Charging Strategy Research Articles

Analysis of an Urban Grid with High Photovoltaic and e-Mobility Penetration

This study analyses the expected utilization of an urban distribution grid under high penetration of photovoltaic and e-mobility with charging infrastructure on a residential level. The grid utilization and the corresponding power flow are evaluated, while varying the control strategies and photovoltaic installed capacity in different scenarios. Four scenarios are used to analyze the impact of e-mobility. The individual mobility demand is modelled based on the largest German studies on mobility “Mobilität in Deutschland”, which is carried out every 5 years. To estimate the ramp-up of photovoltaic generation, a potential analysis of the roof surfaces in the supply area is carried out via an evaluation of an open solar potential study. The photovoltaic feed-in time series is derived individually for each installed system in a resolution of 15 min. The residential consumption is estimated using historical smart meter data, which are collected in London between 2012 and 2014. For a realistic charging demand, each residential household decides daily on the state of charge if their vehicle requires to be charged. The resulting charging time series depends on the underlying behavior scenario. Market prices and mobility demand are therefore used as scenario input parameters for a utility function based on the current state of charge to model individual behavior. The aggregated electricity demand is the starting point of the power flow calculation. The evaluation is carried out for an urban region with approximately 3100 residents. The analysis shows that increased penetration of photovoltaics combined with a flexible and adaptive charging strategy can maximize PV usage and reduce the need for congestion-related intervention by the grid operator by reducing the amount of kWh charged from the grid by 30% which reduces the average price of a charged kWh by 35% to 14 ct/kWh from 21.8 ct/kWh without PV optimization. The resulting grid congestions are managed by implementing an intelligent price or control signal. The analysis took place using data from a real German grid with 10 subgrids. The entire software can be adapted for the analysis of different distribution grids and is publicly available as an open-source software library on GitHub.

Comprehensive exergetic performance assessment and techno-financial optimization of off-grid hybrid renewable configurations with various dispatch strategies and solar tracking systems

The suitable selection of a dispatch strategy in a hybrid renewable configuration is essential in determining optimum scenarios. In this study, to determine the most reliable and economical way to meet the electricity demand of remote areas, an off-grid hybrid renewable configuration comprised of PV panels, wind turbines, a battery bank, and a biogas generator (as backup) is proposed. To this end, exergy and environmental analyses and techno-economic optimization are applied to four dispatch strategies including cycle charging, load following, combined dispatch, and HOMER predictive over a remote off-grid village located in South Khorasan province as a case study. The results of the exergy analysis signposted that for all dispatch modes, the highest share in overall exergy destruction was pertinent to PV modules, and accordingly, the inauguration of three solar tracking systems was examined. To investigate the cost-effectiveness of different configurations and dispatch strategies, a cost-effective index was defined and in all dispatch strategies, the highest belonged to the vertical tracker. On these grounds, the vertical tracking mode was the most cost-effective option to enhance the hybrid system's technical performance. The results indicated that upon forecasting the future load, the HOMER predictive dispatch strategy was performant in terms of employing the storage system and minimizing excess electricity, while the Combined Dispatch elicited the lowest rate of application and appropriate use of the battery. On the economic side, the cycle charging strategy had a cost of energy of 0.128 $/kWh and a net prest cost of $ 152258 and was found as the most affordable scenario with vertical tracking mode. Afterward, sensitivity analyses concerning the financial factors, exergetic parameters, and renewable resources were implemented for generalizing the findings of the investigation to other operating conditions. Eventually, the time series of electrical energy consumption and production, and the frequency of SOC for the battery bank for all dispatch strategies are explored.

