Electric Vehicles Aggregator/System
Operator Coordination for Optimal
Charging Scheduling and Services
Procurement
Miguel Ortega-Vazquez (UW)
Francois Bouffard (McGill)
Vera Silva (EDF)
UNIVERSITY of WASHINGTON
Seattle, USA
Introduction
• Decarbonisation of the transportation sector:
– Electricity as energy carrier
– Ideally, energy from RES
– Large fleets of EVs will constitute a significant share of the
wide system demand
• Advent of the Smart Grid
– Demand will feature higher degrees of communication and
control
.
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
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Electric vehicles (EVs)
• Unlike most existing loads, EVs are equipped with a
battery:
– Waive their energy requirements in time
– Energy storage
– Provide ancillary services, e.g. reserve
.
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
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Impacts
• The pliant nature of the EVs as demand makes them
good candidates to impact the system:
– Technically: provide flexibility to the system
– Economically: trading that flexibility
.
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
4
EVs operating modes
EV operating mode
Schedule
&
p
• Vehicle charging
• Vehicle discharging (V2G)
$
p
$
$
p
$
$
p
• Vehicle plugged-in
• Vehicle unused
$
• Vehicle circulating
.
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
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Power system + EVs
Wholesale
Energy
Co-ordination
Retail
Aggregator
.
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
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Aggregator
• Bridge between EVs and power system players:
tt
ct
lt
Aggregator
et
gT(x,u,p) ≤ 0
gD(x,u,p) ≤ 0
.
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
7
Interaction Aggregator & SO
• Generation scheduling
– Minimize total operating costs
• Power balance constraint (including EVs’ charge and discharge)
• Security: system reserve requirements (unforeseen events)
• Units technical operating constraints:
– Minimum up- and down-times
– Up and down ramp rate limits
• EVs scheduling
– Sell flexibility services to the system
• EVs’ energy requirements
• EVs operating constraints:
– State of charge
– Charging and discharging rates
.
z
V2G
p
Cup
p
Cdn
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
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From day-ahead to real-time
• Schedules are performed a day-ahead on the basis of:
– Wind power generation forecast
– Demand forecast
– Forecasted EVs availabilities
• Deviations between forecasts and actual values
materialize in real-time
– Conventional generation deployment to meet net demand
deviations
– Deployment of EVs’ flexibility services as a function of the
agreed prices the day before
.
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
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Test system
• IEEE-RTS omitting hydro generation
– 26 Units, 3105 MW installed
Group # Units Capacity (MW) Fuel
A
5
12
oil/steam
B
4
20
oil/CT
C
4
76
coal/steam
D
3
100
oil/steam
E
4
155
coal/steam
F
3
197
oil/steam
G
1
350
oil/steam
H
2
400
nuclear
Cost functions
A
5000
B
Cost, $/h
4000
C
3000
D
2000
E
F
1000
G
0
H
0
100
Composition
F
19%
.
A
2% B
2%
C
10%
E
20%
300
400
Power, MW
D
10%
Price, $/MWh
G
11%
H
26%
200
40
30
20
10
0
0
500
1000
1500
2000
Power, MW
Market Clearing at Period 20
2500
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
3000
10
Demand and wind profiles
• Forecasted and actual values…
wind power, MW
demand, MW
300
2500
2000
1500
5
10
15
200
100
0
20
5
demand, MW
time, hrs
10
15
20
time, hrs
2500
Actual
Forecasted
2000
1500
2
4
6
8
10
12
14
16
18
20
22
24
time, hrs
.
