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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]

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]

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]

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS. [ABSTRACT FROM AUTHOR]

Purpose: The provision of healthcare is a substantial global contributor to greenhouse gas (GHG) emissions. Several medical specialties and national health systems have begun evaluating their carbon emission contributions. The aim of this review is to summarise and describe the carbon footprint resulting from the provision of adult, paediatric and neonatal critical care. Methods: A systematic search of Embase, Cochrane and Web of Science was performed in January 2023. Studies reporting any assessment of the carbon footprint of critical care were included. No language restrictions were applied. GHG emissions from life cycle assessments (LCA) were reported, in addition to waste, electricity and water use. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline was followed. Results: In total, 13 studies assessing and describing the environmental impact of 36 adult or paediatric intensive care units (ICUs) were included. Two studies described full LCAs, seven reported waste only, two provided audits of unused medical supplies, one reported electricity use, and one study described a Material Flow Analysis. The estimated carbon emissions from critical care range between 88 kg CO2e/patient/day and 178 kg CO2e/patient/day. The two predominant sources of carbon emissions in critical care originate from electricity and gas use, as well as consumables. Waste production ranged from 1.1 to 13.7 kg/patient/day in the 6 studies where mean waste could be calculated. Conclusion: There is a significant carbon footprint that results from intensive care provision. Consumables and waste constitute important, measurable, and modifiable components of anthropogenic emissions. There remains uncertainty due to a lack of literature, several unstudied areas of carbon emissions from critical care units, and within measured areas, measurement and reporting of carbon emissions are inconsistent. [ABSTRACT FROM AUTHOR]

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

We investigate the power system and climate policy implications of the anticipated expansion in electricity demand from datacentres. We perform a joint optimization of Generation and Transmission Expansion Planning for the island of Ireland considering uncertainty in future datacentre growth under various climate policies. Datacentre expansion imposes significant extra costs on the power system. A renewable energy target is more costly than a technology-neutral carbon reduction policy due to the restriction to invest solely in renewable energy, and the divergence in costs increases non-linearly in electricity demand. A carbon reduction policy is more robust to uncertainties in projected demand than a renewable policy. The increased demand from datacentres, which is relatively constant, is best matched by a power supply with constant output, generally fossil-fuelled power plants, potentially in combination with carbon capture and sequestration technology, than variable supply. Datacentre demand increases power system costs by 6% under a carbon reduction policy, and by 9% to 15% under renewable energy targets. However, the abatement cost of carbon under renewable targets decreases at higher levels of demand, while the opposite is true for the abatement cost under technology-neutral targets. High renewable targets crowd out other low-carbon options, namely CCS, leading to a higher cost solution for the same reduction in emissions, although this result is sensitive to increases in fossil fuel prices. The results suggest that climate-related energy policy should focus on technology-neutral carbon reduction policies. The climate impacts of datacentres on energy demand and thus supply requirements should also feature in climate and energy policy discussions. Climate policy that focuses on renewable energy only is more expensive than a technology-neutral climate policy The costs of a renewable-only focused climate policy increase non-linearly as demand increases [ABSTRACT FROM AUTHOR]

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

In this article we propose a boosting algorithm for regression with functional explanatory variables and scalar responses. The algorithm uses decision trees constructed with multiple projections as the "base-learners", which we call "functional multi-index trees". We establish identifiability conditions for these trees and introduce two algorithms to compute them. We use numerical experiments to investigate the performance of our method and compare it with several linear and nonlinear regression estimators, including recently proposed nonparametric and semiparametric functional additive estimators. Simulation studies show that the proposed method is consistently among the top performers, whereas the performance of existing alternatives can vary substantially across different settings. In a real example, we apply our method to predict electricity demand using price curves and show that our estimator provides better predictions compared to its competitors, especially when one adjusts for seasonality. [ABSTRACT FROM AUTHOR]

