Global Electricity Demand Research Articles

USING AMMONIA FUEL AS A MEASURE TO REDUCE GREENHOUSE GASES

The presented work highlights the possibilities of using ammonia as an alternative fuel in order to achieve low-carbon development of the state and strengthen the fight against greenhouse gases. Since the Industrial Revolution, combustion has been the primary method of energy conversion for human activities, including power generation and transportation. Today, these sectors continue to rely heavily on hydrocarbon fuels. As a result, the largest absolute increase in carbon dioxide emissions in 2022 was from electricity and heat production. With global electricity demand increasing by 2.7%, emissions in the energy sector increased by 1.8% (or 261 Mt), reaching an all-time high of 14.6 Gt. Thus, a large part of CO2 emissions is produced through these sectors, which is the main culprit of global warming, undermining the fight against climate change. The need for decarbonization is taken into account by program documents, including government strategies, which not only warn but also prohibit the excessive formation of pollutants and stimulate the promotion of carbon-free technologies in energy sectors. It is common knowledge that the innovative development of fuel combustion technologies is necessary to achieve the future goals of a carbon-neutral system. Despite the considerable efforts made to reduce greenhouse gas emissions during the combustion of hydrocarbons, for example, by increasing the combustion efficiency, these efforts are not enough to achieve low greenhouse gas emissions. The progress in this field is analyzed and it is found that ammonia is receiving increasing attention as one of the most attractive energy carriers due to its carbon-free nature. However, the use of ammonia in combustion is not without problems, including low flame speed, narrow flammability limits, and tendency to NOx formation. Therefore, the further introduction of ammonia as a fuel in energy and industry requires comprehensive studies of the combustion working process. Research should be based on determining the influence of the main technological factors, the possibility of using mixed fuels, as well as establishing the optimal geometric and mode parameters of the fuel combustion system.

Digital data demand and renewable energy limits: Forecasting the impacts on global electricity supply and sustainability

This study critically evaluates whether the current and projected generation of renewable energy can meet the escalating global demand for electricity from digital data growth. Our modelling forecasts reveal a concerning trend: despite the expansion in renewable energy capacities, they are likely insufficient to satisfy the burgeoning electricity needs of the digital data sector. More alarmingly, there is a real risk that the demand for digital data could soon exceed feasible electricity production capabilities. This paper underscores the urgent necessity for a data-centric sustainability approach across all supply chains, sectors, industries, and nations. Such measures are crucial to increase efficiency, cut energy usage, and transition towards a decarbonized digital ecosystem, thereby supporting the global pursuit of a sustainable, net-zero future. This research highlights a critical junction in energy policy and digital infrastructure planning, urging immediate action to reconcile digital advancement with ecological sustainability.

Space-based solar power: Unlocking continuous, renewable energy through wireless transmission from space

Space-Based Solar Power (SBSP) is an emerging technology that aims to harness the abundant and uninterrupted solar energy available in space and beam it wirelessly to Earth. This innovative approach addresses the limitations of terrestrial solar energy, such as weather variability and the day-night cycle, by positioning solar power stations in space where sunlight is constant. The stations, composed of large arrays of solar panels, capture the solar energy and convert it into microwaves or laser beams, which are then transmitted to receiving stations on Earth for conversion into usable electricity. Research in SBSP focuses on optimal orbital positions, with geostationary and low Earth orbits being considered for maximum energy capture and efficient transmission. Advancements in materials, wireless power transmission technologies, and space-based assembly techniques are critical to the design and deployment of these stations. Additionally, economic and technical feasibility studies explore the challenges of launching, maintaining, and scaling SBSP systems. These include high initial costs, energy transmission efficiency, safety concerns, and the logistics of long-term maintenance. The potential benefits of SBSP are significant. It offers a clean, renewable energy source that could meet the growing global demand for electricity while reducing reliance on fossil fuels. By providing continuous energy, SBSP could enhance energy security and contribute to global efforts to combat climate change. However, several challenges remain, including addressing space debris risks and ensuring the safety of high-power energy transmission to Earth. As research and technological advancements continue, SBSP holds promise as a transformative solution for future energy needs.

