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

Outline

I. Introduction

II. Optimal Transformer Sizing

III. Introduction of PV and EVs

IV. Energy Management System

V. Results

VI. Conclusion

2

Outline

I. Introduction

2

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?

3

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

4

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

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

5

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

Outline

II. Optimal Transformer Sizing (no PV nor EV)

1. Residential & commercial load curves

2. Transformer Operating conditions

2

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

6

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

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

Transformer operating conditions

• Optimal Transformer Sizing, temporary overloading periods

allowed, no PV nor EV:

8

Figure: transformer operating conditions during overloading periods

Figure: Commercial + residential load curves, and corresponding optimal transformer sizing

Outline

III. Introduction of PV and EVs

1. PV and EV modeling

2. Transformer operating conditions

2

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

9

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%

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

Outline

IV. Energy Management System

1. Strategy

2. Transformer operating conditions

2

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

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

Outline

V. Results

2

Comparison of the scenarios

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Figure: District load curves for all the scenarios

Transformer operating conditions

16

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

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

Outline

VI. Conclusion

2

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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