A Statistical Modelling and Analysis of PHEVs' Power Demand in Smart Grids

TL;DR

This paper models the stochastic power demand of PHEVs in smart grids, analyzing how different charging time distributions affect demand profiles and providing insights for infrastructure planning and demand response strategies.

Contribution

It introduces a stochastic model for PHEV charging demand and compares various distribution assumptions, revealing their impact on expected demand profiles.

Findings

Demand profiles are similar across different distributions with same first and second order statistics.

The model aids in long-term power system planning for PHEV integration.

Results support improved demand response algorithm design.

Abstract

Electric vehicles (EVs) and particularly plug-in hybrid electric vehicles (PHEVs) are foreseen to become popular in the near future. Not only are they much more environmentally friendly than conventional internal combustion engine (ICE) vehicles, their fuel can also be catered from diverse energy sources and resources. However, they add significant load on the power grid as they become widespread. The characteristics of this extra load follow the patterns of people's driving behaviours. In particular, random parameters such as arrival time and driven distance of the vehicles determine their expected demand profile from the power grid. In this paper, we first present a model for uncoordinated charging power demand of PHEVs based on a stochastic process and accordingly we characterize the EV's expected daily power demand profile. Next, we adopt different distributions for the EV's charging time following some available empirical research data in the literature. Simulation results show that the EV's expected daily power demand profiles obtained under the uniform, Gaussian with positive support and Rician distributions for charging time are identical when the first and second order statistics of these distributions are the same. This gives us useful insights into the long-term planning for upgrading power systems' infrastructure to accommodate PHEVs. In addition, the results from this modelling can be incorporated into designing demand response (DR) algorithms and evaluating the available DR techniques more accurately.