A Paris aligned 1.5 °C mitigation pathway for the chemical industry based on 100% renewable energy and novel production technologies

With renewables growing at an unprecedented pace and critical carbon budget deadlines approaching, research is shifting to the new frontier of Paris aligned 1.5 °C mitigation pathways for hard-to-abate sectors, including the chemical industry sector. This is a significant challenge with chemical products such as plastics, fertilizers and many more products intertwined with today’s society and with CO2 emissions originating both from energy and from chemical conversions. This paper presents a detailed bottom-up production-based energy and emission calculation to make a 1.5 °C compatible scenario for the chemical industry based on three main measures compared to a business-as-usual scenario. This research found that the holistic combination of demand and supply measures is critical to reduce the different types of emissions in the sector (energy and non-energy process emissions). An overall reduction of 82% of CO2 emissions in 2050 is calculated with a decrease in base chemical production growth (− 21%), 100% renewable energy for heat and electricity (− 50%) and the introduction of novel electrical-based and biomass-based production technologies for ethylene, propylene, methanol and ammonia (− 10%). Critical system developments include slowing chemical demand growth with recycling and circularity best practices, advanced electrification of heat supply and the substantial market penetration of the current most advanced sustainable production technologies for methanol, ammonia, and ethylene based on their reasonable technology maturation trajectories. Here, green methanol and ammonia will require 381 TWh in 2030 and 3232 TWh in 2050 for green hydrogen electrolysis, which will represent 1% and 4% of global electricity demand.

ANALYSIS OF FINANCIAL ECONOMIC INDICATORS FROM A SUSTAINABILITY PERSPECTIVE: STUDY WITH COMPANIES IN THE ENERGY SECTOR LISTED ON B3

Global demand for electricity is growing continuously. Therefore, the analysis of the companies involved becomes timely. This study investigates the impact of the disclosure of sustainability reports, with and without audit opinions, on the economic and financial performance of companies listed on B3 during the period from 2019 to 2023. Methodologically, the research has a descriptive approach, documentary procedure and quantitative nature. Its temporal dimension is longitudinal. The sample, composed of 77 companies selected probabilistically, was divided into: 47 companies that published reports with an audit opinion, 12 that published without an opinion, and 18 that did not publish reports. Through the Kruskal-Wallis test, the results indicated that the companies present similar performances in metrics such as current liquidity, dry liquidity, general liquidity, net margin and asset turnover. However, statistically significant differences were observed in the quick liquidity ratio, debt ratios (level and composition), as well as return on investment (ROI) and return on equity (ROE) indicators. This study contributes to the literature by providing insights into how sustainability reporting can influence various aspects of the financial management of electric power companies.

Analyzing the influence of electric vehicle charging scheduling on distribution transformer lifespan

The ongoing growth in global electricity demand and rapid proliferation of electric vehicles (EVs) are posing challenges to the operation of the low voltage power networks and their components. This study aims to analyze the impacts of peak-load demand on the distribution transformers, considering mild and extreme combinations of electric energy demand and localized charging of plug-in EVs under their different penetration levels, hypothesizing that shifting the demand for charging throughout the day can reduce the aging of distribution transformers. The proposed method employs a fuzzy-logic-based tool, which incorporates the influence of ambient temperature, higher harmonics, reactive power compensation, reverse power flows, and overload. Three different charging/discharging modes have been considered: demand charging, off-peak charging, and synergy of off-peak charging and vehicle-to-house technology. The results indicate that off-peak charging emerges as 2.1 times more effective than demand charging in terms of the transformer's loss of life, particularly for ultimate EV penetration of 100 %. It is demonstrated that the utilization of charging scheduling for EVs should be prioritized to defer costly system upgrades and reinforcements, particularly when EV penetration exceeds 50 %.

