Optimal electric vehicle load control strategy in Malaysia distribution network with distributed energy resources

Mohd Shariff, Noraidah 2025. Optimal electric vehicle load control strategy in Malaysia distribution network with distributed energy resources. PhD Thesis, Cardiff University. Item availability restricted.

Preview	PDF - Accepted Post-Print Version Download (7MB) | Preview
	PDF - Supplemental Material Restricted to Repository staff only Download (221kB)

Abstract

This thesis presents a comprehensive analysis of the impact and management of electric vehicle (EV) integration within Malaysia's low voltage (LV) distribution network, considering distributed generation (DG) resources, particularly photovoltaic (PV) systems. With the growing adoption of EVs and distributed energy resources, there is an increasing need to develop robust strategies to mitigate potential adverse effects on the distribution network. This study investigates the technical challenges posed by uncontrolled EV charging and proposes demand-side management strategies, including optimized charging schedules and distributed generation utilization, to enhance network performance. A key aspect of this research is the development of an EV charging model based on Malaysian urban driving patterns, which incorporates variations in initial state-of-charge (SOC), user commute behaviour, and charging location preferences. The EV charging profiles were created from a database of 130 EV owners, capturing their driving and plug-in behaviours. Using load flow analysis and the Newton-Raphson method, this study examines the power demand, voltage profiles, and power losses under various EV and DG penetration scenarios. Four case studies were conducted to evaluate network impacts: (i) uncontrolled EV charging without DG, (ii) uncontrolled EV charging with full DG penetration, (iii) controlled EV charging without DG, and (iv) controlled EV charging with full DG penetration. Simulation results indicate that, while high EV penetration increases power demand and power losses, controlled charging in conjunction with DG integration can reduce these impacts by utilizing off-peak charging times and renewable energy sources. Additionally, stochastic modelling was employed to address uncertainties in EV charging behaviour, offering a predictive framework to assess the likelihood of network impacts from variable charging demands. This research provides key insights into the technical implications of EV integration in LV networks in urban environment and emphasizes the potential of decentralized, responsive charging strategies to enhance grid resilience. The findings support the adoption of optimized charging schedules and renewable integration policies, laying the groundwork for a sustainable, EV-compatible power infrastructure in Malaysia.

Item Type:	Thesis (PhD)
Date Type:	Completion
Status:	Unpublished
Schools:	Schools > Engineering
Uncontrolled Keywords:	1. Electric Vehicles 2. Distributed Energy Resources 3. Optimal load control 4. Power System Optimization 5. Particle Swarm Optimisation (PSO)
Date of First Compliant Deposit:	17 December 2025
Last Modified:	19 Dec 2025 09:31
URI:	https://orca.cardiff.ac.uk/id/eprint/183316

Actions (repository staff only)

Edit Item