In this paper, the problem of optimal charging of plug-in electric vehicles in a parking lot is addressed. The parking time is assumed to be uncertain and characterized by a statistical distribution with fixed first and second order moments. The energy price during the day is assumed to be known on a day ahead basis. In this context, the problem of maximizing the profit of a charging station is formulated in a receding horizon framework, whose solution provides the optimal charging policy. To accommodate stochastic uncertainty affecting the parking time, a distributionally robust joint chance constraint approach is adopted when formulating the overall optimization problem. The optimal solution guarantees a customer satisfaction criterion expressed as a probabilistic confidence level. Simulation results on a case study show effectiveness and computational feasibility of the proposed approach.

Casini, M., Vicino, A., Zanvettor, G.G. (2019). A distributionally robust joint chance constraint approach to smart charging of plug-in electric vehicles. In Proceedings of the IEEE Conference on Decision and Control (pp.4222-4227). New York : IEEE [10.1109/CDC40024.2019.9029763].

A distributionally robust joint chance constraint approach to smart charging of plug-in electric vehicles

Casini, M.;Vicino, A.;Zanvettor, G. G.
2019-01-01

Abstract

In this paper, the problem of optimal charging of plug-in electric vehicles in a parking lot is addressed. The parking time is assumed to be uncertain and characterized by a statistical distribution with fixed first and second order moments. The energy price during the day is assumed to be known on a day ahead basis. In this context, the problem of maximizing the profit of a charging station is formulated in a receding horizon framework, whose solution provides the optimal charging policy. To accommodate stochastic uncertainty affecting the parking time, a distributionally robust joint chance constraint approach is adopted when formulating the overall optimization problem. The optimal solution guarantees a customer satisfaction criterion expressed as a probabilistic confidence level. Simulation results on a case study show effectiveness and computational feasibility of the proposed approach.
978-1-7281-1398-2
Casini, M., Vicino, A., Zanvettor, G.G. (2019). A distributionally robust joint chance constraint approach to smart charging of plug-in electric vehicles. In Proceedings of the IEEE Conference on Decision and Control (pp.4222-4227). New York : IEEE [10.1109/CDC40024.2019.9029763].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1107652