Fair Energy Trading in Blockchain-Inspired Smart Grid: Technological Barriers and Future Trends in the Age of Electric Vehicles

The global electricity demand from electric vehicles (EVs) increased by 3631% over the last decade, from 2600 gigawatt hours (GWh) in 2013 to 97,000 GWh in 2023. The global electricity demand from EVs will rise to 710,000 GWh by 2030. These EVs will depend on smart grids (SGs) for their charging requirements. Like EVs, SGs are a booming market. In 2021, SG technologies were valued at USD 43.1 billion and are projected to reach USD 103.4 billion by 2026. As EVs become more prevalent, they introduce additional complexity to the SG landscape, with EVs not only consuming energy, but also potentially supplying it back to the grid through vehicle-to-grid (V2G) technologies. The entry of numerous independent sellers and buyers, including EV owners, into the market will lead to intense competition, resulting in rapid fluctuations in electricity prices and constant energy transactions to maximize profit for both buyers and sellers. Blockchain technology will play a crucial role in securing data publishing and transactions in this evolving scenario, ensuring transparent and efficient interactions between EVs and the grid. This survey paper explores key research challenges from an engineering design perspective of SG operation, such as the potential for voltage instability due to the integration of numerous EVs and distributed microgrids with fluctuating generation capacities and load demands. This paper also delves into the need for a synergistic balance to optimize the energy supply and demand equation. Additionally, it discusses policies and incentives that may be enforced by national electricity carriers to maintain grid reliability and manage the influx of EVs. Furthermore, this paper addresses emerging issues of SG technology providing primary charging infrastructure for EVs, such as incentivizing green energy, the technical difficulties in integrating diverse hetero-microgrids based on HVAC and HVDC technologies, challenges related to the speed of energy transaction processing during fluctuating prices, and vulnerabilities concerning cyber-attacks on blockchain-based SG architectures. Finally, future trends are discussed, including the impact of increased EV penetration on SGs, advancements in V2G technologies, load-shaping techniques, dynamic pricing mechanisms, and AI-based stability enhancement measures in the context of widespread SG adoption.

Locating the suitable large-scale solar farms in China's deserts with environmental considerations

Desert areas offer rich solar resources and low land use costs, ideal for large-scale new energy development. However, desert ecosystems are fragile, and large-scale photovoltaic (PV) power facilities pose ecological risks. Current assessments of PV plant sites in deserts lack consideration of wind-sand hazards and ecological impacts. In this study, we have developed a new large-scale photovoltaic (PV) site selection model that integrates the analytic hierarchy process with geographic information system technology, and applies it to the desert regions of China. The results show that the potential for large-scale PV power plants in China's deserts is significant, with 69.4 % of the region assessed as medium or higher. The most suitable area is 12.7 × 104 km2 (7.6 % of the overall study area), mainly centered in the Tibetan Plateau's Qaidam Basin Desert and the deserts of northern China, characterized by favorable solar resources, climate, and terrain. Across all regions, gravel deserts are recognized as more suitable for the construction of large-scale PV power projects than sandy deserts. Considering varying PV installation density scenarios with an installed capacity potential of 36.4–84.9 TW and system costs ranging from 10.0 to 33.5 trillion USD, the study estimates an annual solar power generation potential of 47–110 PWh which is 1.7–3.9 times the global electricity demand. Carbon emissions could be reduced by 26.8–62.6 gigatons annually, offsetting 73–170 % of global emissions. Covering just 4.8–11.5 % of China's desert area (8 × 104–19.4 × 104 km2) would meet the projected 2025 electricity needs of the country. This study lays the groundwork for spatial planning and benefit assessment of large-scale PV projects in desert regions, and reduces conflicts between PV plant construction and local ecosystem.

Towards advanced sustainable criteria for choosing the best site for collecting solar energy in cities using multi-criteria GIS

