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Although there are opportunities to reduce electrical energy demand in unit processes of mechanical pulp-based paper and paperboard production, this may not be financially beneficial. This is generally because energy optimization opportunities connected to reduced refiner electricity demand in mechanical pulping systems also results in less steam available for the drying of the paper. As modern high consistency refiner systems produce approximately one ton of steam for each MWh of electricity when producing one ton of pulp, a reduction in electric energy demand leads to increased fuel demand in steam boilers to compensate for the steam shortage. In this study, we investigated what the financial and environmental situation would look like if we were to expand the system border from a paper mill to a larger system consisting of a mechanical pulp-based paper or paperboard mill, a district heating system with an incineration boiler and a chemical pulp mill. Mechanical pulp production has a wood to product yield of >90%, a high electric energy demand to separate woodchips to pulp and is a net producer of heat and steam while chemical pulp-based production has a wood to product yield of 50%, a low electric energy demand and is a net heat and electricity producer due to the combustion of dissolved wood polymers. The aim of this research is to create useful and robust models of how to use excess heat from certain industry sites to cover the steam shortage in other industry sites by means of utilizing and optimizing the district heating systems connecting these sites. For this purpose, we used a simulation tool which dynamically allows us to evaluate different scenarios. Our results shows that there is great potential to reduce both carbon dioxide emissions and production costs for industry sites and society by means of these tools. [ABSTRACT FROM AUTHOR]

Summary: In the pulp and paper industry, black liquor, which is a biomass resource, is burned to produce electricity. Black liquor is concentrated to 21 wt% water through an evaporator before being burned in a boiler. For the evaporator, a multiple‐effect evaporator (MEE) is mainly used, but it requires a large amount of energy and cost. Therefore, it is crucial to reduce energy and cost of evaporation process. Hence, this study suggested a novel process model that integrated mechanical vapor recompression (MVR) with MEE to increase the energy efficiency. The suggested MVR‐assisted evaporation process was composed of preheating and evaporation processes to effectively concentrate black liquor. In addition, it reduced the steam consumption of MEE by using MVR, which uses relatively inexpensive electric energy in the pre‐evaporation process. In the simulation results, the steam, electricity consumption, and the latent heat recovered from the secondary vapor of the suggested process were quantitatively analyzed to verify the energy efficiency. The results indicate that the proposed process increases substantial energy efficiency compared to the conventional process. Then, the appropriateness of the suggested process was evaluated by the techno‐economic analysis. The total annualized cost (TAC) is determined for both current and potential future economic benefits. TAC of the MVR‐assisted MEE configuration can be reduced by up to 77.54%. [ABSTRACT FROM AUTHOR]

Etulipa demonstrates a full‐color reflective display based on Electrowetting display technology. The CMY color architecture yields very bright colors. The switching speed of electrowetting cells enable displaying of video content. Average power consumption is 7Watt/m 2 implying that the displays can run on solar systems without any need for external charging. [ABSTRACT FROM AUTHOR]

The paper industry is heavily using energy during all the phases of the manufacturing process. This is especially the case in Nordic mills where pressure ground wood is the source of fiber. The purpose of this case study is to show how energy consumption behaves on a monthly basis in a Finnish paper mill that has several paper machine lines. The research examines different categories of energy usage, as well as the overall costs. The results show that the monthly energy usage varies significantly. The electricity consumption of the case mill is rather steady, but there is significant monthly variation in steam and natural gas consumption. Moreover, steam and electricity unit costs increased significantly when coming to the end of the research period. The longitudinal case data shows that it is difficult to enhance energy consumption per produced unit at the paper mill. One reason for this is the bleak demand outlook for the end products, which often result in downsizing and closing operations inside the integrated unit. [ABSTRACT FROM AUTHOR]

The subsector of pulp and paper presented in 2013 an electric power consumption of 9.3% of electricity consumption in the industrial sector in Brazil. It experienced a significant growth of production in the last 20 years, exceeding 160%, and their consumption of electricity increased by 130%, reaching the level of 19,594GWh. Due to its characteristics, with a known structure and production processes, and its importance in the country's industrial sector, this subsector was selected for study in this paper, to evaluate the behavior of its long-term annual electricity demand, at the horizon of 20 years ahead, in energy efficiency scenarios through bottom-up approach. This approach is data intensive and requires data with good quality; it consists of a hierarchical structure that enables to check how specific actions can affect the results at each level and its effect on higher levels. [ABSTRACT FROM AUTHOR]

Energy consumption optimization belongs to the main agendas in sustainable industries. Here, cost‐saving opportunities in the pulp and paper industry as one of the largest industrial energy consumers worldwide are in the focus. Energy‐saving measures are proposed employing a large‐scale mathematical model of a paper machine based on mass and energy balances supported by real‐time experiments in paper machine operation. The presented measures are based on the optimization of paper stock pumping and a drying section operation. The total energy‐saving effect of the four proposed measures accounted for a reduction in specific heat and electricity consumption by more than 2 %. The proposed procedures require no or low investment costs with the overall simple rate of return of less than one year. [ABSTRACT FROM AUTHOR]

The pulp and paper sector is the fourth largest industrial sector in terms of energy use in the world. Of the numerous processes involved in paper-making by a paper machine, the dryer section is the process that consumes the largest amount of energy. A model-based method for estimating energy-saving potentials of the dryer section was put forward in the present study. It was done by four steps: establishing a mathematical model about energy consumption, determining the model parameters, benchmarking the drying performance to obtain the corresponding energy-saving measures, and estimating the energy-saving potentials by using the mathematical model. In a case study, a multi-cylinder dryer section was selected to illustrate the method. After a fundamental field test and observation, several operating problems that restricted the energy performance of paper drying were found. And then several reasonable energy-saving measures were suggested to the operators. Finally, applying the mathematic model, it was found that 0.32 ton of steam will be saved when producing 1 ton of paper in recommended operating conditions. With the designed capacity of 200,000 tons/year, the annual steam-savings will be 64,000 tons. Generally, the price of steam is 130–150 Chinese Yuan (about US$21–24) in China, and the annual economic benefits will be 8.32–9.60 million Chinese Yuan (about US$1.344–1.536 million). [ABSTRACT FROM AUTHOR]

