Distributed Coordination of Electric Vehicles in Unbalanced Distribution Grids: Enhancing Resilience to Peer-to-Peer Communication Failures

被引:0
作者
Nimalsiri, Nanduni I. [1 ]
Ratnam, Elizabeth L. [2 ]
Perera, Maneesha [3 ]
Halgamuge, Saman K. [3 ]
机构
[1] CSIRO, Newcastle, NSW 2304, Australia
[2] Australian Natl Univ, Canberra, ACT 2601, Australia
[3] Univ Melbourne, Parkville, Vic 3010, Australia
基金
澳大利亚研究理事会;
关键词
Electric vehicle charging; Peer-to-peer computing; Batteries; Communication networks; Reactive power; Indexes; Packet loss; Optimization; Costs; Australia; ADMM; communication failures; distributed control; electric vehicle charging; peer-to-peer; unbalanced grid; OPTIMIZATION;
D O I
10.1109/TIA.2025.3531832
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this paper, we propose coordinated charging and discharging of Electric Vehicles (EVs), enabled by a distributed optimization-based approach that is scalable and resilient to communication failures. The underlying optimization problem is formulated to maximize the economic benefits for grid-connected EVs, subject to transformer thermal constraints and nodal voltage constraints enforced on an unbalanced distribution grid. Our distributed coordination approach is underpinned by a consensus-based alternating direction method of multipliers (ADMM) that encompasses an iterative negotiation process amongst neighboring EVs. The charge-discharge profiles are computed by EVs locally (and asynchronously) using limited information exchange via peer-to-peer communications. We prove that our proposed EV coordination algorithm converges, even in the event of communication failures such as packet losses and random disconnections of EVs. Numerical simulations with the IEEE 13-node test feeder, incorporating empirical real-life data of EVs and residential loads, show that the proposed approach yields a 78% reduction in operational costs compared to uncoordinated EV charging, while also preserving network operational limits. In the presence of communication failures, the proposed approach resulted in an 8 times slower convergence speed compared to an ideal communication network.
引用
收藏
页码:1887 / 1895
页数:9
相关论文
共 24 条
[1]   Decentralized Failure-Tolerant Optimization of Electric Vehicle Charging [J].
Aravena, Ignacio ;
Chapin, Steve J. ;
Ponce, Colin .
IEEE TRANSACTIONS ON SMART GRID, 2021, 12 (05) :4068-4078
[2]  
Arnold DB, 2016, IEEE POW ENER SOC GE
[3]  
Bertsekas D.P., 2002, INTRO PROBABILITY
[4]   Distributed optimization and statistical learning via the alternating direction method of multipliers [J].
Boyd S. ;
Parikh N. ;
Chu E. ;
Peleato B. ;
Eckstein J. .
Foundations and Trends in Machine Learning, 2010, 3 (01) :1-122
[5]  
Chang TH, 2015, INT CONF ACOUST SPEE, P3541, DOI 10.1109/ICASSP.2015.7178630
[6]   Multi-Agent Distributed Optimization via Inexact Consensus ADMM [J].
Chang, Tsung-Hui ;
Hong, Mingyi ;
Wang, Xiangfeng .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2015, 63 (02) :482-497
[7]   Grid-aware distributed control of electric vehicle charging stations in active distribution grids [J].
Fahmy, Sherif ;
Gupta, Rahul ;
Paolone, Mario .
ELECTRIC POWER SYSTEMS RESEARCH, 2020, 189
[8]  
Hermans R, 2012, P AMER CONTR CONF, P264
[9]  
Kong FX, 2017, Foundations and Trends® in Electric Energy Systems, V2, P1, DOI 10.1561/3100000016
[10]  
Nimalsiri N., 2023, PROC IEEE INT C ENER, P1