Search

Copyright of Jurnal Ilmu Ekonomi Terapan is the property of Universitas Airlangga 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 Journal of Economics & Administrative Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

Agriculture has played an important role in the growth of the Indian economy. Water and electricity are essential inputs for agriculture today. India produces the third-largest amount of electricity in the world, behind China and the USA. As an incentive to increase production, most states provide free electricity to farmers. Free electricity has decreased groundwater levels and increased electricity consumption. The objective of this study was to find out the factors that influenced farmers to adopt a solar water-pumping system in Gujarat. The samples were randomly selected. This study examined the entire state of Gujarat. One hundred and fifty farmers who adopted solar water-pumping systems were interviewed, including 50 banana farmers, 50 cotton farmers and 50 groundnut farmers. Primary data were collected through an interview. The adoption of solar water-pumping systems by farmers was identified using factor analysis. SPSS software was used to analyse the data collected. In this study, only two factors contributed to the variance of 59.469%. The adoption of solar water pumps by farmers was influenced by government policy and economic benefits. An economic benefit variance of 33.20% was found, while a government policy variance was 26.27%. A solar water-pumping system has low initial costs, flexible credit policies motivate adoption, solar water-pumping systems can save electricity, and maintenance and repair costs are affordable. Government policy that includes solar water-pumping systems is motivated by awareness, subsidies for adoption and a fair price to sell energy surpluses. Farmers are recommended to adopt a solar water-pumping system in conjunction with a drip irrigation system to receive additional income. The successful scheme will be recommended to other states in India for implementation. [ABSTRACT FROM AUTHOR]

The augmented electricity demand requires electrical infrastructure upgrades with system operators instituting strategies to increase Distribution Network (DN) capacity in tandem with load growth. In this study, a simple method of deploying Li-ion Battery Energy Storage Systems (BESSs) to defer DN upgrades is presented by utilizing historical load profiles. The k-means algorithm is employed to cluster the annual load profiles obtained from a substation in groups of 15-minute intervals. The load data are min-max scaled and fed as input to the K-means algorithm. The NPV financial analysis method is followed in the DN upgrade deferral benefit determination with the acquired benefit depending on Li-ion BESS price and feeder upgrade cost. The results indicate economic viability of up to four years with a Net Present Value (NPV) of US$10k for Li-ion 2000kW/3000kWh BESS. More benefits and deferral years are achieved by varying Li-ion BESS and feeder upgrade costs to 80% and 120%, respectively with deferral years increased to six with an NPV of US$110k for Li-ion BESS of 3100kW/6000kWh. [ABSTRACT FROM AUTHOR]

Outlier detection plays a critical role in building operation optimization and data quality maintenance. However, existing methods often struggle with the complexity and variability of building energy data, leading to poorly generalized and explainable results. To address the gap, this study introduces a novel Vision-based Outlier Detection (VOD) approach, leveraging computer vision models to spot outliers in the building energy records. The models are trained to identify outliers by analyzing the load shapes in 2D time series plots derived from the energy data. The VOD approach is tested on four years of workday time-series electricity consumption data from 290 commercial buildings in the United States. Two distinct models are developed for different usage purposes, namely a classification model for broad-level outlier detection and an object detection model for the demands of precise pinpointing of outliers. The classification model is also interpreted via Grad-CAM to enhance its usage reliability. The classification model achieves an F1 score of 0.88, and the object detection model achieves an Average Precision (AP) of 0.84. VOD is a very efficient path to identifying energy consumption outliers in building operations, paving the way for the enhancement of building energy data quality, operation efficiency, and energy savings. [ABSTRACT FROM AUTHOR]

In railway signaling design, the calculation of electricity consumption for railway signal equipment is very complicated. To simplify the process of electricity consumption design for railway signal equipment, this paper analyzes various calculation methods of signal load, and uses the VB6.0 programming software to compile the electricity consumption calculation software for railway signal equipment, reducing the difficulty of electricity consumption design for railway signal equipment and improving design efficiency. [ABSTRACT FROM AUTHOR]

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

The significant growth in demand for electricity has led to increasing congestion on distribution networks. The challenge is twofold: it is needed to expand and modernize our grid to meet this increased demand but also to implement smart grid technologies to improve the efficiency and reliability of electricity distribution. In order to mitigate these congestions, novel approaches by using flexibility sources such as battery energy storage can be used. This involves the use of battery storage systems to absorb excess energy at times of low demand and release it at peak times, effectively balancing the load and reducing the stress on the grid. In this paper, two optimal power flow formulations are discussed: the branch flow model (non-convex) and the relaxed bus injection model (convex). These formulations determine the optimal operation of the flexibility sources, i.e., battery energy storage, with the objective of minimizing power losses while avoiding congestions. Furthermore, a comparison of the performance of these two formulations is performed, analyzing the objective function results and the flexibility operation. For this purpose, a real Spanish distribution network with its corresponding load data for seven days has been used. [ABSTRACT FROM AUTHOR]

With the deteriorating environment and excessive consumption of primary energy, solar energy has become used in buildings worldwide for renewable energy. Due to the fluctuations of solar radiation, a solar photovoltaic (PV) power system is often combined with a storage battery to improve the stability of a building's energy supply. In addition, the real-time energy consumption pattern of the residual houses fluctuates; a larger size for a PV and battery integrated system can offer more solar energy but also bring a higher equipment cost, and a smaller size for the integrated system may achieve an energy-saving effect. The traditional methods to size a PV and battery integrated system for a detached house are based on the experience method or the traversal algorithm. However, the experience method cannot consider the real-time fluctuating energy demand of a detached house, and the traversal algorithm costs too much computation time. Therefore, this study applies Nondominated Sorting Genetic Algorithm-II (NSGA-II) to size a PV and battery integrated system by optimizing total electricity cost and usage of the grid electricity simultaneously. By setting these two indicators as objectives separately, single-objective genetic algorithms (GAs) are also deployed to find the optimal size specifications of the PV and battery integrated system. The optimal solutions from NSGA-II and single-objective GAs are mutually verified, showing the high accuracy of NSGA-II, and the rapid convergence process demonstrates the time-saving effect of all these deployed genetic algorithms. The robustness of the deployed NSGA-II to various grid electricity prices is also tested, and similar optimal solutions are obtained. Compared with the experience method, the final optimal solution from NSGA-II saves 68.3% of total electricity cost with slightly more grid electricity used. Compared with the traversal algorithm, NSGA-II saves 94% of the computation time and provides more accurate size specifications for the PV and battery integrated system. This study suggests that NSGA-II is suitable for sizing a PV and battery integrated system for a detached house. [ABSTRACT FROM AUTHOR]

