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With the development of the economy and society, the importance of a secure and stable electricity supply continues to increase. However, the power grid is facing the test of excess installed capacity, the waste of renewable energy, and a low comprehensive utilization rate. This problem stems from the inconsistent peak–valley differences between power production and consumption, and the lack of clear electricity price signals, which disrupts the safe and stable operation of the power market. This paper combines the interactive transactions among clean energy power generation companies, users, and energy storage, explores how the system optimization model can be reflected in the power market through regulatory measures, and formulates the optimal output scheme of the system under the constraints of clean energy power generation forecast data, user base load forecast data, demand-side resource regulation ability, and energy storage system regulation ability to achieve the goals of comprehensive clean energy power consumption and minimum cost for users. A comprehensive analysis of the proposed model was conducted using actual data from a certain province in China, the results show that the consumption of clean energy will increase by 3% to full consumption and the total cost of users will be 32% lower than that of time-of-use (TOU) power prices, which proves the potential of the proposed joint optimization model in absorbing clean energy and the effectiveness of the market mechanism. [ABSTRACT FROM AUTHOR]

Forecasting energy demand is critical to ensure the steady operation of the power system. However, present approaches to estimating power load are still unsatisfactory in terms of accuracy, precision, and efficiency. In this paper, we propose a novel method, named ELFNet, for estimating short-term electricity consumption, based on the deep convolutional neural network model with a double-attention mechanism. The Gramian Angular Field method is utilized to convert electrical load time series into 2D image data for input into the proposed model. The prediction accuracy is greatly improved through the use of a convolutional neural network to extract the intrinsic characteristics from the input data, along with channel attention and spatial attention modules, to enhance the crucial features and suppress the irrelevant ones. The present ELFNet method is compared to several classic deep learning networks across different prediction horizons using publicly available data on real power demands from the Belgian grid firm Elia. The results show that the suggested approach is competitive and effective for short-term power load forecasting. [ABSTRACT FROM AUTHOR]

This paper presents the analytical and numerical application of a method to determine the parameters and power losses in the core of two medium-power synchronous generators. These generators are used as emergency power sources powered by diesel engines, gas engines, and gas turbines. They cover peak electricity demand but can also be used in traction drives. This article presents a new numerical method for determining losses in the generator core based on the use of a time-stepping solution using the FEM method and calculating these losses using analytical formulas. In calculating the losses for the FEM method, approximations of the loss characteristics of the sheet were used with a wide range of induction values and frequencies. This method is specific to the solution used and was adapted from the authors' previous work on losses in induction machines. A one-phase winding with alternating voltage was supplied to determine the basic parameters in the form of synchronous reactance. Also, an important novelty is the introduction of a new method of determining the saturation state of the magnetic circuit, which significantly affects the machine parameters. The obtained results were used in analytical calculations and implemented in a computer program that allows for the calculation of electromagnetic parameters, operating characteristics, and core losses, taking into account additional losses, total losses, and efficiency, as well as machine parameters in unsteady operating states and the current characteristics of a three-phase symmetrical short circuit at the machine terminals. The calculations obtained were verified experimentally by measurements of real machines. [ABSTRACT FROM AUTHOR]

Low-carbon development of ordinary Portland cement industry is of great significance to China's target "to peak carbon dioxide emissions before 2030 and to achieve carbon neutrality before 2060". Neglecting the regional heterogeneity in China, few studies emphasized the status and developments of energy intensity and CO2 emissions of ordinary Portland cement industry in Guizhou of Southwest China. To bridge this knowledge gap, we present an annual intensity dataset of energy and CO2 emissions at plant-level for Guizhou's ordinary Portland cement industry, which involves the details of clinker rotary kilns, yearly production of clinker and cement products, fuel consumption and electricity consumption, total CO2 emission of cement products, energy intensity indicators of clinker and cement products, utilization ratio of solid-waste in clinker and cement production, and CO2 emission factors of cement products. It is an important supplement and to existing energy intensity and CO2 emissions estimates at plant-level and provincial official emissions inventories that converges all regions of China. [ABSTRACT FROM AUTHOR]

Carbon emission accounting is the basic premise of effective carbon emission reduction and management. This study aimed to establish the carbon emission model and performance evaluation framework of coal mine production enterprises and clarify the low-carbon development path of enterprises. In this study, we took a typical coal production enterprise (K enterprise) in the Shanxi province of China as the research object. We also estimated the carbon emissions of the enterprise mainly according to the Chinese Carbon Emission Accounting Standard (GB/T 32151.11–2018). The triangular model was used to construct the carbon performance evaluation framework. On this basis, we suggested the enterprise's low-carbon development path. The results showed that (1) the carbon emission of K enterprise in 2021 was 36,875.38 tCO2eq; the carbon emission intensity of each ton of coal produced was 0.089 tCO2eq. The critical carbon emissions were electricity consumption and methane fugitive emissions during production. (2) The evaluation indicators for carbon emission performance revealed an imbalance in K enterprise's economic, energy, and environmental development in 2021. The work on energy saving and consumption reduction was relatively weak. (3) Countermeasures for low-carbon development, including a carbon emission ledger, were proposed based on carbon emission accounting and performance evaluation results. This study can help typical underground coal production enterprises in Shanxi province obtain more accurate carbon emission data, providing practical guidance and reference for the same underground coal production enterprises to improve the carbon emission control effect. [ABSTRACT FROM AUTHOR]