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
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Price, $/MWh
40
30
20
EVs’ characteristics
10
0
0
500
EV
parameter
2000type 2500
3000
EV
Power,
BEVMW City-BEV PHEV
Value
Market
Clearing
at
Period
20
EV fleet composition (%)
37
10
53
100
Battery capacity
(kWh)
35
16
18
24.1
40
Clearing Price
Power
Reserve
Energy consumption (kWh/km) 0.2
0.12
0.212345
0.192
30
21 22
EV range (km)
175
133
90
125.5
141516
20
Average distance (km/day) 24 17 18 19 20 10111213
40.0
25
26
Daily consumption
(kWh/day)
7.68
10
Charge/discharge rate (kW/h)
3.70
0
Total number
of vehicles
689,475
0
500
1000
1500
2000
2500
3000
power, MW
1500
price, $/MWh
1000
25
25
a)
15
10
5
0
15
10
5
5
10
15
time, hrs
.
b)
20
EVs, %
EVs, %
20
20
0
5
10
15
20
time, hrs
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
12
wind pow
demand
2000
System with no EVs
1500
5
10
15
100
0
20
5
10
time, hrs
20
10
0
2500 30
20
10
1500
2
4
6
8
10
12
14
16
18
20
22
Actual
Forecasted
2000
0
24
0
$/MWh
Price,$/MWh
price,
demand, MW
2500
b)
2
25
40
4
6
8
10
12
14
16
18
20
22
25
25
price,
%
EVs,$/MWh
SR, MW
22
16
Reserve
15
1000
2000
c)
a)
14
Power
20
20
10 11 12 13
15
500
4
6
8
10
12
14
2000
2400
2200
Power, MW
21
power, MW
Market Clearing at Period 12
16
18
20
22
24
time, hrs
.
14
Power
0
100
24
1000
2
3000
15
10
time, hrs
0
20
30
20
10 11 12 13
20
2000
1500
2000
15
Power,
21
time, hrsMW
Market Clearing at Period 12
time, hrs
3000
1000
10
5
Miguel Ortega-Vazquez - Electrical Engineering -
15
10
5
10
0
3000
2600
22
1525 16
Reserve
b)
20
EVs, %
$/MWh
a)
40
Price, $/MWh
demand, MW
30
time
15
10
5
2000
2200 0
2400
2600
5
10
15
20
5
10
15
20
power,
MW
UNIVERSITY of WASHINGTON
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Uncontrolled EVs – 30% penetration
30
20
100
0
2
4
6
8
10
12
14
16
18
20
22
24
time, hrs
50
0
100
a)
V2G, %
10
Charging, %
$/MWh
a)
5
10
15
b)
50
0
20
5
b)
2000
1500
5
10
15
2
4
6
8
10
12
14
16
18
20
22
24
20
50
0
5
Unused, %
10
15
20
time, hrs
e)
50
5
10
15
20
time, hrs
500
2
20
d)
100
0
c)
15
100
time, hrs
1000
SR, MW
50
0
time, hrs
0
c)
Circulating, %
2500
10
time, hrs
100
3000
Reserve, %
demand, MW
time, hrs
4
6
8
10
12
14
16
18
20
22
24
time, hrs
.
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
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Uncontrolled EVs – 30% penetration
30
20
100
0
2
4
6
8
10
12
14
16
18
20
22
24
time, hrs
50
0
100
a)
V2G, %
10
Charging, %
$/MWh
a)
5
10
15
b)
50
0
20
5
2500
100
b)
2000
1500
2
4
6
8
10
12
14
16
18
20
22
24
10
c)
20
15
20
d)
50
0
5
time, hrs
Unused, %
1000
SR, MW
5
15
100
10
15
20
time, hrs
100
time, hrs
e)
50
0
5
10
15
20
time, hrs
500
0
c)
50
0
10
time, hrs
Circulating, %
3000
Reserve, %
demand, MW
time, hrs
2
4
6
8
10
12
14
16
18
20
22
24
time, hrs
.
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
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Effect of the EVs’ penetration
Uncontrolled
3000
net demand, MW
net demand, MW
Optimized
2500
2000
1500
5
10
15
20
3000
2500
2000
1500
5
time, hrs
30
25
20
15
20% EVs
31% EVs
35
price, $/MWh
price, $/MWh
20
40
20% EVs
60% EVs
100% EVs
35
30
25
20
15
5
10
15
time, hrs
.
