Increase In Electricity Consumption Research Articles

Linear transverse flux generator for wave energy conversion: design optimization and analysis

Abstract The electric power industry impacts each state’s economy significantly, driven by increasing electricity consumption that necessitates expanding power plants and finding alternative energy sources. Among alternative energy sources, ocean and sea wave energy converters can be distinguished as a separate class. Wave energy converters transform wave energy into mechanical and then electrical energy. The purpose of the study is to analyze and optimize the magnetic system of a transverse flux machine (TFM) linear generator and to determine the influence of the distance between the stator cores on the efficiency of the generator. This research included conducting 3D modeling and analysis to identify this rational distance. The methods for investigating the magnetic system and calculating the magnetic field pattern are divided into analytical and numerical. Thanks to advanced software for solving such tasks, numerical calculation methods based on the finite element method play a decisive role. Meanwhile, analytical calculations of the magnetic circuit are performed using Kirchhoff’s second law for preliminary analysis. The article discusses a two-phase linear TFM generator with a U-shaped core and permanent magnets. The results of numerical modeling show that the distance between the stator cores should have a specific size and requires detailed selection when designing the magnetic system in each particular case. In the design studied, it was calculated that 6 mm between the stator cores increases the machine’s performance. 3D modeling is necessary for accurate analysis, considering the axial magnetic flux to minimize stray fields and their mutual demagnetization. Future research will explore an E-shaped core TFM design.

Recycling of hydrogen tolerant La0.6Ca0.4Co0.2Fe0.8O3–d oxygen transport membranes with integrated life cycle assessment for plasma-assisted CO2-conversion

In this study, a recycling approach was adapted for the hydrogen tolerant La0.6Ca0.4Co0.2Fe0.8O3–d (LCCF_6428) oxygen transport membranes that have great potential in plasma-assisted CO2 conversion techniques for producing industrial fuels such as methanol. The major focus was the incorporation of sustainability measures such as integrating life cycle assessment (LCA) into the materials development at an early stage to study and compare the environmental feasibility of the recycled membrane with the primary membrane. The aim was also to ensure reduced resource depletion of critical raw materials such as cobalt and lanthanum by means of recycling. It consisted of microwave-assisted dissolution of the membrane followed by ultrasonic spray synthesis. The recycled membrane exhibited at least 83 % of the oxygen permeability of the primary membrane and maintained hydrogen tolerance up to 600 °C for 25 h which is a remarkable result for LCCF_6428 in terms of potentially enhancing its life span. As per the LCA, recycling did result in lower resource depletion. However, the recycled LCCF had a higher overall environmental impact compared to the primary LCCF, mainly due to increased electricity consumption during recycling. These results accentuate the need for a transition towards more efficient processes accompanied by cleaner and renewable sources of energy and critically indicate integration of LCA into materials development to establish the sustainability profile of materials.

Promoting Sustainability: Collaborative Governance Pathways for Virtual Water Interactions and Environmental Emissions

This study explores the water consumption and greenhouse gas (GHG) emissions in the Yarkand River Basin, focusing on their dynamic interactions across industrial sectors. Utilizing environmental input–output analysis (IOA), the CROPWAT model, and life cycle assessment (LCA), we quantified the historical evolution of physical and virtual water cycles in relation to the water–carbon nexus. Our findings reveal that the planting industry, particularly the production of export-oriented, water-intensive crops like cotton, significantly contributes to both blue and green water consumption, exacerbating regional water scarcity. The persistent external market demand drives this over-extraction, further strained by the basin’s limited water retention capabilities. Although advancements have been made in reducing the per-unit water footprint of crops, total water consumption continues to rise due to agricultural expansion, intensifying pressure on blue water resources. Additionally, agricultural GHG emissions have surged, driven by increased electricity consumption, heavy fertilizer use, and escalating soil N2O emissions. In light of these challenges, our research underscores the critical need for integrated resource management strategies that align with sustainable development goals. By promoting efficient water allocation within the agricultural sector and diversifying crop structures downstream, we can enhance ecosystem resilience and reduce environmental degradation. Furthermore, the advancement of value-added agricultural processing and the implementation of innovative water conservation technologies are essential for fostering economic sustainability. These strategies not only mitigate the environmental impacts associated with agricultural practices but also strengthen the region’s adaptive capacity in the face of climate change and fluctuating market demands. Our findings contribute to the broader discourse on sustainable agricultural practices, emphasizing the interconnectedness of water management, climate resilience, and economic viability in arid regions.

