Bus Charging Stations Research Articles

Load modeling of electric bus charging station from data obtained through Phasor Measurement Units

While the increased adoption of electric vehicles (EVs) is a promising alternative to reduce CO2 emissions, it creates new challenges for the power grid due to increased energy demand and power quality (PQ) issues. These impacts vary depending on several factors such as the level of EV adoption, charging technology, network voltage level, charging patterns, charging station location, battery condition, and driving habits. Analyzing these impacts and developing solutions, such as characterizing the demand curve for charging stations and understanding EV charging patterns, is crucial to ensure a sustainable transition to an electrified EV future. A study using the ZIP load model that represents voltage dependence by combining constant impedance (“Z”), constant current (“I”), constant power (“P”) components, and phasor measurement units (PMUs) demonstrates the effectiveness of EV demand characterization. The importance of this aspect for grid stability and charging management is highlighted.

Optimization of Charging Station Capacity Based on Energy Storage Scheduling and Bi-Level Planning Model

With the government’s strong promotion of the transformation of new and old driving forces, the electrification of buses has developed rapidly. In order to improve resource utilization, many cities have decided to open bus charging stations (CSs) to private vehicles, thus leading to the problems of high electricity costs, long waiting times, and increased grid load during peak hours. To address these issues, a dual-layer optimization model was constructed and solved using the Golden Sine Algorithm, balancing the construction cost of CSs and user costs. In addition, the problem was alleviated by combining energy storage scheduling and the M/M/c queue model to reduce grid pressure and shorten waiting times. The study shows that energy storage scheduling effectively reduces grid load, and the electricity cost is reduced by 6.0007%. The average waiting time is reduced to 2.1 min through the queue model, reducing the electric vehicles user’s time cost. The bi-level programming model and energy storage scheduling strategy have positive implications for the operation and development of bus CSs.

Sustainable and smart electric bus charging station deployment via hybrid spherical fuzzy BWM and MULTIMOORA framework

This study aims to assist public bus operators in locating electric bus charging station (EBCS) facilities from a smart and sustainable view. The selection of the most suitable EBCSs from various possible candidates involves a sophisticated decision-making procedure in terms of several contradictory criteria with imprecise information. The novelty of the study resides in exploring the EBCS site selection problem with spherical fuzzy sets (SFSs), which have shown remarkable effectiveness in limiting information loss by seizing ambiguous, and uncertain data. In this regard, a novel best–worst method (BWM) incorporating Multi-objective optimization via full multiplicative form ratio analysis (MULTIMOORA) methodology in the spherical fuzzy context is proposed to choose the optimal locations for EBCSs. The integrated framework combines the adaptability of the spherical fuzzy BWM (SF-BWM) for determining the criteria weights with the convenience of spherical fuzzy MULTIMOORA (SF-MULTIMOORA) approach for ranking the alternatives. A case study for Istanbul is provided to substantiate the propounded technique and to confirm its viability and efficiency. In the course of making a decision, a four-level hierarchical structure consisting of five main and 22 sub-criteria is built and the comparison matrices are reviewed by a panel of seven experts. A sensitivity analysis is executed, and the results demonstrate that the propositioned approach produces outcomes that are quite robust and consistent. Hence, the findings of this research can benefit public bus operators in choosing the ideal sites for electric charging stations. Finally, the formulated generic methodology is also easily applicable to diverse and complex multiple-criteria problems in the spherical fuzzy domain.

An evaluation method for integrating EVs in distribution networks with clustering algorithms

Introduction: With the installation of advanced metering infrastructures, the operation data of EVs in the distribution networks can be obtained with time intervals of seconds and minutes. Based on these operation data, the impacts of integrating EVs into the distribution networks can be calculated and discussed.Methods: In this paper, an improved clustering algorithm with a new distance index for the daily curves of different types of EVs was proposed. The different types of EVs can be classified into several typical groups and the required number of operation scenarios can be reduced. After reducing the large-scale database to typical clusters, research can be conducted on the characteristics of EVs specific to certain scenarios.Results and discussion: In this way, the capability of integrating different types of EVs into the distribution network, such as fast EV charging stations, slow EV charging stations, and EV bus charging stations, is assessed from the perspective of load capacity size. The proposed clustering algorithm was verified with practical operation data.

