An optimal deployment scheme for extremely fast charging stations

Zhong, Ping; Xu, Aikun; Kang, Yilin; Zhang, Shigeng; Zhang, Yiming

doi:10.1007/s12083-022-01306-7

Cited by 4 publications

(5 citation statements)

References 39 publications

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“…While these methods may not guarantee an optimal solution, they are particularly useful for tackling the complexity and scalability issues often associated with FCS allocation in transportation networks. Several studies propose approximate approaches utilizing specific well-known models such as the set covering approach [28], the arc-based approach [29] as presented by Csiszár and Csonka [30], FRLM [31,32], or the Multipath Refuelling Location Model (MPRLM) [33], which is a more flexible model than FRLM and allows EVs to deviate from the predefined path to reach the FCS. The MPRLM has been utilized for the FCS allocation problem, as presented by Li and Huang [34].…”

Section: B Approximate Approachesmentioning

confidence: 99%

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Optimization Approaches for Fast Charging Stations Allocation and Sizing: A Review

Kandil,

Abdelfatah,

Azzouz

2024

IEEE Access

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The widespread use of fossil fuels in transportation has resulted in significant carbon dioxide emissions and increased reliance on non-renewable energy sources. To address these environmental challenges, the electrification of transportation systems through Electric Vehicles (EVs) has emerged as a promising solution. However, the successful deployment of EVs hinges on the availability of a robust charging infrastructure capable of meeting the charging demands and extending the driving range of EVs. Nonetheless, the large-scale deployment of EV charging infrastructure presents several challenges, including the ability of the electric grid to supply the required energy to accommodate the charging demand. Moreover, determining the optimal locations for fast-charging stations (FCS) in the traffic network to ensure accessibility, convenience, and efficient resource utilization poses a significant challenge. As a result, numerous studies have investigated the optimal allocation and sizing of EV charging infrastructure. The primary objective of this research is to conduct a comprehensive literature review to examine how this optimization problem has been addressed in the literature over the past decade. The review aims to identify the key factors considered in the problem formulation and the optimization techniques for allocating and sizing the charging stations. To achieve this goal, a systematic literature review was conducted following the PRISMA methodology for a comprehensive and unbiased approach. This review contributes to the existing literature by highlighting critical gaps and proposing a framework that can possibly bridge these gaps. The review identifies several critical gaps in current research, including: 1) Transportation-focused studies largely ignore electrical grid constraints; 2) Electrical-focused research often relies on statistically modeled EV charging demand, which may include some geographical assumptions; 3) Multidisciplinary approaches integrating both transportation and electrical networks are still in early stage; 4) Dynamic traffic flow of EVs is rarely considered; 5) Exact optimization methods largely rely on linearized or approximated models; 6) Dominance of approximate methods in transportation and electrical network modeling, primarily relying on evolutionary algorithms; and 7) Hybrid approaches are mainly utilized to solve a specific part of the problem rather than enhancing the quality of the solution. In response to these gaps, the research proposes a novel framework to integrate the transportation network and the electrical grid planning process, offering a holistic and practical solution in FCS infrastructure development.

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Section: B Approximate Approachesmentioning

confidence: 99%

“…MINLP GA [28] Set covering -Most of the introduced or applied algorithms are applied to preselected candidate locations to reduce the search space.…”

Section: B Approximate Approachesmentioning

confidence: 99%

Optimization Approaches for Fast Charging Stations Allocation and Sizing: A Review

Kandil,

Abdelfatah,

Azzouz

2024

IEEE Access

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“…Flow-based models often maximize the total amount of flow in the network (e.g., [6,9,23,24]), which is also our case. For node-based modeling, different objectives have been used, such as maximization of the satisfied EV demand (e.g., [8,34,41]) and minimization of the costs (e.g., [5,12,26,39,40,44,45]).…”

Section: Related Literaturementioning

confidence: 99%

Maximum flow‐based formulation for the optimal location of electric vehicle charging stations

Parent,

Carvalho,

Anjos

et al. 2024

Networks

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With the increasing effects of climate change, the urgency to step away from fossil fuels is greater than ever before. Electric vehicles (EVs) are one way to diminish these effects, but their widespread adoption is often limited by the insufficient availability of charging stations. In this work, our goal is to expand the infrastructure of EV charging stations, in order to provide a better quality of service in terms of user satisfaction (and availability of charging stations). Specifically, our focus is directed towards urban areas. We first propose a model for the assignment of EV charging demand to stations, framing it as a maximum flow problem. This model is the basis for the evaluation of user satisfaction with a given charging infrastructure. Secondly, we incorporate the maximum flow model into a mixed‐integer linear program, where decisions on the opening of new stations and on the expansion of their capacity through additional outlets is accounted for. We showcase our methodology for the city of Montreal, demonstrating the scalability of our approach to handle real‐world scenarios. We conclude that considering both spacial and temporal variations in charging demand is meaningful when solving realistic instances.

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“…We shall describe a generic charging station placement problem [2][3][4][5]8] in this section. A few definitions follow.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Efficiently placing charging stations in cities as part of infrastructure planning to make them EV-friendly has been a very active area of research for at least the last 7 years, and as of today, there is a rich body of literature in this area [1][2][3][4][5][6][7][8][9][10][11][12]. Charging station placement is a specialization of the generic facility location problem that is known to be NP-hard.…”

Section: Introductionmentioning

confidence: 99%

PC-ILP: A Fast and Intuitive Method to Place Electric Vehicle Charging Stations in Smart Cities

Bose,

Dutta,

Shrivastava

et al. 2023

Smart Cities

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The widespread use of electric vehicles necessitates meticulous planning for the placement of charging stations (CSs) in already crowded cities so that they can efficiently meet the charging demand while adhering to various real-world constraints such as the total budget, queuing time, electrical regulations, etc. Many classical and metaheuristic-based approaches provide good solutions, but they are not intuitive, and they do not scale well for large cities and complex constraints. Many classical solution techniques often require prohibitive amounts of memory and their solutions are not easily explainable. We analyzed the layouts of the 50 most populous cities of the world and observed that any city can be represented as a composition of five basic primitive shapes (stretched to different extents). Based on this insight, we use results from classical topology to design a new charging station placement algorithm. The first step is a topological clustering algorithm to partition a large city into small clusters and then use precomputed solutions for each basic shape to arrive at a solution for each cluster. These cluster-level solutions are very intuitive and explainable. Then, the next step is to combine the small solutions to arrive at a full solution to the problem. Here, we use a surrogate function and repair-based technique to fix any resultant constraint violations (after all the solutions are combined). The third step is optional, where we show that the second step can be extended to incorporate complex constraints and secondary objective functions. Along with creating a full software suite, we perform an extensive evaluation of the top 50 cities and demonstrate that our method is not only 30 times faster but its solution quality is also 36.62% better than the gold standard in this area—an integer linear programming (ILP) approach with a practical timeout limit.

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An optimal deployment scheme for extremely fast charging stations

Cited by 4 publications

References 39 publications

Optimization Approaches for Fast Charging Stations Allocation and Sizing: A Review

Optimization Approaches for Fast Charging Stations Allocation and Sizing: A Review

Maximum flow‐based formulation for the optimal location of electric vehicle charging stations

PC-ILP: A Fast and Intuitive Method to Place Electric Vehicle Charging Stations in Smart Cities

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