A Fully Distributed Approach for Plug-in Electric Vehicle Charging

TL;DR

This paper presents a fully distributed multiagent algorithm for coordinating electric vehicle charging schedules, reducing costs and network impacts without centralized control, demonstrated on a fleet of 100 PEVs.

Contribution

It introduces a novel agent-based distributed method for PEV charging coordination that finds optimal solutions through local computations and limited communication.

Findings

The distributed approach achieves near-centralized optimal solutions.

The method scales effectively to 100 PEVs.

Performance is validated through simulation results.

Abstract

Plug-in electric vehicles (PEVs) are considered as flexible loads since their charging schedules can be shifted over the course of a day without impacting drivers mobility. This property can be exploited to reduce charging costs and adverse network impacts. The increasing number of PEVs makes the use of distributed charging coordinating strategies preferable to centralized ones. In this paper, we propose an agent-based method which enables a fully distributed solution of the PEVs Coordinated Charging (PEV-CC) problem. This problem aims at coordinating the charging schedules of a fleet of PEVs to minimize costs of serving demand subject to individual PEV constraints originating from battery limitations and charging infrastructure characteristics. In our proposed approach, each PEVs charging station is considered as an agent that is equipped with communication and computation capabilities. Our multiagent approach is an iterative procedure which finds a distributed solution for the first order optimality conditions of the underlying optimization problem through local computations and limited information exchange with neighboring agents. In particular, the updates for each agent incorporate local information such as the Lagrange multipliers, as well as enforcing the local PEVs constraints as local innovation terms. Finally, the performance of our proposed algorithm is evaluated on a fleet of 100 PEVs as a test case, and the results are compared with the centralized solution of the PEV-CC problem.