Co-Optimization of EV Charging Control and Incentivization for Enhanced Power System Stability

TL;DR

This paper presents a joint optimization and control strategy to manage EV charging demands through incentivization, aiming to improve power system stability and dynamic performance in distribution grids.

Contribution

It introduces a novel combined optimization and control framework for EV charging that minimizes grid transfer function norms and enhances stability, validated via simulations.

Findings

Effective incentivization reduces high charging demands.

Improved dynamic performance demonstrated in simulations.

Framework applicable to unidirectional and bidirectional charging.

Abstract

We study how high charging rate demands from electric vehicles (EVs) in a power distribution grid may collectively cause poor dynamic performance, and propose a price incentivization strategy to steer customers to settle for lesser charging rate demands so that such performance degradation can be avoided. We pose the problem as a joint optimization and optimal control formulation. The optimization determines the optimal charging setpoints for EVs to minimize the $\mathcal{H}_2$-norm of the transfer function of the grid model, while the optimal control simultaneously develops a linear quadratic regulator (LQR) based state-feedback control signal for the battery currents of those EVs to jointly improve the small-signal dynamic performance of the system states. A subsequent algorithm is developed to determine how much customers may be willing to sacrifice their intended charging rate demands in return for financial incentives. Results are derived for both unidirectional and bidirectional charging, and validated using numerical simulations of multiple EV charging stations (EVCSs) in the IEEE 33-bus power distribution model.