Refuse derived fuel (RDF) is an environmentally friendly renewable fuel developed to reduce waste generation. RDF can consist of various kinds of waste such as paper and gardens. One of the critical parameters is the chemical element and calorific value. The purpose of this study was to determine the potential for waste reduction and the relationship of ultimate longevity in RDF to the calorific value. This study's paper and garden waste mixture were P0 (100% paper), P25 (75% paper and 25% garden), P50 (50% paper and 50% garden), P75 (25% paper and 75% garden), and P100 (100% garden). The calorific value of the mixture can reach 3.6-5.2 kWh/kg. Simultaneously the relationship of ultimate elements nitrogen (N), hydrogen (H), oxygen (O), and ash affects the heating value of RDF. Sampling the application in Depok City can reduce waste by 6.67%, with the potential for electrical energy from paper and garden wastes of 358,903.8 kWh and 48,681 kWh, respectively. This shows that this energy waste can supply 0.1% of the total daily electricity demand in Depok City. [ABSTRACT FROM AUTHOR]

This study considered the possibility of reducing the environmental footprint of paper and pulp industry by producing bioenergy from paper sludge by using process wastewater instead of fresh water, and reclaiming water trapped in paper sludge. Experimental studies are conducted with streams from three different pulp and paper mills (virgin pulp mill (VP), corrugated recycling mill (CR), tissue printed recycling mill (TPR)) for sequential bioethanol and biogas production with simultaneous reclamation of water from paper sludge (PS). Total energy yields of 9215, 6387, 5278 MJ/tonne dry PS for VP, CR and TPR, respectively, were obtained for ethanol-biogas production. Virgin pulp paper sludge gave the highest yield for ethanol and biogas in stand-alone processes (275.4 kg and 67.7 kg per ton dry PS respectively) and also highest energy conversion efficiency (55%) in sequential process compared with CR and TPR. Energy and environmental case study conducted on virgin pulp mill has proven the possibility of using paper sludge bioenergy to reduce energy demand by 10%, while reclaiming 82% of the water from the PS, reducing greenhouse gas emissions (GHG) by 3 times and producing solids suitable for land spreading. Integration of a sequential bioprocessing technology into the pulp and paper industry treats wastewater, produces bioenergy and eliminates the need to landfill paper sludge. [Display omitted] • Process wastewater substituted clean water in fermentation and anaerobic digestion. • Sequential bioprocessing reclaimed 65%−85% of entrapped water from paper sludge. • 80% of GHG emissions from landfills is avoided in paper sludge bioprocessing. • Integration of paper sludge bioprocessing in pulp mills can reduce energy demand by 10%. [ABSTRACT FROM AUTHOR]

Abstract: Paper production is an energy-intensive process and accounted for about 9% of industrial energy demand in Germany in 2008. There have only been slow improvements in energy efficiency in the paper industry over the past twenty years. Policies can accelerate the progress made, but knowledge about the remaining efficiency potentials and their costs is a prerequisite for their success. We assess 17 process technologies to improve energy efficiency in the German pulp and paper industry up to 2035 using a techno-economic approach. These result in a saving potential of 34 TJ/a for fuels and 12 TJ/a for electricity, which equal 21% and 16% of fuel and electricity demand, respectively. The energy savings can be translated into mitigated CO2 emissions of 3 Mt. The larger part of this potential is found to be cost-effective from a firm''s perspective. The most influential technologies are heat recovery in paper mills and the use of innovative paper drying technologies. In conclusion, significant saving potentials are still available, but are limited if we assume that current paper production processes will not change radically. Further savings would be available if the system boundaries of this study were extended to e.g. include cross-cutting technologies. [Copyright &y& Elsevier]

Bio-based monomers represent the future market for polymer chemistry, since the political economics of different states promote green ventures toward more sustainable materials and processes. Industrial pulp and paper processing represent a large market that could advance the use of by-products to avoid waste production and reduce pollution. Lignin represents the most available side product that can be used to produce a bio-based monomer. This review is concentrated on the possibility of using bio-based monomer derivates from pulp and the paper industry for UV-curing processing. UV-curing represents the new frontier for thermoset production, allowing a fast reaction cure, less energy demand, and the elimination of solvent. The growing demand for new monomers increases research in the environmental field to substitute for petroleum-based products. This review provides an overview of the main monomers and relative families of compounds derived from industrial processes that are suitable for UV-curing. Particular focus is given to the developments reached in the last few years concerning lignin, rosin and terpenes and the related possible applications of these in UV-curing chemistry. [ABSTRACT FROM AUTHOR]

In this paper, a systematic literature review is presented, through a survey of the main digital databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly electricity demand for residential electricity and to realize the performance evolution and impact of Artificial Intelligence (AI) in STLF. With these specific objectives, a conceptual framework on the subject was developed, along with a systematic review of the literature based on scientific publications with high impact and a bibliometric study directed towards the scientific production of AI and STLF. The review of research articles over a 10-year period, which took place between 2012 and 2022, used the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) method. This research resulted in more than 300 articles, available in four databases: Web of Science, IEEE Xplore, Scopus, and Science Direct. The research was organized around three central themes, which were defined through the following keywords: STLF, Electricity, and Residential, along with their corresponding synonyms. In total, 334 research articles were analyzed, and the year of publication, journal, author, geography by continent and country, and the area of application were identified. Of the 335 documents found in the initial research and after applying the inclusion/exclusion criteria, which allowed delimiting the subject addressed in the topics of interest for analysis, 38 (thirty-eight) documents were in English (26 journal articles and 12 conference papers). The results point to a diversity of modelling techniques and associated algorithms. The corresponding performance was measured with different metrics and, therefore, cannot be compared directly. Hence, it is desirable to have a unified dataset, together with a set of benchmarks with well-defined metrics for a clear comparison of all the modelling techniques and the corresponding algorithms. [ABSTRACT FROM AUTHOR]

