Increase In Energy Consumption Research Articles

Environmentally Friendly Biofuel Production

Changes in the environment and climate conditions, as well as economic growth and the increase in energy consumption, have led to an intensification of research on alternative fuels to replace fossil fuels [...]

The role of automated surface water distribution systems in energy-saving agriculture: A case study from central Iran’s Arid Plateau

Study regionThe NekooAbad Irrigation District in central Iran faces challenges due to inefficient surface water distribution. 82 million m3 of groundwater is extracted annually from 15,000 tube-wells, leading to decreased groundwater levels and increased energy consumption of 234 million kWh per year. Study focusThis study explores implementing automation in surface water distribution to reduce groundwater extraction and conserve energy. A hydraulic simulation model and a centralized Model Predictive Control approach were used to analyze the existing system and propose a recovery plan. The potential impact of automated surface water distribution on reducing groundwater extraction and energy conservation was evaluated through spatial assessment in GIS. New hydrological insights for the regionThe results demonstrate that the introduction of automation can significantly improve surface water distribution and, accordingly, groundwater overexploitation and, consequently, energy conservation, particularly during water scarcity. Energy conservation increased by 42.3 %, 54.8 %, 56.2 %, and 57.7 % under normal conditions, with water shortages of less than 10 %, 10–15 %, and 15–20 %, respectively. However, as the surface water shortages intensified, the energy conservation rates decreased to 57.7 %, 43.7 %, 25.4 %, and 18.9 % for normal conditions, water shortages of 15–20 %, 20–30 %, 30–40 %, and over 40 %, respectively. The automation introduction effectively provided reliable surface water resources, prompting farmers to shut down pumping stations or reduce working hours. Even in extreme scenarios, the project achieved up to 18.9 % energy savings.

Characteristics of the Gut Microbiota Composition of the Arctic Zone Residents in the Far Eastern Region

Background. In many studies over the past decade, scientists have made a connection between the composition of gut microbiota and human health. A number of publications have shown that gut bacteria are involved in many metabolic and physiological processes of the organism. The composition of the gut microbiome is unique for each person and is formed under the influence of various factors associated with both the individual characteristics of the body and the characteristics of the environment. Different regional characteristics make it necessary for the body to adapt to certain conditions, including temperature fluctuations. Living in areas with low temperatures, such as the Arctic zone, dictates the need for increased energy consumption, which affects the composition of the gut microbiome. Methods. In our study, an extensive questionnaire was conducted among the participants, where many questions were included about the dietary preferences of the study participants, which allowed them us to further divide them into groups according to their diets. Stool samples were collected from participants from 3 groups: Arctic native, Arctic newcomer and the control group. The next step was the isolation of bacterial DNA and sequencing the 16S rRNA gene. The analysis of the results of the diversity of the intestinal microbiota was carried out both with and without taking into account the dietary preferences of the participants. Results. As a result of comparing the intestinal microbiota obtained from residents of the Arctic zone with the gut microbiota of residents of other regions with a milder climate, significant differences are found. These differences may be related to limited food resources and a reduction in the variety of food products characteristic of this Arctic region. t was also found that representatives of the bacterial families Christensenellaceae and Muribaculaceae dominated the control group, both with traditional nutrition and with a dairy-free diet in comparison with the Arctic groups. The control group was dominated by representatives of the Prevotellaceae, Enterobacteriaceae and Comamonadaceae families compared to the Arctic group (with a traditional diet). The results also show that the number of representatives of the families Desulfovibrionaceae (with traditional diet) and Enterobacteriaceae (with milk-free diet) is growing in the Arctic group. Conclusions. In the course of this work, bacterial families characteristic of people living in the Arc-tic zone of the Far Eastern region of the Russian Federation were identified. Poor diet, difficult climatic conditions, and problems with logistics and medical care can have a strong impact on the health of this population. The main type of diet for the inhabitants of the Arctic is the traditional type of diet. They consume a large number of low-cost products, obtainget animal protein from poultry and canned food, and also eat a small number of fresh vegetables and fruits. Such a diet is due to the social status of the study participants and the climatic and geographical features of the region (difficulties in agriculture). With such a diet, we observe a decrease in representatives of the Christensenellaceae, Muribaculaceae, Eubacteriaceae, and Prevotellaceae families and an increase in representatives of the Enterobacteriaceae and Desulfovibrionaceae families among Arctic residents. This imbalance in the futuremay cause, this population may to develop various diseases in the future, including chronic diseases such as obesity, intestinal dysbiosis, inflammatory bowel diseases, and type 2 diabetes.

