Energy Sector Research Articles

Integrated geological quality evaluation using advanced wireline logs and core data: a case study on high clay shale oil reservoir in China

Abstract In the unconventional energy sector, shale oil is increasingly recognized as a crucial supplement to production. In recent years, China has made significant advancements in the development of shale oil. A unique type of pure shale reservoir in the Songliao basin, distinct from established shale oil reservoirs worldwide, has emerged as a focal point for exploration and research endeavors. This kind of shale exhibits enhanced heterogeneity, high clay content, intricate rock electricity relationship, posing significant challenges to formation evaluation. This study introduces conversion coefficients for the elements and minerals, a decision tree model, a variable T2 cutoff value method, and 2D NMR cluster analysis, all based on the integration of microimaging, spectroscopy, and 2D NMR logging. These novel approaches facilitate a comprehensive geological quality evaluation of key parameters, including mineral composition, lithology, effective porosity, total organic carbon content, fluid composition, and saturation characteristics within pure shale formations from the Songliao Basin. The established petrophysical model has been validated by core analysis data and successfully applied in exploration wells. Through systematic petrophysical analysis, the optimal landing point for horizontal wells was identified based on the identification of the best reservoir quality interval with high movable oil and oil saturation. The subsequent drilling of horizontal wells demonstrated the reliability of petrophysical evaluation in terms of oil production. This integrated evaluation method for pure shale holds reference significance for similar shale reservoirs in other blocks in the future.

A Novel Proposal in Wind Turbine Blade Failure Detection: An Integrated Approach to Energy Efficiency and Sustainability

This paper presents a novel methodology for detecting faults in wind turbine blades using computational learning techniques. The study evaluates two models: the first employs logistic regression, which outperformed neural networks, decision trees, and the naive Bayes method, demonstrating its effectiveness in identifying fault-related patterns. The second model leverages clustering and achieves superior performance in terms of precision and data segmentation. The results indicate that clustering may better capture the underlying data characteristics compared to supervised methods. The proposed methodology offers a new approach to early fault detection in wind turbine blades, highlighting the potential of integrating different computational learning techniques to enhance system reliability. The use of accessible tools like Orange Data Mining underscores the practical application of these advanced solutions within the wind energy sector. Future work will focus on combining these methods to improve detection accuracy further and extend the application of these techniques to other critical components in energy infrastructure.

Impact of Artificial Intelligence on the Planning and Operation of Distributed Energy Systems in Smart Grids

This review paper thoroughly explores the impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. With the rapid advancement of artificial intelligence techniques such as machine learning, optimization, and cognitive computing, new opportunities are emerging to enhance the efficiency and reliability of electrical grids. From demand and generation prediction to energy flow optimization and load management, artificial intelligence is playing a pivotal role in the transformation of energy infrastructure. This paper delves deeply into the latest advancements in specific artificial intelligence applications within the context of distributed energy systems, including the coordination of distributed energy resources, the integration of intermittent renewable energies, and the enhancement of demand response. Furthermore, it discusses the technical, economic, and regulatory challenges associated with the implementation of artificial intelligence-based solutions, as well as the ethical considerations related to automation and autonomous decision-making in the energy sector. This comprehensive analysis provides a detailed insight into how artificial intelligence is reshaping the planning and operation of smart grids and highlights future research and development areas that are crucial for achieving a more efficient, sustainable, and resilient electrical system.

Numerical Analysis of Hydrogen-Enriched Natural Gas on Combustion and Emission Characteristics

AbstractThe integration of hydrogen into natural gas infrastructure presents a viable strategy for mitigating greenhouse gas emissions and advancing toward carbon neutrality. This study investigates the combustion characteristics and emissions profiles of hydrogen-enriched natural gas mixtures, specifically focusing on the composition of Russian pipeline natural gas. A comprehensive mathematical model was developed to predict emission concentrations and simulate fuel mixture combustion using MATLAB Simulink software. This versatile model facilitates further analysis within the MATLAB ecosystem. The simulation results demonstrate a significant correlation between the hydrogen content in the natural gas mixture and the resulting heat power output. With a constant fuel consumption rate, a notable decrease in heat power was observed as the hydrogen concentration increased, reaching a maximum reduction of 44.9% at a 45% hydrogen content. These findings underscore the feasibility of partially substituting natural gas with hydrogen, while also highlighting the necessity for increased fuel flow rates to maintain equivalent power output levels. This poses additional challenges for natural gas grid operators, necessitating infrastructure adaptations to accommodate higher fuel demands. The insights gained from this research contribute to the growing body of knowledge surrounding hydrogen integration in the energy sector, offering valuable implications for decarbonization strategies and the optimization of natural gas infrastructure.

