Fully Distributed Cooperative Charging for Plug-in Electric Vehicles in Constrained Power Networks

TL;DR

This paper introduces a fully distributed algorithm for cooperative PEV charging that respects power network constraints, improves cost efficiency, and ensures feasible solutions through iterative agent interactions.

Contribution

It presents a novel distributed optimization method for PEV charging that accounts for physical power constraints and guarantees convergence to a feasible, optimal solution.

Findings

Algorithm converges to a global optimum.

Ensures feasibility of each PEV's charging decision.

Effective in managing increased distribution network load.

Abstract

Plug-in Electric Vehicles (PEVs) play a pivotal role in transportation electrification. The flexible nature of PEVs' charging demand can be utilized for reducing charging cost as well as optimizing the operating cost of power and transportation networks. Utilizing charging flexibilities of geographically spread PEVs requires design and implementation of efficient optimization algorithms. To this end, we propose a fully distributed algorithm to solve the PEVs' Cooperative Charging with Power constraints (PEV-CCP). Our solution considers the electric power limits that originate from physical characteristics of charging station, such as on-site transformer capacity limit, and allows for containing charging burden of PEVs on the electric distribution network. Our approach is also motivated by the increasing load demand at the distribution level due to additional PEV charging demand. Our proposed approach distributes computation among agents (PEVs) to solve the PEV-CCP problem in a distributed fashion through an iterative interaction between neighboring agents. The structure of each agent's update functions ensures an agreement on a price signal while enforcing individual PEV constraints. In addition to converging towards the globally-optimum solution, our algorithm ensures the feasibility of each PEV's decision at each iteration. We have tested performance of the proposed approach using a fleet of PEVs.