The development of accurate bioenergetic models, that simulate scenarios for decision making and policy planning contributions, has become a necessity in the actual climate crisis context. The objective of this work is to develop a multicriteria methodology to evaluate scenarios for assessing a region's bioenergetic potential and determining optimal location of biopower plants. This method utilizes a geographic information system that incorporates local biomass distribution, crop harvest statistics, detailed yield data, preference and exclusion maps. The approach examines the current local use of residues in the context of long-term soil preservation, employing sustainable residue removal. When it is applied to the Province of Córdoba, Argentina, specifically assessing main agricultural harvest residues, the model identifies 1.8 million tn/year of theoretical residues, primarily from corn and soybean, revalorizing only 4.1% of the available harvest agricultural residues. When employing the most commonly used approaches in the literature, which involves average crop yield values for extensive regions, our results show an underestimation of theoretical biomass up to 26%. This makes it essential to incorporate greater detail in the modeling. After optimizing five biopower plants, each with a collection radius of 60 km, it becomes possible to convert 59% of these residues into bioenergy, generating 2.3 GWh/year, covering up to 23.3% of Córdoba's annual electricity demand. The novelty of this research resides in the development of a high detailed multicriteria methodology, which facilitates the fast simulation of scenarios to identify optimal biopower plant locations, applicability to different biomass types and bioenergy forms for extensive regions. [ABSTRACT FROM AUTHOR]

Demand response (DR) can provide extra scheduling flexibility for power systems. Different from industrial and residential loads, the production process of manufacturing loads includes multiple production links, and complex material flow and energy flow are closely coupled, which can be seen as a typical nondeterministic polynomial-time (NP) hard problem. In addition, there is a coupling effect between the temperature-controlled loads (TCLs) and the manufacturing loads, which has often been ignored in previous research, resulting in conservative electricity consumption planning. This paper proposes an optimal demand management for the manufacturing industry. Firstly, the power consumption characteristics of manufacturing loads are analyzed in detail. A state task network (STN) is introduced to decouple the relationship between energy and material flow in each production link. Combining STN and production equipment parameters, a general MILP model is constructed to describe the whole production process of the manufacturing industry. Then, a mathematical model of the TCLs considering a comfortable human degree is established. Fully considering the electricity consumption behavior of equipment and TCLs, the model predictive control (MPC) method is adopted to generate the optimal scheduling plan. Finally, an actual seat production enterprise is used to verify the feasibility and effectiveness of the proposed demand management strategy. [ABSTRACT FROM AUTHOR]

This study focuses on carbon emissions of the building sector in relation to local climate zone (LCZ) classification, concentrating on two major parts. First, we estimated carbon emissions in the building sector, which were calculated for weekdays and weekends real-time daily energy consumption patterns. The estimations were divided into direct (from petroleum products consumption) and indirect emissions (from electricity consumption). Second, we examined urban carbon emissions mapping in relation to LCZ. Bangkok Metropolitan Administration (BMA) was used as the case study and 2016 as the base year for examination. The results illustrate that indirect emissions in Bangkok can be up to ten times higher than direct emissions. The analysis indicates that LCZ, such as compact high-rise, large low-rise, light industry, and warehouse zones had a relatively higher carbon emission intensity than others. Additionally, we identified that the compact high-rise zone has the highest indirect emission intensity, while the light industry and warehouse zone have the greatest direct emission intensity. These results provide insights into the dynamics of carbon emission characteristics in the building sector and the methodology purported here can be used to support low carbon city planning and policymaking in Bangkok. [ABSTRACT FROM AUTHOR]

As the share of renewable energy sources, which are weather dependent and consequently volatile, continues to grow, it becomes increasingly important to explore strategies for organising both electricity production and consumption to ensure system stability. People's flexibility in their energy consumption is one option to regulate the system. To better understand people's willingness to align their electricity-consuming activities with a flexible pricing system, an online survey with 962 respondents was conducted. The analysis focused on the factors influencing their willingness to shift electricity-consuming activities away from peak hours, as well as the maximum shift duration of using certain devices. The results indicate that people with more flexible lifestyles and those who perceive shifting activities as taking less effort are more willing to shift their activities and indicate longer shift durations. The data also show that attitudes towards the environment, as well as financial, ecological, and motivational factors, play a role in explaining the variance in the willingness to shift and the shift duration. To conclude, increasing flexibility in everyday life could make a valuable contribution to the optimal use of electricity resources. [ABSTRACT FROM AUTHOR]

When compared to traditional field production, controlled environment agriculture (CEA) such as greenhouses and indoor vertical farms (VF) have sustainability benefits such as reduced land use, less product transportation to consumers, improved resource and land-use efficiencies, food safety, and local food availability. Despite its potential as an environmentally beneficial complement to conventional farming, CEA has numerous issues that limits its adoption and viability as a sustainable option. This review summarizes the literature on key areas of sustainability in CEA, such as (1) sustainability challenges, (2) technologies identified to address sustainability in CEA, (3) quantification and reporting of sustainability in CEA, and (4) gaps and opportunities in addressing CEA sustainability. To filter the available literature from the databases including Web of Science, this scoping review employed a combination of the keywords "sustainability," "controlled environment agriculture," "urban farm," "vertical farm," and "indoor farm." According to the review, main obstacles in CEA were high electricity use, geographical location-related tradeoffs, and an unfavorable public perception of CEA in comparison to field production. These issues are now being addressed by optimized lighting and sensor technology, models, decision support tools to reduce electricity use, and marketing tactics to educate people about the benefits of CEA. This scoping review offers two critical areas to focus sustainability improvement efforts: lowering electrical demand and using circular techniques for organic waste and wastewater reuse in CEA to increase water, nutrient, and energy use efficiency and recovery. In addition, it discusses the techniques and approaches to sustainability assessment in CEA, particularly within the research and application contexts. This scoping review, thus, outlines strategies for enhancing sustainability in CEA, highlighting the importance of integrating circular economy principles and advanced technologies to optimize resource use, and advocates for ongoing research and education to shift public perceptions toward the sustainable potential of CEA. [ABSTRACT FROM AUTHOR]