Solar energy has become a prominent and crucial source of clean energy due to the increasing global demand for electricity. There is a prevailing global trend towards the construction of solar farms as a means of energy generation. This study aims to assess multiple criteria encompassing urban, environmental, and social aspects in order to ascertain the optimal site for constructing solar farms. The evaluation primarily focuses on characteristics such as topography, solar radiation, accessibility, land use, and proximity to roads and power stations. In order to acquire the necessary spatial fit model, the data was evaluated utilizing a Geographic Information System (GIS) program, and the criteria were subsequently consolidated into an integrated geographic information system. A comprehensive review of the existing body of literature reveals that environmental factors, specifically solar radiation and aspect, play a crucial role in determining the suitable places for the establishment of solar energy collection projects (Merrouni et al. in Energy Procedia 49:2270-2279, 2013; McKinney in J Student Res Environ Sci Appalac 4:1-14, 2014). Furthermore, other analysis factors such as proximity to built-up regions, closeness to power lines, and proximity to roadways are taken into account (Hott et al. in GIS-based Spatial Analysis For LargeScale Solar Power And Transmission Line Issues: Case Study of Wyoming, U.S. In: Proceedings of the 41st American Solar Energy Society Meeting, 2012; Effat in Int J Adv Remote Sens GIS 2:205-220, 2013). These criteria have an impact on the cost of solar farms. The objective of this study was to assess several aspects influencing the selection of an optimal location for a solar energy farm, taking into consideration the aforementioned criteria. The study was carried out in New Aswan city, utilizing data obtained from the Urban Planning Authority within the Ministry of Housing and Urban Development, as well as the New Aswan City AuthorityI generated cartographic representations and compiled quantitative data, which informed our selection of places that met the established criteria through the utilization of a Geographic Information System (GIS) software. Upon the conclusion of the study, numerous locations that satisfied the established criteria were selected. The present study focuses on regions with high capacity, and the findings are depicted in the form of spatial and objective maps. One of the primary benefits associated with the utilization of this methodology lies in its versatility, as it can be implemented across several domains. Furthermore, a straightforward adjustment of the criteria facilitates its application in the selection of optimal locations for wind farms.

Using GIS Spatial Analysis for Examination the Utility of the Suggested Suitability Energy Farms Over Kirkuk City

Global electricity demand is rising, prompting increased attention to renewable energy sources like solar power. This research focuses on identifying suitable areas for power lines connecting solar farms to distribution stations in Kirkuk City. Data types, including urban areas, industrial zones, farms, water bodies, and archaeological sites, were analyzed using GIS-based spatial tools. The study highlights accessible and inaccessible regions of Kirkuk's electricity transmission infrastructure, providing insights for decision-makers. The analysis categorizes areas as either benefitted or unbenefited in the context of solar farm suitability. An important consideration is the need for additional electrical distribution stations to accommodate energy transfer from these solar farms.

Prediction Model for Electricity Energy Consumption and Pricing Using Xgboosts With L1 Regularization

The increasing global demand for electricity, driven by rapid industrialization, urbanization, and digitalization, necessitates more accurate and efficient predictive models for electricity generation and consumption. Traditional statistical techniques often fall short in incorporating the complex environmental factors influencing energy demand. This study presents an advanced machine learning model, leveraging XGBoost with L1 regularization, to improve the accuracy of electricity consumption and generation predictions. By integrating comprehensive datasets, including economic indicators and weather variables, the model addresses the critical gaps in existing forecasting methods. The research involved rigorous data pre-processing, feature engineering, and model validation using metrics such as RMSE, MAPE, and MAE. The model achieves a RMSE of 2.212 and MAPE of 2.393%, indicating good predictive performance. Effective data cleaning, feature engineering, and the application of PCA contributed to this result. The results demonstrate significant improvements in prediction accuracy, highlighting the potential of the proposed model to enhance energy management practices, optimize grid operations, and support the integration of renewable energy sources. This study provides a robust framework for future research and practical applications, contributing to the advancement of sustainable energy systems and informed policy-making. The findings underscore the importance of integrating diverse data sources and sophisticated machine-learning techniques to meet the growing challenges in the energy sector.

Machine Learning Algorithms for Prediction of Boiler Steam Production

The continuous increase in global electricity demand has resulted in boiler power plants becoming a significant energy source. The production of steam is a principal indicator of boiler efficiency, and the accurate prediction of steam production is paramount importance for the enhancement of boiler efficiency and the reduction of operational costs. In this study employs a boiler dataset with a steam production capacity of 420 tons per hour. A total of 25 independent variables were extracted from the original 39 variables through data processing and feature engineering for the purpose of prediction analysis. Subsequently, 8 machine learning models were used for modeling predictions. Grid search cross-validation was employed in order to optimise the performance of the model. The models were analysed and assessed using the Mean Squared Error (MSE) metrics. The results show that random forest achieves the highest accuracy among the 8 single models. Based on 8 models, New Bagging ensemble model is proposed, which combined predictions from 8 single models, demonstrated the optimal overall fit and the lowest MSE, achieved the purpose of the research. The present study demonstrates the ability to analyse and predict complex industrial systems with machine learning algorithms, and provides insights into the use of machine learning algorithms for industrial big data analytics and Industry 4.0. Further work could explore using larger datasets and deep learning to make predictions more accurate.

