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Analysis of Large Scale Integration of Electric Vehicles in Nord-Trøndelag

Sagosen, Øystein
Master thesis
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http://hdl.handle.net/11250/2368152
Utgivelsesdato
2013
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  • Institutt for elkraftteknikk [1597]
Sammendrag
In recent years, the shift in attitude towards climate and CO2 emissions has accelerated

the sale of electric and hybrid electric vehicles in Norway. Predictions

indicate that Norway may surpass 200,000 chargeable vehicles by 2020, which corresponds

to seven percent of the total vehicle fleet. This number includes both

electric vehicles and hybrid electric vehicles. To explore the impact a large scale

electric vehicle adoption will have on the power grid, simulations of an existing

low voltage power system have been conducted. The load flow simulation tool

Simpow was used for this purpose, and Nord-Trøndelag Elektrisitetsverk provided

information on the grid structure and consumer consumption data. From the supplied

data, February 2 was chosen for the 24-hour simulation period. This day

has the highest energy consumption, and therefore represents the ?worst case?

scenario. A hypothetically built wind turbine close to the residential areas was

integrated in the system, using wind measurement data from a wind farm in Nord-

Trøndelag. Different scenarios were explored, investigating how sensitive the grid

is to additional load under different assumptions, and how the wind generation can

contribute to a more self-supporting power system. Symmetrical and asymmetrical

distribution of electric vehicle charging loads in relation to physical locations

have been compared, and the results suggest that one cannot give an exact number

of vehicles that the system can handle. The system capacity when operating

with dumb charging strategies is varying depending on where the vehicles are situated

physically. With many electric vehicles located close together, the given

voltage level constraints of the model were violated with a seven percent electric

vehicle penetration share. However, assuming that vehicles are more spread out

physically, the system restrictions were not violated for a electric vehicle share of

20 percent. In other words, the placements of the additional loads are equally

decisive for the system voltage variations as the number of loads. By applying

smart charging strategies, the voltage fluctuations in the system during a day are

mitigated. For the 20 percent EV penetration scenarios, given the assumptions

presented in this thesis, the added load does not seem to put more stress on the

system than it can handle. However, for the 50 percent EV penetration scenarios,

the charging load might present the system with too much stress, even with smart

charging strategies. Other measures will have to be taken if the power system

ever experiences an EV share that high. A long term simulation was performed to verify the results obtained from the 24-hour simulations. It verified that February

2 can be assumed to be the ?worst case? scenario, that is, the lowest voltage levels

throughout the year was observed on that day. It also gave an indication on how

well the wind turbine is suited to relieve the system of increased consumption

due to electric vehicle charging. If the wind generation is assumed to cover the

additional load created by the electric vehicles, the need for imported power in

the system will not increase. Wind generation during February 2 is higher than

the electric vehicles consumption if 20 percent share is assumed. This relation,

however, is not representative for the generation throughout the year. Wind generation

is unpredictable, and generally higher during winter. Installing an energy

storage system makes the wind energy more controllable. Still, days with little

or no wind generation will inflict the need of a huge capacity storage system to

cover the charging loads at all times. Assuming a lower electric vehicle adoption

share, and not requiring the wind generation to cover charging loads at all times,

the needed storage system capacity could be realizable.
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