This study adopts a new approach to reassess the factors influencing urban energy intensity in China. Initially, the factors impacting energy intensity are classified into controllable and uncontrollable categories. Subsequently, employing a single-factor multi-stage method combined with the Adaboost method, 289 Chinese cities are categorized based on uncontrollable factors to eliminate the influence of inherent differences on energy intensity. Finally, panel data regression analyses are conducted using data from 289 Chinese cities between 2005 and 2016, individually for each city type, to evaluate the extent to which controllable factors contribute to energy intensity. The findings indicate that (1) heightened energy prices, an increased share of electricity consumption, and a greater proportion of centralized heating significantly influence the reduction of energy intensity across all city types; (2) to optimize energy consumption, each city type should adopt specific strategies. For instance, cities located in resource-rich heating regions with low economic outputs can reduce their energy intensity by increasing electricity consumption, while cities with high economic outputs can decrease their energy intensity by increasing natural gas consumption. The findings of this study carry substantial implications for the Chinese government in shaping targeted energy policies tailored to different city types. [ABSTRACT FROM AUTHOR]

Nowadays, electricity consumption is increasing rapidly which leads to conventional power systems exhaustion. Therefore, micro-grids (MGs) implantation can enhance the resilience of power systems by implication of new resources, such as renewable energy sources (solar panel and wind power systems), electric vehicles (EV), and energy storage systems (ESS). This paper proposes a new strategy for optimal power consumption inside one microgrid; then, the approach will be extended to optimize the power consumption to enhance the resilience in the case of multi-MGs systems. The system controller of each microgrid has been implemented using ESP32 microcontroller and Raspberry IP4. The proposed approach intends to enhance the resilience of the system to react to any contingency in the system such as loss of power linkage between MG and the network in case of any natural disaster, especially in the rural area. Two controllers are implemented; the first one ensures MG autonomy by the efficient use of its own sources. The second one handles the system resilience cases by demanding/delivering power from/into neighbor microgrids. Hence, this work enhances the system resilience with an optimal cost. Thus, the MG can offer ancillary services for the neighboring MGs. [ABSTRACT FROM AUTHOR]

The pursuit of economic growth has implications for carbon emissions and climate change. Achieving low carbon development is important for attaining the targets of the sustainable development goals. Africa is often described as a largely import-dependent continent. The continent also requires significant investment in information and communication technologies (ICT) and renewable energy to achieve low-carbon economic growth. However, empirical evidence on the joint impacts of imports of goods and services, clean energy use, ICT, and economic growth on carbon emissions in Africa is scanty and mixed. This paper investigated the impacts of information and communication technologies, renewable energy consumption, import and economic growth on carbon emissions by using rich data on total per capita carbon dioxide (CO2) emissions, economic growth, import of goods and services, renewable energy consumption, fixed telephone subscriptions, mobile cellular subscriptions, and individuals using the internet in Africa (2001 ─ 2020) obtained from the World development indicators (WDI) database. Using the Panel autoregressive distributed lag model (PARDL), we found that mobile cellular subscriptions, and level of economic growth significantly increased per capita CO2 emissions in Africa in the long run while renewable energy consumption and technologies and import of goods and services significantly decreased per capita CO2 emissions in the long run. We conclude that information and communication technologies, level of economic growth, import of goods and services, and renewable energy consumption exert impacts on carbon emissions in Africa. Highlights: Achieving low-carbon development in Africa is possible. This research investigated the impacts of information and communication technologies, renewable energy consumption, import and economic growth on carbon emissions in Africa. This study utilized the Panel autoregressive distributed lag model and panel data from 31 African countries. The results shed new light on the emission reduction potential of renewable energy consumption and import of goods and services. The study also shows that ICTs increase carbon emissions in Africa. This article offers recommendations concerning carbon emissions reduction. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco 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 Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study analyzes the asymmetric nexus of coal consumption with environmental quality and economic growth. In this context, the study focuses on eight leading emerging countries that take place in BRICS, E7, and Fragile Five groups. Also, the study uses yearly data from 1989 to 2021 and performs novel quantile methods, such as Granger Causality-in-Quantiles and Quantile-on-Quantile Regression (QQR). Also, quantile regression is used for robustness check. The results present that (i) there are causalities from coal consumption to both environmental quality and economic growth at 10% significance, whereas quantile and country-based results differ; (ii) effects of coal consumption on environmental quality are much stronger in lower quantiles for Brazil, Indonesia, India, South Africa, and, Turkey, but in higher quantiles for China, Mexico, and Russia; (iii) effects of coal consumption on economic growth are much stronger in lower quantiles for Brazil, Indonesia, India, Russia, South Africa, and Turkey; in higher quantiles for China; lower and middle quantiles for Russia; and all quantiles for Mexico; and (iv) the robustness of the QQR results are validated. Hence, empirical outcomes underline the highly crucial effects of coal consumption on environmental quality and economic growth in the countries. The results imply that policymakers should focus on efforts to decrease coal consumption gradually by applying a macro transition plan to increase environmental quality without causing economic decline by considering changing effects of coal consumption at quantiles and countries. [ABSTRACT FROM AUTHOR]

A large number of studies have been conducted to examine China's CO2 emissions problem disaggregated to the city level. However, few studies have delved further into the black box of economic production to examine the characteristics of CO2 emissions at the city supply chain level. In the context of the reality that Beijing takes the lead in achieving CO2 emissions reduction, this study decomposes CO2 emissions change in Beijing at three levels: overall, supply stage, and supply chain, using structural decomposition analysis (SDA) and structural path decomposition (SPD), filling the gap in urban CO2 emissions studies. The results show that: (i) energy consumption intensity is the most significant driver of emissions reduction, while per capita final demand is the largest factor in increasing emissions; (ii) Beijing's emissions reduction contribution is mainly reflected in the first supply stage (76.50%) and the second supply stage (18.85%); (iii) the expansion of domestic exports and thus greater demand for transportation is significant in emissions increase supply chains; (iv) the improvement of the demand structure for electricity from domestic exports contributes a large part in emissions reduction supply chains; (v) the existence of many offsetting effects, such as the ebb and flow of domestic exports on the demand for different products, has led to the loss of emissions reduction. Finally, corresponding policy recommendations are presented from the energy, industry, and demand perspectives. Our study will provide assistance in developing more microscopic policies to reduce emissions and replicating the Beijing experience. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a machine-learning framework that effectively addresses privacy concerns. It harnesses fragmented data from devices across the globe for model training and optimization while strictly adhering to user privacy protection and regulatory compliance. This framework holds immense potential for widespread applications in the smart-grid domain. Through FL, power companies can collaborate to train smart-grid models without revealing users' electricity consumption data, thus safeguarding their privacy. However, the data collected by clients often exhibits heterogeneity, which can lead to biases towards certain data features during the model-training process, therefore affecting the fairness and performance of the model. To tackle the fairness challenges that emerge during the federated-learning process in smart grids, this paper introduces FedCSGP, a novel federated-learning approach that incorporates client sampling and gradient projection. The main idea of FedCSGP is to categorize the causes of unfairness in federated learning into two parts: internal conflicts and external conflicts. Among them, the client-sampling strategy is used to resolve external conflicts, while the gradient-projection strategy is employed to address internal conflicts. By tackling both aspects, FederCSGP aims to enhance the fairness of the federated-learning model while ensuring the accuracy of the global model. The experimental results demonstrate that the proposed method significantly improves the accuracy of poorly performing clients in smart-grid scenarios with lower communication costs, therefore enhancing the fairness of the federated-learning algorithm. [ABSTRACT FROM AUTHOR]