A new process for the production of bioethanol from paper mill sludge (PMS) is described in this work. PMS biomass feedstock was processed via the simultaneous saccharification and fermentation (SSF) with and without accelerants. The enzymatic hydrolysis and fermentation were first evaluated, and the energy demand was 2.2 MJ/L of produced ethanol. When the enzymatic hydrolysis and fermentation were combined, the energy demand was reduced to 1.0 MJ/L ethanol, the sugars production increased, and the overall capital cost of the process decreased. The sugar yield was improved by adding accelerant and selecting the optimal fiber recovery method. The accelerant improved the enzymatic hydrolysis via a pathing/bridging mechanism. The SSF with the chemical fiber recovery method coupled with accelerant addition would be the best process configuration. Upon this combination, the glucose profile was enhanced from 9.8 g/L to 17.0 g/L. The sludge fiber conversion by SSF was improved by selecting an efficient fiber recovery method combined with the accelerant addition. SSF in chemical fiber recovery with accelerant addition was the best process by a 10% improvement of ethanol yield. The proposed process configuration offers a lower cost and sustainable process and contributes to the circular economy of zero waste discharges. • A combined SSF process using PMS feedstock to produce bioethanol was developed. • The energy saving in an integrated process was 1.2 MJ/L ethanol. • The optimal fiber recovery from paper mill sludge was achieved via acid treatment. • The optimal fiber recovery increased fractional glucose by 27%. • The cationic accelerant improved the enzymatic hydrolysis by 10% due to the patching mechanism. [ABSTRACT FROM AUTHOR]

The central role of electricity in shaping the worldwide economy is broadly acknowledged in the energy literature. Having identified the then ongoing crisis of the electricity sector, the present Awami League government initiated restructuring the energy sector since 2009 to quicken the pace of the developmental activities. All these efforts resulted in landmark achievements in the Bangladesh energy sector, especially in increasing the electricity generation capacity. However, the global Covid-19 pandemic and its aftermath effects on the Bangladesh economy require a policy revision in Bangladesh's energy sector and electricity generation mix. To our knowledge, there is no study to analyse the adverse consequences of the Covid-19 in Bangladesh energy sector. By employing a SARIMA forecasting model, we reveal that electricity demand will remain less than the actual generation in the upcoming two years. Also, the DGE model finds that due to Covid-19, the economic growth rate will be around 4.5 percent in the long-run. We further simulate that the steady-state electricity demand and standard consumption values would fall by 8-10 percent and 6 percent, respectively. We recommend that a review of the power sector master plan is needed for future energy security and economic stability in Bangladesh. [ABSTRACT FROM AUTHOR]

Part of the dewatering in a paper machine takes place via vacuum suction boxes situated below the moving web. In addition to the removal of liquid water, considerable amounts of air are sucked through the paper. The air flow that accompanies dewatering is a crucial parameter for the electricity consumption of a vacuum system. The present study models this air flow, combining differential conservation equations with fiber characterization. Measured air flow rates for different vacuum levels, basis weights, and pulp types are compared to model predictions. More than 70% of the data agree within the range of experimental error. [ABSTRACT FROM PUBLISHER]

Purpose: The reason for writing the paper is growing complexity of management of the electric grid. Our collective electric system faces accelerating, multi-dimensional needs that must be addressed to deliver to our communities. Design/methodology/approach: The supply, transmission, distribution, and consumption of electricity are closely coupled, and must be actively coordinated. This requires the coordinated sensing, measurement, and control of devices and systems spread across the grid. This paper assesses the suitability of blockchain for this purpose, as a platform for transactive energy. Findings: Blockchain technology can facilitate secure and transparent record-keeping and transactions and thus may have many potential applications in the energy sector. Practical implications: Blockchain technology is still evolving and faces some challenges, such as scalability, interoperability, regulation, and adoption. However, blockchain has the potential to revolutionize the energy sector by transforming the way energy is produced, distributed, consumed, and traded. Social implications: High electricity demand, aging power grids, and climate disasters are straining our current energy system. As a result, more people are looking to optimize energy usage—and transactive energy may offer an answer. A Blockchain-enabled transacted energy framework can help everyone from consumers to corporations benefit from improved energy efficiency and profit in the process. Originality/value: Distributed energy resources, such as energy efficiency, smart demand response, smart electric vehicle charging, building-level energy storage and distributed solar photovoltaics, become more critical every year. Review of recent papers on these issues may be a valuable source of knowledge for interested parties. [ABSTRACT FROM AUTHOR]