Energy factors affecting environmental pollution for sustainable development goals: The case of India

This study investigates the nexus between electricity consumption, fossil fuel dependency, renewable energy adoption, population growth, trade activities, economic growth, and environmental pollution in India. The primary objective is to understand how these factors interrelate and influence each other, focusing on their implications for sustainable development. The study used data from the World Bank from 2000 to 2023; the methodology adopted includes vector autoregression modeling, Granger causality tests, cointegration analysis, impulse response functions, and variance decomposition. These econometric techniques were selected due to their ability to capture dynamic relationships, determine causality, and identify long-term equilibrium among the variables. The findings reveal that economic growth significantly increases electricity consumption and fossil fuel usage, leading to higher carbon dioxide emissions. On the other hand, renewable energy adoption reduces environmental pollution. The study also highlights the complex interplay between population growth, urbanization, and trade activities in shaping India's energy demand and environmental outcomes. The implications of these findings are critical for India's sustainable development. The results suggest that while economic growth is essential, it must be balanced with sustainable energy practices to mitigate environmental pollution. The findings emphasize the need for policy interventions that promote renewable energy, enhance energy efficiency, and enforce environmental regulations. Recommendations include accelerating renewable energy adoption, implementing stringent energy efficiency standards, and developing integrated policies that simultaneously address the economic, energy, and environmental dimensions. These actions will help India achieve a sustainable balance between economic growth and environmental protection, ensuring a healthier future for its population.

Do policies make a difference? Assessing the impact of China's air pollution prevention and control action plan on carbon emissions

This study aims to balance the dual objectives of improving environmental quality and achieving carbon peaking and carbon neutrality goals. Utilizing panel data from 274 Chinese cities between 2006 and 2020, this paper employs the difference-in-differences method to examine the impact of the Air Pollution Prevention and Control Action Plan (APPCAP) on urban carbon emissions. Empirical results indicate that the impact of the APPCAP on regional carbon emissions follows an inverted U-shaped curve, exhibiting a dual impact path. Although the implementation of the APPCAP increased the annual carbon emissions of the city by an average of 1.637 million tons, it showed a trend of inhibiting the growth of carbon emissions, although it was not significant. Specifically, the APPCAP is most effective in reducing carbon emissions during the "source prevention" stage, primarily by increasing industrial electricity consumption and reducing coal production and consumption. However, a rebound effect occurs at the policy period's end, with compensatory increases in crude oil and electricity consumption, thereby elevating regional carbon emission levels. The APPCAP's effect on carbon emissions is more pronounced in areas with lower population density and higher environmental regulation intensity. It notably raises carbon emission levels in non-industrial base and non-resource-based cities compared to other cities. Furthermore, the APPCAP positively impacts regional carbon emission intensity and economic growth, significantly driving the secondary industry while minimally affecting the primary industry.

ЕФЕКТИВНІСТЬ ЕЛЕКТРОСПОЖИВАННЯ НА ГІРНИЧОРУДНИХ ПІДПРИЄМСТВАХ З УРАХУВАННЯМ ВИКИДІВ

The question of the efficiency of electricity consumption arises acutely at mining enterprises, since in modern conditions the extraction of minerals is largely determined by increased electricity consumption. Therefore, the value of the power consumption fluctuation indicator, which is not taken into account at such enterprises, should naturally be considered as emissions that lead to additional economic losses, that is, to a decrease in the efficien-cy of power consumption. Given that electricity consumption at mining enterprises is usually higher than the specified level, it is advisable to study the corresponding efficiency in the paradigm of the theory of random processes with the determination of emissions. The essence of the problems related to the study of electricity consumption involves the expediency of determining both the actual values of the relevant indicators and some of their normative values. To evaluate electricity consumption by industrial enterprises in general and mining enterprises separately, the "energy efficiency index" is used, which evaluates the loss of energy reserves. The indicator of energy efficiency can be measured through a coefficient, as a ratio of output results to input re-sources, or through the volume of production, its range. The purpose of the article is a statistical analysis of the efficiency of electricity consumption at mining enterprises, taking into account emissions. The research method-ology involves the use of mathematical methods, in particular the theory of probabilities, statistical methods. The content consists in the defined values of the intersection of indicators of electricity consumption by mining en-terprises corresponding to the given (planned) level. Emissions that disrupt the uninterrupted operation of the enterprise are analyzed in order to make appropriate management decisions. The scientific novelty consists in the application of statistical methods in the study of emissions in relation to electricity consumption as a random process. The practical value is in ensuring the possibility of increasing the efficiency of electricity consumption at mining enterprises based on the processing of the values of emission indicators.

