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 real-life 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 plug-in 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 depend-ent on driving behavior.
