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Electricity demand is constantly increasing with the growing population, and the process of establishing new transmission lines has become complicated due to the rise in industrialization and urbanization. One solution to meeting this increased demand is to improve the power transfer capability by increasing the transmission voltage. However, the existing transmission lines may not be able to handle the increased load if they are pushed to their thermal limit. This paper presents a mathematical analysis of a simultaneous AC–DC transmission system transmitting power at different values of parameters. The computational analysis is carried out using MATLAB, and the circuit model simulation is validated by using power systems computer aided design (PSCAD/EMTDC). The transmitted power is evaluated by varying the transmission angle and transmission length for a fixed voltage, and then by varying the transmission angle and voltage for a fixed transmission length. Both these cases are analyzed using approximate and actual formulas. The results of the computation and simulation show close conformity. [ABSTRACT FROM AUTHOR]

We propose a simple dynamic anthropogenic heat (QF) parameterisation for the Weather Research and Forecasting (WRF) single-layer urban canopy model (SLUCM). The SLUCM is a remarkable physically based urban canopy model that is widely used. However, a limitation of SLUCM is that it considers a statistically based diurnal pattern of QF. Consequently, QF is not affected by outdoor temperature changes, and the diurnal pattern of QF is constant throughout the simulation period. To address these limitations, based on the concept of a building-energy model (BEM), which has been officially introduced in WRF, we propose a parameterisation to dynamically and simply simulate QF from buildings (QFB) through a physically based calculation of the indoor heat load and input parameters for BEM and SLUCM. This method allows users to simulate the dynamic QF and the electricity consumption (EC) as the outdoor temperature, building insulation, and heating and air conditioning (HAC) performance change. This is achieved via the simple selection of certain QF options among the urban parameters of WRF. The new parameterisation, SLUCM + BEM, was shown to simulate temporal variations in QFB and EC for HAC (ECHAC) and broadly reproduce the ECHAC estimates of more sophisticated BEM and ECHAC observations in the world's largest metropolis, Tokyo. [ABSTRACT FROM AUTHOR]

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

A demand response (DR) aggregator is a specialized entity designed to collaborate with electricity producers, facilitating the exchange of energy for numerous stakeholders. This application is a pivotal development within the Russian Energy System as it transitions to a Smart Grid. Its successful operation relies on the advancement and implementation of more efficient strategies to manage emerging energy assets and structures. The holonic approach is a distributed management model used to handle systems characterized by random and dynamic changes. This paper analyzes the specific aspects of the electricity demand management mechanism in Russia, primarily aimed at reducing peak load in the energy system by engaging active consumers who are outside the wholesale market. The DR-Aggregator is considered both a cyber-physical system (CPS) with a cluster structure and a business process. The DR-Aggregator exhibits essential holonic properties, enabling the application of a holonic approach to enhance the efficiency of the DR-Aggregator mechanism. This approach will facilitate greater flexibility in managing the load schedules of individual holon consumers, bolster the reliability of power supply by aligning load schedules among holon consumers within the super-holon cluster, and improve the fault tolerance of the DR-Aggregator structure, providing greater adaptability of demand management services. [ABSTRACT FROM AUTHOR]

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

Both the ongoing digital transformation in many statistical agencies around the world and the COVID-19 pandemic outbreak in 2020 have increased the recognition of and demand for infra-monthly economic time series in official statistics during the past decade. Infra-monthly data often display complex forms of seasonality, such as superimposed seasonal patterns with potentially fractional periodicities, that prevent the application of traditional modeling and seasonal adjustment approaches. For that reason, JDemetra+, the official software for harmonized seasonal adjustments of monthly and quarterly data in Europe, has been augmented recently with several methods tailored to the specifics of infra-monthly data. This includes a modified TRAMO-like routine for data pretreatment and extended versions of the ARIMA model-based and X-11 seasonal adjustment approaches alongside the classic STL method. These methods can be accessed through either the graphical user interface or an R package suite, which also provides additional routines for structural time series modeling. We give a comprehensive description of all those methods and discuss the theoretical properties of their key modifications. Selected capabilities are then illustrated using daily births in France, hourly realized electricity consumption in Germany, and weekly initial claims for unemployment insurance in the United States. [ABSTRACT FROM AUTHOR]

