Increase In Electricity Consumption Research Articles

A Multi-Stage Framework for Coordinated Scheduling of Networked Microgrids in Active Distribution Systems Considering Economic and Environmental Impact of Hydrogen Refueling and Charging Stations

Due to the increase in electric energy consumption and the significant growth in the number of electric vehicles (EV) at the level of the distribution network, new networks have started using new fuels such as hydrogen to improve environmental indicators and at the same time better efficiency from the excess capacity of renewable resources. In this article, the services that can be provided by hydrogen refueling stations and charging electric vehicles in the optimal performance of microgrids have been investigated. The model proposed in this paper includes a two-stage stochastic framework for scheduling resources in microgrids, especially hydrogen refueling stations and electric vehicle charging. In this model, two main goals of cost minimization and greenhouse gas emissions are considered. In the proposed framework and in the first stage, the service range of microgrids is determined precisely according to the electrical limitations of distribution systems in emergency situations. Then, in the second stage, the problem of energy management in each microgrid will be solved centrally. In this situation, various indicators including the output energy of renewable sources, smart charging of hydrogen and electric vehicle charging stations (EV/FCV) and flexible loads (FL) are evaluated. The final mathematical model is implemented as a multivariate integer multiple linear problem (MILP) using the GUROBI solver in GAMS software. The simulation results on the modified IEEE 118-Bus network show the positive effect of the presence of flexible loads and smart charging strategies by charging stations. Also, the numerical derivation shows that the operating costs of the entire system can be reduced by 4.77% and the use of smart charging strategies can reduce greenhouse gas emissions by 49.13%.

Reassessing determinants of urban energy intensity in China: insights from controllable and uncontrollable factors.

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.

Study on Household-Level Electricity Consumption of Domestic Consumers in Romania: The Need to Check the Electrical Installation

With technological development and the decrease in the price of electrical appliances for domestic consumers, combined with users’ desire for a certain level of comfort, the number of electrical devices in homes has increased. Although current equipment is largely energy-efficient, the high number of these devices, used simultaneously, has led to an increase in electricity consumption. Electrical installation in homes has been replaced in most cases, but the connections have remained the same, without any checks being carried out in terms of load and operational safety. This paper presents an exploratory case study based on a questionnaire answered by 678 individuals from Romania. The objectives of the questionnaire were to identify the characteristics of the domestic consumer and their opinion on the state of the installation, energy costs, and energy consumption. Based on the analysis, 74.15% of the respondents stated that the power installed in their homes as domestic consumers increased. Based on the analysis conducted on-site, the risk of fire outbreaks in the power supply columns of apartment buildings was identified. To avoid critical situations, it is necessary to periodically check the connection and supply columns so that they are functional and safe to operate.

Competitiveness of companies in the design, engineering and construction of nuclear power plants

Increasing electricity consumption in the modern world makes the unique nuclear power market. It provides growing secured demand, specific external development factors. However, only four companies involved in the design, engineering, and construction of nuclear power plants (NPPs). The competitiveness of those companies included into M. Porter’s theory framework, but has its own characteristics. The purpose of the study is to determine the specifics of companies competitiveness engaged in the design, engineering, and construction of nuclear power plants, and assess the factors and prospects for their development. Besides, we define the theoretical base of international competitiveness with regard to the specific features of this economic sector, characterise the companies operating on the market, and build a model of the competitiveness formation of four representatives under the domestic factors of development. In conclusion, we will present the prospects for ROSATOM development in an internationally competitive environment. The models of competitiveness formation show the positive impact of investments in human capital, ESG agenda implementation, and long-term prospects of R&D projects. The research provides information for developing the theory of competitiveness and improving the company’s activities in this economic sector.

Factors influencing farmers’ adoption of solar water-pumping systems in Gujarat

Abstract 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.

