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Electrochemistry has been widely used in energy storage, metallurgy, chemical synthesis and other fields since its birth using electricity as one of the cheapest reagents. However, compared with the revival of electrochemical synthesis of small molecules, the electrochemical polymerization for preparing macromolecules is still rudimentary due to formidable challenges. Herein, A unique electrochemical polymerization is disclosed between CS2 and diamines at room temperature in an electrolyte‐free manner to access poly thioureas. Polythioureas have high selectivity for four metal ions, including Au3+, Ag+, Pt4+, and Hg2+, and can be used as an efficient adsorbent for recovering gold from metal waste. [ABSTRACT FROM AUTHOR]

In this paper, we study the predictive power of electricity consumption data for regional economic activity. Using unique high‐frequency electricity consumption data from industrial firms for the second‐largest German state, the Free State of Bavaria, we conduct a pseudo out‐of‐sample forecasting experiment for the monthly growth rate of Bavarian industrial production. We find that electricity consumption is the best performing indicator in the nowcasting setup and has higher accuracy than other conventional indicators in a monthly forecasting experiment. Exploiting the high‐frequency nature of the data, we find that the weekly electricity consumption indicator also provides good predictions about industrial activity in the current month with only 2 weeks of information. Overall, our results indicate that regional electricity consumption is a promising avenue for measuring and forecasting regional economic activity. [ABSTRACT FROM AUTHOR]

Traditional mid‐term electricity forecasting models rely on calendar and meteorological information such as temperature and wind speed to achieve high performance. However depending on such variables has drawbacks, as they may not be informative enough during extreme weather. While ubiquitous, textual sources of information are hardly included in prediction algorithms for time series, despite the relevant information they may contain. In this work, we propose to leverage openly accessible weather reports for electricity demand and meteorological time series prediction problems. Our experiments on French and British load data show that the considered textual sources allow to improve overall accuracy of the reference model, particularly during extreme weather events such as storms or abnormal temperatures. Additionally, we apply our approach to the problem of imputation of missing values in meteorological time series, and we show that our text‐based approach beats standard methods. Furthermore, the influence of words on the time series' predictions can be interpreted for the considered encoding schemes of the text, leading to a greater confidence in our results. [ABSTRACT FROM AUTHOR]

The energy transition in Germany requires a high amount of new wind turbines and photovoltaic power for supply of new electricity consumers for heat and traffic. In addition, high electricity demand for production of hydrogen has to be supplied. The corresponding planning for transmission system extension does not take into account economic and environmental burden. As an alternative to the planned oversized transmission grid extension, a second bidding zone for Germany is suggested. [ABSTRACT FROM AUTHOR]

The global energy crisis has been one of the significant challenges for decades, threatening the global economy, and the health of our environment. The government's efforts to enhance the welfare and lifestyle of citizens have been partially undermined by a significant rise in energy intensity, resulting in increased energy consumption. Over the years, researchers have utilized historical energy consumption data to enhance energy efficiency through various technologies. Innovative smart technologies drive energy efficiency, reducing energy usage throughout all areas of the energy industry, from production and supply to consumption. This creates a balance in all sectors and indicates a decrease in energy demand in all areas of building infrastructure. Green buildings that utilize Internet of Things (IoT) technology employ sensors and software to collect data, optimizing, and enhancing building performance. This includes reducing energy and electricity consumption, improving air quality, and optimizing lighting throughout the day. These buildings can contribute to reaching zero‐energy building targets. It becomes challenging to manage green buildings without using centralized control. Therefore, managing and integrating these buildings with intelligent technologies is vital in achieving environmentally friendly management. This study offers a broad overview of the green building concept and explores the use of the green IoT in enhancing services and conserving energy within green buildings. The article aims to deliver an extensive review of green buildings and their advantages, analyze the technology behind the IoT and its integration with solar panels to lower energy consumption, and ultimately identify the challenges present in this area of research. [ABSTRACT FROM AUTHOR]

Due to the diversity and randomness of residential consumption behavior, as well as the different share of domestic electricity demand by appliances, class imbalance problems exist in the non‐intrusive load monitoring (NILM) system. The recognition model becomes biased toward the majority class, which makes the recognition of the minority class difficult. To tackle this challenge, we propose a novel NILM framework for appliance recognition to overcome the insufficient learning problems of the minority class, which combines a multi‐domain feature extraction module, a two‐stage feature selection, and a data oversampling method. Multiple features are extracted to make full of the complementarity of combined features to enhance appliance recognition. Furthermore, a two‐stage feature selection based on minimal‐redundancy‐maximal‐relevance (mRMR) and random forest (RF) is developed to select an optimal feature set. Subsequently, an oversampling method combining K‐medoids and the synthetic minority oversampling technique (KM‐SMOTE) with sampling weights is proposed for data augmentation of the minority class to handle imbalance learning problems. The effectiveness of the proposed method is verified by the experiments on the public dataset PLAID. The results show that, compared with the state‐of‐the‐art techniques, the overall recognition accuracy and F1‐score with the proposed method are enhanced by 4.61% and 2.04%, respectively. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

We address the shortcomings associated with modelling national aggregate electricity consumption in a large and diverse country with unique regional characteristics. Focusing on Saudi Arabia, we employ an econometric approach to analyse the distinct responses of its regions to electricity prices and income levels. The study employs a general to specific modelling approach, utilizing data from 1990 to 2019 within the conventional energy demand theoretical framework. Our region‐specific estimations reveal income and price as the primary drivers, with the southern region exhibiting a higher sensitivity to income, while the eastern region shows a comparatively smaller response to prices attributed to its significant industrial presence. Elasticities for the central and western regions align with previous research, while eastern and southern regions exhibit larger elasticities. Weather impacts are observed only in the warmest western region, characterised by a substantial share of residential electricity consumption. Furthermore, we utilise our estimated model to project a regional baseline demand for electricity in Saudi Arabia and demonstrate how prices affect regions differently. This information is important for an oil‐exporting country like Saudi Arabia, considering the diverse fuel mix used for electricity generation across regions. Assuming moderate economic growth and no price change, our baseline projections indicate a total electricity demand of 366 terawatt hours (TWh) by 2030. [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]

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]

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]

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]

