Queue Based Dynamic Charging Resource Allocation and Coordination for Heterogeneous Traffic in an Electrical Vehicle Charging Station

ABSTRACT

Electric vehicle (EV) fast-charging stations (CSs) with innovative operation management strategies can help to meet the growing EV charging needs. Despite the short charging time associated with the fast charging of EVs, massive deployment of plugged-in EVs for fast charging triggers network surges in the distributed network. Nevertheless, these challenges can be effectively addressed with appropriate EV admission control, charging resource allocation, and coordinated charging strategies. This work proposes an innovative EV-CS operation mechanism that maximizes the CS profit by allowing fast charging EVs to access the CS opportunistically without stressing the distributed network. Furthermore, a queue is employed in the dynamic resource allocation process to favor more fast charging requests through buffering non-critical slow charging EVs, as fast charging users would otherwise be blocked or forcibly terminated. Performance of the proposed priority-based charging coordination is analyzed in terms of the optimum utilization of demand limit, charging completion rate, and charging station utilization. Therefore, it keeps the average charging station utilization above 90% at higher arrival rates of EVs while the distribution transformer is being loaded only up to 50% of its rated capacity. Moreover, the blocking and forced termination probabilities of fast charging users are used to evaluate the service quality of EV charging. The presented dynamic resource allocation and charging coordination strategies for heterogeneous EV traffic maximize the CS profit while assuring quality service to EV users without stressing the distributed network.

KEYWORDS:

• Electric vehicles
• coordinated charging of EVs
• distributed charging control
• hierarchical charging system
• fast charging of EVs

Disclosure statement

No potential conflict of interest was reported by the author(s).

Nomenclature

Table

Notes

1. For each plugged-in EV at time $t$, a row containing the corresponding charging parameters is included into the matrix${\mathbf{A}}_{EV};\mathbf{j}\mathbf{(}\mathbf{n}\mathbf{)}$.

2. Similarly the matrix ${\mathbf{A}}_{j_x(t)\times n}^q$is updated for each queued EV at time t.

3. For each plugged-in x at time t, corresponding charging parameters are included in the matrix ${\mathbf{A}}_{j_x(t)\times n}^x;x\equiv scu,fcu,cscu,ncscu$

Additional information

Notes on contributors

Konara Mudiyanselage Sandun Y. Konara

Konara Mudiyanselage Sandun Y. Konara is currently pursuing PhD degree in renewable energy, majoring in charging coordination of electric vehicles, at the Department of Engineering Sciences, Faculty of Engineering and Science, University of Agder (UiA), Norway. He received his B.Sc.Eng degree (Hons.) in Electrical and Information Engineering from the University of Ruhuna (UoR), Sri Lanka in 2013 and M.Sc. degree in Renewable Energy in 2016 from UiA, Norway. His master’s thesis was awarded as the best thesis in renewable energy at UiA. He is a lecturer at the Department of Electrical and Information Engineering, UoR (currently on study leave for PhD) since 2013. Konara’s research interests include optimum resource allocation and charging coordination in fast electrical vehicle charging stations, demand side management, smart grid, and renewable energy systems.

Mohan Lal Kolhe

Mohan Lal Kolhe is a full professor in smart grid and renewable energy at the Faculty of Engineering and Science of the University of Agder (Norway). He is a leading renewable energy technologist with three decades of academic experience at the international level and previously held academic positions at the world's prestigious universities, e.g., University College London (UK / Australia), University of Dundee (UK); University of Jyvaskyla (Finland); Hydrogen Research Institute, QC (Canada); etc. In addition, he was a member of the Government of South Australia’s first Renewable Energy Board. His research works in energy systems have been recognized within the top 2% of scientists globally by Stanford University’s 2020 matrix. He is an internationally recognized pioneer in his field, whose top 10 published works have an average of over 165 citations each. He has successfully won competitive research funding from the prestigious research councils (e.g., Norwegian Research Council, EU, EPSRC, BBSRC, NRP, etc.) for his work on sustainable energy systems.