Energy Cost Research Articles

Optimization of distributed energy resources planning and battery energy storage management via large-scale multi-objective evolutionary algorithm

This paper investigates the synergistic integration of renewable energy sources and battery energy storage systems to enhance the sustainability, reliability, and flexibility of modern power systems. Addressing a critical gap in distribution networks, particularly regarding the variability of renewable energy, the study aims to minimize energy costs, emission rates, and reliability indices by optimizing the placement and sizing of wind and solar photovoltaic generators alongside battery energy storage systems. An improved large-scale multi-objective evolutionary algorithm with a bi-directional sampling strategy is employed.Two scenarios are considered. In the first scenario, six study cases are analyzed to determine the optimal number, location, and size of distributed generators at peak load demand. The proposed algorithm outperforms existing state-of-the-art methods for small-scale distributed resource allocation. In the second scenario, a multi-period load demand across various seasons is evaluated, introducing new opportunities for battery energy storage systems. The problem is modeled with intertemporal constraints, creating a large-scale optimization challenge. Results demonstrate significant improvements: cost savings of up to 51.67 %, power reductions of up to 76.78 %, and a 99.9 % improvement in voltage deviation. Emission rates decreased by 99.92 % in case 1, 95.19 % in case 2, and 97.38 % in case 3, compared to the base case. The proposed algorithm shows superior convergence and performance in solving both small- and large-scale optimization problems, outperforming recent multi-objective evolutionary algorithms.This study provides a robust framework for optimizing renewable energy integration and battery energy storage, offering a scalable solution to modern power system challenges.

Advancing Economical and Environmentally Conscious Electrification: A Comprehensive Framework for Microgrid Design in Off‐Grid Regions

AbstractThe design of renewable energy systems traditionally emphasizes life cycle costs, often focusing primarily on emissions rather than a comprehensive life cycle impact assessment. This research proposes a four‐tier methodology to balance cost‐effectiveness and sustainability in the electrification of remote areas. Tier 1 focuses on understanding the community context by analyzing electrical load profiles, meteorological data, and component specifications for microgrid design. Tier 2 evaluates the feasibility of various systems, optimizing them through cost analysis and Multi‐Criteria Decision‐Making (MCDM) to rank alternatives. Tier 3 assesses environmental impacts using life cycle assessment, ranking alternatives based on environmental criteria. Tier 4 integrates cost and environmental rankings to determine the most suitable energy configurations, followed by sensitivity analysis to ensure robust decision‐making. The methodology is validated through a case study of an unelectrified remote community, demonstrating that the PV‐Wind Turbine‐Biomass Generator‐Converter configuration is the most robust alternative, proving to be the optimal choice in 50% of the analyzed scenarios, achieving a Cost of Energy of 0.213 USD/kWh while minimizing environmental impact across all 18 criteria considered over a 25‐year life cycle. This novel framework offers a scalable approach to designing renewable energy systems, enhancing sustainable electrification efforts in developing regions.

Off-grid PV/biomass/DG/battery hybrid renewable energy as a source of electricity for a farm facility

Reliable, cost-effective energy systems are pivotal for sustainable development in the agricultural sector. Using the energy balance analysis of the Hybrid Optimization Model for Electric Renewable (HOMER), this study presents a techno-economic assessment of a hybrid renewable energy system for a farm facility. The energy system components considered in the analysis include solar photovoltaics, wind turbines, diesel generators, biomass, and battery storage. The results shows that ten feasible energy systems can technically power the typical farm facility, with a PV-Biomass-DG battery being the best in terms of the total net present cost. The PV-Biomass-DG-battery systems consist of 561.023 kW of PV, 88 kW of diesel generator, 10 kW of biomass, 81 kW of converter, and 584 batteries operated in a load-following mode. When the cost of energy (COE) of EA1 0.206325 $/kWh is compared to EA7 0.407681 $/kWh, a difference of $0.201356 will be saved for every kWh. Also, a comparison of the operating cost of EA1, $65,713.23 and the operating cost of EA7 $212,027.4 showed a margin of $146,314.17 being saved. Energy systems EA3 and EA4 had the lowest carbon dioxide production levels of 15.8 kg annually, respectively. The simple payback period metrics increased from EA1 to EA6 between 2.97 years to 6.99 years. The result of the study shows that adopting a low-carbon energy transition is technically and economically viable for the agricultural sector, especially in developing countries.