Cryptocarbon: How much is the corrective tax?

With increasing awareness of past environmental damage from crypto mining, questions arise as to how persistent the problem will be in the future and how taxation can help in addressing this negative externality. We estimate that the global demand for electricity by crypto miners reached that of Australia or Spain, resulting in 0.28% of global CO2 emissions in 2022. Projections suggest sustained future electricity demand and indicate further increases in CO2 emissions if crypto prices significantly increase and the energy efficiency of mining hardware is low. To address global warming damages, we estimate the corrective excise on the electricity used by crypto miners to be USD 0.045 per kWh, on average. Considering also air pollution costs raises the tax to USD 0.085 per kWh. Country-specific estimates vary depending on their electricity sources. Other new electricity reliant technologies are also driving up CO2 emissions. In our calculation, the corrective tax on electricity used by data centers is USD 0.048, on average.

CFD modelling and analysis of a radial ORC turbine with backswept rotor blades

Abstract Global electricity demand has steadily risen over the past five decades due to increased industrialization and improved accessibility worldwide. Addressing this escalating demand requires expanding electricity production, achievable through adopting renewable energy sources or enhancing production efficiency. Organic Rankine Cycle (ORC) power systems offer a promising solution for efficient electricity generation by utilizing organic fluids with lower critical parameters. Unlike conventional steam power plants, ORC systems operate at lower temperatures, making them adaptable to various applications. The efficiency of ORC power systems heavily relies on the design and performance of their turbines. This study focuses on modelling and analyzing the ORC turbine using Computational Fluid Dynamics (CFD) techniques, specifically employing a radial-inflow design with backswept rotor blades. Geometric parameters were derived from a simplified zero-dimensional (0-D) turbine model developed in MATLAB. CFD modelling was conducted using ANSYS software, integrating the BladeGen module for turbine geometry generation and the TurboGrid toolbox for mesh creation. Comparative analysis with the 0-D design method validated the accuracy of the CFD turbine modelling and the achievement of desired operational parameters. This research underscores the potential of CFD techniques in optimizing ORC turbine design thus allowing to achieve efficient electricity generation, contributing to the advancement of sustainable energy technologies.

Coal-based generators’ corporate response to an emissions trading scheme in Kazakhstan

The power industry remains a central focus of modern climate mitigation policies because of its significant contribution to global GHG emissions and increased global electricity demand. This paper examines Kazakhstan's coal-based power-generation companies' responses to a national emissions trading scheme (ETS) regulation introduced in 2013 by applying corporate climate strategy and sociotechnical systems frameworks. The in-depth case study is based on 20 interviews with managers of the six largest Kazakhstan coal-based generators and ten interviews with independent industry experts. The empirical data indicate that Kazakhstan's ETS has impacted the country's generators. The companies have responded in the form of non-compliance (i.e., application for additional free allowances), compliance (i.e., purchase of allowances from the ETS), and compliance-plus activities (i.e., exploring investment into renewable energy and fuel switching to natural gas). At the same time, Kazakhstan's generators continue the business-as-usual approach by investing in modernizing their coal-generating assets. Therefore, Kazakhstan may consider complementing its ETS regulation with additional policy reforms to support its ambitious objective to achieve carbon neutrality by 2060.

Conceptual Sizing Approach for Electric Power Generation Turbine Aerostat

Addressing the challenges of increasing global electricity demand and the environmental impact of conventional energy sources requires innovative solutions. This study proposes a practical approach to overcome the limitations of wind power, which is dependent on weather patterns and land constraints. The solution involves using tethered aerostats equipped with wind turbines to generate power. By connecting these aerostats to transmission towers, the system optimizes efficiency and utilizes existing infrastructure. The research explores the design methods, parameters, and complexities involved in implementing this groundbreaking solution, emphasizing the benefits of advancements in wind turbine technology. This self-sustaining energy system offers a reliable and cost-effective alternative for renewable energy generation, reducing carbon emissions and meeting the ever-growing global electricity demand. By paving the way for a sustainable and eco-friendly future in energy production, this solution holds promise for a brighter tomorrow.