Globally, the carbon footprint (CF) is constantly increasing, contrasting with the decreasing trend observed for decades in the European Union (EU) countries, where EU guidelines are responsibly followed and outlined in its strategic documents. Information and communication technology (ICT) carbon emissions have historically increased in parallel with global emissions, contributing to continuous increases in ICT's CF over time, even when excluding full life cycle emissions. This study examines the impact of ICT on household electricity consumption, aiming to quantify the potential reduction of greenhouse gas (GHG) emissions through improved household energy efficiency. The methodology includes the data collection on ICT device usage in households within the city of Novi Sad (Republic of Serbia), employing the survey method that queries respondents on device quantities and their usage patterns. This study provides results for decision-makers to recognize concrete benefits from the transition to a circular economy (CE) and low-carbon emissions, which are reflected as benefits for the local community and socio-economic environment. [ABSTRACT FROM AUTHOR]

Fluctuations in electricity demand can lead to inefficiencies in diesel generators (DEG) and potentially trigger blackouts, as experienced by PT Indolakto. This study aims to identify the performance parameters of a 2 MW DEG and formulate strategies to prevent blackouts caused by the 2 MW DEG at PT Indolakto. The research used data collection techniques including field observations, interviews, literature studies, and performance test analysis. The results showed that the efficiency of the 2 MW DEG at load reached 44%, indicating there is still reserve power. An overload of 21% from the generator's capacity resulted in a blackout and a system frequency drop of 13.4 Hz from the normal frequency. Although the 2 MW DEG has adequate efficiency, optimization is necessary to prevent blackouts. One strategy is to use the 2 MW DEG as a system reserve for critical loads. [ABSTRACT FROM AUTHOR]

Germany has not yet made significant progress toward achieving SDGs 7 and 13. This challenge can be attributed to the underlying financialization problem in Germany, as well as implementation issues related to energy efficiency, particularly in coal and gas, and renewable energy consumption. Given these circumstances, Germany is facing challenges in reducing greenhouse gas emissions. Addressing this issue may necessitate a policy realignment, which is the primary focus of this study. In this context, our study employs various wavelet-based tools, including wavelet coherence, multivariate wavelet coherence, and wavelet-based causality, to delve into the co-movements, causality, and direction of causality between the load capacity factor (LF) and key factors such as energy efficiency (gas and coal), technological innovation, renewable electricity, and financial development, focusing on Germany during the period from 1990Q1 to 2020Q4. The results from wavelet coherence, corroborated by wavelet cohesion, indicate that in the short and medium term, improvements in coal efficiency, gas efficiency, financial development, and renewable energy are associated with an increase in LF, thereby contributing to ecological quality. Furthermore, the multivariate wavelet coherence results underscore the significant impact of considering or not considering the effect of a third variable in the interrelationship between LF and its determinants, with the effect being more pronounced when the third variable is taken into account. Although this policy framework primarily targets the objectives of SDG 13 and 7, its applicability extends to other EU nations. The primary significance of this study is its proposal of an SDG-focused policy framework. [ABSTRACT FROM AUTHOR]

In the context of power system decarbonization, the demand-side strategy for increasing the share of renewable energy is studied for two constrained energy systems. This strategy, which is currently widely suggested in policies on the energy transition, would impact consumer behavior. Despite the importance of studying the latter, the focus here is on decisions regarding the type, location, and timeframe of implementing the related measures. As such, solutions must be assessed in terms of cost and feasibility, technological learning, and by considering geographical and environmental constraints. Based on techno-economic optimization, in this paper we analyze the evolution of the power system and elaborate plausible long-term trajectories in the energy systems of two European islands. The case studies, Procida in Italy and Hinnøya in Norway, are both electrically connected to the mainland by submarine cables and present issues in their power systems, which are here understood as relatively isolated power systems. Renewable energy integration is encouraged by legislative measures in Italy. Although not modeled here, they serve as a backbone for the assumptions of increasing these investments. For Procida, rooftop photovoltaics (PV) coupled with energy storage are integrated in the residential, public, and tertiary sectors. A price-based strategy is also applied reflecting the Italian electricity tariff structure. At a certain price difference between peak and off-peak, the electricity supply mix changes, favoring storage technologies and hence decreasing imports by up to 10% during peak times in the year 2050. In Norway, renewable energy resources are abundant. The analysis for Hinnøya showcases possible cross-sectoral flexibilities through electrification, leading to decarbonization. By fine-tuning electric vehicle charging tactics and leveraging Norway's electricity pricing model, excess electricity demand peaks can be averted. The conclusions of this double-prospective study provide a comparative analysis that presents the lessons learnt and makes replicability recommendations for other territories. [ABSTRACT FROM AUTHOR]