Toward Real-Time Pricing and Allocation for Surplus Resources in Electric Bus Charging Stations

Toward Real-Time Pricing and Allocation for Surplus Resources in Electric Bus Charging Stations

Optimization Model of Hybrid Renewable Energy Generation for Electric Bus Charging Stations

This paper introduces a comprehensive approach for sizing grid-connected hybrid renewable energy systems tailored for electric bus fleet operations. The study involves two main steps. First, a mathematical model that optimizes the configuration of such systems by considering daily electric bus consumption, solar irradiance, wind speed, and biomass potential is formulated. The model utilizes Pareto frontier multi-objective optimization to minimize the net present cost, the cost of energy, and greenhouse gas emissions. Second, the model is rigorously applied and tested in a real-world case study in Fez, Morocco, using HOMER Pro; the case study centers on the daily energy requirements of the buses, estimated at 2.5 megawatt hours per day, with a peak demand of 345 kilowatts. Two scenarios are explored, revealing a discernible trade-off dilemma between the full hybrid renewable energy scenario (Scenario 1) and the grid-connected hybrid renewable energy scenario (Scenario 2). In Scenario 2, the grid-connected hybrid renewable energy system demonstrates a notable 42.8% reduction in the net present cost, totaling USD 984,624. Similarly, the levelized cost of energy experiences a significant decrease, reaching approximately 0.08 USD/kWh, marking a 38.1% reduction. However, this apparent economic advantage is juxtaposed with a critical consideration—an increase in greenhouse gas emissions from null to 330,418 kg/year.

A two-stage approach to siting electric bus charging stations considering future-current demand

With the global acceleration of the carbon-neutral process and the widespread adoption of electric buses in various countries, the placement of charging stations plays a crucial role in promoting low-carbon development in public transportation systems. This paper proposes a two-stage approach for siting electric bus charging stations considering future-present needs. Initially, perspectives on future and current needs, a community discovery model is constructed using bus space-time swipe card data to identify the municipal bus travel link network. This model considers the OD distribution of bus passenger flow, which is essential for determining the line network and capacity layout. Additionally, a bus first and last station hotspot identification-Voronoi diagram model is established based on the service area partition of bus charging stations, incorporating the weight of charging demand. The model assesses the aggregation of charging demand in each service area using demand aggregation and spatial aggregation indicators. It utilizes the current bus charging requirements aggregation pattern to determine the siting of bus charging stations in the sub-regions, resulting in a data-driven two-stage method for siting electric bus charging stations considering future and current demand. The city is divided into 11 bus charging station subzones using the community discovery model, and sites are selected within each subzone. The results indicate that the demand aggregation degree has increased to some extent in 10 service areas compared to the initial sub-district and an overall increase of 22.79% for the entire city, from 0.487 to 0.598. Among them, 8 service areas have a demand aggregation degree greater than 0.5, indicating that charging demand in most sub-districts is concentrated in high-value aggregation areas. The spatial aggregation degree of all service areas is less than 1, with a decrease in the spatial aggregation degree of 7 service areas and an overall decrease of 11.47% for the entire city, from 0.680 to 0.602. Most service areas experience a decrease in spatial aggregation degree while the demand aggregation degree increases, optimizing the structure of the service areas and reducing the time cost of charging the buses. The model demonstrates good applicability to charging stations and offers an effective approach to siting them.

Optimizing electric bus charging infrastructure deployment under two charging modes considering seasonal differences

ABSTRACT The innovations of battery and charging technologies facilitate worldwide bus electrification, which can conserve energy and reduce emissions. The emergence of wireless power transfer technology offers the potential to alleviate bus range anxiety. This paper constructs the layout models of static wireless and centralized charging stations based on the seasonal differences associated with battery capacities and passenger flow, which aims to obtain the bus charging station layout scheme of the lowest cost. Then, the heuristic algorithm is utilized to solve the layout models as the mixed linear programming problem. This study results confirm that the combined layout of static wireless and centralized charging stations is more advantageous than a single model. Finally, the required fixed cost of supporting bus system operation is explored for future capital reserves.

