Natural Gas Energy Research Articles

Renewable energy intensity and efficiency of fossil energy fuels in the nordics: How environmentally efficient is the energy mix?

In recent years, energy intensity-efficiency has improved in the Nordic states and largely among the European Union member states, thus accounting for the region's impressive performance in terms of environmental sustainability. Clearly, there is paucity of literature on the criticality of renewable energy efficiency/intensity as compared with disaggregated fossil fuel efficiency. To offer more novelty, environmental sustainability (de) merit of renewable energy intensity is compared with fossil (oil, natural gas, and coal) fuel efficiency in the panel of Nordic states over the period 1990–2020 by employing relevant econometric approaches such as the cross-sectional autoregressive distributed lag (CS-ARDL). Importantly, the result shows that a percentage increase in renewable energy intensity mitigates greenhouse gas (GHG) emissions by ∼0.45% against ∼0.29% by oil energy efficiency in the long run, thus suggesting that environmental performance of renewable energy intensity outweighs the efficient use of oil fuel. Additionally, renewable energy intensity also outperforms efficient use of natural gas and coal energy by ∼0.51%–∼0.37% and ∼0.43%–∼0.17% respectively. However, economic growth and environmental-related innovations spur GHG emissions, implying that the Nordic countries are decoupling economic performance from environmental setbacks amidst structural shift via technological innovations. The results are considered formidable for the specificity of energy mix policy especially in Denmark, Finland, Norway, and Sweden.

Power enhancement in PV arrays under partial shaded conditions with different array configuration

Solar Photovoltaic systems are used for electrical power generation, and they provide an alternative source to non-renewable energy sources like coal, oil, natural gas and nuclear energy. Photovoltaic arrays used in PV systems may be subjected to partial shading conditions, thereby affecting power generation because of higher power mismatch losses. Due to an uneven distribution of irradiation condition, some of the bypass diodes turned on and affect the power generation in a photovoltaic array. The mismatch losses are due to the output from PV panels subjected to different irradiations because of non-uniform partial shading conditions. The power loss can be reduced by uniformly distributing the partially shaded condition over the entire PV array. In this work different shaped 4 × 4 array configuration is proposed to overcome the effect of partial shading condition, thereby providing lower mismatch losses. Simulations under different partial shading conditions are carried out using MATLAB Simulink, and the experimental setupis carried out for the proposed array configuration for 4 × 4 PV array and the results are discussed.

Investigating the Frontiers of Deep Learning and Machine Learning: A Comprehensive Overview of Key Challenges in Missing Well Log Estimation

Well logging is a significant method of geological formation description and resource assessment in the exploration and development of oil, natural gas, minerals, groundwater, and sub-surface thermal energy, as well as geotechnical engineering and environmental research. However, the challenging problem of estimating well logging data always exists because well logs can only be measured through a drilling process involving costly and time-consuming field trials. This study provides a brief overview of Deep Learning (DL) models and examines the well-logging issues in estimating missing well-log data. In addition, it discusses a literature review focusing on the application of DL models for well-log estimation. The outcome of this exploratory work necessitates suitable requirements for the design and implementation.

Procedure for conformity assessment of automated systems for control and accounting of natural gas energy

The paper analyses the regulatory and technical requirements for the construction of automated systems for control and accounting of natural gas energy. Typical diagrams of gas energy accounting systems (gas measuring stations) are presented. Existing metrological requirements for accuracy classes in regulatory documents for the specified systems are provided. The approaches to conduct testing during the conformity assessment are considered. A procedure for conducting testing during the conformity assessment of the gas energy accounting system using a modular approach has been developed. The expediency of applying accuracy classes A, B, C and D according to DSTU EN 1776 during the conformity assessment is substantiated. The stages of testing and inspections of the installation of pressure, pressure drop, temperature, meter, flow chromatograph and energy calculator sensors are described. The necessity of using calibrated gas mixtures, the component composition of which corresponds to the transported gas, for testing the chromatograph is argued. The calculation of the maximum permissible error of the automated system for control and accounting of natural gas energy for two ranges of the gas volume flow, depending on the gas meter, is presented. The calculation under the condition of creating a new gas energy accounting system and supplementing the existing gas volume accounting system with a flow chromatograph are considered. The calculation of the error of the automated system for control and accounting of natural gas energy using the method of variable differential pressure is also presented.