The use of fossil fuel-based energy systems that provide heat and electricity to a building has adverse environmental impacts. These impacts can be mitigated, to a certain extent, through the incorporation of renewable energy sources (RES). The primary objective of this study was to conduct an environmental assessment of the performance of energy systems in existing public facilities located in Poland. Based on the findings, we proposed and implemented changes to these systems and validated the environmental impact of the RES systems used. SimaPro 8.1 software and the Ecoinvent 3.0 database were employed for the analysis, which entailed an environmental assessment of six public facilities located in Poland. The installation of RES resulted in an average 27% reduction in electricity consumption from the national electricity grid. This reduction was observed to be the least in the hospital and the most in the religious building. This was reflected in the environmental assessment of heating systems. The implementation of RES reduced the environmental impact of the religious building by an average of 20%. Concurrently, the CO2 emissions decreased by 35%, SO2 by 44%, and PM10 by 42%. Significant investments and the installation of advanced RES will not prevent the occurrence of unintentional environmental consequences unless the demand for electricity and thermal energy is reduced. The use of RES in the analyzed buildings and the associated avoided emissions do not entirely offset the negative emissions resulting from the utilization of other (conventional) energy sources in the analyzed energy systems of public buildings. Consequently, the analyzed facilities collectively exert a detrimental impact on the environment. [ABSTRACT FROM AUTHOR]

This work presents an economic analysis of the use of electricity storage in PV installations, based on previously adopted assumptions, i.e., the type and location of the tested facility and comparative variants, divided into the share of the storage in the installation, and the billing system. The work takes into account the share of the energy shield and assumes a consumption limit of 2000 kWh. The cost of building the installation is based on July 2023 prices. The work assumes potential directions of changes in electricity prices, based on which the degree of investment profitability for a given price situation is determined. Depending on the adopted change in the direction of electricity prices, with a low price increase rate, for installations in the new billing system (net-billing), the optimal choice is an installation without energy storage with a power exceeding the actual energy demand. Assuming a high increase in electricity prices, the optimal choice is an installation with energy storage with an installation capacity exceeding the actual demand. For installations billed using the net-metering system, the optimal choice is an installation without storage with an appropriately selected installation power. This article shows how much you can gain after installing a PV installation and not only what costs must be incurred to complete the investment. Profit analysis will enable a more complete assessment of the profitability of investing in PV panels (with or without energy storage). It describes the verification of the profitability of a PV installation for a standard user depending on various types of settlements with the electricity supplier and the lack or installation of an energy-storage facility. [ABSTRACT FROM AUTHOR]

Estimating city–scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple gridded inventories and a Kalman filter fusion method, we developed an optimal city–scale (3 km) FFCO2 emission product that incorporates quantified uncertainties and connects global–regional–city scales. Our findings reveal the following: (1) Kalman fusion post–reconstruction reduces estimate uncertainties for 2000–2014 and 2015–2021 to ±9.77% and ±11.39%, respectively, outperforming other inventories and improving accuracy to 73% compared to ODIAC and EDGAR (57%, 65%). (2) Long–term trends in the Greater Bay Area (GBA) show an upward trajectory, with a 2.8% rise during the global financial crisis and a −0.19% decline during the COVID-19 pandemic. Spatial analysis uncovers a "core–subcore–periphery" emission pattern. (3) The core city GZ consistently contributes the largest emissions, followed by DG as the second–largest emitter, and HK as the seventh–highest emitter. Factors influencing the center–shift of the pattern include the urban form of cities, population migration, GDP contribution, but not electricity consumption. The reconstructed method and product offer a reliable solution for the lack of directly observed emissions, enhancing decision–making accuracy for policymakers. [ABSTRACT FROM AUTHOR]