The Dynamics of Electricity Consumption in the Economic Prosperity of Nepal

The paper examines the relationship between the dynamics of electricity consumption and Nepal's economic prosperity. Descriptive methodologies and statistical tools are used in this quantitative study that gathers data from secondary sources. The patterns of power consumption are influenced by population increase, urbanization, government regulations, and technical developments, all of which affect Nepal's economic development and social well-being. This paper uses an interdisciplinary approach to show how crucial it is to address energy-related issues to realize socioeconomic potential. Economic growth is the main factor driving the dynamic of power consumption. The finding reveals that the autonomous electricity consumption is 4.13 kilowatt hours shows if income increases by USD 100, the estimated increase in average electricity consumption is 54 kWh. Indicates that increase in per capita income, increases the consumption of electricity, and economic growth are strongly correlated. The result from the forecast analysis shows that the unrestrained demand for electricity is projected to increase from an estimated 7,888 million kilowatt-hours (million kWh) in 2019–2020 to 13,831 million kilowatt-hours in 2029–2030 which seems closer and validates the Electricity Demand Forecast for 2014 to 2040 by Government of Nepal. The result shows that the dynamics of electricity consumption and economic prosperity are strongly correlated. Policymakers, energy planners, investors, and other stakeholders must acknowledge the issue and make a call to action to support initiatives that enhance economic growth, access to energy, and sustainable energy use.

Green-gray imbalance: Rapid urbanization reduces the probability of green space exposure in early 21st century China

Green space exposure provides greater beneficial effects on residents compared to unnatural spaces, commonly referred to as “gray spaces”. However, during rapid urbanization, gray spaces expand more quickly than green spaces, often encroaching upon and overtaking these natural environments. This unchecked growth leads to detrimental impacts on the human habitat and overall environmental quality. Consequently, it is essential to meticulously assess the spatial and temporal patterns of residents' exposure levels, as well as to thoroughly investigate the underlying driving mechanisms behind these changes. This study used population-weighted exposure level measurements to assess gray and green space exposure in Chinese cities in the early 21st Century (2000–2019). Additionally, the Gray-Green space Exposure Ratio (GER) was calculated, and the spatiotemporal driving mechanism of GER by each factor was analyzed by geostatistical modeling. The results show that gray space exposure is generally increasing in China, especially in eastern parts of China. The probability of exposure to gray spaces exceeds that of green spaces in some high urbanization rate cities. This trend will continue, albeit at a slower rate. Urban sprawl, built-up area density, and increased electricity consumption were the main drivers of rising GER, whereas greenspace integrity contributed to lower GER; the driving mechanisms for GER changes were spatiotemporal heterogeneous. This study provides a reliable reference for restoring the green space exposure to promote the living environment constructing and residents' access to nature.

Design of solar thermal absorption air conditioning system using CO2 with synthetic building load

