Energy Consumption Cost Research Articles

CBWO: A novel multi-objective load balancing technique for cloud computing

In cloud computing systems, the growing demand for diverse applications has led to challenges in resource allocation and workload distribution, resulting in increased energy consumption and computational costs. To address these challenges, we propose a novel load-balancing method, namely CBWO, that integrates Chaos theory with the Black Widow Optimization algorithm. Our approach is designed to optimize cloud computing environments by improving energy efficiency and resource utilization. We employ CloudSim for simulations, evaluating key performance metrics such as energy consumption, resource utilization, makespan, task completion time, and imbalance degree. The experimental results demonstrate the superiority of our method, achieving average improvements of 67.28% in makespan and 29.03% in energy consumption compared to existing solutions.

High-current decoupled hydrogen and oxygen evolution via nickel–cobalt based redox mediators and bifunctional catalyst of 3D printing substrates

The conversion of renewable energy sources with relatively large energy fluctuations into hydrogen represents a crucial aspect of energy storage. Nevertheless, the direct water electrolysis process is known to require excessive instantaneous energy consumption and high cost. Two-step alkaline water electrolysis is regarded as a secure and effective method of generating hydrogen from renewable energy sources when compared to direct water electrolysis. Here we propose a two-step alkaline water electrolysis using nickel–cobalt based hydroxide (Ni0.9Co0.1(OH)2) as a redox mediator, and a high-performance bifunctional catalyst as gas evolution electrodes (GEE). The substrates for the GEE were prepared using 3D printing and then loaded with in-situ grown Ru-doped MoS2/NiFe-LDH hierarchical heterostructure catalysts (MS-NiFe-Ru-3D). The oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) of the MS-NiFe-Ru-3D catalyst can reach up to 500 mA cm−2 at 300 and 250 mV overpotentials, respectively. It can meet the requirement of high catalyst performance for two-step alkaline water electrolysis. The direct water electrolysis using the bifunctional MS-NiFe-Ru-3D catalyst only requires a voltage of 1.85 V at 500 mA cm−2 with minimal attenuation over 300 h. For the two-step alkaline water electrolysis using MS-NiFe-Ru-3D as bifunctional catalysts and Ni0.9Co0.1(OH)2 as redox mediator, only 1.70 V and 0.27 V were required for HER and OER at 500 mA cm−2, respectively. This work offers a promising avenue for the efficient conversion of renewable secondary energy sources into hydrogen.

PROBLEMS OF MANAGING TECHNOLOGICAL REGIMES IN GAS DISTRIBUTION NETWORKS AND MODERN APPROACHES TO THEIR SOLUTION

This article systematically discusses the problems of managing technological regimes in gas distribution networks and modern approaches to their resolution. The scientific-theoretical and practical aspects of managing gas distribution networks (GDN) are analyzed. Based on the obtained results, a scientifically grounded comparative explanation of the existing contradictions in modern and traditional gas distribution networks is provided, and the role of key factors in the emergence of these contradictions is investigated. It is shown that technological backwardness in traditional GDNs increases energy consumption and operational costs, thereby reducing the economic efficiency of the system and increasing the risk of accidents. The research suggests decision-making methods for solving management problems in traditional GDNs by maintaining the technical-structural level and implementing telemetry systems. It is indicated that the widespread use of telemetry and telemechanics tools in the gas distribution industry enhances the safe delivery of gas to end-users and significantly reduces service costs. Another focus of the research is on the issues related to the processing of spatial information for the management of the technical components of gas distribution systems. The article also analyzes scientific research dedicated to the application of modern IT methods and tools in the management of GDNs, investigating management and forecasting models using neural networks and intelligent methods. Keywords: Gas distribution networks, modern GDN networks, traditional GDN networks, GIS systems, digital technologies, technogenic risks, neural networks, forecasting methods

A comprehensive study on the effects of electrocoagulation integrated in a membrane bioreactor treating sunflower oil refinery wastewater on treatment performance, biological properties, and fouling behavior.