The Pt‐cathode hydrogen evolution reaction (Pt‐HER) of acetone cyanohydrin provides an alternative method for introducing the cyano group onto electrophilic carbons of imines and chalcones. The synthesis of α‐aminonitriles and β‐cyano ketones has been achieved smoothly with a small excess of cyanating agent (2 equivalent), catalytic amount of supporting electrolyte and low electricity consumption (0.02 F/mol). Besides demonstrating a general atom economy, the high Faradaic efficiency, allows for a scale‐up with low‐demanding electrochemical design and short electrolysis times. [ABSTRACT FROM AUTHOR]

With the rapid development of digital technologies such as intelligent control and information communication, digitization has gradually become an inevitable trend to promote technology innovation and industrial upgrading for the power grid. As the basis of digital technology, digital sensing technology will gradually penetrate all aspects of the power system for comprehensive perception and high operational reliability. First, this article introduces the application status and technical requirements of power intelligent sensor technology in various aspects of the power system, including the generation, transmission, transformation, distribution, and consumption of electricity. Next, domestic and foreign scholars' latest research achievements in power sensing technology are described from three major aspects, that is, optical fiber sensing technology, MEMS sensing technology, and sensor self‐powered technology. Finally, future challenges for the electric power intelligent sensors are discussed and an outlook of future widespread application in the clean, safe, and efficient digital power grid is provided. [ABSTRACT FROM AUTHOR]

Precise electricity forecasting is a pertinent challenge in effectively controlling the supply and demand of power. This is due to the inherent volatility of electricity, which cannot be stored and must be utilised promptly. Thus, this study develops a framework integrating canonical cointegrating regressions (CCR), time series artificial neural network (ANN) and a multilayer perceptron ANN model for analysing and projecting India's gross electricity consumption to 2030. Annual data for the years 1961–2020 have been collected for variables like gross domestic product (GDP), population, inflation GDP deflator (annual %), annual average temperature and electricity consumption. The study was conducted in three phases. In the first phase of the study, the CCR method was used to check the significance of the selected variables. In the second phase, the projected values of independent variables (GDP, population, inflation GDP deflator [annual %] and annual average temperature) were predicted using the time series ANN model. Finally, a multilayer perceptron ANN model with independent variables was used to forecast the gross electricity consumption in India by 2030. The result shows that the electricity consumption in India will increase by around 50% in the next 10 years, reaching over 1800 TWh in 2030. The proposed approach can be utilised to effectively implement energy policies, as an accurate prediction of energy consumption can help capture future demand. [ABSTRACT FROM AUTHOR]

We construct a sustainable development model by examining the short‐run relationship, long‐run relationship and causality between renewable energy consumption and economic growth. We consider a sample of 142 countries for 1991–2019 and employ time series analysis. The study noticed the long‐run and short‐run relationship between economic growth and renewable energy consumption in the case of all Countries Groups and regions such as Europe and Central Asia, Middle East and North Africa, Sub‐Saharan Africa and low‐income economies. Overall, we conclude that renewable energy consumption positively influences economic growth, which can motivate countries to achieve sustainable development goals. A nation cannot only focus on economic development when growing environmental and climatic concerns exist. What is essential is the prioritisation of sustainable development goals. The constructed model will help the countries plan, modify and implement national and international sustainable development policies. The results will also be helpful to intergovernmental organisations for channelling countries' efforts towards sustainable development. [ABSTRACT FROM AUTHOR]

The cost of manufacturing and electricity consumption are key considerations in encouraging the adoption of solar air heaters (SAH) in sunny and low‐income areas. These parameters can have a significant impact on promoting the use of these devices. In this study, solar air heating was designed with the objective of achieving the highest possible efficiency/cost ratio. It is a corrugated channel SAH whose structure is equipped with two barriers perforated with a sufficient number of holes for a good airflow distribution. A new model was developed to evaluate the qualitative parameters that describe the thermos‐energetic behavior of a heating system. These parameters were measured using experimental data obtained under real operating conditions. The thermal model assumes a uniform temperature for the glass, absorber, and insulation of the collector, while the temperature of the circulating air is assumed to vary linearly along the collector. To ensure that these assumptions were valid, the collector was cut into a number of 0.1 m sections in the direction of flow. By comparing the numerical results with the experimental data, the model was validated and then used to calculate the temperature profiles of the different elements of the collector, as well as to estimate the impact of certain operational parameters on its thermal performance. Relative percentage error values, between the numerical and the experimental results, of 1.7517%, 1.0750%, 0.8577%, 2.2371%, and 2.3637% for absorber plate temperature, outlet airflow temperature, useful power, thermal, and effective efficiencies, respectively, are recorded. [ABSTRACT FROM AUTHOR]

Load forecasting is crucial for the operation and planning of electricity generation, transmission, and distribution. In the context of short‐term electricity load prediction for residential users, single‐task learning methods fail to consider the relationship among multiple residential users and have limited feature extraction capabilities for residential load data. It is challenging to obtain sufficient information from individual residential user load predictions, resulting in poor prediction performance. To address these issues, we propose a framework for multi‐task residential short‐term load prediction based on contrastive learning. Firstly, clustering techniques are used to select residential users with similar electricity consumption patterns. Secondly, contrastive learning is employed for pre‐training, extracting trend and seasonal feature representations of load sequences, thereby enhancing the feature extraction capability for residential load Feature. Lastly, a multi‐task learning prediction framework is utilized to learn shared information among multiple residential users' loads, enabling short‐term load prediction for multiple residences. The proposed load prediction framework has been implemented on two real‐world load data sets, and the experimental results demonstrate that it effectively reduces the prediction errors for residential load prediction. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

As both the population and economic output of India continue to grow, so does its demand for electricity. Coupled with an increasing determination to transition to net zero, India has responded to this rising demand by rapidly expanding its installed renewable capacity: an increase of 60% in the last 5 years has been driven largely by a quintupling of installed solar capacity. In this study, we use broad variety of data sources to quantify potential and realized capacity over India from 1979 to 2022. For potential capacity, we identify spatiotemporal patterns in solar, wind, hydro and wave power. We show that solar capacity factor is relatively homogeneous across India, except over the western Himalaya, and is highest during the pre‐monsoon. Wind capacity factor is highest during the summer monsoon, and has high values off the southern coast, along the Western Ghats, and in Gujarat. We argue that wave power could be a useful source of renewable energy for the Andaman and Nicobar Islands, which are not connected to the main Indian power grid. Using gridded estimates of existing installed capacity combined with our historical capacity factor dataset, we create a simple but effective renewable production model. We use this model to identify weaknesses in the existing grid—particularly a lack of complementarity between wind and solar production in north India, and vulnerability to high‐deficit generation in the winter. We discuss potential avenues for future renewable investment to counter existing seasonality problems, principally offshore wind and high‐altitude solar. [ABSTRACT FROM AUTHOR]

