Probabilistic load flow analysis of electric vehicle smart charging in unbalanced LV distribution systems with residential photovoltaic generation. (September 2021) - Ramadhani, Umar Hanif; Fachrizal, Reza; Shepero, Mahmoud; Munkhammar, Joakim; Widén, Joakim
Highlights: The grid impact of smart charging with an objective to reduce the net-load variability in unbalanced distribution systems with residential photovoltaic generation is evaluated. The variability of electric vehicle mobility, household load, photovoltaic system generation, and the adoption of photovoltaic and electric vehicle in society are modelled. Two different methods of electric vehicle and photovoltaic allocation are compared: randomly distributed and concentrated. Smart charging scheme improves the system performance and increase the correlation between nodes. Concentrated allocation has more severe impacts, in particular at lower penetration levels. Abstract: Several studies have presented electric vehicle smart charging schemes to increase the temporal matching between photovoltaic generation and electric vehicle charging, including a smart charging scheme with an objective to minimize the net-load variance. This method has proved, through simulations, that the self consumption could be increased, but the benefit of the approach has not been tested on a low voltage distribution system. To increase the quality of grid impact analyses of the smart charging scheme, probabilistic methods that include input and spatial allocation uncertainties are more appropriate. In this study, a probabilistic load flow analysis is performed by modelling the variability of electric vehicle mobility, household load, photovoltaic system generation, and the adoption of photovoltaic system and electric vehicle in society. The results show that the smart charging scheme improves the low voltage distribution system performance and increases the correlations between network nodes. It is also shown that concentrated allocation has more severe impacts, in particular at lower penetration levels. This paper can form the basis for the development of probabilistic impact analysis of smart charging to allow society to integrate more electric vehicles and photovoltaic systems for a more sustainable future.