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Energy efficiency has become a major concern for manufacturing systems, due to industry being the largest user of scarce, finite energy sources, and also to recent events which have pushed energy prices to alarming levels. In the present Industry 4.0 context, Reconfigurable Manufacturing Systems (RMS) are therefore one of the most promising manufacturing paradigm. In this paper, we investigate the suitability of one of the most common types of RMS, the Parallel-Serial manufacturing line with Crossover, to help minimise the peak of the electric power consumption. More specifically, the balancing of such a production line is studied, so as to integrate power peak minimisation from the design stage. Thus, we define the Parallel-Serial-with-Crossover Assembly Line Balancing Problem with Power Peak Minimization, a new combinatorial NP-hard problem. We also propose a suitable time-indexed Integer Linear Program that integrates balancing and scheduling decisions and a matheuristic algorithm designed to tackle large-size instances. Both approaches are tested on a wide set of instances. The computational results show that relevant power peak reductions can be achieved (33% on average), opening up promising perspectives from both algorithmic and managerial viewpoints. [ABSTRACT FROM AUTHOR]

Manufacturers are facing pressure to reduce electricity costs. Onsite renewable energy generation may be a solution, but its high capital cost and intermittent power generation limit its use. Grid-responsive smart manufacturing could effectively incorporate renewables in industrial processes. This study integrates grid-responsive smart manufacturing with renewables on an industrial plant scale and demonstrates both a favourable economic and environmental outcome. A user-friendly decision-aid model for energy management is provided to manufacturers. A case study shows how solar panels, industrial batteries, smart pumping strategies, and various combinations of those elements can save on electricity costs. Dynamic simulation results demonstrate that grid-responsive smart manufacturing can effectively lower peak demand. The economic results show that grid-responsive smart manufacturing and renewables synergistically optimise cost reductions. The solar coupled with smart pumping scenario shows annual cost savings of $755,200, accounting for 4.6% of the total electricity cost. Smart pumping alone saves $371,900 annually with a 0.7-year payback period, demonstrating how the manufacturing sector can utilise its own processes in load shifting. This study supports that incorporating grid-responsive smart manufacturing with renewables can effectively reduce electricity costs and emissions for industry. Abbreviations: e: Equivalent; GHG: Greenhouse gas; PBP: Payback period; PV: Photovoltaics; SP: Setpoint; VFD: Variable speed drives [ABSTRACT FROM AUTHOR]

This paper proposes a novel intelligent scheduling method based on deep reinforcement learning (DRL) to solve the multi-objective steelmaking-continuous casting (SCC) scheduling problem, under time-of-use (TOU) tariffs for the first time. The intelligent scheduling system architecture is designed, and a mathematical model is established to minimise the total sojourn time and electricity cost. To effectively reduce production costs by avoiding peak periods of electricity consumption, the 'start time' of the system is generated based on the Markov Decision Process (MDP), and heuristic scheduling rules related to power cost are used as the action space, with corresponding reward functions designed according to the characteristics of these two objectives. To satisfy the continuous casting which is a particular SCC constraint, a backward strategy is developed. Additionally, a branching duelling double deep Q-network (BD3QN) is adapted to guide action selection and avoid blind search in the iteration process, and then applied to real-time scheduling. Numerical experiments demonstrate that the proposed method outperforms comparison algorithms in terms of solution quality and CPU times by a large margin. [ABSTRACT FROM AUTHOR]

The purpose of long-term forecasting is to meet the needs of practical applications, such as the prediction of the development trend of infectious diseases and the planning of electricity consumption. In this paper, we study the long-term forecasting of time series. Studies have shown that previous Transformer-based models have the potential to improve prediction capabilities, but there are also some problems, such as the lack of location information and the slow training speed. To solve these problems, we designed an efficient Transformer-based model called AD-Autoformer, which is specifically designed for long-term series prediction. By introducing position embedding, the model can better understand the patterns and relationships in the sequence, to improve the performance and generalization ability of the model when processing sequence data, and the self-attention distilling mechanism realizes the compression and acceleration of the model by halving the cascading layer input to highlight the dominant attention. This method significantly reduces the computational complexity of the model and improves the training speed of the model while maintaining the performance of the model. Experimental results on five large datasets show that the proposed AD-Autoformer model has different degrees of improvement in MSE and MAE indicators compared with other benchmark methods. [ABSTRACT FROM AUTHOR]

The increasing adoption of electric vehicles is expected to substantially raise electricity demand. This could require significant grid investment to maintain secure electricity supply, which has traditionally been provided through infrastructure upgrades. The potential of smart technologies like Vehicle-to-Grid (V2G) to contribute to security of supply has prompted the need to quantify their impact. We hypothesize that the F-Factor methodology can effectively quantify V2G's security of supply contribution. Applying F-Factor analysis to V2G through optimization modeling and sensitivity studies, we find that key parameters like V2G charger ratings, EV battery capacities, and load profile peakiness significantly influence the results. We conclude that the F-Factor provides a valuable tool for assessing V2G's potential to enhance security of supply, with implications for more efficient grid planning in the context of transport electrification. [ABSTRACT FROM AUTHOR]

