Electric Vehicle Charging Impact on Load Profile
Abstract: One barrier to sustainable development is considered to be greenhouse gas emissions and pollution caused by transport, why climate targets are set around the globe to reduce these emissions. Electric vehicles (EVs), may be a sustainable alternative to internal combustion engine vehicles since having EVs in the car park creates an opportunity to reduce greenhouse gas emissions. This is due to the efficiency of the electric motor. For EVs with rechargeable batteries the opportunity to reduce emissions is also dependent on the generation mix in the power system. EVs with the possibility to recharge the battery from the power grid are denoted plug-in electric vehicles (PEVs) or plug-inhybrid electric vehicles (PHEVs). Hybrid electric vehicles (HEVs), without external recharging possibility, are not studied, hence the abbreviation EV further covers PHEV and PEV.With an electricity-driven private vehicle fleet, the power system will experience an increased amount of variable electricity consumption that is dependent on the charging patterns of EVs. Depending on the penetration level of EVs and the charging patterns, EV integration creates new quantities in the overall load profile that may increase the load peaks. The charging patterns are stochastic since they are affected by the travel behavior of the driver and the charging opportunities which imply that the EV integration also will have an effect on the load variations. Increased load variation and load peaks may create a need for upgrades in the grid infrastructure to reduce the risk for losses, overloads or damaging of components. However, with well-designed incentives to the EV users the variable electricity consumption due to electric vehicle charging (EVC) may become a flexible load that can help the power system mitigate load variations and load peaks.The aim with this licentiate thesis is to investigate the impact of EVC on load profiles and load variations. The thesis reviews and categorizes EVC models in previous research. The thesis furthermore develops electric vehicle charging models to estimate the charging impact based on charging patterns induced by private car travel behavior. The models mainly consider uncontrolled charging (UCC) related to stochastic individual car travel behavior and induced charging needs for PHEVs. Moreover, the thesis comments on the potential of individual charging strategies (ICS) with flexible charging and external charging strategies (ECS).Three key factors are identified when considering the impact of EVC on load profiles and load variations. The key factors are: The charging moment, the charging need and the charging location. It is concluded that the level of details concerning the approach to model these key factors in EVC models will impact the estimations of the load profiles. This means that models taking into account a higher level of mobility details will be able to create a more realistic estimation of a future UCC behavior, enabling for more accurate estimates of the impact on load profiles and the potential of ICS and ECS.
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