EV Fleet Flexibility Estimation on the Distribution Network
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Tipus de documentProjecte Final de Màster Oficial
Data2020-05-10
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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 smart grids. Along with a clear political will and adjustments of some regulatory barriers, this potentially opens the market for an increased share in electric vehicles (EVs). Presenting an opportunity to analyze the role of EVs and their capacity to provide flexibility services for system operators, the
distribution service operator (DSO) in particular. Specifically, it allows for an alternative route the DSO could take rather than resort to traditional measures to fix grid contingencies, which are not only 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 to estimate the value of instantaneous power that could be dispatched on the distribution grid at specific 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 already participating 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 as social care to citizens. The data is acquired for the months of January and July 2020, which presents an added value to the analysis as peak electric demand in Denmark usually occurs during the winter season 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 a database 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 it provides a platform that generates the possible amounts of flexible power that could be dispatched to the grid in addition to the power that is available for charging at different time slots and for a chosen duration. The analysis focuses on the flexible power that could be injected on the grid as part of services provided to the DSO to alleviate grid congestions and prevent the overloading of transformers. The results show how the EV fleet of 8 V2G chargers can satisfy the forecasted increase in peak demand of 13% by 2030. These are estimated for the transformer operating at 70% load at peak and full load at peak, albeit at a slightly lower confidence level for the latter. Although the model
does not take into consideration battery aging and charger optimization schemes, the results still provide such estimations at a 95% confidence level (i.e. -2 standard deviations) from a normal distribution curve for the month of January during peak times. With more types of EVs connected in the future, such as residential and commercial, flexibility levels can be predicted to further increase.
TitulacióMÀSTER UNIVERSITARI EN ENGINYERIA DE L'ENERGIA (Pla 2013)
Col·leccions
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fares-bilal-itm-ex-2020-551.pdf | 4,156Mb | Accés restringit |