This study evaluated the effects of electrocoagulation integrated in a laboratory-scale membrane bioreactor (MBR), namely EC-MBR, on the treatment performance, activated sludge morphological characterization, and membrane fouling of MBR treating actual sunflower oil refinery wastewater. The EC-MBR system exhibited significantly higher chemical oxygen demand (COD) and oil and grease (O&G) removal efficiency compared to the MBR system. Additionally, both systems achieved excellent turbidity removal, with a percentage above 99%. The membrane fouling rate was higher in the EC-MBR system compared to the MBR system. Despite the decrease in the soluble microbial product (SMP) and extracellular polymeric substance (EPS) concentration in the EC-MBR system, especially their protein fraction, the significant increase in MLSS and carbohydrates/protein ratio, and the decrease in the mixed liquor and the cake layer particles size were the main membrane fouling factors. The membrane fouling resistance distribution showed that the EC-MBR system had a higher percentage of pore blocking resistance compared to the MBR. FTIR analysis identified a greater proportion of carbohydrate compounds in the cake layer of the EC-MBR system. SEM images revealed dense microbial clusters, mainly rod- and oval-shaped bacteria, in the EC-MBR system. Furthermore, EDX analysis detected elements such as Ca, K, O, Al, and P in both systems, with the EC-MBR system showing a higher Al content. The EC-MBR system showed low energy consumption (0.431kWhm-3) and total operating costs ($0.90m-3), showcasing its effectiveness and affordability for sustainable wastewater management.

Multi-User Optimal Load Scheduling of Different Objectives Combined with Multi-Criteria Decision Making for Smart Grid

Load balancing between required power demand and the available generation capacity is the main task of demand response for a smart grid. Matching between the objectives of users and utilities is the main gap that should be addressed in the demand response context. In this paper, a multi-user optimal load scheduling is proposed to benefit both utility companies and users. Different objectives are considered to form a multi-objective artificial hummingbird algorithm (MAHA). The cost of energy consumption, peak of load, and user inconvenience are the main objectives considered in this work. A hybrid multi-criteria decision making method is considered to select the dominance solutions. This approach is based on the removal effects of criteria (MERECs) and is utilized for deriving appropriate weights of various criteria. Next, the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method is used to find the best solution of load scheduling from a set of Pareto front solutions produced by MAHA. Multiple pricing schemes are applied in this work, namely the time of use (ToU) and adaptive consumption level pricing scheme (ACLPS), to test the proposed system with regards to different pricing rates. Furthermore, non-cooperative and cooperative users’ working schemes are considered to overcome the issue of making a new peak load time through shifting the user load from the peak to off-peak period to realize minimum energy cost. The results demonstrate 81% cost savings for the proposed method with the cooperative mode while using ACLPS and 40% savings regarding ToU. Furthermore, the peak saving for the same mode of operation provides about 68% and 64% for ACLPs and ToU, respectively. The finding of this work has been validated against other related contributions to examine the significance of the proposed technique. The analyses in this research have concluded that the presented approach has realized a remarkable saving for the peak power intervals and energy cost while maintaining an acceptable range of the customer inconvenience level.

Economic and Environmental Aspects of Applying the Regeneration of Spent Moulding Sand

This article presents issues related to the rational management of foundry sand in the context of sustainable development. Attention was drawn to the need to take appropriate measures to protect available natural deposits of good foundry sands in terms of their depletion. The main objective of the analyses undertaken was to find out whether more expensive but more efficient thermal regeneration can compensate for the higher energy consumption in relation to mechanical regeneration of spent moulding sand with an organic binder. This aspect was considered from the point of view of the multiple operations performed to clean the grain matrix from the spent binder, taking into account the direct and indirect costs of the process. This paper presents a comparative analysis of the mechanical and thermal regeneration of spent moulding sand on equipment offered by an exemplary manufacturer. Attention was drawn to the successively increasing price of the regeneration process. When analysing the grain matrix recovery process for sustainability reasons, attention was drawn to an important factor in grain matrix management related to its yield in different regeneration methods. Based on an analysis of the costs of regenerating 1 tonne of spent moulding sand, it was concluded that, in the long term, thermal regeneration, which is more expensive due to the cost of equipment and energy consumption, can offset the outlay incurred. Sand consumption was found to be 4.6 times higher by mechanical regeneration in the case studied. At the same time, the grain matrix after thermal regeneration was found to have significantly better and more stable technological parameters in subsequent sand mould preparation cycles. The reproducibility and stability of the technological process can also be an important component of economic growth as part of sustainable development.

