| dc.description.abstract | In recent years, the shift in attitude towards climate and CO2 emissions has acceleratedthe sale of electric and hybrid electric vehicles in Norway. Predictionsindicate that Norway may surpass 200,000 chargeable vehicles by 2020, which correspondsto seven percent of the total vehicle fleet. This number includes bothelectric vehicles and hybrid electric vehicles. To explore the impact a large scaleelectric vehicle adoption will have on the power grid, simulations of an existinglow voltage power system have been conducted. The load flow simulation toolSimpow was used for this purpose, and Nord-Trøndelag Elektrisitetsverk providedinformation on the grid structure and consumer consumption data. From the supplieddata, February 2 was chosen for the 24-hour simulation period. This dayhas the highest energy consumption, and therefore represents the ?worst case?scenario. A hypothetically built wind turbine close to the residential areas wasintegrated in the system, using wind measurement data from a wind farm in Nord-Trøndelag. Different scenarios were explored, investigating how sensitive the gridis to additional load under different assumptions, and how the wind generation cancontribute to a more self-supporting power system. Symmetrical and asymmetricaldistribution of electric vehicle charging loads in relation to physical locationshave been compared, and the results suggest that one cannot give an exact numberof vehicles that the system can handle. The system capacity when operatingwith dumb charging strategies is varying depending on where the vehicles are situatedphysically. With many electric vehicles located close together, the givenvoltage level constraints of the model were violated with a seven percent electricvehicle penetration share. However, assuming that vehicles are more spread outphysically, the system restrictions were not violated for a electric vehicle share of20 percent. In other words, the placements of the additional loads are equallydecisive for the system voltage variations as the number of loads. By applyingsmart charging strategies, the voltage fluctuations in the system during a day aremitigated. For the 20 percent EV penetration scenarios, given the assumptionspresented in this thesis, the added load does not seem to put more stress on thesystem 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 smartcharging strategies. Other measures will have to be taken if the power systemever 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 February2 can be assumed to be the ?worst case? scenario, that is, the lowest voltage levelsthroughout the year was observed on that day. It also gave an indication on howwell the wind turbine is suited to relieve the system of increased consumptiondue to electric vehicle charging. If the wind generation is assumed to cover theadditional load created by the electric vehicles, the need for imported power inthe system will not increase. Wind generation during February 2 is higher thanthe electric vehicles consumption if 20 percent share is assumed. This relation,however, is not representative for the generation throughout the year. Wind generationis unpredictable, and generally higher during winter. Installing an energystorage system makes the wind energy more controllable. Still, days with littleor no wind generation will inflict the need of a huge capacity storage system tocover the charging loads at all times. Assuming a lower electric vehicle adoptionshare, and not requiring the wind generation to cover charging loads at all times,the needed storage system capacity could be realizable. | nb_NO |