Investigation of the Hydrogen Gas Production by Using Solar Energy

Hydrogen is an energy source that offers both environmental benefits and reliability, making it a promising solution for promoting sustainable growth in various sectors worldwide. As businesses and societies increasingly seek sustainable alternatives, hydrogen has emerged as a key player in the transition to a low-carbon economy based on renewable energy sources. Consequently, hydrogen production holds immense potential for replacing traditional energy sources such as petroleum, natural gas, and non-renewable energy. Various methods can be employed for hydrogen production, including fossil fuel-based processes, nuclear energy, and renewable energy sources. To achieve the goal of zero emissions, the focus has shifted towards utilizing renewable energy, specifically through photovoltaic electrolysis, to generate hydrogen gas. Photovoltaic electrolysis employs solar energy to split water molecules into hydrogen and oxygen. However, the efficiency of this method is currently quite low. Temperature plays a crucial role in increasing the efficiency of water electrolysis, making it an important factor to consider in enhancing hydrogen gas production through photovoltaic electrolysis. Therefore, there is significant value in investigating the impact of temperature on the efficiency of hydrogen gas production in photovoltaic electrolysis. In summary, hydrogen energy production has emerged as a promising solution for sustainable growth, and renewable energy sources such as photovoltaic electrolysis hold great potential for achieving zero emissions. Exploring the influence of temperature on the efficiency of hydrogen gas production in photovoltaic electrolysis is of considerable interest, aiming to improve the overall process and enhance the viability of hydrogen as a sustainable energy source.

Современное состояние глобального рынка сжиженного природного газа и перспективы реализации российских арктических СПГ-проектов

Transitioning from coal to natural gas and renewable energy sources has become a global trend over the past decade. Despite efforts made by the EU, USA, Japan, and several other nations to replace fossil fuels with alternative sources, significant success in this domain remains elusive, with hydrocarbons continuing to dominate the energy landscape. Notably, the share of liquefied natural gas (LNG), once perceived as a weak alternative to pipeline gas, constituted 56 % of the global gas exports by the end of 2022, with the number of consumer countries reaching 47. This study aims to assess the potential capabilities of key LNG suppliers in increasing production and export volumes to meet the growing demand in the global LNG market. A primary focus is placed on evaluating the capabilities and plans of Russian companies to execute large-scale LNG projects. The research employs methods of systems and statistical analysis, along with economic forecasting. The article discusses the key trends in the global LNG market's development and identifies the factors exerting the most significant impact on its trajectory. An analysis of the current long-term program supporting LNG production in Russia is conducted, highlighting the key support measures available to domestic companies. The practical significance of this study lies in identifying countries poised to increase their production volumes and enter the LNG production and export arena within six to seven years. The research pinpoints key large-scale domestic LNG projects set to make the Russian LNG share in the world market grow from 8 % to 15–17 %. Subsequent steps of this research will involve assessing the prospects for selling Russian and American LNG in the Asia-Pacific (APAC) market.

Resources extraction and access to clean energy a curse or blessing for the economy? Middle-income economies case study

The study evaluates the long-lasting and debatable issue of the resource-growth nexus, which unearths whether natural resources have a curse or blessing on economic growth. Hence, this research revisits the nexus of natural resources and economic growth in middle-income economies with more relevant and effective variables: energy efficiency and access to clean fuel technology from 1988 to 2021. The study leveraged various time series methods, including the ADF-GLS stationary analysis to govern the stationary sequence and the Bayer-Hanck cointegration test for long-term cointegration. The primary methods include FMOLS, DOLS, and CCR, which reveal long-term elasticities between dependent and independent variables. Both non-parametric quantile and parametric robust regression are used for robustness checks. The results reveal that time series were found static D (1) and long-term cointegration occurs between economic growth and its regressors: mineral rents, natural gas rents, energy efficiency, and access to clean fuel technology. The empirical findings exhibit that natural gas and mineral rents show an encouraging influence on the economy's growth in middle-income economies, confirming the blessings of these resources. Moreover, energy efficiency and access to clean fuel technology allow effective use of natural resource rents and endorse supportable economic growth. The robustness check scrutiny confirms the results via robust regression, while the robustness test of quantile regression displays asymmetries in the mineral rents and natural gas rents across quantiles. These results indicate several policy implications to policymakers regarding the use of energy efficiency and access to clean fuel technologies should be increased and encouraged to allow the effective use of natural resources for sustainable expansion of the economy.