Design and multiperformance evaluation of the ternary azeotrope separation process via extractive distillation based on different extractants

Resource utilization and sustainable wastewater treatment have received widespread attention because of their efficient use of energy. In this study, an ethanol (EtOH)/ethyl tert-butyl ether/water ternary azeotropic system was used to explore the separation efficiency of various extractants. The relative volatility, toxicity, and process modeling of extractants were analyzed to provide a theoretical basis and practical guidance for screening the optimal extractant among nine commonly used extractants. Extractive distillation (ED) was optimized for six processes with a focus on total energy consumption (TEC) and total annual cost (TAC). The ED-EtOH process was the most economical, whereas the extractive ED-DMSO process had the lowest TEC. After heat integration (HI) treatment, the TAC and TEC of each process were reduced, among which HI-ED-EtOH and HI-ED-glycerol (GLY) had the best and lowest economy, respectively. Although high-boiling point and high latent heat extractants (such as GLY and EtOH) increase the heat demand and operating costs, their high latent heat characteristics can effectively recycle heat energy during the HI process. The optimal balance between economy and energy consumption was determined through multi objective optimization considering the physical and chemical properties, environmental impact, and adaptability of extractants.

Reducing CO[formula omitted] emissions, energy consumption, and decarbonization costs in manganese production by integrating fuel-assisted solid oxide electrolysis cells in two-stage oxide reduction

Manganese is one of the most consumed metals due to its use in iron and steel making, which generates between 7%–9% of all CO2 emissions produced globally per annum. For the first time, this work explores the potential of a novel process concept to reduce CO2 emissions, energy consumption, and decarbonization costs in manganese production, which involves integrating fuel-assisted solid oxide electrolysis cells (FASOECs) in a two-stage scheme for the reduction of raw manganese ores. In this scheme, higher oxides that are present in raw manganese ores (MnO2, Mn2O3, Mn3O4) are pre-reduced using CO, yielding manganese monoxide (MnO) that is further reduced to Mn in a submerged arc furnace (SAF) using coke and electricity. In the proposed FASOEC integration concept, off-gas from the manganese ores after pre-reduction (a mixture of H2, CO, and CO2) is supplied to the FASOECs’ anode as fuel, in order to produce high-purity H2 in the cathode that is directed to the first stage of manganese reduction. H2 has enhanced reaction kinetics for reducing higher manganese oxides in comparison to CO; therefore, integrating FASOECs can improve manganese oxide conversion rates during pre-reduction, resulting in lower consumption rates of coke and energy in the SAF. The off-gas supplied to the FASOECs’ anode also lowers the amount of energy required for H2 production in the cathode (can also generate electricity, simultaneously) and produces anode exhaust gas containing only CO2 and steam. Since steam can be easily condensed from this stream, the integration of FASOECs also enables efficient decarbonization of the manganese production process, thus eliminating the need for a designated CO2 capture system. The techno-economic analysis performed herein demonstrates that directing the full supply of off-gas produced by the SAF to the FASOECs’ anode at 800 °C reduces the overall energy consumption of manganese production by up to 18% in comparison to conventional processes. This results in decarbonization cost reductions by as much as 3%–15% and a corresponding decarbonization price range of $4-32 per ton of manganese product for plant capacities of 50 and 200 kt, respectively.

Cholesteric Liquid Crystal Mirror-Based Smart Window Controlled with Ambient Temperature

In this paper, the authors demonstrate a small prototype of a smart window based on the thermo-optical properties of cholesterol liquid crystals. Due to its polymer-free design, the manufactured smart window is transparent and can reflect certain portions of visible or infrared light without requiring an external power source, and thus is easier to install and operate. The proposed smart window technology based on a cholesteric liquid crystal mirror will reduce energy consumption costs by reflecting excess sunlight and heat transfer, increasing comfort for residents of buildings and structures.

Advancing Sustainability in a Residential Building in Europe: A Case Study on LCA and LCC across Different Scenarios

