Location Planning of Fast Charging Station Considering its Impact on the Power Grid Assets

Mao, Daijiafan; Wang, Jiankang; Tan, Jun; Xu, Yiran; Li, Jie

doi:10.1109/itec.2019.8790573

Cited by 12 publications

(14 citation statements)

References 21 publications

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“…Integer programming, linear integer programming, MIP, and various theoretical methods all provide support for CS sizing and placement problem. 128,129 Model-based objective functions and constraints, the economic cost and power loss of CS can be reduced to a certain extent. This section summarizes each analytical method as described in Table 10.…”

Section: Methodsmentioning

confidence: 99%

Recent advances of optimal sizing and location of charging stations: A critical overview

Yang

Shu

et al. 2022

Intl J of Energy Research

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Summary With the rapid development of the global economy and technology, the demand for fossil fuels in transportation and power industries is ever‐growing, which inevitably leads to lower fossil fuel storage and higher prices. Electric vehicles (EVs) are widely used as an effective solution to energy shortage and environmental pollution. However, there exist numerous obstacles, such as unmatchable capacity and unsuitable location. Therefore, an appropriate size and location of the charging station (CS) are very crucial. Based on a thorough investigation of 138 literature, this paper comprehensively summarizes the past from three aspects including charging technology, CS model, and methodologies. Besides, a comprehensive summary of the charging way and its level is proposed for a more overall analysis with enriched technologies diversity. Furthermore, two different CS models are discussed detailly in the aspects of the objective function, factors, advantages and disadvantages, applicability, and complexity. Additionally, a detailed summary of three methodologies, including meta‐heuristic, multi‐criteria decision making (MCDM), and analytical methods, are proposed and a comprehensive evaluation and their advantages/disadvantages, applicability, and complexity are summarized and assessed thoroughly. Finally, 11 valuable and insightful findings are drawn and two promising challenges, two opportunities, and three future directions are proposed for further research. Highlights Key technical difficulties and future challenges of sizing and placement of charging stations (CSs) are discussed. Most representative papers are selected and presented in table form for readers to compare more conveniently. Two different kinds of models and evaluation indexes are introduced in detail and each model is compared and evaluated fairly from different angles. Thirty‐three methods of sizing and location determination are summarized and compared strictly, and the advantages and disadvantages, applicability, and complexity of each method are dedicated to being described. Eleven main findings and seven constructive suggestions are put forward because of the development status of electric vehicles.

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Section: Methodsmentioning

confidence: 99%

Recent advances of optimal sizing and location of charging stations: A critical overview

Yang

Shu

et al. 2022

Intl J of Energy Research

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“…Using the cross‐entropy optimisation method, Mao et al. [37] investigated the impact of fast charging stations on the power grid. Yenchamchalit et al.…”

Section: Related Research Workmentioning

confidence: 99%

Optimal locating and sizing of charging stations for large‐scale areas based on GIS data and grid partitioning

Prakobkaew,

Sirisumrannukul

2023

IET Generation Trans & Dist

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This research proposes an optimisation model for identifying the optimal locations and sizes of public charging stations for large‐scale areas based on geographic information system (GIS) data and grid partitioning. A grid partitioning technique is utilised to generate subareas and investigate the demand for electric vehicle (EV) chargers calculated based on the number of EVs in each subarea. The optimal locations are formulated as a maximum covering location problem and solved by integer programming to ensure that all vehicle access subareas, according to the GIS data, are covered by charging stations. The sizes of charging stations are formulated as a knapsack problem and solved by the EV‐charge point ratio to guarantee each subarea has the appropriate number of chargers. The Voronoi diagram is integrated to estimate vehicle density and improve visualisation. The model is extended to include potential power loss reduction by adjusting the distribution of chargers in relation to the proximity to electrical substations. The developed methodology has been implemented by the whole country area of Thailand, which covers an area of approximately 510,000 km2. The study results are represented in a map blueprint that is very useful in strategic planning to support the so‐called EV30@30 government policy's ambitious national target of 2,050,000 units of EVs in 2030.

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“…Other works use classic optimization algorithms to solve this particular location problem, for example in [29,30] it is formulated as a mixed-integer linear problem and solved by deterministic branch-and-bound method. However, the charging stations location problem is a NP-hard problem [13,31], and thus, the use of heuristic methods and artificial intelligence algorithms have become some of the most recurrent solving techniques [27,32]. In [16] the problem is formulated as MINLP and solved using genetic algorithm technique.…”

Section: Literature Reviewmentioning

confidence: 99%

Electric vehicle charging stations’ location in urban transportation networks: A heuristic methodology

Franco

Durán-Tovar

Pradilla

et al. 2021

IET Electrical Syst in Trans

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The lack of public charging infrastructure has been one of the main barriers preventing the technological transition from traditional vehicles to electric vehicles. To accelerate this technological transition, it is necessary to elaborate optimal charging station location strategies to increase the user confidence, and maintain investment costs within acceptable levels. However, the existing works for this purpose are often based on multipath considerations or multi‐objective functions, that result in taxing computational efforts for urban transportation networks. This article presents a heuristic methodology for urban transportation networks, that considers the deployment of the charging stations for coverage purposes, and the fulfilment of user preferences and constraints as two separated processes. In this methodology, a Reallocation Algorithm is formulated to prioritize the selection of Locations of Interest, and to reduce the number of stations with overlapping covering areas. The methodology results are compared to those drawn from a Greedy Algorithm based on a multipath consideration, in an extensive metropolitan transportation network. The results show that the proposed methodology significantly reduce the computational time required for solving the location problem, and furthermore, allows for similar results to those obtained when considering k = 2 and k = 3 deviation paths.

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Location Planning of Fast Charging Station Considering its Impact on the Power Grid Assets

Cited by 12 publications

References 21 publications

Recent advances of optimal sizing and location of charging stations: A critical overview

Recent advances of optimal sizing and location of charging stations: A critical overview

Optimal locating and sizing of charging stations for large‐scale areas based on GIS data and grid partitioning

Electric vehicle charging stations’ location in urban transportation networks: A heuristic methodology

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