Smart Online Charging Algorithm for Electric Vehicles via Customized Actor-Critic Learning

TL;DR

This paper introduces a novel online EV charging algorithm using customized actor-critic learning, effectively reducing costs and peak loads while handling uncertainties in EV behaviors.

Contribution

It develops a new actor-critic learning-based charging strategy that adapts to unknown EV behaviors and improves computational efficiency with a customized approach.

Findings

SCA reduces EV charging costs by over 24% compared to baseline algorithms.

CALC achieves near SCA performance with significantly lower computational complexity.

Proposed algorithms effectively manage EV charging under uncertainty.

Abstract

With the advances in the Internet of Things technology, electric vehicles (EVs) have become easier to schedule in daily life, which is reshaping the electric load curve. It is important to design efficient charging algorithms to mitigate the negative impact of EV charging on the power grid. This paper investigates an EV charging scheduling problem to reduce the charging cost while shaving the peak charging load, under unknown future information about EVs, such as arrival time, departure time, and charging demand. First, we formulate an EV charging problem to minimize the electricity bill of the EV fleet and study the EV charging problem in an online setting without knowing future information. We develop an actor-critic learning-based smart charging algorithm (SCA) to schedule the EV charging against the uncertainties in EV charging behaviors. The SCA learns an optimal EV charging strategy with continuous charging actions instead of discrete approximation of charging. We further develop a more computationally efficient customized actor-critic learning charging algorithm (CALC) by reducing the state dimension and thus improving the computational efficiency. Finally, simulation results show that our proposed SCA can reduce EVs' expected cost by 24.03%, 21.49%, 13.80%, compared with the Eagerly Charging Algorithm, Online Charging Algorithm, RL-based Adaptive Energy Management Algorithm, respectively. CALC is more computationally efficient, and its performance is close to that of SCA with only a gap of 5.56% in the cost.