This study evaluates the environmental and economic performance of the "ESTIA of Athens" residential building considering its energy consumption and greenhouse gas (GHG) emissions. It also explores the potential for improvements in these areas. The European Union's strategy to reduce GHG emissions from buildings by 55% compared to 1990 levels by 2030, as part of its broader aim for climate neutrality by 2050 is a background for the study. The methodology involves a Life Cycle Assessment (LCA) using SimaPro software, monitoring energy consumption, temperature and humidity of the building. The thirteen impact categories and damage impact assessment that include human health, ecosystem and resources were examined. The study compares various scenarios, including historical data from 2018 to 2022 and two hypothetical scenarios to assess environmental impacts across different categories. Additionally, Life Cycle Costings (LCC) are performed to evaluate the economic aspects of the building's performance. The results highlight substantial differences in energy consumption, GHG emissions and economic costs among the scenarios. The findings suggest that a hypothetical scenario, referred to as Case 3, demonstrates lower environmental impacts and economic costs compared to other scenarios, indicating its potential as an optimal renovation strategy for the building. This includes reductions of 32%, 40% and 58% in the human health, ecosystems, and resources categories, respectively. A similar trend is observed across the impact categories, with reductions ranging from 4% in the Mineral Resource category to 47% in the Global Warming category. The study underscores the importance of holistic assessments in informing energy policy and renovation strategies for achieving both environmental sustainability and economic viability in buildings.

Possibilities of Changes in Energy Intensity of Production Depending on the Scale of Farm Investments in a Polish Region

The purpose of this paper was to analyze the possibility of changes in energy intensity of production in the context of farm investment scale. The empirical section relies on unpublished FADN microdata. The study answers the question of whether investments and fixed capital growth can contribute to improvements in environmental performance of agricultural production. As it turns out, the group of farms with the greatest amount of investments saw an increase in energy consumption costs, though at a much slower rate than growth in production value. As a result, there was a drop in energy intensity of production, defined as the ratio between energy costs and production value. These findings corroborate the hypothesis advanced in this study, namely that upon reaching a sufficiently large amount of investments, farms can become capable of reducing the energy intensity of their production activity. Hence, higher levels of capital productivity are attained when the farms invest in more energy-efficient fixed assets. Conclusions cannot be universal, as the analysis involved a portion of selected farms. However, the research indicates a trend in the study of energy intensity of production depending on the scale of investment and the validity of the investment comprehensiveness approach. These conclusions provide a basis for recommendations for an agricultural policy which should include support for investments that are large and comprehensive (in relation to the farm’s assets) and go beyond the simple renewal of assets. Therefore, in its investment-oriented measures, the agricultural policy should take the innovativeness (including energy efficiency) criterion into account.

Coordinated scheduling optimization of building integrated energy system with flexible load

Under the background of China's "dual carbon" goals of peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, this paper proposes a method for the optimization of source-load coordinated scheduling in Building Integrated Energy System (BIES) with Flexible Load (FL) to promote sustainable and efficient energy development and enhance energy utilization efficiency. The method starts by analyzing the energy consumption behavior characteristics on the load side of buildings, determining the potential for FL reduction based on national standards and human comfort requirements. It then applies price-based demand response measures to shiftable and transferrable loads and incentives-based demand response measures to FL identified as reducible through potential analysis. Subsequently, a multi-objective optimization model focusing on economic efficiency and carbon emissions is established to coordinate the response curves of FL on the demand side and the operation of energy equipment on the supply side. Finally, the coordination optimization model is solved using a synergistic approach combining the logistic chaos mapping and adaptive t-distribution enhanced Non-dominated Sorting Dung Beetle Optimizer (NSDBO) algorithm, ensuring superior search effectiveness. Simulation results demonstrate that the optimization of FL responses using the Logistic-t-NSDBO algorithm effectively reduces peak-load variations while significantly lowering energy consumption costs and carbon emissions on the energy supply side.

Multi-objective optimization in order to allocate computing and telecommunication resources based on non-orthogonal access, participation of cloud server and edge server in 5G networks

Mobile edge processing is a cutting-edge technique that addresses the limitations of mobile devices by enabling users to offload computational tasks to edge servers, rather than relying on distant cloud servers. This approach significantly reduces the latency associated with cloud processing, thereby enhancing the quality of service. In this paper, we propose a system in which a cellular network, comprising multiple users, interacts with both cloud and edge servers to process service requests. The system assumes non-orthogonal multiple access (NOMA) for user access to the radio spectrum. We model the interactions between users and servers using queuing theory, aiming to minimize the total energy consumption of users, service delivery time, and overall network operation costs. The problem is mathematically formulated as a multi-objective, bounded non-convex optimization problem. The Structural Correspondence Analysis (SCA) method is employed to obtain the global optimal solution. Simulation results demonstrate that the proposed model reduces energy consumption, delay, and network costs by approximately 50%, under the given assumptions.