With the continuous growth of electricity demand, the safe and stable operation of distribution lines is crucial for power transportation. Unmanned aerial vehicle (UAV) inspection has been widely used for the maintenance and repair of distribution lines. Due to the limitations of computational power and endurance, it is difficult for UAVs to independently complete data processing. Combined with mobile edge computing (MEC), this paper proposes a computing offloading strategy based on multi‐agent reinforcement learning and double‐layer offloading mechanism, which can further utilize the computing power of non‐task devices and edge servers. Firstly, three‐layer system architecture, named MEC‐U‐NTDC (MEC‐UAV‐Non‐task Device Cloud), is built. Secondly, double‐layer offloading mechanism is designed to comprehensively utilize the computing power of edge servers and neighbouring non‐task devices. Finally, a multi‐agent algorithm DLMQMIX is proposed to minimize the total cost for UAV inspection. Simulation experiments show that the proposed algorithm can effectively solve the task offloading problem of UAV‐aided distribution line inspection, and compared with algorithms such as PSO, GA, and QMIX, it performs better in terms of average delay, system cost, and load balancing, achieving a smaller total system cost. [ABSTRACT FROM AUTHOR]

China's rapid industrialization and urbanization have led to significant nitrogen oxide (NOx) emissions, contributing to severe atmospheric pollution. Understanding the driving factors behind these emissions is crucial for effective pollution control and environmental management. Therefore, this study is an attempt to provide insights into the influence of socioeconomic factors and explore spatial dependencies of NOx emissions in China in 2022 employing spatial regression models (SRMs). Among the SRMs considered, the spatial Durbin model (SDM) is identified as the most suitable for analyzing regional NOx emissions. The study highlights the importance of controlling electricity consumption and vehicle emissions for addressing air pollution in Chinese regions. Specifically, a one billion kilowatt-hour increase in electricity consumption leads to approximately 549.6 tons of NOx emissions, and an increase of 1000 vehicles in a region results in an average increase of 7113.4 tons of NOx emissions in the same region. Furthermore, per capita consumption expenditure (PCEXP) and research and development (R&D) expenditure exhibit negative direct and spillover impacts. Contrary to previous studies, this research finds that changes in urban population density do not have a significant direct or indirect effect on NOx emissions within the studied areas. Moreover, we conducted additional investigations to assess the effectiveness of government action plans in reducing NOx emissions. Specifically, we evaluated the impact of Phases 1 and 2 of the Clean Air Action Plan, launched in 2013 and 2018, respectively, on the socioeconomic drivers of NOx emissions. Therefore, the data were modeled for the years 2013 and 2017 and compared to the results obtained for 2022. The findings indicate that over the entire period (2013–2022), the emission controls mandated by the action plan resulted in significant reductions in the impact of many of the studied NOx drivers. In conclusion, based on the results, this study presents recommendations to mitigate NOx emissions. [ABSTRACT FROM AUTHOR]

Covalent organic framework (COF) materials, known for their robust porous character, sustainability, and abundance, have great potential as cathodes for aqueous Zn‐ion batteries (ZIBs). However, their application is hindered by low reversible capacity and discharge voltage. Herein, a donor–acceptor configuration COF (NT‐COF) is utilized as the cathode for ZIBs. The cell exhibits a high discharge voltage plateau of ≈1.4 V and a discharge capacity of 214 mAh g−1 at 0.2 A g−1 when utilizing the Mn2+ electrolyte additive in the ZnSO4 electrolyte. A synergistic combination mechanism is proposed, involving the deposition/dissolution reactions of Zn4SO4(OH)6·4H2O and the co‐(de)insertion reactions of H+ and SO42− in NT‐COF. Meanwhile, the NT‐COF with a donor–acceptor configuration facilitates efficient generation and separation of electron‐hole pairs upon light exposure, thereby enhancing electrochemical reactions within the battery. This leads to a reduction in charging voltage and internal overvoltage, ultimately minimizing electricity consumption. Under ambient weather conditions, the cell exhibits an average discharge capacity of 430 mAh g−1 on sunny days and maintains consistent cycling stability for a duration of 200 cycles (≈19 days) at 0.2 A g−1. This research inspires the advancement of Zn‐organic batteries for high‐energy‐density aqueous electrochemical energy storage systems or photo‐electrochemical batteries. [ABSTRACT FROM AUTHOR]

This paper addresses the question whether energy consumption variables and the disaggregation of output matter in the context of efficiency analysis of container terminals. While it is obvious that the energy consumption of refrigerated cargo is higher than the energy consumption of non-refrigerated cargo, this work investigates whether those differences show in an overall efficiency analysis of terminals. This would point to a potentially important input for efficiency measures, to be considered in future productivity and efficiency analysis of terminals. Starting with a discussion on theoretical concepts and variable selection for measuring the energy dimension of terminal efficiency, this is the first paper that applies data envelopment analysis (DEA) comparing results with and without energy consumption, as well as differentiating productive outputs (dry and reefer container handling). The results reveal how the output disaggregation leads to substantially different efficiency scores and are a first step to show the relevance of output disaggregation and the inclusion of the energy variables as inputs in container terminal efficiency studies. [ABSTRACT FROM AUTHOR]

