EV Charging Optimization based on Day-ahead Pricing Incorporating Consumer Behavior

TL;DR

This paper proposes a day-ahead pricing model for EV charging that minimizes costs and peak demand by considering consumer behavior and historical usage data, offering a scalable and simple optimization approach.

Contribution

It introduces a novel, scalable EV charging optimization method based on an ideal consumption profile and day-ahead pricing, incorporating consumer convenience tradeoffs.

Findings

Reduced electricity costs through optimized pricing

Lowered peak system demand with the proposed model

Scalable approach effective for different community sizes

Abstract

With the increasing penetration of electric vehicles (EVs) into the automotive market, the electricity peak demand would increase significantly due to home-EV-charging. This paper tackles this problem by defining an 'ideal' EV consumption profile, from which a day-ahead pricing model is derived. Based on historical residential EV-use data ranging over a year, we demonstrate that the proposed optimization process results in a pricing profile that achieves a dual objective of minimizing the total electricity cost, as well as the peak aggregate system demand. Importantly, the proposed formulation is simple, and accounts for the tradeoff between consumer convenience in terms of the number of available charging slots during a day and the reduction in the total electricity cost. This technique is demonstrated to be scalable with respect to the size of the community whose EV charging demands are being optimized.