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

The economic recovery of the tourism industry after the Covid-19 pandemic to find modern and efficient trends to increase profitability is accompanied by the adoption of comprehensive accommodation approaches towards resilience and environmental sustainability. The research aims at the application of environmental management elements and measures in all types of accommodation facilities in the Czech Republic (n1 = 1,016). A qualitative focus group method complemented the quantitative research using correspondence analysis, Levene's, Kruskal-Wallis, and Tukey's HSD tests (n2 = 9 + moderator). The results indicate that the differences in the number of environmental measures implemented were minimal for the monitored hotels and guesthouses. On the other hand, the star rating of accommodation facilities is not a key parameter in the environmental impact assessment. The most used environmental measures were devices reducing electricity consumption (hotels 94%, guesthouses 94%), separating waste (hotels 88%, guesthouses 89%), and water consumption reduction (hotels 85%, guesthouses 86%). At the same time, the most minor used were measures reducing chemical consumption (hotels 23%, guesthouses 22%) communication and environmental education of employees and guests (hotels 32%, guesthouses 18%). [ABSTRACT FROM AUTHOR]

With the increasing demand for electricity, microgrid systems are facing issues such as insufficient backup capacity, frequent load switching, and frequent malfunctions, making research on microgrid resilience crucial, especially to improve system power supply reliability. This paper proposes a method for analyzing the resilience metric of new energy grid-connected microgrid system, and proposes optimization strategies to improve resilience. Firstly, a measurement method for the resilience of the microgrid system is established based on the operating characteristics of the system components. Secondly, the sensitivity relationship between system resilience and parameters is established, and an optimization model for resilience improvement strategies of microgrid systems based on parameter sensitivity is constructed. Finally, simulation verification is conducted based on the IEEE 37-node microgrid system. The results show that the proposed measurement method can scientifically and reasonably measure the resilience of the microgrid system, and the resilience improvement strategy significantly improves the operational resilience, verifying the effectiveness and robustness of the proposed analysis method. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) plays a vital role in ensuring the safe, efficient, and economical operation of power systems. Accurate load forecasting provides numerous benefits for power suppliers, such as cost reduction, increased reliability, and informed decision-making. However, STLF is a complex task due to various factors, including non-linear trends, multiple seasonality, variable variance, and significant random interruptions in electricity demand time series. To address these challenges, advanced techniques and models are required. This study focuses on the development of an efficient short-term power load forecasting model using the random forest (RF) algorithm. RF combines regression trees through bagging and random subspace techniques to improve prediction accuracy and reduce model variability. The algorithm constructs a forest of trees using bootstrap samples and selects random feature subsets at each node to enhance diversity. Hyperparameters such as the number of trees, minimum sample leaf size, and maximum features for each split are tuned to optimize forecasting results. The proposed model was tested using historical hourly load data from four transformer substations supplying different campus areas of the University of Beira Interior, Portugal. The training data were from January 2018 to December 2021, while the data from 2022 were used for testing. The results demonstrate the effectiveness of the RF model in forecasting short-term hourly and one day ahead load and its potential to enhance decision-making processes in smart grid operations. [ABSTRACT FROM AUTHOR]

This study investigated the impact of information and communication technology (ICT) on electricity intensity, incorporating electricity prices, financial development, and population growth in Korea from 1990 to 2020, using the ARDL (autoregressive distributed lag) model. Three cases were considered, each relating to a different ICT proxy: Internet use, mobile cellular phone use, and exports of ICT-related products. The results varied depending on the proxy used to represent ICT. An increase in mobile cellular phone use leads to an increase in electricity intensity in the long run; however, the short-run effects of this change are unclear. An increase in Internet use also leads to an increase in electricity intensity in the long run but induces a decrease in electricity intensity in the short run. Increments in the exports of ICT-related products lead to an increase in electricity intensity in the short run; however, this effect is negligible in the long run. Electricity prices do not affect electricity intensity in all cases, and financial development reduces the intensity of electricity in the cases of the use of both mobile cellular phones and the Internet as proxies for ICT, whereas population growth increases electricity intensity in all cases. [ABSTRACT FROM AUTHOR]

