renewable Energy Research
and Applications (ICRERA), 2015 IEEE International Conference on, pp. 1-6
 G. Graber, V. Galdi, V. Calderaro, A. Piccolo, L. Fratelli – “Experimental Validation of a Steady-State
Metro Network Simulator for Eco-Drive Operations”, in IEEE International Conference on
Environment and Electrical Engineering (EEEIC), June 2016, (accepted)
43
Journals
• V. Calderaro, V. Galdi, G. Graber, A. Piccolo - “Optimal Generation Rescheduling in Microgrids
under Uncertainty”, in International Review of Electrical Engineering (IREE) - Num. 5 , Vol. 8, pp. 1-8,
(Oct. 2013)
• V. Calderaro, V. Galdi, G. Graber, A. Piccolo - “Generation Rescheduling and Load Shedding in
Distribution Systems under Imprecise Information”, in IEEE Systems Journal - Vol. PP, Issue 99,
pp. 1-9, (Jan. 2016)
• G. Graber, V. Galdi, V. Calderaro, A. Capasso, R. Lamedica, A. Ruvio - “Energy Management of
Auxiliary Battery Substation Supporting High-Speed Train on 3 kV DC Systems” in IEEE
Transactions on Industrial Application, (under review)

ELECTRIC MOBILITY: SMART TRANSPORTATION IN
SMART CITIES
I n g . G I U S E P P E G R A B E R
THANK YOU FOR YOUR ATTENTION
UNIVERSITÀ DEGLI STUDI DI SALERNO
Scuola Dottorale di Ingegneria
Dottorato in Ingegneria dell’Informazione

PhD_Dissertation

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