With the peak of carbon dioxide emissions and carbon neutrality, China is placing greater emphasis on energy expenditure. Office buildings occupy a prominent position in building energy consumption, which is one of the main energy consumption areas. Taking an administration building in Chengdu as an example, this article simulates the building energy consumption based on Design Builder software, examines the variables influencing energy consumption, and suggests energy-saving strategies combined with fresh ideas for sustainable architectural design. The results showed that the modeling building was a high-energy-consuming building, with an energy consumption of 724,857.59 kWh, and a unit area energy consumption of 288.17 kWh/m2 in Chengdu. For energy conservation and emission reduction, this article proposes the following three energy-saving measures. The first is to apply heat recovery technology for air conditioning systems. The second is photovoltaic glass, which provides partial electricity demand for buildings and reduces dependence on traditional energy sources. The third is roof greening, which utilizes the plants to purify the air and beautify the environment. The results showed that the heat recovery technology in air conditioning systems reduced the total energy consumption of buildings from 642144.04 kWh/m2 to 502937.83 kWh/m2, photovoltaic glass reduced 552243.87 kWh/m2, and roof greening reduced to 635947.35 kWh/m2. All of these have good energy-saving and emission reduction effects. The above three strategies not only help reduce building energy consumption, but also provide substantial support for China to achieve carbon neutrality. [ABSTRACT FROM AUTHOR]

Water and energy are essential resources for human life, and carbon emissions (CEs) occur in tandem with their use. Thus, water, energy, and carbon are closely inter-related. Approximately 4% of the global energy is used in urban water sectors (UWSs), which encompass various processes such as water intake, treatment, and distribution and wastewater collection and treatment, all of which consume significant energy and emit CO2. Several countries are actively working toward achieving carbon neutrality by 2050–2060. Therefore, increasing energy efficiency and reducing CEs through comprehensive evaluations of UWSs is essential. This study aimed to quantify energy consumption and CEs in UWSs and proposed a methodology for analyzing water–energy–carbon (WEC) links at the city level. By applying it to Incheon Metropolitan City (IMC), we first identified the UWSs and established a WEC database. Based on this database, the WEC consumption and emissions were analyzed by process or administrative district, and visualizations using Sankey diagrams and Geographic Information System Mapping were created to enhance their understandability. In 2021, the UWSs in IMC consumed 308,496,107 kWh of energy, representing 32.7% of the public electricity consumption of IMC, with an average energy intensity of 0.46 and 0.38 kWh/m³ for water supply systems (WSSs) and sewerage systems (SSs), respectively. Their carbon emissions totaled 315,765,358 kg CO2, accounting for 2.7% of IMC's total carbon emissions, with an average carbon intensity of 0.21 and 0.58 kg CO2/m³ for WSSs and SSs, respectively. The proposed methodology was used to comprehensively evaluate WEC consumption and emissions in IMC. It is expected to enable relevant stakeholders to develop measures, such as water reuse and increasing renewable energy usage in water treatment and wastewater treatment plants, to build sustainable UWSs. [ABSTRACT FROM AUTHOR]

Despite its high share in national energy demand, the tertiary sector is an undifferentiated residual in the energy balances of most countries. To develop effective policy measures for enhancing energy efficiency, policymakers need a sound understanding of how the energy demand is structured. To date, only a few studies assess energy demand in the tertiary sector quantitatively. This paper aims to close this gap by introducing an integrated model that transparently breaks down the energy demand according to statistical subsectors, energy carriers and end-uses. The model combines a technology-based bottom-up with a top-down approach and applies it to a case study on Germany based on survey data from companies. Model validity is analyzed using a set of indicators. The results for Germany show that the model is consistent with the national energy balance showing an aggregate deviation of only 1%. The results for 2019 indicate that electricity demand in Germany's tertiary sector is dominated by lighting (35%), information and communication technologies (33%) and the provision of mechanical energy (9%), while fuel demand is mainly due to space heating (78%), followed by mechanical energy (15%). [ABSTRACT FROM AUTHOR]

As the global average temperature has increased due to climate change, the use of air conditioning equipment for cooling homes has become more popular. Inverter equipment is advertised as a better energy option than systems with an on/off control; however, there is a lack of sufficient studies to prove this. This work aims to analyze and compare the electricity consumption associated with cooling equipment with an on/off control and inverter equipment. A heat transfer model coupled with energy balance for a room is developed and implemented in Python 3.12. The indoor temperature is controlled by simulating an on/off control and a PID control for the inverter system. Subsequently, the electricity consumption of the two systems is compared, and a sensitivity analysis is performed to determine which variables have the greatest impact on electricity consumption. The results show that the inverter equipment has lower electricity consumption compared to the equipment with the on/off control. However, the sensitivity analysis shows that the indoor temperature set point plays a more relevant role since a 15% variation in its value impacts electricity consumption by up to 77%. [ABSTRACT FROM AUTHOR]

Accurate forecasting of electric vehicle (EV) power consumption per unit mileage serves as the cornerstone for determining diurnal variations in EV charging loads. To enhance the prediction accuracy of EV power consumption per unit mileage, this paper proposes a modelling method grounded in an improved Ant Colony Optimization-Support Vector Regression (ACO-SVR) framework. This method integrates the effects of both temperature and speed on the power consumption per unit mileage of EVs. Initially, we analyze the influence mechanism of driving speed and ambient temperature on EV power consumption, elucidating the relationship between power consumption per unit mileage and these factors. Subsequently, we construct an ACO-SVR model utilizing an improved ant colony optimization algorithm, fitting the relationship between power consumption, speed, and temperature to derive the EV power consumption per unit mileage model. Finally, leveraging operational data from EVs in Guangdong, Hong Kong, and Macao as a case study, we validate the energy consumption model of EVs by considering factors such as ambient temperature and driving speed. The results demonstrate that the model proposed in this paper is both accurate and effective. [ABSTRACT FROM AUTHOR]