Urban carbon emissions are an important area for addressing climate change, and it is necessary to establish scientific and effective carbon emission prediction models to formulate reasonable emission reduction policies and measures. In this paper, a novel model based on Lasso regression, an ARIMA model, and a BPNN is proposed. Lasso regression is used to screen the key factors affecting carbon emissions, and the ARIMA model is used to extract the linear components of the carbon emission sequences, while the BPNN is used to predict the residuals of the ARIMA model. The final result is the sum of that from the ARIMA model and the BPNN. The carbon peak, carbon neutralization time, and emissions were analyzed under different scenarios. Taking Suzhou City as an example, the results show that the electricity consumption of the whole population is one of the key drivers of carbon emissions; the carbon emission prediction accuracy and stability of the ARIMA-BPNN combined model are better than those of the single model, which improves the reliability as well as the accuracy of the model's prediction. However, under the constraints of the current policies, the goal of achieving carbon peaking by 2030 in Suzhou City may not be realized as scheduled. This novel carbon emission prediction model built was validated to provide a scientific basis for low-carbon urban development. This study presents an important reference value for predicting carbon emissions and formulating emission reduction measures in other cities. [ABSTRACT FROM AUTHOR]

Since microgrids can effectively integrate renewable energy, energy storage devices, and controllable loads, this advantage promotes the rapid development and application of microgrid technology. However, with the high proportion of renewable energy access, only considering how energy is optimally distributed in microgrids can no longer meet the actual demand. How to aggregate user-side controllable loads to form regulation resources has become a research hotspot, and the users, as a passive party in the load scheduling process, should also be an important consideration in their perception of the use of electricity. First, a control model for temperature-controlled loads and a time-shift model for time-shiftable loads are developed. Then, the comprehensive electricity satisfaction model of users is established, and the two-layer optimal scheduling model of microgrids considering users' satisfaction is proposed, with users as the upper layer and microgrids as the lower layer, and the two-layer model is transformed into a single-layer model according to the KKT condition for solving. Finally, the effect of the weighting factor for satisfaction on the economy is discussed through the analysis of examples, which verifies the effectiveness of the two-layer model. [ABSTRACT FROM AUTHOR]

With the rapid development of the information age, smart meters play an important role in the smart grid. However, there are more and more attacks on smart meters, which mainly focus on the identity authentication of smart meters and the security protection of electricity consumption data. In this paper, an efficient lightweight smart meter authentication scheme is proposed based on the Chinese Remainder Theorem (CRT), which can realize the revocation of a single smart meter user by publishing a secret random value bound to the smart meter identity. The proposed scheme not only protects the security of smart meter electricity consumption data by using encryption, but also resists identity attacks from both internal and external adversaries by using hash functions and timestamps. Experiment shows that the proposed scheme has lower computation overhead and communication overhead than other authentication schemes and is more suitable for smart meter authentication. [ABSTRACT FROM AUTHOR]

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

As the world's largest electricity-consuming country, China faces the challenge of energy conservation and environmental pollution. Therefore, it is imperative that China takes decisive action to address these issues. Based on the panel data of 30 provinces (cities, districts) in China from 2011 to 2020, we use the entropy method to measure the air pollution index in different provinces, construct two fixed effects models, panel quantile model, and spatial Durbin model to empirically analyze the impact of electricity consumption on air pollution in China's provincial regions. The experimental results show that: (1) Electricity consumption has a significant positive impact on the provincial air pollution index in China and the higher the index is, the more serious the air pollution is. When the electricity consumption increases 1%, the air pollution index will increase of by 0.0649% as accompanied. (2) Through comparison of different times, we found that the degree of increase in air pollution index caused by electricity consumption would be reduced due to the improvement of environmental protection efforts. From the perspective of different geographical locations, the electricity consumption in the southeast side of the "Hu Line" has exacerbated the impact on air pollution index. (3) According to the panel quantile regression results, the marginal effect of electricity consumption on air pollution is positive. With the increase of quantiles, the impact of electricity consumption on air pollution is increasing. (4) Spatial effect analysis shows that electricity consumption has a significant positive spatial spillover effect on air pollution index. The increase in electricity consumption not only increases the air pollution index in the local region, but also leads to an increase in the air pollution index in surrounding areas. These findings contribute to the governance of air pollution and the promotion of sustainable economic, environmental and energy development. [ABSTRACT FROM AUTHOR]

