Mathematical model of fast charging station with integrated accumulators impact on low voltage distribution network

Mastny, Petr; Morávek, Jan; Vrana, Michal; Wasserbauer, Vojtech; Vojtek, Martin

doi:10.1109/epe.2017.7967302

Cited by 1 publication

(1 citation statement)

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“…Moreover, the addition of EV charger load can increase the load of the power grid. Unfortunately, placement of chargers at the weak points of the power network and uncoordinated charging may lead to voltage instability, spikes in load curve, degradation of reliability indices, power losses, and harmonics [1][2][3][4][5][6][7][8][9]. Thus, the placement of EV charger must consider both the convolution of transport and distribution network [10].…”

Section: Introductionmentioning

confidence: 99%

A hybrid ant lion optimization chicken swarm optimization algorithm for charger placement problem

Deb

Gao

2021

Complex Intell. Syst.

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Transportation electrification is known to be a viable alternative to deal with the alarming issues of global warming, air pollution, and energy crisis. Public acceptance of Electric Vehicles (EVs) requires the availability of charging infrastructure. However, the optimal placement of chargers is indeed a complex problem with multiple design variables, objective functions, and constraints. Chargers must be placed with the EV drivers’ convenience and security of the power distribution network being taken into account. The solutions to such an emerging optimization problem are mostly based on metaheuristics. This work proposes a novel metaheuristic considering the hybridization of Chicken Swarm Optimization (CSO) with Ant Lion Optimization (ALO) for effectively and efficiently coping with the charger placement problem. The amalgamation of CSO with ALO can enhance the performance of ALO, thereby preventing it from getting stuck in the local optima. Our hybrid algorithm has the strengths from both CSO and ALO, which is tested on the standard benchmark functions as well as the above charger placement problem. Simulation results demonstrate that it performs moderately better than the counterpart methods.

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