The objectives of this paper were to: (i) analyze field testing results to find the reason of current unreasonable operation strategy; (ii) propose operation strategy based on the above work and demonstrate its effectiveness. The results indicated that, unreasonable operation strategies were mainly due to the mismatching of chilled water storage capacity and user side cooling demand. And heat pumps inefficiently operating under low part load ratio (PLR) condition from 11:00 to 19:00 in design day resulted in high operation cost, too. In this paper, proposed operation strategy considered operation objective, operation constraints, time of use electricity price and heat pumps efficient operation. Compared to current operation strategy, proposed operation strategy yielded 30.5% operation cost saving in design day and 15.1% seasonal operation cost saving in summer. Proposed operation strategy is effective to guide system actual operation. [ABSTRACT FROM AUTHOR]

This research aims to reduce electricity use and promote activities to reduce electricity use within the organisation through a case study from the PCB assembly department. It was found that the total annual electricity consumption is approximately 2,099,093 kWh/y. The amount of PCB production per time is 7 sheets, and the delay time to release PCB sheets is 15 s per sheet. Therefore, the researcher used the AHP techniques to identify the problems. The solution is to introduce innovative insulation wrapping that can help reduce heat radiation from the machine. From the experiment, it was found that after the improvement, the production number increased to 11 sheets, with a delay time of 10 s per sheet. Production increased by 33.3%, heat loss decreased by 53.5%. As a percentage per power dropped 51% and the percentage of the electricity cost was 70.1% the payback period was 1.69 years. [ABSTRACT FROM AUTHOR]

Demand for electricity and cooling is increasing due to population growth, which in turn increases pollution. Renewable energy sources are gaining popularity due to their lower levels of pollution. Resource utilisation efficiency is an important factor in maximising the efficiency of low-temperature heat sources. In this study, a vapour absorption cooling cycle and a Kalina power cycle were merged. At the economiser, heat is recovered from the heat exchanger and divided into two basic solution streams. The high-pressure flow passes through the boiler evaporator to produce wet steam, while another stream is throttled to an intermediate pressure. The thermal efficiency of the cycle improves with increasing separator temperature, separator pressure, concentration, and heat recovery temperature, but there is an optimal separator temperature for resource utilisation capacity. Increasing separator pressure and heat recovery temperatures improves resource utilisation efficiency. The proposed cycle has a better temperature match in the heating process. The resource utilisation efficiency of the present cycle is 31.13%, which is 90.63% higher than the reference study. [ABSTRACT FROM AUTHOR]

Cables are of vital importance for the transmission and distribution of electricity within the power grid. However, as the demand for electricity continues to grow, the transmission capacity of a single high-voltage cable is no longer sufficient to meet the required standards. It is therefore imperative to increase the transmission capacity of cables. The deployment of parallel cable transmission represents the most effective strategy for enhancing transmission capacity. However, due to the complex electromagnetic coupling relationship between cables, there is a possibility of current imbalance between cable lines in the same phase, which will have an adverse effect on the normal operation of power equipment and pose a significant threat to the security of the power system. In light of the aforementioned considerations, this paper primarily investigates the mitigation of parallel cable line imbalance. Initially, it examines the parallel cable line current imbalance factors through a theoretical lens, encompassing cable impedance and electromagnetic coupling arrangement. Subsequently, it assesses the efficacy of two proposed methods for reducing the imbalance degree of the parallel cable, namely, the adjustable reactor in series and the addition of a magnetic ring device. Furthermore, finite element simulation software is employed to simulate the two methods and verify their efficacy. Ultimately, a parallel cable plus magnetic ring test is conducted to verify the effectiveness of the magnetic ring. [ABSTRACT FROM AUTHOR]

Given the increasing use of electricity and the current environmental challenges, ensuring efficiency and sustainability in power generation must be a major concern for society. In this context, energy and exergy analysis are useful to assess the efficiency and sustainability of different economic sectors. Hence, an exergy-based sustainability analysis of the Brazilian power generation sector was performed based on operational data from 2000 to 2020. The overall exergy efficiency of this sector varied from 63.3% to 75.0%. Thermal power generation was identified with the largest improvement potential. Several sustainability indicators were used to address the sector's sustainability. It was found that the depletion number varied between 0.25 and 0.37, while the sustainability index ranged from 2.73 to 4.00. The behavior of the indicators, such as relative irreversibility, lack of productivity, waste exergy ratio, and environmental effect factor, revealed that the exergetic cost of natural gas generation is already equivalent to that of hydropower generation, while electricity generated from natural gas represents only about 14% of the hydropower generation in the country. Over the evaluated period, cumulative exergy loss increased to more than twice the exergy provided by the system. In addition, a hypothetical scenario with no natural gas power generation was evaluated and compared; results showed a huge potential for exergy savings. This study is expected to contribute to enhancing energy policy formulation for sustainable development in Brazil. [ABSTRACT FROM AUTHOR]