This study addresses the pressing issue of quality electricity access in remote regions, with a specific focus on Tumbatu Island in Tanzania. Most studies on quality improvement concentrate on low‐voltage distribution lines and leave high‐voltage (HV) transmission lines behind. It is essential to address quality issues in HV lines due to their critical role in electricity transmission and distribution infrastructure. The objective of the study is to improve the voltage profile on the island's HV transmission line by identifying the optimal hybrid energy system comprising solar PV, wind turbine, and battery technologies. The study begins by presenting the total power demand and consumption on Tumbatu Island, which are important factors in designing an efficient energy system. The findings reveal a power demand of 7173 kW and a consumption of 28540 kWh/day, with an average scaled value of 1507.9 and 6000 kWh/day. To achieve the desired voltage profile improvement, the research incorporates HOMER Pro software for simulating and analyzing various hybrid system configurations. Essential input parameters, such as costs, resources, technology components, and load, are considered in the simulation process. The software generates a ranked list of options based on the system's net present cost (NPC). The simulation results demonstrate that the integration of solar PV, wind turbine, and HV lines provides a substantial improvement in the voltage profile, increasing it from 29.6 to 31.23 kV during peak demand periods. This solution proves to be the most economically viable, with the lowest NPC of $4,003,851 and a relatively short payback period of 3.79 years. In addition, a sensitivity analysis is performed to identify the most influential parameter in the system's performance. Wind speed is found to have the greatest impact, emphasizing its significance in the design and operation of the hybrid energy system. The implementation of this optimal hybrid renewable energy system on Tumbatu Island will not only improve the voltage profile and meet the island's energy needs but also shall contribute to global efforts in reducing pollution and the cost of electricity to the Tumbatu Island population. Moreover, it addresses the current and future demands for clean energy, underlining its importance in achieving sustainable and accessible electricity in sub‐Saharan Africa and beyond. [ABSTRACT FROM AUTHOR]

In recent years, one of the primary causes of high electricity consumption is the growing global population and the availability of power‐demanding smart devices. As a result, accurate load forecasting tools are required for effective energy conservation in microgrids. Various simulation tools and artificial intelligence (AI)‐based methods have been used to make the best forecasts of electricity demand. Furthermore, conventional systems are static and based solely on historical data. To address this practical need, this work aims to create a machine learning (ML) model for short‐term load forecasting based on feature selection and parameter optimization. This research proposes a hybrid short‐term load demand prediction approach that combines an adaptive barnacle‐mating optimizer (ABMO) and an artificial neural network (ANN). When compared to the regression tree (RT), support vector machine (SVM), ANN, and particle swarm optimization‐based ANN (PSO‐ANN) algorithms, the proposed ABMO‐ANN algorithm reduces mean absolute percentage errors by 67.69%, 64.58%, 59.18%, and 42.02%, respectively. Compared to conventional RT, SVM, ANN, and PSO‐ANN algorithm, the hybrid ABMO with ANN outperforms them based on mean absolute percentage error, root mean square error, correlation coefficient (R2)$\left(\right. R^{2} \left.\right)$, symmetric mean absolute percentage error, and agreement index (d). [ABSTRACT FROM AUTHOR]

Ancillary service provision and peak shaving (PS) play essential roles in the current day‐to‐day power system operation, which is challenged by the increasing renewable generation penetration. Providing these critical services using classical approaches such as peak load generators has been limited due to high operational costs and environmental impacts. The use of battery energy storage systems (BESS) is another popular method that is limited by high initial investment costs and high degradation rates. In this work, a novel approach to utilize industrial loads and BESS to provide multiple power system services in different stages is proposed. Industrial loads such as aluminium crushers are known for their intensive electricity consumption. Nevertheless, when applied in frequency regulation (FR), they perform poorly due to their discrete nature in operation. This drawback and the aforementioned BESS shortcomings are addressed by combining on‐site BESS with plant machinery to provide FR services and recover BESS related costs. Later, depending on the optimal capacity distribution, BESS usage is extended into the energy arbitrage market to provide PS services. This approach resulted in higher earnings for participating customers and network operators, as well as in less CO2${\rm {CO}}_{2}$ emissions, and minimal BESS degradations. An Australian case study of the South West Interconnected System, along with Worsley Alumina refinery data of Western Australia has been used to showcase the model performances. [ABSTRACT FROM AUTHOR]

A run‐of‐the‐river power plant is a renewable energy source used for electricity production. Its power output depends on the varying water flow rate over time, which can impact the reliability of the electric network. Previous research has not studied the effects of water flow rate and temperature variations on the hazard rate of the plant's components. This paper addresses this gap by investigating the impact of these variations on the reliability of electric networks with run‐of‐the‐river power plants. The analysis considers the hazard rates of the plant's components, incorporating the relationship between hazard rate and temperature based on the Arrhenius law. Parameters such as power output, current, power loss, operating temperature, and hazard rate are calculated for different water flow rates and ambient temperatures. Numerical simulations on a test system are conducted to examine the influence of water flow rate and temperature on the reliability indices of the electric network. The results demonstrate that water flow rate and temperature significantly affect the hazard rates of run‐of‐the‐river power plants. This highlights the need to consider these factors in the reliability analysis of electric networks incorporating these renewable resources. [ABSTRACT FROM AUTHOR]

Here, a novel tri‐level energy market model aimed at addressing the challenges posed by demand side management (DSM) in the electricity distribution company (EDC) is introduced. DSM has emerged as a new strategy employed by EDCs to manage and control electricity demand by encouraging end‐users to modify their electricity consumption patterns. This is achieved through the participation of demand response (DR) aggregators, which play a crucial role in assisting end‐users with strategies and technologies to reduce their electricity consumption during peak hours. The proposed tri‐level energy market model consists of four distinct players: EDC, microgrids, aggregators, customers. The interactions between these four actors are modelled within a tri‐level game framework, where the EDC and aggregators act as leaders, and the micro‐grids and customers are followers. This multi‐level and multi‐player game structure allows for a more realistic representation of the complexities involved in DSM programs within the energy market. To demonstrate the effectiveness of the proposed model, a real case study is utilized, showing that the new model better resembles real‐life market conditions. The results illustrate how the tri‐level energy market model can significantly reduce demand fluctuations during peak hours, leading to improved efficiency and effectiveness within DSM programs. [ABSTRACT FROM AUTHOR]