Electricity is fundamental to modern societies and will become even more so as its use expands through different technologies and population growth. Power generation is currently the largest source of carbon-dioxide (CO2) emissions globally, but it is also the sector that is leading the transition to net zero emissions through the rapid rise of renewables. The impacts of COVID-19 on the electricity sector led to a reduction in the demand for electricity, while at the same time, the current global energy crisis has placed the security and affordability of electricity at the top of the political agenda in many countries. In this way, the decrease in the demand for electricity, as well as its gradual recovery, makes it necessary to carry out energy planning that considers the adverse effects caused by global events with a high socioeconomic impact. In this article, the Low Emission Analysis Platform (LEAP) 2020 software has been used to determine the distribution of energy sources to 2050 for Mexico. The variables that lead to the possible profiles for 2050 are social, economic, and technological. The results correspond to a possible future based on official data from the National Electric System (SEN) of Mexico. The forecast for 2050 indicates that the electricity sector will have almost double the current installed capacity; however, emissions do not correspond to twice as much: they are practically 50% higher. [ABSTRACT FROM AUTHOR]

Non-invasive load monitoring (NILM) deduces changes in energy consumption patterns and operational statuses of electrical equipment from power signals in the feed line. With the emergence of fine-grained power load distribution, the importance of utilizing this technology for implementing demand-side energy management in smart grid development has become increasingly prominent. To address the issue of low load identification accuracy stemming from complex and diverse load types, this paper introduces a NILM method based on uniform manifold approximation and projection (UMAP) reduction and enhanced density-based spatial clustering of applications with noise (DBSCAN). Firstly, this paper combines the characteristics of user load under transient and steady-state conditions and selects data with significant differences to construct a load-characteristic database. Additionally, UMAP is employed to reduce the dimensionality of high-dimensional load features and rebuild a load feature database. Subsequently, DBSCAN is utilized to categorize typical user loads, followed by a correlation analysis with the load-characteristic database to determine the types or classes of loads that involve switching actions. Finally, this paper simulates and analyzes the proposed method using the electricity consumption data of industrial users from the CER–Electricity–Data dataset. It identifies the electricity load data commonly utilized by users in a specific area of Zhejiang Province in China. The experimental results indicate that the accuracy of the proposed non-invasive load identification method reaches 95%. Compared to the wavelet transform, decision tree, and backpropagation network methods, the improvement is approximately 5%. [ABSTRACT FROM AUTHOR]

Accurately predicting industrial electricity consumption is of paramount importance for optimizing energy management and operational efficiency. Traditional forecasting approaches face significant challenges in capturing the complex factors influencing industrial electricity consumption, often due to the inadequate representation of correlations, thus limiting their predictive capabilities. To overcome these limitations, we propose a novel graph-based forecasting model termed Industry–Geography Time Series Forecasting Model (IG-TFM). Our approach leverages historical electricity consumption data and geographical information relevant to similar industries to construct an industry–geography relationship graph. This graph serves as the foundation of a comprehensive network that encompasses all industries of interest, allowing us to identify sectors closely associated with the target industry. The structured graph data are then processed within a graph convolutional neural network framework, which effectively captures the impact of geographical location, industry similarities, and inter-industry relationships on electricity consumption patterns. Utilizing this enriched representation, we develop our IG-TFM for accurate time series forecasting of industrial electricity consumption. Experiments conducted on real-world data, including 31 industries across 9 cities in a southern province of China, demonstrate the significant advantages of our proposed method across key performance indicators such as the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). These findings underscore the importance and efficacy of employing complex networks to encode sequence-related information, thereby substantially improving prediction accuracy in industrial electricity consumption forecasting. [ABSTRACT FROM AUTHOR]

This review provides a detailed inventory analysis of the manufacturing process of a cosmetic cream using gold nanoparticles (AuNPs) and hydroxylated fullerene water complex (3HFWC) as novel nanocomponents for cream. The inventory analysis was focused on the evaluation of the two raw materials of the nanocomponents, the consumption of electricity and water, which enabled an insight into the process flows within the production process. The data obtained from this analysis of the inventory of nanocomponents provide an insight into the potential improvements that can be made in the manufacturing process of nanocomponents, in order to reduce the environmental impact of the production of new cosmetic creams. These results will serve as the basis for the second part of the analysis, where a life cycle analysis will be carried out to assess the environmental impacts of cream production from the acquisition of raw materials to the disposal of the final product. [ABSTRACT FROM AUTHOR]

The instability of the increasingly important photovoltaic (PV) electricity generation in Korea due to a dependence on meteorological conditions is an emerging problem. In this study, we examined the influence of various meteorological conditions on the prediction accuracy of PV generation and module temperature. The predictions were based on weather data acquired from the Korea Meteorological Administration and compared with actual PV generation data from the Dangjin coal yard PV power facility. The results were analyzed on the basis of meteorological conditions, insolation, ambient temperature, and wind velocity. [ABSTRACT FROM AUTHOR]