A direct-readout photoelectrochemical (PEC) lab-on-paper device based on coupled an electricity generating system and paper supercapacitors was established for highly sensitive detection of adenosine triphosphate (ATP). Concretely, CdSe quantum dots (QDs) decorated ZnO networks assembled sensing surface provided outstanding photoelectric properties, on which glucose oxidase (GOx) labeled aptamer was subsequently immobilized via the hybridization chain reaction. With analytes present, specific recognition was stimulated by aptamer, resulting in labeled GOx released. Such released GOx could flow to electrochemical cell to conduct electrochemical redox reactions, which could effectively produce electricity that was stored by capacitor I. Sequentially, photoactive material produced an outstanding voltage due to the decrease of steric hindrance on the sensing interface, which was utilized for charging an external capacitor II. The two instantaneous current was acquired along with the discharge of capacitor I and II by digital multimeter (DMM) readout, respectively. The summational current values performed an increment in pace with the addition of target ATP concentration with the dynamic working range from 10 nM to 3 μM and a detection limit of 6.3 nM attained. Significantly, the signal amplified strategy utilizing as-generated electricity from electrochemical redox reactions were isolated from the photoelectrodes, which was beneficial for amplifying the signal response in the PEC matrices and the development of more efficient signal performance. Image 1 • A Photoelectrochemical lab-on-paper device based on coupled electricity generating system and paper supercapacitors. • High sensitivity and selectivity for ATP detection. • Use of electricity generated from redox electrochemical reactions for signal amplification. • A portable digital multimeter for signal readout. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization in China, urban energy consumption increases rapidly, leading to energy shortages and environmental pollution, of which building operational energy consumption carbon emissions (BECCE) account for a large proportion. It has a vital impact on global warming and urban green and sustainable development. Chengdu city in Sichuan Province is taken as the research area in this paper. First, basic information and power data on four types of single buildings, including large-sized buildings, small- and medium-sized buildings, government agencies, and residential buildings, are collected. Second, the characteristics of the four types of buildings are extracted, and the calculation model of BECCE ("electricity-carbon" model) based on particle swarm optimization algorithm-support vector machine (PSO-SVM) is constructed, and the model is trained and verified using the method of five-fold cross-validation. Then, according to the mean absolute error (MAE), root mean square error (RMSE), and R² evaluation indicators, the constructed "electricity-carbon" model is compared and evaluated. Finally, the generalization ability of the "electricity-carbon" model is verified. The research results show that (1) the "electricity-carbon" model constructed in this paper has a high accuracy rate, and the fitting ability of the PSO-SVM model is significantly better than that of the support vector regression (SVR) model; (2) in the testing stage, the fitting situation of large buildings is the best, and MAE, RMSE, and R² are 858.7, 1108.6, and 0.91, respectively; and (3) the spatial distribution map of regional BECCE can be quickly obtained using the "electricity-carbon" model constructed in this paper. The "electricity-carbon" model constructed in this paper can provide a scientific reference for building emission reduction. [ABSTRACT FROM AUTHOR]

Integrated energy microgrids and shared energy storage have significant benefits in improving the energy utilization of the system, which is gradually becoming the current research hotspot. And the uncertainty of new energy output also significantly affects the stable and economic operation of integrated energy microgrid. So how to establish a set of integrated energy microgrids optimization operation model considering photovoltaic (PV) output uncertainty and shared energy storage is an urgent problem to be solved nowadays. Firstly, this paper introduces the framework of an integrated energy system microgrid containing a shared energy storage operator (ESO), and analyzes the scheduling method of the upper tier operator within the system as well as the economic benefits at the lower tier user end. Secondly, to address the randomness of PV output, Monte Carlo method is used to generate the scenarios, and then the scenarios are cut down by using the fast antecedent elimination technique. Then, an optimal operation model is established for micro grid operator (MGO) and user aggregator (UA), respectively, and based on the master-slave game relationship, so that the MGO is the leader and the UA is the follower, a Stackelberg game model is proposed to consider the integrated demand response of electricity and heat between the MGO and UA in the context of the participation of ESO in the auxiliary service of the UA. Finally, the proposed model is brought into a typical residential building community for simulation verification, and the results show that the model proposed in this paper can effectively balance the interests of MGOs and UAs, and realize win-win benefits for UA and ESO. [ABSTRACT FROM AUTHOR]

Science and technology, with their miraculous advancements, have fascinated human life to such an extent that imagining a world without these innovations is hardly possible. While technology is on the rise, an increase in immoral activities should also be noted. From a technical perspective, "power theft" is a non-ignorable crime that is highly prevalent and directly affects the economy of a nation. Today's life is unimaginable without electricity, almost all things, equipment, and appliances used daily rely on electric power to run smoothly. Solar energy is an alternative but has limitations, such as environmental changes and high initial costs. From rural to urban areas and from domestic to industrial sectors, the use of electricity has increased, but so has power theft. Detecting and eradicating such crimes with the help of the developing scientific field is the "need of the hour." With these views, the paper was conceived and designed. The paper provides a complete and comprehensive tool to stop power theft, which is extremely simple to understand and easy to implement. [ABSTRACT FROM AUTHOR]

The European Union, in pursuit of the goal of reducing emissions by at least 55% by 2030 and achieving climate neutrality by 2050, is deploying different actions, with industry decarbonization as a key strategy. However, increasing electricity demand requires an intensification of energy generation from clean technologies, and the energy system's expansion is hindered by renewable generation's climatic dependencies and the imperative for substantial electrical infrastructure investments. Although the transmission grid is expected to grow, flexibility mechanisms and innovative technologies need to be applied to avoid an overwhelming growth. In this context, this paper presents a thorough assessment, conducted within the FLEXINDUSTRIES project, of the flexibility potential across seven energy-intensive industries (automotive industry, biofuel production, polymer manufacturing, steel manufacturing, paper mills, pharmaceutical industry, and cement production). The methodology followed during the analysis entails reviewing the state-of-the-art existing flexibility mechanisms, industries' energy markets engagement, and technical/operational readiness. The results highlight the feasibility of the proposed actions for enabling energy market flexibility through demand-response programs, quantifying energy opportunities, and pinpointing regulatory and technical barriers. [ABSTRACT FROM AUTHOR]

This paper proposes a smart home appliance scheduling management (HASM) system based on demand response. An IoT based HASM system is developed to provide an efficient electricity consumption scheme to residential consumers for scheduling their appliances in a cost-effective way and smartly throughout a day. This smart load scheduler takes residential electricity users' preferences to shift the appliances with deferrable characteristics at cost effective periods considering the user is aware of dynamic tarif f information. The user interaction to the scheduling of loads provides maximum comfort level as well as saving of total electricity bill, and the effective utilization of demand response strategies to reduce peak hour electricity consumption. The results of this development indicate a reduction of daily energy consumption confirming the consumer comfort. [ABSTRACT FROM AUTHOR]