The environmental impact of magnetic resonance imaging (MRI) has recently come into focus. This includes its enormous demand for electricity compared to other imaging modalities and contamination of water bodies with anthropogenic gadolinium related to contrast administration. Given the pressing threat of climate change, addressing these challenges to improve the environmental sustainability of MRI is imperative. The purpose of this review is to discuss the challenges, opportunities, and the need for action to reduce the environmental impact of MRI and prepare for the effects of climate change. The approaches outlined are categorized as strategies to reduce greenhouse gas (GHG) emissions from MRI during production and use phases, approaches to reduce the environmental impact of MRI including the preservation of finite resources, and development of adaption plans to prepare for the impact of climate change. Co‐benefits of these strategies are emphasized including lower GHG emission and reduced cost along with improved heath and patient satisfaction. Although MRI is energy‐intensive, there are many steps that can be taken now to improve the environmental sustainability of MRI and prepare for the effects of climate change. On‐going research, technical development, and collaboration with industry partners are needed to achieve further reductions in MRI‐related GHG emissions and to decrease the reliance on finite resources. Level of Evidence: 5 Technical Efficacy: Stage 6 [ABSTRACT FROM AUTHOR]

The article presents the results of a study on the generation of electricity using porous Ca6H2O19Si6 structures as an example during moisture adsorption. It has been determined that the potential difference at the contacts of a single‐layer structure of compacted Ca6H2O19Si6 particles depends on the time of formation of the potential‐determining and diffusion layers and processes that destroy the diffusion layer or prevent its formation can increase the efficiency of moisture electric generator. The concept for creating the moisture electric generators based on multilayer structures, which may allow the use of such structures as moisture electric generators for continuous operation under constant external conditions, has been proposed. [ABSTRACT FROM AUTHOR]

Electrocatalytic hydrogenations (ECH) enable the reduction of organic substrates upon usage of electric current and present a sustainable alternative to conventional processes if green electricity is used. Opposed to most current protocols for electrode preparation, this work presents a one‐step binder‐ and additive‐free production of silver‐ and copper‐electroplated electrodes. Controlled adjustment of the preparation parameters allows for the tuning of catalyst morphology and its electrochemical properties. Upon optimization of the deposition protocol and carbon support, high faradaic efficiencies of 93 % for the ECH of the Vitamin A‐ and E‐synthon 2‐methyl‐3‐butyn‐2‐ol (MBY) are achieved that can be maintained at current densities of 240 mA cm−2 and minimal catalyst loadings of 0.2 mg cm−2, corresponding to an unmatched production rate of 1.47 kgMBE gcat−1 h−1. For a continuous hydrogenation process, the protocol can be directly transferred into a single‐pass operation mode giving a production rate of 1.38 kgMBE gcat−1 h−1. Subsequently, the substrate spectrum was extended to a total of 17 different C−C−, C−O− and N−O−unsaturated compounds revealing the general applicability of the reported process. Our results lay an important groundwork for the development of electrochemical reactors and electrodes able to directly compete with the palladium‐based thermocatalytic state of the art. [ABSTRACT FROM AUTHOR]

Load demand forecasting is very important for the management, designing and analysis of an electrical grid system. Load forecasting has progressively become a crucial component of the energy management system with the growth of the smart micro grid. This study presents a new framework to long term load forecasting in the world of electricity power with the help of historical load trends. The main objective of this research work is estimating monthly electricity demand of an Indian state Chhattisgarh, in terms of per day average load demand using a machine learning model—Long Short‐Term Memory (LSTM). This framework considers average of each day load demand for every month of years 2018–2022 and forecasted per day average load demand for each month of the year 2023. Furthermore, the predicting accuracy is evaluated for training and testing phase, in terms of error metrics like Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The MAPE values under the training and testing phase are in the range of 0.010%–0.652% and 0.378%–10.54%, respectively. A comparative study of LSTM model with Artificial Neural Network (ANN) model indicates the proposed LSTM model is more accurate and can be applied for real time load demand forecasting. [ABSTRACT FROM AUTHOR]

From the point of production to consumption, electrical energy should be utilized effectively and qualitatively. Efficiency at the electricity consumption stage is not sufficient, and the desired criteria must be met at the production stage. The efficient and high‐quality generation of electrical energy is directly related to the performance of the generator used. In order to achieve the desired criteria for a generator design, many issues should be considered together. In order for this study to do, a 14‐pole and 5 kW permanent magnet synchronous generator (PMSG) was designed using the innovative optimizer named the weighted mean of vectors (INFO), the jumping spider optimization algorithm (JSOA), the teaching–learning‐based optimization (TLBO), the pelican optimization algorithm (POA) and the teaching‐learning‐studying‐based optimization (TLSBO) algorithms. The generator performance was optimized within the determined inequality constraints by the offset and embrace value and six parameters of the stator slot structure. The generator designed with the parameters determined by the TLSBO was more successful than the generators designed according to all the other proposed algorithms. The total harmonic distortion (THD) value decreased by 51.983% compared to the initial design at approximately the same efficiency. [ABSTRACT FROM AUTHOR]

To improve the multiscale dynamic dispatching and distribution capacity of regional integrated energy, a multiscale dynamic time‐domain dispatching model of regional integrated energy with seasonal load difference is proposed. The master–slave game coordination planning model of regional comprehensive energy multiscale dispatching is constructed, and the difference fusion model of regional comprehensive energy multiscale dynamic dispatching is established by using the active and reactive power coordination control method according to the influence factors of voltage deviation and line overload absorption. Maximize the weight factors of the positive contribution of distributed photovoltaic (PV) power to the active distribution network. On the basis of the quantitative regulation analysis method of demand response power based on the equivalent slope of the load curve, establish an energy absorption capacity analysis model of seasonal load differentiation. On the basis of the dynamic interaction between different systems, a multiscale dynamic time‐domain analysis and dynamic simulation model of regional integrated energy is constructed by using the method of PV absorptive capacity assessment on both sides of demand uncertainty, to achieve multiscale dynamic time‐domain feature extraction of integrated energy and integrated scheduling of seasonal load differences. The simulation results show that the multiscale dynamic time‐domain allocation of regional comprehensive energy using this method improves the energy dispatching and balanced allocation capability, and the time‐domain dynamic allocation capability is good. Considering the terminal load demand and the electricity price demand formulated by the distribution network investment operators, the seasonal load response and comprehensive dispatching capability are improved. [ABSTRACT FROM AUTHOR]

