A probabilistic capacity planning methodology for plug-in electric vehicle charging lots with on-site energy storage systems
Bayram, I. Safak and Galloway, Stuart and Burt, Graeme (2020) A probabilistic capacity planning methodology for plug-in electric vehicle charging lots with on-site energy storage systems. Journal of Energy Storage, 32. 101730. ISSN 2352-152X (https://doi.org/10.1016/j.est.2020.101730)
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Abstract
Plug-in electric vehicles (PEV) have gained popularity to support environmental sustainability and reach net-zero emission goals. However, accommodating large numbers of PEVs is a complex problem as concurrent PEV demand significantly increases peak demand and stresses supporting network elements. In this paper, we present a large-scale PEV charging lot equipped with an on-site storage. Power drawn from the grid is utilized to meet customer demand and charge the storage unit which, in return, is employed to lower peak load and demand charges. By considering the probabilistic nature of the customer demand, the proposed architecture is modelled by a Markov-modulated Poisson Process and a matrix-geometric based algorithm is developed to solve the associated capacity planning problem. Station outage probability (defined as the probability of not serving PEV demand) is used as the main metric to size station resources. Case studies show that by accounting for the statistical variations in customer demand, the power required for the station is significantly less than the sum of chargers' rated power. In addition, on-site storage can considerably reduce the stress on the supporting grid components and lower stations' running cost.
ORCID iDs
Bayram, I. Safak ORCID: https://orcid.org/0000-0001-8130-5583, Galloway, Stuart ORCID: https://orcid.org/0000-0003-1978-993X and Burt, Graeme ORCID: https://orcid.org/0000-0002-0315-5919;-
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Item type: Article ID code: 73587 Dates: DateEvent31 December 2020Published12 August 2020Published Online3 August 2020AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Strategic Research Themes > Ocean, Air and Space
Strategic Research Themes > Measurement Science and Enabling Technologies
Strategic Research Themes > EnergyDepositing user: Pure Administrator Date deposited: 13 Aug 2020 11:15 Last modified: 25 Sep 2024 00:44 URI: https://strathprints.strath.ac.uk/id/eprint/73587