Electrification of oil refineries through multi-objective multi-period graph-theoretical planning: A crude distillation unit case study

Electrification using renewable energy sources is the key to paving a sustainable and cleaner future for the oil and gas sector, which is known to be a significant carbon dioxide emitter. Nevertheless, the suitability of the electrification designs heavily depends on the seasonal availability of renewable energy sources. This work proposes to use a multi-period graph-theoretical (P-graph) approach to determine the optimal retrofit strategy to achieve electrification with consideration of economic and environmental factors. Both single-period and multi-period models are considered via a graph-theoretical approach to rank and evaluate all the combinatorically feasible electrification pathways based on the overall performance. The effectiveness of the proposed method is developed using a crude distillation unit (CDU) case study adopted from a multinational company. The effectiveness of the proposed method is illustrated using a crude distillation unit (CDU) case study shown in three different scenarios that include prioritizing economic aspect (Scenario 1), prioritizing environmental aspect (Scenario 2), and considering equal importance of both aspects (Scenario 3). For single-period operation, the results showed a mix of natural gas and hydropower energy, exclusive use of onshore wind energy, and a mix of onshore wind energy and biogas cogeneration energy for Scenario 1, Scenario 2, and Scenario 3, respectively. In contrast, the multi-period model also utilized nuclear energy for Scenario 2 and Scenario 3 given the seasonal availability constraint. Following that, a sensitivity analysis is conducted to see the effect of the absence of the most influential energy sources on the optimal solution of each scenario and the top solutions under budget and CO2 emission constraints. Pareto analysis is outlined to offer an understanding of tradeoffs between differently prioritized solutions that decision-makers can select. The combination of the proposed analysis provides a systemic approach towards transforming traditional industries towards a cleaner future via electrification.

Characteristics of cavitating flows in cryogenic pump units in a combined cycle power system based on liquefied natural gas and solar energy

The cavitation of cryogenic pump units is an important cause for the low cold energy recovery efficiency and low utilization efficiency of solar energy in combined cycle power systems based on liquefied natural gas (LNG) and solar energy. Through numerical simulation of cavitation in cryogenic pump units, the research established the correlation mechanism between cavitation evolution and energy characteristics. By using the transport-based cavitation model considering the thermomechanical effect of cryogenic media, a novel numerical simulation model for two-phase flows during cryogenic cavitation was developed. In addition, the numerical model of cavitation was verified to be reliable. The cavitation performance of the two-stage LNG submerged pump in cryogenic pump units was studied under multiple conditions. In addition, the evolution of cavitating flow regimes under conditions of different flow rates and its influences on flow patterns of various flow passage components were investigated by combining analysis on temperature changes and flow fields. The entropy production diagnostic model (EPDM) was also adopted to discuss the distribution of the irreversible entropy production rate (EPR) in the evolution process of cavitation. Results show that the interfacial EPR that characterizes the phase-transition intensity after inception of cavitation exerts significant influences. Analysis of influencing mechanisms of changes in temperature, changes in flow fields, and distribution characteristics of energy consumption can provide a theoretical basis for further optimization.

Energy price bubbles and extreme price movements: Evidence from China's coal market

This study investigates the factors behind the extreme price movements in China's coal market, with a particular focus on the impact of climate risk and energy transition in recent years. The Generalized Sup Augmented Dickey-Fuller (GSADF) method is employed to detect coal price bubbles, and a dynamic model averaging (DMA) approach is then used to analyze the causes of these price bubbles. The findings reveal that price bubbles in the Chinese coal market are mainly triggered by fluctuations in international energy prices. The extreme prices are rooted in supply-demand imbalances resulting from energy transition, economic development, and geopolitical conflicts. Policies aimed at adjusting coal supplies can effectively mitigate abnormal coal price fluctuations in China, while normal coal price fluctuations are significantly influenced by changes in energy demand driven by macroeconomic development. During the green transition towards renewable energy, the current high prices of fossil energy present challenges to China's energy supply security but also offer opportunities for the development of the renewable energy market. However, energy transition has facilitated the spread of price bubbles across coal, natural gas, and renewable energy markets, potentially leading to contagion effects.

