Energy Consumption Cost Research Articles

ANÁLISE DE RENTABILIDADE DE INVESTIMENTOS EM SISTEMAS DE REFRIGERAÇÃO COM ALTA EFICIÊNCIA ENERGÉTICA

This study analyzes the profitability of investments in high-energy-efficiency refrigeration systems, addressing the economic and environmental impacts of these systems compared to conventional technologies. Energy efficiency is essential for reducing operational costs and promoting sustainability, especially in sectors with high continuous refrigeration demand. The objective is to evaluate the economic feasibility of efficient systems, highlighting variables that influence the return on investment. A descriptive and qualitative literature review was conducted, analyzing studies on energy efficiency, environmental impact, and cost-benefit in commercial and industrial sectors. The findings indicate that, although the initial investment in high-efficiency technologies is high, these systems significantly reduce energy consumption and operational costs over time. It concludes that adopting efficient refrigeration systems is economically viable across various sectors, especially those with high energy demand, and an effective strategy for meeting environmental requirements and reducing emissions.

Reconfiguration and compensation design method for single-DoF closed-chain leg mechanism

Compared with a wheeled robot propelled by a rotary drive, a legged robot equipped with multiple motors on a single leg possesses superior adaptability, while at the cost of high energy consumption and less velocity. Inspired by the California mite, the design method of reconfigurable closed-chain leg mechanism with single degree of freedom (DoF) is proposed for effective striding and smooth propelling. Furthermore, the lower-mobility closed-chain leg mechanism is more appropriate for obtaining high frequency and fast movement with the merits of simple control system and rotary drive. Firstly, a reconfigurable design is performed in the swing phase for a high-knee motion intermittently driven by a walking motor through dynamic coupling. Secondly, its fluctuations in vertical displacement and horizontal speed are compensated through the contact outline curve and variable speed input in the supporting phase. Finally, the simulated performance is analyzed and compared, and a prototype is fabricated for fluctuating and obstacle-crossing experiments.

Research on time-varying path optimization for multi-vehicle type fresh food logistics distribution considering energy consumption

With the increasing demand for fresh food markets, refrigerated transportation has become an essential component of logistics operations. Currently, fresh food transportation frequently faces issues of high energy consumption and high costs, which are inconsistent with the development needs of the modern logistics industry. This paper addresses the optimization problem of multi-vehicle type fresh food distribution under time-varying conditions. It comprehensively considers the changes in road congestion at different times and the quality degradation characteristics of fresh goods during distribution. The objectives include transportation cost, dual carbon cost, and damage cost, subject to constraints such as delivery time windows and vehicle capacity. A piecewise function is used to depict vehicle speeds, proposing a dynamic urban fresh food logistics vehicle routing optimization method. Given the NP-hard nature of the problem, a hybrid Tabu Search (TS) and Genetic Algorithm (GA) approach is designed to compute an optimal solution. Comparison with TS and GA algorithm results shows that the TS-GA algorithm provides the best optimization efficiency and effectiveness for solving large-scale distribution problems. The results indicate that using the TS-GA algorithm to optimize a distribution network with one distribution center and 30 delivery points resulted in a total cost of CNY 12,934.02 and a convergence time of 16.3 s. For problems involving multiple vehicle types and multiple delivery points, the TS-GA algorithm reduces the overall cost by 2.94–7.68% compared to traditional genetic algorithms, demonstrating superior performance in addressing multi-vehicle, multi-point delivery challenges.

Optimization of a Bayesian Game for Peer-to-Peer Trading among Prosumers under Incomplete Information via a CNN-LSTM-ATT

In modern low-carbon industrial parks, various distributed renewable energy resources are employed to fulfill production needs. Despite the growing capacity of renewable energy generation, a significant portion of the power produced by these renewable resources remains unconsumed, resulting in a waste of resources. Within an industrial park, microgrids that both generate and consume energy resources act as energy prosumers. Peer-to-peer (P2P) trading provides an efficient means of utilizing renewable energy among these energy prosumers, who possess both power generation and consumption capabilities. However, within the current market mechanism, each prosumer retains private information that is not disclosed on the network. To address the issue of incomplete information among multiple prosumers during the decision-making process, we develop a Bayesian game model based on the CNN-LSTM-ATT prediction method for P2P electricity transactions among multiple prosumers. The energy prosumers in each industrial park aim to minimize their energy consumption costs by adjusting strategies that include P2P energy trading and managing thermal loads. Prosumers make decisions on the basis of their own characteristics and estimates of other prosumer characteristics, which are obtained from the joint probability distribution predicted by the CNN-LSTM-ATT method. These decisions are aimed at minimizing each prosumer's electricity costs. The simulation results demonstrate the effectiveness of the Bayesian game model proposed in this study.

