LAPSE:2023.13682
Published Article
LAPSE:2023.13682
A Backwards Induction Framework for Quantifying the Option Value of Smart Charging of Electric Vehicles and the Risk of Stranded Assets under Uncertainty
Spyros Giannelos, Stefan Borozan, Goran Strbac
March 1, 2023
The anticipated electrification of the transport sector may lead to significant increase in the future peak electricity demand, resulting in potential violations of network constraints. As a result, a considerable amount of network reinforcement may be required in order to ensure that the expected additional demand from electric vehicles that are to be connected will be safely accommodated. In this paper we present the Backwards Induction Framework (BIF), which we use for identifying the optimal investment decisions, for calculating the option value of smart charging of EV and the cost of stranded assets; these concepts are crystallized through illustrative case studies. Sensitivity analyses depict how the option value of smart charging and the optimal solution are affected by key factors such as the social cost associated with not accommodating the full EV capacity, the flexibility of smart charging, and the scenario probabilities. Moreover, the BIF is compared with the Stochastic Optimization Framework and key insights are drawn.
Keywords
backwards induction framework, electric vehicles, option value, smart charging of EV, Stochastic Optimization
Suggested Citation
Giannelos S, Borozan S, Strbac G. A Backwards Induction Framework for Quantifying the Option Value of Smart Charging of Electric Vehicles and the Risk of Stranded Assets under Uncertainty. (2023). LAPSE:2023.13682
Author Affiliations
Giannelos S: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Borozan S: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK [ORCID]
Strbac G: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Journal Name
Energies
Volume
15
Issue
9
First Page
3334
Year
2022
Publication Date
2022-05-03
Published Version
ISSN
1996-1073
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Original Submission
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PII: en15093334, Publication Type: Journal Article
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doi:10.3390/en15093334
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