Demand response plays a pivotal role in modern smart grid systems, aiding in balancing energy consumption. However, the increasing energy demands of contemporary society have placed a significant burden on power systems. To simulate the interaction between electricity supply and demand, this paper introduces the concept of Deep Q-Network (DQN) to the domain of demand response. Additionally, a novel multivariate forecasting model, referred to as PreAttention-CNN-GRU (PACG), is proposed to predict in real time the impact of electricity prices on consumer electricity usage behavior. Finally, a load control method, denoted as DQN-PreAttention-CNN-GRU (DQN-PACG), is presented to achieve price-based demand response. The performance of PACG was tested on a real-world German dataset, demonstrating superior predictive accuracy compared to traditional forecasting models such as Long Short-Term Memory Networks. Furthermore, the test results of DQN-PACG on the same dataset contribute to alleviating the load and stress on the power grid. This paper also includes a case study of southern provinces in China, where the model was able to reduce electricity consumption by 1.64% and electricity cost by 5.42%, both of which outperform the current electricity pricing policies. [ABSTRACT FROM AUTHOR]

Electricity generation in the United States entails significant water usage and greenhouse gas emissions. However, accurately estimating these impacts is complex due to the intricate nature of the electric grid and the dynamic electricity mix. Existing methods to estimate the environmental consequences of electricity use often generalize across large regions, neglecting spatial and temporal variations in water usage and emissions. Consequently, electric grid dynamics, such as temporal fluctuations in renewable energy resources, are often overlooked in efforts to mitigate environmental impacts. The U.S. Department of Energy (DOE) has initiated the development of resilient energyshed management systems, requiring detailed information on the local electricity mix and its environmental impacts. This study supports DOE's goal by incorporating geographic and temporal variations in the electricity mix of the local electric grid to better understand the environmental impacts of electricity end users. We offer hourly estimates of the U.S. electricity mix, detailing fuel types, water withdrawal intensity, and water consumption intensity for each grid balancing authority through our publicly accessible tool, the Water Integrated Mapping of Power and Carbon Tracker (Water IMPACT). While our primary focus is on evaluating water intensity factors, our dataset and programming scripts for historical and real‐time analysis also include evaluations of carbon dioxide (equivalence) intensity within the same modeling framework. This integrated approach offers a comprehensive understanding of the environmental footprint associated with electricity generation and use, enabling informed decision‐making to effectively reduce Scope 2 water usage and emissions. Key Points: We offer hourly U.S. electricity mix, water, and CO2‐eq intensities of end users, providing comprehensive local grid impact assessmentThe Water IMPACT tool provides modeling framework for historical and real‐time estimates of electricity mix and environmental impactsThe dataset and modeling framework support DOE's energyshed management initiative, helping users effectively reduce Scope 2 footprints [ABSTRACT FROM AUTHOR]

The electricity market is a complex and dynamic environment characterized by a multitude of factors that influence electricity prices. Accurate and reliable electricity price forecasting (EPF) is crucial for market participants, including power generators, consumers, and policymakers. Electricity prices are influenced by temporal dependencies and electricity consumption patterns. Therefore, dependencies across different feature dimensions (cross-dimensional dependencies) and temporal trend information are essential. To address the aforementioned issues, we propose Multimodal Integration and Linear Enhanced Transformer (MILET), which combines cross-dimensional dependencies with single-dimensional modal features. First, we decompose electricity price data into three regular modals using Variational Mode Decomposition and Sample Entropy. This approach enables us to uncover the intrinsic patterns within the variable, thereby simplifying the complexity of the data series. Then integrate these three modals and the original dataset into a five-channel encoder (Modal Integration Encoder, MIE) with both single and multi-dimensional information. MIE is composed of Overall Two-Stage Attention (OTSA) and Long Short-Term Memory (LSTM), where OTSA handles cross-dimensional dependencies, and LSTM addresses long-term dependencies. Additionally, we capture trend information in electricity consumption features through linear layers and linearly integrate the data to obtain the forecasting results. Extensive experimental results on five electricity price datasets demonstrate the effectiveness of MILET compared to state-of-the-art techniques. Our code is available at . [ABSTRACT FROM AUTHOR]