Explaining the environmental efficiency capability of energy mix innovation among the Nordic countries

Improving the environmental efficiency performance of energy mix is important to the global carbon neutrality goal. Given this motivation, this study delves into providing more explanation about the role of innovation approaches to coal, oil, and natural gas energy resources in the Nordic economies i.e., Denmark, Finland, Norway, and Sweden during the period 2000–2019. To achieve this objective and reach an evidence-based insight for policy formulation, the present study leveraged robust panel econometrics estimators that circumvent cross-sectional dependence and heterogeneity issues in the investigated blocs. The empirical evidence shows that economic growth (GDP), coal energy, oil and natural gas contribute to the Nordic economies' environmental degradation in the short- and long-run. Contrarily, innovation via environmental-related patents mitigates environmental degradation, especially in the long run. Furthermore, the interaction models, i.e., applying innovative approaches in the energy mix's consumption, can mitigate carbon emissions across the countries, thus significantly improving environmental sustainability. Specifically, innovative approaches in coal, oil, and natural gas consumption improve environmental sustainability by mitigating carbon emissions, thus delivering respective environmental efficiency improvements of ∼94%, ∼108%, and ∼123%. Additionally, Granger causality evidence further suggests that the examined factors are significant drivers of the environmental indicator. Regarding policy insight, this study affirms that further improvement in energy savings is vital to continuously deploying the conventional energy mix.

Optimal operation of coordinated multi-carrier energy hubs for integrated electricity and gas networks

"In the field of energy supply, coordinated multi-carrier systems, which include natural gas and electric energy, offer unique opportunities to enhance energy efficiency and flexibility. Nevertheless, the interdependence between electricity and natural gas networks poses various challenges related to the flow of electricity and gas in feeders, pipes, and connection points. To address these challenges, the concept of an energy hub emerges as an essential component in multi-carrier energy systems, serving as a connection point between the electricity grid and gas grids. This study introduces an optimal operation method for coordinating gas and electricity distribution networks through interconnected energy hubs. The infrastructure of the proposed energy hub comprises a combined heat and power unit, a boiler, an electric energy storage system, a heat pump, and a gas-burning unit, effectively meeting heating and electrical load requirements. The model formulation follows a stochastic approach based on a two-stage scenario. Considering uncertainties related to wind energy, electric load, and real-time energy prices, the primary objective of this study is to minimize the total operating cost of the energy system. The optimization problem is solved using Wall's optimization algorithm. To meet its energy requirements, the integrated energy system presented in this paper actively participates in the real and daily electric energy markets, as well as the natural gas market, providing the necessary energy resources. Additionally, the model incorporates realistic descriptions of electric power and gas flow in feeders and gas pipelines, considering AC load distribution and the Weymouth equation. With consideration of connectivity constraints, the proposed model offers a comprehensive representation of the integrated electricity and gas networks. The simulations conducted on the integrated energy system, comprising a 33-bus electric network and a 6-node gas network with interconnected energy hubs, highlight a remarkable 12 % decrease in electricity purchase costs and a substantial 18 % reduction in natural gas procurement expenses, underscoring the cost-efficiency and effectiveness of the stochastic planning approach.

Welfare Level of Population in the Context of Prices Evolution in the Republic of Moldova

Recent evolutions in the prices of energy resources and consumer goods have caused major pressures on household incomes. Thus, the share of expenses for food products and the payment of bills for consumed energy resources constitute more than half of the families’ budget in the Republic of Moldova. The real expenses for the mentioned goods and services would have been substantially higher if the state institutions had not intervened in the support of the population by: capping the commercial addition to socially important products, establishing reduced rates of Value Added Tax, granting compensations when paying invoices for the use natural gas, electric and thermal energy. Through this policy, the state aims to minimize the impact of inflation on real incomes and, respectively, on the purchasing power and the level of well-being of the population.