The decarbonisation of electricity generation requires real-time monitoring and control of grid components to efficiently and timely dispatch demand. This highly automated system, known as the Smart Grid, relies on smart or sensor-equipped distribution network components to optimise energy flow and minimise losses. However, energy theft, a major obstacle to efficient resource utilisation, poses a significant challenge to achieving this goal. This study proposes and evaluates a real-time telemetry and control system designed to mitigate energy theft in agricultural irrigation applications. The system increases energy efficiency by tracking the energy use in agricultural irrigation. The key challenge is to identify the source of illegal electricity consumption, classify it, and localise it. To address these difficulties, two distinct classification problems are addressed through the utilisation of machine learning methodologies. The initial classification task concerns the categorisation of loads that consume illegal electricity in agricultural irrigation. The subsequent classification problem pertains to the categorisation of feeder branches where such loads are activated. Therefore, a pilot distribution grid feeder has been simulated, and irrigation motors have been used as illegal loads which are activated at different points along the distribution feeder. The data collected from these simulations are used to create a data set where three-phase current data are collected from the transformer substation. The generated data set is employed to train machine learning models for the classification of illegal loads and feeder branches. The performance results of machine learning methods is obtained using the following metrics: accuracy, precision, recall, and F1-score. The results of the classification of loads stealing electricity in agricultural irrigation demonstrate that the bagged trees (BAT) algorithm achieves 99.64% in each criterion. In branch classification, the algorithm achieves the best results, with 97.64%, 97.40%, 96.22%, and 96.81%, respectively. Both classification performance results indicate that the proposed algorithm is effective for solving both classification problems. This research demonstrates the efficacy of ML-powered real-time monitoring and control in combating energy theft and promoting efficient resource utilisation within agricultural irrigation networks. It is a pioneering study in the field of determining and classifying illegal loads in agricultural irrigation. Further research will investigate the potential for expanding the system’s capabilities to include different load types and exploring alternative ML techniques for broader applicability within the context of low voltage distribution network monitoring. [ABSTRACT FROM AUTHOR]

Electricity consumption pattern recognition is the foundation of intelligent electricity distribution data analysis. However, as the scale of electricity consumption data increases, traditional clustering analysis methods encounter bottlenecks such as low computation speed and processing efficiency. To meet the efficient mining needs of massive electricity consumption data, in this paper a parallel processing method of the density-based k-means clustering is presented. First, an initial cluster center selection method based on data sample density is proposed to avoid inaccurate initial cluster center point selection, leading to clustering falling into local optima. The dispersion degree of the data samples within the cluster is also used as an important reference for determining the number of clusters. Subsequently, parallelization of density calculation and clustering for data samples were achieved based on the MapReduce model. Through experiments conducted on Hadoop clusters, it has been shown that the proposed parallel processing method is efficient and feasible, and can provide favorable support for intelligent power allocation decisions. [ABSTRACT FROM AUTHOR]

Insulators are vital components used in electrical transmission lines. It's crucial to keep their production cost low since they are widely used in electricity transmission. Instead of expensive alumina-based insulators, we can make cheaper ones using clay and bauxite found in nature. Adding more alumina makes them stronger, which is important for high voltage transmission. This paper aims to study the production process of bauxite-based insulators. We examined the insulators' structure using SEM images and EDAX spectrum analysis to see their formations, porosity ratio, and density. This research helps us learn more about these insulators and how they can be used effectively for high voltage transmission lines. [ABSTRACT FROM AUTHOR]

The ever-growing demand for electricity necessitates innovative approaches to power system control. Traditional methods often struggle to handle the complexities of modern grids. This study explores the potential of Fuzzy Logic Control (FLC) and Genetic Algorithms (GAs) for optimizing Power System Stabilizer (PSS) settings at the Peerdawd Gas Power Station (PPGS) under normal load conditions (80% power factor). FLC excels at mimicking human decisionmaking in uncertain situations, making it ideal for power systems with fluctuating loads. GAs, inspired by natural selection, efficiently searches for optimal solutions in complex problems. By combining these techniques, we can effectively fine-tune PSS2B parameters, leading to significant improvements. This study utilizes MATLAB Simulink to compare the performance of FLC and GA-based optimization with traditional methods. Key power system parameters are monitored, including voltage terminal (VT), rotor speed (ωm), active and reactive power output (Peo and Qeo), alongside transient response characteristics like damping ratio (ζ), overshoot (%MP), settling time (ts), peak time (tp), natural frequency (ωn), and damping frequency (ωd). Optimizing PSS2B parameters using FLC and GAs is expected to demonstrably reduce power oscillations, minimize overshoot, and accelerate system stability restoration. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to assess user awareness of electricity consumption at a public university in Ghana based on the norm activation model (NAM). Design/methodology/approach: The study adopted a survey design, using closed-ended questionnaires as the main data collection instruments. Data was collected from 300 respondents (staff and students) of the university and analysed descriptively using Statistical Package for the Social Sciences. Findings: The study found that the level of user awareness (as defined by NAM) of electricity consumption among staff and students is moderate. Therefore, the more energy campaigns users are exposed to the higher their level of user awareness (awareness of consequence, ascription of responsibility and personal norm). Practical implications: The findings of this study can assist university management boards and other organisations in recognising the potential for reducing electricity use by increasing user knowledge. Ultimately, this can result in added value. Originality/value: The study makes a significant contribution to the literature on the NAM by exclusively focusing on the variables of NAM to assess the level of user awareness of electricity consumption in a university setting. [ABSTRACT FROM AUTHOR]