Can combined wind and solar power meet the increased electricity load on heatwave days in China after the carbon emission peak? A case study in southern Hebei

Wind and photovoltaic (PV) power are the fastest-growing renewable energy sources; however, they are vulnerable to weather extremes. In the context of global warming, the frequency of extreme weather events, particularly heatwaves (HW), is anticipated to increase. Compared to normal summer days, HW days not only cause a rise in electricity demand but also exhibit a complementary growth in wind/PV generation. Therefore, determining whether the increase in wind/PV generation on HW days can meet the corresponding surge in electricity load is pivotal for replacing fossil fuel-based electricity. To address this question, this study conducts a case study for southern Hebei Province, which has the highest combined wind/PV installations in China, for the period 2031–2040 under the scenario that China will achieve its carbon peak (2030) and carbon neutrality goals as planned. We use recently developed load and wind power models and calibrate a boosting ensemble learning model to simulate PV generation. The results reveal that the total increase in wind/PV generation on HW days can fully meet the total increase in electricity consumption starting in 2039. However, due to the uneven distribution of wind/PV generation and load changes throughout the HW days, 9 GWh of energy storage capacity is required to ensure electricity supply in the early morning hours. Under China's climate change adaptation strategy, Hebei Province can harness wind/PV energy to address peak load demands during HW conditions and initiate climate-resilient urban development pilot projects.

Electrolysis of low-carbon methanol for point-of-use hydrogen generation: Opportunities and challenges for the direct use of unrefined feedstocks

Low-carbon methanol (LCM) is a promising hydrogen carrier that can reduce the overall transportation and storage costs associated with the industrial use of H2 as a chemical feedstock or fuel. Herein, LCM refers to methanol produced via processes that emit lower greenhouse gases (by 90% or more) relative to conventional methanol production derived from fossil fuels like pipelined natural gas or coal. LCM production pathways can valorize stranded methane as well as from sources such as biomass and biogas (e.g., landfill and digester gases). The distributed nature of these stranded feedstocks lends themselves well to methanol synthesis within a smaller-scale, decentralized infrastructure. While process intensification and relatively cheaper feedstock can support these distributed productions, forgoing economies of scale can significantly increase the cost of conventional upgrading steps such as distillation. Fortunately, these upgrading steps may be unnecessary by targeting specific applications, where high methanol purities are not required, thereby lowering the holistic cost and carbon intensity of an overall process.Here, we consider methanol as a hydrogen carrier and its conversion to H2 at the point of use through an electrolytic process by experimentally comparing the oxidation of methanol, of varying purity levels (e.g., grade (99.9+% purity) and crude forms) using a proton exchange membrane (PEM) electrolyzer. In this study, the supply of crude methanol derives from a distributed gas-to-liquid process, developed by M2X Energy Inc. The impurity content is speciated and quantified within the crude methanol sample, which is used directly with water co-reactants to evaluate the effect of impurity type and concentration on electrolytic performance on commercial Pt–Ru catalysts. Our experimental results show that about 1 wt% of C2–C5 alcohols in the starting crude methanol readily poison the catalyst, leading to an increased electricity consumption by 5.1–9.8 kWh per kg H2 produced when operating the electrolyzer at high current densities (>200 mA cm−2). A sensitivity analysis is developed according to the measured energy penalty, which can be used to decide whether bypassing methanol distillation provides economic benefit based on site-specific electricity and distillation costs.