Removal of Diclofenac Sodium from Simulated Wastewater through an Optimized Plasma Technology System Synergized with Activated Carbon and Persulfate

Diclofenac sodium (DCF) has received much attention due to the contaminations associated with its frequent usage. Current technologies for DCF removal from wastewater have presented higher energy consumption and lower cost efficiency, thus efficient removal of DCF without secondary pollution would be required. In this study, removal of DCF by plasma technology was modeled and optimized via the response surface methodology (RSM). The DCF removal rate could reach 66.3% after a treatment of 60 min when the voltage was 12.7 kV, the discharge gap was 15 mm, and the number of discharge needles was 5. Subsequently, further addition of granular activated carbon (GAC) and persulfate (PS) enhanced the removal of DCF through the construction of an optimized plasma/GAC/PS system. The improved removal rate of DCF could rise to 90.4%, when GAC was 2 g/L and PS was 2 mmol/L under the optimal conditions of RSM. Based upon the analysis of UPLC-QTOF-MS/MS and UV-vis and free radical quenching, the removal of DCF in the plasma/GAC/PS system mainly relied on the attack of •OH and SO4•⁻ on the benzene ring, involving the C-N breakage of DCF and the generation of by-products. Finally, residual toxicity and cost of the DCF treatment were evaluated.

Energy Consumption and Energy Costs Based on Time-of- Day Tariffs in Seafood Processing Units in Kerala, India

Energy conservation in seafood processing is crucial due to the highly energy-intensive nature of its main stages such as pre-processing, processing, and storage, which must operate under cold chain conditions. The variability of energy tariffs based on Time of Day (TOD) significantly impacts the economic sustainability of these operations. This study evaluates the electrical energy consumption patterns of seafood processing units in South India, categorized by production capacity. Primary data were collected from 10 seafood processing units, assessing energy consumption during Normal Load Time (NLT), Peak Load Time (PLT), and Off-Peak Load Time (OLT) over a one-year period. Resultsrevealed that smaller capacity units (<30 T/D) accounted for 56% of total production during PLT, despite energy tariffs being highest during this time.These low-capacity units also exhibited higher mean energy consumption and costs compared to larger units, suggesting inefficiencies. The findings highlight the need for optimized energy management strategies, such as rescheduling production to lower-tariff periods and adopting more energy-efficient technologies. Implementing such measures could reduce energy costs and improve the overall profitability of seafood processing operations, particularly for smaller units.

Research on Intelligent Energy System and Power Metering Optimization Based on Multi-objective Optimization Decision Algorithm

In this paper, the intricate problem of optimizing power metering within an intelligent energy system, utilizing a multi-objective optimization decision-making algorithm, is thoroughly explored. Given the current energy landscape, achieving efficient energy utilization and environmental sustainability has become a focal point of research. As a pivotal aspect of future energy management, the precision and optimization of power metering in intelligent energy systems directly influence the effectiveness and cost of energy consumption. To begin, this paper delves into the fundamental principles and application backdrop of intelligent energy systems, highlighting the significance of power metering in such systems. Subsequently, addressing the multi-objective optimization challenges in power metering, a novel optimization method based on a multi-objective optimization decision algorithm is introduced. This algorithm achieves comprehensive optimization of power metering, encompassing multiple objectives such as power cost reduction, enhanced energy efficiency, and environmental protection. The experimental results underscore the remarkable performance of this algorithm, which not only elevates the precision of power metering but also achieves substantial savings in energy costs and significantly boosts energy efficiency. Furthermore, the algorithm exhibits robust adaptability, making it capable of addressing power metering optimization challenges across diverse scenarios. Finally, this paper discusses the practical application prospects of the optimization algorithm in intelligent energy systems, and points out the direction of future research. The research in this paper provides new ideas and methods for the optimization of power metering in intelligent energy systems, and has important theoretical and practical significance for promoting the intelligence and refinement of energy management.

