Smart electric vehicle charging strategies for sectoral coupling in a city energy system

2021 ◽

Author(s):

Venkatesh Boddapati ◽

S Arul Daniel

Keyword(s):

Electric Vehicle ◽

Autonomous Vehicles ◽

Operating Cost ◽

Energy System ◽

Solar Pv ◽

Diesel Generator ◽

Hybrid Energy ◽

Charging Station ◽

Electric Vehicle Charging ◽

Vehicle Charging

Mobility has been changing precipitously in recent years. With the increasing number of electric vehicles (EV), travel-sharing continues to grow, and ultimately, autonomous vehicles (AV) move into municipal fleets. These changes require a new, distributed, digitalised energy system, maintenance, and growing electrification in transportation. This paper proposes the designing of an Electric Vehicle Charging Station (EVCS) by using hybrid energy sources such as solar PV, wind, and diesel generator. The proposed system is mathematically modelled and designed using the Hybrid Optimization Model for Multiple Energy Resources (HOMER). The system is analysed and assessed in both autonomous mode and grid-connected mode of operation. The optimum sizing, energy yields of the system in each case is elaborated, and the best configuration is found for design. The variations in Levelized Cost Of the Energy (LCOE), Net Present Cost (NPC), initial cost, and operating cost of the various configuration are presented. From the results, it is observed that the grid-connected EVCS is more economical than the autonomous EVCS. Further, a sensitivity analysis of the EVCS is also performed.

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Comparing Power-System- and User-Oriented Battery Electric Vehicle Charging Representation and its Implications on Energy System Modeling

10.20944/preprints202001.0224.v1 ◽

2020 ◽

Author(s):

Niklas Wulff ◽

Felix Steck ◽

Hans Christian Gils ◽

Carsten Hoyer-Klick ◽

Bent van den Adel ◽

...

Keyword(s):

Renewable Energy ◽

Power Systems ◽

Power System ◽

Electric Vehicle ◽

Electric Vehicles ◽

System Optimization ◽

Energy System ◽

Battery Electric Vehicle ◽

Electric Vehicle Charging ◽

Vehicle Charging

Battery electric vehicles provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. In particular, state-of-charge assumptions and representing fast charging drive curtailment of renewable energy feed-in and required gas power plant flexibility. A detailed representation of fleet charge connection is less important. Peak load can be significantly reduced by 5% and 3% in both scenarios, respectively. Shifted load is very robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.

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Comparing Power-System and User-Oriented Battery Electric Vehicle Charging Representation and Its Implications on Energy System Modeling

Energies ◽

10.3390/en13051093 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1093 ◽

Cited By ~ 1

Author(s):

Niklas Wulff ◽

Felix Steck ◽

Hans Christian Gils ◽

Carsten Hoyer-Klick ◽

Bent van den Adel ◽

...

Keyword(s):

Renewable Energy ◽

Power Systems ◽

Power System ◽

Electric Vehicle ◽

Electric Vehicles ◽

System Optimization ◽

Energy System ◽

Battery Electric Vehicle ◽

Electric Vehicle Charging ◽

Vehicle Charging

Battery electric vehicles (BEV) provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. Assumptions on fleet battery availability and a detailed representation of fast charging are found to have a strong impact on wind curtailment, renewable energy feed-in, and required gas power plant flexibility. A representation of fleet connection to the grid in high temporal detail is less important. Peak load can be reduced by 5% and 3% in both scenarios, respectively. Shifted load is robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.

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