Internet of Things (IoTs) is a promising technology that combines communication and data networking. Integration of Smart Grids (SGs) and IoT will fulfill an increased demand for energy requirements by transforming the reliable and efficient traditional power grids. The SG enables bi-directional transmission between the Service Provider (SP) and Smart Meter (SM) to send and receive information regarding electricity consumption over a public channel. The public channel allows an adversary to intercept the information exchanged between them and tamper with the SM as it is installed outside which leads to forging or modification of the messages and privacy violation. In addition, the SM has limited computational and storage capacity. To protect SM privacy and securely communicate in the SG network, Physically Unclonable Functions (PUFs) based Authentication and Key Agreement (AKA) schemes were suggested in the literature. However, they may suffer from the machine learning modeling attack and several other security issues. Thus, we propose a finite state machine enabled controlled PUF based AKA (PF-AKA) Framework for the IoT based SG (IoT-SG) network. The PF-AKA framework is verified formally using the Real-or-Random (RoR) model, AVISPA tool, and BAN logic. It shows that PF-AKA achieves the security requirements along with protection from the SM physical and modeling attacks. The performance analysis is carried out and it represents that the PF-AKA yields competitive computation and communication costs compared to AKA schemes in the literature for the IoT-SG network. [ABSTRACT FROM AUTHOR]

A smart meter is an automation technology that sends real-time power consumption of electric appliances to the outsourced cloud through the aggregator node. An outsourced cloud is used by the Utility providers to release computation and storage overhead. The real-time smart meter data helps in the management of demand and supply in the smart grid. However, the real-time smart meter data exposes the privacy of smart meter customers and inefficient aggregated smart meter data results in unbalanced power management decisions in the smart grid. Therefore, a smart meter data storage (SMDS) model is proposed that aggregates the encrypted smart meter data at the fog node with the property of homomorphic encryption and stores it on the outsourced cloud. Two clouds are used to process the smart meter data and only the utility provider is able to retrieve the actual power consumption of the smart meter. Additionally, a secure query processing model is designed to retrieve the smart meter data on the outsourced cloud. Experimental results show the effectiveness of the proposed work and the feature comparison demonstrates the superiority of the proposed over the existing works. [ABSTRACT FROM AUTHOR]

The transformation towards cleaner energy consumption in Togo is progressing at a slow pace due to a combination of unfavourable socioeconomic, demographic, and spatial factors that favour traditional fuel use over clean and efficient energy. This study tries to quantify the inequality in modern energy access by applying the inequality of opportunity framework. We use a parametric approach to the 2015 Togolese Living Standard Survey; our results show that, on average, 40.76% of inequalities in household modern energy access and consumption in Togo are due to unequal circumstances beyond their control. A Shapley-value decomposition shows that inequalities of opportunity are greater for older persons and women than for youths and men, respectively. Moreover, these inequalities are more pronounced in rural areas and poor regions, especially the Savanna and Kara regions. Consequently, policies aimed at reducing inequality of opportunity in modern energy access and consumption in Togo should emphasise demographic factors, such as gender, age composition, and geographic location of households. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The electrocatalytic conversion of CO2 to produce fuels and chemicals holds great promise, not only to provide an alternative to fossil feedstocks, but also to use renewable electricity to convert and recycle the greenhouse gas CO2 to mitigate climate problems. However, the selectivity and reaction rates for the conversion of CO2 into desirable carbon-based products, especially multicarbon products with high added value, are still insufficient for commercial applications, which is attributed to insufficiently favourable microenvironmental conditions in the vicinity of the catalyst. The construction of catalysts/electrodes with confined structures can effectively improve the reaction microenvironment in the vicinity of the electrodes and thus effectively direct the reaction towards the desired pathway. In this review, we firstly introduce the effects of the microenvironment at the electrode-electrolyte interface including local pH, local intermediate concentration, and local cation concentration on CO2 reduction reaction (CO2RR) as well as the mechanism of action, and then shed light on the microenvironmental modulation within the confined space, and finally and most importantly, introduce the design strategy of CO2RR catalyst/electrode based on the confinement effect. [ABSTRACT FROM AUTHOR]

In order to solve the problem of large-scale power grid, complex connection relationship, and large demand for electricity carbon factor calculation resources, this paper proposes an algorithm that is easy to model the power grid topology and has high computational efficiency to implement the application of "One Electricity Carbon Chart". In this paper, the graph computing component is used to realize the graph modelling, distributed storage, and high-performance calculation of the electric carbon factor for the data representing the topology of the power grid, such as the power grid table, AC line segment table, and AC line end table. This paper realizes the rapid grid topology graph modelling of EMS dispatching system data, so that the grid structure and active power flow can be intuitively displayed and dynamically studied. This paper also realizes the distributed parallel calculation of electric carbon factor in the case of a ring network, which solves the problem of difficult analysis and calculation of large-scale power grid topology. We conduct experiments on real datasets, and the experimental results demonstrate the effectiveness of the proposed method. In addition, the research on the "One Electricity Carbon Chart" makes the carbon emission of power plants depend on the power flow of the power grid and transfers the accounting method, realizing the calculation of the carbon emission factor of the power grid at the plant level, and provides a new perspective for the sharing of carbon emission responsibilities in the power system. [ABSTRACT FROM AUTHOR]

