ESRU EV Demand Model

Model Input Data

The focus was on Electric Vehicles (EVs), that have the smallest amount of CO2 emissions. The EV model is from the data collection of EV charging station in Glasgow. In this model shown the EV specification that using car park to be a public charging category by a different type of battery capacity.

The model was developed ESRU using the Visual Basic for Applications within Excel and Visual Basic Software. It was originally developed to generate the demand profile for 100 electric vehicles and 14 charging points.

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There are three main types of electric vehicles (EVs), classed by the degree that electricity is used as their energy source. BEVs, or battery electric vehicles, PHEVs of plug-in hybrid electric vehicles, and HEVs, or hybrid electric vehicles. Only BEVs are capable of charging on a level 3, DC fast charge [4].

There were 3 types of chargers used, EVOLT, Rapid DC and an OC4S. There was pretty much the same number of each charger used however, their charging power was drastically different. The OC4S has a significantly higher charger power compared to the other chargers meaning it can charge the vehicles a lot faster.

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The tool is developed to generate the demand for public charging over the course of a year and allows charging to occur at anytime, without any restrictions inhibiting the vehicles. We decided to focus the model on public charging rather than home charging as few people living in cities have access to home charging points. Therefore, the decision was made to limit the models so that they would only charge during the day.

After generating a demand for the 100 vehicles, we decided to increase the number of vehicles to 10,000 EVs and the number of charging points from 14 to 98 in order to generate a more realistic demand profiles.

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Base Model Schematic

The base model schematic shows the process of the coding in the original ERSU model. The model selects the electric vehicles at random to determine whether or not the they will charge today. For the vehicle that are to charge, the model will determine when the EV is able to charge and whether or not a charger is free. If the selected charger is not free at the specified time, the EV will wait 1 time step (30 minutes) before trying again. Once the charger the available, the model will calculate how much power is needed from the gird to fully charge the EV. After the EV is fully charged, the charger will move on onto the next EV and begin the process all over again.

Base Unconstraint Model

From the simulations we were able to determine the annual emissions of CO2 from the three regions we were investigating.  From the basic model, the UK produces an emission of 129 kg of CO2 per car, 143 kg of CO2 per car for North Wales and Scotland only has an emissions of 26 kg of Co2 per car.

The graph only shows a week over the course of the year but, from the basic model, we can see that there is a high charging demand throughout the day, with some spikes surpassing 2000kW and the demand only drops in the late hours in the evening and early morning. The main issue of the basic model is a lot of demand in the morning which has a significant impact on the grid, and we can’t utilise our renewable resources, so we added in the time constraints to combat this.