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

In this paper, we present a study on forecasting hourly electricity spot prices for India. Forecasting electricity prices is challenging for its special characteristics. Based on hourly data covering several years, ARMAX models are estimated for forecasting for individual regions. Along with ARMA, intra-day, inter-day and hourly variations and seasonalities on weekdays, holidays and festive days are incorporated. Combinations of static and dynamic forecasts are also examined. The results indicate that prices for Sundays and holidays differ significantly from weekdays; also, a clear region-specific difference between prices in winter and summer months is observed. Price behaviour in the Southern region is different from other regions. The fitted models perform well for forecasting horizons of hourly prices of up to five days. The fitted ARMAX models have an easily implementable simple structure. Such forecasts provide valuable insights for policymakers and market participants, as they incorporate several features of the Indian electricity market, including various dimensions of seasonality of demand. JEL Classifications : C53, Q47, C51 [ABSTRACT FROM AUTHOR]

The aim of this article is to analyze theoretically whether changes in an access charge pricing policy can promote green electricity use. The model considers two hypothetical firms: One firm offers green electricity product, and the other firm provides conventional electricity. Each firm pays an access charge to the natural monopoly in order to have access to the transportation network. Through using a vertical differentiation model, we try to analyze the link between the access charges paid by the two firms and the qualities offered. We also attempt to explain why the consumers do not often choose green electricity. Finally, we suggest a new policy that can lead the consumers to choose the green electricity product instead of conventional electricity. Our model shows that changes in the access charge pricing policies can constitute a relevant component in the reforms of the electricity sector and can have impacts on the consumption of the green electricity. A differentiating access charge policy could allow a better penetration of the green electricity product and an increase in the social welfare when consumers have greater preferences in environmental qualities. Otherwise, their preservation at a uniform level is desirable in order to maintain the competition. JEL Classifications : B21, C72, L13, Q21, P28 [ABSTRACT FROM AUTHOR]

Smart grids are modernized, intelligent electricity distribution systems that integrate information and communication technologies to improve the efficiency, reliability, and sustainability of the electricity network. However, existing smart grids only integrate renewable energies when it comes to active demand management without taking into consideration the reduction of greenhouse gas emissions. This paper addresses this problem by forecasting CO2 emissions based on electricity consumption, making it possible to transition to renewable energies and thereby reduce CO2 emissions generated by fossil fuels. This approach contributes to the mitigation of climate change and the preservation of air quality, both of which are essential for a healthy and sustainable environment. To achieve this goal, we propose a transformerbased encoder architecture for load forecasting by modifying the transformer workflow and designing a novel technique for handling contextual features. The proposed solution is tested on real electricity consumption data over a long period. Results show that the proposed approach successfully handles time series data to detect future CO2 emissions excess and outperforms state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

With the progress in renewable energy and smart grid technologies, electricity users are evolving into prosumers, capable of both consuming and generating electricity through distributed photovoltaic (DPV) systems. Concurrently, the liberalization of the electricity retail market has prompted retailers to design customized electricity packages based on users' needs and preferences, aiming to enhance service quality, efficiency, and user retention. However, previous studies have not fully addressed the multidimensional characteristics and electricity consumption behaviors that influence package selection. This paper initially dissects user characteristics across three key dimensions: electricity demand preferences, price sensitivity, and risk tolerance. Therefore, leveraging utility functions and autonomous choice behavior models, we propose two innovative electricity purchase and sale combination packages: a fluctuating pricing package and a discount-based pricing package. Furthermore, we introduce the Self-Adaptive Weight and Reverse Learning Particle Swarm Optimization (SAW&RL-PSO) algorithm to address the complexities of these choices. Simulation results indicate that the methodologies presented significantly enhance user benefits and retailer revenues while also effectively managing electricity usage fluctuations and the challenges of integrating large-scale DPV systems into the electrical grid. [ABSTRACT FROM AUTHOR]