Optimization for fuel consumption and TCO of a heavy-duty truck with electricity-propelled trailer

As renewable energy grows in the heavy-duty truck sector, exploration of diverse solutions with varied energy types and powertrain configurations becomes a necessity. While most studies focus on powertrain configurations in tractors, this study takes a different approach: propose and evaluate new plug-in hybrid configurations with multiple power sources for heavy-duty trucks. The tractor retains its engine powertrain, while the trailer is equipped with electric axle(s), forming a fuel-electricity hybrid propulsion system. The specifications of the electric propulsion system are optimized, using a multi-objective particle swarm optimization algorithm and dynamic programming control algorithm to evaluate the energy consumption and total cost of ownership (TCO). The results highlight the potential of the configuration with three electric axles and single reduction gear in regard to vehicle energy consumption. Conversely, the configuration with one electric axle and a two-speed transmission exhibits the lowest TCO. Under the China World Transient Vehicle Cycle, the fuel consumption of the diesel truck is reduced by up to 44.59 % with the use of the optimized hybrid configuration. The truck's TCO can be then reduced by up to 832 thousand CNY in one-million-kilometer operation. For an actual road operation, these figures change to 34.79 % and 284 thousand CNY, respectively.

Fault tolerant & priority basis task offloading and scheduling model for IoT logistics

The Internet of Things (IoT) has connected millions of devices to the internet for communication and computation purposes. Due to constraints such as limited battery power, processing capabilities, and storage capacity, IoT devices often rely on remote fog nodes for task execution, a practice known as task offloading. The NP-hard nature of offloading and scheduling IoT tasks onto fog nodes poses significant challenges. Current IoT task offloading often relies on single parameters like device MIPS, RAM, or battery power. Similarly, task allocation on fog nodes frequently prioritizes VM MIPS. To address energy consumption and task failures in IoT systems, robust fault tolerance, efficient task offloading, and effective scheduling are crucial. This paper introduces a novel Fault-Tolerant, Priority-Based Task Offloading and Scheduling Model (FP-TOSM) for IoT logistics. Utilizing the Analytic Hierarchy Process (AHP) for multi-criteria decision-making, IoT tasks are prioritized. Tasks below a specified threshold are executed locally, while those within a defined range are offloaded to a fog node. Tasks exceeding this range are delegated to the cloud. The Euclidean formula is used to determine the proximity between the IoT logistics vehicle and fog nodes for offloading decisions. Dynamic re-clustering fault tolerance mechanisms are utilized to manage task failures after offloading. Successful offloaded tasks are allocated to suitable VMs in the cloud and fog nodes. The proposed strategy selects the most suitable VM for offloaded tasks using a multi-criteria decision-making process to reduce energy consumption, SLA violations, and execution costs. The model's performance is evaluated through simulations in iFogSim2. Results demonstrate reductions in energy consumption by up to 5.8 % and 10.4 %, decreases in SLA violations by up to 25.2 %, and a 16.28 % improvement in response time compared to an ACO-based model with a response time of 17.8 %. Additionally, the task failure ratio shows a significant reduction of up to 22.1 %. These findings highlight the effectiveness of FP-TOSM in enhancing efficiency and reliability within fog computing environments.

Algorithm for solving multi-objective hybrid flow shop scheduling problem

Aiming at the multi-objective unrelated parallel machine hybrid flow shop scheduling problem, a multi-objective mathematical model is established with the goal of minimizing the maximum completion time, total machine energy consumption and machine processing cost. An improved multi-objective evolution algorithm based on decomposition (IMOEAD) is proposed. The uniform design table is used to generate the initial weight vector to improve the population diversity. Normal distribution crossover and adaptive Gaussian mutation are used to improve the global and local search capabilities of the algorithm. Individuals are selected in the neighborhood of the weight vector to generate new solutions. The non-dominated rank and crowding distance are used to update the external archive. The inverse generation distance, generation distance and number of non-dominated solutions are used as performance indicators. Through a large number of case simulations, the algorithm is compared with the non-dominated sorting genetic algorithm II and the multi-objective evolution algorithm based on decomposition. The results verify the effectiveness of the algorithm.

