Transport is essential for the existence and development of mankind, but it brings with it many negative features such as pollution, noise, traffic accidents and so on. One of the biggest problems is environmental pollution due to the emission of greenhouse gases from internal combustion vehicles, which is why the solution of green and cleaner transport powered by electricity has come up. This is how electric vehicles have entered the market, to prevent environmental pollution. In addition to being more environmentally friendly, EVs also have the advantage of cheaper transport, as the price of fuel is more expensive per km than the price of electricity. This is why some people choose to buy EVs as an investment vehicle in the hope of saving on fuel. In addition, one of the major problems in transport is traffic accidents.
Electric vehicles have brought with them other problems such as higher vehicle prices, low range, and the inability to charge quickly and charge wherever we want, but one of the main problems is the impact of EVs on the grid. EVs are stochastic loads, which means that they are unpredictable to the grid in terms of when and where they will plug in, which in turn causes congestion and voltage deviations. Therefore, in this thesis, I decided to find out and investigate the topic of adjusting the charging time of EVs, so that the EVs impact on the grid will be as small as possible. I will look for the optimal time intervals when to plug in an electric vehicle so that the grid is least affected.
In my thesis, I have studied the given low voltage distribution network, which has been captured based on real data of our network. Several electric vehicles were then added to the grid and I observed the response of the grid. I examined the allocated network and determined when the household consumption was lowest and switched on the EV chargers at that time. The lowest household consumption is in the morning and at night, so I connected the EVs to the grid at that time and it turned out that I avoided power peaks and spread the load of the grid nicely throughout the week. The most optimal time for charging is at night.
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