The building sector is one of the largest final consumers of energy, especially for air conditioning, and research has shown that there is a great potential for energy savings in this sector. The performance of a chiller can vary significantly depending on system partial loads and evaporator and condenser operating temperatures. The condensers of water-only chillers take the heat of condensation from the refrigerant only through the sensible heat of the refrigerant, leading to significant water flows. The outside air temperature and the intensity of solar radiation directly influence the consumption of electricity on the chiller. Having available the values for the solar radiation intensity I [W/m2 ] and the outside air temperature te [⁰C], with the help of the proposed mathematical model, validated by experimental measurements, the optimal values for the minimum electricity consumption of the system are obtained, under the conditions which the chilled water temperature is 7/12⁰C and the indoor air temperature is 27⁰C [ABSTRACT FROM AUTHOR]

The main goal of the power system is to congregate the power demand within the power grid while maintaining economical operation, system security and minimal environmental impact. Due to the increasing demand for electrical energy, many problems have arisen with the power systems. These problems include excessive load, uneven system performance, unsatisfactory voltage profile, and an increase in network power losses. To address these issues, more generation sources and improved transmission capacity are required. In order to meet increasing electricity demand, it is more efficient to integrate a sufficient number of smaller generation units. Utilities and consumers can get the significant benefit from installation of distributed generation (DG), which reduces power losses, progress voltage profile, increases power quality and reliability, delays system updates, supports local reactive power, standby generation and peak limiting. This article aims to enrich the performance of the entire network through the best possible placement and penetration of wind energy and solar photovoltaic (PV) dispersed generation. [ABSTRACT FROM AUTHOR]

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

Electricity consumption in buildings is a significant contributor to greenhouse gas emissions, which drive climate change. Reducing electricity use in residential buildings, which account for approximately 20% of Thailand's total electricity consumption, represents a key opportunity for lowering greenhouse gas emissions. The aim of this study was to assess the potential reduction in greenhouse gas emissions through the use of appropriate solar absorptance in wall paint, conducted via an energy simulation using a representative residential building model from Bangkok. The DOE2.1E program was employed to simulate a standard two-story house commonly found in Thailand, with an approximate floor area of 120 square meters. The window-to-wall ratios were set at 10% and 20%, and air conditioning usage was modeled for nighttime hours. External wall paint was assigned varying solar absorption coefficients, ranging from 10% to 90%. Greenhouse gas emissions were calculated by multiplying the simulated annual electricity consumption by the emission factor, expressed in kgCO2eq/kWh, provided by the Thailand Greenhouse Gas Management Organization. The results indicated that adjusting wall paint solar absorptance from 10% to 90% led to a 10% variation in both energy consumption and greenhouse gas emissions, potentially reducing CO2 emissions by approximately 411–456 kgCO2eq per house per year. Therefore, implementing regulations that mandate the use of wall paints with appropriate solar absorption coefficients could significantly reduce greenhouse gas emissions and contribute to environmental protection efforts in Thailand. [ABSTRACT FROM AUTHOR]

With the global shift to electric vehicles, countries face unique challenges and opportunities shaped by their geographical and economic contexts. This paper presents a system that leverages smart transport technologies, the Internet of Things, and decision-making algorithms, such as PROMETHEE, to optimize charging stations and their positioning in diverse urban and rural settings. The system addresses key obstacles, including managing charging infrastructure, balancing energy consumption, and enhancing transport accessibility. By analyzing local conditions, the proposed solution incorporates innovative algorithms for electricity demand forecasting, charging station management, and integration with urban transport systems. This approach ensures a flexible, scalable, and sustainable electric vehicle management system that aligns with international standards and evolving technological trends. [ABSTRACT FROM AUTHOR]

