SYDCSYOCJun 28, 2018

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

arXiv:1806.11190h-index: 57
Originality Incremental advance
AI Analysis

This work addresses the need for scalable, privacy-preserving charging coordination for PEVs in constrained distribution networks, which is important for grid operators and PEV aggregators.

The paper proposes a fully distributed algorithm for cooperative charging of plug-in electric vehicles (PEVs) that respects power constraints from charging station infrastructure, such as transformer limits. The algorithm achieves convergence to the globally optimal solution while ensuring feasibility at each iteration, tested on a fleet of PEVs.

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.

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