Optimal location of PEVCSs using MAS and ER approach

Jiang, Changxu; Jing, Zhaoxia; Ji, Tianyao

doi:10.1049/iet-gtd.2017.1907

Cited by 18 publications

(7 citation statements)

References 44 publications

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“…Vehicle battery capacity max e is set at 25 kWh, and charging power c p is set at 12 kW. According to (16) T 8:00-9:00, 17:00-19:00 3:00-5:00, 16:00-18:00 0:00-7:00 8:00-9:00, 17:00-19:00 23:00-5:00…”

Section: A Parameter Settingmentioning

confidence: 99%

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Optimal Decision-Making Method for a Plug-In Electric Taxi in Uncertain Environment

et al. 2021

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This paper studies the optimal decision-making problem for a plug-in electric taxi (PET) in a time-varying complex environment, i.e., a passenger environment, charging station environment, traffic environment, and taxi company management system, in order to maximize PET profit in a short-term operating cycle. First, this problem is formulated as a sequential decision-making problem composed of multiple decision slots. Then, to make the model more practical, the model is divided into two parts: an external environment and an electric taxi model for refinement. The uncertainty and time-varying characteristics of four environmental aspects, including passengers, charging stations, traffic, and taxi company management systems, are analysed and modelled. The transitions between adjacent processes and the environmental feedback of each process are modelled by further subdividing both the serving process and the charging process of the PET into multiple subprocesses, including cruising, carrying passengers, driving to the charging station, queueing before charging, and connecting to the power grid for charging. There are several uncertain factors in the sequential decision-making process for the PET, which leads to difficulty in solving the problem. To address this difficulty, the model-free algorithm SARSA is chosen. Finally, the effectiveness of the proposed method is verified by simulation results.INDEX TERMS Plug-in electric taxi, decision making, uncertainty, SARSA algorithm, load modelling.

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Section: A Parameter Settingmentioning

confidence: 99%

“…Ref. [15] and [16] proposed a multiobjective optimizing model for electric taxi charging station deployment based on taxi trajectory data. Ref.…”

Section: Introductionmentioning

confidence: 99%

Optimal Decision-Making Method for a Plug-In Electric Taxi in Uncertain Environment

et al. 2021

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“…The goal is to maximize the satisfaction of charging demand for ET drivers based on 800 data from Wien on taxi cars. Likewise, the work of [32] provides an optimal location for charging stations using a multi-agent systems simulation framework to simulate the PETs' daily operation in real life such as cruising, picking up passengers, and charging. Pan et al [33] authors discuss the installation of charging stations that take the impact of travelers, taxi drivers, electricity distributors, transport networks, distributors, and electricity users into account.…”

Section: B Siting Et Charging Stationsmentioning

confidence: 99%

Urban Traffic Flow Mapping of an Andean Capital: Quito, Ecuador

et al. 2020

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Several efforts have been devoted to developing sustainable cities to address global environmental challenges and the growth of urban areas. In particular, transportation has various issues such as air pollution, noise, and traffic, which have to be addressed by collecting significant information of the traffic and road conditions of the cities. Automating the data extraction process and street network construction will allow building more useful models to study traffic behavior. This work presents a network modeling approach to identify interest points (extreme and internal) of the city, through a winner-takes-all edge trimming, and mapping the traffic flow between them. Such points can be considered as entries of an Origin-Destination matrix, where such information can be used to model traffic behavior between interest points. The case study of Quito, Ecuador is considered. Besides, to address environmental issues, this paper encourages the replacement of internal combustion taxis with electric vehicles. From the understanding of the vehicle traffic behavior, a pre-feasibility siting of electric taxi (ET) charging stations was carried out. The results will allow performing the sizing of each charging station considering electric power network constraints. This work aims to ensure a sustainable transportation system based on this crucial information. INDEX TERMS charging station, driving time and distance, electric vehicle, graph trimming, urban traffic network

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“…However, the authors do not account for the SOC when ETs arrive at charging station. Similarly, the optimal location of charging stations was presented in [66]. Their methodology was based on a multi-agent system to simulate plug-in hybrid ET daily operations, and minimized charging costs of plug-in ETs, power losses, and voltage deviations.…”

Section: Electric Taxis (Ets) Approachesmentioning

confidence: 99%

Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies

et al. 2019

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The market for electric vehicles (EVs) has grown with each year, and EVs are considered to be a proper solution for the mitigation of urban pollution. So far, not much attention has been devoted to the use of EVs for public transportation, such as taxis and buses. However, a massive introduction of electric taxis (ETs) and electric buses (EBs) could generate issues in the grid. The challenges are different from those of private EVs, as their required load is much higher and the related time constraints must be considered with much more attention. These issues have begun to be studied within the last few years. This paper presents a review of the different approaches that have been proposed by various authors, to mitigate the impact of EBs and ETs on the future smart grid. Furthermore, some projects with regard to the integration of ETs and EBs around the world are presented. Some guidelines for future works are also proposed.

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Optimal location of PEVCSs using MAS and ER approach

Cited by 18 publications

References 44 publications

Optimal Decision-Making Method for a Plug-In Electric Taxi in Uncertain Environment

Optimal Decision-Making Method for a Plug-In Electric Taxi in Uncertain Environment

Urban Traffic Flow Mapping of an Andean Capital: Quito, Ecuador

Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies

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