Integrating Machine Learning and Genetic Algorithms to Optimize Building Energy and Thermal Efficiency Under Historical and Future Climate Scenarios

As the global energy demand rises and climate change creates more challenges, optimizing the performance of non-residential buildings becomes essential. Traditional simulation-based optimization methods often fall short due to computational inefficiency and their time-consuming nature, limiting their practical application. This study introduces a new optimization framework that integrates Bayesian optimization, XGBoost algorithms, and multi-objective genetic algorithms (GA) to enhance building performance metrics—total energy (TE), indoor overheating degree (IOD), and predicted percentage dissatisfied (PPD)—for historical (2020), mid-future (2050), and future (2080) scenarios. The framework employs IOD as a key performance indicator (KPI) to optimize building design and operation. While traditional indices such as the predicted mean vote (PMV) and the thermal sensation vote (TSV) are widely used, they often fail to capture individual comfort variations and the dynamic nature of thermal conditions. IOD addresses these gaps by providing a comprehensive and objective measure of thermal discomfort, quantifying both the frequency and severity of overheating events. Alongside IOD, the energy use intensity (EUI) index is used to assess energy consumption per unit area, providing critical insights into energy efficiency. The integration of IOD with EUI and PPD enhances the overall assessment of building performance, creating a more precise and holistic framework. This combination ensures that energy efficiency, thermal comfort, and occupant well-being are optimized in tandem. By addressing a significant gap in existing methodologies, the current approach combines advanced optimization techniques with modern simulation tools such as EnergyPlus, resulting in a more efficient and accurate model to optimize building performance. This framework reduces computational time and enhances practical application. Utilizing SHAP (SHapley Additive Explanations) analysis, this research identified key design factors that influence performance metrics. Specifically, the window-to-wall ratio (WWR) impacts TE by increasing energy consumption through higher heat gain and cooling demand. Outdoor temperature (Tout) has a complex effect on TE depending on seasonal conditions, while indoor temperature (Tin) has a minor impact on TE. For PPD, Tout is a major negative factor, indicating that improved natural ventilation can reduce thermal discomfort, whereas higher Tin and larger open areas exacerbate it. Regarding IOD, both WWR and Tin significantly affect internal heat gains, with larger windows and higher indoor temperatures contributing to increased heat and reduced thermal comfort. Tout also has a positive impact on IOD, with its effect varying over time. This study demonstrates that as climate conditions evolve, the effects of WWR and open areas on TE become more pronounced, highlighting the need for effective management of building envelopes and HVAC systems.

Optimisation of Integrated Heat Pump and Thermal Energy Storage Systems in Active Buildings for Community Heat Decarbonisation

The electrification of residential heating systems, crucial for achieving net-zero emissions, poses significant challenges for low-voltage distribution networks. This study develops a simulation model to explore the integration of heat pumps within active building systems for community heating decarbonisation. The model optimises heat pump operations in conjunction with thermal energy storage units to reduce peak demand on low-voltage networks by using real-time measured electricity demand data and modelled heat demand data for 76 houses. The study employs an algorithm that adjusts thermal storage charging and discharging cycles to align with off-peak periods. Three scenarios were simulated: a baseline with unoptimised heat pumps, a fixed threshold model, and an active building model with daily optimised thresholds. The results demonstrate that the active building model achieves a 21% reduction in peak demand on the low-voltage substation compared to the baseline scenario; it also reduces the total electrical energy consumption by 12% and carbon emissions by 17%. The fixed threshold scenario shows a 16% improvement in peak demand reduction, but it also shows an increase in energy consumption and emissions. These findings highlight the potential of active buildings to enhance the efficiency and sustainability of residential energy systems, marking a significant step toward decarbonising residential heating while maintaining grid stability.