An online reinforcement learning approach to charging and order-dispatching optimization for an e-hailing electric vehicle fleet

Given the uncertainty of orders and the dynamically changing workload of charging stations, how to dispatch and charge electric vehicle (EV) fleets becomes a significant challenge facing e-hailing platforms. The common practice is to dispatch EVs to serve orders by heuristic matching methods but enable EV drivers to independently make charging decisions based on their experiences, which may compromise the platform’s performance. This study proposes a Markov decision process to jointly optimize the charging and order-dispatching schemes for an e-hailing EV fleet, which provides pick-up services for passengers only from a designated transportation hub (i.e., no pick-up from different locations). The objective is to maximize the total revenue of the fleet throughout a finite horizon. The complete state transition equations of the EV fleet are formulated to track the state-of-charge of their batteries. To learn the charging and order-dispatching policy in a dynamic stochastic environment, an online approximation algorithm is developed, which integrates the model-based reinforcement learning (RL) framework with a novel SARSA(Δ)-sample average approximation (SAA) architecture. Compared with the model-free RL algorithm and approximation dynamic programming (ADP), our algorithm explores high-quality decisions by an SAA model with empirical state transitions and exploits the best decisions so far by an SARSA(Δ) sample-trajectory updating. Computational results based on a real case show that, compared with the existing heuristic method and the ADP in the literature, the proposed approach increases the daily revenue by an average of 31.76% and 14.22%, respectively.

An Electric Vehicle Assisted Charging Mechanism for Unmanned Aerial Vehicles

The global greenhouse effect and air pollution problems have been deteriorating in recent years. The power generation in the future is expected to shift from fossil fuels to renewables, and many countries have also announced the ban on the sale of vehicles powered by fossil fuels in the next few decades, to effectively alleviate the global greenhouse effect and air pollution problems. In addition to electric vehicles (EVs) that will replace traditional fuel vehicles as the main ground transportation vehicles in the future, unmanned aerial vehicles (UAVs) have also gradually and more recently been widely used for military and civilian purposes. The recent literature estimated that UAVs will become the major means of transport for goods delivery services before 2040, and the development of passenger UAVs will also extend the traditional human ground transportation to low-altitude airspace transportation. In recent years, the literature has proposed the use of renewable power supply, battery swapping, and charging stations to refill the battery of UAVs. However, the uncertainty of renewable power generation cannot guarantee the stable power supply of UAVs. It may even be very possible that a large number of UAVs need to be charged during the same period, causing congestion in charging stations or battery swapping facilities and delaying the arranged schedules of UAVs. Although studies have proposed the method of that employing moving EVs along with wireless charging technology in order to provide electricity to UAVs with urgent needs, the charging schemes are still oversimplified and have many restrictions. In addition, different charging options should be provided to fit the individual need of each UAV. In view of this, this work attempts to meet the mission characteristics and needs of various UAVs by providing an adaptive flight path and charging plan attached to individual UAVs, as well as reducing the power load of the renewable power generation during the peak period. We ran a series of simulations for the proposed flight path and charging mechanism to evaluate its performance. The simulation results revealed that the solutions proposed in this work can be used by UAV operators to fit the needs of each individual UAV.

Flexible electric vehicle charging and its role in variable renewable energy integration

Uptake of electric vehicles is accelerating as governments around the world aim to decarbonize transportation. However, swift and widespread electric vehicle (EV) adoption will require some degree of controlled charging to mitigate the adverse impacts of electric vehicle adoption. Simulating the interaction between transportation and power requires new modelling tools with operational detail and spatial-temporal granularity. This analysis evaluates the potential benefits of utility-controlled charging (UCC) with the objective of reducing variable renewable energy (VRE) curtailment in decarbonized power systems using a framework that links travel and power system models using an intermediate charging model. Results show that the addition of VRE generation infrastructure shows the most impact on electricity system operating emissions and costs, but EV charging plays a significant role as well. Within EV charging strategies, UCC charging decreases emissions by 7% compared to uncontrolled charging. UCC is proven to be most effective in the summer due to higher electric vehicle fuel economy. Finally, the type of VRE generation infrastructure on the grid may have implications for siting of EV charging infrastructure due to the typical temporal peaks of wind and solar energy. These findings demonstrate how the use of distinct but linked travel and power sector models can be deployed to reduce multi-sector emissions and costs.

Application of Fast-Charging Graphite-Anode Lithium-Ion Batteries in Black Start

Industrial and commercial energy storage are widely used. Large industrial users can adjust the peak and valley power consumption during the summer peak power consumption to reduce the burden on transformers and to gain certain benefits from the peak and valley power difference. For users with important loads, the energy storage system is a alternative power supply to ensure the continuity of power supply for important loads. This paper systematically introduced the research progress based on the optimization of electrolyte and charging strategy in the field of improving the rapid charging of graphite anodes in recent years, which provided a reference for promoting the commercial application of rapid charging technology and the high-quality development of domestic energy storage technology. The high-power energy storage battery, in particular, can recover power supply as soon as possible after large-scale power system shutdown accidents or even power outages, reducing economic losses and social impact.

Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview

The increasing electric vehicle (EV) adoption in the context of transportation electrification and carbon neutrality pursuit brings both new challenges and opportunities for all the stakeholders in EV-grid integration. To fully unleash the potentials of EVs as flexible distributed energy storage to facilitate efficient EV-grid interactions, it is imperative to predicting spatio-temporal distributions of EV charging demand, optimizing charging infrastructure layout and implementing smart charging scheduling schemes. Appropriate EV behavior modeling plays a fundamental role to realize these targets. This paper aims to provide a comprehensive review on EV behavior modeling and its applications in EV-grid integration algorithm development. Various models have been developed to describe EV usage patterns, charging decision making process and response to smart charging schemes. In particular, the existing usage pattern models including temporal, spatial and energy sub-models are expounded, and different sub-models of charging choice and response to smart charging are also presented. An EV behavior modeling paradigm is proposed to provide guidance for EV behavior model selection in different application scenarios by developing different portfolios of temporal, spatial, energy usage, charging choice and response models. Accordingly, enabling EV behavior modeling for EV charging demands prediction and smart charging scheduling is covered in details. This study provides in-depth behavioural insights and viable approaches to developing efficient EV behavior models for advancing EV-grid integration and provides perspectives towards future research directions.

ELECTRIC VEHICLE ONBOARD CHARGING VIA HARRIS HAWKS OPTIMIZATION-BASED FRACTIONAL-ORDER SLIDING MODE CONTROLLER

Electric vehicles (EVs) have become more popular due to their excellent efficiency and pollution-free benefits. The technology requirements for onboard chargers are increasing as the number of electric vehicles increases. This research proposes a fractional-order sliding mode controller (FOSMC) for power converters to improve the efficiency of the onboard battery charger. The Harris Hawks optimization (HHO) algorithm chooses the FOSMC parameters. Independent controllers are used in a two-stage charging scheme. The grid-side ac–dc converter helps to smooth the current and voltage in the dc bus while reducing the harmonic frequency in the grid. A dc-dc converter with a constant current–constant voltage curve regulates the charging parameters of the battery on the battery side. Experiments show that HHO-based FOSMC improves the overall dynamic response of the onboard battery charger. Moreover, the proposed method performs with a current total harmonic distortion (THD) of less than 2 %. The proposed method improves 98% efficiency than existing methods such as SSA-PID and SSA- FOAFPIDF controllers.

An Adaptive Fast Charging Strategy Considering the Variation of DC Internal Resistance

In this article, an adaptive fast charging strategy for lithium-ion batteries considering the variation of dc internal resistance (DCIR) is proposed, which applies to mobile applications. The experiments prove that the DCIR obeys the Gaussian distribution during the normal charging stage. However, the DCIR no longer obeys Gaussian distribution once the battery is fully charged. The proposed strategy employs the maximum-likelihood method to estimate the expectation and variance of the Gaussian distribution. The charging mode switches from constant current (CC) to constant voltage (CV) immediately when the DCIR deviates significantly from the expectation. Under the premise of satisfying the high accuracy for the parameter estimates, a constrained optimization problem is established to determine the DCIR minimum number of measurements ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> ), and the constraints are transformed into hypothesis testing for solving. The CC operating time is maximized based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> and the adaptive control of the transition point. The experiments verify the validity of the proposed method. The results show that compared with the CC-CV method, the proposed strategy saves 52.2%, 41.57%, and 29.72% of the charging time at 1C, 0.75C, and 0.5C, respectively, which effectively improves the charging speed and prevents overcharging.

Multi Agent Interaction Mechanism of Intelligent Network Connection for Interaction Between EV and Power Grid

As a new type of flexible load, the charging behavior of electric vehicles is random and intermittent in space and time, which puts forward higher requirements for the safe, stable grid operation and power quality. When a large-scale electric vehicle charging load is connected to the power grid, its disordered charging behavior will also bring adverse effects to the power system. From the perspective of charging behavior, the gradual increase of devices such as electric vehicles and charging piles have formed the Internet of Electric Vehicles, power distribution network, and charging equipment Internet of Things. At the same time, more and more charging loads are connected to the grid, and the charging behavior also contains a lot of load information on the grid side. These data are fused through the server, which can provide an economical and reasonable charging strategy for the charging behavior of electric vehicles on the regional grid side.