This is a study of Norway's sustainable environment development amidst the interactions of natural resources, external investment (FDI) and economic development. Much has been done with respect to the study of Norway's economic performance in relation to the link between the natural resources and FDI with little emphasis on the environmental performance of the resource‐based economy. Also, Norway is classified as among the top countries in Europe with a greater percentage of adopting renewable energy, and no study has done a critical review of the impact of natural resources and FDI which are part of drivers of carbon emission that can counter the positive impact of renewable energy towards the Norway's sustainable environment. On this basis, this study adopts a time series data of Norway, 1970 to 2018 to study its environmental performance. Approaches such as structural break analysis, Autoregressive Distributed Lag (ARDL)‐bound testing and Granger causality estimations are utilised in this study for in‐depth analysis of the subject. Findings from ARDL confirmed a positive association between fuels and carbon emission, other indicators (economic growth and natural resources) are improving the quality of the country's environment. FDI even though shows positive sign remains insignificant in impacting the environmental performance in the short run reverted to a significant negative relationship with carbon emissions. This confirms the pollution halo hypothesis and rejects the pollution haven hypothesis (PHH) for Norway, and this trend can be sustained with the constant implementation of environmental rules in the country. Granger test confirms, a one‐way transition from fossil fuels to carbon emission, from carbon emission to growth, and from economic growth to fossil fuels. Also, a two‐way transmission is found between fossil fuels and FDI. These findings from Granger causality are consistent with the findings from ARDL, hence, two ways interactions between FDI and fossil fuel energy source consumption from Granger causality and the two variables (fossil fuels and FDI) are seen impacting on Norway's environmental performance. Findings from the estimates suggest that natural resources and FDI are mitigating pollution, hence, Norway's policy is expected to be resources and FDI driven in sustainable environment development. [ABSTRACT FROM AUTHOR]

Mother Earth is dying. Humanity needs to step up. Otherwise, the planet we call home will soon be uninhabitable. The destiny of our planet inarguably depends on consumers, as their choices have the power to turn them into eco‐superheroes (sustainable consumption) or eco‐villains (unsustainable consumption). This article aims to review the current progress of consumption research and propose strategies to inspire consumers to embrace their inner eco‐warrior spirit, fostering sustainable behavior that protects the environment and supports human well‐being. To achieve this, this article employs an integrative approach, merging a meta‐perspective on theoretical development with a multi‐study methodological approach. Harnessing the power of scientometrics, this article delivers meta‐insights from a multi‐study analysis on consumption trends in relation to environmental values (micro‐perspective; Study 1) and planetary health (macro‐perspective; Study 2). These findings pave the way for future research and intervention strategies designed to nurture ecological sustainability and unleash the eco‐warrior in every consumer. [ABSTRACT FROM AUTHOR]

Among the total 17 UN‐SDGs (sustainable development goals) proposed by the United Nations, the goal 7 basically ensures easy global availability of sustainable, clean, cost effective, reliable, and modern energy. Researchers are primarily concentrating on renewable energy sources in this direction since they are sustainable, clean, and environmentally friendly. But there are numerous obstacles, such as their low reliability, irregular nature, remote availability, and so forth, that prevent them from being widely used in commercial applications. Combining these renewable energy sources with effective and affordable energy storage technologies can be a promising solution to approach concerns including rising energy consumption, reliable and consistent electric power, and better grid stability. Modern energy storage systems such as electric double layer capacitor (EDLC) and lithium‐ion batteries have a great deal of potential for a wide range of applications. Carbon‐derived materials are the most flexible and fundamental materials for the storage and conversion of modern energy. Since it requires the pyrolysis and activation of expensive starting materials like wood, petroleum, and coal, the commercially produced activated carbon is costly and unsustainable. Due to rapidly increasing amount of plastic waste and the requirement for sustainable development, the conversion of waste plastics into valuable yet inexpensive carbon nanomaterials have attracted significant research interest. Their outstanding capability for charge accumulation and transportation is due to their enormous surface area, high chemical stability, and electrical conductivity in combination with low density. The present article examines the necessity and the efforts undertaken to develop supercapacitors and Li‐ion batteries as sustainable modern energy storage devices using recycled waste plastic. This article is categorized under:Sustainable Development > GoalsEmerging Technologies > Energy Storage [ABSTRACT FROM AUTHOR]

We report herein an electrocatalytic CO2 reduction‐coupled sulfion oxidation system for the co‐productions of valuable formate and sulfur at much enhanced atom utilization. Specifically, an organic ligand‐assisted two‐step reconstruction approach has been developed to fabricate the highly dispersed p‐Bi nanosheets (p‐Bi NSs) for cathodic CO2 reduction reaction (CO2RR), and meanwhile porous Co−S nanosheets (Co−S NSs) was applied for anodic sulfion oxidation reaction (SOR). Significantly high Faradaic Efficiencies of about 90 % for formate production by CO2RR in a wide potential range from −0.6 V to −1.1 V, and excellent SOR performances including an ultra‐low onset potential of about 0.2 V and recycle capacity of S2− in the 0.1 M and 0.5 M S2− solutions, have been simultaneously achieved. In the meantime, both the structure transformation of the catalysts and the reaction pathways are explored and discussed in detail. A two‐electrode CO2RR||SOR electrolyzer equipped with above electrocatalysts has been established, which features as low as about 1.5 V to run the electrolyzer at 100 mA cm−2, manifesting extremely lowered electricity consumption in comparison to conventional CO2RR system. Moreover, a sulfur separation approach has been proposed by using CO2, which is efficient, environmentally friendly and cost effective with value‐added NaHCO3 be obtained as the byproduct. [ABSTRACT FROM AUTHOR]

