Energy price forecasting for optimal managing of electric vehicle fleet

Vuelvas, José; Ruiz, Fredy; Gruosso, Giambattista

doi:10.1049/iet-est.2020.0043

Cited by 4 publications

(6 citation statements)

References 46 publications

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“…In practice this means that in this case the range constraints are not relevant. Consequently, the number of DBs needed to achieve a timetable can be acquired using a MIP having the objective function given in Equation ( 6) and only the constraints in ( 9)- (11). This model is significantly less computationally expensive than the one for EBs.…”

Section: Case Studiesmentioning

confidence: 99%

“…For the same reason, a high penetration of EVs can be observed within car hailing and taxi services. In parallel, there is a growing research interest on the efficient planning of charging infrastructure for large fleets and exploiting smart charging scheduling in such systems [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…The set of constraints can be divided into three groups. The first one, in ( 9)- (11), is dedicated to the completion of all the trips in the timetable by some EB. This set of constraints does not consider the range of EBs.…”

mentioning

confidence: 99%

“…Note that the starting at the depot and finalizing the day at the depot are also considered as trips (pull-out and pull-in). The constraint in Equation (11) states that there is an equal number of EBs that leave the starting depot and arrive at the ending depot.…”

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confidence: 99%

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A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems

et al. 2021

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Electrifying public bus transportation is a critical step in reaching net-zero goals. In this paper, the focus is on the problem of optimal scheduling of an electric bus (EB) fleet to cover a public transport timetable. The problem is modelled using a mixed integer program (MIP) in which the charging time of an EB is pertinent to the battery’s state-of-charge level. To be able to solve large problem instances corresponding to real-world applications of the model, a metaheuristic approach is investigated. To be more precise, a greedy randomized adaptive search procedure (GRASP) algorithm is developed and its performance is evaluated against optimal solutions acquired using the MIP. The GRASP algorithm is used for case studies on several public transport systems having various properties and sizes. The analysis focuses on the relation between EB ranges (battery capacity) and required charging rates (in kW) on the size of the fleet needed to cover a public transport timetable. The results of the conducted computational experiments indicate that an increase in infrastructure investment through high speed chargers can significantly decrease the size of the necessary fleets. The results also show that high speed chargers have a more significant impact than an increase in battery sizes of the EBs.

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Section: Case Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems

et al. 2021

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“…It is, therefore, essential to have energy storage systems capable of promoting an optimal use of the resources. One of the most interesting and challenging solutions is to use Electric Vehicles (EV) as virtual storage systems [8][9][10][11]. Different optimization-based solutions to manage the charging profile of electric vehicles were proposed [12].…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Fleets as Balancing Instrument in Micro-Grids

Gruosso

Ruiz

2021

Energies

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Micro-grids have become the building block of modern energy systems, where distributed resources are the characterizing feature. The charging operation of electric vehicles can be exploited as a flexible load to achieve operational goals of the micro-grid. In the particular case of car-sharing fleets, the degrees of freedom in the charging procedures are reduced when compared to private users. In this work, we illustrate how a car sharing fleet can be incorporated as a flexible load in the micro-grid management system. A linear optimization problem is formulated, where the cost function makes a trade-off between the gain in flexibility in the micro-grid and the loss incurred by the car-sharing service for delaying the recharging procedure of the EV. The proposed approach is evaluated on a data set of charging events generated by a real car-sharing fleet showing that the EMS allows reducing the daily peak demand requested to the public grid and diminishes the operational costs.

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