Optimizing electrokinetic remediation for pollutant removal and electroosmosis/dewatering using lateral anode configurations

Soil electrokinetics (SEK) research has been widely used in various fields such as soil remediation, dewatering, land restoration, geophysics, sedimentation, pollution prevention, consolidation, and seed germination. According to our most recent published research on SEK process design modifications during the last 30 years (1993–2022), more than 150 designs have been introduced to assure SEK’s maximum performance. Incorporating lateral electrodes/anodes was not documented in the existing literature, which motivated us to investigate the output of this design. In this study, we aimed to enhance the performance of the perforated cathode pipe soil electrokinetic remediation (SEKR) system (PCPSS) for removing inorganic pollutants by installing lateral anodes (LA-PCPSS) using two approaches. In the first approach, the LA-PCPSS was connected to different sources of applied voltages (DSAV) from different power supplies, while in the second approach, the entire operation system was connected to the same source of applied voltage (SSAV). We used the Taguchi approach (L9OA) to determine the optimal levels of applied voltages for the DSAV system. The results indicated that the DSAV-(LA-PCPSS) could be optimized at an applied voltage of 1 V cm−1 for the surface and the first and second lateral anodes. The indigenous Sr (elements found in the tested soil without artificial pollution) in kaolinite showed the best response among other elements (Ni and other indigenous elements) when optimizing the DSAV-(LA-PCPSS) using the Taguchi approach. Installing lateral anodes (position B) supplied to low applied voltage (0.5 V cm−1) improved the electroosmosis (EO) rate/dewatering. Reverse migration of ions was observed during the remediation of real contaminated soil using the SSAV-(LA-PCPSS). The DSAV-(LA-PCPSS) is considered an appropriate design for the SEKR of inorganic pollutants, and increases the EO flow/dewatering. Additionally, the increased energy consumption employing the DSAV-(LA-PCPSS) was extremely minimal compared to the traditional PCPSS, which is an economic advantage for SEKR research. The DSAV-(LA-PCPSS) is still under optimization/intensification process, and subsequent processes will be examined to achieve high efficiency.

Research and Case Application of Zero-Carbon Buildings Based on Multi-System Integration Function

This study focuses on developing and implementing zero-carbon buildings through the integration of multiple systems to meet China’s carbon neutrality goals. It emphasizes the significant role of the building sector in carbon emissions and highlights the challenge of increasing energy consumption conflicting with China’s “dual carbon” targets. To address this, the research proposes a comprehensive framework that combines multifunctional envelope structure (MES) systems, photovoltaic power generation, energy storage, direct current (DC) systems, flexible energy management (PEDF), and regional energy stations. This framework integrates different technologies such as phase change materials, radiation cooling, and carbon mineralized cement, aiming to reduce carbon emissions throughout the building’s lifecycle. The method has been successfully applied in the Yazhou Bay Zero Carbon Post Station project in Sanya, Hainan, with precise calculations of carbon emission reductions. The carbon emission calculations revealed a reduction of 44.13 tons of CO2 annually, totaling 1103.31 tons over 25 years, primarily due to the rooftop photovoltaic systems. It demonstrates that the multi-system integration can reduce carbon emissions and contribute to China’s broader carbon neutrality goals. This approach, if widely adopted, could accelerate the transition to carbon-neutral buildings in China.

Freezing time, freezing in time: reconfiguring freezing practices for reducing meat waste and energy consumption

Considering that many fresh food items require domestic cold storage, understanding the role of appliances such as freezers is critical to reducing greenhouse gas emissions relating to domestic food waste and energy use. Using a social practices approach, this article reports on a study of cold storage and food waste practices, particularly freezing relating to meat, in 20 Australian households. Householders were interviewed twice, over winter and summer, 2022–2023. We studied intersecting practices of eating, shopping, cooking and cold storage, and their connection to larger social phenomena such as cold supply chains to gain insights into how and why meat was wasted. Although we set out to study household practices, we found that retailer practices of pricing specials and bulk buying must also be considered. Retailer practices combined with ambiguity in consumer understandings of date labels, shelf life and freezing times mean that domestic cold storage has become a normalised extension of the cold supply chain of food, especially for meat. We argue that understandings such as convenience and thriftiness, in conjunction with retailer expectations of domestic cold storage, may lead to the ratcheting up of household freezer use for food storage. We illustrate through our findings that this may potentially increase energy consumption and accelerate food waste, which becomes particularly problematic for carbon-intensive foods such as red meat. In innovatively reimagining domestic cold storage as an extension of industrial and retailer cold supply chains, we explore what kind of capabilities may be required for an equitable and low waste cold supply chain that reduces reliance on household freezers.

