Performance and consumer satisfaction‐based bi‐level tariff scheme for EV charging as a VPP

Argade, Sachin; Aravinthan, Visvakumar; Büyüktahtakın, İ. Esra; Joseph, Siny

doi:10.1049/iet-gtd.2018.5754

Cited by 17 publications

(3 citation statements)

References 31 publications

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“…x price = { min{c 1 c 2 c k } t k Î t peak min{g 1 g 2 g k } t k Î t valley (11) x time = { min{c 1 c 2 c k } g pk ³ g slowk min{g 1 g 2 g k } g slowk > g pk (12) where g k is the charging duration of the k th EV; c k is the charging cost of the k th EV; t k is the charging start time of the k th EV; t peak and t valley are the peak and valley time for electricity price, respectively; g pk is the parking duration of the k th EV; and g slowk is the charging duration when the k th EV selects slow charging. The charing cost stands for the cost when the EV selects slow charging mode and the minimum charging duration refers to the fast charging mode.…”

Section: ) Charging Mode Selection Modulementioning

confidence: 99%

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Electric Vehicle Charging Simulation Framework Considering Traffic, User, and Power Grid

Liu

Shi

Zhao

et al. 2021

Journal of Modern Power Systems and Clean Energy

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The traffic and user have significant impacts on the electric vehicle (EV) charging load but are not considered in the existing research. We propose a novel integrated simulation framework considering the traffic, the user, and power grid as well as the EV traveling, parking and charging based on cellular automaton (CA). The traffic is modeled by the traffic module of the proposed framework based on CA, while the power grid and user are modeled in the EV charging module. The traffic flow, user' s charging preference, user' s charging satisfaction, and the total supply capability (TSC) in the surveyed region are considered in the proposed framework. Two cases are carried out to show the interactions between the user and power grid. It is shown that the proposed framework can accurately simulate the interactions among traffic situation, user' s behavior and TSC，which are significantly lacking in the existing research. The proposed framework is scalable in considering additional interrelated elements.

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Section: ) Charging Mode Selection Modulementioning

confidence: 99%

“…The distributed decision-making method [9] has been used to model the charging behavior under different transmission conditions. The user' s satisfaction is considered in the charging sequence optimization [10] and a bi-level tariff scheme [11].…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Charging Simulation Framework Considering Traffic, User, and Power Grid

Liu

Shi

Zhao

et al. 2021

Journal of Modern Power Systems and Clean Energy

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show abstract

“…Even though several EV mobile apps have been popped up these days for getting various information on CSs that will provide user convenience [14, 15], only a few of them furnish real‐time information on EVSEs such as availability status and allow to reserve EVSEs before going to the charging points.…”

Section: Motivationmentioning

confidence: 99%

Smart electric vehicle charging management for smart cities

Vaidya

Mouftah

2020

IET Smart Cities

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In recent years, attraction to alternative urban mobility paradigms such as electric vehicles (EVs) is increasing since EVs can significantly minimise fossil fuel dependency and reduce carbon emission in urban areas. Nonetheless, there are several barriers toward widespread adoption of EVs. Moreover, as EV penetration increases in urban areas, uncoordinated charging may cause power outage. Deployment of EV charging network can allow EVs to communicate with the service provider to coordinate charging activities. Taking into account, increased growth of EVs, number of charging facilities will be inadequate in urban areas, so efficient EV charging management is required for managing and allocating scarce charging station (CS) resources. In this study, the authors have designed and implemented a smart EV charging management system utilizing charging strategy that includes effective reservation management and efficient slot allocation of CSs. Considering composite cost that includes waiting time, estimated charging time, estimated charging cost, user discontent factor and CS congestion impact in such a method, their scheduling scheme shall furnish a set of optimal solutions. Viewing user discontent factor and average waiting time, they have evaluated performance of proposed strategy. The proposed charging strategy is effective than the existing one in terms of average waiting time. Nomenclature ϵ i req requested energy of EV i χ i ini initial SoC value of EV i χ i tar targeted SoC value of EV i Δ χ i difference SoC value between targeted and initial for EV i t i ar expected arrival time of EV i at the CS t i dep expected departure time of EV i from the CS t i wa waiting (queuing) time of EV i at the CS t i rs reservation start time t i rf reservation finish time of EV i τ i r reservation duration for EV i δ time-slot interval 2

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Virtual Power Plants and Integrated Energy System: Current Status and Future Prospects

Mishra

Bordin

Leinakse

et al. 2021

Handbook of Smart Energy Systems

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Performance and consumer satisfaction‐based bi‐level tariff scheme for EV charging as a VPP

Cited by 17 publications

References 31 publications

Electric Vehicle Charging Simulation Framework Considering Traffic, User, and Power Grid

Electric Vehicle Charging Simulation Framework Considering Traffic, User, and Power Grid

Smart electric vehicle charging management for smart cities

Virtual Power Plants and Integrated Energy System: Current Status and Future Prospects

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