Purpose: Electrical energy is in this day and age fundamental to the functioning of every household. The use of gas, heating oil or solid fuels for home heating also involves the supply of electricity. Today, electric power is the most environmentally friendly source of energy. Experience has shown that substitution of an oil heating system with a heat pump resulted in significant financial savings for the household. Design/methodology/approach: The purpose of this paper shall entail the construction of a multi-equation econometric model describing the mechanisms of electricity consumption in a specified household. The model takes the nature of a system of interdependent equations. Described shall be the monthly volume of electricity consumption, payment amount of for this energy (in PLN), and price per 1 MWh in a given month. Monthly time series from September 2015 to November 2022 have been used, which resulted in a time series with a count of 87 statistical observations. Findings: The econometric model of household electricity consumption presented in this paper confirms both the hypothesis about feedback between the variables USAGE and PRICE as well as the recursive effect of electricity consumption volume on its value in monetary units. In addition to the cognitive value of the econometric modeling results obtained, the empirical tool constructed makes enables forecast estimation of the energy consumption volume, its value and unit price in subsequent months, for at least 12 consecutive months. Practical implications: This type of research has great practical utility. They make it easier for rational interaction between electricity sellers and specific consumers. Social implications: Knowledge about the mechanisms of electricity consumption on the farm home may influence the rationalization of consumption and spending. This type of rationalization should have a positive impact on the environment natural, contributing to the reduction of greenhouse gas emissions. Originality/value: The novelty and originality of the work are the identification of the mechanism behavior of a single household consuming energy electric. Feedback between magnitudes revealed electricity consumption and its price on a specific farm home. The recipients of the work will be electricity sellers, consumers, and researchers of household market behavior. [ABSTRACT FROM AUTHOR]

This paper is an attempt to discuss the role of green practices/services which are incorporated within libraries towards environmentally sustainable. With continuous use and easy availability of new technologies like computer printers, faxes, and photocopiers etc. it is very important to protect the environment. Since, all these machines have an impact using paper, ink, and electricity on the environment. This paper begins with an explanation of sustainability, environmental sustainability, green printing, and copying etc. Further, demonstrates the various sustainable strategies for the libraries. And also highlights the strategies for overcoming the impact of paper use, ink use and electricity. This paper will also provide a different approach for librarians to achieve green practices/services. The adverse impact of new technologies on the environment also increases the requirement of green practices/services within libraries and reducing the carbon footprint, which will automatically enhance the environmentally sustainable practices, a valuable step for enhancing the lifestyle of society. [ABSTRACT FROM AUTHOR]

Smart meters for measuring electricity consumption are fast becoming prevalent in households. The meters measure consumption on a very fine scale, usually on a 15 min basis, and the data give unprecedented granularity of consumption patterns at household level. A multitude of papers have emerged utilizing smart meter data for deepening our knowledge of consumption patterns. This paper applies a modification of Okoli’s method for conducting structured literature reviews to generate an overview of research in electricity customer classification using smart meter data. The process assessed 2099 papers before identifying 34 significant papers, and highlights three key points: prominent methods, datasets and application. Three important findings are outlined. First, only a few papers contemplate future applications of the classification, rendering papers relevant only in a classification setting. Second; the encountered classification methods do not consider correlation or time series analysis when classifying. The identified papers fail to thoroughly analyze the statistical properties of the data, investigations that could potentially improve classification performance. Third, the description of the data utilized is of varying quality, with only 50% acknowledging missing values impact on the final sample size. A data description score for assessing the quality in data description has been developed and applied to all papers reviewed. [ABSTRACT FROM AUTHOR]

The three-phase imbalance in low-voltage distribution networks (LVDNs) seriously threatens the security and stability of the power system. At present, a standard solution is automatic phase commutation, but this method has limitations because it does not address the branch imbalance and premature convergence or instability of the commutation algorithm. Therefore, this paper proposes a novel refined regulation commutation system, combined with a modified optimized commutation algorithm, and designs a model and simulation for feasibility verification. The refined regulatory model incorporates branch control units into the traditional commutation system. This effectively disperses the main controller's functions to each branch and collaborates with intelligent fusion terminals for precise adjustment. The commutation algorithm designed in this paper, combined with the above model, adopts strategies such as symbol encoding, cubic chaotic mapping, and adaptive adjustment based on traditional genetic algorithms. In addition, in order to verify the effectiveness of the proposed method, this paper establishes a mathematical model with the minimum three-phase imbalance and commutation frequency as objectives and establishes a simulation model. The results of the simulation demonstrate that this method can successfully lower the three-phase imbalance of the low-voltage distribution network. It leads to a decrease of the main circuit's three-phase load imbalance rate from 27% to 6% and reduces each branch line's three-phase imbalance ratio to below 10%. After applying the method proposed in this paper, the main and branches circuit three-phase imbalance are both lower than the limit ratio of the LVDNs, which can improve the quality and safety of electricity consumption. Additionally, the results also prove that the commutation algorithm under this method has faster convergence speed, better application effect, and better stability, which has promotion and application value. [ABSTRACT FROM AUTHOR]

The presence of condensate inside the dryer cylinders results in a significant increase in the power and torque required until the condensate transitions to the rimming stage. This paper evaluates the effect of the condensate during the starting of a dryer section using the experimental curves reported by several authors, as well as a dimensionless model of the input power as a function of the condensate quantity and roll velocity. Through the evaluation of a nine-cylinder dryer section for various condensate quantities and varying number of cylinders containing condensate, a comparison is made to the power requirements of several actual paper machine dryer sections undergoing various starting procedures. These comparisons indicate that if the percent condensate (\condensate volume/full cylinder volume \times 100) in the range of 0.5%–1%, or greater, is nominally assumed to be present, then the sizing of the section drive capacity must take into consideration the additional indrive load resulting from this condensate. Options are proposed to consider the effect of the condensate without increasing the Technical Association of the Pulp and Paper Industry recommended drive capacity of dryer sections. [ABSTRACT FROM PUBLISHER]

