Agent-based charging scheduling of electric vehicles

Azar, Armin Ghasem; Jacobsen, Rune Hylsberg

doi:10.1109/onlinegreencom.2016.7805408

Cited by 6 publications

(6 citation statements)

References 14 publications

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“…However, as noted in (Ferro et al 2019), event-triggered approach present in the literature commonly consists of on-line scheduling algorithms capable of "correcting" a pre-existing schedule when a new vehicle requiring charging service arrives at the station. Instead, the approach presented in this paper consists in developing a predictive discrete event optimization model for the optimal scheduling of electric vehicles in charging stations with multiple sockets that can be used for parallel charging.…”

Section: State Of the Artmentioning

confidence: 99%

“…Instead, the approach presented in this paper consists in developing a predictive discrete event optimization model for the optimal scheduling of electric vehicles in charging stations with multiple sockets that can be used for parallel charging. This is essentially the same approach followed in (Ferro et al 2019), but it is now extended to the case in which several vehicles may be charged simultaneously. Besides, a further extension is provided with respect to the model in (Ferro et al 2019), consisting of the possibility of power flows from the vehicles, that is, the so-called V2G model.…”

Section: State Of the Artmentioning

confidence: 99%

“…This is essentially the same approach followed in (Ferro et al 2019), but it is now extended to the case in which several vehicles may be charged simultaneously. Besides, a further extension is provided with respect to the model in (Ferro et al 2019), consisting of the possibility of power flows from the vehicles, that is, the so-called V2G model.…”

Section: State Of the Artmentioning

confidence: 99%

“…For this reason, heuristics and metaheuristics have been applied as well as decision architectures based on decentralized optimization. Differently from the majority of papers in literature, following the same approach as in (Ferro et al 2019), in this paper, a discrete event approach is used to formalize the optimization model. The reason lies in the necessity of reducing the number of variables and computational time.…”

Section: Introductionmentioning

confidence: 99%

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Discrete event optimization of a vehicle charging station with multiple sockets

Ferro

Minciardi

Parodi

et al. 2020

Discrete Event Dyn Syst

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The relevance and presence of Electric Vehicles (EVs) are increasing all over the world since they seem an effective way to fight pollution and greenhouse gas emissions, especially in urban areas. One of the main issues related to EVs is the necessity of modifying the existing infrastructure to allow the installation of new charging stations (CSs). In this scenario, one of the most important problems is the definition of smart policies for the sequencing and scheduling of the vehicle charging process. The presence of intermittent energy sources and variable execution times represent just a few of the specific features concerning vehicle charging systems. Even though optimization problems regarding energy systems are usually considered within a discrete time setting, in this paper a discrete event approach is proposed. The fundamental reason for this choice is the necessity of limiting the number of the decision variables, which grows beyond reasonable values when a short time discretization step is chosen. The considered optimization problem regards the charging of a series of vehicles by a CS connected with a renewable energy source, a storage element, and the main grid. The objective function to be minimized results from the weighted sum of the (net) cost for purchasing energy from the external grid, the weighted tardiness of the services provided to the customers, and a cost related to the occupancy of the socket during the charging. The approach is tested on a real case study. The limited computational burden allows also the implementation in real-case applications.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Discrete event optimization of a vehicle charging station with multiple sockets

Ferro

Minciardi

Parodi

et al. 2020

Discrete Event Dyn Syst

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“…Flowchart of generating a feasible behavior pair BP k i (t), where τ j,i (t) is the summation of load demands of appliances with insufficient flexibility. Earliest Deadline First (EDF) mechanism[9] is applied on the remaining appliances and relevant power actions are, then, updated. Feasibility is related to constraints formulated in each prosumer's MO-MINLP.…”

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confidence: 99%

A Non-Cooperative Framework for Coordinating a Neighborhood of Distributed Prosumers

Azar

Nazaripouya

Khaki

et al. 2019

IEEE Trans. Ind. Inf.

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