A CVaR-based stochastic framework for storm-resilient grid, including bus charging stations

This paper proposes a two-stage stochastic framework for improving distribution system resilience against storms, in which the uncertainties associated with load demands, solar irradiance after a storm event, and maximum gust wind speed are considered. In the first stage, the available idle electric buses (EBs) are optimally allocated to the charging stations prior to storm arrival. In the second stage, critical loads are restored through island formation after the storm. In this framework, the solar-powered charging stations of EBs and the distributed generators (DGs) are part of the electric energy resources. The solar charging stations contribute to supplying the critical loads in two ways: 1- The electricity generation through rooftop-installed photovoltaic modules 2- By discharging the batteries of the pre-allocated EBs. The objective of this optimization model is to minimize the expected priority-weighted curtailed energy. At the same time, the risk imposed by the uncertain parameters is taken into account through the conditional value-at-risk (CVaR) index. The proposed framework is expressed in terms of a stochastic mixed-integer linear programming (MILP) optimization problem. This framework is implemented on the 33-node distribution test system, and its superior resilient performance is demonstrated through four case studies.

Optimal Location and Line Assignment for Electric Bus Charging Stations

This article considers an optimization problem aiming at planning the service stations to recharge a fleet of electric buses (EBs) for public transportation. The planning problem includes the selection of the sites of the stations (among a set of pre-specified eligible sites) and their sizing (both in terms of the number of sockets and maximum output power). The optimization problem also integrates issues related to the assignment of the various lines to the activated stations and the bus fleet's sizing. The formalization of the optimization problem is carried out by taking into account the nonlinear charging process characteristic of the batteries of EBs. The optimization problem is defined as a minimum cost design while guaranteeing a minimum quality level of the service, represented in this case by a minimum frequency for each of the lines. The overall decision problem turns out to be a mixed nonlinear one. It can be efficiently solved by common commercial software tools for moderate-medium problem sizes, as demonstrated in the presented application.

Electric bus charging station location optimization considering queues

The transition to new forms of urban transport, which are environment friendly, efficient, and cost-effective has become a pressing issue of modern times. Battery electric buses are being deployed worldwide, marking a change in public transport planning and operations. The emergence of fast and ultra-fast wireless charging for electric buses has brought about the need for new planning processes and associated decision-support models for their deployment. The objective of this paper is the development of a model for optimally locating fast wireless chargers in an electric bus network, which considers delays of buses queuing for opportunity charging at charger locations. An integer linear programming model is formulated for that purpose: the model minimizes the investment cost for deploying opportunity charging facilities. An M/M/1 queuing model is employed to incorporate bus queuing considerations at terminal charging locations. The proposed model is demonstrated to a benchmark instance and results are discussed.

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

In recent years, there has been growing attention on the electrification of the public transit network. Battery electric buses (BEBs) are among the promising alternatives to replace diesel-powered buses. However, the possible driving range from a full charge has proved a matter of concern, as has the waiting times of BEBs returning to terminal stops after completing their journeys. This study aimed to design an efficient electric transit network considering waiting times at terminal stops and two configurations of charger to avoid BEBs running out of charge: a fast charger with energy storage (ES) technology and one without. A queuing-based mathematical model was proposed. To validate the proposed model, we tested it on two sizes of network: the Mumford0 (small) and the Mumford2 (large). By conducting a sensitivity analysis, certain model parameters, including the power of fast chargers, duration of service interval, BEB energy consumption, and maximum allowable waiting time were found to have substantial impacts on the electric public transit network. ES chargers were found to have the potential to save 15.35% of total costs. Other analyses confirmed that altering the capacity of fast- and ES chargers could affect the number of chargers required in the transit network and the total cost. Policies are suggested for transit agencies to plan to optimize their electric transit networks.