Traditional air conditioning and refrigeration solutions rely on compressor-driven systems, leading to increased electricity consumption and intensified greenhouse gas (GHG) emissions. These systems primarily utilize synthetic refrigerants such as CFCs, HCFCs, and HFCs. Despite their advantages, these refrigerants are either banned or subject to restricted permissions under the Montreal Protocol (1987) and the Kyoto Protocol (1997) due to environmental concerns. According to the Paris Accord, ratified by 196 parties as of 2017, there is a global shift towards low global warming refrigerants and eco-friendly alternatives. COP27 reaffirmed global commitments to limit temperature rise to 1.5 °C, emphasizing urgent action and stronger climate plans to combat global warming. CO2 envisaged by ASHRAE is resurrected as a promising natural refrigerant with the advent of high-pressure technologies the design employs a solar evacuated glass tube collector (EGTC) with U-shaped copper tubes and flat plate collector (FPC) to harness solar thermal energy. An immersed heat exchanger's (HX) based thermal storage tank is incorporated to address the intermittency of solar energy. The CO2 refrigerant offers advantages over other natural refrigerants due to its low critical point.This research conducts a TRNSYS® simulation of a 35.2 kW absorption cooling system with synthetic building load driven by energy captured through FPC and EGTC using R-744, additionally supported by a supplementary boiler for traditional office timing. The system design comprises two solar collectors, the EGTC and FPC, integrated with two different cooling loop configurations (C1 and C2). In C1, the hot water that exits the generator of the absorption chiller is forwarded toward the immersed HX-based hot storage tank, which is connected to the solar and absorption cooling loop. In C2, the working fluid leaving the absorption chiller is diverted directly toward the auxiliary boiler/furnace, if the temperature in the storage tank falls below the necessary level (108.89 °C) and if the tank temperature is greater than this set point temperature, then the flow passes through the storage tank. Both system designs have been developed using TRNSYS, with dynamic simulations conducted throughout the summer.The solar fraction and collector efficiency are considered as key performance indicators to evaluate and compare the system's effectiveness. Then the optimization of system parameters is performed for all four cases (EGTC C1 & C2 and FPC C1 & C2) based on these key performance indicators, all configurations integrated with a fixed synthetic building cooling load. The optimum parameters are found for all four cases, and the optimum parameters for EGTC C1 & C2 are 100 m2, 4 m3, and 11° for collector area, storage tank volume, and collector slope respectively. Similarly, for FPC based configurations, these parameters are 200 m2, 32 m3 for C2 and 4 m3 for C1, and 20° respectively. The results revealed better C2 performance than C1, with EGTC outperforming FPC. The final results were computed for the EGTC based designs due to their high solar fractions, with EGTC C2 emerging as the optimal configuration, achieving a seasonal solar fraction of 0.649.

Exploring hydrogen metallurgy to CO2 emissions reduction in China’s iron and steel production: An analysis based on the life cycle CO2 emissions – LEAP model

Previous research has primarily assessed the environmental impact of hydrogen metallurgy from technical or project perspectives, lacking a life cycle perspective. The impact of continued cleanliness of the hydrogen production mix and power generation structure on carbon dioxide (CO2) emissions from the hydrogen metallurgy production process has not been investigated on a medium to long-term scale, which does not provide an adequate and comprehensive view of the contribution of hydrogen metallurgy to China’s emissions reduction, resulting in short-sighted technology choices. To address this lack, we construct a bottom-up energy system model that combines life cycle CO2 emissions with long-range energy alternatives planning (LEAP), including iron and steel (IS) production, power generation, and hydrogen production. Setting development paths of two typical hydrogen metallurgy technologies, including business-as-usual (BAU) and hydrogen metallurgy (HM) scenarios, we carefully evaluate the CO2 emissions impact of hydrogen metallurgy from a whole industry perspective and in each IS production stage. The results suggest that the hydrogen metallurgy transition will help the iron and steel industry (ISI) to achieve peak and deep carbon reduction. Under the HM scenario, CO2 emissions from the ISI would decrease to 816.28 million tons (Mt) in 2050, representing an additional 281.13 Mt reduction over the BAU scenario. Hydrogen metallurgy can accelerate CO2 reduction in raw material acquisition and material processing phases but may increase CO2 emissions in manufacturing and recycling phases. In 2050, hydrogen metallurgy will contribute an additional 12.49 Mt and 300.95 Mt of CO2 reduction to the raw material acquisition and material processing phases, respectively. However, CO2 emissions in the manufacturing and recycling phases would be 32.15 Mt and 0.22 Mt more than in the BAU scenario due to the increased electricity consumption and scrap demand of electric arc furnace (EAF) steelmaking, respectively. Over time, the optimization of hydrogen production and power supply mix will gradually reduce CO2 emissions in each IS production process. In 2050, the CO2 intensity of the hydrogen-rich shaft furnace direct reduced iron (H2-DRI)-EAF process will drop to 753.41 kgCO2/t, which is lower than all other processes except the 100 % scrap-based EAF process. Advancing the development of the H2-DRI-EAF process can effectively achieve CO2 emissions reduction while alleviating dependence on scrap resources. The results are expected to inform government policy on the development of hydrogen metallurgy in China and other countries.