15
time, hrs
40
10
10
20
10
5
10
15
20
time, hrs
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
16
400
a)
MW
200
0
Effect of the V2G markup
-200
-400
0
5
10
30
charge, kWh
$/MWh
a)
20
2
4
6
8
10
12
14
16
18
20
22
24
MW MW
power,
capacity,
Reserve
demand, MW
2
4
6
8
10
12
14
16
b)
2000
6
8
10
12
14
22
24
Injected in V2G
Discharging losses
60
1000
4
20
100
1500
80
2
18
time, hrs
1000
0
0
10
3000
1500
25
10
2000
0
time, hrs
2500
20
20
3000
10
0
15
time, hrs
16
18
20
22
24
time, hrs
40
500
20
00
1 2
42
6
38
10 4 12
14
5
time,
hrs
zV2G
, $/MWh
16
6 18
20 7
22
24
8
SR, MW
1000
c)
500
0
2
4
6
8
10
12
14
16
18
20
22
24
time, hrs
.
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
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Effect of the pricing of the reserve
from EVs
30
a)
$/MWh
- 20 The price of the reserve from the supply
10 side is 20% of the bids of energy
- Reserve from EVs at 2 $/MW
4
6
8
10
12
14
16
18
20
22
24
time, hrs
50
0
100
a)
V2G, %
2
Charging, %
0
100
5
10
15
b)
50
0
20
5
b)
2000
1500
5
10
15
2
4
6
8
10
12
14
16
18
20
22
24
20
50
0
5
Unused, %
10
15
20
time, hrs
e)
50
5
10
15
20
time, hrs
500
2
20
d)
100
0
c)
15
100
time, hrs
1000
SR, MW
50
0
time, hrs
0
c)
Circulating, %
2500
10
time, hrs
100
3000
Reserve, %
demand, MW
time, hrs
4
6
8
10
12
14
16
18
20
22
24
time, hrs
.
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
18
30
Reducing the exercise price for reserve
deployment
$/MWh
a)
100
Charging, %
24
time, hrs
50
0
5
10
15
100
a)
Scheduled
Deployed
V2G, %
- 20 Price of the reserve from the supply side at
10 30% of the bids of energy
- Reserve from EVs at 2 $/MW
0
2
4
6
8 at 5
10 $/MWh
12
14
16
18
20
22
- Exercise
price
b)
50
0
20
5
10
b)
50
200
2000
5
4
6
8
10
12
14
16
18
20
22
24
time, hrs
0
5
charge, kWh
SR, MW
1000
500
2
4
6
8
10
12
14
16
18
20
22
24
50
a)
0
5
10
15
20
time, hrs
e)
10
15
20
25
time, hrs
50
0
20
5
10
15
20
time, hrs
10
0
0
20
d)
100
-200
c)
15
20
100
time, hrs
0
2
10
Unused, %
1500
0
Circulating, %
Reserve, %
c)
400
2500
15
time, hrs
100
3000
MW
demand, MW
time, hrs
2
4
6
8
10
12
14
16
18
20
22
24
time, hrs
time, hrs
.
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
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Summary
• Presented the necessary adaptations to a typical USstyle short-term forward electricity markets including
EVs fleet aggregators
• The operating constraints of the EVs are explicitly
modeled
• Represents adequately the collective operation of the
EVs to attain benefits while reducing operating costs
• Several ancillary services and their deployment are
represented: V2G, Reserve capacity as additional charge
and interruption of scheduled charging/discharging
.
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
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Conclusions
• If no coordination is implemented, only a limited
amount of EVs can be successfully integrated in the
system
• Implementing an approach such as the proposed,
allows the integration of large volumes of EVs without
needing to invest in generation assets
• The participation of the aggregator(s) as service
providers determined by the services bids
.
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
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Conclusions
• Charging process is scheduled at the cheapest periods
• Services from EVs are scheduled only if these are
competitive with those from conventional sources
• V2G is attractive to avoid synchronization of expensive
generation
• Reserve is in the form of interruption of charging is
attractive, but it can only be offered when EVs are
charging  light loads…
• Reserve as additional charge are attractive, but only
when their supply equivalent is expensive
.
Miguel Ortega-Vazquez - Electrical Engineering - UNIVERSITY of WASHINGTON
22