Purpose: On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data. Design/methodology/approach: First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption. Findings: Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability. Originality/value: This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability. Highlights: The stability of electricity consumption is important to the stable operation of the grid. An improved FP-growth algorithm is employed to explore the influencing factors. The improved algorithm enables the mining of rules containing specific attribute labels. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to explore ChatGPT's (generative pre-trained transformers) potential as a tool for retailers to improve customer experience and boost sales. While it provides benefits like personalized recommendations and 24/7 assistance, there are limitations, like difficulty in understanding unconventional language. The paper stresses careful integration to overcome these limitations and create a better customer experience. Additionally, it discusses the potential for further development and integration of ChatGPT in retail, such as generating product descriptions and virtual try-on experiences. Finally, the paper encourages retailers to embrace ChatGPT to meet their customer needs. Design/methodology/approach: Case-based methodology involves using specific cases or examples to explore a broader issue or phenomenon. Researchers have analysed real-world cases to identify patterns, themes and insights that can be applied to other contexts or situations. This was useful for understanding complex and multifaceted issues as it allowed us to delve deeper into specific examples and explore the nuances of the situation. Findings: While ChatGPT is a powerful tool for retailers, limitations such as difficulty in understanding non-standard accents and unconventional language can arise, causing customer frustration. Retail managers must integrate ChatGPT in a way that enhances customer experience. In the future, ChatGPT has the potential to generate product descriptions, provide virtual try-on experiences and integrate with augmented or virtual reality technology to offer more immersive experiences. Careful consideration and integration can help retailers overcome these limitations and offer personalized recommendations, round-the-clock assistance and an engaging customer experience that improves sales. Originality/value: The case topic is very much in a novel stage of research and writing. [ABSTRACT FROM AUTHOR]

A reasonable allocation of production schedules and savings in overall electricity costs are crucial for large manufacturing conglomerates. In this study, we develop an optimization model of off-site industrial production scheduling to address the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's power supply, and the difficulty in promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and variations in electricity prices in different places to optimize the scheduling of industrial production in various locations. The model is embedded with two sub-models, i.e., an electricity price prediction model and a distributed photovoltaic power cost model to complete the model parameters, in which the electricity price prediction model utilizes a Long Short-Term Memory (LSTM) neural network. Then, the particle swarm optimization algorithm is used to solve the optimization model. Finally, the production data of two off-site pharmaceutical factories belonging to the same large group of enterprises are substituted into the model for example analysis, and it is concluded that the optimization model can significantly reduce the electricity consumption costs of the enterprises by about 7.9%. This verifies the effectiveness of the optimization model established in this paper in reducing the cost of electricity consumption on the demand side. [ABSTRACT FROM AUTHOR]

In light of the escalating concerns surrounding climate change and air quality degradation, the imperative for energy conservation and emission reduction has garnered widespread attention. Given that factories represent a significant portion of electricity consumption within the power network, a comprehensive analysis of the electricity consumption behavior of energy- intensive enterprises becomes paramount. To meticulously dissect the electricity consumption patterns of energy-intensive enterprises, this paper categorizes them into four distinct production modes: 24-hour all-day production factories, pure daytime production factories, pure nighttime production factories, and environmentally friendly peaking production factories. Employing the dynamic electricity-carbon factor as a guiding force, the analysis encompasses electricity consumption, tariff expenditure, peaking costs, carbon emissions, and comfort levels associated with each production method throughout the year. A delicate equilibrium is sought among multiple objectives, aiming to optimize the user experience while simultaneously mitigating costs and carbon emissions. Furthermore, this paper conducts a comparative analysis of each objective, employing single-objective genetic algorithms and the interior point method. The resultant findings serve as invaluable insights for business users, aiding in informed decision- making processes. [ABSTRACT FROM AUTHOR]

The digitization of distribution power systems has revolutionized the way data are collected and analyzed. In this paper, the critical task of harnessing this information to identify irregularities and anomalies in electricity consumption is tackled. The focus is on detecting non-technical losses (NTLs) and energy theft within distribution networks. A comprehensive overview of the methodologies employed to uncover NTLs and energy theft is presented, leveraging measurements of electricity consumption. The most common scenarios and prevalent cases of anomalies and theft among consumers are identified. Additionally, statistical indicators tailored to specific anomalies are proposed. In this research paper, the practical implementation of numerous artificial intelligence (AI) algorithms, including the artificial neural network (ANN), ANFIS, autoencoder neural network, and K-mean clustering, is highlighted. These algorithms play a central role in our research, and our primary objective is to showcase their effectiveness in identifying NTLs. Real-world data sourced directly from distribution networks are utilized. Additionally, we carefully assess how well statistical methods work and compare them to AI techniques by testing them with real data. The artificial neural network (ANN) accurately identifies various consumer types, exhibiting a frequency error of 7.62%. In contrast, the K-means algorithm shows a slightly higher frequency error of 9.26%, while the adaptive neuro-fuzzy inference system (ANFIS) fails to detect the initial anomaly type, resulting in a frequency error of 11.11%. Our research suggests that AI can make finding irregularities in electricity consumption even more effective. This approach, especially when using data from smart meters, can help us discover problems and safeguard distribution networks. [ABSTRACT FROM AUTHOR]