The surge in urban household electricity consumption (HEUR) has posed a challenge to China's energy conservation efforts. In this paper, from the new perspective of SHAP interpretable machine learning, we address the "black box" drawbacks of machine learning in HEUR prediction research, study the influencing factors and mechanisms of the surge of urban household electricity consumption, and explore the interactions of critical factors, such as the aging of the population, the per capita income (PCI), and the electricity consumption rate of a single-person household (SPHR). The findings are as follows: (1) The key influencing factors of HEUR in China (number of general higher education graduates, per capita living space, urbanization rate, number of older people, electricity consumption per square meter of urban housing, and per capita income). (2) PCI is not the developed region's HEUR's dominant factor; on the contrary, PCI significantly affects less developed regions. (3) The influence of key influencing factors on HEUR is a left-low-right-high U-shape nonlinear effect, while AS and FR show inverted U-shape nonlinearity. (4) Higher education has not increased people's awareness and action to consume HEUR; in the future, SPHR will become the main force of HEUR consumption. In addition, compared to men, women with lower incomes save more HEUR. It provides a set of scientific research bases and suggestions for energy conservation, emission reduction and sustainable energy development in China and other countries. [ABSTRACT FROM AUTHOR]

Higher education institutions (HEIs) play a fundamental role in sustainability, promoters of innovation, science, and technology. Therefore, every day more institutions are joining the fight against global warming. One of the contributions of HEIs is the carbon footprint (CF) report, to implement policies and management systems to establish strategies to reduce polluting gas emissions from their campuses. In the present study, a systematic review was carried out for 50 reports of CF, where 94% of the studies were published from 2018 to 2022. This research compiles methodologies, scope, results, and trends in Carbon Footprint calculations and provides a procedure to evaluate CF on college campuses. This research shows that the most evaluated emission sources were the consumption of electrical energy (78%), transportation (74%), and the use of fuels (64%). In addition, the HEIs with the lowest emission factors for electricity consumption are Finland, England, and Colombia. Furthermore, establishing a specific carbon footprint guideline for universities would improve reports and allow better comparisons between HEIs. [ABSTRACT FROM AUTHOR]

This study re-examines the stationarity properties of per capita electricity consumption in 18 MENA countries from 1980–2021. We use a novel panel stationarity test with a Fourier approximation to capture structural breaks and nonlinearities in the data. This Fourier panel test accounts for cross-sectional dependence and allows heterogeneity across cross-sections in the panel. Moreover, we apply several new panel stationarity tests that complement the aforementioned Fourier test. The study finds strong empirical evidence supporting the stationarity of per capita electricity consumption in the MENA region when considering smooth structural breaks, and our results remain unchanged using sharp structural breaks panel stationarity test. Therefore, we conclude that policies to manage energy demand will have no long-run effect as per capita electricity consumption will, after a shock, return to its long-run trend path. [ABSTRACT FROM AUTHOR]

In recent years, electricity systems have been undergoing rapid renewable energy transitions. This has introduced myriad challenges, namely, matching variability in both supply and demand sides, low plant load factors, resource constraints, etc. Managing these challenges requires effective demand-side solutions. Therefore, demand response (DR) approaches become relevant and essential in better-performing supply- and demand-side planning and management. For this purpose, in this article, two scenarios representing different situations and needing different interventions are developed for future years to study the effectiveness of demand-side solutions in managing the adverse supply-side constraints through an optimization model. The two scenarios are (i) Demand Response in Constrained Electricity System and (ii) Demand Response in High Renewables Electricity System. The model results suggest that the optimization model is robust enough to achieve an optimal as well as desirable solutions for both scenarios and provide evidence for the effectiveness of DR in addressing such challenges. DR solutions, if implemented effectively, can alter and moderate load curves to achieve perfect balance with supply profiles that are affected by resource constraints or by high penetration of solar and wind energy resources. [ABSTRACT FROM AUTHOR]

Ensuring effective forecasting of buildings' energy consumption is crucial in establishing a greater understanding and improvement of buildings' energy efficiency. In Singapore, domestic electricity usage in public residential buildings takes up a significant portion of the country's annual energy consumption. Having effective forecasting approaches is thus important in supporting relevant strategies and policy making. In this research, we proposed a hybrid approach that was based on a combination of building characteristics and urban landscape variables to predict residential housing electricity usage in Singapore. XGboost was also incorporated inside the hybrid approach as the preferred machine learning approach for energy consumption predictions. To demonstrate our proposed approach's predictive strength, the performance of our proposed hybrid machine learning approach was compared with two other models, Geographically Weighted Regression (GWR) model and the Random Forest (RF) model. Results showed that our proposed hybrid model had outperformed these abovementioned approaches with higher accuracy (r2 value of 0.9). The proposed approach had thus been effective in forecasting electricity consumption for public housing in Singapore, and it could also be utilised in other similar urban areas for future electricity consumption forecasting. [ABSTRACT FROM AUTHOR]

