Capacity Optimization of EV Charging Networks: A Greedy Algorithmic Approach

Abstract

In the recent years, there has been a steady increase in the use of electrical vehicles (EV). Their further adoption is becoming more dependent on the quality of service provided by the charging infrastructure. In this paper, the focus is on optimizing the charging infrastructure from the point of minimizing the service drop modelled using the standard M/M/c/c loss queue. To be exact, a mathematical model is proposed for the problem of optimizing capacities at individual stations in an EV charging network. The novelty is in considering the relation of capacity of a charging station to its arrival rate. Due to the non-linearity of the problem, a greedy algorithm combined with a local search is developed for finding near optimal configurations of the system. The new model is evaluated using real-world data for population density and existing charging infrastructure for metropolitan areas. The conducted computational experiments, show that charging networks optimized using the proposed model, significantly better reflect the state-on-the-ground than standardly used models, while maintaining a low service drop rate.

Applications of artificial neural networks for calculation of the Erlang B formula and its inverses

“…2 The Erlang's formulas, aimed at the beginning to be used for performance calculation in public telephone networks, over the years have been applied in systems where the resources are limited, such as in mobile communication systems, VoIP (Voice over Internet Protocol) systems, computer networks, call centers, and electric vehicle power systems. [3][4][5][6][7][8][9][10][11][12] During planning and design of trunking and queuing systems and evaluating their performance, in addition to determining the probability of blocking and the probability of call waiting, which are the basis for estimating the quality of the service, it is also necessary to solve the inverse problem, that is, to determine the traffic intensity or the number of required channels (resources) for a given probability that corresponds to the desired GOS (Grade of Service) value. Given the nature of Erlang formulas, there is no inverse function of these formulas in a closed form.…”

“…The Erlang B formula determines the probability of blocking for a given offered traffic in the system with the given number of channels, while the Erlang C formula determines the probability that a call request will be on hold for the considered value of the offered traffic 2 . The Erlang's formulas, aimed at the beginning to be used for performance calculation in public telephone networks, over the years have been applied in systems where the resources are limited, such as in mobile communication systems, VoIP (Voice over Internet Protocol) systems, computer networks, call centers, and electric vehicle power systems 3–12 …”

Određivanje Inverzne Vrednosti Erlang B Formule Primenom Veštačkih Neuronskih Mreža

“…Imajući u vidu prirodu Erlangovih formula, ne postoji inverzna funkcija ovih formula u zatvorenom obliku. Zbog toga se vrše istraživanja na pronalaženju rešenja (numeričkih modela ili aproksimacija) za određivanje inverzne vrednosti Erlangovih formula [12]- [17] ili se koriste look-up tabele, pogotovo u aplikacijama koje se odnose na call centre.…”