An easy method for simultaneously enhancing power system voltage and angle stability using STATCOM

The global electricity demand is growing rapidly and is expected to double by 2050. This growth requires significantly expanding the current high-voltage transmission lines, which is a long-term and costly solution. As an alternative, utilities use advanced technologies like Flexible Alternating Current Transmission System (FACTS) devices as a temporary measure, postponing the expansion. However, it is necessary to locate these devices in the network properly to avoid negative outcomes. Traditional methods for FACTS placement focus on voltage profile improvement and power loss reduction, neglecting rotor angle stability. This can lead to oscillatory instability, especially in weak power grids. To address this issue, the paper proposes a simple approach to optimize the placement of shunt FACTS devices in power systems. The method improves both voltage and rotor angle stability without compromising power loss. The proposed approach considers rotor angle stability constraints, which is essential for system stability during transient events. The study was demonstrated on Nigeria’s 50-bus 330 kV power system, with results indicating a 31% improvement in angle stability with an enhanced voltage profile. The method is straightforward, non-iterative, and provides a cost-effective solution for FACTS device placement in power systems.

Photovolitaics Concept Integrated on the Grid With the STT-PLN Building

As we enter the 21st century, concerns over dwindling oil and gas reserves have become more pronounced. With energy demand on the rise, particularly in developed nations, projections indicate a 70% increase between 2000 and 2030. By the year 2017, global electricity demand is expected to reach 25.4 trillion kWh. Solar energy emerges as a promising solution, especially in regions like Indonesia, where the entire mainland covers roughly 2 million km2 and receives an average daily radiation distribution of 4.8 kWh/m2. This translates to a solar energy potential of 5.10 mW, equivalent to 112,000 GWp. Technically speaking, solar panels have shown an efficiency improvement of 17.4%. When considering components and the quality absorbed by solar power plants (PLTS) synchronized by KWH EXIM, calculations reveal a performance ratio of 81%, confirming the technical feasibility of implementing such systems

Recyclability and ecological-economic analysis of a simple photovoltaic panel

Photovoltaic industry has displayed an exponential expansion rate over the past two decades. Currently, 1185 GW of the global electricity demand is derived from photovoltaic (PV) panels with more to come in the next two decades. With the growing installment rate, a concern regarding managing panels that reached the end of their life-span grows. Despite the fact that the approximate life-span of a panel is 25 years, oftentimes this interval is shortened due to weather conditions, low-quality installations, and poor maintenance. Therefore, a global attention is focused on research and law-regulation of proper recycling and disposal of the PV waste. In this contribution, recyclability of one PV panel is determined upon subjecting it to the simplest recycling process, which is manual mechanical separation using common hand-tools. In addition, an ecological-economic analysis of the panel, which yields an absolute profit of the panel is presented.

A hydroxy containing, naphthalene-based and imine linked porous organic polymer for rapid iodine uptake

Nuclear energy is turning out to be one of the best alternatives to fossil fuels in order to meet the global electricity demand. Radioactive iodine is a volatile and hazardous component that may be present in aqueous nuclear waste and off-gas streams of a spent fuel processing plant. Iodine species are also quite mobile and their accidental leakage into the environment can adversely affect the health of animals and humans to an alarming extent. Thus, design and development of materials capable of capturing/storing iodine effectively for a long time, is of utmost importance in contemporary research. Herein, facile synthesis of a unique nitrogen rich and thermally stable porous organic polymer (POP_DH) is described that has a porous structure comprising of both micro and mesopores. The POP_DH has a specific surface area (128.3 m2 g-1), and can capture 2.855 g g−1 of iodine (vapours) at 75 °C. The POP_DH can retain the trapped iodine up to seven days without any significant release which makes it a suitable material suitable for storing iodine. The presence of O and N heteroatoms (imine linkages) also enable POP_DH to remove iodine species dissolved in water. Herein, the iodine removal is quite rapid with about 99 % of iodine captured within 2 h. POP_DH samples can be recycled up to five rounds for iodine capture with only small changes in performance as an adsorbent. Overall, POP_DH is promising material for capture of radioactive iodine from various media.