Adoption of clean electric energy depends not only on administrative regulations, but also on public support, in particular, the public is willing to pay for environmental improvements. However, the increase of solar photovoltaic power generation willingness to pay (WTP) associated with higher education attainment and the identification of their causality has been missing. Present paper used the enactment of the Compulsory Schooling Law as an instrumental variable to solve the causal relationship between education and willingness to pay for photovoltaic power generation. The results are as follows:Heckman two-stage model and instrumental variable both confirmed that higher education has a positive impact on WTP for solar photovoltaic power generation. For each level of public education in the east, the WTP of photovoltaic power generation will increase by 7.540 CNY, 8.343 CNY and 8.343 CNY respectively, the central public will increase by 9.637 CNY, 10.775 CNY and 11.758 CNY, and the western public will increase by 12.723 CNY, 15.740 CNY and 17.993 CNY respectively. The positive influence of education level is smaller among the people who know the ladder price better, but it is bigger among the people who are male, older than 45 years old, healthier, higher income and stronger awareness of safe electricity use. The total socio-economic value of photovoltaic power generation is significantly different in eastern, central and western region China. [ABSTRACT FROM AUTHOR]

In the application of new energy consumption system engineering, in order to evaluate the contribution of electric power industry expansion, an evaluation model of electric power industry expansion contribution considering the influencing factors of new energy consumption is constructed. In the process of power industry expansion, the growth of new energy installed capacity, power system regulation ability, power grid interconnection and electricity demand are the core factors that affect the change of power contribution to power industry expansion. Using the characteristic extraction method of power consumption behavior of users with industrial expansion, after extracting two characteristics, namely, the utilization hours of user's industrial expansion capacity and the proportion of new energy load put into operation under the change of four major factors, the monthly industrial expansion power consumption of typical users is predicted by the monthly industrial expansion power consumption forecasting method of users considering industrial expansion capacity, and then the growth curve of user's industrial expansion power consumption is drawn. Based on the forecast method of monthly industry expansion electricity generated by industry expansion quantity, the industry expansion quantity of typical individual users is calculated, and the industry expansion quantity is input into RBF network model trained by particle swarm optimization algorithm to complete the forecast of monthly industry expansion electricity; Finally, the contribution ratio of each influencing factor is calculated, and the evaluation of power industry expansion contribution considering the influencing factors of new energy consumption is completed. After testing, this model can be used as an available model for evaluating the contribution of electric power industry under the condition of considering the influencing factors of new energy consumption. [ABSTRACT FROM AUTHOR]

Electricity consumption of mining enterprises depends on many factors. The influence of factors on the process of electricity consumption is complex and diverse, and it is impossible to describe it within the framework of deterministic methods, due to the uncertainty of the conditions that determine the influence of factors. These factors include mining geological, mining technological, climate-meteorological, energetic, organizational, operational and others. Energy consumption regimes of mining enterprises depend on many mining technological factors, most of which are insufficiently taken into account in the analysis and planning of electricity consumption. [ABSTRACT FROM AUTHOR]

Mining and processing enterprises are large consumers of electricity, with a large share of energy costs in the cost of production. Thus, the price of electricity in the cost of production takes higher values for Hydrometallurgical Plant 1. In this regard, the development of energy consumption models that can be used as a basis for managing the energy consumption process is an important component of learning. It is important to analyze the energy consumption to estimate the electricity consumption under different operating conditions of the mining industry. In order to study and plan electricity consumption and develop methods for regulating electricity consumption, it is necessary to determine the place of each technological stage in the energy consumption balance [1]. This article examines the laws of electricity consumption in mining and processing enterprises. The study of such laws helps to determine the level of energy consumption management for the mining and processing plant. [ABSTRACT FROM AUTHOR]

Microgrid optimization scheduling, as a crucial part of smart grid optimization, plays a significant role in reducing energy consumption and environmental pollution. The development goals of microgrids not only aim to meet the basic demands of electricity supply but also to enhance economic benefits and environmental protection. In this regard, a multi-objective optimization scheduling model for microgrids in grid-connected mode is proposed, which comprehensively considers the operational costs and environmental protection costs of microgrid systems. This model also incorporates improvements to the traditional particle swarm optimization (PSO) algorithm by considering inertia factors and particle adaptive mutation, and it utilizes the improved algorithm to solve the optimization model. Simulation results demonstrate that this model can effectively reduce electricity costs for users and environmental pollution, promoting the optimized operation of microgrids and verifying the superior performance of the improved PSO algorithm. After algorithmic improvements, the optimal total cost achieved was CNY 836.23, representing a decrease from the pre-improvement optimal value of CNY 850. [ABSTRACT FROM AUTHOR]

