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.
Highlights
Societal awareness of greenhouse gas emissions and their impact on the environment has led to an increase in the adoption of both photo voltaic (PV) and electric vehicles (EVs) as sustainable alternatives, both of which are predicted to have a vital share of the future energy mix (Comello, Reichelstein, & Sahoo, 2018; International Energy Agency, 2018)
This section first presents the result of the probabilistic grid impact analysis of smart charging in terms of voltage profiles and phase un balance
The phase unbalance is defined as the maximum deviation, among the three phases, between phase and average line voltage (IEEE, 2002)
Summary
Societal awareness of greenhouse gas emissions and their impact on the environment has led to an increase in the adoption of both photo voltaic (PV) and electric vehicles (EVs) as sustainable alternatives, both of which are predicted to have a vital share of the future energy mix (Comello, Reichelstein, & Sahoo, 2018; International Energy Agency, 2018). The currently increasing penetration of these technologies in residential buildings poses potential challenges to distribution grid operation due to the temporal mismatch between on-site power con sumption and production (Fachrizal & Munkhammar, 2020) This mismatch may lead to voltage deviation problems, component over loading and increases in power losses (Aleem, Suhail Hussain, & Ustun, 2020; Khalid, Alam, Sarwar, & Asghar, 2019). These adverse effects of PV power generation and EV charging, limit the adoption of these technologies in the electricity system and could prevent the transition of the society towards a sus tainable future. Previous studies have shown that improved on-site matching between PV power generation and EV charging load could effectively decrease the negative impacts on the power system resulting from PV and EV deployment (Fachrizal, Shepero, van der Meer, Munkhammar, & Widen, 2020; Luthander, Widen, Nilsson, & Palm, 2015)
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