Reducing the power consumption of oil production wells is one of the important directions of oil industry development in the context of transition of many fields to the late stages of operation. At the same time, there is often a need to limit hydrocarbon production, which is often accomplished by shutting down wells completely. In this paper, the authors propose an approach to improve the energy efficiency of the electric centrifugal pump unit through the use of periodic operation mode, which allows to reduce the specific power consumption of the electric centrifugal pump unit. Also, this mode can be considered as a way to regulate the well flow rate. According to the results of calculations it was obtained that the reduction of specific power consumption of the unit when operating at nominal frequency can reach 5% relative to the continuous mode of operation. An assessment was made of the effect of reducing the frequency of the supply voltage in a periodic mode on the parameters of the operation of the installation and it was found that the well flow rate increased by 1.43% with a reduction in specific power consumption by 1.64%. [ABSTRACT FROM AUTHOR]

The coupled water electrolysis is a promising strategy for green hydrogen production. Pollutants are attractive feedstocks for the fungible anode reactions of the sluggish oxygen evolution reaction, with the advantages of trash-to-treasure and cost reduction. There is an urgent need for real waste sample tests from the view of both contaminant abatement and electrochemical hydrogen production. This work offers a systematic evaluation of the coupled water electrolysis with real wastewater, where four real wastewater with sulfur element compounds in different states are taken as examples. The technical and economic potentials are evaluated particularly based on the coupled water electrolysis performances, including cell voltage, current density, hydrogen production rate, hydrogen electricity consumption, and pollution abatement. The coupled water electrolysis with the refractory wastewater, e.g. the desulfurization wastewater from coal-fired power plants and the H acid wastewater, does not show advantages from the view of hydrogen production, due to the high overpotential and limited current density. Nevertheless, hydrogen is a high-value-added byproduct for operation cost reduction from the view of wastewater treatment. The readily oxidizable pollutants, e.g. sulfite and sulfide, exhibit notable potentials for anodic depolarization and electricity consumption reduction of hydrogen production. However, the coupled water electrolysis adopting the sulfide spent caustic stream may suffer a low hydrogen production rate for sulfide to sulfur. The coupled water electrolysis adopting sulfite wastewater indicates excellent potential for both space-time yield and electricity consumption for hydrogen production. This work may offer a reference for the systematic evaluation and selection of coupled water electrolysis with real wastewater. • An evaluation of coupled water electrolysis with real wastewater is presented. • Pollutants are attractive feedstocks for the coupled water electrolysis. • The readily oxidizable pollutants exhibit excellent potential for the coupled water electrolysis. • Hydrogen is a high-value-added byproduct for cost reduction of wastewater treatment. [ABSTRACT FROM AUTHOR]

Providing sustainable, affordable, and reliable electricity through low-carbon energy development in the Nigerian energy sector is fundamental to ensuring energy security. Currently, efforts to harness the potential of renewable energy, to provide universal electricity access for all have not translated into significant economic development in Nigeria. Investment in green hydrogen could strengthen Nigeria's net-zero transition plan (NETP) and achieve sustainable energy access. The study explored the role of green hydrogen among five Electricity Distribution Companies (DisCos), from three geopolitical zones in Nigeria—North West, North Central, and North East. A bottom-up optimization linear programming methodology based on an open energy modelling framework (OEMOF) was used as the modelling paradigm. Secondary data mined from the Nigeria Energy Commission, Nigeria Electricity Regulatory Commission, NECAL 2050 report and international reports, and 2020 was used as a reference year to benchmark the model. The basic characteristics of the generation of electricity from green hydrogen, fuel cells, electrolyzers, and hydrogen storage, among other existing generation plants, were modelled till 2060 using modelled daily data obtained from Toktarova et al. (Electrical Power and Energy Systems 111:160–181, 2019). Outcomes from benchmarking led to two planning scenarios; these investigated possible insights that explored green hydrogen in Nigeria. Results showed that an integrated distributed approach would enhance harnessing green hydrogen in Nigeria, that is, electricity distribution among the DisCos. The study also revealed the following (1) the levelized cost of electricity could drop by about 8%, so also the cost of the investment; (2) access to electricity showed an improvement compared to the base year; and (3) emissions were cut in the power sector. To attain sustainable NETP with green hydrogen, the study recommends that a distributed generation approach among DisCos would support the national net-zero transition plan. [ABSTRACT FROM AUTHOR]

In Africa, mitigating climate change in a context of a growing human population and developing economies requires a bold transition to renewable energy (RE) resources. Declining costs for solar photovoltaics (by 90% between 2009 and 2023) and wind turbines (by 57% between 2010 and 2023) fuelled their construction, and hybrid forms such as floating photovoltaics (FPV) on existing hydropower reservoirs are increasingly being explored. Nevertheless, 65% of the proposed RE capacity in Africa remains hydropower, despite confirmed ecological, socioeconomic, and political ramifications on different spatiotemporal scales. The 673 proposed hydropower plants (HPPs) would increasingly affect river systems and threaten their biodiversity. While there is clear evidence that a transition to RE in Africa is technically feasible, there is a lack of spatially explicit studies on how this transition could be implemented. Hence, the aim of the present study is to explore options for an RE mix that avoids additional hydropower construction and, therefore, further river fragmentation. Attribute data of the open-accessible Renewable Power Plant Database (RePP Africa) were analysed to assess the amount of lost capacity due to operation stops. Geospatial analyses of solar irradiation and existing reservoir data were used to derive the potential for FPV. The degree of possible replacement of future hydropower was assessed under consideration of economically feasible wind and solar photovoltaic (PV) potential. To enhance electricity generation from existing HPPs, efficient and sustainable renewable power plant planning must integrate the risk of failure, as it has diminished the available capacity in the past up to 24%. Our findings further reveal that 25 African countries could replace the proposed hydropower development by FPV covering less than 25% of the surface area of their existing hydropower reservoirs. All 36 African countries could replace proposed hydroelectricity generation by fully exploiting feasible onshore wind and solar PV potential with a mean surplus of 371 TWh per year. In summary, our findings provide scientific evidence to support policy discussions on the potential electricity gains from (1) minimizing plant failure, (2) installing FPV as a co-use option, and (3) exploiting wind and solar resources. This study provides quantitative, data-based, and spatially explicit scenarios on the implementation of an RE mix that could relieve the dam building pressure on African rivers. [ABSTRACT FROM AUTHOR]