Background and aims: As a part of the framework of the EU-funded Energy efficiency through Behavior CHANge Transition (ENCHANT) project, the present paper intends to provide a "Research Protocol" of a web-based trial to: (i) assess the effectiveness of behavioral intervention strategies––either single or in combination––on electricity saving, and (ii) unravel the psychological factors contributing to intervention effectiveness in households across Europe. Methods and materials: Six distinct interventions (i.e., information provision, collective vs. individual message framing, social norms, consumption feedback, competitive elements, and commitment strategies) targeting electricity saving in households from six European countries (i.e., Austria, Germany, Italy, Norway, Romania, and Türkiye) are evaluated, with an initial expected samples of about 1500 households per country randomly assigned to 12 intervention groups and two control groups, and data is collected through an ad-hoc online platform. The primary outcome is the weekly electricity consumption normalized to the last seven days before measurement per person per household. Secondary outcomes are the peak consumption during the last day before measurement and the self-reported implementation of electricity saving behaviors (e.g., deicing the refrigerator). The underlying psychological factors expected to mediate and/or moderate the intervention effects on these outcomes are intentions to save electricity, perceived difficulty of saving energy, attitudes to electricity saving, electricity saving habit strength, social norms to save electricity, personal norms, collective efficacy, emotional reaction to electricity consumption, and national identity. The intervention effectiveness will be evaluated by comparing psychological factors and consumption variables before and after the intervention, leading to a 14 (groups including 2 control groups) × 6 (time) mixed factorial design, with one factor between (group) and one factor within subjects (time)–6 measurements of the psychological factors and 6 readings of the electricity meters, which gives then 5 weeks of electricity consumption. Results: Data collection for the present RCT started in January 2023, and by October 2023 data collection will conclude. Discussion: Upon establishing feasibility and effectiveness, the outcomes of this study will assist policymakers, municipalities, NGOs, and other communal entities in identifying impactful interventions tailored to their unique circumstances and available resources. Researchers will benefit from a flexible, structured tool that allows the design, implementation and monitoring of complex interventions protocols. Crucially, the intervention participants will benefit from electricity saving strategies, fostering immediate effectiveness of the interventions in real-life contexts. Trial registration: This trial was preregistered in the Open Science Framework: https://osf.io/9vtn4. [ABSTRACT FROM AUTHOR]

This document is an editorial note from the journal Economics & Business Letters. It provides information on the most cited papers in the journal from 2015 to 2023, as well as statistics on submissions and acceptance rates. The note highlights the journal's commitment to being a quality, free, online letter-type journal that covers all areas of economics and business. It also includes acknowledgements to authors, referees, and associate editors, and provides information on the journal's indexing, metrics, open access policy, and publication process. The document concludes with a list of referees and the editorial board of the journal. [Extracted from the article]

The adaptation of renewable energy has been increasing in a very encouraging way all over the world. Among various renewable energy resources, wind and solar energy are the promising sources of alternative energy. Wind and solar photovoltaic (PV) have been employed in parallel as a hybrid system for better electricity service. This paper presents a case study and modeling of wind-solar hybrid system in Hriharpur Gadi village, Sindhuli District, Nepal. The hybrid system yields 110kWh of energy per day meeting the village's electricity demand of 87 kWh per day. Moreover, the hybrid power system with battery storage system is modeled using MATLAB simulator. Further, improvising in the existing modeling has been presented to enhance the efficiency and effectiveness of the system. [ABSTRACT FROM AUTHOR]

In the grey models, the GM(1,1) model is an important type of prediction model. The traditional grey GM(1,1) model has good prediction results in the case the original data show exponential variations at a slow rate. However, in practical problems, although showing exponential variations or approximately exponential variations, original data vary very fast sometimes. In these cases, the traditional grey GM(1,1) model tends to have poor prediction accuracy, mainly because the data fails to meet the laws presented by the traditional model. Therefore, the paper makes improvements in the following two aspects: first, the paper transforms the traditional accumulated generating sequence of original data; second, the paper extends the traditional grey model's structure, i.e., building a grey EGM(1,1,Σe^(ck)) model. The paper offers the parameter optimization method of the grey EGM(1,1,Σe^(ck)) model. Using the novel modeling method proposed, the paper builds the grey EGM(1,1,Σe^(ck)) models for China's total electricity consumption and China's GDP per capita, respectively, in the final section. Results show that the models built with the proposed modeling method have high simulation precision and prediction precision. [ABSTRACT FROM AUTHOR]

In order to reduce the peak–valley difference of the power grid load, reasonably arrange users' electricity consumption time and realize the intelligent management of the power grid, we construct a user electricity consumption information acquisition system based on unmanned aerial vehicles (UAVs) by using a sensor network. In order to improve the service quality of the system and reduce the system delay, this paper comprehensively considers the factors that affect the user's electricity consumption information collection system, such as the UAV trajectory, the unloading decision of the data receiving point and so on. Therefore, this paper puts forward an effective iterative optimization algorithm for joint UAV trajectory and unloading decisions based on a deep Q network (DQN), in order to obtain the optimal UAV trajectory and unloading decision design, acquire the optimal solution to minimize the time delay of the monitoring system and maximize the service quality of the user electricity information collection system, thus ensuring the stable operation of the user electricity information collection system. In this paper, different complexity algorithms are used to solve this problem. Compared with the greedy algorithm, the proposed algorithm, CDQN, improves the system service quality by approximately 2 % and reduces the system delay by approximately 16 % , so that the user's electricity consumption information can be analyzed and processed faster. [ABSTRACT FROM AUTHOR]

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

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

The ongoing electrification of the transport sector is expected to cause an increase in electricity demand and, therefore, trigger significant network investments to accommodate it. This paper focuses on investment decision-making for electricity distribution grids and specifically on the strategic and incremental investment network planning approaches. In particular, the former involves network planning with the consideration of a long-term multi-stage study horizon, as opposed to a shorter–term view of the future that applies to the latter case. An investment analysis that is carried out underlines the economic savings generated from adopting a strategic investment perspective over an incremental one. These economic savings are achieved from the fact that the associated fixed investment costs are incurred only once in the horizon under strategic planning. On the other hand, incremental planning involves a series of network reinforcement decisions, thereby incurring the fixed cost multiple times. In addition, sensitivity analyses that are carried out capture the effect of key parameters, such as investment cost, discount rate and investment delay, on the generated economic savings. [ABSTRACT FROM AUTHOR]