The increasing demand for electricity in daily life highlights the need for Smart Cities (SC) to use energy efficiently. Both technical and Non‐Technical Losses (NTL), particularly those resulting from electricity theft, present powerful obstacles; NTL alone can reach billions of dollars. Although Machine Learning (ML) based approaches for NTL detection have been embraced by numerous utilities, there is still a lack of thorough analysis of these methods. Limited research exists on NTL identification evaluation criteria and unbalanced data management in the context of SC. This research compares ML algorithms and data balancing methods to optimize electricity consumption detection. The given research applied the 15 ML techniques of Logistic regression, Bernoulli naive Bayes, Gaussian naive Bayes, K‐Nearest Neighbour, perceptron, passive‐aggressive classifier, quadratic discriminant analysis, SGD classifier, ridge classifier, linear discriminant analysis, decision tree, nearest centroid classifier, multi‐nomial naive Bayes, complement naive Bayes and dummy classifier. While SMOTE, AdaSyn, NRAS, and CCR are considered for data balancing. AUC, F1‐score, and seven relevant performance metrics were used for comparison. We have also implemented SHapely Additive exPlanations (SHAP) for feature importance and model interpretation. Results show varying classifier performance with different balancing methods, emphasizing data preprocessing's role in NTL detection for smart grid security. [ABSTRACT FROM AUTHOR]

Ensemble methods, such as Bagging, Boosting, or Random Forests, often enhance the prediction performance of single learners on both classification and regression tasks. In the context of regression, we propose a gradient boosting‐based algorithm incorporating a diversity term with the aim of constructing different learners that enrich the ensemble while achieving a trade‐off of some individual optimality for global enhancement. Verifying the hypotheses of Biau and Cadre's theorem (2021, Advances in contemporary statistics and econometrics—Festschrift in honour of Christine Thomas‐Agnan, Springer), we present a convergence result ensuring that the associated optimization strategy reaches the global optimum. In the experiments, we consider a variety of different base learners with increasing complexity: stumps, regression trees, Purely Random Forests, and Breiman's Random Forests. Finally, we consider simulated and benchmark datasets and a real‐world electricity demand dataset to show, by means of numerical experiments, the suitability of our procedure by examining the behavior not only of the final or the aggregated predictor but also of the whole generated sequence. [ABSTRACT FROM AUTHOR]

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As an important part of the construction industry, rural residential buildings are characterized by low energy utilization, unreasonable structure, and low consumption level, so it is particularly important to study the low carbon transformation and evaluation system. In this study, the coefficient of variation method was used to identify the important factors affecting the low‐carbon transformation of rural residential buildings, and the evaluation system of low‐carbonization of rural residential buildings was determined by Analytic Hierarchy Process (AHP), and the system was used in a rural residential building low‐carbonization evaluation study. The results show that "Energy utilization," "Envelope structure," and "Economic factors" have obvious influence on the low carbonization of buildings, with the weights of 36.4%, 24.5%, and 19.5%, respectively. Among the secondary indicators, the top four weightings are "clean energy utilization rate" (0.152), "external wall insulation system" (0.090), "window performance" (0.088), and "electricity consumption" (0.084), which are the most critical factors influencing the low carbonization level of clean energy utilization in rural residential buildings. Finally, based on the constructed low‐carbonization evaluation system, we propose targeted solution strategies to provide a theoretical basis for establishing an effective assessment system for the low‐carbonization of clean energy in rural residential buildings. [ABSTRACT FROM AUTHOR]

This paper proposes a situational awareness‐based method of load control for safe electricity consumption by industrial users to assess the risk of regulated load equipment within industrial users and to ensure their safe use of electricity. Firstly, in the situational perception stage, collecting key parameters such as controllable equipment within industrial users, and constructing a risk indicator system for industrial users in the process of load control from four perspectives: the degree of controllability of load equipment, the correlation and mutual influence of load equipment, the uncertainty of self‐provided power and economic losses; Then, in the situational understanding and presentation stage, a risk assessment method that integrates a self‐learning weight model and a cloud model is proposed to analyze the risk of industrial users under control; Finally, in the situational guidance stage, based on the assessment results of the risk situational of industrial users, industrial users' safe electricity consumption is controlled in multiple rounds to achieve the objective of maintaining safe and reliable operation of the power grid by accepting control at a low‐risk level. The simulation of the industrial users of steel mills is used to verify the rationality and effectiveness of the method proposed here. [ABSTRACT FROM AUTHOR]

Under the new power system, a high proportion of new energy is widely connected to the power grid, and it is necessary to increase investment in centralized and distributed energy storage, flexible resource regulation, and transmission and distribution grids, resulting in an increase in power system costs. The current electricity capacity price does not reflect the economic value of the added system adequately, and flexible capacity is needed to ensure the safety of the power grid under the new power system. In the context of the transformation of the new power system and investment function, it is increasingly difficult to recover the costs according to the traditional electricity price. It is necessary to establish a coordinated development mechanism for the electricity capacity price and the electricity price that recovers the fixed costs according to the investment function to ensure a reasonable return on investment in flexible resources and to provide effective investment signals. Therefore, this work first studied and proposed a mechanism for the formation of capacity and electricity prices based on the proportion of allowed income and fixed costs at different voltage levels and calculates the level of electricity capacity (demand) prices and electricity consumption prices. Then, using the BARY curve, genetic algorithm, and clustering of user load rates, a collaborative mechanism between the electricity capacity price and the electricity price was designed. With the goal of optimizing the electricity capacity price and considering constraints such as the flexibility and reliability of the new power system, the ratio of the capacity cost allocated to the electricity capacity price by voltage level users to the capacity cost allocated to the electricity price was calculated, and the optimal combination of electricity capacity price and electricity price for different load‐rate intervals was determined. Finally, the effectiveness of the model was verified through numerical simulations. On the basis of the investment characteristics of the new power system, suggestions for a capacity pricing mechanism under the new power system were proposed from the perspectives of transfer capacity function, transfer electricity function, grid reliability, and environmental and social benefits. [ABSTRACT FROM AUTHOR]

