Scenario 1 : Time Constraint

The Model Description

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Time constraint scenario was created to test the feasibility of limiting the EV charging periods in reducing total carbon emissions, also as a steppingstone to achieve the next carbon constraint scenarios, specifically ‘Working Day’. Creating time constraint required modifying the ‘Base Model’ to have sense of control over the automated algorithms and impose the time variant/limitation. 

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Time constraint: in which the allocation of EV charging times are shifted to avoid times of peak demand in the grid. For example, shifting the morning-time charging demand to times of reduced demand

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In UK national grid, there are two common periods which the demand almost always peaks at [http://grid.iamkate.com/]. The first peak of demand at Morning-time period (8am – 12pm), The demand peaks again at Night-time period (5pm – 9pm). In this scenario, the allocation of EV charging times were shifted to avoid times of peak demand in the grid. For example, shifting the Morning-time’s charging demand to times of reduced demand (12pm – 4pm). For this scenario it was assumed that EV charging would take place at a public car park which EV commuters might leave their cars for up to 8 hours. Along with the fact that the peak demand during Morning-time is usually higher than the second peak.

The time constraint model worked on mitigating the grid demand by shifting the demand from before mid-day to after mid-day and up until 4pm abiding the assumption of public charging for commuters. Thus, any vehicle that were originally assigned to charger before 12pm will be fully charge by 4pm in the time constraint. 

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Time Constraint Model Schematic

In time constraint, public car park will not receive any electric vehicles before midday, every day. Instead they will be rescheduled to arrive and be served at a time window between 12 pm – 4 pm. Moreover, in case there are not enough of chargers in carpark to serve all the newcomers, the car que and the EV commuters might be required around 30 mins until a charger is emptied and ready to provide the charging service. Once the EV is seen to a charger and plugged in, charging unit/station starts calculating the total power drawn from the grid to fully charge the EV. After the EV leaves the car park the charger will be available to charge the next EV.

Time Constraint Charging Demand

A week worth of obtained results of time constraint were plotted (the one in orange) and by comparing it with the unconstrained results (the one in the dotted black) for the same period. We can say that the time constrained tool has indeed shifted off the morning time demand to mid-day and afternoon period.

Specifically 12pm to 4 pm where it peaks at just over 9000 kW and momentarily drops to zero at 4pm. This one result of time limitation and when compared to unconstrained we could notice that it barely used to pass the 2000 kW and the night-time for time constrained almost remained the same. However, the demand distribution of time constraint model is still dependent on the same probabilities as the unconstrained one, in which it has higher probabilities for EV charging is during afternoon period, resulting in undesirable single sharp peak of demand in each day, in real life this could add a strain to the grid which may not be cover by renewables.

Time Constraint: CO2 Emission

The shift of charging time, hence the demand resulted in it being less distributed and have higher demand peaks. CO2 emissions wise, time constraint model achieved slightly lower carbon mass emitted annually from charging a single EV. However, the carbon reduction is deemed to be insignificant if compared with individual carbon footprint for a given period.