The non-technical losses caused by abnormal power consumption behavior of power users seriously affect the revenue of power companies and the quality of power supply. To assist electric power companies in improving the efficiency of power consumption audit and regulating the power consumption behavior of users, this paper proposes a power consumption anomaly detection method named High-LowDAAE (Autoencoder model for dual adversarial training of high low-level temporal features). High-LowDAAE adds an extra "discriminator" named AE3 to USAD (UnSupervised Anomaly Detection on Multivariate Time Series), which performs the same function as AE2 in USAD. AE3 performs the same function as AE2 in USAD, i.e., it is trained against AE1 to enhance its ability to reconstruct average data. However, AE3 differs from AE2 because the two "discriminators" correspond to the high-level and low-level time series features output from the shared encoder network. By utilizing different levels of temporal features to reconstruct the data and conducting adversarial training, AE1 can reconstruct the time-series data more efficiently, thus improving the accuracy of detecting abnormal electricity usage. In addition, to enhance the model's feature extraction ability for time-series data, the self-encoder is constructed with a long short-term memory (LSTM) network, and the fully connected layer in the USAD model is no longer used. This modification improves the extraction of temporal features and provides richer hidden features for the adversarial training of the dual "discriminators". Finally, the ablation and comparison experiments are conducted using accurate electricity consumption data from users, and the results show that the proposed method has higher accuracy. [ABSTRACT FROM AUTHOR]

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In recent years, interest in artificial intelligence has been increasing in the energy sector, with the aim of improving efficiency and decreasing overall consumption. Innovative methods such as pattern recognition, artificial neural networks or other machine learning methods are being investigated to improve the accuracy of forecasting. Moreover, a suitable operation strategy may improve the efficiency of energy systems. The electric equivalent load is an operation strategy proposed for systems where electricity and heat (or cooling) are requested simultaneously, and there is equipment such as a heat pump to cover the heating (or cooling) load. In this paper, the electric equivalent load is defined to cover both the electricity request of the user and the electricity consumption of the heat pump. Thus, the energy system follows the electric equivalent load to cover both electricity and heating/cooling requests. This study aims to analyse how the accuracy varies when forecasting energy requests (e.g. electricity and heat) separately with respect to forecasting directly the electric equivalent load. The results show that electric equivalent load forecasting averages the accuracy of electricity and heat, improving the operation system by decreasing the electricity demand from the grid and increasing energy production during peak hours. [ABSTRACT FROM AUTHOR]

Time series data analysis and forecasting have recently received considerable attention, supporting new technology development trends for predicting load fluctuations or uncertainty conditions in many domains. In particular, when the load is small, such as a building, the effect of load fluctuation on the total load is relatively large compared to the power system, except for specific factors, and the amount is very difficult to quantify. Recently, accurate power consumption prediction has become an important issue in the Internet of Things (IoT) environment. In this paper, a traditional time series prediction method was applied and a new model and scientific approach were used for power prediction in IoT and big data environments. To this end, to obtain data used in real life, the power consumption of commercial refrigerators was continuously collected at 15 min intervals, and prediction results were obtained by applying time series prediction methods (e.g., RNN, LSTM, and GRU). At this time, the seasonality and periodicity of electricity use were also analyzed. In this paper, we propose a method to improve the performance of the model by classifying power consumption into three classes: weekday, Saturday, and Sunday. Finally, we propose a method for predicting power consumption using a new type of ensemble model combined with three time series methods. Experimental results confirmed the accuracy of RNN (i.e., 96.1%), LSTM (i.e., 96.9%), and GRU (i.e., 96.4%). In addition, it was confirmed that the ensemble model combining the three time series models showed 98.43% accuracy in predicting power consumption. Through these experiments and approaches, scientific achievements for time series data analysis through real data were accomplished, which provided an opportunity to once again identify the need for continuous real-time power consumption monitoring. [ABSTRACT FROM AUTHOR]

The article presents a study on the determination of the efficiency improvement potential for public water supply systems of centrifugal network pumps. The study analyzed the efficiency and calculated energy usage of variable speed centrifugal pumps. Result showed energy savings of 1110 megawatt hour (MWh) annually when control mode of variable speed network pumps is changed from constant to proportional pressure control mode.

The "load-following" characteristic of the power system makes the electricity consumption behavior on the load side crucial for the low-carbon operation of the distribution network. To address this, this paper proposes an improved dynamic carbon emission factor for the distribution network, taking into account the spatiotemporal characteristics of carbon emission intensity and the integration capacity of photovoltaics (PV). Based on this, a calculation method for the carbon emissions of the distribution network load is provided. Subsequently, for commercial and industrial user scenarios, demand response models are separately constructed for commercial and industrial loads based on different driving mechanisms. Using time-of-use electricity prices as decision variables, optimization scheduling of the distribution network is carried out with the objectives of minimizing scheduling costs and carbon emissions. At the same time, a case study is conducted in an improved IEEE-33 node distribution network. The results indicate that, under the guidance of the improved dynamic carbon emission factor, load transfer can be achieved through fluctuating electricity prices, effectively reducing the scheduling costs of the distribution network, decreasing carbon emissions, and enhancing the PV integration capacity of the distribution network in different user scenarios. Finally, it is hoped that in the future, this optimization method can be widely applied, and further research can explore coordinated strategies among generation, network, load, and storage to advance the development of the power industry. [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]

The objective of this paper is to investigate the nonlinear relationship between ICT and CO2 emissions by controlling for economic growth, foreign direct investment, energy consumption, and trade openness. Using data from 16 Middle East and North African (MENA) countries over the period 1990–2019, we apply the Panel Smooth Transition Regression (PSTR) model, as introduced by (González A, Teräsvirta T, vanDijk D (2005) Panel smooth transition regression models. SEE/EFI Working Paper Series in Economics and Finance, No. 604), to study the potential regime-switching behavior of the relationship between the variables. The results reveal the existence of a strong regime-switching effect between ICT and CO2 emissions. It was found that after reaching a certain threshold, ICT use and penetration starts to significantly mitigate environmental degradation. Our results show that high levels of ICT not only improve environmental quality but can also be part of the solution to combat the environmental challenges that the MENA region has faced over the past decades. In addition, to account for the potential endogeneity bias, we also develop and estimate a PSTR model with instrumental variables (IV-PSTR) using the approach of (Fouquau et al., Econ Model 25:284–299, 2008). The results obtained confirm those initially found by the PSTR model. The study concludes with policy implications. [ABSTRACT FROM AUTHOR]