The Smart Grid operates autonomously, facilitating the smooth integration of diverse power generation sources into the grid, thereby ensuring a continuous, reliable, and high-quality supply of electricity to end users. One key focus within the realm of smart grid applications is the Home Energy Management System (HEMS), which holds significant importance given the fluctuating availability of generation and the dynamic nature of loading conditions. This paper presents an overview of HEMS and the methodologies utilized for load forecasting. It introduces a novel approach employing Quantum Support Vector Machine (QSVM) for predicting periodic power consumption, leveraging the AMPD2 dataset. In the establishment of a microgrid, various factors such as energy consumption patterns of household appliances, solar irradiance, and overall load are taken into account in dataset creation. In the realm of load forecasting in Home Energy Management Systems (HEMS), the Quantum Support Vector Machine (QSVM) stands out from other methods due to its unique approach and capabilities. Unlike traditional forecasting methods, QSVM leverages quantum computing principles to handle complex and nonlinear electricity consumption patterns. QSVM demonstrates superior accuracy by effectively capturing intricate relationships within the data, leading to more precise predictions. Its ability to adapt to diverse datasets and produce significantly low error values, such as RMSE and MAE, showcases its efficiency in forecasting electricity load consumption in smart grids. Moreover, the QSVM model's exceptional flexibility and performance, as evidenced by achieving an accuracy of 97.3% on challenging datasets like AMpds2, highlight its distinctive edge over conventional forecasting techniques, making it a promising solution for enhancing forecasting accuracy in HEMS.The article provides a brief summary of HEMS and load forecasting techniques, demonstrating and comparing them with deep learning models to showcase the efficacy of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Electricity consumption in homes is on the rise due to the increasing prevalence of home appliances and longer hours spent indoors. Home energy management systems (HEMSs) are emerging as a solution to reduce electricity consumption and efficiently manage power usage at home. In the past, numerous studies have been conducted on the management of electricity production and consumption through solar power. However, there are limited human-centered studies focusing on the user's lifestyle. In this study, we propose an Intelligent Home Energy Management System (i-HEMS) and evaluate its energy-saving effectiveness through a demonstration in a standard house in Korea. The system utilizes an IoT environment, PID sensing, and behavioral pattern algorithms. We developed algorithms based on power usage monitoring data of home appliances and human body detection. These algorithms are used as the primary scheduling algorithm and a secondary algorithm for backup purposes. We explored the deep connection between power usage, environmental sensor data, and input schedule data based on Long Short-Term Memory network (LSTM) and developed an occupancy prediction algorithm. We analyzed the use of common home appliances (TV, computer, water purifier, microwave, washing machine, etc.) in a standard house and the power consumption reduction by the i-HEMS system. Through a total of six days of empirical experiments, before implementing i-HEMS, home appliances consumed 13,062 Wh. With i-HEMS, the total consumption was reduced to 10,434 Wh (a 20% reduction), with 9060 Wh attributed to home appliances and 1374 Wh to i-HEMS operation. [ABSTRACT FROM AUTHOR]

The general development of technology and universal access means that gastronomy furnishings can be found in every corner of the world. Therefore, it is important to develop these devices and the machines that constitute them. We are talking about refrigeration systems located inside gastronomy furnishings. The R404A refrigerant, popular in recent years, is being withdrawn from use due to its harmful impact on the environment. Modern synthetic refrigerants or natural refrigerants can be used as alternative substances. In modern solutions, it is expected that devices and all cooperating elements have the lowest possible harmful impact on the environment and the user while, at the same time, having the highest possible energy efficiency. First, tests were carried out with the R404A refrigerant. Then, the working medium was replaced without changing any element of the refrigeration system with the modern R455A refrigerant. The system was tested in terms of the operating parameters achieved and in terms of electricity consumption. It was found that there is an alternative R455A refrigerant operating in the refrigeration system of catering furnishings, which provides the system with an average of 34% better energy efficiency than the reference refrigerant R404A. It was also found that the time needed to achieve the set working conditions decreased. An alternative refrigerant allows a refrigeration system to be built based on components available on the market or one that can be used as a direct replacement for the old refrigerant. [ABSTRACT FROM AUTHOR]

Demand response management (DRM) plays a crucial role in the prospective development of smart grids. The precise estimation of electricity demand for individual houses is vital for optimizing the operation and planning of the power system. Accurate forecasting of the required components holds significance as it can substantially impact the final cost, mitigate risks, and support informed decision-making. In this paper, a forecasting approach employing neural networks for smart grid demand-side management is proposed. The study explores various enhanced artificial neural network (ANN) architectures for forecasting smart grid consumption. The performance of the ANN approach in predicting energy demands is evaluated through a comparison with three statistical models: a time series model, an auto-regressive model, and a hybrid model. Experimental results demonstrate the ability of the proposed neural network framework to deliver accurate and reliable energy demand forecasts. [ABSTRACT FROM AUTHOR]

Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications. [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]

In the contemporary energy landscape, characterized by a global commitment to sustainability, the effective management and forecasting of energy consumption play pivotal roles in achieving environmental and economic goals. As nations strive to meet sustainable development targets, optimizing energy use becomes imperative. This paper addresses these challenges by focusing on load forecasting and energy management within the context of a Savona campus microgrid. In this thesis, the NNR algorithm based load profile prediction model was proposed. The development process involved a detailed exploration of the correlation between weather information and electricity consumption. Furthermore, the outputs of the load forecasting model, namely the predicted load profiles, were subsequently utilized in the Energy Management System (EMS) to optimally manage power flows in the campus microgrid using an Internet of things as a service (IoTaaS) with day-ahead forecasting model. The overall results of the model evaluation across all periods reveal a Mean Absolute Error (MAE) of 9.63 kW, a Coefficient of Determination (R2) of 0.79, and a Mean Absolute Percentage Error (MAPE) of 9.02%. These metrics provide a comprehensive assessment of the model's performance across various temperature conditions. The proposed load profile forecasting model was integrated into the Energy Management System (EMS) developed for Savona campus microgrid in Italy. The findings provide a valuable framework for optimizing microgrid operations, aligning with global sustainability objectives. [ABSTRACT FROM AUTHOR]

