On the Effects of Distributed Electric Vehicle Network Utility Maximization in Low Voltage Feeders
For researchers and engineers designing real-time EV charging control in distribution networks, this work validates the effectiveness of NUM-based algorithms under specific conditions and highlights the advantage of the primal approach.
The paper evaluates distributed optimization solutions for EV charging control based on Network Utility Maximization (NUM) in low voltage feeders, finding that NUM effectively captures operational constraints when ampacity violations are the main bottleneck, and that the primal NUM solution outperforms the dual NUM solution in preventing system overload.
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.