Quantification of nutrient fluxes during acute exercise in mice.

Despite the known metabolic benefits of exercise, an integrated metabolic understanding of exercise is lacking. Here, we use invivo steady-state isotope-labeled infusions to quantify fuel flux and oxidation during exercise in fasted, fed, and exhausted female mice, revealing several novel findings. Exercise strongly promoted glucose fluxes from liver glycogen, lactate, and glycerol, distinct from humans. Several organs spared glucose, a process that broke down in exhausted mice despite concomitant hypoglycemia. Proteolysis increased markedly, also divergent from humans. Fatty acid oxidation dominated during fasted exercise. Ketone production and oxidation rose rapidly, seemingly driven by a hepatic bottleneck caused by gluconeogenesis-induced cataplerotic stress. Altered fuel consumption was observed in organs not directly involved in muscle contraction, including the pancreas and brown fat. Several futile cycles surprisingly persisted during exercise, despite their energy cost. In sum, we provide a comprehensive, integrated, holistic, and quantitative accounting of metabolism during exercise in an intact organism.

Techno-Economic Analysis of a Carbon Molecular Sieve-Based Xylene Isomer Purification Process.

The separation and purification of xylene isomers is critical to the production of polyethylene terephthalate (PET). These separations are complex to operate and demand tremendous amounts of energy and thus are an opportunity for reducing industrial energy consumption. Membranes provide a low-energy footprint technology with simpler operation, and recently, membrane materials have been developed that are capable of separating xylene isomers. While these materials have demonstrated the ability to separate xylenes, they have yet to be commercially deployed. This work conducts a techno-economic analysis (TEA) to provide insight on the commercial attractiveness of a p-xylene selective carbon molecular sieve (CMS) membrane from both a cost and energy standpoint. This TEA was conducted through the pairing of a Maxwell-Stefan transport framework for rigid microporous materials with process modeling. Single-stage organic solvent reverse osmosis (OSRO) and pervaporation processes were used to evaluate the effects of recovery, selectivity, and diffusivity on the energy intensity and cost of the xylene separation. The final analysis used two systems, a single pervaporation stage followed by two OSRO stages, which was compared against a three-stage OSRO cascade. These were benchmarked against the commercial Parex process. We estimate that the membrane processes have the potential to enable impressive cost savings compared to the Parex process. While both systems outperformed the Parex process in terms of cost, the pervaporation/OSRO hybrid process was able to achieve the lowest cost of all due to its reduced membrane surface area compared to standalone OSRO. These findings demonstrate the potential of membrane systems in the field of difficult small molecule solvent separations.

Study of microwave hardwood board modification and drying: experiments and technical-economic assessment

The very low permeability of many hardwood species causes problems during lumber drying. These include very long drying times, drying defects, large material losses after drying, high energy consumption, and expensive drying processes. Jarrah (Eucalyptus marginata) is a species which very difficult to dry. Microwave (MW) wood modification and MW drying can provide an increase in wood permeability and solve some hardwood drying problems. Two 60 kW MW conveyor plants at frequencies 2.45 and 0.922 GHz were used for experiments. The study of MW Jarrah (29x87mm) board drying after pre-drying MW modification showed that it is possible to dry boards in the moisture content range from 70 to 10% during 147-159 hours (6 -7 days) using the cycle method of MW power application. Such intensive MW drying does not bring perceptible damage to the wood and provides low variability of moisture content in the board cross sections. The shorter drying times reduce associated capital, space, energy and labor costs whilst the reduction in drying defects can increase yields. A short time of MW drying provides an opportunity to develop conveyor systems for hardwood sawn timber drying. Economic assessment of MW Jarrah board drying showed that a specific drying cost ranges from US$ 50 to $277/m3 at electricity prices from US$0.08 to $0.32/kWh. The electricity costs form the largest share in total specific drying costs - 44 to 82%. Jarrah boards are expensive product and MW drying costs can be acceptable for industry taking into consideration savings provided by fast drying and wood loss reduction.

