Optimal Placement of Battery Electric Bus Charging Stations Considering Energy Storage Technology: Queuing Modeling Approach

Momenitabar, Mohsen; Ebrahimi, Zhila Dehdari; Bengtson, Kelly

doi:10.1177/03611981221149729

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…In addition, infrastructure configuration schemes interact with each other [23,24]. For instance, the number of chargers and the charging station location affect the EB charging plan, thus influencing the fleet size [25,26].…”

Section: Integrated Optimization Of Vehicle Dispatching and Charging ...mentioning

confidence: 99%

Optimal Vehicle Scheduling and Charging Infrastructure Planning for Autonomous Modular Transit System

Chang,

Cong,

Wang

et al. 2024

Sustainability

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Prioritizing the development of public transport is an effective way to improve the sustainability of the transport system. In recent years, bus passenger flow has been declining in many cities. How to reform the operating mode of the public transportation system is an important issue that needs to be solved. An autonomous modular bus (AMB) is capable of physical coupling and uncoupling to flexibly adjust vehicle capacity as well as provide high-quality service under unbalanced passenger demand conditions. To promote AMB adoption and reduce the operating cost of the bus route, this paper presents a joint optimization method to simultaneously determine the AMB dispatching plan, charging plan, and charging infrastructure configuration scheme. Then, a mixed-integer programming model is formulated to minimize the operating costs of the bus route. A hybrid intelligent algorithm combining enumeration, cloning algorithm, and particle swarm optimization algorithm is designed to resolve the formulated model. Subsequently, an actual bus route is taken as an example to validate the proposed method. Results indicate that the developed method in this paper can reduce the operating costs and operational energy consumption of the route compared with the real route operating plan. Specifically, the reduction ratio of the former is 23.85%, and the reduction ratio of the latter is 5.92%. The results of this study validate the feasibility and advantages of autonomous modular transit service, contributing positively to the sustainable development of the urban public transportation system.

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Section: Integrated Optimization Of Vehicle Dispatching and Charging ...mentioning

confidence: 99%

Optimal Vehicle Scheduling and Charging Infrastructure Planning for Autonomous Modular Transit System

Chang,

Cong,

Wang

et al. 2024

Sustainability

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show abstract

“…It was noted that L = W means that for both M/M/1 and M/M/2, the anticipated number of patrons waiting in line is times the anticipated wait time. This is known as Little's law and is generally true [27]- [31]. The main attributes of the queuing model are the population source, the number of servers, the arrival with service patterns, and the queue discipline [33].…”

Section: 1mentioning

confidence: 99%

Queuing analysis and optimization of public vehicle transport stations: A case of South West Ethiopia region vehicle stations

Alem

2024

Int. J. Ind. Optim.

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Modern urban environments present a dynamically growing field where, notwithstanding shared goals, several mutually conflicting interests frequently collide. However, it has a big impact on the city's socioeconomic standing, waiting lines and queues are common occurrences. This results in extremely long lines for vehicles and people on incongruous routes, service coagulation, customer murmuring, unhappiness, complaints, and looking for other options, sometimes illegally. The root cause is corruption, which leads to traffic jams, stops and packs vehicles beyond their safe carrying capacity, and violates passengers' human rights and freedoms. This study focused on optimizing the time passengers had to wait in public vehicle stations. This applied research employed both data-gathering sources and mixed approaches. Then, 166 samples of key informants of transport stations were taken using the Slovin sampling formula. The time vehicles, including the drivers and auxiliary drivers ‘Weyala', had to wait was also studied. To maximize the service level at vehicle stations, a queuing model was subsequently devised ‘Menaharya’. Time, cost, and quality encompass performance, scope, and suitability for the intended purposes. The study also focused on determining the minimal response time required for passengers and vehicles queuing to reach their ultimate destinations within the transportation stations in Tepi, Mizan, and Bonga. A new bus station system was modeled and simulated by Arena simulation software in the chosen study area. 84% improvement on cost reduced by 56.25%, time 4 hours to 1.5 hours, quality, safety and designed load performance calculations employed. Stakeholders are asked to implement the model and monitor the results obtained.

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“…To the authors’ knowledge, no studies have considered both types of fast and ES chargers to deal with an ETRNDP. Also, the concept of demand charge has been largely overlooked, even for charging station location problems (CSLPs) studied by many researchers ( 1 , 6 , 7 ). This study, by proposing a bi-level optimization model, has two aims: on the one hand, the transit route will be generated using the heuristic algorithm (upper-level problem); on the other hand, the total charging costs of the transit network, which is called a lower-level problem, are minimized using generated transit routes in the upper-level problem.…”

mentioning

confidence: 99%

Designing an Electric Transit Route Network Utilizing Energy Storage Technology to Mitigate Annual Demand Charge

Momenitabar

Ebrahimi

Mattson

et al. 2023

Transportation Research Record: Journal of the Transportation R

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The electrification of traditional transit route networks has been a promising option for urban public transit. Compared with fast chargers, energy storage (ES) technology benefits the planner in relation to cost savings and avoiding high electricity demand charges. Although many studies ignored the potentiality of utilizing ES chargers, this study uses this concept with demand charges to design an electric transit route network by proposing a bi-level optimization model. The upper-level model aims to minimize the total travel times of passengers along with the total number of indirect trips. In comparison, the lower-level problem plans to minimize the total costs of purchasing two types of fast and ES chargers and demand charges for the transit network that entirely operates with battery electric buses (BEBs). Because of the NP-hard nature of the proposed model, the discrete multi-objective grey wolf optimizer algorithm is suggested in a discrete space to search for a feasible solution by providing the Pareto frontier of two objective functions. Also, the initial route set generation algorithm has been applied to generate a new set of transit routes. To validate the proposed model, our model has been tested on a benchmark network of Mumford0 in a small and a Fargo–Moorhead area in a medium size. Our results confirmed that considering ES chargers could save 4.8% and 76.5% (for the user cost), 2.3%, and 111.9% (for the operator cost) of the total network and demand charges.

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Optimal Placement of Battery Electric Bus Charging Stations Considering Energy Storage Technology: Queuing Modeling Approach

Cited by 8 publications

References 28 publications

Optimal Vehicle Scheduling and Charging Infrastructure Planning for Autonomous Modular Transit System

Optimal Vehicle Scheduling and Charging Infrastructure Planning for Autonomous Modular Transit System

Queuing analysis and optimization of public vehicle transport stations: A case of South West Ethiopia region vehicle stations

Designing an Electric Transit Route Network Utilizing Energy Storage Technology to Mitigate Annual Demand Charge

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