Solar photovoltaics have emerged as an economic source of renewable electricity generation for regions with high solar irradiation. Still, the installation of utility-scale PV systems is often disrupted due to the unavailability of land. Sparsely populated areas with a hot, dry climate can mitigate the land-energy conflict as they experience higher solar irradiation. In arid areas, the PV systems incur more losses due to dust deposition on PV modules and reduced energy conversion efficiency at higher temperatures. The shortage of water makes the module cleaning procedures cost-prohibitive. Floating Solar photovoltaic (FSPV) systems resolve these problems due to their reduced land footprint, higher efficiency, and reduce evaporative water loss. This study estimates the energy generation of a 4.8 MW FSPV-based microgrid in a hot, semi-arid urban area. By utilizing 5% area of the waterbody, the FSPV system yields 8.876 GWh of electricity annually, to meet 41.7% electricity demand of the study area. The system generates 16.7% more power compared to the land-based PV system. The performance ratio of 82%, and a DC capacity utilization factor of 20.3% is achieved. The system saves 52,810.32 m3 of water per year, compensating for the water requirement of 786.99 m3. for cleaning and dust removal of PV modules. The weekly cleaning procedure limits the soiling loss to 2.79%, compared to 8.47% after a month without cleaning. The FSPV-based microgrid reduces annual CO2 emissions by 2.93 tonnes. This study shall enable engineers, urban planners, and policymakers to develop clean energy-based, sustainable microgrid solutions for urban areas. [ABSTRACT FROM AUTHOR]

Load shifting for existing electric resistance water heaters (EWHs) presents a potential solution to utility challenges caused by the temporal mismatch between solar generation and electricity consumption. A case study in the Florida Power & Light (FPL) territory for both an individual and a fleet of EWHs evaluates this potential. Two optimization problems are solved using mixed-integer linear programming to maximize solar self-consumption and minimize electricity cost for an individual EWH. Compared to default operation, the solar maximization strategy results in nearly twice as much heating electricity from solar (80% vs. 42%) and 14% increased electricity cost based on FPL rates. An energy balance approach shows the aggregate impact of all FPL EWHs on days of peak and minimal load, June 25th and December 3rd. Publicized goals of increased solar capacity by 2030 will cause changes to the FPL net load, total load minus solar generation. The four million EWHs in the territory can stabilize the net load in 2030 as shifting their load causes a 24% decrease in the standard deviation of the predicted load on both dates. These results encourage widespread adoption of retrofit devices that control EWHs based on solar generation profiles and water consumption schedules. [ABSTRACT FROM AUTHOR]

Studying user electricity consumption behavior is crucial for understanding their power usage patterns. However, the traditional clustering methods fail to identify emerging types of electricity consumption behavior. To address this issue, this paper introduces a statistical analysis of clusters and evaluates the set of indicators for power usage patterns. The fuzzy C-means clustering algorithm is then used to analyze 6 months of electricity consumption data in 2017 from energy storage equipment, agricultural drainage irrigation, port shore power, and electric vehicles. Finally, the proposed method is validated through experiments, where the Davies-Bouldin index and profile coefficient are calculated and compared. Experiments showed that the optimal number of clusters is 4. This study demonstrates the potential of using a fuzzy C-means clustering algorithm in identifying emerging types of electricity consumption behavior, which can help power system operators and policymakers to make informed decisions and improve energy efficiency. [ABSTRACT FROM AUTHOR]

With the large-scale development and utilization of renewable energy, industrial flexible loads, as a kind of load-side resource with strong regulation ability, provide new opportunities for the research on renewable energy consumption problem in power systems. This paper proposes a two-layer active power optimization model based on industrial flexible loads for power grid partitioning, aiming at improving the line over-limit problem caused by renewable energy consumption in power grids with high proportion of renewable energy, and achieving the safe, stable and economical operation of power grids. Firstly, according to the evaluation index of renewable energy consumption characteristics of line active power, the power grid is divided into several partitions, and the inter-zone tie lines are taken as the optimization objects. Then, on the basis of partitioning, a two-layer active power optimization model considering the power constraints of industrial flexible loads is established. The upper-layer model optimizes the planned power of the inter-zone tie lines under the constraint of the minimum peak-valley difference within a day; the lower-layer model optimizes the regional source-load dispatching plan of each resource in each partition under the constraint of the minimum operation cost of the partition, so as to reduce the line over-limit phenomenon caused by renewable energy consumption and save the electricity cost of industrial flexible loads. Finally, through simulation experiments, it is verified that the proposed model can effectively mobilize industrial flexible loads to participate in power grid operation and improve the economic stability of power grid. [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]

In response to the practical application of the electricity consumption information collection system in the online monitoring business of measuring equipment, the author introduces a method for analyzing the abnormal flying away of electricity meters based on the IoT technology LOF local anomaly detection algorithm. This method can effectively determine whether the abnormal energy representation value belongs to accidental or trend anomalies by calculating the abnormal factor of the energy representation value. After excluding the influence of accidental data, perform a secondary judgment on the abnormal flight of the energy meter. The experimental results show that when calculating the LOF factor of the electricity meter, it can be found that the LOF curve data range is mainly concentrated in the range of 0.8 to 1.3, and there is no significant change in the LOF factor near the mutation point. This proves that this method can effectively improve the accuracy of anomaly detection, avoid misjudgment of faults, and improve the efficiency of on-site fault handling. [ABSTRACT FROM AUTHOR]

