Impact of Electric Vehicle Loads on the System Load Profile of Sri Lanka

R M G D Ranathunga,Lalith A Samaliarachchi

doi:10.4038/engineer.v50i4.7270

R M G D Ranathunga, Lalith A Samaliarachchi

Open Access

https://doi.org/10.4038/engineer.v50i4.7270

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Abstract

Electric vehicles (EVs) are being promoted worldwide because of the potential they have to address atmospheric pollution issues and relieve countries from the burdens associated with the use of liquid petroleum based transport fuels. Ever increasing environmental concerns, improvements to battery technologies, entry of new manufacturers and new vehicle models, and introduction of favourable fiscal policies have all contributed to the increase in EV penetration rates worldwide including Sri Lanka. However, it is not clear how the power system in Sri Lanka would face the challenge of this new and unknown demand which would get added to the already exiting demand profile and which would be a result of the stochastic nature of the battery charging behaviour of EVs. In this research study, EV charging modes and charger types available in the market were considered and a model was established to ascertain the relationship between the charging demand and the other contributory factors such as EV battery sizes, charge remaining at the commencement of recharging, charging rates at different places of the chronological load profile, charging habits of customers and time-of-use pricing (TOU) policies. The probability distribution of variables such as the time of commencement of charging and battery charge duration was considered at a significantly acceptable level. By combining the probability distribution curves of the said variables, several EV charge demand curves were established using Monte Carlo method and the charging demand curves were subsequently superimposed on the system load profile. While the proposed methodology gives an insight into the impact of the EV load on the system load profile, it also shows how an effective control of EV charging could bring down the operational costs and investment cost of a power system.

Highlights

Introduction behaviours ofElectric vehicles (EVs) have their own randomness and intermittency and their impact on the
Internal factors include EV battery size, charging rates that differ depending on the amount of battery charge remaining at the commencement of charging, geographical distribution of the charging infrastructure and charging habits
All the distributions were based on the fact that 3 kW of power were required by each EV during the charging period [8]

Summary

Introduction

Introduction behaviours ofEVs have their own randomness and intermittency and their impact on the. Because of the emphasis placed on low carbon emissions and the advantages of electricity when used in transport in place of liquid fuels, the development of EVs is being promoted in the transport sector at an accelerated pace. Power system is high, especially when all EV users start charging their batteries at the same time during low tariff and peak load hours, requiring more power from the grid to balance the resulting increase in the generation and operation costs. With the increasing EV population, the evaluation of the impact of aggregated EV loads with associated battery charging characteristics on the system load profile becomes important. The factors influencing the charging characteristics can be categorized as internal and external [1]. The only factor that can be considered as external is the time-of-use (TOU)

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Conclusion