Smart Grid Agent: Plug-in Electric Vehicle

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractAbstract-This study describes a method for programming a plug-in electric vehicle agent that can be used in power system models and in embedded systems implemented in real plug-in electric vehicles. Implementing the software in reallife applications and in simulation tools enables research with a high degree of detail and practical relevance. Agent-based programming, therefore, is an important tool for investigating the future power system. To demonstrate the plugin electric vehicle agent behavior, an optimization algorithm is presented and two battery aging methods as well as their effect on V2G operation are analyzed. Aging costs based on the depth of discharge result in shallow cycles and a strong dependency on driving behavior, because the state-of-charge affects the discharging process. In contrast, aging costs based on energy throughput calculations results in deeper cycles and V2G operation which is less dependent on driving behavior.

Efficient integration of plug-in electric vehicles via reconfigurable microgrids

“…This term measures the expected economic losses caused by any interruption in the energy supply. It is shown in the literature that this index can help planners to confirm the adequate level of reliability for electric customers, grant economic justification for determining the network reinforcement and redundancy allocation, recognize weak points in the system, establish suitable maintenance scheduling, and develop proper operation policies [32]. In (7), the price of interruption at each bus is calculated using the composite customer damage function (CCDF) [22].…”

Stochastic Reconfiguration and Optimal Coordination of V2G Plug-in Electric Vehicles Considering Correlated Wind Power Generation

“…According to this figure, as the DoD is increased, the number cycles to battery failure is decreased. Mathematically, Wöhler curve is modeled as follows [16]:…”

“…Mathematically, the degradation cost of the battery for one discharge from fully charged state (DoD = 0) to a specified level (DoD = DoD s ) is calculated as follows [16]:…”

A novel model of electric vehicle fleet aggregate battery for energy planning studies

“…However, in that case the (aggregate) battery model and/or the cost function should be extended to account for the battery degradation effect, i.e. to leverage the energy cost and battery degradation cost in the charging optimisation process [18]. In order to validate and compare the basic and novel aggregate battery models, the charging power distribution P c,agg (k) should first need to be determined as the control input of both models.…”