Development of Carbon Emission Disclosure Indicators in Indonesia and Analysis of Determining Factors

The first objective of this research is to develop indicators of carbon emission disclosure in Indonesia, while the second objective is to analyse the determinants of carbon emission disclosure. This research resulted in the development of carbon emission disclosure indicators. The development recommendations were derived from a review of scientific literature. The results of the recommendation are 14 theme indicators as a measure in carbon emissions disclosure and also become dependent variable of this research. Furthermore, this research analyses various determinants that affect carbon emission disclosure. There are four main groups of variables determined in this study, namely financial performance, ownership, committee, and board of directors. The scope of the research is 225 companies sample listed on the Indonesia Stock Exchange. The industry sectors that are the scope of the research are the energy sector, industrial sector, basic material sector, non-cyclical consumer sector, and transportation sector. The method used in this research is a combination of qualitative and quantitative. The development of carbon emission disclosure indicators was examined using qualitative methods through a literature review. While the determinant analysis uses quantitative methods with probability analysis (logistic regression analysis). The results showed that the group of financial performance variables (represented by leverage and market value variables), ownership (represented by International Listing and state ownership variables), committee (represented by environmental committee), and board of directors (represented by Board Diversity) on carbon emission disclosure in Indonesia have significant effects. The development of indicators and the results of this research have supported the Legitimacy theory as a basis that can influence company's carbon emissions disclosure. The novelty of carbon emissions disclosure indicator can be used as a reference for companies and regulators as a basis for complete carbon emissions disclosure.

Impactos Socioambientais em Assentamentos Urbanos Informais

Aim – Analyze, critically, the environmental licensing process of informal urban settlements, seeking to improve the instrument in a context of climate change. Methodology - This is a qualitative approach in which the documentary research method was used in environmental licensing processes of informal urban land subdivisions in the Federal District. Originality/relevance - There is a scarcity of studies investigating licensing in informal urban settlements. Considering that environmental licensing is required for activities that may cause socio-environmental damage and aims to mitigate or compensate for such damage, research that empirically evaluates the application of the instrument should be encouraged. Results - In general, environmental licensing in informal urban settlements has not been efficient, mitigation measures are not complied with, monitoring is unsatisfactory, there is a lack of indicators to measure urban environmental quality, and there is a lack of analysis of technological alternatives that consider climate projections and nature-based solutions. Theoretical/methodological contributions: Research on environmental licensing processes for informal urban settlements for low-income (ARIS) and high-income populations (ARINE) has contributed to complement the global literature, focused on investigating the mining and energy sectors. Comparative research on informal urban land subdivision in other socioeconomic contexts is recommended. Social and environmental contributions - Low-income settlements in ARIS are more vulnerable to socio-environmental risks and climate change than settlements in ARINE, requiring greater attention from managers to reduce this vulnerability.

The role of utilizing artificial intelligence and renewable energy in reaching sustainable development goals

Many nations want to use only renewable energy by 2050. Given the recent rapid expansion in RE use in the global energy mix and its progressive impact on the global energy sector, the evaluation and analysis of Renewable Energy's impact on achieving sustainable development goals is insufficient. Wind energy could be renewable. For smart grid supply-and-demand issues, wind power forecasting is critical. One of the biggest challenges of wind energy is its significant fluctuation and intermittent nature, which makes forecasting difficult. This study's goal is to develop data-driven models to predict wind speed and power. This paper uses Machine Learning (ML) and Deep Learning (DL) to improve a wind speed prediction and recommendation system for wind turbine power production using site climatological data. This system optimizes turbine use by selecting the right number of turbines to operate solely based on the required energy, which is related to wind power and speed, and recommends the best power station location to determine the best turbine run time. The Proposed Enhanced Recommendation System (PERS) includes the Wind Speed Prediction Module (WSPM), Wind Speed vs. Power Consumption Calculation (WSPC), and Recommendation Module (RM). Test results showed the suggested method works in run time. The XGBoost or Random Forest regressor predicted 15-day power output with 94 % accuracy and 6 % mean average percentage error.

