Distributed Control for Charging Multiple Electric Vehicles with Overload Limitation

TL;DR

This paper presents an online control algorithm for managing electric vehicle charging across multiple stations, aiming to reduce grid overload and energy costs while utilizing renewable energy sources.

Contribution

It introduces a novel Lyapunov optimization-based online algorithm for coordinated PEV charging and energy management that ensures overload avoidance and cost minimization.

Findings

Reduces on-grid energy costs in simulations.

Prevents distribution network overload during peak loads.

Effectively manages random uncontrollable loads.

Abstract

Severe pollution induced by traditional fossil fuels arouses great attention on the usage of plug-in electric vehicles (PEVs) and renewable energy. However, large-scale penetration of PEVs combined with other kinds of appliances tends to cause excessive or even disastrous burden on the power grid, especially during peak hours. This paper focuses on the scheduling of PEVs charging process among different charging stations and each station can be supplied by both renewable energy generators and a distribution network. The distribution network also powers some uncontrollable loads. In order to minimize the on-grid energy cost with local renewable energy and non-ideal storage while avoiding the overload risk of the distribution network, an online algorithm consisting of scheduling the charging of PEVs and energy management of charging stations is developed based on Lyapunov optimization and Lagrange dual decomposition techniques. The algorithm can satisfy the random charging requests from PEVs with provable performance. Simulation results with real data demonstrate that the proposed algorithm can decrease the time-average cost of stations while avoiding overload in the distribution network in the presence of random uncontrollable loads.