THE RELATIONSHIP BETWEEN ELECTRICITY CONSUMPTION, TRADE, AND ECONOMIC GROWTH IN NIGERIA

The study examines the relationship between electricity consumption, trade, and economic growth in Nigeria. The results show that increased electricity consumption positively impacts economic growth in both the short and long run. Foreign direct investment (FDI) also has a positive and significant effect, indicating that higher FDI leads to long-term economic growth. Trade openness was found to contribute to environmental growth. The study recommends that policymakers strengthen the manufacturing sector to better harness the benefits of trade openness and reduce its negative impacts

The Impact of Economic Growth, Energy and Electricity Consumption Usage on CO2 Emissions: A Case Study of Morocco

Given the uncontrolled increase in environmental pollution and degradation of environmental quality worldwide, the environmental impact of economic growth has become a major focus in recent decades the most important question is: How does economic growth affect environmental quality? This study addressed this issue, examining the nature of the long-run relationship between economic growth per capita and pollutant emissions in Morocco. For this reason, the ARDL (Autoregressive Distributed Lag) modeling approach was used from 1990 to 2020. This approach mainly includes time series analysis and cointegration. The relationship tested introduces four variables: carbon dioxide (CO2), energy consumption per capita, electricity consumption per capita and GDP per capita. The main results indicate a long-term relationship between economic growth, energy consumption and CO2 emissions, indicating that a 1% increase in electricity consumption increases CO2 emissions by 0.36%. On the other hand, a 1% increase in GDP per capita increases CO2 emissions by 0.76%, and a 1% increase in energy consumption increases emissions by 2.06%. Thus, all variables show a significant effect. In conclusion, the strategy that Morocco needs to follow to solve our problems is to prioritize investment in research, development and artificial intelligence, while providing adequate training in energy-related fields.

Sustainability education and resource curse control in the selected resource-rich economies

The “resource curse” paradoxically impacts countries abundant in natural resources, presenting challenges despite their apparent benefits. In 20 such economies, the focus has shifted to using sustainability measures to address this curse, analyzed through Cross-Sectional Fully Modified Ordinary Least Squares. Between 1999 and 2018, sustainable education proved essential in mitigating resource curse vulnerabilities. For instance, a 1% rise in primary school enrollment correlates with a 0.16 decrease in the Resource Curse Vulnerability Index, aiding economic diversification. Similarly, a 1% increase in state education investment leads to a 0.54% reduction in the index, improving human capital and governance. However, higher inflation exacerbates vulnerability, while trade liberalization reduces dependence on resources. Increased electricity consumption and internet access may intensify risks. Effective policies should focus on increasing education investment, supporting Education for Sustainable Development, enhancing media advocacy, promoting renewable energy, and advancing sustainable digital transformation.

A Proposed Method and Case Study of Waste Heat Recovery in an Industrial Application

Abstract Waste heat recovered from a refrigeration machine is associated with the double benefit of generating cold and heat with just one unit. Additional energy is required in most cases to achieve these benefits. To evaluate the efficiency of waste heat recovery, two novel efficiency indicators are described. The overhead coefficient of performance (OCOP) describes additional electrical power required to raise the temperature to make waste heat usable. The coefficient of savings describes power reduction when condenser heat is fed into a cold district heating network instead of exhausting it to high-temperature outside air. Results are reported from a case study in a food logistic center with high cooling demand in Isny, Germany. Waste heat at this facility was previously released unused to outside air. We describe how this waste heat can be used to supply sustainable heat supply to a new residential area. During the design phase, it is difficult to choose the best operating temperature for district heating networks (DHNs). The novel indicators are used to value the effort to make waste heat usable. Whereas a supply temperature of 20 °C has no disadvantages for the operator, a supply temperature of 40 °C is associated with an increase in electricity consumption. The resulting OCOPs are above 5.0 even under unfavorable conditions and exceed the theoretically calculated (Holm and Pehnt, 2023, Wärmeschutz und Wärmepumpe-Warum Beides Zusammengehört, Forschungsinstitut für Wärmeschutz e.V., Institut für Energie und Umweltforschung, München/Berlin/Heidelberg, p, 13, 14; Agora Energiewende, Fraunhofer IEG, 2023, Roll-out von Großwärmepumpen in Deutschland, Strategien für den Markthochlauf in Wärmenetzen und Industrie, Berlin) and measured (Fraunhofer Institut für Solare Energiesysteme, 2020, “Wärmepumpen in Bestandsgebäuden, Ergebnisse aus dem Forschungsprojekt, WP-Smart im Bestand.”) coefficients of performance (COPs) for air-sourced heat pumps. Although using waste heat is not free, it is beneficial when overall efficiency is considered.