A General Picture of Cucurbit[8]uril Host–Guest Binding: Recalibrating Bonded Interactions

Atomic-level understanding of the dynamic feature of host-guest interactions remains a central challenge in supramolecular chemistry. The remarkable guest binding behavior of the Cucurbiturils family of supramolecular containers makes them promising drug carriers. Among Cucurbit[n]urils, Cucurbit[8]uril (CB8) has an intermediate portal size and cavity volume. It can exploit almost all host-guest recognition motifs formed by this host family. In our previous work, an extensive computational investigation of the binding of seven commonly abused and structurally diverse drugs to the CB8 host was performed, and a general dynamic binding picture of CB8-guest interactions was obtained. Further, two widely used fixed-charge models for drug-like molecules were investigated and compared in great detail, aiming at providing guidelines in choosing an appropriate charge scheme in host-guest modelling. Iterative refitting of atomic charges leads to improved binding thermodynamics and the best root-mean-squared deviation from the experimental reference is 2.6 kcal/mol. In this work, we focus on a thorough evaluation of the remaining parts of classical force fields, i.e., the bonded interactions. The widely used general Amber force fields are assessed and refitted with generalized force-matching to improve the intra-molecular conformational preference, and thus the description of inter-molecular host-guest interactions. The interaction pattern and binding thermodynamics show a significant dependence on the modelling parameters. The refitted system-specific parameter set improves the consistency of the modelling results and the experimental reference significantly. Finally, combining the previous charge-scheme comparison and the current force-field refitting, we provide general guidelines for the theoretical modelling of host-guest binding.

Reinforcement Learning-Enabled Electric Vehicle Load Forecasting for Grid Energy Management

Electric vehicles are anticipated to be essential components of future energy systems, as they possess the capability to assimilate surplus energy generated by renewable sources. With the increasing popularity of plug-in hybrid electric vehicles (PHEVs), conventional internal combustion engine (ICE)-based vehicles are expected to be gradually phased out, thereby decreasing greenhouse gases and reliance on foreign oil. Intensive research and development efforts across the globe are currently concentrated on developing effective PHEV charging solutions that can efficiently cater to the charging needs of PHEVs, while simultaneously minimizing their detrimental effects on the power infrastructure. Efficient PHEV charging strategies and technologies are necessary to overcome the obstacles presented. Forecasting PHEV charging loads provides a solution by enabling energy delivery to power systems based on anticipated future loads. We have developed a novel approach, utilizing machine learning methods, for accurately forecasting PHEV charging loads at charging stations across three phases of powering (smart, non-cooperative, and cooperative). The proposed Q-learning method outperforms conventional AI techniques, such as recurrent neural and artificial neural networks, in accurately forecasting PHEV loads for various charging scenarios. The findings indicate that the Q-learning method effectively predicts PHEV loads in three scenarios: smart, non-cooperative, and cooperative. Compared to the ANN and RNN models, the forecast precision of the QL model is higher by 31.2% and 40.7%, respectively. The Keras open-source set was utilized to simulate three different approaches and evaluate the efficacy and worth of the suggested Q-learning technique.

Modelling of Electric Bus Operation and Charging Process: Potential Contribution of Local Photovoltaic Production

The transition from diesel to electric buses allows the reduction of greenhouse gas emissions. However, the impacts of charging strategies on the quality of bus services and the utility grid must be assessed to ensure the feasibility of the energy transition in the public transportation sector. This study investigates the performances of different locations and sizes of charging infrastructures by presenting the comprehensive modelling of a bus network. It also estimates the potential benefits of a local photovoltaic (PV) production to reduce negative impacts on the utility grid. The presented approach is used for modelling one urban bus line in Compiègne, France, and simulations are performed for various case studies. The results demonstrate that the proposed method allows analysing the impact of the charging process on the quality of bus services by determining the delays of arrivals. The simulations also show the impacts of charger placement on bus on-board battery capacity, total peak power demand of battery charging, and PV self-consumption ratio. The amount of PV energy used directly to charge buses remains low, although it varies between scenarios. PV energy during winter is not sufficient to fully charge buses; however, it can be enough with additional stationary storage in the summer.