How does Hamas–Israel war impact stock markets in the Middle East? Country and sector-level analysis

ABSTRACT This paper investigates the sensitivity of a set of Middle Eastern stock markets to the most recent armed confrontation between Hamas and Israel. The analysis reveals a discernible negative impact across almost all markets considered, with the Israeli stock market experiencing the most pronounced effect on the event day. Interestingly, the Israeli stock market appears to be relatively more efficient than those of neighbouring countries. At the sector level, among the negatively affected sectors, the energy sector experienced the most pronounced impact. However, some sectors, notably materials, industrials, and information technology, were relatively resilient to this geostrategic shock.

Energy Storage Integration for Renewable Energy Supply in Malaysia: A Review

Malaysia intends to increase the share of renewable energy (RE) in its installed power capacity from the current 23%, or 8.5 GW, to 40%, or 18.0 GW, by 2035. Renewable energy sources, specifically solar and biogas, are unpredictable and subject to sudden changes due to weather conditions and feedstock availability. Furthermore, their integration with the current conventional plants is complicated. In relation to this, deploying energy storage has become the main agenda under the Renewable Energy Roadmap to achieve 40% installed capacity by 2035. When selecting the most adequate energy storage system, the technological, environmental and economic aspects need to be considered. Therefore, the purpose of this article is to review the current state of energy storage and the renewable energy situation in Malaysia. The significance of this paper is that it encourages RE consumption in Malaysia in parallel with the government’s target due to the deployment of energy storage in the energy supply system. It can serve as a guideline for policymakers in their penetration of the renewable energy sector.

Strategic Management of Clean Energy Investments: Financial Performance Insights by Using BWM-based VIKOR and TOPSIS Methods

Market share of renewable energy companies increases with the increase in green-energy production. Companies increase their investments depending on the increase in energy production and can provide capital by offering the company to the public. Companies are traded in the exchange market in order to fund their increasing investments and access a large investor base. The increase in the market values of clean energy companies in recent years draws attention of investors. The study aims to identify key financial indicators that influence company growth and investment value by analyzing companies listed in the S&P North America and Europe Clean Energy Index. Therefore, the study evaluates the financial performance of clean energy companies using Best-Worst Method (BWM)-based VIKOR and TOPSIS methods to provide investors with a comprehensive assessment of investment opportunities in the clean energy sector. Also, the comparison of the two methods will contribute to the literature. The sample of this study consists of 10 big companies in S&P North America and Europe Clean Energy Index. Financial tables and exchange market data of the companies for the years 2019, 2020, and 2021 were used in this study. Remarkably, the analysis findings of the two methods differ from each other. The findings reveal significant insights into the financial health and growth potential of clean energy companies, offering valuable guidance for future.

Electricity Industry Strategies in Ecuador and Peru: Their Impacts on Energy Efficiency and Prices

Through an empirical analysis of the impact of determinants on energy efficiency and electricity prices using monthly data, we compare the outcomes of energy strategies in Ecuador and Peru from 2014 to 2023. OLS regression models with Newey-West matrices are applied in two contexts: Energy efficiency and energy prices. Energy efficiency is explained through variables such as the proportion of renewable energies, national income, and the international oil price. The results indicate that in Ecuador, the proportion of renewable energies is positively related to energy efficiency, whereas in Peru, energy efficiency mainly depends on the trend component capturing changes in productivity and the decline in fuel prices. Furthermore, electricity prices are sensitive to fluctuations in oil prices, especially in Peru, due to its dependence on thermoelectric generation. The energy strategies of both countries, including the integration of renewables and the management of fossil fuel costs, play crucial roles in determining energy efficiency and prices. This study provides a comprehensive view of how various economic and structural factors influence the performance of the energy sector in both nations, highlighting the differences in their approaches and outcomes.