A Hybrid Deep Learning Model to Estimate the Future Electricity Demand of Sustainable Cities

Rapid population growth, economic growth, and technological developments in recent years have led to a significant increase in electricity consumption. Therefore, the estimation of electrical energy demand is crucial for the planning of electricity generation and consumption in cities. This study proposes a hybrid deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) techniques, both of which are deep learning techniques, to estimate electrical load demand. A hybrid deep learning model and LSTM model were applied to a dataset containing hourly electricity consumption and meteorological information of a city in Türkiye from 2017 to 2021. The results were evaluated using mean absolute percent error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) metrics. The proposed CNN-LSTM hybrid model was compared to the LSTM model, with lower MAPE, MAE, and RMSE values. Furthermore, the CNN-LSTM model exhibited superior prediction performance with an R2 value of 0.8599 compared to the LSTM model with an R2 value of 0.8086. These results demonstrate the effectiveness of the proposed deep learning model in accurately estimating future electrical load demand to plan electricity generation for sustainable cities.

Rainy and dry seasons impact on electricity demand in Indonesia

Electricity consumption has become an integral part of daily life and is pivotal in supporting various aspects of human life. North Aceh Regency, a tropical region in Indonesia, experiences significant seasonal fluctuations between the rainy and dry seasons. This research aims to investigate and analyze the impact of these seasonal differences on electricity consumption patterns by consumers in the region using the IBM SPSS statistical method. Monthly electricity consumption data from consumers in North Aceh Regency over a specific period were collected and analyzed using IBM SPSS software. Descriptive statistical analysis, hypothesis testing, and regression models were employed to identify significant differences in electricity consumption between the rainy and dry seasons and to understand the factors influencing consumption patterns. The results of the analysis indicate a significant difference in electricity consumption between the rainy and dry seasons in North Aceh Regency. The dry season shows an increase in electricity consumption, possibly related to factors such as the use of air conditioning and additional lighting.

Theoretical analysis of using multiple borehole heat exchangers for production of heating and cooling energy in shallow geothermal reservoirs with underground water flow

In this work the feasibility of using the double-U borehole heat exchangers (BHE) in multiple arrangements for production of heating and cooling energy has been investigated.Theoretical case studies are given for a few hypothetical lithological cases corresponding to the lithology that can be found in City of Zagreb. The results show interference between the borehole heat exchangers and limits of use of shallow reservoir with respect to slow convection and loss of accumulated energy in the reservoir. Special emphasis is placed on the description of the groundwater flow determined by the parameters of the shallow geothermal reservoir, such as permeability, water saturation, thermal conductivity, specific heat capacity and soil density, topography, precipitation, etc. Simulations have been performed in FeFlow coupled with Python user-scripts for individual control of the BHE’s, and the surface equipment is modelled using RES2GEO. To assess the accuracy of numerical predictions in simulating thermal responses, FeFlow software was utilized to conduct Thermal Response Tests (TRT) for individual Borehole Heat Exchangers (BHEs). These numerical results were then juxtaposed with empirical TRT data collected from a site near Velika Gorica, a town within the City of Zagreb. For each case simulation is done for one year with one hour resolution. The temperature distribution analysis highlights notable differences between scenarios, where the temperature field with lower permeability experiences significant variations, particularly near the BHE. Here, temperatures can soar to 20 °C during cooling periods and plummet to 4 °C in heating seasons. These fluctuations cause overheating and subcooling of the reservoir, impairing the heat pump’s efficiency and necessitating increased electricity consumption. By using Multivariate linear regression and Multi-layer perceptron regressor method, the main parameter for determination of COP of ground source heat pump. The analysis reveals that the majority of errors fall within the range of ± 5 %, although the MLA prediction method exhibits slightly more frequent errors. Additionally, the R2 scores demonstrate strong performance: the MLA procedure achieves 0.95 for heating and 0.99 for cooling, while the MLP procedure attains 0.99 for both heating and cooling phases.