Application of Non-Parametric and Forecasting Models for the Sustainable Development of Energy Resources in Brazil

To achieve Sustainable Development Goal 7 (SDG7) and improve energy management efficiency, it is essential to develop models and methods to forecast and enhance the process accurately. These tools are crucial in shaping the national policymakers’ strategies and planning decisions. This study utilizes data envelopment analysis (DEA) and bootstrap computational methods to evaluate Brazil’s energy efficiency from 2004 to 2023. Additionally, it compares seasonal autoregressive integrated moving average (SARIMA) models and autoregressive integrated moving average (ARIMA) forecasting models to predict the variables’ trends for 2030. One significant contribution of this study is the development of a methodology to assess Brazil’s energy efficiency, considering environmental and economic factors to formulate results. These results can help create policies to make SDG7 a reality and advance Brazil’s energy strategies. According to the study results, the annual energy consumption rate is projected to increase by an average of 2.1% by 2030, which is accompanied by a trend of GDP growth. By utilizing existing technologies in the country, it is possible to reduce electricity consumption costs by an average of 30.58% while still maintaining the same GDP value. This demonstrates that sustainable development and adopting alternatives to minimize the increase in energy consumption can substantially impact Brazil’s energy sector, improving process efficiency and the profitability of the Brazilian industry.

Transparent, Flexible, Responsive Switching "Delayed" Amphiphilic Coatings Designed on the Basis of the Full-Cycle Antifouling Strategy.

Marine fouling on the surface of ships and equipment not only creates problems of enhanced resistance to navigation and increased energy consumption but also leads to unclear vision and inaccurate data collection. Antifouling coatings to resist fouling are effective, but it is difficult to achieve long-lasting fouling protection with a single interface state. Switching the status of the interface by intelligent response is a reasonable way to achieve full-cycle efficient antifouling. In this study, the hydrophobic and active antifouling interface in the initial state was achieved by adopting the fluorine-containing group and the natural extract (citronellol) as the antifouling active site. The switching of the interface relies on silanes, which respond to the generation of zwitterions in a seawater environment. Eventually, the interface switched from the hydrophobic state to the amphiphilic state with delayed formation, which achieved continued antifouling. Based on the full-cycle antifouling concept, the combination of low surface energy and antifouling active ingredients in the initial state sustainably switched surfaces in the midterm (free radicals generated during the hydrolysis process), and amphiphilic interfaces formed by "delays" produced an antifouling effect from the initial stage to the subsequent stage. The excellent antifouling activity (bacterial and diatom attachment inhibition by over 90% and significantly reduced mussel adhesion force), optical transparency, and flexibility of these coatings indicate the potential for the application of antifouling coatings prepared from hyperbranched silicone-based resins; they can also be used for data extraction sensors, underwater probes, marine photovoltaics, and other areas where transparency is required.

A National-Scale Study of Polycyclic Aromatic Hydrocarbons in Surface Water: Levels, Sources, and Carcinogenic Risk

Elucidating pollution characteristics of polycyclic aromatic hydrocarbons (PAHs) in water and assessing the associated carcinogenic risks is crucial for improving public health. PAHs in the surface water of seven main river basins across China, compiled from 95 studies from 2004 to 2022, were used to investigate geographic variations of occurrence, source, and carcinogenic risk. Total PAH concentrations exhibited substantial geographic distributions ranging from 300 to 7552 ng·L−1. Low molecular weight PAHs predominated, showing three-ring PAHs abundant in the north, while two-ring PAHs dominated in the south due to distinctions regarding energy consumption. The northern basins exhibited higher concentrations of PAHs than the southern owing to the synergistic impacts of low temperature, increased energy consumption, and higher industrial activities. Coal combustion and industrial emissions were the primary contributors in the northern basins, accounting for 23–44% and 20–38%, respectively, which were associated with pollutants released from heavy industries and space heating during cold periods. In contrast, vehicle exhaust emissions and petroleum leakage from river transport constituted the principal sources in the relatively economically developed southern basins, accounting for 24–35% and 31–57%, respectively. A lifetime carcinogenic risk model revealed that the highest health risks existed in adults, followed by adolescents and children. Toxic concentrations of BaP and the daily intake of water directly enhanced the PAHs’ carcinogenic risks, while body weight featured negative correlations with the risks.

