Hierarchical Game for Networked Electric Vehicle Public Charging Under Time-Based Billing Model

Yu, Yue; Su, Chunxia; Tang, Xiao; Kim, BaekGyu; Song, Tiecheng; Han, Zhu

doi:10.1109/tits.2020.2994192

Cited by 23 publications

(5 citation statements)

References 27 publications

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“…In Figure 1, the individual blocks K i F represent the cost minimizing controllers in charge of optimally splitting the ride-hailing fleets. Inspired by objective functions analyzed in [6], [7], [21], [22], we model the cost as the sum of three terms: the expected queuing cost, the charging cost, and the negative expected revenue.…”

Section: B Company's Objectivementioning

confidence: 99%

“…Pricing mechanisms are usually studied in the context of maximizing revenue. Papers [6]- [8] model the charging stations as selfish revenue-maximizing agents, whereas [9] describes a game for modeling the regulatory aspects such as taxes and transportation prices as well as the operational matters for mobility service providers. From the perspective of congestion control in urban networks, different tolling mechanisms, congestion taxes, and congestion-aware routing schemes have been proposed in [10]- [13] based on the congestion game vehicle routing.…”

Section: Introductionmentioning

confidence: 99%

“…However, it also implies that the companies do not know the prices before deciding how to direct their vehicles but rather how their joint decision will influence the charging cost. Finally, the ridehailing company's total operational cost in this paper combines similar elements from the models found in [6], [7], [21], [22].…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Pricing Game for Balancing the Charging of Ride-Hailing Electric Fleets

Maljkovic

Nilsson

Geroliminis

2023

IEEE Trans. Contr. Syst. Technol.

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Due to the ever-increasing popularity of ridehailing services and the indisputable shift towards alternative fuel vehicles, the intersection of the ride-hailing market and smart electric mobility provides an opportunity to trade different services to achieve societal optimum. In this work, we present a hierarchical, game-based, control mechanism for balancing the simultaneous charging of multiple ridehailing fleets. The mechanism takes into account sometimes conflicting interests of the ride-hailing drivers, the ride-hailing company management, and the external agents such as power-providing companies or city governments that will play a significant role in charging management in the future. The upper-level control considers charging price incentives and models the interactions between the external agents and ride-hailing companies as a Reverse Stackelberg game with a single leader and multiple followers. The lower-level control motivates the revenue-maximizing drivers to follow the company operator's requests through surge pricing and models the interactions as a single leader, multiple followers Stackelberg game. We provide a pricing mechanism that ensures the existence of a unique Nash equilibrium of the upper-level game that minimizes the external agent's objective at the same time. We provide theoretical and experimental robustness analysis of the upper-level control with respect to parameters whose values depend on sensitive information that might not be entirely accessible to the external agent. For the lowerlevel algorithm, we combine the Nash equilibrium of the upper-level game with a quadratic mixed integer optimization problem to find the optimal surge prices. Finally, we illustrate the performance of the control mechanism in a case study based on real taxi data from Shenzhen in China.

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Section: B Company's Objectivementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hierarchical Pricing Game for Balancing the Charging of Ride-Hailing Electric Fleets

Maljkovic

Nilsson

Geroliminis

2023

IEEE Trans. Contr. Syst. Technol.

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“…When a numerous number of EVs require charging is a major challenge for both scientists and companies. The goal is to reduce the impact on the power grid by solving the charging problem [3], [23]- [29] One such approach is MCSs known as a new type of EV charging equipment that offers charge service at any location and time requested [30], [31]. One main advantage of MCSs is that they can harvest idle energy from the grid efficiently by being charged from substations during off-peak hours or renewable resources [8], [10].…”

Section: Related Workmentioning

confidence: 99%

Distributed Auction-Based Incentive Mechanism for Energy Trading Between Electric Vehicles and Mobile Charging Stations

Kim

Shin

et al. 2022

IEEE Access

Self Cite

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With the increasing number of electric vehicles, deploying fixed charging stations (FCSs) has been a widely adopted solution for providing charging services to EVs. However, the charging requirement of EVs being near overload FCSs and/or in areas of inadequate charging infrastructures such as highways and rural areas will surpass the capabilities of FCSs. To address this challenge, the realized peer-to-peer (P2P) energy trading between electric vehicles (EVs) and mobile charging stations (MCSs) can be utilized to relieve the overload on FCSs, leverage under-utilized energy resources among cities and achieve trading benefits. By deploying this energy trading model, EVs purchase available energy from MCSs instead of FCSs to fulfill their charging demands. However, such an energy trade requires an incentive mechanism to ensure fair trading and prevent personal gain, which motivates us to study the incentive mechanism design for the energy trading model. In this paper, first we consider an energy trading system involving multiple MCSs and EVs. Then, we formulate the incentive mechanism between MCSs and EVs as an auction game, in which the MCSs are auctioneers and EVs are bidders. In the formulated problem, each EV secretly submits its bid to MCSs, and each MCS distributively decides winners without knowledge about other MCSs. To achieve this, we design a distributed action-based energy trading mechanism that ensures fairness of providing charging service, validity in trading price's resource determination, and nothing incentivizing the EVs to cheat on payment decisions. The proposed energy trading scheme achieves certain critical properties, including truthfulness, individual rationality, budget balance, and computational efficiency for both buyers and sellers. Finally, we perform simulation experiments to verify the proposed scheme's effectiveness compared with the baseline in literature.

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“…The work in [8] proposes a non-cooperative game pricing strategy framework by the approach of profit-sharing and user equilibrium principles to maximize the social welfare of the electrified transportation system stakeholders consisting of electricity wholesalers, fast charging stations, and EV users, but this work also does not propose a closed form function to show how EVs select their desired CS for charging. The work in [9] is focused on the EV public charging market with heterogeneous CSs under the time-based billing model. In the proposed hierarchical game, each CS sets the charging price to maximize its own revenue first, then the EVs choose their desired CSs and determine the charging time; however, the power flow constraints, which affect the pricing strategy and relevant results, are not considered in this work.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Pricing Strategy for Electric Vehicle Charging Stations to Distribute the Congestion and Maximize the Revenue

Kazemtarghi,

Mallik,

Chen

2023

Preprint

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Hierarchical Game for Networked Electric Vehicle Public Charging Under Time-Based Billing Model

Cited by 23 publications

References 27 publications

Hierarchical Pricing Game for Balancing the Charging of Ride-Hailing Electric Fleets

Hierarchical Pricing Game for Balancing the Charging of Ride-Hailing Electric Fleets

Distributed Auction-Based Incentive Mechanism for Energy Trading Between Electric Vehicles and Mobile Charging Stations

Dynamic Pricing Strategy for Electric Vehicle Charging Stations to Distribute the Congestion and Maximize the Revenue

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