Positive Spillovers from Infrastructure Investment: How Pipeline Expansions Encourage Fuel Switching

Abstract This paper studies the role of the U.S. pipeline infrastructure in the country's transition from coal to natural gas energy. I leverage the Environmental Protection Agency's Mercury and Air Toxics Standards as a plausibly exogenous intervention, which encouraged many coal plants to convert to natural gas. Combining this quasi-experimental variation with a plant's preexisting proximity to the pipeline network, I isolate implied pipeline connection costs within a dynamic discrete choice model of plant conversions. Key model results indicate that infrastructure-related costs prevent $9 billion in emissions reductions from taking place, suggesting a $2.4 million per mile external benefit of pipeline expansions.

Thermodynamic performance analysis of a novel integrated energy cascade system of liquid air energy storage and two-stage organic Rankine cycles

Liquid air energy storage can enhance the absorptive capacity for renewable energy due to its high energy storage density and extensive application scenarios. This paper proposes an integrated cascade energy system including liquid air energy storage, two-stage organic Rankine cycle, organic Rankine cycle, liquid natural gas regasification and absorption heat pump/chiller to use waste heat and liquid natural gas's cold energy fully and improve the round-trip efficiency. The waste heat of liquid air energy storage and the cold energy of liquid natural gas could increase the temperature and condense the working fluid of organic Rankine cycles. The system's thermodynamic model in design and off-design conditions are established. The charge and discharge minimum loads of liquid air energy storage are 82.5 % and 33.5 %, respectively. The round-trip efficiency of liquid air energy storage obtains a maximum of 49.6 % and a minimum of 29 % in the load ranges. Based on the load changes, the effects on the organic Rankine cycle are discussed, and four control strategies are proposed to absorb the waste heat and cold energy efficiently under off-design conditions. Besides, the organic Rankine cycles are affected by the mass flow of liquid natural gas and split ratio, which are discussed, and its underlying causes are analyzed. The results show that the integrated cascade energy system's round-trip efficiency reaches a maximum of 80.3 % in design conditions and a minimum of 62.7 % in off-design conditions, increasing by 61.9 % and 116.2 %, respectively.

Modeling the impacts of changes in nuclear energy, natural gas, and coal in the environment through the novel DARDL approach

There is no doubt that energy consumption and production are the primary sources of greenhouse gases that contribute to climate change and global warming. It will be extremely helpful to develop policies to reduce carbon dioxide emissions by analyzing and estimating the factors responsible for these harmful gases. Against this background, the purpose of this study is to perform an analysis of energy services at a disaggregated level in the environment using a novel methodology called Dynamic Autoregressive Distributed Lag (DARDL) for Pakistan utilizing time series data from 1975 to 2021. Further robustness check is performed by Kernel-based regularized least squares (KRLS). Results show that (i) energy indicators and CO2 emissions are cointegrated; (ii) there is a statistically significant effect of nuclear, hydro, natural gas, oil, and coal energy use on CO2 emissions; (iii) nuclear energy reduces CO2 emissions; (iv) Nuclear energy dwindles CO2 emissions to a great extent when a positive shock is applied to it, even if it is 300%; (v) The use of hydro energy also declines CO2 emissions; (vi) Further, CO2 emissions increases dramatically with positive shocks to coal, even if they are 25 %; and, the robustness of the results is verified through KRLS method. This implies that energy consumption is a significant determinant of CO2 emissions in Pakistan, so efforts to reduce CO2 emissions should be considered in order to ensure a sustainable future.

Heat source replacement strategy using catalytic infrared: A future for energy saving drying of fruits and vegetables.

Drying is an important process for fruits and vegetables, which requires a lot of heat and the heat sources are mainly coal, electricity, natural gas, and solar energy. Most of the heat is usually wasted due to the long drying process and poor transfer efficiency. The use of coal also pollutes the environment. The national electricity curtailment policy regulates the drying industry. Therefore, the fruits and vegetables drying industry is facing new challenges due to its own development needs and external factors. Catalytic infrared drying (CIR) technology brings solutions to these problems. Compared with other drying technologies, CIR has a high drying efficiency and can effectively reduce the use of electric energy, avoid waste, and minimize pollution of water. However, improper processing conditions still cause quality deficits such as severe browning, and the drying is difficult due to weak infrared penetration. Although CIR has shortcomings, it is still expected to establish an energy-saving and efficient fruit and vegetable drying system.