Electricity and water systems in the Western US (WUS) are closely connected, with hydropower comprising 20% of total annual WUS generation, and electricity related to water comprising about 7% of total WUS electricity use. Because of these interdependencies, the threat of climate change to WUS resources will likely have compounding electricity impacts on the Western Interconnect grid. This study describes a WUS‐wide water system model with a particular emphasis on estimating climate impacts on hydropower generation and water‐related electricity use, which can be linked with a grid expansion model to support climate‐resilient electricity planning. The water system model combines climatically‐driven physical hydrology and management of both water supply and demand allocation, and is applied to an ensemble of 15 climate scenarios out to 2050. Model results show decreasing streamflow in key basins of the WUS under most scenarios. Annual water‐related electricity use increases up to 4%, and by up to 6% during the summer months, driven by growing agricultural demands met increasingly through a shift toward energy‐intensive groundwater to replace declining surface water. Total annual hydropower generation changes by +5% to −20% by mid‐century but declines in most scenarios, with decreases in summer generation by up to nearly −30%. Water‐related electricity use increases tend to coincide with hydropower generation declines, annually and seasonally, demonstrating the importance of concurrently evaluating the climate signal on both water‐for‐energy and energy‐for‐water to inform planning for grid reliability and decarbonization goals. Plain Language Summary: Electricity and water systems in the Western United States (WUS) have a strong dependency. Water fuels hydropower generation and electricity is used to pump, transport, treat, heat, and dispose of water. Climate change poses a serious threat to water availability in the WUS and is likely to also affect hydropower generation and electricity use related to water. This study develops a WUS water system model to evaluate the impact of a set of climate change scenarios on the dynamic interplay between water availability and demand and hydropower generation and electricity use by the water sector by 2050. We find that in many key basins, streamflow decreases under the climate scenarios. At the same time, reliance on groundwater increases to meet growing agricultural water demand. Hydropower generation shows decreases in most cases while energy use related to water increases. Because these hydropower generation declines tend to occur during periods when electricity demand for water grows, electricity grid planners in the Western Interconnect will benefit from this type of study by informing future grid buildouts that maintain reliability and decarbonization goals. Key Points: Interactions among hydro‐climate, hydropower, and water's energy use are critical for evaluating Western US (WUS) water‐energy climate risksUnder climate change, our WUS water model finds declining streamflows, growing agricultural demands, and increased groundwater useWith warming and drying, water‐related energy use grows while hydropower generation decreases under most climate scenarios by mid‐century [ABSTRACT FROM AUTHOR]

The study analyses the effect of global value chains participation (GVCP) on CO2 emissions and assesses whether digitalization technologies can enhance environmental quality. We use panel data estimation techniques for 112 developing countries over the period 1990–2018. Findings show that the GVCP contributes to environmental degradation. Other factors that increase environmental damage include FDI inflows, industrial value‐added and electricity consumption. However, renewable energy consumption significantly reduces CO2 emissions. Findings show that digitalization is an effective channel in reducing CO2 emissions in the GVCP in developing countries. These findings have important policy implications in exploring the GVCP's development dynamics in upgrading opportunities from digital technologies to reduce environmental pollution and promote sustainable growth in developing economies. [ABSTRACT FROM AUTHOR]

In order to solve the collaborative optimization scheduling of multi‐microgrid under the high penetration rate of new energy, this paper considered the energy interaction between micro‐grids in multi‐microgrid and the relationship between new energy consumption and electricity cost, constructed a collaborative scheduling model considering both micro‐grid load and main grid wind and optical energy storage, proposed objective function based on economic cost, and improved Genetic Algorithm (GA). The elite thought and catastrophe thought are used to optimize the selection operation, and the particle swarm optimization algorithm is used to optimize the mutation operation. Furthermore, three scenarios were selected to verify the effectiveness of the proposed model and the improved GA. The results show that the proposed model can ensure the stable operation of the multi‐microgrid system with a high proportion of new energy access and reduce the operation cost. In addition. the improved algorithm solves the problems of easy to fall into local optimum and slow iteration speed, and has better performance in solving the model. [ABSTRACT FROM AUTHOR]

Strategies to control ongoing biological invasions are often developed by modelling the invasive species' population and aiming to reduce its abundance. However, if the ultimate objective is to protect and restore native species, focussing solely on the invader may not be optimal because it does not account for (i) species interactions that can cause the invader's impacts to depend nonlinearly on its abundance, (ii) collateral damages to native species incurred due to nonspecific removal methods or (iii) native‐invader trait differences.To identify an invader suppression strategy that maximizes average native population size, we applied optimal control theory to a two‐species model of a native species threatened by an invasive competitor. We examined trade‐offs between iterative physical removals that selectively target invaders and intensifiable chemical control that is nonselective but has higher efficacy.We found that while iterative removals were capable of supporting large native populations when applied continuously, cost could be prohibitively high. In contrast, when favourable native‐invader trait differences enabled native species to re‐establish more quickly than invaders, intensifiable methods could achieve substantial restoration benefits at lower cost by focussing removal effort into periodic, high‐efficacy events.In a metapopulation, removals that rotated among spatial patches were optimal when the native species had higher dispersal, whereas synchronous removals were preferred when native recovery was initiated locally and the invader could disperse.For a case study in Hawaiian streams, we compared how effective two alternative methods of removing invasive live‐bearing fishes (poeciliids) might be at restoring the endemic freshwater goby Sicyopterus stimpsoni. We found that rotenone (a piscicidal chemical) offered superior benefits when the control budget was small and efficacy was high, but that electrofishing (use of electricity to manually collect target fish) was better with larger budgets and in many lower‐efficacy scenarios.Synthesis and applications. Our findings demonstrate that, by accounting for species interactions and collateral damage, invasive species control strategies can be optimized in light of species traits. Choices about the timing, locations and types of removal events present opportunities to increase the efficiency with which invasive species suppression benefits native species. [ABSTRACT FROM AUTHOR]

Owing to the complexity of metros, the energy consumption characteristics of metro systems exhibit variability and the energy‐saving management of the systems encounters challenges. To encapsulate the essential characteristics of energy usage and to objectively assess the energy performance of metro systems, this study presents a generalized framework and applies it to a case study conducted in Tianjin. The study also employs correlation analysis to investigate the applicability of the indicators relevant to ridership. The results indicate that the monthly traction electricity consumption exhibits slight variation, while station electricity usage demonstrates substantial fluctuation with seasonal changes. For Tianjin Metro, the passenger factor hardly shows any effect on the electricity use of metro lines. The median value of traction electricity use is approximately 2.0 kWh/(car‐km) and that of the average annual station electricity use of underground lines ranges from 95 to 155 kWh/m2. The emission from the traction sector is 12.2 kgCO2/(vehicle‐km) and from the station sector is 118.6 kgCO2/m2. The study also identifies the energy‐intensive lines of the Tianjin Metro and compares the energy utilization among various global metro systems. The authors hope that this study can help shed light on the assessment of the energy status of metro systems and serve as a source of information for other City‐Metros to implement energy‐saving management. [ABSTRACT FROM AUTHOR]