Integrating machine learning and parametric design for energy-efficient building cladding systems in arid climates: Sport hall in Kerman

Climate change, driven by fossil fuel dependence, presents a significant challenge for the construction industry, particularly in energy-intensive regions like arid climates. This research investigates the potential of integrating machine learning and parametric optimization to enhance the energy efficiency of spatial structure domes in such environments. Focusing on a sports pavilion in Kerman, Iran, the study examines the crucial role of cladding systems in building energy performance. Employing a rigorous four-phase methodology, the research optimizes dome cladding materials for hot, dry climates using a dual objective function: energy cost and material cost. The process involves a comprehensive literature review, data-driven material selection, advanced energy simulations, and optimization analysis. Parametric modeling tools (Rhino, Grasshopper, Honeybee) facilitate the comparative analysis of various cladding systems. Multivariate Polynomial Regression (MPR) enables predictive modeling of energy consumption and material costs, streamlining the design process for architects. The optimized solution is a hybrid cladding model composed of 10 % polycarbonate and 90 % aluminum. Analysis reveals that the hybrid system offers superior energy optimization compared to pure aluminum (4.58 %) and polycarbonate (5.70 %). While polycarbonate has a lower initial material cost, the hybrid system achieves a balance between material expenditure and long-term energy efficiency. This highlights the importance of considering life-cycle costs when evaluating building envelope materials. This research advances a framework that leverages machine learning and parametric design for building envelope optimization. This framework empowers architects and engineers to create energy-efficient structures within arid environments, promoting a more sustainable built environment.

Wearable Fall Detectors Based on Low Power Transmission Systems: A Systematic Review

Early attention to individuals who suffer falls is a critical aspect when determining the consequences of such accidents, which are among the leading causes of mortality and disability in older adults. For this reason and considering the high number of older adults living alone, the development of automatic fall alerting systems has garnered significant research attention over the past decade. A key element for deploying a fall detection system (FDS) based on wearables is the wireless transmission method employed to transmit the medical alarms. In this regard, the vast majority of prototypes in the related literature utilize short-range technologies, such as Bluetooth, which must be complemented by the existence of a gateway device (e.g., a smartphone). In other studies, standards like Wi-Fi or 3G communications are proposed, which offer greater range but come with high power consumption, which can be unsuitable for most wearables, and higher service fees. In addition, they require reliable radio coverage, which is not always guaranteed in all application scenarios. An interesting alternative to these standards is Low Power Wide Area Network (LPWAN) technologies, which minimize both energy consumption and hardware costs while maximizing transmission range. This article provides a comprehensive search and review of that works in the literature that have implemented and evaluated wearable FDSs utilizing LPWAN interfaces to transmit alarms. The review systematically examines these proposals, considering various operational aspects and identifying key areas that have not yet been adequately addressed for the viable implementation of such detectors.

Optimised rule-based peak shaving and its impact on annual energy cost reduction

This paper presents an optimised rule-based peak-shaving method which is tested on an educational institute to know its impact on annual energy cost reduction. In recent years, energy management in low-voltage distribution systems has drawn more attention. Peak shaving is an important energy management method which helps consumers and grid operators. Peak shaving reduces voltage drop and voltage-rise issues, ratings of the different components and energy consumption costs and power losses. In this work, some of the most effective peak-shaving techniques to handle the problem of peak shaving and a few real-world implementations are discussed. Furthermore, an optimised rule-based peak-shaving (RBPS) method, which considers grid import and export power limits, is presented. The impact of this peak-shaving method, on a case study of an educational institute with a photovoltaic (PV)-battery energy storage system (BESS) based on its net load data over an annum for annual operating energy cost reduction, is presented. The results indicate that the total cost reduction for a year is 68.48% using PV and BESS with the peak-shaving application.

A fuzzy multi-objective model for aggregate production planning considering energy consumption, carbon emission, chemical, solid waste, and wastewater