For the purpose of effectively providing protection and utilization of historical earthen sites, the fully-enclosed exhibition halls with electricity driven air-conditioning have been successfully adopted to provide a suitable preservation air environment. However, its electricity consumption and carbon emissions are considerable due to the continuous operation of environmental control, the energy consumption of air cooling in exhibition halls can reach more than 10 times that of office buildings. To further reduce energy consumption, this current study drew on the concept of NBS (Nature Based Solution) and proposed a low-carbon environmental control system for an enclosed exhibition hall for storage of earthen relics by applying natural energy (solar photovoltaic and earth-air-cooling) and space optimization. Firstly, a numerical model was established to simulate the preservation environment in exhibition halls and the electricity consumption of each energy subsystem. Then the model was applied to optimize the design parameters (e.g. earth-air tunnel and airflow pattern) for the environmental control system. Numerical results showed that the electricity consumption was reduced by 58% by applying earth-air-cooling and solar photovoltaic. After optimization of the exhibition hall height, electricity consumption was approximately reduced by 65%. This research is of great value to mitigate energy consumption problems encountered in earthen relics museums and other similar environments. [ABSTRACT FROM AUTHOR]

This document provides a concise summary of various articles related to energy-saving practices and household energy consumption in Ecuador. The articles explore factors such as gender, education, income, and internet access that influence energy-saving behaviors in households. They also highlight regional disparities in energy-saving practices and recommend strategies such as education initiatives, improved internet access, and citizen participation in social organizations to promote energy conservation. The document emphasizes the need for caution in interpreting the findings and suggests further research on the interrelation between different energy-saving practices and comparisons between different locations. Additionally, it includes a list of references and further reading on related topics. [Extracted from the article]

This study undertakes a Life Cycle Assessment (LCA) of red and white wine production in Brazil, focusing on a leading winery in the Serra Gaúcha region. Given the notable lack of similar research in this area, our study employs LCA methodologies to evaluate the environmental impacts associated with the winery stage. Our results indicate that although white wine generally exhibits a lower environmental impact than red wine, the specific impacts differ substantially across various environmental categories, highlighting the necessity for context-specific assessments. We also find that electricity consumption, sugar usage, and liquid CO2 production are the primary drivers of environmental impacts, especially in toxicity-related categories. Moreover, our research critically examines the suitability of the Product Environmental Footprint Category Rules (PEFCR) within the Brazilian context and proposes a revised set of impact categories. This revision enhances the accounted impacts from 55% in the existing PEFCR 2.0 to 80% in our recommended framework, suggesting the need for more tailored environmental impact assessment tools in regional settings. Through these findings, our study not only addresses a significant gap in LCA research within the Brazilian wine industry but also sets the stage for improved environmental management practices across Brazilian wine industries. These enhancements are crucial for aligning local production processes with global sustainability standards and advancing the environmental stewardship of the wine sector in Brazil. [ABSTRACT FROM AUTHOR]

In recent years, the "power restriction" measures were taken in many localities, industrial electricity consumption was interrupted, mainly in the "two high" (high energy consumption and high pollutant emission) enterprises. Take the "power restriction" event in 2021 as an example, this event affected a wide range of regions and disrupted people's livelihood. It not only refers to the power switch and rationing measures taken by individual regions, but also includes differentiated policies implemented by over 20 provinces to limit power supply and industrial production. This paper investigated in detail and summarized the different requirements of power restriction across the country, through the comparison of annual growth rates of power generation and industrial added value，the comparison results of energy intensity, electricity production, power consumption and the industrial growth rate in each province are analyzed. Industrial enterprises and related industrial chains were most heavily affected by the "power restriction" event，mainly in steel, cement, electrolytic aluminum, industrial silicon, textile printing and dyeing, chemical fiber, chemical and other enterprises with high energy consumption and high pollutants emission. The measures to limit electricity will affect the normal production of enterprises to a certain extent, leading to not only the decrease of production efficiency, but also the increase of energy consumption and pollutant emission per unit product. Energy management measures such as differentiated electricity price have limited effect on the output of "two high" industrial products, and the economic leverage of differentiated electricity price is gradually being diluted by the price rise of end products. This paper analyzed the reasons and explored several key problems that need special attention. then recommended certain countermeasures for its appropriate application in the future. [ABSTRACT FROM AUTHOR]