Due to the rapid development of science and information technology, electricity information acquisition system has been widely used in the electricity data collection of users. With the massive electricity data collection, it is difficult to adopt traditional data processing methods to meet the abnormal data processing. In order to effectively mine abnormal information in electricity consumption data, an anomaly detection model based on the Isolation Forest (iForest) algorithm is proposed. Firstly, the daily load curve with strong regularity is used as the characteristic index of anomaly monitoring, and the users with abnormal electricity consumption data are preliminarily screened. Secondly, on the basis of electrical variables, the suspected abnormal users are further analyzed, and the anomaly identification model of electricity data is established to automatically classify the voltage at the metering point. Moreover, combined with the current data, the abnormality of the electric energy metering device is identified, and then the validity of the model is determined through on-site verification. Finally, according to the participating voltage of the fault phase, the 96-point voltage data frozen during the failure period is analyzed and the correction coefficient is adjusted. The results reveal that the electricity data detection model based on the iForest algorithm has significant advantages in computational efficiency. Through the cumulative recall and Precision-Recall (P-R) curves of the model, it is found that the majority of abnormal users can be detected only by detecting a few users with high abnormal scores, which shows that the model has high efficiency. The decision tree algorithm combined with the current data can effectively identify the anomalies of the energy metering device, which verifies the validity of the anomaly identification model of the electricity consumption data. [ABSTRACT FROM AUTHOR]

Purpose: Cellobiose lipids (CL) are biosurfactants produced by various Ustilaginaceae species in aerobic fermentations. They show high potential for application as alternatives to conventional oleochemical- or petrochemical surfactants. To ensure their environmentally friendly performance, we aimed to assess CL production from a life cycle perspective at an early developmental stage to identify process steps that have the highest impact on the environment. With this information, optimisation approaches can be derived. Materials and methods: Following a cradle-to-gate approach, we modelled the CL fermentation and purification process based on experimental data from the lab scale and process simulation data at a 10 m3 scale. For LCA, the impact categories (IC) abiotic depletion potential (ADP), eutrophication potential, photochemical ozone creation potential, global warming potential, acidification potential, and the primary energy demand were calculated for all process steps. Based on the obtained results, process bottlenecks were identified, and alternative process scenarios varying the related process parameters were simulated. These were used to assess the environmental impact reduction potential (EIRP) of an optimised process and draw recommendations for experimental process optimisation. Results and discussion: The obtained results showed that the fermentation caused ~ 73% of ADP and more than 85% of all other ICs. The major contributor was the electricity consumption for continuous fermenter aeration. Thus, reducing the fermentation duration from the initial 14 to 5 days would result in a decrease in all investigated ICs of up to ~ 27–52%. An increase in CL concentration results in a decrease in all ICs of a similar magnitude due to the higher yield per batch at comparable energy and material consumption. Although the share of purification process steps to all ICs is overall relatively small, implementing foam fractionation for in situ product recovery showed an additional EIRP of 18–27% in all purification IC shares. Conclusions: The conducted LCA showed that overall, more EIRP can be achieved by optimising fermentation process parameters compared to purification process steps. This is mainly due to the long fermentation duration and large energy consumption for fermenter aeration. This highlights the importance of using LCA as a driver for process optimisation to identify process steps with high EIRP. While some of the results are specific to CL, other obtained results can be transferred to other fermentations. [ABSTRACT FROM AUTHOR]

Over the last two decades, research increasingly has paid attention to resilience as a way to strengthen electricity systems against the cascading impacts caused by electricity disruptions. Although much of the electricity resilience literature has focused on scale of large grids, a growing segment of research has focused on smaller-scale electricity systems, particularly with applications for communities. Research on financing these systems could encourage their uptake in local communities, particularly by including community in the ownership or operation of these systems; however, much of this research remains comparatively nascent. This paper seeks to review what previous studies have identified as some of the conditions that shape financing electricity resilience in local communities in G7 countries and how this field uses the term "electricity resilience" compared to broader uses of electricity resilience. The review provides a technical overview of smaller-scale systems for communities and a review of three socio-economic research areas—governance, cost-benefits, and business models—which shape financing electricity resilience in local communities. The discussion section finds that costs and the level of community involvement seem to play a fundamental role in shaping the conditions for financing electricity resilience across much of the research. Comparing this field to broader uses of "electricity resilience" suggests that more work is needed to understand the role of adaptation in financing electricity resilience for local communities, particularly over the long term. We posit that the field's approach costs and its inclusion of the community in electricity resilience may contribute to its general lack of attention to long-run adaptation. Despite potential benefits of continued advancements from technical research, the maturity of the field and age of some of the early cases suggests that researchers could begin to study adaptation to electricity disruptions at the community level more than in the past. [ABSTRACT FROM AUTHOR]

The economic development of any nation depends on energy production and consumption, thereby, needing more exploration from researchers. The current article thus makes an attempt to examine the role of REW and non-REW energy on the economic development in China. The current article has used renewable energy production (REP), and renewable energy consumption (REC) to measure renewable energy. The current research has used fossil fuel energy consumption, energy use, energy import, and electric power consumption to measure non-renewable energy. The present study has taken secondary data from world development indicators from 1986 to 2020. The article has applied the nonlinear autoregressive distributed lagged model to evaluate the association among the understudy constructs. The findings indicated that REP and REC, fossil fuel energy consumption, energy use, energy import, and electric power consumption are linked with economic development in a positive manner in China context. The study guides the regulators in establishing the regulations related to economic development through both type of energy resources. [ABSTRACT FROM AUTHOR]