The Environmental Stake of Bitcoin Mining: Present and Future Challenges

The environmental impact of Bitcoin mining has raised severe concerns considering the expected growth of 30% by 2030. This study aimed to develop a Life Cycle Assessment model to determine the carbon dioxide equivalent emissions associated with Bitcoin mining, considering material requirements and energy demand. By applying the impact assessment method IPCC 2021 GWP (100 years), the GHG emissions associated with electricity consumption were estimated at 51.7 Mt CO2 eq/year in 2022 and calculated by modelling real national mixes referring to the geographical area where mining takes place, allowing for the determination of the environmental impacts in a site-specific way. The estimated impacts were then adjusted to future energy projections (2030 and 2050), by modelling electricity mixes coherently with the spatial distribution of mining activities, the related national targeted goals, the increasing demand for electricity for hashrate and the capability of the systems to recover the heat generated in the mining phase. Further projections for 2030, based on two extrapolated energy consumption models, were also determined. The outcomes reveal that, in relation to the considered scenarios and their associated assumptions, breakeven points where the increase in energy consumption associated with mining nullifies the increase in the renewable energy share within the energy mix exist. The amount of amine-based sorbents hypothetically needed to capture the total CO2 equivalent emitted directly and indirectly for Bitcoin mining reaches up to almost 12 Bt. Further developments of the present work would rely on more reliable data related to future energy projections and the geographical distribution of miners, as well as an extension of the environmental categories analyzed. The Life Cycle Assessment methodology represents a valid tool to support policies and decision makers.

Influence of inlet velocity on the separation performance of a combined hydrocyclone

Hydrocyclone separation exploits centrifugal force to differentiate particles based on their sizes and densities, yet challenges arise when small, dense particles and large, low-density ones settle at similar velocities. To address this, we propose a two-stage combined hydrocyclone for accurate separation. Using numerical simulations, we examine the internal flow field and performance of this system. Our findings reveal that the primary hydrocyclone achieves size-dependent classification, while the secondary one achieves density-dependent sorting. Increasing inlet velocity enhances separation efficiency and accuracy by improving flow field dynamics, albeit at the cost of increased energy consumption and material residence time. Thus, optimizing inlet velocity is vital for maximizing the separation performance and operational efficacy of the combined hydrocyclones.

A novel perspective of addressing the hydraulic dynamic imbalance in district heating network: The application of nonlinear programming

Hydraulic balance within the heating system's pipeline network is essential for efficient operation. Hydraulic imbalances can result in increased energy consumption and heat loss. Accordingly, a new dynamic hydraulic model has been developed, leveraging the topological structure of the network. This model incorporates the nonlinear constraints associated with flow coupling between electric valves and variable frequency pumps, with the objective of minimizing energy used in pump operation. It determines the optimal pump frequency and valve settings for operational adjustments. The model's predictive accuracy was validated using real operational data from a campus heating system. Additionally, it calculates the campus's ideal load and evaluates the potential for energy savings. Three adjustment strategies were established based on different temporal scales: hourly, day-and-night, and daily. Findings indicate that the optimized system offers substantial energy savings, particularly the hourly adjustment, which reduces pump energy consumption by 38.2 % and total system flow by 9.0 %. The day-and-night variable frequency was the next most effective, reducing pump energy consumption by 32.8 %, and total system flow by 7.6 %. The daily variable frequency strategy showed the least energy savings, with pump energy consumption by 25.0 %, and total system flow by 5.5 %. This dynamic hydraulic regulation optimization model serves as a valuable tool for developing intelligent heating systems.