Charging for Electric Vehicles Using A Zeta Converter

Electric vehicles make up a large portion of the transportation system of the future. The development of electric vehicles has made them more inexpensive and energy-efficient than traditional gasoline or diesel-powered vehicles, and it is paving the way for the electrification of transportation in the future. The key difference between electric vehicles andIts success is largely dependent on the converter's availability of high efficiency charging techniques the goal of this project is to design and implement fast charging infrastructures while achieving the highest possible standards for variables like output power, power factor, and dependability. This will help to overcome the many difficulties encountered. To comprehend how the prototype works and to develop the optimum strategy for quick charging, it is simulated using MATLAB Simulink. In order to develop a quicker, more efficient charging process, it also assesses the various converter types that can be incorporated into charging stations. This project addresses several environmental concerns that are involved in sustainable development in addition to the technical components of EV charging stations. Putting this intoPractise in the future could be one of the most simple, quick, and effective designs for charging micro electric vehicles, and it could pave the way for a simple, quick technique of charging electric vehicles from low range to larger range of ratings and capacity.

Combining Power-to-Heat and Power-to-Vehicle strategies to provide system flexibility in smart urban energy districts

The deployment of distributed renewable generation in urban districts requires managing local energy excess by means of distributed systems. Sector coupling strategies can be a solution to increase self-consumption, representing an alternative to electric batteries, however their limitations need to be assessed in order to properly plan urban systems.. This study investigates the potential role of heat pumps and smart charging of electric vehicles to provide system flexibility in residential neighbourhoods. Photovoltaic self-consumption, district self-sufficiency, annual costs and CO2 emissions have been assessed by simulating the district using the MATLAB toolbox for EnergyPLAN. Furthermore, the impact of the current energy market crisis has been evaluated. Cross-sector integration is crucial to improve system flexibility. Power-to-Heat system has a positive effect on all parameters, however its potential is limited. Power-to-Vehicle strategy is more effective and allows for doubling the community's self-consumption and self-sufficiency. However, the participation of vehicle owners in smart charging schemes is necessary. The combined implementation considerably improves energy, economic and environmental performance and turns out to be much more effective than electric batteries. Finally, the current increase in energy prices also improves the cost-effectiveness of electric batteries and makes the combined implementation of the three systems the best configuration.

Quantitative study on steam system thermal inertia based on a dynamic hydraulic analytical model

Integrated energy system (IES) in industrial parks is pivotal to China's carbon neutrality path, where steam plays a critical role as an energy carrier and raw materials in the production process. Utilization of the thermal inertia of steam systems may become a means to address challenges in industrial park IES dispatch. However, researchers have less discussed the dynamic modeling and inertia quantification of industrial steam systems compared to district heating. This work proposes a dynamic hydraulic analytical model (DHAM) of steam systems reflecting the spatial and temporal transportation of steam by introducing the reference temperature and momentum linearization assumptions. Further, a steam thermal inertia model (STIM) which comprises five storage key parameters and two indexes, is developed to quantify the thermal inertia based on DHAM. Validation results of DHAM in an industrial park demonstrate that the relative error is less than 3%, achieving high accuracy. Whilst the thermal inertia of a 163-node steam system in two operation conditions is quantified and compared. Results show that the net heat storage of the steam system could reach 7440.30 kg while the positive utilization rate in both scenarios never rises above 3%. It indicates that the thermal inertia as virtual storage can fully cover the dispatching demand while is not utilized and lacks the regulation strategy of active charging in operation. DHAM and STIM proposed in this study lay the quantitative basis for steam system analysis and operation optimization of industrial park IES.