With the development of Non-Intrusive Load Monitoring (NILM), it has become feasible to perform device identification, energy consumption decomposition, and load switching detection using Deep Learning (DL) methods. Similar to other machine learning problems, the research and validation of NILM necessitate substantial data support. Moreover, different regions exhibit distinct characteristics in their electricity environments. Therefore, there is a need to provide open datasets tailored to different regions. In this paper, we introduce the Transient Dataset of Household Appliances with Intensive Switching Events (TDHA25). This dataset comprises switch instantaneous data from 10 typical household appliances in China. The TDHA dataset features a high sampling rate, accurate labelling, and realistic representation of actual appliance start-up waveforms. Additionally, appliance switching is achieved through precise control of relay switches, thus mitigating interference caused by mechanical switches. By furnishing such a dataset, we aim not only to enhance the recognition accuracy of existing NILM algorithms but also to facilitate the application of NILM algorithms in regions sharing similar electricity consumption characteristics to those of China. [ABSTRACT FROM AUTHOR]

Off-grid photovoltaic systems have been proposed as a panacea for economies with poor electricity access, offering a lower-cost "leapfrog" over grid infrastructure used in higher-income economies. Previous research examining pathways to electricity access may understate the role of off-grid photovoltaics as it has not considered reliability and carbon pricing impacts. We perform high-resolution geospatial analysis on universal household electricity access in Sub-Saharan Africa that includes these aspects via least-cost pathways at different electricity demand levels. Under our "Tier 3" demand reference scenario, 24% of our study's 470 million people obtaining electricity access by 2030 do so via off-grid photovoltaics. Including a unit cost for unmet demand of 0.50 US dollars ($)/kWh, to penalise poor system reliability increases this share to 41%. Applying a carbon price (around $80/tonne CO2-eq) increases it to 38%. Our results indicate considerable diversity in the level of policy intervention needed between countries and suggest several regions where lower levels of policy intervention may be effective. This study investigates the role of off-grid solar in achieving SDG7 in Africa, focusing on understanding the impact of carbon pricing and supply reliability. It uses high-resolution spatial analysis and demand modelling to explore policy interventions for universal electricity access. [ABSTRACT FROM AUTHOR]

Accurate electricity demand forecasting serves as a vital planning tool, enhancing the reliability of management decisions. Apart from that, achieving these aims, particularly in managing peak demand, faces challenges due to the industry's volatility and the ongoing increase in residential energy use. Our research suggests that employing deep learning algorithms, such as recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU), holds promise for the accurate forecasting of electrical energy demand in time series data. This paper presents the construction and testing of three deep learning models across three separate scenarios. Scenario 1 involves utilizing data from all-day demand. In Scenario 2, only weekday data are considered. Scenario 3 uses data from non-working days (Saturdays, Sundays, and holidays). The models underwent training and testing across a wide range of alternative hyperparameters to determine the optimal configuration. The proposed model's validation involved utilizing a dataset comprising half-hourly electrical energy demand data spanning seven years from the Electricity Generating Authority of Thailand (EGAT). In terms of model performance, we determined that the RNN-GRU model performed better when the dataset was substantial, especially in scenarios 1 and 2. On the other hand, the RNN-LSTM model is excellent in Scenario 3. Specifically, the RNN-GRU model achieved an MAE (mean absolute error) of 214.79 MW and an MAPE (mean absolute percentage error) of 2.08% for Scenario 1, and an MAE of 181.63 MW and MAPE of 1.89% for Scenario 2. Conversely, the RNN-LSTM model obtained an MAE of 226.76 MW and an MAPE of 2.13% for Scenario 3. Furthermore, given the expanded dataset in Scenario 3, we can anticipate even higher precision in the results. [ABSTRACT FROM AUTHOR]

As stated in the 2016 Paris Agreement, concerns about global climate change and carbon emissions have increased, and organizations, in particular, have embarked on an annual measurement process to estimate their contribution to global climate change. Carbon footprint, one of the measurement methods, is a widely applied tool to assess the environmental impact of organizations. This study presents a real case study of a denim-washing company's activities based on ISO standard calculation methods of greenhouse gas emissions. Accordingly, the annual carbon footprint of the denim-washing company was 2482.09 tCO2e for the year 2021 in total for the overall carbon footprint. Direct emission was calculated at 1575.75 tCO2e, indirect energy–related emission at 798.09 tCO2e, and indirect non-energy–related emission at 108.25 tCO2e. The highest CO2 emissions are related to heating from greenhouse gas direct emission sources, followed by purchased electricity consumption, and the lowest CO2 emissions are related to fire–CO2 tube storage. In conclusion, this study is particular in that it analyzes not only the specific processes of a denim-washing company but also the overall organizational carbon footprint calculation, assesses the importance of indirect non-energy in the total carbon footprint, and evaluates the calculation findings with sector-specific mitigation strategies. [ABSTRACT FROM AUTHOR]

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

This work determined the ecological footprint of the National Center for Applied Electromagnetism, a science institution attached at the Universidad de Oriente, Santiago de Cuba. The categories analyzed to carry out the calculations of CO2 emissions and the ecological footprint were: Water, Built surface, Electricity Consumption, Mobility, Paper, Non-hazardous solid waste, Hazardous waste, Food, Services and Production of Magnetic Conditioners. The methodology of Doménech (2009) and the methodology for calculating the ecological footprint in universities of López Álvarez et al. (2009) of the Sustainable Development Office, University of Santiago de Compostela were used as a basis. The results obtained for the year 2022 reflect that the National Center for Applied Electromagnetism needs an area of 52,984 ha/year of forest to regenerate the natural resources that the center uses for the satisfaction of it social demand. Similarly, it requires 89,096 hag standard forest to assimilate CO2 emissions. [ABSTRACT FROM AUTHOR]