Heat transfer and economic characteristics of direct oil cooling for electric traction motor under driving cycles

Currently, the energy density of electric traction motor is continuously increasing to match the performance and reliability of electric vehicles with IC engine vehicles. The commercially employed water jacket cooling fails to achieve the desired efficiency of high energy density electric traction motor. Direct oil cooling is one of potential cooling strategies to replace the conventional cooling method for achieving the desired thermal management of next generation electric traction motors. However, the database to commercialize the direct oil cooling based on thermal management and economic aspects is not enriched sufficiently and thus extensive research studies need to be conducted in this field. Therefore, the heat transfer and economic characteristics of direct oil cooling for electric traction motor are investigated considering different cooling oils and driving duty cycles in the present work. The integrated electromagnetic-thermal numerical model is developed using ANSYS Motor-Cad commercial software to simulate the heat transfer and economic characteristics. The reliability of coupled numerical model is assured by validating the simulated results with experimental and reference data in error range of ± 5 %. The winding temperature, heat transfer coefficient, expected insulation lifetime, levelized cost of energy (LCOE), and net cumulative savings are evaluated as heat transfer and economic characteristics. Three driving duty cycles namely, UDDS (Urban Dynamometer Driving Schedule), NEDC (New European Driving Cycle) and US06 (Highway Driving Cycle) and transmission oil, shell thermal oil and Novec dielectric oil are investigated. The performance of electric traction motor with direct oil cooling is superior compared to that with water jacket cooling. The direct cooling with Novec dielectric oil shows improved heat transfer and economic characteristics compared to transmission oil and shell thermal oil. The direct cooling with Novec dielectric oil restricts the maximum winding temperature within 80.21 °C, 67.65 °C and 116.82 °C under driving cycles of UDDS, NEDC and US06, respectively. In addition, the enhanced insulation lifetime, heat transfer coefficient, LCOE and net cumulative savings of 7.91 × 105h, 23036.73 W/m2-K, 0.170 $/kWh and 47151.13 $, respectively are reflected for direct cooling with Novec dielectric oil.

Design and performance analysis of a net-zero energy building in Owerri, Nigeria

Building cooling load depends on heat gains from the outside environment. Appropriate orientation and masonry materials play vital roles in the reduction of overall thermal loads buildings. A net-zero energy building performance has been analyzed in order to ascertain the optimum orientation and wall material properties, under the climatic conditions of Owerri, Nigeria. Standard cooling load estimation techniques were employed for the determination of the diurnal interior load variations in a building incorporating renewable energy as the major energy source, and compared with the situation in a conventionally powered building. The results show a 19.28% reduction in the building’s cooling load when brick masonry was used for the wall construction. It was observed that a higher heat gain occurred when the building faced the East-West direction than when it was oriented in the North-South direction. Significant diurnal cooling loads variation as a result of radiation through the windows was also observed, with the east facing windows contributing significantly higher loads during the morning hours while the west facing windows contributed higher amounts in the evening. The economic analysis of the net-zero energy building showed an 11.63% reduction in energy cost compared to the conventional building, with a 7-year payback period for the use of Solar PV systems. Therefore, the concept of net-zero energy building will not only help in energy conservation, but also in cost savings, and the reduction of carbon footprint in the built environment.

Optimal scheduling of battery energy storage system operations under load uncertainty

This paper investigates the optimal scheduling of battery energy storage system operations considering energy load uncertainty. We develop a novel two-stage distributionally robust optimization model to determine an optimal battery usage schedule that minimizes the worst-case energy costs considering peak load costs. The model leverages deep-learning-based probabilistic forecasting in the construction of the ambiguity set. Specifically, we develop a Deep Autoregressive Recurrent Networks model to generate a probabilistic forecast of energy loads over a time horizon. The output of the forecasting model is then used to construct a marginal-moment ambiguity set for the distributionally robust optimization model. To solve the proposed model, we establish a closed-form characterization of the optimal second-stage objective function value. Leveraging this closed-form expression and using second-order conic duality, we derive an exact single-level mixed integer second-order conic reformulation of the problem. Extensive computational experiments, conducted on a real dataset, demonstrate the value of our proposed model and the effectiveness of the resulting battery schedule. The results show that the proposed model outperforms several benchmarks, including two-stage stochastic programming. Furthermore, the accuracy of the load forecast significantly impacts the effectiveness of the optimal battery schedule in eliminating peak loads by achieving up to 18% reduction in the maximum energy load.