Alternative solvents based on aqueous solutions of amino acids have been recently developed as possible substitutes for Mono Ethanol Amine (MEA) for CO2 removal from flue gas streams. The potassium taurate solvent has the advantages of degradation resistance, low toxicity and low energy requirements for its regeneration. With any type of solvent, CO2 removal applied to a power production plant decreases the revenues obtained from selling electricity because of the energy requirements. Operating the CO2 removal section in flexible mode avoids significant effects on the profits of the power plant, while accomplishing environmental regulations. This work is the first journal paper focusing on the application in flexible mode of the potassium taurate system for treating a flue gas stream from a 500 MW coal-fired power plant. Techno-economic evaluations are performed to determine the best operating conditions considering the variation in the electricity demand and its price, and different values of carbon tax. In the summer period, with high electricity prices and demands, carbon tax values between 45 EUR/tCO2 and 60 EUR/tCO2 favor CO2 absorption in the flexible mode, without periods of full CO2 emissions during the day. [ABSTRACT FROM AUTHOR]

Non-technical losses (NTL) have been a growing problem over the years, causing significant financial losses for electric utilities. Among the methods for detecting this type of loss, those based on Artificial Intelligence (AI) have been the most popular. Many works use the electricity consumption profile as an input for AI models, which may not be sufficient to develop a model that achieves a high detection rate for various types of energy fraud that may occur. In this paper, using actual electricity consumption data, additional statistical and temporal features based on these data are used to improve the detection rate of various types of NTL. Furthermore, a model that combines both the electricity consumption data and these features is developed, achieving a better detection rate for all types of fraud considered. [ABSTRACT FROM AUTHOR]

Electricity consumption in buildings is one of the major causes of energy usage and knowledge of this can help building owners and users increase energy efficiency and conservation efforts. For Pacific Island countries, building electricity demand data is not readily accessible or available for constructing models to predict electricity demand. This paper starts to fill this gap by studying the case of schools in Fiji. The aim of the paper is to assess the factors affecting electricity demand for grid-connected Fijian schools and use this assessment to build mathematical models (multiple linear regression (MLR) and artificial neural network (ANN)) to predict electricity consumption. The average grid-connected electricity demand in kWh/year was 1411 for early childhood education schools, 5403 for primary schools, and 23,895 for secondary schools. For predicting electricity demand (ED) for all grid-connected schools, the stepwise MLR model shows that taking logarithm transformations on both the dependent variable and independent variables (number of students, lights, and air conditioning systems) yields statistically significant independent variables with an R2 value of 73.3% and RMSE of 0.2248. To improve the predicting performance, ANN models were constructed on both the natural form of variables and transformed variables. The optimum ANN model had an R2 value of 95.3% and an RMSE of 59.4 kWh/year. The findings of this study can assist schools in putting measures in place to reduce their electricity demand, associated costs, and carbon footprint, as well as help government ministries make better-informed policies. [ABSTRACT FROM AUTHOR]

Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, consequently leading to a reduction in carbon footprint. Accurately forecasting peak demand in commercial buildings provides benefits to both the suppliers and consumers by enhancing efficiency in electricity production and minimizing energy waste. Precise predictions of energy peaks enable the implementation of proactive peak-shaving strategies, the effective scheduling of battery response, and an enhancement of smart grid management. The current research on peak demand for commercial buildings has shown a gap in addressing timestamps for peak consumption incidents. To bridge the gap, an Energy Peaks and Timestamping Prediction (EPTP) framework is proposed to not only identify the energy peaks, but to also accurately predict the timestamps associated with their occurrences. In this EPTP framework, energy consumption prediction is performed with a long short-term memory network followed by the timestamp prediction using a multilayer perceptron network. The proposed framework was validated through experiments utilizing real-world commercial supermarket data. This evaluation was performed in comparison to the commonly used block maxima approach for indexing. The 2-h hit rate saw an improvement from 21% when employing the block maxima approach to 52.6% with the proposed EPTP framework for the hourly resolution. Similarly, the hit rate increased from 65.3% to 86% for the 15-min resolution. In addition, the average minute deviation decreased from 120 min with the block maxima approach to 62 min with the proposed EPTP framework with high-resolution data. The framework demonstrates satisfactory results when applied to high-resolution data obtained from real-world commercial supermarket energy consumption. [ABSTRACT FROM AUTHOR]

