Data-driven Distributed Control to Scale EV Integration into Power Grid

TL;DR

This paper presents a scalable, data-driven distributed control method using AIMD for managing EV charging congestion in power grids, reducing communication needs while maintaining fairness and effectiveness.

Contribution

It introduces a novel distributed, data-driven congestion detection and control algorithm based on AIMD that operates with minimal communication and off-line data utilization.

Findings

The distributed AIMD algorithm closely matches ideal AIMD in fairness.

Significant reduction in communication requirements.

Provides insights into grid dynamics through data analysis.

Abstract

Electric vehicles (EVs) are finally making their way onto the roads, but the challenges concerning long charging times and impact on congestion of the power distribution grid are still not resolved. Proposed solutions depend on heavy communication and rigorous computation and mostly need real-time connectivity for optimal operation; thereby, they are not scalable. With the availability of historical measurement data, EV chargers can take better-informed actions while staying mostly off-line. This study develops a distributed and data-driven congestion detection methodology together with the Additive Increase Multiplicative Decrease (AIMD) algorithm to control mass EV charging in a distribution grid. The proposed distributed AIMD algorithm performs very closely to the ideal AIMD in terms of fairness and congestion handling, and its communication need is significantly low. The results can provide crucial insights on how data can be used to reveal the inner dynamics and structure of the power grid and help develop more advanced data-driven algorithms for grid integrated power electronics control.