Development of improved reinforcement learning smart charging strategy for electric vehicle fleet

Due to its environmental and energy sustainability, electric vehicles (EV) have emerged as the preferred option in the current transportation system. Uncontrolled EV charging, however, can raise consumers; charging costs and overwhelm the grid. Smart charging coordination systems are required to prevent the grid overload caused by charging too many electric vehicles at once. In light of the baseload that is present in the power grid, this research suggests an improved reinforcement learning charging management system. An optimization method, however, requires some knowledge in advance, such as the time the vehicle departs and how much energy it will need when it arrives at the charging station. Therefore, under realistic operating conditions, our improved Reinforcement Learning method with Double Deep Q-learning approach provides an adjustable, scalable, and flexible strategy for an electric car fleet. Our proposed approach provides fair value which solves the overestimation action value problem in deep Q-learning. Then, a number of different charging strategies are compared to the Reinforcement Learning algorithm. The proposed Reinforcement Learning technique minimizes the variance of the overall load by 68 % when compared to an uncontrolled charging strategy.

Genetic algorithm‐based partial charging schedule of rechargeable sensor networks

SummaryThe use of rechargeable sensors is a promising solution for wireless sensor networks. On this type of network, mobile charging vehicles (MC) are used for charging sensors using wireless energy transfer (WET) technology. In on‐demand charging, a sensor transmits a charging request to the service station, and the MC visits the sensor to transfer energy. The key disadvantages of utilizing MC‐based WET are its high energy expenditure rate due to mobility, long service time, and slow charging rate. Because of these reasons, sensors deplete their energy and become dead before the MC reaches the requesting nodes to recharge. We have adapted a genetic algorithm‐based partial charging scheme to serve the charging requests. Our objective is to improve the survival ratio of the network. Using comprehensive simulations, we analyze the performance of our proposed method and compare it to two other existing approaches. The simulation results demonstrate that our proposed algorithm improves the survival ratio by up to 20 % by developing a dynamic energy threshold function for transmitting charging requests from the sensors and a partial charging schedule using a genetic algorithm.

Auranofin Targeting the NDM-1 Beta-Lactamase: Computational Insights into the Electronic Configuration and Quasi-Tetrahedral Coordination of Gold Ions.

Recently, the well-characterized metallodrug auranofin has been demonstrated to restore the penicillin and cephalosporin sensitivity in resistant bacterial strains via the inhibition of the NDM-1 beta-lactamase, which is operated via the Zn/Au substitution in its bimetallic core. The resulting unusual tetrahedral coordination of the two ions was investigated via the density functional theory calculations. By assessing several charge and multiplicity schemes, coupled with on/off constraining the positions of the coordinating residues, it was demonstrated that the experimental X-ray structure of the gold-bound NDM-1 is consistent with either Au(I)-Au(I) or Au(II)-Au(II) bimetallic moieties. The presented results suggest that the most probable mechanism for the auranofin-based Zn/Au exchange in NDM-1 includes the early formation of the Au(I)-Au(I) system, superseded by oxidation yielding the Au(II)-Au(II) species bearing the highest resemblance to the X-ray structure.

Metaheuristic for the integrated electric vehicle and crew scheduling problem

Encouraged by international efforts to reduce greenhouse gases and local emissions, many public transport operators are converting their fleets to battery-powered electric buses. Public transport operators can choose between different electric bus concepts, with the total cost of ownership being the most important decision criterion. The associated strategic decisions regarding charging strategy, vehicle concept, and charging infrastructure have a significant impact on the operational planning of the electric buses.Motivated by this, this paper aims to analyze the interactions between electrification and operational planning, especially vehicle scheduling and crew scheduling. This allows us to make a more comprehensive comparison of different electrification concepts. Prior work has addressed the impact of electrification on vehicle scheduling but has neglected the interactions with crew scheduling. Crew scheduling dominates operational costs and planning for many public transport operators and must therefore be considered in all strategic decisions. For this reason, in this work we focused on integrated electric vehicle and crew scheduling problem. This allows us to calculate the total cost of ownership of different electric bus concepts under better representation of local conditions. We deal with the electric vehicle and crew scheduling problem with a metaheuristic based on Adaptive Large Neighborhood Search. We tested the developed methodology for a real-world bus route. Our results indicate that the constraints for crew scheduling significantly impact the total cost of ownership and the required number of vehicles of the different electrification concepts. Our case study suggests that the choice of the most cost-effective concept depends significantly on crew scheduling constraints. These findings imply that crew scheduling constraints should be considered as part of the local framework for bus fleet electrification.