Modeling demand flexibility of electric vehicles

Ramesh, Srivatsa, Chirag; Rengarajan, Desik; Tumuluru, Vamsi Krishna

doi:10.1109/smartgridcomm.2017.8340702

Cited by 4 publications

(2 citation statements)

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“…One of the traditional pricing mechanisms is to use price elastic matrix (PEM) to evaluate the charging demand response [10], [11]. The PEM is defined as the ratio of the change in charging demand to the base charging demand over the change in price to the base price.…”

Section: Literature Reviewmentioning

confidence: 99%

A Two-phase On-line Joint Scheduling of Pricing and Control for Welfare Maximization of Charging Station

Huang¹,

Jia²,

Yang³

et al. 2022

Preprint

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The large adoption of EVs brings practical interest to the operation optimization of the charging station. The joint scheduling of pricing and charging control will achieve a win-win situation both for the charging station and EV drivers, thus enhancing the operational capability of the station. We consider this important problem in this paper and make the following contributions. First, a joint scheduling model of pricing and charging control is developed to maximize the expected social welfare of the charging station considering the Quality of Service and the price fluctuation sensitivity of EV drivers. It is formulated as a Markov decision process with variance criterion to capture uncertainties during operation. Second, a two-phase on-line policy learning algorithm is proposed to solve this joint scheduling problem. In the first phase, it implements event-based policy iteration to find the optimal pricing scheme, while in the second phase, it implements scenario-based model predictive control for smart charging under the updated pricing scheme. Third, by leveraging the performance difference theory, the optimality of the proposed algorithm is theoretically analyzed. Numerical experiments for a charging station with distributed generation and hydrogen energy storage demonstrate the effectiveness of the proposed method and the improved social warfare of the charging station.

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Section: Literature Reviewmentioning

confidence: 99%

A Two-phase On-line Joint Scheduling of Pricing and Control for Welfare Maximization of Charging Station

Huang¹,

Jia²,

Yang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A behavioural modeling of electric vehicles using price elasticities is proposed in [29] to provide insights into the degree of demandshifting that can occur across various day-ahead electricity market scenarios. An approach to model demand flexibility of electric vehicles is proposed in [30], where the demand flexibility offered by an EV is represented using a price elasticity matrix which is calculated with respect to a flat reference price scenario. However, the work does not provide enough insights into the value of an elasticity matrix due to the lack of elasticity data.…”

Section: Literature Reviewmentioning

confidence: 99%

Behavioural Change in Green Transportation: Micro-Economics Perspectives and Optimization Strategies

Bordin

Tomasgård

2021

Energies

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The increasing demand for Electric Vehicle (EV) charging is putting pressure on the power grids and capacities of charging stations. This work focuses on how to use indirect control through price signals to level out the load curve in order to avoid the power consumption from exceeding these capacities. We propose mathematical programming models for the indirect control of EV charging that aim at finding an optimal set of price signals to be sent to the drivers based on price elasticities. The objective is to satisfy the demand for a given price structure, or minimize the curtailment of loads, when there is a shortage of capacity. The key contribution is the use of elasticity matrices through which it is possible to estimate the EV drivers’ reactions to the price signals. As real-world data on relating the elasticity values to the EV driver’s behaviour are currently non-existent, we concentrate on sensitivity analysis to test how different assumptions on elasticities affect the optimal price structure. In particular, we study how market segments of drivers with different elasticities may affect the ability of the operator to both handle a capacity problem and properly satisfy the charging needs.

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