Controlling the Charging of Electric Vehicles with Neural Networks

TL;DR

This paper introduces decentralized neural network controllers for electric vehicle charging that optimize grid load by balancing peak and valley consumption without requiring external communication.

Contribution

It presents novel decentralized neural network controllers optimized by evolutionary and gradient methods for EV charging coordination.

Findings

Controllers effectively balance grid load and reduce peak consumption.

Neural network architectures perform comparably in realistic scenarios.

Decentralized controllers operate without external communication.

Abstract

We propose and evaluate controllers for the coordination of the charging of electric vehicles. The controllers are based on neural networks and are completely de-centralized, in the sense that the charging current is completely decided by the controller itself. One of the versions of the controllers does not require any outside communication at all. We test controllers based on two different architectures of neural networks - the feed-forward networks and the echo state networks. The networks are optimized by either an evolutionary algorithm (CMA-ES) or by a gradient-based method. The results of the different architectures and the different optimization algorithms are compared in a realistic scenario. We show that the controllers are able to charge the cars while keeping the peak consumptions almost the same as when no charging is performed. Moreover, the controllers fill the valleys of the consumption thus reducing the difference between the maximum and minimum consumption in the grid.