Thanks to various European directives, individuals are empowered to share and trade electricity within Renewable Energy Communities, enhancing the operational efficiency of local energy systems. The digital transformation of the energy market enables the integration of decentralized energy resources using cloud computing, the Internet of Things, and artificial intelligence. In order to assess the feasibility of new business models based on data-driven solutions, various electricity consumption time series are necessary at this level of aggregation. Since these are currently not yet available in sufficient quality and quantity, and due to data privacy reasons, synthetic time series are essential in the strategic planning of smart grid energy systems. By enabling the simulation of diverse scenarios, they facilitate the integration of new technologies and the development of effective demand response strategies. Moreover, they provide valuable data for assessing novel load forecasting methodologies that are essential to manage energy efficiently and to ensure grid stability. Therefore, this research proposes a methodology to synthesize electricity consumption time series by applying the Box–Jenkins method, an intelligent sampling technique for data augmentation and a probabilistic forecast model. This novel approach emulates the stochastic nature of electricity consumption time series and synthesizes realistic ones of Renewable Energy Communities concerning seasonal as well as short-term variations and stochasticity. Comparing autocorrelations, distributions of values, and principle components of daily sequences between real and synthetic time series, the results exhibit nearly identical characteristics to the original data and, thus, are usable in designing and studying efficient smart grid systems. [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]

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]

In this study we test if electricity consumption has asymmetric effects on carbon dioxide emissions in Kuwait, over the period 1971–2018. The study applied the recently developed nonlinear autoregressive distributed lags model of Shin et al. (Festschrift in honor of Peter Schmidt. Springer, New York, 2014) that allows for estimating asymmetric long-and short-run effects. The asymmetric effect was introduced via decomposing the electricity consumption into positive and negative changes. The findings of the nonlinear autoregressive distributed lags model show evidence of asymmetric long-run effect, where the long-run effect of the electricity consumption increase was significantly positive, but the long-run effect of the electricity consumption decrease was not significant. Furthermore, asymmetric short-run effect was found as well. In addition, the modified method of the Granger causality test; namely Toda and Yamamoto (Journal of Econometrics 66:225–250, 1995), shows bidirectional causality between carbon dioxide emissions and both electricity consumption increase and decrease. In view of the empirical outcomes, some policy implications have been drawn. One suggested important implication is moving to use more clean energy sources (wind, solar, hydropower) in electricity production, given that Kuwait has sufficient economic and technological capacity to develop and promote new energy sources. [ABSTRACT FROM AUTHOR]

Considering Africa's increasing electricity demands and the pivotal role of electricity in socioeconomic development, this study addresses a research gap in understanding the stochastic properties of per capita electricity consumption in African contexts. Through examining the integration properties of per capita electricity consumption across 19 African countries, the research determines whether 'shocks' yield transitory or permanent effects, aiding policy formulation and future demand forecasting. Annual electric power consumption data (1971 – 2014) (kWh per capita) from the WDI across 19 African countries are used. Our methodology starts with formally testing for cross-sectional dependence (CSD) using various tests to avoid potential size distortions. Subsequently, we employ the novel panel stationarity test of Nazlioglu and Karul (2017), a Fourier KPSS panel stationarity test. The robustness of the analysis is enriched by incorporating the Hadri and Kurozumi (2011) panel stationarity test and Nazlioglu et al. (2021) new panel stationary tests. The CSD test results reveal mutual dependency in electricity consumption among the sampled African countries, underscoring the need for a panel testing methodology that accommodates CSD. The results of the panel stationarity tests that allow CSD and structural breaks—specifically, the Nazlioglu and Karul (2017) test, which models structural breaks as a gradual process—reject the null hypothesis of trend stationarity for both single frequencies, i.e., k =1, and 2. Similarly, all new, complementary panel stationarity tests from Nazlioglu et al. (2021) reject the null hypothesis of stationarity for both frequencies. Collectively, the outcomes from various panel stationarity tests present strong evidence of nonstationary per capita electricity consumption within the sampled African countries. Additionally, the Nazlioglu and Karul panel stationarity test enables an analysis of the individual Fourier KPSS test for cross-sectional units in the panel. Of the 19 countries, only Togo does not reject the null hypothesis of stationarity for both frequencies. These individual country results align with the panel outcomes, providing compelling evidence for the nonstationarity of the per capita electricity consumption series across the investigated African nations. Accordingly, shocks to electricity consumption will have permanent effects, indicating that per capita electricity consumption will not return to its pre-shock equilibrium path. In this case, demand or consumption stabilizing policies will have long-run effects. [ABSTRACT FROM AUTHOR]