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

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

This research focuses on analyzing the aerodynamic characteristics of residual air currents generated by vehicle movement and evaluating their feasibility for energy generation, then designing a vertical axis wind turbine. The parameters assessed include the characteristic velocity profile, the average and maximum velocities, disturbance lifetimes, as well as the frequency and probability of recurrence of these disturbances. Using the data, projections are made on the electrical energy amount that can be produced by a wind turbine operating under such wind conditions. Measurements were taken at four locations: three within Mexico City (CDMX) and one on the outskirts. The measurement station, consisting of a 2.35 m vertical tower equipped with eight vertically aligned thermos-resistive anemometers, is installed on medians less than 0.50 m from moving vehicles. The data from within CDMX show maximum wind velocities ranging from 6 to 8 m/s at ground level, while measurements on the outskirts record velocities of up to 19.5 m/s. A probabilistic analysis reveals that usable air currents could be present 58% of the time. Based on electrical production calculations, it is estimated that harnessing this residual energy could power approximately 4500 homes, considering the national cost per kWh and the average electricity consumption of a four-person household. [ABSTRACT FROM AUTHOR]

The 2022 energy crisis highlighted the dependence of the Europe electricity sector on imported natural gas and the need to accelerate the adoption of renewables to the power system. However, operating a reliable power system with high share of renewables might require curtailing some renewables and activating conventional generators not scheduled in the day-ahead markets to ensure system reliability. These actions can result in environmental impacts, higher system costs and welfare impacts for customers. We use a novel high-granularity data from the Spanish power system for the period 2019–2022 to estimate the effects of these actions and forecast future impact of implementing ambitious targets of the European Gas Reduction Plan. We show that reliance on conventional generators will sharply increase with the addition of renewables. However, higher electricity consumption reduces the negative welfare impacts of integrating renewables. Until renewables and storage technologies advance further, conventional generators are needed for reliable operation of the systems. [ABSTRACT FROM AUTHOR]

The anthropic causes of climate change are well known, but the influence of climate change on society needs to be better estimated. This study estimates the impact of climate change on energy consumption on small tropical islands using monthly temperatures and energy production/consumption statistics during the last decades. Here, we show, using energy, meteorological, demographic, and economic datasets, as well as statistical correlations, that energy consumption is sensitive to (i) cyclonic activity and (ii) temperature warming. On small tropical islands, increased electricity consumption correlates with temperatures rising above 26 °C in relation to air conditioner electricity consumption. On La Réunion Island, a +1 °C increase is expected to cause an electricity production of 1.5 MWh/inhabitant per year, representing a growth of 3.2%. Considering that non-renewable sources are primarily used to produce electricity, this feedback contributed significantly (i.e., 2000 to 4000 TWh) to the greenhouse gas increase caused by climate warming over the last decades on tropical islands. Demographic and wealth variations, as well as socio-economic crises, also have a significant impact on energy consumption (2 kWh for 1000 inhabitants, 0.008 GWh/inhabitant growth for a 10,000 GDP/inhabitant growth, and a 0.2 GWh/inhabitant decrease during COVID-19, for annual consumption, respectively) and must be taken into account for decadal variation analysis. The relationship between climate change and energy consumption in tropical areas should be better integrated into climatic scenarios to adapt building isolation and energy production. [ABSTRACT FROM AUTHOR]

In order to improve the vehicle driving range of a pure electric light commercial vehicle, this paper proposes to reduce the electric power consumption per 100 kilometers by reducing power consumption and increasing coasting energy recovery power. Firstly, for ECO energy-saving mode, the paper proposes the energy-saving control technology based on different vehicle usage scenarios and working conditions, which significantly reduces the energy consumption of the vehicle in the whole scenario. Secondly, in the view of the coasting energy recovery deceleration of commercial vehicles with empty and full load varies greatly, the paper proposes the energy recovery control based on the vehicle mass to achieve the same coasting deceleration under different loads to recover more energy. Then, for the problem that coasting energy recovery is usually designed with low adhesion coefficient road surface, the paper proposes adaptive adjustment of coasting energy recovery based on road adhesion coefficient. Finally, the pure electric light truck is tested on real road. The results show that the ECO mode reduces the electricity consumption per 100 kilometers by 2.9%, and by 2.1% for scenarios of turning on the air conditioning and opening the car door. The energy recovery strategy based on the vehicle' s mass shows a 2.7% reduction in electricity consumption per 100 kilometers. [ABSTRACT FROM AUTHOR]

