Optimization based AIMD saturated algorithms for public charging of electric vehicles. (May 2019)

• Record Type: Journal Article
• Authors: Nisar Shah, Saqib; Incremona, Gian Paolo; Bolzern, Paolo; Colaneri, Patrizio
• Is Part Of: European journal of control. Volume 47(2019)
• Page Start: 74
• Page End: 83
• Publication Date: 2019-05
• Subjects: AIMD -- Distributed control -- Electric vehicles -- Optimal scheduling -- Distributed management; Control theory -- Periodicals; Automatic control -- Periodicals; Automatic control -- Mathematics -- Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.ejcon.2018.12.009
• Languages: English
• ISSNs: 0947-3580

Abstract

Abstract: The Additive Increase Multiplicative Decrease (AIMD) algorithm is an interesting approach in congestion control of communication networks, as it maintains the good features of a distributed strategy, without sacrificing the network stability and robustness. Recent applications of these algorithms also concern other industrial fields such as Electric Vehicles (EVs) based transportation systems, for which the introduction of an optimal charging policy is an important challenge for power systems operation. Moreover, saturation constraints on the resource allocated to each vehicle need to be taken into account in order to avoid peak power requirements and grid overloads. Optimization based AIMD algorithms with saturation constraints are proposed in this paper for public charging of EVs. Specifically, a new AIMD approach is presented in order to capture the main advantages of optimal algorithms which minimize either the sum of charging times or the operation time of each vehicle, giving rise to a mixed AIMD strategy. Simulation results illustrate the performance of the proposal, even in comparison to the corresponding centralized optimal solutions.