Prediction of hydrogen production in proton exchange membrane water electrolysis via neural networks

Advancements in water electrolysis technologies are crucial for green hydrogen production. Proton exchange membrane water electrolysis (PEMWE) is characterized by its efficiency and environmental benefits. The prediction and optimization of hydrogen production rates (HPRs) in PEMWE systems is difficult and still challenging because of the complexity of the system as well as the operational parameters. The integration of artificial intelligence (AI) and machine learning (ML) appears to be effective in optimization within the energy sector. Hence, this work employs the artificial neural network (ANN) to develop a model that accurately predicts HPR in PEMWE setups. A novel approach is introduced by employing the Levenberg–Marquardt backpropagation (LMBP) algorithm for training the ANN. This model is designed to predict HPR based on critical operational parameters, including anode and cathode areas (mm2), cell voltage (V) and current (A), water flow rate (mL/min), power (W), and temperature (K). The optimized ANN configuration features an architecture with 7 input nodes, two hidden layers of 64 neurons each, and a single output node. The performance of the ANN model was evaluated against conventional regression models using key metrics: mean squared error (MSE), coefficient of determination (R2), and mean absolute error (MAE). The findings of this study reveal that the developed ANN model significantly outperforms traditional models, achieving an R2 value of 0.9989 and an MAE of 0.012. In comparison, random forest (R2 = 0.9795), linear regression (R2 = 0.9697), and support vector machines (R2 = − 0.4812) show lower predictive accuracy, underscoring the ANN model's superior performance. This work demonstrates the efficiency of the LMBP in enhancing hydrogen production forecasts and sets a foundation for future improvements in PEMWE efficiency. By enabling precise control and optimization of operational parameters, this study contributes to the broader goal of advancing green hydrogen production as a viable and scalable alternative to fossil fuels, offering both immediate and long-term benefits to sustainable energy initiatives.

Maturity assessment of grid-scale flexibility and energy storage services towards a decarbonized Europe

Background The study evaluates the maturity of grid-scale flexibility services, essential for Europe's transition to a decarbonized economy. Conducted under the Horizon-Europe SINNOGENES project, it focuses on energy storage technologies, regulatory frameworks, and stakeholder perspectives. Methods The methodology comprises three phases. First, significant grid-scale energy storage and flexibility technologies, including Battery Energy Storage Systems (BESS) and Vehicle-to-Grid (V2G) systems, were reviewed. Second, the regulatory framework for flexibility services at the EU transmission systems level was assessed, analyzing key EU directives and national regulations from Spain, Portugal, Germany, Cyprus, and Switzerland. Third, stakeholder perspectives were gathered through a survey of forty-four stakeholders in the European energy sector and bilateral interviews with companies and organizations in the energy sector. Results Technological advancements in grid-scale flexibility services have been identified, along with persistent regulatory challenges. The survey and interviews revealed a varied perception among stakeholders regarding the maturity and implementation of these services. A high demand for flexibility was noted, with 77% of stakeholders expressing a need for or active use of flexibility services. Electrochemical energy storage and demand response were highlighted as the most relevant technologies. However, high implementation costs, perceived insufficient benefits, and regulatory uncertainties were major barriers to widespread adoption. Conclusions The study concludes that despite notable technological progress and some regulatory advancements, several barriers still impede the full maturity and integration of grid-scale flexibility services in Europe. Addressing these regulatory and market challenges is crucial for achieving a fully functional and integrated flexibility service framework across the European grid. Harmonized regulations, financial incentives, and clear long-term pricing signals are essential to support the market.

Educational paradigm shift: assessing the prospects of a master's course in green energy transition

This study examines the integration of emerging engineering technologies into STEM education with a focus on the green energy transition. Using a mixed-methods approach, including an international student survey conducted via Google Forms, the research identifies key factors influencing the demand for an innovative Master's program in Green Energy. Quantitative analysis through JASP software reveals a significant positive correlation between student interest in renewable energy related MSc courses and the perception of improved job opportunities in the green energy sector. Furthermore, the survey results indicate that students prioritize the inclusion of Mechanical, Electrical, and IT skills in the curriculum. Qualitative feedback highlights the critical need for practical skills in new research areas such as nanotechnology, quantum chemistry, Carbon capture, and Solar/Wind-based energy sources to meet the evolving demands of the green energy industry. These findings suggest that academic programs must be restructured to align more closely with the skills required for sustainable energy systems, thereby bridging the gap between academic preparation and market readiness.