Evaluation the impact of electricity consumption on China's air pollution at the provincial level.

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

Income and electricity consumption issue: evidence from European households

Research on electricity consumption plays an important role in the development of sustainable national strategies and mitigation of global climate change. With global economic development in general, and Europe in particular, the demand for electricity in households is increasing. Therefore, based on demand theory, this study evaluates the impact of income on electricity consumption in European households. Data were collected from 35 European countries and analyzed. The instrumental variable regression results indicate that income positively impacts electricity consumption in European households. The electricity consumption of European households provides evidence that supports the prediction of demand theory, as an increase in household income as well as a decrease in price leads to a rise in overall consumption. However, the effect of price is not statistically significant. The findings also show that population growth and inflation positively influence household electricity consumption. The outcomes of this study have theoretical and practical implications for improving energy efficiency and effective electricity use in European households. Therefore, it is necessary to continue implementing energy-saving strategies at the household level in the context of increasing electricity consumption in Europe.

An automated energy management framework for smart homes

Over the last fifty years, societies across the world have experienced multiple periods of energy insufficiency with the most recent one being the 2022 global energy crisis. In addition, the electric power industry has been experiencing a steady increase in electricity consumption since the second industrial revolution because of the widespread usage of electrical appliances and devices. Newer devices are equipped with sensors and actuators, they can collect a large amount of data that could help in power management. However, current energy management approaches are mostly applied to limited types of devices in specific domains and are difficult to implement in other scenarios. They fail when it comes to their level of autonomy, flexibility, and genericity. To address these shortcomings, we present, in this paper, an automated energy management approach for connected environments based on generating power estimation models, representing a formal description of energy-related knowledge, and using reinforcement learning (RL) techniques to accomplish energy-efficient actions. The architecture of this approach is based on three main components: power estimation models, knowledge base, and intelligence module. Furthermore, we develop algorithms that exploit knowledge from both the power estimator and the ontology, to generate the corresponding RL agent and environment. We also present different reward functions based on user preferences and power consumption. We illustrate our proposal in the smart home domain. An implementation of the approach is developed and two validation experiments are conducted. Both case studies are deployed in the context of smart homes: (a) a living room with a variety of devices and (b) a smart home with a heating system. The obtained results show that our approach performs well given the low convergence period, the high level of user preferences satisfaction, and the significant decrease in energy consumption.

Income, extreme temperature, and residential electricity consumption in the Spanish Provinces

The effect of income and extreme temperatures on the residential electricity consumption in Spain is analyzed. An electricity demand function is estimated by means of ordinary least squares-Driscoll and Kraay, feasible generalized least squares, and quantiles panel data techniques, for the total of the sample, and by coastal climatic zones. The results support the energy-environmental Kuznets curve hypothesis for Spain, the continental, and Mediterranean zones. An N form is found for the Atlantic zone. The results also reflect that increases in temperature above 22∘C and decreases below 15∘C increase electricity consumption, this increase being progressive as temperatures vary. Thus, extreme temperature generates electricity consumption growth. The Mediterranean zone is most sensitive to these temperature changes. The results also show that electricity consumption is more sensitive to cold than to heat. The results suggest an inefficient use of heating and cooling appliances in the areas with the highest electricity consumption.

Multiobjective Stochastic Power System Expansion Planning Considering Wind Farms and Demand Response

In recent years, due to the increase in electricity consumption and environmental problems, power system expansion planning requires new technologies. In this regard, the incorporation of renewable energy sources (RESs) and utilization of demand response (DR) programs need disruptive variations in the present power system configurations. This paper proposes a mixed-integer linear robust multiobjective model for generation and transmission expansion planning (GEP-TEP) taking into account wind farms (WFs) and a DR program based on time-of-use pricing. The suggested model is presented via mixed-integer nonlinear programming (MINLP) at the first stage and then transformed into mixed-integer linear programming (MILP) using the Big M linearization technique. Moreover, long- and short-term uncertainties of load demand and WFs are incorporated into the recommended model to achieve more accurate results. The interval-based method is applied for taking into account long-term uncertainties while the scenario-based stochastic model is applied for modeling short-term uncertainties in the recommended GEP–TEP model. Lastly, the suggested model is investigated on various standard test systems to evaluate the effectiveness of the GEP-TEP model.