In Silico Analysis of Vitamin D Interactions with Aging Proteins: Docking, Molecular Dynamics, and Solvation Free Energy Studies

Aging is a natural process that is also influenced by some factors like the food someone eats, lifestyle decisions, and impacts on general health. Despite the recognized role of nutrition in modulating the molecular and cellular mechanisms underlying aging, there is a lack of comprehensive exploration into potential interventions that can effectively mitigate these effects. In this study, we investigated the potential anti-aging properties of vitamin D by examining its interactions with key molecular targets involved in aging-related pathways. By using molecular docking and dynamics techniques, we evaluate the interactions and stability of vitamins D2 and D3 with key proteins involved in aging pathways, such as SIRT1, mTOR, AMPK, Klotho, AhR, and MAPK. Our results reveal promising binding affinities between vitamin D and SIRT1 forms, with energy values of −48.33 kJ/mol and −45.94 kJ/mol for vitamins D2 and D3, respectively, in aqueous environments. Moreover, molecular dynamics simulations revealed that the vitamin D3–SIRT1 complex exhibited greater stability compared with the vitamin D2–SIRT1 complex. The study calculated the solvation free energy to compare the solubility of vitamins D2 and D3 in water and various organic solvents. Despite their strong interactions with water, both vitamins exhibited low solubility, primarily due to the high energy cost associated with cavity formation in the aqueous environment. Compared with other solvents, water demonstrated particularly low solubility for both vitamins. This suggested that vitamins D2 and D3 preferred binding to aging receptors over dissolving in bulk aqueous environments, supporting their strong therapeutic interactions with these receptors. These findings shed light on the molecular mechanisms underlying vitamin D’s potential anti-aging effects and lay the groundwork for developing nutraceuticals targeting aging and associated diseases. Understanding these mechanisms holds promise for future interventions aimed at promoting healthy aging and enhancing overall well-being.

A socio-economic assessment of an emerging technology in the mining industry

Abstract Purpose This article provides methodological insights to evaluate the socio-economic risk of an emerging froth flotation technology for the mining sector with the goal of guiding the design and development process. This technology is used to separate valuable particles based on surface properties among minerals and, if properly developed, could be used to valorize fine particles that currently existing technology cannot separate and thus become waste material. Methods The Social Hotspot assessment utilized the Product Social Impact Life Cycle Assessment (PSILCA) database to analyze social hotspots in the relevant industrial sector. In addition, a survey captured the viewpoints of technology developers regarding additional potential social risk and opportunities. The final results were defined by combining these two analyses, conducted according to the 2020 UNEP guidelines for Social Life Cycle Assessment of Products and Organizations. For the economic assessment, the Material Flow Cost Accounting (MFCA) methodology (ISO14051) was applied, considering material costs, system costs, energy costs, and waste management costs for both the current situation and a future industrial-scale scenario. Results and discussion The study emphasizes the importance of tailoring methodological approaches for case studies involving low Technology Readiness Level (TRL) technologies based on available data. The state of technology development has led to different results for the economic and social analyses, primarily due to the difficulty in accurately predicting potential social impacts at this stage. The social analysis identified potential risks and 28 subcategories of impacts across different stakeholder categories. The economic assessment found that energy costs (49%) were the highest contributor to the MFCA cost of the future scenario, followed by system costs (29%). Conclusions and recommendations The study concludes that conducting a socio-economic analysis during the developmental stage of a technology is valuable for identifying critical hotspots that require monitoring, effectively guiding the research and development phase. This application represents a unique case in the mining sector and could be a first step in defining a methodological approach suitable for low TRL technologies. Analyzing both social and economic risks provides a more comprehensive perspective on sustainability, complementing environmental risk assessments.

Energy-efficient heat pump-assisted pre-concentration integrated with sequential [EMIM][BF4] and ethylene glycol-based extractive distillation for enhanced recovery of ethanol and isopropyl alcohol from wastewater