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

As global temperatures rise as a result of climate change, there is an urgent need to find ways to keep buildings cool without increasing energy demand, especially in the residential sector. This is especially true in hot climate regions, where demand for air-conditioning drives high household electricity consumption rates, and, in some cases, overburdens the energy supply. This study evaluates the impact of thermal insulation type, thickness, and location on reducing the annual energy consumption in a representative low-rise residential building in Riyadh, the capital of Saudi Arabia, using the DesignBuilder energy simulation tool. The most effective materials and locations are identified, and the life cycle cost model is used to establish the optimum thickness in each location. Reductions in energy demand, CO2 emissions, and cost savings are also calculated, and these are shown to exceed those made by applying current Saudi building standards. The findings reveal that applying the optimal thickness of thermal insulation to the walls and roof of a two-storey villa significantly enhances its thermal performance and reduces total costs, cutting overall energy consumption, and carbon emissions by up to 42.5% compared with the base case model, with a cost savings of 33% over 30 years, life cycle savings of 86.1 USD/m2, and a payback period of 7.98 years. This study demonstrates the positive impacts of thermal insulation from both an environmental and an economic perspective, with the aim of increasing its application. Its findings are immediately applicable in other hot-arid regions, notably other Gulf states, and its focus on the importance of ensuring that building regulations that mandate thermal insulation are properly enforced gives it wider relevance in other climate regions. [ABSTRACT FROM AUTHOR]

Decarbonising the energy system requires high shares of variable renewable generation and sector coupling like power to heat. In addition to heat supply, heat pumps can be used in future energy systems to provide flexibility to the electricity system by using the thermal storage potential of the building stock and buffer tanks to shift electricity demand to hours of high renewable electricity production. Bridging the gap between two methodological approaches, we coupled a detailed building technology operation model and the open-source energy system model Balmorel to evaluate the flexibility potential that decentral heat pumps can provide to the electricity system. Austria in the year 2030 serves as an example of a 100% renewable-based electricity system (at an annual national balance). Results show that system benefits from heat pump flexibility are relatively limited in extent and concentrated on short-term flexibility. Flexible heat pumps reduce system cost, CO2 emissions, and photovoltaics and wind curtailment in all scenarios. The amount of electricity shifted in the assessed standard flexibility scenario is 194 GWhel and accounts for about 20% of the available flexible heat pump electricity demand. A comparison of different modelling approaches and a deterministic sensitivity analysis of key input parameters complement the modelling. The most important input parameters impacting heat pump flexibility are the flexible capacity (determined by installed capacity and share of control), shifting time limitations, and cost assumptions for the flexibility provided. Heat pump flexibility contributes more to increasing low residual loads (up to 22% in the assessed scenarios) than decreasing residual load peaks. Wind power integration benefits more from heat pump flexibility than photovoltaics because of the temporal correlation between heat demand and wind generation. [ABSTRACT FROM AUTHOR]

The electrical energy expenditure of households in Turkey has been increasing over the years, and households account for one-fifth of the overall electrical energy consumption. For this reason, it is of vital importance to determine the factors that affect household electricity expenditure, reveal consumption patterns for different consumer groups, and propose suggestions for the efficient use of electrical energy. In the present study, determinants of household electricity expenditure were analyzed with the quantile regression approach using the Household Budget Survey published by the Turkish Statistical Institute. In conclusion, it was determined that demographic, economic, and residential factors had significant effects on the electricity expenditures of households. The present study will serve as a guide for policymakers and decision-makers in developing strategies and conducting studies toward reducing the electricity expenditures of households, which is an important factor for energy usage. [ABSTRACT FROM AUTHOR]