Numerical simulation of integrating an air conditioner with an evaporative air cooler

During the summer, air conditioning is increasingly used in commercial and residential structures to provide thermal comfort. The elevated condenser pressure caused by high ambient temperatures has led to a significant increase in electricity consumption in dry, hot climates. Therefore, this article presents the results of a numerical simulation investigation into the feasibility of enhancing the performance of a conventional air conditioning unit by applying a direct evaporative cooling system. The objective is to increase the cooling capacity while decreasing power consumption. The program used a small window-type air conditioner configured to simulate various weather conditions. The numerical findings indicate that incorporating evaporative cooling aids improved the system's ability to overcome numerous obstacles, resulting in a 10–20% increase in refrigeration capacity. Additionally, power consumption was reduced by approximately 3%, and the discharge temperature fell by 6–10°C. The predicted mean vote standard (PMV) for thermal conditions for human occupants yielded favorable outcomes (normal-type). Despite the challenging climate conditions prevailing during the five sweltering summer months, water production demonstrated good results, particularly in September.

ЭЛЕКТРОСНАБЖЕНИЕ РЕСПУБЛИКИ ДАГЕСТАН: ОТ КОЛЛАПСА К БЕЗАВАРИЙНОСТИ И НАДЕЖНОСТИ

The article examines the regional problems of electricity supply. The role of the electric power industry in the socio-economic development of the country is noted. Global trends in the production and consumption of electric power are highlighted. Dagestan, before market transformations, fully provided itself with the energy produced in the republic, and over the past twenty years, electricity consumption has doubled, but its production has not increased, and moreover, in some years there has even been a decrease. The reasons for the sharp increase in electricity consumption by the population of the republic are the increase in living standards, housing construction in cities and the development of the shadow economy, especially mining activities. But the main problem in the article is not so much the shortage of energy itself, as the wear and tear of the grid complex. The wear rate of the networks is more than 20%. The loss of electricity in the networks exceeds 75% of the power consumption. The article emphasizes that without attracting large investments from the federal center, the reconstruction of the energy grid system is impossible. In parallel, the directions of development of distributed energy are highlighted: the construction of small hydroelectric power plants on mountain rivers, geothermal, solar and wind power plants. All this is aimed at ensuring trouble-free reliable electricity supply to the population.

Identifying conditions leading to power quality events in Arctic Norway: Feature selection

Higher penetration of renewable energy and increased electricity consumption makes delivering a reliable power supply a complex task. Power system operators are nevertheless required to provide a reliable power supply with a power quality within specified limits. Power quality events can cause malfunctioning of sensitive equipment for consumers in the distribution network. Communities in the periphery of radial distribution networks, such as rural and island communities, often experience more challenges with power quality than other areas. This study focuses on a small island community in the Norwegian Arctic that exemplifies the challenges of power reliability. It explores an alternative approach to utilise the existing electrical infrastructure. By analysing one year of measurements from an electrical substation near a large industry complex, combined with weather forecast data, machine learning models are employed to recognise conditions leading to low power quality. Although these models show satisfactory performance, some data is missing to fully understand the causes of such events. To address this, feature selection techniques are applied to identify the most significant data features and find new important features. Through this process, a refined dataset is obtained, enabling more accurate detection of power quality events in the Arctic community. This research not only enhances the detection capability but also provides valuable insights into the specific data features that contribute to identifying power quality events, offering opportunities for targeted mitigation strategies and generalisation to other locations.