A multiphase flow model of water droplets dielectrophoretic-induced air dehumidification phenomena

Air humidity in indoor spaces plays a critical role in human comfort and health. Dehumidification systems are used for building humidity controls, but they can take significant energy consumption, especially in geographic locations with high outdoor humidity and warm climates. Consequently, there is a growing demand for innovative dehumidification processes that consume minimal energy. Dielectrophoretic air dehumidification represents one such promising approach. However, it has not garnered significant attention due to the absence of engineering models and simulation tools capable of evaluating its performance and limitations at large-scale airflows. A new numerical multiphase CFD model, which is also experimentally validated, is developed in a customized Reacting Foam solver based on OpenFOAM® version 9. The newly developed model seeks to decrease substantial energy consumption and lower costs by leveraging the dielectrophoretic phenomenon to regulate moisture levels in the air. The solver integrates a hybrid Eulerian-Lagrangian framework to track the droplet's trajectory and growth rate while solving the continuum equations for the moist air. An electrospray produces electrically charged droplets, which grow during their in-flight trajectories as water vapor condenses onto their surfaces. The role of electrostatic forces in promoting vapor condensation within a high-gradient electrical field is investigated, and the dielectrophoretic vapor nucleation process on charged water droplets is discussed. The CFD model was validated against results from the literature and from proof-of-concept experiments conducted by the authors, which showed a 2 % air dehumidification with a single electrospray and airflow rate of 5 cubic feet per minute. The simulation results indicated that augmenting the number of electrically charged spray droplets increased the dehumidification of the air to 25 %. The initial mean droplet diameter, the orientation of the injector and relative humidity significantly influence the assessment of dehumidification. Scaling up this approach to larger airflow volumes is identified as a potential future research direction.

Research on Ship Data Analysis Platform Development and Its Application in Teaching

This paper delves into the significant importance of developing a ship data analysis platform, which aims to enhance the operational efficiency and safety of the shipping industry by integrating big data processing technologies. The development of the platform centers on crucial aspects such as data collection, preprocessing, analysis, and visualization, enabling real-time monitoring and intelligent prediction of ship operational status. This platform not only assists in optimizing route planning and reducing energy consumption costs but also provides robust support for navigational safety. In project-based teaching, this platform serves as a bridge between theory and practice, cultivating students' abilities in big data processing and analysis through case studies, practical exercises, and other forms, thereby promoting the cultivation of high-quality talents and technological innovation in the field of shipping.

A multi-objective dual dynamic genetic algorithm-based approach for thermoelectric optimization of integrated urban energy systems

In order to reduce the loss of energy in the process of conversion and utilization, and to improve the energy utilization effect and economic benefits of urban integrated energy system, a thermoelectricity optimization method of urban integrated energy system based on multi-objective double dynamic genetic algorithm is proposed. The method analyzes the operation characteristics of the urban integrated energy system through its operation model, constructs a multi-objective optimization model for the thermoelectricity of the urban integrated energy system, determines the optimization multi-objective function that minimizes the operation cost, minimizes the carbon emission, minimizes the technical dissatisfaction and the related constraints, and then solves the optimization model by using the dual dynamic genetic algorithm to output the results of the optimization of the thermoelectricity of the urban integrated energy system.The test results show that the algorithm has a crowding distance of 0.75 or above when solving, and the overall unit energy consumption cost reduction ratio is about 22.97 %. The state of charge results of the four energy storage power stations are all below 10 %, and the lowest convergence speed is only 7 seconds, effectively improving energy utilization efficiency.

Multi‐objective based container placement strategy in CaaS

AbstractIn contrast to a conventional virtual machine (VM), a container is a lightweight virtualization technology. Containers are becoming a prominent technology for cloud services because of their portable, scalable, and flexible deployments, especially in the Internet of Things (IoT), smart devices, and fog and edge computing. It is a type of operating system‐level virtualization in which the kernel allows multiple isolated containers to run independently. Container placement (CP) is a nontrivial problem in Container‐as‐a‐Service (CaaS). CP is mapping to a container over virtual machines (VMs) to execute an application. Designing an efficient CP strategy is complex due to several intertwined challenges. These challenges arise from a diverse spectrum of computing resources, like on‐demand and unpredictable fluctuations of IT resources by multiple tenants. In this article, we propose a modified sum‐based container placement algorithm called a multi‐objective optimization‐based container placement algorithm (MSBCPA). In the proposed algorithm, we have considered two metrics: makespan and monetary costs for optimizing available IT resources. We have conducted comprehensive simulation experiments to validate the effectiveness of the proposed algorithm over the CloudSim 4.0 simulator. The proposed optimization algorithm (MSBCPA) aims to minimize the makespan and the execution monetary costs simultaneously. In the simulation, we found that the execution cost and energy consumption cost reduce by 20% to 30% and achieve the best possible cost‐makespan trade‐offs compared to competing algorithms.