Recovering ethanol and isopropyl alcohol (IPA) from wastewater offers significant economic and environmental advantages. In this work, 1-ethyl-3-methylimidazolium tetrafluoroborate ([EMIM][BF4]) and ethylene glycol (EG) are proposed as double entrainers for separating IPA/ethanol/water mixtures. The novel process sequentially uses [EMIM][BF4] to separate ethanol/water and IPA/water azeotropes, followed by EG for near-boiling mixture of ethanol/IPA. Energy-economic comparisons of extractive distillation process using single and double entrainers are performed. To address the issue of high water content in mixtures, the heat pump-assisted pre-concentration (HPP) integrated with extractive distillation processes are proposed. Subsequently, the effect of water content in the mixtures on process performances are analyzed. The results indicate that compared to using a single entrainer [EMIM][BF4] or EG process, the double entrainers process reduces energy cost and total annual cost (TAC) by 19.9% to 45.3% and 18.6% to 52.7%, respectively. The HPP scheme effectively reduces the mole fraction of water from 60% to 26.05%, further decreasing energy costs by 4.2% to 22.2%. The energy cost and TAC of the double entrainers process decreases by 4.8% to 50%, and 2.8% to 52.1% compared with the single entrainer process under HPP schemes. The double entrainers [EMIM][BF4]-EG process shows promising prospects from energy and economic perspectives.

Techno-economic and environmental analysis of hybrid energy system for industrial sector of Pakistan

The industrial sector of Pakistan is currently facing severe load-shedding, which ultimately affects its unit production. The greater dependency on conventional energy resources (Thermal, Nuclear, etc.) results in higher production costs and environmental pollution. A sustainable, cost-effective, and environment-friendly solution can help the industrial growth of Pakistan. This article proposes an optimal hybrid energy system (HES) for the industrial sector of Pakistan to overcome the mentioned challenges. The proposed HES is developed in HOMER Pro. Three different energy cases (Case I: Existing energy system including a utility grid and diesel generator, Case II: On-grid Biogas system, and Case III: On-grid PV system with batteries) are considered for the Gourmet food Industry in the Sundar Industrial estate, Pakistan. The Load profile of the selected site was calculated through on-site visits and data provided by the designated utility grid feeder. The analysis shows that Case III is more effective than other cases, indicating reduced Net Present Cost (NPC), Cost of Energy (COE), and Operating Cost (OC) to $ 19.2 million, $0.034/kWh, and $ 573,371/year respectively. Moreover, the On-grid PV system with batteries (Case III) provides an environmentally friendly solution by reducing 63.82% \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:C{O}_{2}$$\\end{document} by and 62.22% \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{NO}_{x}$$\\end{document}. Comparing the sensitivity analysis for various grid sell-back prices ($0/kWh, $0.043/kWh, $0.061/kWh, and $0.09/kWh), Case III is more cost-effective than Case II. The revenue generation in Case III is $128,499.41/yr, considering the supply of excess electricity into nearby small industrial loads at $0.065/kWh, this indicates that installing optimal HES in industries will not only help in overall cost reduction but also support in mitigating environmental pollution and load shedding.

Exploring the nexus of oil price shocks: Impacts on financial dynamics and carbon emissions in the crude oil industry

This empirical study delves into the intricate interplay between oil price shocks (OPS) and their dual impacts on both carbon emissions and the financial performance of the crude oil industry. Utilizing a sophisticated econometric approach, the study employs a Nonlinear ARDL model to meticulously scrutinize the enduring and immediate associations between oil prices and key financial indicators such as stock prices, dividends, and profit ratios of oil exploration industries. Given the significant fiscal and ecological transformations in China during this period, the chosen factors provide a robust framework for measuring changes at both microeconomic and macroeconomic levels, contributing valuable insights to sustainable economic development strategies. Our findings elucidate a statistically significant influence of OPS on the financial performance of the oil industry, particularly in the short term. The increase in the volatility level of OPS manifests a discernible decline in stock prices and profit ratios. Intriguingly, the study further uncovers a nuanced facet of OPS, revealing a prolonged and beneficial impact on carbon emissions in the long term. More importantly, heightened oil prices serve as a catalyst, motivating oil exploration companies to intensify production and exploration activities, thereby increasing carbon emissions in a direct relationship. The implications of these findings are pivotal for policy making, underscoring the imperative for decision-makers and industry stakeholders to pivot towards cleaner, more sustainable energy sources. The research augments the ongoing discourse surrounding the intricate nexus between energy costs and carbon emissions, furnishing invaluable insights that could steer the development of policies geared towards fostering sustainability and mitigating the adverse effects of climate change.