With a growing focus on environmental conservation, enterprises continuously reevaluate their conventional profit-driven business models. This shift has led to the development of green production planning models, attracting considerable attention in recent years. Previously, energy consumption or carbon emission were the most considered green parameters in the production planning problems. Nevertheless, several other essential green aspects such as reductions in chemical, solid waste, and wastewater are usually neglected. Therefore, this study proposes a multi-objective model for environmentally conscious aggregate production planning, simultaneously considering the above-mentioned green objectives. Additionally, the uncertain parameters of the model, including the green parameters, were modelled through fuzzy programming. Furthermore, this study incorporated a contradicting relationship between energy consumption cost and associated carbon emission, which was previously presented as non-contradicting. This relationship was illustrated by utilizing the time-of-use electricity tariff scheme. To solve the model, the initial step involved the defuzzification of uncertain attributes, followed by the transformation of the multi-objective model into a single-objective model. Finally, the IBM ILOG CPLEX Optimization Studio solver was utilized to solve the model based on the data collected from a precision engineering company. The generated production plan achieved an overall satisfaction level of 71%, which is reasonably acceptable to the decision maker. Moreover, sensitivity analysis has been conducted for the degree of optimism of the objective functions and the feasibility degree of the constraints. A general overview of the results associated with the performed sensitivity analysis indicated that the optimum values of the objective functions deteriorate as the parameters increase, though all remain within an acceptable range. The proposed study will assist decision makers in generating production plans that are both economically and environmentally feasible.

Effects of different corrugated rolls on the performance of BTW1/Q345R composite plate

Abstract BTW1/Q345R composite plate not only has the advantages of high strength and high wear resistance, but also helps to reduce energy consumption and production cost. This paper proposes a novel rolling process based on corrugated rolls for the preparation of wear-resistant steel BTW1/Q345R composite plates. In order to analyze and study the microstructure, and mechanical properties of BTW1/Q345R composite plates after rolling, techniques such as electron backscatter diffraction (EBSD) were used. The results indicate that the composite plate obtained through rolling with the corrugated roll of 72 cycles (3# corrugated roll) exhibits a yield strength of 550 MPa, tensile strength of 705 MPa, elongation at break of 17.57%, and shear strength of the composite interface at 242 MPa. Notably, the rolling process with a 3# corrugated roll yields the best combination of tensile mechanical performance and interface bonding performance for the composite plate.

Industrial park wastewater treatment using a combined sono-pulsed electrochemical-coagulation process

Maintaining sustainable water management practices, preserving ecosystems, and safeguarding public health has depend on the treatment of wastewater. It has a significant impact on reducing water pollution and protecting the water supplies. Sono-pulsed electrochemical oxidation (S-PEO) process is a wastewater treatment method that uses ultrasound (US) waves and electrochemistry to enhance the pollutant removal efficiency. This study aim is to investigate the mineralization of industrial park wastewater by using pulsed electrochemical oxidation (PEO) and a combination of sonolysis and pulsed electrochemical oxidation (S-PEO) processes. The research focuses on the percentage elimination of chemical oxygen demand (COD) and color, as well as the assessment of power consumption. The study considers the influence of experimental parameters like initial pH, electrolysis time, and current to find the optimum conditions for maximum percentage removal efficiency with minimization of power consumption. According to the study, the optimum values of responses for the S-PEO process were obtained at pH=7, electrolysis time=40 min, and current =0.5 Amp. The optimum values for the maximum removal percentages of the responses COD and color were 97.04 %, and 99.70 %, respectively. The minimum of power consumption for S-PEO was 0.19 kWh/m3. These results demonstrate that, the superior efficiency of the S-PEO process and its feasibility in the removal of industrial wastewater pollutants. Optimizing parameters like pH, time, and current intensity is crucial for optimal performance, reducing energy consumption and operational costs. The S-PEO technique eliminates pollutants from wastewater efficiently and effectively, making it the suitable process when compared to the others.

On the impact of co-optimizing station locations, trip assignment, and charging schedules for electric buses

As many public transportation systems around the world transition to electric buses, the planning and operation of fleets can be improved via tailored decision-support tools. In this work, we study the impact of jointly locating charging facilities, assigning electric buses to trips, and determining when and where to charge the buses. We propose a mixed integer linear program that co-optimizes planning and operational decisions jointly and an iterated local search heuristic to solve large-scale instances. Herein, we use a concurrent scheduler algorithm to generate an initial feasible solution, which serves as a starting point for our iterated local search algorithm. In the sequential case, we first optimize trip assignments and charging locations. Charging schedules are then determined after fixing the optimal decisions from the first level. The joint model, on the other hand, integrates charge scheduling within the local search procedure. The solution quality of the joint and sequential iterated local search models are compared for multiple real-world bus transit networks. Our results demonstrate that joint models can help further improve operating costs by 14.1% and lower total costs by about 4.1% on average compared with sequential models. In addition, energy consumption costs and contracted power capacity costs have been reduced significantly due to our integrated planning approach.