Driving Factors of NOx Emissions in China: Insights from Spatial Regression Analysis

China’s rapid industrialization and urbanization have led to significant nitrogen oxide (NOx) emissions, contributing to severe atmospheric pollution. Understanding the driving factors behind these emissions is crucial for effective pollution control and environmental management. Therefore, this study is an attempt to provide insights into the influence of socioeconomic factors and explore spatial dependencies of NOx emissions in China in 2022 employing spatial regression models (SRMs). Among the SRMs considered, the spatial Durbin model (SDM) is identified as the most suitable for analyzing regional NOx emissions. The study highlights the importance of controlling electricity consumption and vehicle emissions for addressing air pollution in Chinese regions. Specifically, a one billion kilowatt-hour increase in electricity consumption leads to approximately 549.6 tons of NOx emissions, and an increase of 1000 vehicles in a region results in an average increase of 7113.4 tons of NOx emissions in the same region. Furthermore, per capita consumption expenditure (PCEXP) and research and development (R&D) expenditure exhibit negative direct and spillover impacts. Contrary to previous studies, this research finds that changes in urban population density do not have a significant direct or indirect effect on NOx emissions within the studied areas. Moreover, we conducted additional investigations to assess the effectiveness of government action plans in reducing NOx emissions. Specifically, we evaluated the impact of Phases 1 and 2 of the Clean Air Action Plan, launched in 2013 and 2018, respectively, on the socioeconomic drivers of NOx emissions. Therefore, the data were modeled for the years 2013 and 2017 and compared to the results obtained for 2022. The findings indicate that over the entire period (2013–2022), the emission controls mandated by the action plan resulted in significant reductions in the impact of many of the studied NOx drivers. In conclusion, based on the results, this study presents recommendations to mitigate NOx emissions.

Reducing Electric Vehicles Charging Costs Utilizing Optimal Control Method Based on Diverse Charging Patterns

Plug-in electric vehicles (PEVs) present a promising solution for decreasing greenhouse gas emissions and conserving natural oil reserves. However, the growing number of PEVs connected to the electric grid raises concerns regarding the reliable and safe operation of the network. This concern stems from the fact that the charging operation of electric vehicles (EVs) involves a significantly high level of electricity consumption due to the size of EVs' battery charging period. Unscheduled charging activities can lead to increased electricity consumption, increase in PEV charging costs and overload on the distribution grid. To address this issue, this paper proposes a coordinated charging schedule for PEVs, utilizing an optimized EV battery charging model with an optimal control method that deals with finding the best possible control signal for a dynamic system over period of time aiming to optimize a particular performance index, while satisfying various constraints such as boundary condition, state, and control path. The results demonstrate that the proposed optimized charging schedule reduces charging costs by up to 21% compared to an unscheduled charging pattern. Index Terms— Charging cost, coordinated charging, optimal control, plug-in electric vehicle, penetration level, unscheduled charging.

Electrification of residential and commercial buildings integrated with hybrid renewable energy systems: A techno-economic analysis

This study assesses the techno-economic viability of city-scale building electrification, comparing a base case scenario with conventional HVAC systems to an electrified scenario with heat pump systems. EnergyPlus simulations reveal a 28% increase in annual electricity consumption in the electrified scenario, shifting the peak consumption from summer to winter with a 49% surge. Hybrid renewable energy systems (HRESs) are then evaluated using HOMER Pro, considering renewable penetration from 0% to 100%. The net present cost (NPC) of the base case and electrified scenarios range from $10.34 to $24.38 billion and $8.79 to $50.31 billion, respectively. Sensitivity analysis explores the impact of carbon tax, fuel price, and renewable subsidies on HRESs in the electrified scenario. Results indicate that applying a carbon tax up to $120/tonne of CO2 increases the optimum renewable fraction from 5% to 26%, while higher fuel prices and renewable subsidies further enhance the feasibility by shifting the optimum renewable fraction to 60% and 54%, respectively, demonstrating the interplay between economic factors and renewable integration in electrified urban contexts.

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.

A multi-stage framework for coordinated scheduling of networked microgrids in active distribution systems with 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%.