Introduction: With the increasing fluctuations in the current domestic and international economic situation and the rapid iteration of macroeconomic regulation and control demands, the inadequacy of the existing economic data statistical system in terms of agility has been exposed. It has become a primary task to closely track and accurately predict the domestic and international economic situation using effective tools and measures to compensate for the inadequate economic early warning system and promote stable and orderly industrial production. Methods: Against this background, this paper takes industrial added value as the forecasting object, uses electricity consumption to predict industrial added value, selects factors influencing industrial added value based on grounded theory, and constructs a big data forecasting model using a combination of "expert interviews + big data technology" for economic forecasting. Results: The forecasting accuracy on four provincial companies has reached over 90%. Discussion: The final forecast results can be submitted to government departments to provide suggestions for guiding macroeconomic development. [ABSTRACT FROM AUTHOR]

The fluctuations in power output from distributed power sources are rapid and dramatic, causing voltage fluctuations in the distribution network that threaten the safety of electricity consumption. Soft open points (SOPs) can replace traditional contact switches and are expected to suppress voltage fluctuations. However, traditional power regulation methods based on the reference values are unable to address rapid and large voltage fluctuations. Therefore, this paper proposes an adaptive power control method for SOPs based on virtual impedance. The SOP is modeled as a series link of adjustable impedance and a voltage source. Then, the voltage difference component is used to calculate the reference for the DQ domain to regulate the power flow in real time automatically. By doing so, the proposed method can smooth the voltage fluctuations in the distribution network. Additionally, the virtual impedance is also optimized to minimize the power loss. Finally, the method is validated through simulation and experiments, demonstrating that this method can automatically regulate power and significantly reduce voltage fluctuations. [ABSTRACT FROM AUTHOR]

With the continuous increase in air conditioning installations, the proportion of air conditioning electricity consumption to total electricity consumption is growing. Research on the decomposition of air conditioning electricity consumption is of great significance for assessing electricity usage, formulating electricity scheduling plans, and ensuring the stable operation of the power grid. Currently, typical day selection strategies for the decomposition of air conditioning electricity consumption often overlook the corresponding relationship between typical daily electricity consumption and temperature. Therefore, this paper proposes an air conditioning electricity consumption decomposition method based on the dependence between temperature and electricity consumption. This method filters typical days based on Copula-based dependence indicators and equiprobable ellipses, determines the baseline electricity consumption curve through model selection voting, and ultimately calculates the air conditioning electricity consumption. The effectiveness of the proposed method is validated by applying it to electricity consumption data in Fuzhou from 2019 to 2022. [ABSTRACT FROM AUTHOR]

Background and aim: Traffic-related ultrafine particle pollution near highways is associated with adverse health. Reducing exposure by use of portable air purifiers in homes is one approach to reducing this risk. However, the reaction of residents to having air purifiers in homes is not well studied. Methods: Within the framework of our randomized crossover trial of air purifiers in homes near a major highway, we collected data about participants' use and reactions to air purifiers using questionnaires at their 30-day and 90-day home visits, recorded electricity consumption using HOBO monitors, and conducted structured interviews with participants. Results: Nearly all 150 participants reported running the air purifiers virtually 24 h every day in both their living room and their bedroom in the prior month. The units' HOBO electricity use, from a subset of 45 participants, supported the participants' responses from the questionnaire. Approximately 80% of participants reported setting their air purifier on the medium setting. Tolerance to air purifier noise increased significantly between the 30-day and 90-day home visits, with approximately two thirds reporting not being bothered at all by the noise. The qualitative interviews in a subset of 26 participants yielded consistent responses to those from the questionnaires. Size of unit, airflow, and energy consumption were additional concerns that emerged during the interviews. Conclusions: Results from the questionnaires, HOBO data, and structured interviews all suggest participants had positive reactions towards the presence of in-home APs, and therefore may be receptive to using air purifiers in their homes on a regular basis. Trial registration: ClinicalTrials.gov, NCT04279249. Registered 09 October 2019. [ABSTRACT FROM AUTHOR]

Despite the abundance of research on reducing carbon emissions, there is a significant gap in understanding the influence of macroeconomic factors on carbon dioxide (CO2) emissions from a spatial-structural perspective. This study aims to contribute to the literature by investigating the impact of macroeconomic factors on carbon dioxide emissions in six East African countries between 1989 and 2020. Using spatial econometric panel models, the study analyzed spatial dependence among the variables. The empirical findings indicate that gross domestic product (GDP) per capita and electricity consumption have positive direct and indirect effects on carbon emissions, while fuel prices and exports have negative direct effects, but positive spillover effects on neighboring countries. Imports have a positive impact on local economies, but negative spillover effects. Additionally, the urban population has no significant impact on the environment. These findings provide important policy implications for optimizing spatial growth patterns and achieving a low-carbon economy in East African countries. [ABSTRACT FROM AUTHOR]

The power generation capacity must be increased to accommodate population growth and address the lack of electricity access in rural areas. Traditional power plants in South Africa are unable to keep up with the growing demand for electricity. By strategically planning and building clusters of renewable energy sources like solar and wind, microgrid operators can provide a sustainable solution that boosts electricity supply while being cost-effective and environmentally friendly. Utilizing renewable energy can help alleviate strain on power plants by reducing peak demand in constrained distribution networks. The benefits of renewable energy include lower electricity expenses, enhanced system reliability, investment reallocation, and reduced environmental impact. These advantages will enhance the efficiency of the power system and contribute economic value to society. However, integrating solar power into the network infrastructure presents challenges such as fundamental changes in network structure, its intermittent nature due to unpredictability, and geographical constraints, which can complicate the task of grid operators in balancing electricity supply and demand within system limits while minimizing costs. The study employs Homer Pro 3.18.1 software to assess the economic costs and benefits of effectively integrating renewable technologies into the power grid. The aim is to evaluate the economic and technical feasibility of investing in renewable energy projects within the network. The research outcomes can guide power system operators, planners, and designers in leveraging solar energy to drive economic growth and industrial advancement, as well as assist independent power producers in making informed investment choices. [ABSTRACT FROM AUTHOR]