Building operations account for a large amount of energy use and CO2 emissions, and the morphology of buildings in residential clusters strongly impacts energy efficiency performance. However, little research has focused on the morphology and energy electricity usage of high-rise residential clusters in hot summer and cold winter (HSCW) regions. We investigated 96 residential clusters in Hangzhou, China, and established a corresponding morphology database. Additionally, we obtained annual electricity consumption for 16 of these residential clusters. With this database, we performed optimization of morphological parameters upon energy use intensity (EUI) using a genetic algorithm (GA). Specifically, the cooling, heating, and lighting EUIs of high-rise residential clusters were studied. After implementing the optimized morphological parameters, there was a reduction of up to 7.73% in EUI. According to regression analysis, the average aspect ratio was the most significant factor influencing EUI (r = −0.907), followed by floor area ratio (r = −0.755), average orientation (r = 0.502), and average number of floors (r = −0.453). These results indicate that a higher intensity of land development with a greater floor area ratio, average aspect ratio, and average number of floors can reduce total energy consumption. Additionally, we found that an average building orientation of southwest 15° (with respect to south) is optimal. The findings of this study can assist urban planners and designers in developing more sustainable residential clusters, leading to decreased energy costs and CO2 emissions. [ABSTRACT FROM AUTHOR]

Purpose: Most life cycle inventory (LCI) datasets include location-based electricity mixes. Using these LCI datasets in combination with market-based electricity accounting in life cycle assessments (LCAs) leads to double counting of electricity from specific sources, such as renewable energy. The goal of this paper is to analyze market-based accounting by replacing location-based electricity consumption with market-based residual mixes in all processes of the ecoinvent database. The python script for this replacement procedure is openly available. Materials and methods: We use the Brightway 2 software package to replace European location-based electricity consumption in all 21,238 processes of the ecoinvent database with residual electricity mixes on the corresponding voltage level. The ecoinvent database provides residual electricity mixes for all countries involved in European trade with market-based energy attribute certificates—Guarantees of Origin. We analyze the induced changes to the database both on an individual process level and on a database-wide level. The analysis focusses on the impact category climate change but is further extended to the impact categories terrestrial acidification, freshwater eutrophication, marine eutrophication, ozone depletion, and particulate matter formation. Results and discussion: For European processes, the implementation of the residual electricity mixes leads to average changes in the life cycle impact assessment (LCIA) results for climate change of 1%, 10%, 20%, and 50% for 2958, 733, 387, and 107 processes, respectively. Changes are especially pronounced for Norwegian and Icelandic processes due to the large differences among their location-based and residual electricity mixes. For the other analyzed impact categories, the changes in LCIA results are particularly high for freshwater eutrophication. With the push towards more primary data in LCA and GHG accounting, the impacts of using residual electricity mixes instead of location-based electricity mixes need further research on basis of quantified figures, which we present in this paper. Conclusion: This paper presents an approach towards enabling more consistent market-based accounting throughout LCAs and thereby contributes to the avoidance of double counting. Since only European residual mixes are considered, the database of this paper leads to distortion of LCA results. Further, research is needed to address double counting beyond the European electricity sector. This includes an expansion of this research to a global level and other industrial sectors. Furthermore, research is needed regarding industry-specific electricity sources in LCI datasets, which may also lead to double counting. [ABSTRACT FROM AUTHOR]

European governments are addressing plastic pollution by implementing the Directive EU 2019/904 that bans single‐use items like cutlery, plates, straws, and beverage stirrers. One strategy proposed by several industries to continue their production is the conversion of single‐use items into multiuse items. Herein, the focus is on the production of polystyrene reusable plates and the suitability of this product for microwave and dishwasher use is assessed: in proper conditions, the structure is preserved without cuts or visible deformations, and the chemical stability is guaranteed. To get a full picture of the benefits and impacts of this new product, a life cycle assessment methodology is applied to compare the performances of one reusable plate with 20 single‐use plates. Results indicate that proper use is crucial: a reusable plate disposed after one use is more impactful than a single‐use plate. In contrast, the reuse of plates for 20 times allows to reduce carbon footprint and acidification potential. Eutrophication and ozone layer depletion, on the other hand, increase due to the washing phase. The solutions that can mitigate these issues and enhance the sustainability of the reusable plates include the improvement of the detergent compositions and the use of electricity from renewable sources. [ABSTRACT FROM AUTHOR]

