Fast-charging station location problem: A two-phase approach with mathematical program with equilibrium constraints considering charging choice behaviour

Qiao, Dong; Wang, Guangmin; Xu, Meng

doi:10.1016/j.scs.2023.104678

Cited by 12 publications

(2 citation statements)

References 43 publications

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“…This function includes factors such as electricity prices and driving distance to the station, as presented in [37], and may further include considerations like crowdedness, brand of charging station, and level of attraction [38]. Additionally, to incorporate traffic flow, researchers have adopted the concept of system optimum [39] or user equilibrium [31] or even developed a novel mathematical program to minimize social costs with equilibrium constraints [40].…”

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%

“…-Dynamic traffic flow is not considered -GA depends on the parameter's selection. [35] Minimize driving distance and waiting time [40] Mathematical program with equilibrium constraints (MPEC) to minimize total social cost (public parking lots) MINLP PSO [39] Minimize cost and driving time PSO may get trapped in a local optimum.…”

Section: B Approximate Approachesmentioning

confidence: 99%