Techno-economic analysis and optimisation of Piperazine-based Post-combustion carbon capture and CO2 compression process for large-scale biomass-fired power plants through simulation

This study aims to investigate a cost-effective and energy-efficient amine-based post-combustion carbon capture (PCC) process for large-scale supercritical biomass-fired power plants (SC BFPP). Thus, we have quantified the energy and economic performance of the PCC process with different configurations and solvents. Three process configurations which included the standard configuration, the absorber intercooler (AIC) with the advanced flash stripper (AFS) and the AIC, AFS and side stream extraction (SSE) were simulated in Aspen Plus® V11 using 30 wt% and 40 wt% piperazine (PZ) as solvent. In addition to this, CO2 compression trains using the heat pump (HP) and supercritical CO2 cycle (s-CO2) were also simulated. Sensitivity analysis of the PCC process was carried out to investigate the impact of important parameters on the energy performance of the process. Furthermore, energy analysis shows that a minimum energy consumption of 2.78 GJ/tCO2 was achieved with the PCC process using 40 wt% PZ, a combination of the AIC, AFS and SSE for capture and s-CO2 for compression. This achieved a significant energy saving of 1.01 GJ/tCO2 compared with the standard PCC process using 30 wt% monoethanolamine that is used as the benchmark in this study. The economic analysis results showed that the minimum CO2 capture cost of 55.70 $/tCO2 was achieved using the AIC-AFS-SSE-sCO2 configuration and 40 wt% PZ as solvent. This represents a 19.5 % reduction in cost compared with the standard 40 wt% PZ process with a cost of 69.18 $/tCO2. The optimisation of the PZ-based PCC process was carried out to determine the optimal solvent concentration with the minimum carbon capture cost. It was found that the optimal PZ concentration for the PCC process based on standard and AFS configurations were 37.5 wt% and 32.5 wt%, respectively. The optimisation of the stripper pressure for the minimum carbon capture cost was conducted. As a result, compared with the standard PCC process using 40 wt% PZ, the energy consumption and CO2 capture cost of the optimised process at the suggested pressure of 7 bar were reduced by 41.6 % and 32.4 %, respectively.

How sustainable are low-noise pavements? Answers from a life-cycle-assessment including the use-phase and noise mitigation of typical Swiss pavements

Despite continuous efforts to reduce road noise emissions, road noise is still the dominant source of noise in Switzerland. Low-noise pavements are an effective measure for road noise mitigation, but besides their higher cost, their major drawback is a reduced service lifetime as compared to conventional pavements. How do these arguments compare in a comprehensive view? To address this question, we performed a cradle-to-grave life-cycle-analysis of four different pavement types: a conventional pavement as reference, two types of semi-dense low-noise pavements and an acoustically optimized type of asphalt concrete. Besides the needed raw materials, the construction, maintenance and demolition, this life-cycle-analysis also includes the use-phase of the road. Thus, the noise mitigation (based on latest acoustic ageing models) and changes in fuel consumption (based on rolling resistance) are included in the assessment. We evaluate the results in the dimensions of ecological scarcity, greenhouse gas emissions, primary energy consumption and life cycle costs. The impact assessment method "Swiss ecological scarcity" allows to weight noise emissions and fuel consumption directly against resource demands of the different pavement types. Our results show that in densely populated areas, the noise reduction and energy savings clearly outweigh the increased resource demand of low-noise pavements.

Анализ влияния климатических факторов на потребление электроэнергии предприятиями в неперевозочных видах деятельности ОАО «РЖД»

Purpose: analysis of the costs of energy consumption by enterprises of JSC Russian Railways by type of fuel, identification of the most costly type of energy. Systematization of factors influencing energy consumption. Characteristics of climatic factors affecting energy consumption. The task was set to assess the possibility of using ASKUE data to improve the system for analyzing and planning energy consumption in non-transportation activities of JSC Russian Railways. Analysis of climatic factors affecting electricity consumption by enterprises in non-transportation activities of JSC Russian Railways. Determine the maximum time interval for analyzing energy consumption data. Methods: collecting information on electricity consumption and changes in climate characteristics. The main research method is the method of correlation analysis, which allows us to identify the most important factors influencing electricity consumption. Based on the correlation coefficient, a correlation matrix will be compiled using the Statistica software package. Results: climatic factors have been identified that have the greatest impact on the energy consumption of the enterprise in non-transportation activities of JSC Russian Railways. The maximum enlarged interval for analyzing energy consumption data has been clarified. Practical significance: climatic factors have been proposed, the characteristics of which must be relied upon when planning electricity consumption at non-transportation enterprises of JSC Russian Railways. These factors can be used in the further development of a model for planning energy consumption. The maximum enlarged interval for analyzing influencing factors has also been identified, allowing to obtain accurate results. The possibility of using hourly ASCAE data to assess and identify climatic factors affecting electricity consumption by non-transportation enterprises has been proven.