Risk path prediction and early warning method of LNG unloading system based on improved kinetic model

The LNG unloading system is an important transit station for LNG storage, processing and outgoing transmission. The main processes include ship unloading, storage, gasification and outgoing transmission, BOG condensation, flare venting and other processes, which involve intensive personnel operation and safety management activities. Once LNG is leaked, frostbite will happen to personnel and fire and explosion will occur when encountering ignition sources, which will cause serious consequences that are difficult to bear. The risk path of the static safety evaluation method is based on qualitative judgment, and the dynamic evolution direction of the LNG unloading system risk factors is not accurately predicted, so how to accurately predict and warn the risk path during the risk evolution is a key issue. In this paper, based on the improved system dynamics model, a propagation method with risk entropy as the information carrier is established to identify two different types of dynamic risk nodes, energy diffusion and energy conduction. Compared with the traditional dynamics model, the prediction accuracy of dynamic risk nodes of the improved model is increased by 22%, 20% and 14% for the unloading process, storage process and loading and transfer process, respectively. The prediction of the path of energy-conducting type risk evolution direction is increased by 29.3%, 32.4% and 30%, respectively. The simulation results are beneficial to the dynamic risk control and accident prevention of LNG unloading system, and provide new ideas for natural gas energy safety and security.

Urban Building Energy Modeling with Parameterized Geometry and Detailed Thermal Zones for Complex Building Types

Urban building energy modeling (UBEM) has attracted wide attention to the requirement for global carbon emission reduction. This paper presents a UBEM tool, AutoBPS-Param, to generate building energy models (BEMs) with parameterized geometry and detailed thermal zones, especially for complex building types, considering the shading effect from surrounding buildings simultaneously. Three building number scales and four scenarios were analyzed in the hotel-related buildings in Changsha, China. For the prototype modeling of Scenario 1, eighteen prototype building energy models for six building types in three vintages were created, and their simulation results were aggregated based on their representative floor areas. For AutoBPS-Param of Scenario 4, the method created one EnergyPlus (Version: 9.3.0) model for each building. The geometry of the prototype model was scaled and modified based on the target building’s length, width, and number of stories. The surrounding buildings were also added to the AutoBPS-Param simulation to better capture the urban dynamic impact. The results showed that the annual electricity and natural gas energy use intensity (EUI) of the pre-2005 HotelOffice prototype model was 172.25 and 140.45 kWh/m2. In contrast, with the AutoBPS-Param method, the annual electricity EUIs of 71 HotelOffice buildings constructed before 2005 ranged from 159.51 to 213.58 kWh/m2 with an average of 173.14 kWh/m2, and the annual gas EUIs ranged from 68.02 to 229.12 kWh/m2 with an average of 108.89 kWh/m2. The proposed method can better capture the diversity of urban building energy consumption.

Prediction of RCCI combustion fueled with CNG and algal biodiesel to sustain efficient diesel engines using machine learning techniques

This study used microalgae biodiesel as a high-reactive fuel directly injected along with various Compressed Natural Gas (CNG) energy shares (10, 20, 30, and 40%) as low-reactive fuel injected into the intake system. The experiments are performed in a single-cylinder, water-cooled, 1500 rpm, 3.5 kW power Compression Ignition (CI) engine under various loading conditions to examine the effects of CNG energy share on performance and emissions in Reactivity Controlled Compression Ignition (RCCI) combustion mode. The study found that the 30%CNG share decreased Nitrogen oxides (NOx) and smoke by 25 and 31%, as well as an increase in thermal efficiency of 4.35% in comparison to traditional biodiesel combustion. Finally, two machine learning (ML) models, namely the Gradient Boosting Regressor (GBR) and LASSO (Least Absolute Shrinkage and Selection Operator) Regression, were developed for predicting the dependent variables individually from the independent variables. Both the LASSO and GBR models achieved high accuracy with R2 values of 0.98–0.99 and relatively low Root Mean Square Error (RMSE) values.