An Advanced Data Driven Model for Residential Plug- in Hybrid Electric Vehicle Charging Demand

As the plug-in hybrid electric vehicle (PHEV) is becoming a very significant component in residential loads, an accurate and valid model for the PHEV charging demand is the key for load forecast, demand respond, system planning and so forth. As a result, we propose a data driven queuing model for residential PHEV charging demand by performing data analytics on smart meter measurements. The data driven model captures the non-homogeneity and periodicity of the residential PHEV charging behaviors through a self-service queue with a periodic and non-homogeneous Poisson arrival rate, an empirical distribution for charging duration and a finite calling population. Upon parameter estimation, we further validate the model by comparing the simulated data with real measurements. The hypothesis test shows the proposed model captures the charging behaviors well. And we acquire the long-run steady state performance of the PHEV charging demand through simulation output analysis.