The MGM (1, n) model has the characteristics of less data required, simple modeling, and high prediction accuracy. It has been successfully applied to short-term forecasting across various economic, social, and technological domains, yielding promising outcomes. There is insufficient attention paid to the interpolation coefficient of the model. The interpolation coefficients determine the extent of model fitting, which, in turn, impacts its prediction accuracy. This study made some improvements to the interpolation coefficients and proposed an improved MGM (1, n) model. IMGM (1, n) model and MGM (1, n) model were employed to compare the performance of the improved MGM (1, n) model. Upon a series of comparisons and analyses, it was concluded that the improved MGM (1, n) model has higher fitting and prediction accuracy than the other two forecasting methods. The method was used to forecast the short-term electricity consumption of Linfen City. The findings revealed that by 2030, the electricity demand in Linfen City is projected to be 563.7 billion kWh. [ABSTRACT FROM AUTHOR]

Background: There is a close link between the lack of electricity access and poverty indicators such as illiteracy, high infant mortality, lack of access to health care and malnutrition among others. Most rural farming communities in Ghana lack access to electricity due to the high cost of extending the grid to these communities. This lack of access tends to worsen the gap between urban and rural inhabitants regarding access to education, healthcare and development. Methods: This study assessed the technical and theoretical potential of agricultural residues in providing electricity to off-grid communities. The study used crop production figures of maize, cassava, millet and groundnut in the Soma and Goyiri farming communities in the Sawla-Tuna-Kalba District to conduct an assessment of the theoretical and technical potential of residues from the crops. The production figures of these crops were obtained from the District Office of the Ministry of Food and Agriculture. Expected electricity demand of households, schools and health centers in the study communities were collected and employed for the projected load demand estimates. Results: The study found that 312.23 MWh/day of electricity could be generated from the combined residues of maize, cassava, millet and groundnut from the two communities. This amount of electricity is capable of providing ~ 202 to 263 times the peak electricity demand of the studied communities. Out of the total electricity demand of the two communities, only about 91 kWh/day is needed for use in a school and Community Health Promotion and Services (CHPS) compound, implying that the electricity from crop residues can also help to improve education and health provision in the rural communities. Conclusion: It is concluded that the potential of crop residues in meeting the electricity demand of off-grid communities is enormous. Hence, it must be considered in Ghana's energy development plans to achieve universal electricity access. [ABSTRACT FROM AUTHOR]

Electric bicycles offer convenient short-distance travel, but improper battery charging poses a fire risk, especially indoors, potentially causing significant accidents, property damage, and even threats to life. Recognizing the charging state of electric bicycle batteries is crucial for safety. This paper proposes a novel method to identify the charging process of lithium batteries in electric bicycles. Methods that do not require physical alterations to the equipment are used to acquire users' electricity consumption data, with current signals preprocessed and input into a combined model integrating convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks. The proposed model captures complex patterns and features in the charging data, effectively identifying the charging characteristics of lithium batteries. Validation using NASA's lithium battery dataset and real experimental data shows that the combined model achieves recognition accuracy of 96% and 97% on training data and 93% and 94% on validation data. Further validation under multiple device loads and comparison with other models indicate that the proposed method is highly accurate, outperforming traditional CNN and LSTM models by 4–9%. This research enhances the safety and regulation of electric bicycle battery charging and provides a reliable method for non-intrusive load identification in smart monitoring systems, contributing to improved safety measures and energy management in residential environments. [ABSTRACT FROM AUTHOR]

Introduction: Aquaponics provide multiple benefits due to the simultaneous yield of vegetables and fish, however they are characterized by increased greenhouse gas emissions owing to intensive production system. The most appropriate method for quantifying the environmental effects of these systems is Life Cycle Assessment with which the identification of hotspots and the suggestion of improved production plans can be achieved. The purpose of the present study was to evaluate the environmental impact of a pilot high-tech aquaponic system utilized for the simultaneous production of baby lettuce and rocket as well as rainbow trout, in indicators such as Global Warming Potential. Materials and methods: To achieve this goal, data on inputs and outputs were collected from 12 case studies that were implemented, combining different fertilizer treatments, substrate choices, plant species cultivated and water source provision. Life Cycle Assessment was performed using SimaPro v.9.4.0.2 software. Results: The results showcase that the optimal case studies include the cultivation of baby lettuce and rocket in perlite substrate using wastewater from fish and partial use of synthetic fertilizers. Indicatively, Global Warming Potential of these cases was calculated at 21.18 and 40.59 kg CO2-eq/kg of vegetable respectively. The parameter with the greatest impact on most of the environmental indicators was electricity consumption for the operation of the oxygen supply pump for the fish tanks, while greenhouse infrastructure had the greatest impact in Abiotic Depletion and Human Toxicity impact categories. In an alternative production scenario tested where renewable energy sources were used, system impacts were reduced by up to 50% for Global Warming Potential and 86% for Eutrophication impact. The results of this study aspire to constitute a significant milestone in environmental impact assessments of aquaponic production systems and the adoption of more sustainable farming practices. [ABSTRACT FROM AUTHOR]