Global electricity demand is increasing with the rising population and rapid urbanization. Building Integrated photovoltaic (BIPV) system is a new method of renewable energy generation where solar photovoltaic (PV) modules are integrated into the building surfaces such as façade, shades, windows, roofs, and tiles. BIPV systems reduce the urban energy demand. Utilization of vertical surfaces makes the BIPV system a preferable choice where land scarcity affects the implementation of large PV systems. The economic viability depends on the maximum power generated by the BIPV array. In urban environments, the BIPV arrays experience severe partial shading conditions (PSCs). The PSCs cause mismatch losses, reducing the global maximum power of the BIPV array and efficiency. Fixed array configurations such as series-parallel (SP), total-cross-tied (TCT), triple-tied (TT), and quarter-tied (QT) are designed to solve this issue. The cross ties across the rows of the BIPV array improve the performance at the expense of more wiring. Researchers proposed various optimal array configurations with different shading patterns. This research attempts to generalize the design of the BIPV array configurations by considering the trade-off between wiring requirements and shading losses. The performance of SP, TT, QT, and TCT configurations under four different shading conditions is simulated with the proposed BIPV array design algorithm. A 9 × 8 BIPV array of 3.6 kW is considered. QT configuration reduces the wiring requirement by 10.45% compared to TCT and improves up to 8.43% maximum power than SP. The fill factor is improved to 48.49%, and the mismatch loss is limited to 34.10%. Therefore, QT and TCT are considered favorable configurations for BIPV array design. [ABSTRACT FROM AUTHOR]

The growing demand for energy, driven by rapid economic development, necessitates higher electricity consumption. However, conventional energy systems relying on fossil fuels present environmental challenges, prompting a shift towards renewable energy sources. In Kosovo, coal-fired power plants dominate electricity production, highlighting the need for cleaner alternatives. Worldwide efforts are underway to increase the efficiency of photovoltaic systems using sustainable materials, essential for ecological and human health. Solar and wind energy are emerging as sustainable alternatives to traditional fossil fuels. However, global concerns about energy security and environmental sustainability are driving countries to prioritize renewable energy development. In Kosovo, the integration of renewable energy sources, such as wind and solar energy, is progressing rapidly. However, challenges such as voltage stability and power losses need to be addressed. Distributed generation offers a solution by increasing energy reliability and reducing greenhouse gas emissions. Further research is needed to assess the technical, economic, and environmental implications of integrating renewable resources into Kosovo's energy system, focusing on power quality, system reliability, and voltage stability. The research focused on the eastern region of the country, operating at the 110 kV substation level. Challenges in energy quality arise due to the lack of 400 kV supply and the continuous increase in energy consumption, especially in the Gjilan area. This paper investigated integrating renewable energy, especially wind and solar sources, into the medium- and long-term plans at the Gjilan 5 substation to enhance energy quality in the area. Successful integration requires detailed analysis of energy flows, considering the impact of photovoltaics (PVs) on distribution system operation and stability. To simulate and analyze the effects of renewables on the transmission system, voltage profile, and power losses, a case study was conducted using ETAP software. The simulation results present a comparison between scenarios before and after integrating renewable systems to improve energy quality in the identified area. [ABSTRACT FROM AUTHOR]

The demand for different energy goods and services is a fundamental component in a country's economic structure for development. Understanding it is vital in designing economic policies, such as taxes, that can improve the welfare of the population. A comprehension of the distributional effects of elasticities and the application of them to simulate household responses to price changes, as well as a calculation of the welfare impacts on poor and rich households in Mexico, should inform policy design. This paper uses the Household Income and Expenditure Survey (ENIGH) from 1996 to 2018 to estimate the demand of Mexican households for fuels, specifically electricity, liquefied petroleum gas, and gasoline. A Quasi Ideal Quadratic Demand System (QUAIDS) is employed to analyse the effects of removing energy subsidies and introducing a carbon tax. The results indicate that welfare losses would be regressive concerning electricity price increases, while changes in gasoline prices would be progressive. Redistributing the tax revenues accrued by removing energy subsidies and imposing the carbon tax would have more progressive effects on the economy of Mexican households, with welfare gains of up to 350% for the poorest households in the case of electricity consumption taxes. [ABSTRACT FROM AUTHOR]

Current developments in vehicles have generated great interest in the research and optimization of heating, ventilation, and air conditioning (HVAC) systems as a factor to reduce fuel consumption. One of the key trends for finding solutions is the intensive development of electric transport and, consequently, additional requirements for reducing energy consumption and modifying climate systems. Of particular interest is the optimal functioning of comfort and life support systems during air recirculation, i.e., when there is a complete or partial absence of outside air supply, in particular to reduce energy consumption or when the environment is polluted. This work examines numerical models of airfields (temperature, speed, and humidity) and also focuses on the concentration of carbon dioxide and oxygen in the cabin, which is a critical factor for ensuring the health of the driver and passengers. To build a mathematical model, the Navier–Stokes equations with energy, continuity, and diffusion equations are used to simulate the diffusion of gases and air humidity. In the Ansys Fluent finite volume analysis package, the model is solved numerically using averaged RANS equations and k-ω turbulence models. The cabin of a mainline locomotive with two drivers, taking into account their breathing, is used as a transport model. The problem was solved in a nonstationary formulation for the design scenario of summer and winter, the time of stabilization of the fields was found, and graphs were constructed for different points in time. A comparative analysis of the uniformity of fields along the height of the cabin was carried out with different locations of deflectors, and optimal configurations were found. Energy efficiency values of the climate system operation in recirculation operating modes were obtained. A qualitative assessment of the driver's blowing directions under different circulation and recirculation modes is given from the point of view of the concentration of carbon dioxide in the breathing area. The proposed solution makes it possible to reduce electricity consumption from 3.1 kW to 0.6 kW and in winter mode from 11.6 kW to 3.9 kW and save up to 1.5 L/h of fuel. The conducted research can be used to develop modern energy-efficient and safe systems for providing comfortable climate conditions for drivers and passengers of various types of transport. [ABSTRACT FROM AUTHOR]