Does Privatization improve Corporate Governance and Profitability? Evidence from Cement Company of Northern Nigeria

The paper study how Privatization improves Corporate Governance and Profitability of Cement Company of Northern Nigeria. Data was collected from secondary sources, and the statistical tools employed in the Methodology were descriptive statistics and OLS regressions. The study aimed at bridging literature gap on studies that relate corporate governance and privatization policy in Nigeria. The results suggest that, The result suggest that, the company has lower market capitalization, private sector and foreign investors have concentrated ownership, bigger board size, lower percentage of executive directors and higher percentage of non-executive directors, Board chairmen were not the chief executives and chairmen audit committees were non-executive directors, lower workforce, higher percentage of management staff small percentage of non-management staff, lower profitability, products demand depended on government capital projects, The Company purchased 5MW power plant in 1991, macro-economic challenges such as devaluation of naira, energy sector crisis, foreign exchange rate, inflation, trade liberalization, inconsistent stabilization policies, political instability and banks’ strikes and weak private sector. Inferential result reveals that, has state ownership, percentage of management staff, percentage of non-management staff and privatization have positive and significant relationship with profitability. However, minority ownership has a negative and significant relationship with profitability. Based on these findings, the researcher arrived at the conclusion that, Corporate governance has significant impacts on profitability of Cement Company of Northern Nigerian. Even though, unfavourable macroeconomic environment militated against its efficiency. The study recommends that, government needs to introduce macroeconomic stabilization measures that will improve effective demand of cement products particularly with the problem of withdrawal of fuel subsidy in the economy. The company should, as a matter of necessity, design strategies that will; create international market opportunities, enhance security measures, cheap inputs, efficient financial and inventories management that will improve profitability post privatization.

Optimization of Marketing Strategy for State-Owned Energy Products through Sentiment Analysis with VADER and LSTM on Social Media

Sentiment analysis is also known as opinion mining. It has an important role in natural language processing and data mining. It involves extracting and analyzing subjective information from textual data to determine the sentiment. With the advancement of technology, it is increasingly important to understand users' opinions and sentiments regarding a particular product, service or issue. This research aims to optimize the marketing strategy of energy products in SOE subsidiaries through sentiment analysis using the VADER and LSTM methods on social media.The researcher analyzed data from twitter to identify consumer sentiment towards energy products. The results of the analysis show that the combination of VADER and LSTM is effective in identifying the nuances of sentiment, with the accuracy of sentiment classification reaching 85%. Based on the analysis, the researchers developed recommendations for marketing strategies that are more focused and responsive to public perception. This study also highlights the importance of two-way interaction between companies and consumers as part of an effective marketing strategy. The findings of this study are expected to be used as a reference by state-owned companies in the energy sector to improve the effectiveness of their marketing strategies through a sentiment analysis approach on social media.

Advancing thermoelectric potential: strontium telluride under compression strain analyzed with HSE hybrid functional and Wannier interpolation

In the present era, the energy sector is undergoing an intense transformation, which encourages numerous research efforts aimed at reducing and reusing energy waste. One of the main areas of focus is thermoelectric energy, where telluride compounds have attracted researchers due to their remarkable ability to convert thermal energy into electrical energy. We focused this study on finding out how well strontium telluride (SrTe) can be used to generate thermoelectric power by testing it under up to 10% compression strain. We have used advanced computational approaches to increase the accuracy of our results, specifically the HSE hybrid functional with the Wannier interpolation method. This method is primarily employed to analyze electronic properties; however, our research extends its utility to investigate thermoelectric characteristics. Our findings provide accurate predictions for both electronic and thermoelectric properties. The above method has successfully achieved a significant improvement of 58% in the electronic band gap value, resulting in a value of 2.83 eV, which closely matches the experimental results. Furthermore, the Figure of Merit 0.95 is obtained, which is close to the ideal range. Both the band gap value and the thermoelectric figure of merit decrease when the compression strain is increased. These findings emphasize the importance of using SrTe under specific conditions. The findings of this work provide motivation for future researchers to investigate the environmental changes in the thermoelectric potential of SrTe.

