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 and Goran Strbac
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Spyros Giannelos: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Stefan Borozan: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Goran Strbac: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK

Energies, 2022, vol. 15, issue 9, 1-22

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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