A time-of-use pricing strategy for managing electric vehicle clusters

Vuelvas, José; Ruiz, Fredy; Gruosso, Giambattista

doi:10.1016/j.segan.2020.100411

Cited by 39 publications

(12 citation statements)

References 27 publications

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“…To regulate EV charging behavior, a stochastic two-level optimization model of EV participation in demand response was presented. The upper layer was intended for maximizing EV aggregator profit, while the lower layer was optimized for minimizing EV charging costs [15]. A real-time charging price method is proposed for controlling the charging of plug-in hybrid electric vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is worth noting that • represents the synthesis operation of matrix rather than matrix operation. Following the computation of the transitive closure matrix t(R) = (r ts ) T×T , different confidence levels λ ∈ [0, 1] are selected to obtain corresponding intercept matrix t(R) λ , as shown in Equation (15).…”

Section: Load Time Division Based On Fuzzy Clusteringmentioning

confidence: 99%

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Orderly Charging Strategy Based on Optimal Time of Use Price Demand Response of Electric Vehicles in Distribution Network

et al. 2022

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In order to manage electric vehicles (EVs) connected to charging grids, this paper presents an orderly charging approach based on the EVs’ optimal time-of-use pricing (OTOUP) demand response. Firstly, the Monte Carlo approach is employed to anticipate charging power by developing a probability distribution model of the charging behavior of EVs. Secondly, a scientific classification of the load period is performed using the fuzzy clustering approach. Then, a matrix of demand price elasticity is developed to measure the link between EV charging demand and charging price. Finally, the charging scheme is optimized by an adaptive genetic algorithm from the distribution network and EV user viewpoints. This paper describes how to implement the method presented in this paper in an IEEE-33-bus distribution network. The simulation results reveal that, when compared to fixed price and common time-of-use pricing (CTOUP), the OTOUP charging strategy bears a stronger impact on reducing peak–valley disparities, boosting operating voltage, and decreasing charging cost. Additionally, this paper studies the effect of varied degrees of responsiveness on charging strategies for EVs. The data imply that increased responsiveness enhances the likelihood of new load peak, and that additional countermeasures are required.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Load Time Division Based On Fuzzy Clusteringmentioning

confidence: 99%

Orderly Charging Strategy Based on Optimal Time of Use Price Demand Response of Electric Vehicles in Distribution Network

et al. 2022

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“…The authors have taken the network and battery parameters as optimization constraints. A price based bi-level optimization strategy is proposed by [17] to maximize the benefits of aggregator while realizing the dynamic driving pattern. Although the reported work has exhibited reasonable profit of the EV aggregator, it has failed to capture network performance.…”

Section: Introductionmentioning

confidence: 99%

A Coordinated Charging Scheduling of Electric Vehicles Considering Optimal Charging Time for Network Power Loss Minimization

et al. 2021

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Electric vehicles’ (EVs) technology is currently emerging as an alternative of traditional Internal Combustion Engine (ICE) vehicles. EVs have been treated as an efficient way for decreasing the production of harmful greenhouse gasses and saving the depleting natural oil reserve. The modern power system tends to be more sustainable with the support of electric vehicles (EVs). However, there have been serious concerns about the network’s safe and reliable operation due to the increasing penetration of EVs into the electric grid. Random or uncoordinated charging activities cause performance degradations and overloading of the network asset. This paper proposes an Optimal Charging Starting Time (OCST)-based coordinated charging algorithm for unplanned EVs’ arrival in a low voltage residential distribution network to minimize the network power losses. A time-of-use (ToU) tariff scheme is used to make the charging course more cost effective. The concept of OCST takes the departure time of EVs into account and schedules the overnight charging event in such a way that minimum network losses are obtained, and EV customers take more advantages of cost-effective tariff zones of ToU scheme. An optimal solution is obtained by employing Binary Evolutionary Programming (BEP). The proposed algorithm is tested on IEEE-31 bus distribution system connected to numerous low voltage residential feeders populated with different EVs’ penetration levels. The results obtained from the coordinated EV charging without OCST are compared with those employing the concept of OCST. The results verify that incorporation of OCST can significantly reduce network power losses, improve system voltage profile and can give more benefits to the EV customers by accommodating them into low-tariff zones.

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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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A time-of-use pricing strategy for managing electric vehicle clusters

Cited by 39 publications

References 27 publications

Orderly Charging Strategy Based on Optimal Time of Use Price Demand Response of Electric Vehicles in Distribution Network

Orderly Charging Strategy Based on Optimal Time of Use Price Demand Response of Electric Vehicles in Distribution Network

A Coordinated Charging Scheduling of Electric Vehicles Considering Optimal Charging Time for Network Power Loss Minimization

Electric Vehicle Fleets as Balancing Instrument in Micro-Grids

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