The EU De‐Risking of Energy Dependencies: Towards a New Clean Energy Geopolitical Order?

The mounting geopolitical tensions and rivalries between the world’s major economies transform the goals and instruments of domestic and external policies. Industrial strategies of leading global powers call for technological decoupling, strategic autonomy, and the de-risking of dependencies in critical value chains. Economic interdependencies become a liability and de-globalisation tendencies come to the fore. The energy sector is not exempted from these trends, leading even to the weaponisation of energy in some cases. In that vein, this article explores the character and directions of EU international energy engagement through the geoeconomic lens. Taking inspiration from literature on energy security and the geopolitics of energy transition, the article theorises the concept of de-risking in energy to investigate how the EU is positioning itself as a power while ensuring security and competitiveness. Looking at three illustrative examples of the energy transition—supply of natural gas, access to energy-critical minerals, and international hydrogen markets—the article shows that EU de-risking means not only diversifying suppliers but, most notably, constructing new economic, sustainable, and potentially long-lasting international relations. As a result, despite the deep geopoliticisation of energy and the new global “disorder,” the EU’s de-risking has the potential to reshape international relations by forging new partnerships or reconfiguring existing ones, thus establishing a new economic order driven by clean energy while offering new economic opportunities to create local value chains and decarbonise economies in third countries.

Optimising Flywheel Energy Storage Systems: The Critical Role of Taylor–Couette Flow in Reducing Windage Losses and Enhancing Heat Transfer

Amidst the growing demand for efficient and sustainable energy storage solutions, Flywheel Energy Storage Systems (FESSs) have garnered attention for their potential to meet modern energy needs. This study uses Computational Fluid Dynamics (CFD) simulations to investigate and optimise the aerodynamic performance of FESSs. Key parameters such as radius ratio, aspect ratio, and rotational velocity were analysed to understand their impact on windage losses and heat transfer. This study reveals the critical role of Taylor–Couette flow on the aerodynamic performance of FESSs. The formation of Taylor vortices within the airgap was examined, demonstrating their effect on temperature distribution and overall system performance. Through a detailed examination of the skin friction coefficient and Nusselt number under different conditions, this study identified a nonlinear relationship between rotor temperature and rotational speed, highlighting the accelerated temperature rise at higher speeds. The findings indicate that optimising these parameters can significantly enhance the efficiency of FESSs, reducing windage losses and improving heat transfer. This research provides valuable insights into the aerodynamic and thermal optimisation of FESSs, offering pathways to improve their design and performance. The results contribute to advancing guidelines for the effective implementation of FESSs in the energy sector, promoting more sustainable energy storage solutions.

How Local Pollution and Transboundary Air Pollution Impact Air Quality in Jakarta?

Jakarta’s rapid population growth and industrialization have severely impacted air quality, with limited monitoring stations and increasingly complex pollutant sources influenced by meteorological factors. Source apportionment studies are critical for addressing urban air quality issues because knowing the source of pollution is an important step in overcoming urban air quality problems. This study uses the Geographically Weighted Regression (GWR) and Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) models to analyze the distribution and sources of NO2, SO2, and CO in Jakarta and surrounding areas. Data includes spatial information from road networks, industries, and the energy sector during the wet (December) and dry (June) months of 2023. GWR results show that network density significantly contributes to pollutant concentrations, particularly during the dry month, while industrial and energy sectors display seasonal variations. The HYSPLIT model indicates that in December, air masses carrying pollutants mainly originate from the northwest and north, and in June, from the southeast and southwest. These patterns underscore the regional influence of transboundary air pollution from industrial and energy plants. The combined GWR and HYSPLIT findings provide a comprehensive understanding of air pollution dynamics and the critical role of meteorological factors in pollutant distribution.