Taxonomy of optimization algorithms combined with CNN for optimal placement of virtual machines within physical machines in data centers

Energy management in datacenters is a major challenge today due to the environmental and economic impact of increasing energy consumption. Efficient placement of virtual machines in physical machines within modern datacenters is crucial for their effective management. In this context, five algorithms named CNN-GA, CNN-greedy, CNN-ABC, CNN-ACO and CNN-PSO, have been developed to minimize hosts’ power consumption and ensure service quality with relatively low response times. We propose a comparative approach between the developed algorithms and other existing methods for virtual machine placement. The algorithms use optimization algorithms combined with Convolutional Neural Networks to build predictive models of virtual machine placement. The models were evaluated based on their accuracy and complexity to select the optimal solution. The necessary data is collected using the CloudSim Plus simulator, and the prediction results were used to allocate virtual machines according to the predictions of the models. The main objective of this research is to optimize the management of Information Technology resources within datacenters. This is achieved by seeking a virtual machine placement policy that minimizes hosts’ power consumption and ensures an appropriate level of service for users' needs. It considers the imperatives of sustainability, performance, and availability by reducing energy consumption and response times. We studied six scenarios under specific constraints to determine the best model for virtual machines’ placement. This approach aims to address current challenges in energy management and operational efficiency.

Green Order Sorting Problem in Cold Storage Solved by Genetic Algorithm

This study investigates the efficiency of cold storage warehouses and contributes to sustainable supply chain management by integrating eco-friendly practices into storage operations. In facilities for milk and its derivatives, unregulated order handling significantly increases energy consumption due to frequent door openings in the cooler. To address this challenge, we developed a novel mathematical model aimed at optimizing order sequences and minimizing energy costs, addressing a previously unexplored gap in the literature. A genetic algorithm (GA) was employed to solve this model, with careful consideration of carbon emissions generated during the algorithm’s execution. We utilized the Yates notation, an experimental design technique, to systematically optimize the GA’s parameters, ensuring robust and statistically valid results. This methodology enabled a thorough analysis of the factors influencing energy consumption. The findings enhance energy efficiency in cold storage warehouses, leading to reduced carbon dioxide emissions and fostering sustainable practices within supply chain management. Ultimately, this study successfully integrates green practices into cold storage operations, supporting broader sustainability objectives.

Mission-Based Design and Retrofit for Energy/Propulsion Systems of Solar-Powered UAVs: Integrating Propeller Slipstream Effects

Over twenty Solar-Powered Unmanned Aerial Vehicle (SPUAV) designs exist worldwide, yet few have successfully achieved uninterrupted high-altitude flight. This shortfall is attributed to several factors that cause the actual performance of SPUAV to fall short of expectations. Existing studies identify the propeller slipstream as one of these adverse factors, which leads to a decrease in the lift–drag ratio and an increase in energy consumption. However, traditional design methods for SPUAVs tend to ignore the potential adverse effects of slipstream at the top-level design phase. We find that this oversight results in a reduction in the feasible mission region of SPUAVs from 109 days to only 46 days. To address this issue, this paper presents a high-fidelity multidisciplinary design framework for the energy/propulsion systems of SPUAVs that integrates the effects of a propeller slipstream. Specifically, deep neural networks are employed to predict the lift–drag characteristics of SPUAVs under various slipstream conditions, and the energy performance is further analyzed by evaluating the time-varying state parameters throughout a day. Subsequently, the optimal solutions for the energy/propulsion systems specific to certain latitudes and dates are obtained through optimization design. The effectiveness of the proposed design framework was demonstrated on a 30-m wingspan SPUAV. The results indicated that, compared to the traditional design method, the proposed approach led to designs that more effectively accomplished closed-loop flight in designated regions and prevented the reduction of the feasible mission region. Additionally, through the targeted retrofit of the energy/propulsion systems, SPUAVs exhibited enhanced adaptability to the solar radiation characteristics of different mission points, resulting in a further expansion of the feasible mission region. Furthermore, this research also explored the variation trends in optimal solutions across different latitudes and dates and investigated the reasons and physical mechanisms behind these variations.

