Smart Charging with Hourly Pricing: Reducing Costs and Grid Congestion for Promoting Electric Vehicles in Pakistan /

Abstract

This study explores how smart charging with dynamic hourly pricing can address grid congestion concerns arising from the growing adoption of electric vehicles (EVs) in Pakistan. We propose an optimization technique to establish an hourly pricing model for Pakistani distribution companies, promoting off-peak charging behavior among EV owners. An agent-based energy management system is then introduced to facilitate coordination between EV aggregators and the grid. This system employs machine learning to accurately predict battery state-of-charge and integrates both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) functionalities for optimized energy flow. The model is evaluated using real distribution network data with seasonal load variations. The results reveal that with 10% EV penetration, hourly pricing can significantly reduce charging costs for EV owners (up to 28% and 31% during summer and winter, respectively). Additionally, it offers substantial relief for the grid by considerably reducing peak transformer load compared to flat or 2-part tariffs. This research demonstrates the potential of smart charging with dynamic pricing as a cost-effective and efficient solution for promoting EVs in Pakistan while mitigating grid congestion.