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

The current electricity default rates in continental countries, such as Brazil, pose risks to the economic stability and investment capabilities of distribution utilities. This situation results in higher electricity tariffs for regular customers. From a regulatory perspective, the key issue regarding this challenge is devising incentive mechanisms that reward distribution utilities for their operational and investment choices, aiming to mitigate or decrease electricity non-payment rates and avoid tariff increases for regular customers. Despite adhering to the principles of incentive regulation, the Brazilian Electricity Regulatory Agency (ANEEL) uses a methodological approach to define regulatory targets for electricity defaults tied to econometric models developed to determine targets to combat electricity non-technical losses (NTLs). This methodology has been widely criticized by electricity distribution utilities and academics because it includes many ad hoc steps and fails to consider the components that capture the specificities and heterogeneity of distribution utilities. This study proposes a fuzzy inference-based model for defining regulatory default targets built independently of the current methodological approach adopted by ANEEL and aligned with the principles of incentive regulation. An empirical study focusing on the residential class of electricity consumption demonstrated that it is possible to adopt a specific methodology for determining regulatory default targets and that the fuzzy inference approach can meet the necessary premises to ensure that the principles of incentive regulation and the establishment of regulatory targets are consistent with the reality of each electricity distribution utility. [ABSTRACT FROM AUTHOR]

According to the recent revision of the Greek National Energy and Climate Plan, the country sets out to accomplish an ambitious target concerning the integration of renewables in the local electricity mix during the ongoing decade, at the levels of 80% by 2030. This implies the need to more than double the existing wind and PV capacity at the national level, which in turn introduces numerous challenges. Amongst them, spatial planning for new RES installations seems to be the most demanding, with the adoption of novel technological solutions in the field of RES potentially holding a key role. New technologies, like offshore wind and floating solar, are gradually gaining maturity and may offer such an alternative, challenged at the same time however by the need to entail minimum disruption for local ecosystems. To that end, we currently assess the theoretical potential of floating PVs for lentic water systems of the Greek mainland. We do so by looking into 53 different lentic water systems across the Greek territory that meet the constraint of 1 km2 minimum surface area, and we proceed with the estimation of the relevant floating PV capacity per system under the application of a minimal intervention approach, assuming PV coverage of 1% over the total lentic water system area. In this context, our findings indicate a maximum, aggregate theoretical capacity that could exceed 2 GWp at the national level, with the respective annual energy yield reaching approximately 4 TWh or, equivalently, >6% of the country's anticipated annual electricity consumption in 2030. Finally, our results extend further, offering a regional level analysis and a set of policy directions and considerations on the development of floating solar in Greece, while also designating the energy merits of floating PVs against similar, land-based installations. [ABSTRACT FROM AUTHOR]

In the wake of the dual-carbon objective, the call for low-carbon attributes in integrated energy systems is ascending, with an amplified imperative to integrate wind and solar power efficiently. This study introduces an advanced low-carbon optimization framework for integrated energy systems, incorporating a sophisticated time-differentiated carbon accounting mechanism attentive to consumer emissions. A nuanced carbon accounting model is crafted to assess consumer emissions with greater accuracy. Predicated on these emissions, a refined low-carbon demand response model is articulated, factoring in the influence of carbon emission factors pertinent to electricity and heat procurement on user conduct. This model integrates the consideration of heat reclaimed from methanation processes, which in turn informs the carbon emission factors associated with purchased heat, and evaluates the subsequent optimization impact on the system. The proposed model is designed to curtail the system's operational expenditures and is operationalized via the CPLEX solver. Through the establishment of various scenarios for evaluative comparison, the model is corroborated to substantially augment the system's proficiency in assimilating wind and solar energy, markedly curtail carbon emissions, and facilitate a sustainable and cost-efficient operation of the integrated energy system. [ABSTRACT FROM AUTHOR]

This paper takes a low-energy building in Changchun, China, as an object to test and study the characteristics of two heating modes, AC/DC (Alternative current/Direct current) switching and AC/DC synthesis, from the perspectives of temperature change, irradiation intensity, power generation, electricity consumption, etc. Firstly, the experimental research was conducted under two heating cable modes by establishing mathematical models and a test rig, and it was found that the photoelectric conversion efficiency on sunny, cloudy, and overcast days was 18%, 14.5%, and 12%, respectively. A simulation model was established by TRNSYS to run an ultra-low-energy building throughout the year. It was found that the highest and lowest monthly power generation occurred in February and July, respectively. The annual power generation of the system was 6614 kWh, and the heating season power generation was 3293.42 kWh. In the current research, the DC electricity consumption was slightly higher than the AC electricity consumption. Under conditions of similar radiation intensity and power generation, the indoor temperature of the AC/DC synthesis cable heating mode were 1.38% higher than the AC/DC switching heating able mode, and the electricity consumption were 10.9% and 4.76% higher, respectively, than those of the AC switching heating cable mode. This is of great significance for clean-energy heating, energy savings, and emissions reduction in northern China. [ABSTRACT FROM AUTHOR]

This article aims to demonstrate the technical capabilities and effectiveness of an energy production and management system for school facilities using a modular solution. The system is assumed to generate electricity from renewable sources, such as wind or sun. The potential of renewable energy sources in Cracow, Poland, was assessed, with a focus on solar energy (photovoltaic panels, PV). Taking into account the installation of heating and other equipment, an analysis of the facility's electricity demand was carried out. The study recommended the use of a heat pump system to heat and cool the facility. Renewable energy sources will meet 81% of the facility's projected annual demand, according to the study. An analysis of the energy consumption and production profiles shows that almost 69% of the energy produced by the PV panels is consumed on site. Of the remaining energy, 31% is fed back into the grid and sold to the grid operator or used by other facilities within the shared settlement. The overall balance results in a small electricity deficit that must be covered by the grid. If suitable sites are available, the facilities under study could consider installing a wind turbine as a potential supplement to the energy deficit. [ABSTRACT FROM AUTHOR]

Low productivity and high electricity consumption are considered problems of the hammer mill, which is widely used in current feed production. In this paper, a folded V-shaped hammer was designed to improve the performance of the hammer mill. To determine the optimal design parameters of the new hammer, the single-factor test and orthogonal tests were carried out with the inclination angle of hammer, the angle of hammer head, and the distance of hammer head as the influencing factors, and the productivity and output per kWh as evaluation indexes. The order of the influence on the productivity and output per kWh were the inclination angle of hammer>the angle of hammer head> the distance of hammer head. The parameters were optimized based on the orthogonal tests with the following results: the angle of hammer head was 160°, inclination angle of hammer was 110°, and the inclination distance of hammer head was 24 mm. The static analysis and modal analysis were carried out on the optimized hammer by using ANSYS software. The results showed that the new hammer satisfies the strength and stiffness requirements during working, does not resonate, and has good dynamic characteristics. The new hammer can effectively improve the performance of the hammer mill, and the research results can provide theoretical basis for the optimization design of the hammer mill. [ABSTRACT FROM AUTHOR]