Assessing energy savings and visual comfort with PDLC-based smart window in an Istanbul office building: A case study

Lighting applications in architectural structures have a high share in energy consumption. Inefficient lighting applications and technologies will increase energy consumption and do not guarantee the visual comfort desired by users. Recognizing the importance of user comfort, this study takes a deeply user-centric approach in proposing feasible lighting control methods that can improve energy efficiency in smart and carbon-neutral future buildings and change daylight's benefits in favor of the user. Smart glass technology, which can adjust the color and transmittance of the light in response to external stimuli (applied voltage) and can be dynamically modulated, changing window and door characteristics, has high potential in the market for modern societies. This paper develops an innovative lighting control method to improve energy efficiency and visual comfort and reduce energy consumption. The daily performance of the proposed control methods regarding illuminance, uniformity factor, and energy consumption is experimentally analyzed in a test bench modeling an office environment with IG80 glass (for comparison), artificial lighting source (LED lamp), and polymer-dispersed liquid-crystal (PDLC)-based smart window. The research methodology involved a detailed analysis of the relationship between performance metrics and dimmer percentages and comparing the energy consumption performance of the lighting methods with other design parameters and technological advances. This algorithm prioritizes user comfort and considers the PDLC-based smart window light transmittance and LED lamp dimmer control depending on daylight. The results show that the proposed algorithm can increase energy savings by up to 22 %. While efficient lighting control is closely related to weather conditions and indoor specifications, it has similar energy consumption performance compared to research into design parameters and technological advances, eliminating further efforts.

Designing a Bidirectional Power Flow Control Mechanism for Integrated EVs in PV-Based Grid Systems Supporting Onboard AC Charging

This paper investigates the potential use of Electric Vehicles (EVs) to enhance power grid stability through their energy storage and grid-support capabilities. By providing auxiliary services such as spinning reserves and voltage control, EVs can significantly impact power quality metrics. The increasing energy consumption and the global imperative to address climate change have positioned EVs as a viable solution for sustainable transportation. Despite the challenges posed by their variable energy demands and rising numbers, the integration of a smart grid environment with smart charging and discharging protocols presents a promising avenue. Such an environment could seamlessly integrate a large fleet of EVs into the national grid, thereby optimizing load profiles, balancing supply and demand, regulating voltage, and reducing energy generation costs. This study examines the large-scale adoption of EVs and its implications for the power grid, with a focus on State of Charge (SOC) estimation, charging times, station availability, and various charging methods. Through simulations of integrated EV–PV charging profiles, the paper presents a lookup-table-based data estimation approach to assess the impact on power demand and voltage profiles. The findings include multiple charging scenarios and the development of an optimal control unit designed to mitigate the potential adverse effects of widespread EV adoption.

The influence of economic growth, fossil and renewable energy, technological innovation, and globalisation on carbon dioxide emissions in South Africa

Carbon dioxide (CO2), whose atmospheric volume has been increasing, is the principal greenhouse gas (GHG) that causes global warming and climate change. Climate change from increasing greenhouse gases (GHGs) has broad health and environmental influences. Human-emitted GHGs constitute a significant cause of global warming. The main objective of this article is to assess the influence of technological innovation, economic growth, fossil energy use, renewable energy consumption, and globalisation on CO2 emissions (CO2Es) in South Africa over the observation period 1990–2020. The Dynamic Ordinary Least Squares (DOLS) estimator was implemented to examine the relationship between the constituents. The study results indicate that economic growth, fossil energy use, technological innovation, and globalisation boost CO2Es, whereas renewable energy consumption reduces CO2Es. The output of the canonical cointegrating regression and fully modified least squares estimators also supports the output of the DOLS estimator. We concluded that, in South Africa, a rise in economic growth, fossil energy use, technological innovation, and globalisation results in an upsurge in CO2Es. In contrast, an increase in renewable energy consumption leads to a reduction in CO2Es. This research work contributes to the discourse on CO2Es in the South African context. The study recommends that policymakers in South Africa should design policies that foster the formation of a low-carbon country, the consumption of renewable energy, and the implementation of environment-friendly technological innovations that reduce the use of fossil energy.Graphical