Investing in renewable energy is of utmost importance, especially in the context of addressing climate change. However, while numerous studies have explored the role of renewable energy in achieving carbon neutrality, there is a noticeable gap in the literature regarding how uncertainties affect the impact of renewable energy on electric power CO2. To bridge this gap, the present study employs recently developed nonparametric techniques, namely multivariate quantile-on-quantile regression (MQQR) and time-varying quantile causality (TVQC), to investigate the relationship between renewable energy and electric power CO2 in the presence of uncertainties. The study utilizes monthly data spanning from January 1988 to May 2023. The bivariate results reveal that renewable energy and low uncertainties contribute to improving ecological quality by reducing electric power CO2. Furthermore, the multivariate quantile-on-quantile regression results highlight the substantial influence of low uncertainties on the impact of renewable energy in lowering electric power CO2. Additionally, the TVQC analysis demonstrates that both renewable energy and uncertainties possess predictive power regarding electric power sector CO2. The study discoveries showcase that the influence of renewable energy on electric power CO2 is subject to external moderation. The suggested policy framework in this study is structured to assist the United States in accomplishing the goals outlined in SDG 7. [ABSTRACT FROM AUTHOR]

Global concern over socioeconomic activities' environmental impact is undeniable, given their implications for climate and human welfare. In our study, we analyze Pakistan's landscape, with emphasis on the effects of electricity generation and consumption (both symmetric and asymmetric), energy trade, and the role of information and communication technology (ICT). We harnessed data from 1993 to 2020, sourced from World Development Indicators, Climate Watch, and the USA EIA. Our analysis involved application of the Nonlinear Autoregressive Distributed Lag model and the Autoregressive Distributed Lag bounds testing methods. Our results reveal that electricity consumption, whether experiencing positive or negative shocks, exhibits a tendency to elevate emissions over the long term. Similarly, shocks in electricity generation from fossil fuels, whether positive or negative, have adverse environmental consequences both in the short and long run. A negative shock in electricity generation from renewable and alternative sources triggers environmental degradation. Remarkably, nuclear energy plays a pivotal role in bolstering environmental sustainability in Pakistan. Conversely, we identified factors contributing to ecological degradation, including imports of coal, crude oil, metallurgical coke, and ICT usage, in the long run. Equally, trade and natural resources rent emerge as mitigating factors over time. Ultimately, our study underscores the importance of diversifying the energy mix, with a particular emphasis on expanding the renewable sources for reducing emissions and fostering environmental sustainability in Pakistan. Additionally, our research suggests that leveraging natural resources rent and trade capabilities can pave the way for an eco-friendlier future in the country. This study assesses the environmental impact of electricity generation by source, electricity consumption, energy trade, and ICTs in Pakistan. The findings confirm that both positive and negative shocks in fossil fuel electricity generation and consumption detrimentally affect the environment. Coal, crude oil, metallurgical coke imports, and ICTs degrade the environment, while nuclear electricity generation, natural resources rent, and trade improve it. Integrating energy diversification with focus on promoting renewable energy sources has the potential to significantly reduce emissions and advance long-run environmental sustainability. Strategically utilizing natural resources rent and trade capabilities can aid in establishing an environmental sustainability in Pakistan. [ABSTRACT FROM AUTHOR]

Floating photovoltaic (FPV) systems have generated much interest in the literature, due to the potential synergies with existing hydropower reservoirs. This is of particular relevance to Aotearoa New Zealand, where the national grid's heavy reliance on hydro resources has left the country prone to energy shortages during dry years. This paper investigates the potential implications of a 1-gigawatt peak fleet of distributed FPV systems on seven hydro schemes around the country. The results show that an FPV fleet in Aotearoa New Zealand could provide additional electricity generation when the controlled hydropower storage is low. A larger, wider rollout would allow for the reservoirs to replenish after the country's unique winter peak in electricity demand. [ABSTRACT FROM AUTHOR]