Research on Carbon Emission Characteristics and Mitigation Pathways in the Vehicle Fuel Cycle: A Case Study of Guangdong Province

This study presents a comprehensive analysis of vehicle ownership, energy consumption, and carbon emissions in Guangdong Province, China, from 2020 to 2035 under different scenarios. Key findings highlight the province’s pursuit of carbon peak goals and provide valuable insights into strategies to achieve them. Vehicle ownership in Guangdong is projected to exceed 48 million by 2035, which represents a doubling from 2020. Under both scenarios, internal combustion engine vehicle ownership will peak around 2030 and then gradually decline, while under the enhanced scenario, electric vehicle ownership will exceed 40% by 2035. Enhanced vehicle energy efficiency and reduced annual mileage will lead to a 17% reduction in gasoline and diesel consumption by 2035 in both scenarios. At the same time, there will be a substantial five- to six-fold increase in electricity consumption for vehicles compared to 2020. Both scenarios peak in carbon emissions before 2030, with the enhanced scenario achieving this peak a year earlier. The enhanced scenario outperforms the baseline, reducing carbon emissions by about 21.2% from the peak and 8% relative to 2020. Pure electric vehicles exhibit a significant advantage in reducing carbon emissions per vehicle compared to their internal combustion engine counterparts. Encouraging new energy vehicles, especially pure electric ones, accelerates the carbon emissions peak and lowers overall peak emissions. Accelerating the adoption of electric vehicles, reducing per-vehicle fuel consumption and annual average mileage, and optimizing transportation modes are crucial for carbon peaking from the vehicle fuel cycle. Policy recommendations focus on promoting new energy vehicles, optimizing transportation, and advancing research and technology.

Setting Optimization of Distribution Network Current Protection Based on Constrained Multi‐Objectives Backbone Particle Swarm Optimization

With the increase in residential electricity consumption, the number of branch lines connected to the 10 kV main line and the load all increase. When the main line is segmented into several shorter lines, the current protections of these lines are difficult to coordinate with each other. This condition may cause the number of protection mal‐operation increases, the power outage scope expands, and the reliability of the power supply decreases. Therefore, this paper studies the setting optimization method of distribution network current protections. First, a mathematical model of protection setting optimization is established with three objective functions keeping the fault clearing time minimal, the protection sensitivity highest, and the outage load minimal. Some constraints are considered, such as the cooperation between the protections of the adjacent lines. Second, the multi‐objective backbone particle swarm optimization algorithm is used. The problems of the traditional particle swarm optimization algorithm that lacks population diversity and easily falls into local optimization are solved by adopting the time‐varying variation and linear decreasing dynamic allocation strategy. Finally, the K‐means clustering algorithm is used to get the most representative optimal solution providing a variety of feasible solutions for the practical application. This paper takes a 10 kV line as an example to test the setting optimization method and compares the optimized settings with the traditional ones. The results show that the setting optimization method based on the constrained multi‐objective backbone particle swarm can effectively reduce the protection mal‐operations and improve the overall performance of distribution network protections. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Mineral resources and the green economy: A blueprint for sustainable development and innovation

This study assesses the factors influencing sustainable growth in agriculture and industry across 14 Asian economies, utilizing data spanning from 2000 to 2020 and employing the System-GMM (Generalized Method of Moments) technique. The findings reveal a noteworthy positive impact of a 1% increase in patent applications on the green growth of the industry, while no such effect is observed in agriculture. Mineral resource efficiency emerges as crucial for green growth, exhibiting significant improvements. Conversely, heightened internet access is associated with reduced green growth due to increased electricity consumption from fossil fuels. Inward foreign direct investment is found to impede green growth, highlighting the need for a transition to sustainable sectors. Additionally, the Consumer Price Index negatively affects potential green growth. Lastly, a higher GDP per capita positively influences green growth, underscoring the role of income levels in prioritizing sustainability. Recommendations include supporting green research and development, fostering a robust green finance market, digitalizing green finance, and implementing emissions taxation on mineral exploitation companies to promote resource efficiency and sustainability.