Robust Integration of Electric Vehicles Charging Load in Smart Grid’s Capacity Expansion Planning

Sajad Aliakbari Sani (), Olivier Bahn (), Erick Delage () and Rinel Foguen Tchuendom ()
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Sajad Aliakbari Sani: HEC Montréal
Olivier Bahn: HEC Montréal
Erick Delage: HEC Montréal
Rinel Foguen Tchuendom: CIM and McGill University

Dynamic Games and Applications, 2022, vol. 12, issue 3, No 12, 1010-1041

Abstract: Abstract Battery charging of electric vehicles (EVs) needs to be properly coordinated by electricity producers to maintain the network reliability. In this paper, we propose a robust approach to model the interaction between a large fleet of EV users and utilities in a long-term generation expansion planning problem. In doing so, we employ a robust multi-period adjustable generation expansion planning problem, called R-ETEM, in which demand responses of EV users are uncertain. Then, we employ a linear quadratic game to simulate the average charging behavior of the EV users. The two models are coupled through a dynamic price signal broadcasted by the utility. Mean field game theory is used to solve the linear quadratic game model. Finally, we develop a new coupling algorithm between R-ETEM and the linear quadratic game with the purpose of adjusting in R-ETEM the uncertainty level of EV demand responses. The performance of our approach is evaluated on a realistic case study that represents the energy system of the Swiss “Arc Lémanique” region. Results show that a robust behaviorally-consistent generation expansion plan can potentially reduce the total actual cost of the system by 6.2% compared to a behaviorally inconsistent expansion plan.

Keywords: Multi-period adjustable robust optimization; Mean field game; Demand response; Electric vehicles (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s13235-022-00454-y

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