More than half of the materials extracted from natural environments eventually accumulate as building material stock (BMS). From a linear‐to‐circular economy perspective, BMS transforms the building sector from a virgin material consumer and a waste generator to a future depository of secondary resources. Studies characterizing the amount and distribution of BMS adopt different approaches, but high data requirements restrict their applicability. This research proposes an alternative method for regional BMS quantification. The method leverages the permanent population, electricity consumption, and BMS of a sample city to develop a statistical regression model; then uses it to estimate the BMS of a larger, homogenous region. With relatively low data requirements, the new method is especially applicable in underdeveloped areas where data required for BMS quantification methods are usually unavailable or incomplete. We apply the method to characterize the spatiotemporal evolution of BMS in China's Greater Bay Area. From 2000 to 2021, the total BMS in this region increased from 4.4 to 7.7 billion tonnes, with concrete, brick, and steel accounting for 72.32%, 17.57%, and 4.71% of the total BMS, respectively. The most rapid BMS growth occurred in Guangzhou (from 534.75 to 1277.82 Mt) and Shenzhen (517.80 to 1235.48 Mt). A core–edge BMS accumulation pattern emerged in this area while the BMS peak showed a coast‐to‐inland shift. Future studies can explore generalizing this new method to characterize BMS in other developing regions. [ABSTRACT FROM AUTHOR]

The growing electricity demand of IT infrastructure has raised significant concerns about its carbon footprint. To mitigate the associated emissions of computing systems, current efforts therefore increasingly focus on aligning the power usage of software with the availability of clean energy. To operate, such carbon‐aware applications require visibility and control over relevant metrics and configurations of the energy system. However, research and development of novel energy system abstraction layers and interfaces remain difficult due to the scarcity of available testing environments: Real testbeds are expensive to build and maintain, while existing simulation testbeds are unable to interact with real computing systems. To provide a widely applicable approach for developing and testing carbon‐aware software, we propose a method for integrating real applications into a simulated energy system through software‐in‐the‐loop simulation. The integration offers an API for accessing the energy system, while continuously modeling the computing system's power demand within the simulation. Our system allows for the integration of physical as well as virtual compute nodes, and can help accelerate research on carbon‐aware computing systems in the future. [ABSTRACT FROM AUTHOR]

This paper aims to study the forecasting capabilities of several models under the Markov regime‐switching (MRS) and the extreme value theory (EVT) frameworks applied to daily electricity prices in the New Zealand electricity market. The MRS models in this study include up to five regimes, with time‐varying transition probabilities and incorporation of external market variables. We apply Hamilton's filter with maximum likelihood estimation for parameter estimation. The EVT peaks‐over‐threshold (EVT‐PoT) framework is also considered, and its relationship to the MRS class of models is discussed. We generate out‐of‐sample forecasts under various market scenarios. The MRS models are able to replicate real price densities under stable market conditions. The EVT‐PoT model performs well despite its lack of complexity compared to the MRS framework. We attribute this to the usage of the generalized Pareto distribution to model price extremities. [ABSTRACT FROM AUTHOR]

A highly selective and durable oxygen evolution reaction (OER) electrocatalyst is the bottleneck for direct seawater splitting because of side reactions primarily caused by chloride ions (Cl−). Most studies about OER catalysts in seawater focus on the repulsion of the Cl− to reduce its negative effects. Herein, we demonstrate that the absorption of Cl− on the specific site of a popular OER electrocatalyst, nickel‐iron layered double hydroxide (NiFe LDH), does not have a significant negative impact; rather, it is beneficial for its activity and stability enhancement in natural seawater. A set of in situ characterization techniques reveals that the adsorption of Cl− on the desired Fe site suppresses Fe leaching, and creates more OER‐active Ni sites, improving the catalyst's long‐term stability and activity simultaneously. Therefore, we achieve direct alkaline seawater electrolysis for the very first time on a commercial‐scale alkaline electrolyser (AE, 120 cm2 electrode area) using the NiFe LDH anode. The new alkaline seawater electrolyser exhibits a reduction in electricity consumption by 20.7 % compared to the alkaline purified water‐based AE using commercial Ni catalyst, achieving excellent durability for 100 h at 200 mA cm−2. [ABSTRACT FROM AUTHOR]

Sustainability is a global mandate and has set agendas for regions, nations, sectors, and corporations. Because of the globalized nature of the economy, the pursuit of sustainability in one place has implications for others. The aim of this paper is to describe how a sector—the Guatemalan Sugarcane Agro‐industry (GSAI)—can make progress toward sustainability and contribute to the Sustainable Development Goals of the country in which it operates. The methodology used was a case study description, based largely on data reported by the GSAI. The main findings of the study show that the contributions of the industry to water and sanitation in its area of operations have been important, but limited, due to several factors that include issues such as: lack of political will, lack of technical capacity, financing, and appropriate legislation. On the other hand, the contributions of the GSAI to energy have been remarkable for the Guatemalan energy matrix, highlighting that cogeneration from sugarcane bagasse represents up to 30% of the electricity demand in the country during the 6‐month harvest season. Building on social corporate responsibility theory, the actions related to the goals on water and energy are explained. What was found is that the contributions of a sector, though important, are possibly not outstanding at the national scale, and that the conditions of the country limit the reach or pace of progress of the sector. An exception is the contributions toward sustainable energy and associated emissions, which are noteworthy. The interplay of actors at different scales needs to be understood if globally we are to achieve the goals. This understanding is key to put in place policies and enabling conditions for sectors to make progress faster, helping their country advance as well as contributing to sustainability of buyers in other countries or regions. [ABSTRACT FROM AUTHOR]

Electricity theft is a primary concern for utility providers, as it leads to substantial financial losses. To address the issue, a novel extreme gradient boosting (XGBoost)‐based model utilizing the consumers' electricity consumption patterns for analysis is proposed for electricity theft detection (ETD). To remove the imbalance in the real‐world electricity consumption dataset and ensure an even distribution of theft and non‐theft data instances, six different artificially created theft attacks were used. Moreover, the utilization of the XGBoost algorithm for classification, especially to identify malicious instances of electricity theft, yielded commendable accuracy rates and a minimal occurrence of false positives. The proposed model identifies electricity theft specific to the regions, utilizing electricity consumption parameters, and other variables as input features. The authors' model outperformed existing benchmarks like k‐neural networks, light gradient boost, random forest, support vector machine, decision tree, and AdaBoost. The simulation results using the false attacks for balancing the dataset have improved the proposed model's performance, achieving a precision, recall, and F1‐score of 96%, 95%, and 95%, respectively. The results of the detection rate and the false positive rate (FPR) of the proposed XGBoost‐based detection model have achieved 96% and 3%, respectively. [ABSTRACT FROM AUTHOR]