Impact of electric vehicles in sizing the power ... · Impact of electric vehicles in sizing the...
Transcript of Impact of electric vehicles in sizing the power ... · Impact of electric vehicles in sizing the...
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EVS28 KINTEX, Korea, May 3-6, 2015
Impact of electric vehicles in sizing the
power transformer in micro-grid system
X-L. Dang1, P. Codani1,2, M. Petit1
1Department of Power and Energy Systems, Supelec, France 2Advanced Technologies and Innovation Research Department, PSA
Peugeot Citroen, France
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Outline
I. Introduction
II. Optimal Transformer Sizing
III. Introduction of PV and EVs
IV. Energy Management System
V. Results
VI. Conclusion
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Outline
I. Introduction
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Introduction (1)
• Objectives in CO2 emission reduction
• Increasing share of Renewable Energy Sources (RES)
• Increasing interest in Plug-in Electric Vehicles (PEV)
• Renewable Energy Sources:
• Intermittent
• Asynchronous
• Located at the DSO side
• PEV:
• Peak power related problems
• Located at the DSO side
• Problem:
• Concerns about grid security
• Flexibility of different types of EVs in the distribution network?
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Introduction (2)
• Research topic:
• Evaluating the impacts of introducing PV panels and EVs in an eco-dis
trict on the substation transformer
• Defining an energy management strategy for flexible loads
• Using EVs as flexibility sources
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Distribution
Grid
Transformer
Psub
EV fleet AResidential households
Commercial Buildings
PV panels
EV fleet B
EV fleet C
Eco-district
Prod
Cons
Cons + Stor
Figure: System overview
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Introduction (3)
• Approach:
1. Optimal transformer sizing, without no PV nor EVs, with temporary
overloadings allowed
2. Introduction of PV and EVs analysis of overloading conditions
3. Definition of an energy management strategy for EVs charging, with
V2G capabilities analysis of new overloading conditions
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Distribution
Grid
Transformer
Psub
EV fleet AResidential households
Commercial Buildings
PV panels
EV fleet B
EV fleet C
Eco-district
Prod
Cons
Cons + Stor
Figure: System overview
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Outline
II. Optimal Transformer Sizing (no PV nor EV)
1. Residential & commercial load curves
2. Transformer Operating conditions
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Residential consumption
• Residential consumption modeling1
• This model takes into account the specific nature of particular consumers
• It also includes a model of the use of household lightings2
• District composed of 200 households
• With a mean of 4 people per household
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1I. Richardson, M. Thomson, D. Infield, and C. Clifford, Domestic electricity use: A high-resolution energy demand model, Energy and Buildings, vol. 42, no. 10, pp.18781887, Oct. 2010 2Ian Richardson, Murray Thomson, David Infield, and Alice Delahunty, "Domestic lighting: A high-resolution energy demand model ," Energy and Buildings , vol. 41, no. 7, pp. 781-789, 2009.
Figure: residential power consumption over one day
12AM 3AM 6AM 9AM 12PM 3PM 6PM 9PM 12AM0
75
150
225
P (
kW
)
Presi
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Commercial consumption
• Modeling of the commercial building consumption:
• Heating / Air cooling, ventilation, IT hardware, lightings and misc
• Summing up all the consumptions
• Data processing (15 minute time stamp)
• On site data from a commercial building
• 1000 people working in the district
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12AM 3AM 6AM 9AM 12PM 3PM 6PM 9PM 12AM0
25
50
75
100
P (
kW
)
Pterti
Figure: commercial consumption load curve over one day
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Transformer operating conditions
• Optimal Transformer Sizing, temporary overloading periods
allowed, no PV nor EV:
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Figure: transformer operating conditions during overloading periods
Figure: Commercial + residential load curves, and corresponding optimal transformer sizing
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Outline
III. Introduction of PV and EVs
1. PV and EV modeling
2. Transformer operating conditions
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PV model
• Assumption: the commercial building rooftop is covered with
PV panels:
• 3000m2 of PV panels
• On site data near Paris (France)
• Measured over one year
• 15 minute time stamp
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EV modeling (1)
• Full electric vehicles
• 22 kWh
• SOC ∈ [20% ; 90%]
• Three types of fleet
• EV fleet A: people living in the district (20% take rate, i.e. 160 EVs)
• EV fleet B: people working in the district (10% take rate, i.e. 100 EVs)
• EV fleet C: company fleet (10 EVs)
• Several driver behaviors considered:
• Range anxiety
• Charge-at-work “selfish” behavior
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EV modeling (2)
• Electric Vehicle Supply Equipment (EVSE) charging powers:
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• Use for transportation:
• PSA Peugeot Citroen data
• CROME project results
EVSE power plug Fleet A Fleet B Fleet C
Slow (a) – 3kW 93% 35% 0%
Slow (b) – 7kW 7% 34% 0%
Intermediate charging – 22kW 0% 29% 100%
Fast charging – 43kW 0% 2% 0%
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Transformer operating conditions
• Introducing PV and EVs, highlighting forbidden overloading
periods
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(a) PV only (b) PV & EVs
Figure: transformer operating conditions with the introduction of PV and EVs
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Outline
IV. Energy Management System
1. Strategy
2. Transformer operating conditions
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Energy management system strategy
• EV fleets used as flexible sources (some EVs are not flexible
due to charging needs for transportation)
• Determination of the power flow between the district and the
grid due to non-flexible units:
• Determination of the power provided by the flexible EVs:
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𝑃𝑓𝑙𝑜𝑤 𝑡 = 𝑃𝑃𝑉 𝑡 − 𝑃𝑟𝑒𝑠𝑖 𝑡 + 𝑃𝑐𝑜𝑚 𝑡 + 𝑃𝐸𝑉𝑛𝑜𝑛𝐹𝑙𝑒𝑥𝑖(𝑡)
Pflow
Prated
- Prated
A B C D
t
Over
production
Over
consumption
Charging strategy
Discharging strategy
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Transformer operating conditions
• Implementation of the Energy Management System
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0 2 4 6 8 10 12100
110
120
130
140
150
160
t(h)
% in P
rate
d
overloading occurrences
guideline limitations
Figure: transformer operating conditions with the EMS
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Outline
V. Results
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Comparison of the scenarios
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Figure: District load curves for all the scenarios
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Transformer operating conditions
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0 2 4 6 8 10 12100
110
120
130
140
150
160
t(h)
% in P
rate
d
overloading occurrences
guideline limitations
Figure: transformer operating conditions
(a) No PV nor EV (b) PV only
(c) PV & EV, no EMS (b) PV & EV, EMS
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Numerical results
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Items Pmax_global (kW)
Eex (MWh) Duration (h)
Psub_ave (kW)
Non EMS 488 21,2 613 35
EMS 376 2,0 186 11
Improvement ratio (%)
23 90 70 71
Table: Numerical gains with the EMS
• Pmax_global: maximum daily peak power
• Eex: Energy exchanged during overloading periods
• Duration : duration of the overloading periods
• Psub_ave: average power during overloading periods
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Outline
VI. Conclusion
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Conclusion
• The transformer is first sized with respect to the thermal limitations
• The various operating conditions of the transformer are presented with the introduction of PV and EVs
• The EMS implemented:
• Enables to reduce significantly the overloading periods
• Allows to increase the penetration of EVs in the district
• Enables to reduce the transformer contracted power, or allows for more power consumption during specific periods
• Future work:
• Economical analysis of the gains
• Identify the maximum level of penetration for EVs
• Determination of the optimal relationship between the PV surface and the number of EVs
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