A Statistical Modelling and Analysis of Residential Electric Vehicles' Charging Demand in Smart Grids

TL;DR

This paper models and analyzes the stochastic charging demand of electric vehicles in smart grids, proposing a demand response technique and V2G integration to manage load and maintain grid stability.

Contribution

It introduces a novel stochastic model for EV charging demand, incorporating random arrival, charging, and departure times, and proposes an autonomous demand response method with V2G to optimize grid load.

Findings

EV charging demand can be effectively modeled using stochastic processes.

Demand response and V2G can significantly reduce peak load.

The model supports long-term grid infrastructure planning.

Abstract

Electric vehicles (EVs) 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 charging time of the vehicles determine their expected charging demand profile from the power grid. In this paper, we first present a model for uncoordinated charging power demand of EVs based on a stochastic process and accordingly we characterize an EV's expected daily power demand profile. Next, we illustrate it for different charging time distributions through simulations. This gives us useful insights into the long-term planning for upgrading power systems' infrastructure to accommodate EVs. Then, we incorporate departure time as another random variable into this modelling and introduce an autonomous demand response (DR) technique to manage the EVs' charging demand. Our results show that, it is possible to accommodate a large number of EVs and achieve the same peak-to-average ratio (PAR) in daily aggregated power consumption of the grid as when there is no EV in the system. This peak value can be decreased further significantly when we add vehicle-to-grid (V2G) capability in the system.