The Impact of Street Trees on Temperature Reduction in a Nature-Based Climate Adaptation Program in George Town, Malaysia

Nature-based solutions have been promoted as an effective strategy to address climate impacts, including urban temperature reduction. In this paper, we analyze the impacts of the introduction of street trees on temperature (Universal Thermal Climate Index, UTCI) for three different dates, 2000, 2023, and 2050. A 3D model was developed in Rhinoceros software for a part of George Town, on Penang Island. Four different sections of streets were simulated after integration of the model with the Grasshopper plug-in, where a parametric system was built for temperature measurements based on simulations in the Ladybug and Honeybee plug-ins. The tree species used were selected from a pool of tree species commonly planted in urban settings in Malaysia that have low and medium sensitivity to climate impacts. The results show a maximum reduction of 7 °C between 2000 and 2050, achieved on a street with an NW–SE orientation that was planted with three rows of trees. The minimum UTCI reduction achieved was 3 °C, between 2023 and 2050, in a street with NW–SE orientation that was planted with one tree row. The two streets with a SW–NE orientation showed a 5 °C temperature reduction between 2023 and 2050. Both streets have only one row of trees but different species and sizes, with the bigger trees reducing the temperature in a slightly larger area. The results show the importance of introducing and safeguarding street trees to reduce urban temperatures in the country, potentially keeping temperatures below life-threatening levels, thereby safeguarding urban health, while also reducing costs of energy consumption. Solar orientation, the number of tree rows, and their distribution impact the outcomes. The findings provide useful guidance for climate-conscious urban planning practices in Malaysia.

Joint intelligent optimizing economic dispatch and electric vehicles charging in 5G vehicular networks

In recent years, with the rapid development of 5G networks, the road traffic network composed of vehicles with different energy sources has become more and more complex, and the problems of environmental pollution and road congestion have also become increasingly serious. Electric vehicles are favored by people due to their environmental protection and energy-saving characteristics. However, improper charging dispatching will cause excess energy in charging stations, affecting the power grid and road traffic, such as energy shortages and lower traffic throughput. Therefore, how to design a reasonable charging strategy that can maximize the user’s charging satisfaction and consume the energy of the charging station as much as possible becomes a challenge. Meanwhile, this strategy should consider power economic dispatch to reduce power generation costs and polluting gas emissions. With the support of 5G’s high-bandwidth and low-latency characteristics, this paper designs an intelligent charging model which indirectly reflects the charging satisfaction through the time cost, energy consumption cost, charging cost, and the user’s range anxiety, while consuming the remaining energy of the charging station as much as possible. Due to the uncertainty of wind and photovoltaic power generation, this paper proposes a two-stage economic dispatch model to improve the accuracy of power dispatch and reduce power generation costs and carbon emissions. Due to the highly variable traffic environment and energy demand, we employ proximal policy optimization-based deep reinforcement learning algorithms to realize electric vehicle charging dispatching and charging station power dispatching. Numerical results show the efficiency of our proposed strategy for electric vehicle charging in terms of the convergence speed.

Optimal battery electric bus system planning considering heterogeneous vehicles, opportunity charging, and battery degradation

Battery electric buses (BEBs) have been seen as a viable alternative for sustainable mobility. The charger placement and fleet configuration play a crucial role in the operational efficiency of BEBs. In this study, a joint optimization model is developed to strategically plan the charger placement and fleet configuration for a BEB system. Distinct from existing research, we simultaneously incorporate opportunity charging, heterogeneous vehicles, and battery degradation to integratively minimize the costs of charger installation, BEB purchase, energy consumption, and battery degradation. Based on a model reformulation, a heuristic-exact hybrid solution method is customized to solve the model. A real-world case study from Oslo, Norway is provided to demonstrate the optimization model and solution method. The results indicate that the proposed model can reduce the total costs by 21.27% in comparison to the conventional model. Sensitivity analyses are further conducted to provide insightful findings and managerial implications for BEB systems.