Optimal location planning of electric bus charging stations with integrated photovoltaic and energy storage system

AbstractThis study presents a novel bus charging station planning problem considering integrated photovoltaic (PV) and energy storage systems (PESS) to smooth the carbon‐neutral transition of transportation. This paper illustrates a two‐stage stochastic programming model capturing the uncertainty of PV power outputs and designs a step‐wise solution approach in which a conventional charging station location problem is solved in the first step and an improved L‐shaped algorithm is developed in the second step to determine charging station locations with PESS. The numerical experiments indicate that the step‐wise solution approach can provide high‐quality solutions within acceptable computational time. A case study is performed using a real‐world transit network in Beijing, China, with 34 bus routes and 15 candidate bus charging stations. Compared with the benchmark model, both recharging cost and carbon emission are reduced considerably. This paper provides novel insights into the development of integrated transportation and renewable energy systems.

Optimal electric bus scheduling by considering photovoltaic charging infrastructure

Battery electric buses (BEBs) are being introduced to substitute for diesel buses worldwide owing to their zero emissions. Investigating BEB scheduling problems is crucial for minimising charging costs and satisfying passenger demands. In addition, photovoltaic and energy-storage system (PESS) adoption in public transport could offer a promising alternative towards reducing charging costs and carbon dioxide emissions. This paper presents a novel BEB scheduling problem in which a PESS is deployed at bus-charging stations. The proposed problem is formulated as mixed-integer linear programming in which BEB scheduling and photovoltaic (PV) energy scheduling are optimised. To improve solution efficiency, a two-step solution approach is proposed: a BEB scheduling problem without considering the PESS is solved in the first step, and then a PV energy scheduling problem is solved in the second step. A case study is performed using the operational data of buses 404 and 109 in Beijing, China. The results show that the sum of charging and carbon dioxide emission costs will be reduced considerably if the PESS is introduced in a transit system. This study provides new insights for engineering a sustainable future in green transportation.

Planning of Regional Urban Bus Charging Facility: A Case Study of Fengxian, Shanghai

The electrification of public transport is of great significance to alleviating environmental pollution and energy problems. The construction of charging stations for electric buses (EBs) is the key step for the electrification of public transport and receives more and more attention. This paper proposes a new urban electric bus charging station planning algorithm which consists of two parts, park-maintaining (PM) charging station planning and midway supply (MS) charging station planning. Firstly, bus routes are classified based on charging demands. Accordingly, the PM charging station planning model is divided into full slow charging (FSC) model, Bus Rapid Transit (BRT) model and Hybrid model. Secondly, the improved grid AP algorithm is applied to plan MS charging stations to enhance the EB operation reliability. Then by multi-terminal charging pile optimization model, the economics of charging facilities construction is enhanced. Finally, via an ordered control charging algorithm, the economic profits of overall planning schemes are enhanced. The bus system in Fengxian, Shanghai is taken as an example to demonstrate the proposed method. Results prove that the proposed method can effectively meet the charging demands of EBs and improve the operating reliability of the EB system.

Sharing the electric bus charging stations by scheduling the charging strategy

Electric cars (ECs) play a vital role in reducing carbon emissions and improving sustainable development. Due to the lack of public charging facilities, the application of private ECs is limited. Motivated by the sharing economy, this is the first study to investigate the idea of sharing electric bus (EB) charging stations to alleviate the plight of private EC owners. However, existing EB charging strategies do not cater to the sharing and hinder the implementation of the policy. Therefore, this paper proposed a general mathematic mixed-integer non-linear programming model to coordinate the optimal charging strategy and sharing operation to meet the external and internal goals of bus systems, namely, maximizing charging station availability and satisfying the EB charging demand. Harnessing real-world bus operating data, the proposed model effectively generates multiple charging strategies. The results show that some hybrid strategies can achieve a similar sharing availability period as the public-oriented strategy with lower costs. In addition, sensitivity analysis indicates that increasing battery capacity can extend the sharing availability period while increasing charging power has only a minor impact. Overall, the proposed model obtains desired charging scheduling and provides concrete suggestions to promote the sustainable development of ECs and EBs.