In this research, a renewable energy hybrid system (PV-Wind) is modeled to compare different design options based on their economic and technical features. The energy requirements of a Reversible Osmosis desalination plant located on the island of Tenerife with a water production capacity of up to 20,000 m3/day was considered. The system is connected to the electricity grid. The HOMER software, version 2.75 was used to produce optimum strategies for renewable energy. The assumptions input into the model were: the technical specifications of the devices, electricity demand of the desalination plant, as well as the solar radiation and the wind speed potentials. Numerous arrangements were considered by the software, version 2.75. The optimal results were obtained based on the use of renewable energy. The data used in the study were recorded in Tenerife in the Canary Islands. The experience of this research could be transferred to other Atlantic islands with similar renewable energy sources (specifically the wind) and water scarce conditions. [ABSTRACT FROM AUTHOR]

The great advantage of plasma technology in harnessing abundant clean energy for electrifying and decentralizing the chemical industry holds the promise of attaining carbon neutrality. Therefore, recent research efforts have been dedicated to reducing the energy costs of plasma processes to facilitate the commercialization of this technology. However, it has been noted an inconsistency in reporting energy costs across the literature resulted from inaccurate estimation of power consumption within the system, leading to the misevaluation of the process, its underlying mechanism, and the significance of critical factors. This study comprehensively addresses these challenges by discussing and refining methods for estimating power consumption in a plasma system. Insights are drawn from our ongoing research in plasma NOx synthesis, specifically a thorough analysis of the discharge dynamics in a recently developed reactor "high-frequency spark discharge" using a high-speed camera, ICCD camera, and high-performance oscilloscope at various pulse widths of the applied voltage. The investigation revealed the importance of accounting for the post-spark period in the voltage cycle during power estimation, as it demonstrates an influence on NOx synthesis. Furthermore, the study highlighted and addressed critical errors in power measurement and energy cost estimation in the literature. It is found that a significant error, exceeding ± 70%, arises from overlooking signals delay in the setup and improper adjustment of oscilloscope functions, particularly channel impedance, data averaging, bandwidth, and sampling rate. This paper serves as a valuable guide towards establishing standardized measurements toward the precise estimation of energy costs in plasma processes. [ABSTRACT FROM AUTHOR]

In Europe, there is increasing recognition of the ethical challenges associated with designing and developing Artificial Intelligence (AI) and Machine Learning (ML) systems, especially concerning their potential impact on fundamental rights. As a result, ethics frameworks and guidelines have been issued by stakeholders in both the private and public sectors. At the same time, fostering innovation in the field is also crucial. The implementation of the European Union (EU) sustainability goals is one area where AI/ML is expected to make a significant contribution, but there are important considerations to address. The expanding reliance on data storage and the subsequent increase in electricity consumption due to advancements in AI/ML have significant implications for greenhouse gas emissions. This paper examines the regulatory landscape for environmental sustainability using an institutional analysis contrasting the ethical frameworks and current normative standards applicable within the EU. The purpose is to identify a realistic and practical interpretation that prioritizes the development and implementation of AI/ML in a way that facilitates a smooth transition towards the sustainable usage of digital and automated technologies. [ABSTRACT FROM AUTHOR]

This study introduces a quantitative evaluation method for the lifecycle of electric hydrogen production in microgrids, considering line electric thermal couplers. By analyzing the operational mechanism and benefits of hydrogen production through residual electricity, we systematically evaluated the cost‐effectiveness of Proton Exchange Membrane electrolytic hydrogen production. A comprehensive lifecycle cost model was constructed, including equipment investment, maintenance, operating costs, and line thermocouple failure costs. By combining hydrogen sales, electricity consumption, new energy subsidies, and delayed construction benefits, a lifecycle benefit model was developed to measure economic performance. Experimental results showed that under a 5% power outage rate, the hydrogen distribution network has a short investment payback period, high production, and significant economic benefits, reaching 23.19 million US dollars by the sixth year. This study provides valuable insights for economic decision‐making in electric hydrogen microgrids. [ABSTRACT FROM AUTHOR]

Energy saving and the development of renewable energies already form part of the daily discourse in our society. Climate change and the environmental problems arising from it affect all of us on the planet. In view of the recent energy supply crisis in Europe and in line with the EU's Sustainable Development Goals, it is necessary to accelerate the ecological transition. This means, among many other things, encouraging energy efficiency and energy savings. This research is based on the EURONET 50/50 max project, which applied a methodology with no upfront capital outlay, but based on changing user habits to save energy in educational buildings. It was carried out in twelve educational centres of the Spínola Foundation throughout Spain. Significant reductions in energy and water consumption were achieved. The maximum reduction obtained in water consumption was 29%, 24% in electricity consumption and 33% in heating consumption. The average reduction in consumption obtained was 13% compared to 11% achieved in the 50/50 max project. It was concluded that, on the journey to optimising our energy consumption, working on patterns of usage is fundamental, raising awareness and involving users in order to give rise to permanent processes over time, gradually reversing climate change. [ABSTRACT FROM AUTHOR]