Charging Coordination of Plug-in Electric Vehicles Considering Machine Learning Based State-of-Charge Prediction for Congestion Management in Distribution System

Abstract

This article proposes a novel strategy for congestion mitigation in a distribution system by charging coordination strategy of plug-in electric vehicle (PEV) considering grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. The G2V mode of a large figure of PEVs creates a congestion scenario in the distribution system. Therefore, a coordinated charging strategy has been considered in this work to mitigate distribution system congestion. Moreover, a precise estimation and prediction of PEVs state-of-charge (SOC) is necessary while formulating PEVs coordinated strategy. The study of the work is two folded. First, for the first time, a combination of machine learning approach like gradient boosting method-Bayesian optimization (GBM-BO) is considered in prediction of PEVs SOC at the finishing of trip. Second, a coordinated charging scheme is established based on particle swarm optimization (PSO) and firefly algorithm (FA) using the PEVs SOC at the finishing of trip. The charging coordination strategy is analyzed on the 38-bus radial distribution system integrated with solar powered charging-cum parking lot (SPCPL). The machine learning based prediction results reveal the significant reduction in errors between predicted and calculated values. The results further reveal the reduction in PEVs charging cost and congestion scenario while considering SPCPL in the distribution system.

Keywords:

• congestion management
• plug-in electric vehicle (PEV)
• gradient boosting method-Bayesian optimization (GBM-BO)
• state-of-charge (SOC)
• grid to vehicle (G2V)
• and vehicle to grid (V2G)

Additional information

Notes on contributors

Subhasish Deb

Subhasish Deb has completed his B.E. in Electrical Engineering from National Institute of Technology Agartala, Tripura, India, in 2009. He has completed his Master of Technology (M.Tech.) in Power and Energy System Engineering and Ph.D. in Electrical Engineering from National Institute of Technology Silchar, Assam, India, in 2012 and 2021 respectively. He is presently working as Assistant Professor in the Department of Electrical Engineering, Mizoram University, Mizoram, India. His research interest includes congestion management, electric vehicle integration and application of soft computing and machine learning.

Arup Kumar Goswami

Arup Kumar Goswami received the Bachelor of Technology (B.Tech.) in Electrical Engineering from the Regional Engineering College Kurukshetra, Haryana, India, in 1997, the Master of Engineering (M.E.) in Electrical Engineering from the Birla Institute of Technology Mesra, Ranchi, India in 2005 and the Ph.D. degree from the Indian Institute of Technology Roorkee, Uttarakhand, India in 2010. Currently, he is a Professor of Electrical Engineering at the National Institute of Technology Silchar, Assam, India. His main research areas include electric vehicle and congestion management, power quality and energy markets, renewable energy management and smart grid, and condition monitoring of electrical equipment.

Rahul Lamichane Chetri

Rahul Lamichane Chetri received Bachelor of Technology (B.Tech.) in Electrical Engineering from the National Institute of Technology Silchar, Assam, India in 2021. His research interest includes electric vehicle and optimization algorithms.

Rajesh Roy

Rajesh Roy received Bachelor of Technology (B.Tech.) in Electrical Engineering from the National Institute of Technology Silchar, Assam, India in 2021. His areas of interest include electric vehicles, prediction and optimization algorithm for power system using machine learning, and database management system.