[Display omitted] • Se-vacancy rich FeSe 2 nanoarrays on iron foam is fabricated. • FeSe 2 /IF-V Se catalyst presents excellent electrocatalytic SOR activity. • SOR-hybrid seawater electrolysis is demonstrated for chlorine-free hydrogen production. • Se-vacancy can regulate d-p band center of surface atoms on FeSe 2. • Zn-nitrite battery is first constructed to power SOR-hybrid seawater electrolysis. Defect engineering is considered one of the most powerful strategies for regulating the catalytic activity of electrocatalysts. A deep understanding of the defect-involved mechanism in electrocatalytic process is of great importance but remains a challenging task. In this study, an anionic Se-vacancy (V Se) was introduced into iron diselenide (FeSe 2) nanoarrays, enabling the catalyst to exhibit improved electrocatalytic performance for sulfion oxidation reaction (SOR). The Se vacancy incorporated can modulate surface electronic structure and optimize the proximity of the d-p band center, strengthening the interaction between FeSe 2 -V Se and S atom. This endows the FeSe 2 /IF-V Se catalyst with the excellent electrocatalytic activity for SOR. As an alternative reaction to oxygen evolution reaction (OER), the low-potential SOR enables an ultralow electricity consumption in SOR-hydrid seawater electrolysis, avoiding detrimental chlorine electrochemistry. A proof-of-concept self-powered system based on FeSe 2 -V Se electrodes was constructed by integrating Zn-nitrite battery and SOR-hybrid seawater electrolyzer to produce high-value hydrogen, sulfur and ammonia. This work offers a fascinating strategy for the construction of an advanced energy system to produce hydrogen while utilizing seawater and wastewater. [ABSTRACT FROM AUTHOR]

From 2016 to 2040, global energy demand is expected to increase by almost 50%. A substantial proportion of this expansion will remain concentrated in emerging economies, predominantly India and China. The energy demand, namely for coal, will increase due to reasons such as population growth, industrialization, and the remarkable expansion of the middle class. In India, the coal employed is categorized as low-grade and exhibits a notable ash content, ranging from 30 to 45 percent. Using lignite or coal in thermal power stations leads to generating a significant quantity of fly ash. The issues of controlling fly ash due to its propensity to cause air and water pollution must be addressed efficiently, especially given the large volume of ash produced and the environmental impact it causes in India. This article thoroughly examines Indian fly ash, encompassing its distinctive attributes, a wide array of uses, environmental ramifications, and regulatory structure. The volume of fly ash produced has experienced a significant rise in the last ten years, primarily because coal-fired thermal power plants are responsible for meeting more than 70% of the nation’s electricity demands. Currently, India is responsible for the production of about 180 million metric tonnes of fly ash. Moreover, this article provides a thorough examination of the global landscape about the manufacturing and utilization of fly ash, with a particular focus on India. [ABSTRACT FROM AUTHOR]

The electric sector simultaneously faces two challenges: decarbonization to mitigate, and adaptation to manage, the impacts of climate change. In many regions, these challenges are compounded by an interdependence of electricity and water systems, with water needed for hydropower generation and electricity for water provision. Here, we couple detailed water and electricity system models to evaluate how the Western Interconnection grid can both adapt to climate change and develop carbon-free generation by 2050, while accounting for interactions and climate vulnerabilities of the water sector. We find that by 2050, due to climate change, annual regional electricity use could grow by up to 2% from cooling and water-related electricity demand, while total annual hydropower generation could decrease by up to 23%. To adapt, we show that the region may need to build up to 139 GW of additional generating capacity between 2030 and 2050, equivalent to nearly thrice California's peak demand, and could incur up to $150 billion (+7%) in extra costs. The authors link water and electricity system models to evaluate how the electric grid can both adapt to climate change impacts and decarbonize, while also accounting for dependencies and climate vulnerabilities of the closely coupled water sector. [ABSTRACT FROM AUTHOR]