On the Effects of Distributed Electric Vehicle Network Utility Maximization in Low Voltage Feeders

TL;DR

This paper evaluates distributed network utility maximization algorithms for real-time EV charging control in low voltage feeders, highlighting their effectiveness and operational constraints through simulations on a standard test feeder.

Contribution

It demonstrates that NUM-based algorithms can effectively incorporate operational constraints and shows the superiority of primal solutions in preventing overloads.

Findings

NUM algorithms effectively model operational constraints.

Primal NUM solutions better prevent system overloads.

Ampacity violations are the main bottleneck.

Abstract

The fast charging of Electric Vehicles (EVs) in distribution networks requires real-time EV charging control to avoid the overloading of grid components. Recent studies have proposed congestion control protocols, which result from distributed optimization solutions of the Network Utility Maximization (NUM) problem. While the NUM formulation allows the definition of distributed computations with closed form solutions, its simple model does not account for many of the feeders operational constraints. This puts the resulting control algorithms effectiveness into question. In this paper, we investigate the impact of implementing such algorithms for congestion control in low voltage feeders. We review the latest NUM based algorithms for real-time EV charging control, and evaluate their behavior and impact on the comprehensive IEEE European Low Voltage Test Feeder. Our results show that the EV NUM problem can effectively capture the relevant operational constraints, as long as ampacity violations are the main bottleneck. Moreover, the results demonstrate an advantage of the primal NUM solution over the more conventional dual NUM solution in preventing a system overload.