Semi-stochastic load model for heavy goods electric vehicles depot charging considering the potential for demand side management

Shariati, O. ORCID: https://orcid.org/0000-0002-1790-7165, Coker, P., Smith, S. T. ORCID: https://orcid.org/0000-0002-5053-4639 and Potter, B. (2022) Semi-stochastic load model for heavy goods electric vehicles depot charging considering the potential for demand side management. In: 2022 International Conference on Communications, Information, Electronic and Energy Systems (CIEES), 24-26 November 2022, Veliko Tarnovo, Bulgaria. doi: 10.1109/CIEES55704.2022.9990821 (9781665491495)

Abstract/Summary

Research and development on the electrification of road vehicles have been dominated by light-duty passenger vehicles and vans. Recent developments in Electric Vehicle (EV) technology, however, are bringing attention to the electrification of heavy-duty vehicles and raising questions about fleet charging profiles and related implications for power networks. In this paper, a semi-stochastic model is developed for the simulation of Heavy Goods Electric Vehicle (HGEV) depots' charging demand profiles. The modelling of daily fleet charging profiles, managed and unmanaged, are addressed in this paper to investigate the characteristics of the typical load profile and the potential of demand side management in response to price signals. Issues of charging constraint and optimal depot charging, from a power network perspective, have been applied to understanding how the fleet demand is met. The findings of this work paves the way to study the impacts of heavy-duty vehicle electrification on the network and the maximum power demanded from the grid based on the charging strategies taken. Adoption of flexibility programs or network reinforcement plans around sites of fleet charging can be informed by this study - helping to mitigate the overall stress that the grid could face due to increasing future demands.

Item Type	Conference or Workshop Item (Paper)
URI	https://reading-clone.eprints-hosting.org/id/eprint/110567
Identification Number/DOI	10.1109/CIEES55704.2022.9990821
Refereed	Yes
Divisions	Interdisciplinary centres and themes > Energy Research
Publisher	IEEE
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