Efficiency Assessment of the Production of Alternative Fuels of High Usable Quality within the Circular Economy: An Example from the Cement Sector

This article aims to present the mechanisms regulating the waste management system of one of the European countries that affect the cement industry. This publication analyses the possibility of using selected fractions of municipal and industrial waste as alternative fuels, including an analysis of ecological costs and benefits. The methodology includes the analysis of production data and the calculation of savings resulting from the use of alternative fuels. On this basis, ecological aspects were also indicated that should be taken into account when analyzing the profitability of the investment. Production data from an example Polish cement plant were used to analyze the research problem. Based on the guidelines of environmental standards and technical specifications, the parameters that PASr alternative fuels should meet were calculated in the company laboratory. This fuel type was then calculated in terms of emission intensity and production efficiency. The research results obtained in this paper study emphasize that the change in cement clinker production technology toward the use of waste raw materials and secondary fuels does not lead to an increase in heavy metal emissions to the extent that would justify qualifying cement as a material requiring systematic control of its harmful impacts on humans and the natural environment. The conclusions show that the use of alternative fuels reduces CO2 emissions and production costs, without negatively affecting the efficiency and production volume. The average energy requirement for the production of 1 ton of cement is approximately 3.3 GJ, which corresponds to 120 kg of coal with a calorific value of 27.5 MJ per kg. Energy costs account for 30–40% of the total cement production costs. Replacing alternative fuels with fossil fuels will help reduce energy costs, providing a competitive advantage for cement plants that use it as an energy source. The presented considerations can provide an answer to all interested parties, including representatives of the executive and legislative authorities, on what path the sector should follow to fit into the idea of sustainable building materials and the circular economy.

Sustainable mega-seaports with integrated multi-energy systems: Life-cycle environmental and economic evaluation

Ports play a critical role in modern society by acting as crucial links between water and land transportation, and integrating transportation with energy systems. This integration results in a high demand for various types of energy uses, with polluting emissions produced by the diverse energy sources. Integrated renewable energy systems represent promising solutions to achieving high levels of energy supply while lowering carbon footprints. In this research, a framework is proposed for a port multi-energy system that encompasses solar energy, wind energy, a hydrogen system and a number of energy storage systems. The proposed framework is tailored for implementation at Ningbo Zhoushan Port, the largest port globally. Different system design schemes are compared based on the number and rated power of wind turbines. Then, a comprehensive life-cycle economic and environmental assessment of the system is conducted through simulation. The economic and environmental metrics such as LCOE, REF and CO2 emissions are considered. The outcomes demonstrate that the design scheme incorporating two wind turbines with high rated power outperforms others in both environmental and economic metrics. Over the life-cycle, the renewable energy fraction exceeds 72%, and the levelized cost of energy plummets to 0.46 yuan/kWh. The implementation of the proposed port integrated multi-energy system yields substantial environmental and economic benefits. Specifically, it allows for a reduction of 66.68% in CO2 emissions and a cost reduction of 70.94%. These outcomes highlight the potential that the proposed system holds for enhancing environmental sustainability and economic efficiency within the port context.

Ups and downs: copepods reverse the near-body flow to cruise in the water column

Copepods are negatively buoyant organisms actively participating in large-scale vertical migrations as primary consumers in marine ecosystems. As such, these organisms need to overcome their own weight to swim upwards, incurring extra energy costs that are not offset by any mechanism intrinsic to drag-based propulsion. While copepod vertical migrations are well documented, it is still unclear how they achieve extensive upward cruising despite this limitation. In this study, we found suction to be a compensatory mechanism enhancing thrust in upward-swimming copepods. Using experimentally derived velocity and pressure fields, we observed that copepods pull water in front of them to generate sub-ambient pressure gradients when cruising upward, thereby inducing an upstream suction force to complement the thrust produced by the legs. Contrary to expectations that drag always dominates the leg recovery phase, we found that the upstream suction generates net thrust for about a third of the recovery stroke. In contrast, downward-swimming copepods push rather than pull on water and do not benefit from thrust-enhancing suction effects during the recovery stroke. Differences in the induced flows are associated with contrasting leg kinematics, indicating a response to the body orientation rather than a fixed effect. These results offer insights into an important swimming mechanism that can inform the role mesozooplankton play in biogenic hydrodynamic transport and its impact on marine biogeochemistry.

