Evaluation of Distinct EV Scheduling at Residential Charging Points in an Unbalanced Power Distribution System

Abstract

Electric vehicles (EVs) have significant potential and sustainability in the future transportation sector due to their less pollution and low operating cost. The uncoordinated charging of these EVs may push the power delivery system towards lower performance and even at the risk of damaging the power system equipment. The proper scheduling and coordination of EVs with the system may lessen the negative impact. In this paper, a multi-objective scheduling approach for individual candidate-based EVs is proposed to relieve the distress in power delivery in the best possible way. Minimization of average demand deviation and system unbalance reduction are selected as the objectives to restore the power delivery performance. The proposed approach has been verified on a realistic three-phase power distribution infrastructure. A simple linear regression model is employed to predict the near future number of EVs in the test region. An optimizitaion algorithm based on particle swarm optimization (PSO) technique is operated to find the finest solution satisfying EV charging constraints. The stochastic nature of load demands at the node points of the test network is analyzed with the help of a probabilistic demand model and it is further incorporated into the optimization framework. It is possible to reduce the system’s average demand deviation and unbalance level by 53.74% and 51.36%, respectively, with the proposed EV scheduling approach. A comparative study with the existing methods further claims the efficacy of the proposed approach.

HIGHLIGHTS

• Framework for home charging schedule of distinct EVs

• Compliance with multi-objective power supply efficiency

• Probabilistic modeling of load demand from real collected data

• Optimization-driven solution for a test case in Mysore city in India

KEYWORDS:

• EV scheduling
• Load model
• Optimization
• Unbalanced distribution system

ACKNOWLEDGEMENTS

The acquisition of real-time distribution load data by Chamundeshwari Electricity Supply Corporation, Mysore (CESCOM) is gratefully acknowledged by the authors.

DISCLOSURE STATEMENT

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

Additional information

Notes on contributors

Pritam Das

Pritam Das is a MTech student in the specialization in power and energy systems engineering from the National Institute of Technology Silchar, Assam, India. He was awarded BTech degree in electrical engineering from Jalpaiguri Government Engineering College, West Bengal, India in 2020. His research interest includes electric vehicle, energy management, power system optimization, and artificial intelligence and its application in power and energy systems. Email: [email protected]

Soumyakanata Samantaray

Soumyakanta Samantaray is pursuing PhD in Electrical Engineering Department at the National Institute of Technology Silchar, Assam. He was awarded MTech degree in power and energy systems engineering from the National Institute of Technology Silchar, Assam, India in 2010. He received his BTech degree in electrical and electronics engineering from the Silicon Institute of Technology in 2005. During the period from 2005 to 2008 and from 2010 to 2018, he worked as a lecturer in various colleges. His research interest includes electrical distribution system analysis, renewable energy, energy storage systems, and load forecasting. Email: [email protected]

Partha Kayal

Partha Kayal has been working as an assistant professor with the Department of Electrical Engineering, National Institute of Technology, Assam, India, since June 2018. Earlier, he worked with the Future Institute of Engineering and Management till early June 2018. He was awarded ME and PhD from the Indian Institute of Engineering Science and Technology Shibpur, West Bengal, India in 2010 and 2016, respectively. He has published papers in journals and presented papers in conferences. His current research interest includes optimization of distributed energy resources, operation, and control of microgrid, enhanced distribution system planning and modeling, impacts of energy storage and electric vehicles on distribution network, reliability and power quality issues, statistical modeling and forecasting of energy, demand response and electricity market, energy management in smart grid and power system dynamics and stability phenomenon. Corresponding author. Email: [email protected]