Optimal Charging Scheduling by Pricing for EV Charging Station With Dual Charging Modes

Zhang, Yongmin; You, Pengcheng; Cai, Lin

doi:10.1109/tits.2018.2876287

Cited by 204 publications

(64 citation statements)

References 32 publications

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“…Small-scale: Numerous studies [1,4,10,11,19,22,29,34,39,40,[43][44][45][46][51][52][53] have been carried out for dealing with real problems in the ET domain, e.g., charging station siting and deployment, charging schedule. However, most of the existing works are based on small-scale real-world ET data or utilize conventional gas taxi data to simulate ET scenarios.…”

Section: Short-term Investigationmentioning

confidence: 99%

Experience

Wang

Chen

Zhang

et al. 2019

The 25th Annual International Conference on Mobile Computing and Networking

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Due to the ever-growing concerns on the air pollution and energy security, many cities have started to update their taxi fleets with electric ones. Although environmentally friendly, the rapid promotion of electric taxis raises problems to both taxi drivers and governments, e.g., prolonged waiting/charging time, unbalanced utilization of charging infrastructures and reduced taxi supply due to the long charging time. In this paper, we make the first effort to understand the long-term evolving patterns through a five-year study on one of the largest electric taxi networks in the world, i.e., the Shenzhen electric taxi network in China. In particular, we perform a comprehensive measurement investigation called ePat to explore the evolving mobility and charging patterns of electric vehicles. Our ePat is based on 4.8 TB taxi GPS data, 240 GB taxi transaction data, and metadata from 117 charging stations, during an evolving process from 427 electric taxis in 2013 to 13,178 in 2018. Moreover, ePat also explores the impacts of various contexts and benefits during the evolving process. Our ePat as a comprehensive investigation of the electric taxi network mobility and charging evolving has the potential to advance the understanding of the evolving patterns of electric taxi networks and pave the way for analyzing future shared autonomous vehicles.

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Section: Short-term Investigationmentioning

confidence: 99%

Experience

Wang

Chen

Zhang

et al. 2019

The 25th Annual International Conference on Mobile Computing and Networking

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“…In [24], Faridimehr et al proposed a two-stage stochastic programming model to optimize the charging network of CSs, and also a sample average approximation method was adopted to solve this problem for large-scale instances. In [25], an optimal pricing scheme was studied by Zhang et al to minimize the dropping rate of the charging service, where the CSs were modelled as queuing network with multiple servers and heterogeneous service rates.…”

Section: Related Workmentioning

confidence: 99%

Congestion-Balanced and Welfare-Maximized Charging Strategies for Electric Vehicles

Tang

Wang

Yang

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

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With the increase of the number of electric vehicles (EVs), it is of vital importance to develop the efficient and effective charging scheduling schemes for all the EVs. In this paper, we aim to maximize the social welfare of all the EVs, charging stations (CSs) and power plant (PP), by taking into account the changing demand of each EV, the changing price, the capacity and the congestion balance between different CSs. To this end, two efficient scheduling algorithms, i.e., Centralized Charging Strategy (CCS) and Distributed Charging Strategy (DCS) are proposed. CCS has a slightly better performance than the DCS, as it takes all the information and make the decision in the central control unit. On the other hand, DCS dose not require the private information from EVs and can make decentralized decision. Extensive simulation are conducted to verify the effectiveness of the proposed algorithms, in terms of the performance, congestion balance and computing complexity.

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“…As a result, the grid receives full impact from the irregular charging/discharging loads of EVs, thus increasing the level of power quality (PQ) problems in the power distribution system. In [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42], ECSs not using RE and ESSs were investigated. Based on the results presented in the reviewed papers, in some cases, only limited EVs can possibly be connected.…”

Section: Ecss Without Res and Esssmentioning

confidence: 99%

“…A multi-objective grey wolf optimizer (MOGWO) algorithm and fuzzy satisfaction-based decision-making method were used to obtain the final result, which was tested on the IEEE 123-bus distribution system connected to a transportation network. Control strategies were studied in [39][40][41][42]. P. You [39] proposed a cooperative charging strategy based on dual decomposition and Benders decomposition to allow EVs to share energy with each other, which is similar to using EVs as ESSs.…”

Section: Ecss Without Res and Esssmentioning

confidence: 99%

“…In [40], randomized algorithms were used for ECS energy management with a day-ahead upper limit of power consumption. The charging schedule was optimized in [41] by incorporating fast and regular charging modes into one ECS. X. Tan et al [42] used a generalized Benders decomposition algorithm to optimize the charging schedule of an ECS with a battery swapping service.…”

Section: Ecss Without Res and Esssmentioning

confidence: 99%

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System Planning of Grid-Connected Electric Vehicle Charging Stations and Key Technologies: A Review

2019

Energies

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The optimal planning of electric vehicle (EV) charging stations (ECSs) with advanced control algorithms is very important to accelerate the development of EVs, which is a promising solution to reduce carbon emissions of conventional internal combustion engine vehicles (ICEVs). The large and fluctuant load currents of ECSs can bring negative impacts to both EV-related power converters and power distribution systems if the energy flow is not regulated properly. Recent review papers related to EVs found in open literature have mainly focused on the design of power converter-based chargers and power interfaces, analyses of power quality (PQ) issues, the development of wireless charging techniques, etc. There is currently no review paper that focuses on key technologies in various system configurations, optimal energy management and advanced control issues in practical applications. To compensate for this insufficiency and provide timely research directions, this paper reviews 143 previously published papers related to the aforementioned topics in recent literature including 17 EV-related review papers found in Institute of Electrical and Electronics Engineers (IEEE)/Institution of Engineering and Technology (IET) (IEEE/IET) Electronic Library (IEL) and ScienceDirect OnSite (SDOS) databases. In this paper, existing system configurations, related design methods, algorithms and key technologies for ECSs are systematically reviewed. Based on discussions given in the reviewed papers, the most popular ECS configuration is a hybrid system design that integrates renewable energy (RE)-based power generation (REBPG), various energy storage systems (ESSs), and utility grids. It is noteworthy that the addition of an ESS with properly designed control algorithms can simultaneously buffer the fast, fluctuant power demand during charging, smooth the intermittent power generation of REBPG, and increase the overall efficiency and operating flexibility of ECSs. In addition, verifying the significance of the flexibility and possible profits that portable ESSs provide in ECS networks is a potential research theme in ECS fields, in which the potential applications of portable ESSs in the grid-tied ECSs are numerous and could cover a full technical spectrum.

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Optimal Charging Scheduling by Pricing for EV Charging Station With Dual Charging Modes

Cited by 204 publications

References 32 publications

Experience

Experience

Congestion-Balanced and Welfare-Maximized Charging Strategies for Electric Vehicles

System Planning of Grid-Connected Electric Vehicle Charging Stations and Key Technologies: A Review

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