Optimizing textile dyeing and finishing for improved energy efficiency and sustainability in fleece knitted fabrics

In the industrial range, optimizing dyeing and finishing energy is important to control environmental pollution. In the Dyeing stage to finishing of textiles gas, electricity, steam, and water are used 260 m3/hour, 591 kWh, 1.2 pounds/hour, and 8.69 tons/hour respectively. If textile professionals do not match the desired shade and quality of fabrics with the use of minimal resources the energy cost will be multiple times higher. This study investigates the change in the shade of fleece knitted fabrics from the dyeing unload to the finish stage and assumes a dyeing recipe adjustment, focusing on the impact of optimized dyeing and finishing processes. Also, it focuses on qualitative changes in properties across various color variations. Identical dyeing recipes for light, medium, and dark shades of red, blue, and navy. Properties such as GSM (grams per square meter), width, color strength, shade (darker/lighter, red/green, blue/yellow), shrinkage, spirality, pilling, bursting strength, and color fastness were analyzed. Dyeing to post-finishing, an increase in color strength (K/S) values was observed, with examples including minimum increases from 2.9 to 3.18 for light red and maximum from 19.3 to 22.9 for dark navy shade. Darker shades (DL*) were observed after stenter 1st pass (among all variants, red: 1.2 % to 8.1 %, blue: 4.5 % to 6.7 %, navy: 1.6 % to 2 %), while lighter shades (DL*) were observed following sueding and napping (among all variants, red: 3.1 % to 19.7 %, blue: 11.8 % to 19.7 %, navy: 14.8 % to 27.6 %). Greenish (Da*) and yellowish (Db*) tones are prominent across all colors in the finishing stages. Besides, other properties shrinkage, spirality, pilling, bursting strength, and color fastness significantly changed. These findings offer valuable guidance for dyeing professionals aiming to achieve the desired adjustment of shades that match the quality standard and produce sustainable fleece fabrics. To compensate for the shade lightening that occurs during the finishing process, it is recommended to keep the fabric shade slightly darker (5.70 % to 23.10 %) at the dyeing stage.

Electrical vehicles impact on unbalanced distribution networks considering different loads and energy prices

The expansion of distributed generation and erratic loads create many challenges for electricity distribution networks (DN), like grid congestion and load unbalance. Technological advances in recent years have made the use of electric vehicles (EVs) more economical and justified for network connection. The use of smart EVs in the network scale, in addition to the peak shedding, if it is used in a single phase, it can also lead to the improvement of load unbalance. This paper proposes an energy and cost management model for the appropriate and distributed deployment of single phase EVs in distribution networks to provide network support for a DN including renewable resources. By connecting or disconnecting single-phase smart EVs to the DN, unbalanced loads in this network will be balanced as much as possible. To evaluate the proposed model, a network of 13 buses has been used, and the calculations of these energy costs have been measured during 24 h by two types of household and industrial loads. These loads are unbalanced in the DN and single-phase EVs have the ability to improve the unbalanced load of the DN. Also, the energy price is variable during the day and depends on the peak load. From the sensitivity analysis and with the relative increase or decrease of load, load change effects on the cost of each DN per-unit power have been measured and compared with the presence or absence of single-phase smart EVs to obtain more practical results. According to this paper, the use of EVs, in addition to improving the load of the DN, greatly reduces the costs of DN.

UAV p­­­arcel Delivery System with Deep Reinforcement Learning Based Collision Avoidance and Route Optimization

Unmanned Aerial Vehicles (UAVs), or drones, have recently become a favorable solution for fast parcel delivery due to their maneuverability and advances in navigation technologies. With the limitation of battery capacity and payload of drones, it is crucial to consider both efficiency and cost while conducting the tasks. Meanwhile, UAVs should not collide with each other while traveling to customers. In this article, we propose a UAV parcel delivery system involving deep reinforcement learning (DRL) approach for collision avoidance and a genetic algorithm for route optimization. Specifically, a delivery center generates near-optimal routes, loading UAV with parcels according to demands. Each UAV takes charge of delivering packages in compliance with the assigned route while avoiding collision with each other. We utilize DRL to achieve collision avoidance without having prior knowledge about the trajectories of other UAVs. Additionally, we adopt a genetic algorithm to obtain the lowest energy cost path for each UAV. To find such an optimized path, we solve a capacitated vehicle routing problem (CVRP) with a modified cost function and extra constraints. Realistic simulations using a physics engine and software-in-the-loop (SITL) are conducted to evaluate the feasibility of the proposed methods.