Electric bus charging station site selection based on the combined DEMATEL and PROMETHEE-PT framework

Electric buses (EBs) are one of the most promising transportation modes to reduce air pollution and greenhouse gas emissions. With the promotion of EBs, the selection of EBs charging station (EBCS) site plays a key role in bus electrification. In order to deal with the information uncertainty and the complicated logical relationship between evaluation criteria, this paper establishes a combined framework of fuzzy decision-making trial and evaluation laboratory (DEMATEL), preference ranking organization method for enrichment evaluations (PROMETHEE) and prospect theory (PT) to evaluate EBCS sites. Firstly, the evaluation criteria system consisting of transportation, economy, technology, and environment for the EBCS site selection problem is established. Secondly, the fuzzy DEMATEL method is adopted to calculate the weights of EBCS evaluation criteria and analyze their causal relationships. Thirdly, the fuzzy PROMETHEE method with PT is combined to obtain the ranking of the alternative EBCS sites. Finally, based on the case study in Nanjing city, the proposed combined framework proves to be effectiveness and scalability. The numerical results show that the long-term influencing criteria (e.g., coordination with power grid planning, total investment cost), the net cause criteria and the criteria with strong mutual influence have great impact on EBCS site selection.

A framework for the assessment of electric bus charging station construction: A case study for Stockholm's inner city

Electromobility has gained momentum in the last years following the efforts to reduce transportation-related emissions. In this context, efficient charging infrastructure is vital to foster the expansion of electric vehicles. This paper presents a standardized framework for micro-scale analysis of potential charging locations for electric buses aiming at easing the analysis process and promoting the expansion of electric buses. The framework is tailor-made for the Municipality of Stockholm and tested in two city-centre public transport hubs, namely Odenplan and Slussen. However, the framework can be used in other locations by implementing minimum changes. Connecting charging stations to the power grid is identified as the main drawback in city-centre locations due to their high impact on the grid. Lack of available capacity at nearby connection points results in long distance connections, reaching up to 1 km in some cases. Such connections impact the overall cost of electrification directly, as they may account for up to 63% of the total cost. Although other issues regarding space availability and operational efficiency also need to be addressed, such as the lack of enough dwell time to charge the batteries, the framework proves the suitability of these inner-city locations as charging points.

Evaluation of the multi-dimensional growth potential of China's public charging facilities for electric vehicles through 2030

Based on almost 6 million charging records of 1588 charging stations in Jiangsu Province, China, this paper quantitatively analyzes the growth potential of public charging facilities for electric vehicles (EVs) in China towards 2030, from both aspects of saving power cost and providing flexible regulation capability. Based on the single EV charging optimization model, the unsupervised method is adopted to handle the efficiency problem of sampling calculation for massive actual data, and loop calculations were then conducted for sampling optimization. Bus charging stations (BCS), urban public charging stations (UPCS), and highway charging stations (HWCS)) are considered. Calculation results show that charging operators or users can save more than 20% of the power purchase cost (about 88.1 billion yuan) through intelligent charging management. By 2030, large-scale adoption of EVs would enable schedulable capacity that accounts for more than 10% of China's annual electricity generation from renewable energy sources (wind and photovoltaic energy). Moreover, electrical buses have the most potential now, while the figure for electrical passenger cars charged in UPCS would account for the greatest proportion in the future.

Electric bus charging station supplied by urban electrical DC railway network

The increasing charging stations for electric buses in urban areas bring critical challenges to the electricity distribution network. One potential solution is to interconnect a charging station to the urban Electrical DC Railway Network (EDCRN) to design an electrical smart grid. This work develops a charging strategy to ensure that the electric bus can circulate without stop at the bus depot for recharging during the daytime. To minimize the power demand from the EDCRN, the braking energy of urban trains is recovered to recharge electric bus. The battery is added to store more instantaneous braking energies for recharging the bus later, and its storage capacity is properly sized. Moreover, renewable energies are integrated to achieve an environment-friendly urban electric mobility system. The energy management system is formulated to ensure a reliable and efficient operation of the DC railway micro-grid. An energetic simulation model is built for a period of 24 hours by using the Energetic Macroscopic Representation. Critical results are provided to verify the proposed charging strategy.