EV Fleet Flexibility Estimation on the Distribution Network

Abstract: A new electricity market model was drafted in Denmark (1) to define the roles of different players and promote the integration of renewable generation as well as the gradual shift towards smartgrids. Along with a clear political will and adjustments of some regulatory barriers, this potentiallyopens the market for an increased share in electric vehicles (EVs). Presenting an opportunity toanalyze the role of EVs and their capacity to provide flexibility services for system operators, thedistribution service operator (DSO) in particular. Specifically, it allows for an alternative route theDSO could take rather than resort to traditional measures to fix grid contingencies, which are notonly costly and time consuming but have negative environmental and social impacts as well. The thesis tackles flexibility from a fleet of EVs on the Danish island of Bornholm in a bid toestimate the value of instantaneous power that could be dispatched on the distribution grid atspecific times. A real data set is analyzed corresponding to an EV fleet at 8 vehicle-to-grid (V2G)charger points, all connected to the same 400 kVA distribution transformer and alreadyparticipating in energy sell-back to the grid. The fleet itself is part of the regional municipality,operating during working hours from 8am to 4pm on weekdays and providing homecare as well associal care to citizens. The data is acquired for the months of January and July 2020, which presentsan added value to the analysis as peak electric demand in Denmark usually occurs during the winterseason and the month of January around 7 pm in particular. Flexibility estimation is provided through a model that is coded in python. It takes as an input adatabase formed from the provided data, including information on vehicle and charger models.The code then outputs a power time series, which is the basis of the analysis in this thesis as itprovides a platform that generates the possible amounts of flexible power that could be dispatchedto the grid in addition to the power that is available for charging at different time slots and for achosen duration. The analysis focuses on the flexible power that could be injected on the grid aspart of services provided to the DSO to alleviate grid congestions and prevent the overloading oftransformers. The results show how the EV fleet of 8 V2G chargers can satisfy the forecasted increase in peakdemand of 13% by 2030. These are estimated for the transformer operating at 70% load at peakand full load at peak, albeit at a slightly lower confidence level for the latter. Although the modeldoes not take into consideration battery aging and charger optimization schemes, the results stillprovide such estimations at a 95% confidence level (i.e. -2 standard deviations) from a normaldistribution curve for the month of January during peak times. With more types of EVs connectedin the future, such as residential and commercial, flexibility levels can be predicted to furtherincrease.