Natural Gas Demand Research Articles

Assessment of deep learning and classical statistical methods on forecasting hourly natural gas demand at multiple sites in Spain

Prediction of natural gas demand can help to better manage energy demand and supply and recent developments of deep learning methods make it possible to improve forecast performance. This study examined the feasibility of hourly natural gas demand forecast, and compared statistical and deep learning methods to evaluate their prediction performance at five different sites in Spain. Hourly forecast can achieve an adjusted R2 ∼0.99 and MAPE lower to 2.7%. SMLR yields high prediction accuracy (MAPE: 3%–10%) in four sites, but suffers issues of missing data (5%–17%) and has relatively more extreme predictions (653 observations; ±100% away from the values). MLP has less amount of extreme predictions (517 observations) with a similar accuracy (MAPE: 3%–11%), but still suffers issue of missing data (11%–28%). LSTMs also achieves good prediction accuracy (MAPE: 3%–13%) and is able to manage most extreme values. Other methods are generally less promising but can be site-specific. Understanding their distinctive characteristics can help decision-makers to rule better decisions. Exploration of future forecasts based on LSTMs shows promising (adjusted R2: 0.90–0.99; MAPE: 11%–32%) in near future (<7 h), while model optimization can be used to further improve the performance, especially for a longer gap.

A Surrogate Weather Generator for Estimating Natural Gas Design Day Conditions

Natural gas customers rely upon utilities to provide gas for heating in the coldest parts of winter. Heating capacity is expensive, so utilities and end users (represented by commissions) must agree on the coldest day on which a utility is expected to meet demand. The return period of such a day is long relative to the amount of weather data that are typically available. This paper develops a weather resampling method called the Surrogate Weather Resampler, which creates a large dataset to support analysis of extremely infrequent events. While most current methods for generating weather data are based on simulation, this method resamples the deviations from typical weather. The paper also shows how extreme temperatures are strongly correlated to the demand for natural gas. The Surrogate Weather Resampler was compared in-sample and out-of-sample to the WeaGETS weather generator using both the Kolmogorov–Smirnov test and an exceedance-based test for cold weather generation. A naïve benchmark was also examined. These methods studied weather data from the National Oceanic and Atmospheric Administration and AccuWeather. Weather data were collected for 33 weather stations across North America, with 69 years of data from each weather station. We show that the Surrogate Weather Resampler can reproduce the cold tail of distribution better than the naïve benchmark and WeaGETS.

Natural Gas Demand Response Pilot Program (DOE)

Federal Grants & ContractsVolume 45, Issue 23 p. 4-5 Grants alerts Natural Gas Demand Response Pilot Program (DOE) First published: 01 November 2021 https://doi.org/10.1002/fgc.32032Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume45, Issue234 November 2021Pages 4-5 RelatedInformation

Data-driven short-term natural gas demand forecasting with machine learning techniques

Natural gas demand forecasting is one of the most crucial steps in the proper planning and operation of natural gas supply systems. The demand and supply of natural gas must be balanced at all times.Large error in forecasts of natural gas demand can cost Local Distribution Companies (LDCs) millions of dollars. In this study, techniques for accurate forecasting of natural gas demand are examined. The models are tested and validated on real data from nPower forecasting competition 2018, which consists of historical natural gas consumption and the corresponding weather forecast at 6-h intervals. The methodology presents a holistic approach that includes data pre-processing, feature engineering, feature selection, model development, and post-processing. To capture the intra-day variability in natural gas demand a block-wise approach is used to develop the forecasting models. In this approach, a separate model is developed for each block of the day. Subsequently, four different forecasting models are developed using the block-wise technique, namely, a block-wise gradient boosting model using features from sensitivity analysis (GB), a block-wise gradient boosting model using features from PCA (GB-PCA), a block-wise ANN-CG model using features from sensitivity analysis (ANN-CG) and a block-wise ANN-CG model using features from PCA (ANN-CG-PCA). Three hybrid forecasts are also developed by combining the forecasts from the four individual models. The results show that the combined models outperform the individual models, with an improvement of around 15% in terms of Mean Absolute Percentage Error (MAPE).

Potential role of natural gas infrastructure in China to supply low-carbon gases during 2020–2050

As natural gas (NG) demand increases in China, the question arises how the NG infrastructure fit into a low greenhouse gas (GHG) emissions future towards 2050. Herein, the potential role of the NG infrastructure in supplying low-carbon gases during 2020–2050 for China at a provincial resolution was analyzed for different scenarios. In total, four low-carbon gases were considered in this study: biomethane, bio-synthetic methane, hydrogen, and low-carbon synthetic methane. The results show that the total potential of low-carbon gas production can increase from 1.21 EJ to 5.25 EJ during 2020–2050, which can replace 20%–67% of the imported gas. In particular, Yunnan and Inner Mongolia contribute 17% of China’s low-carbon gas production. As the deployment of NG infrastructure can be very different, three scenarios replacing imported pipeline NG were found to reduce the expansion of gas infrastructure by 35%–42%, while the three scenarios replacing LNG imports were found to increase infrastructure expansion by 31%–53%, as compared to the base case. The cumulative avoided GHG emissions for the 6 analyzed scenarios were 6.0–8.3 Gt CO2. The GHG avoidance costs were highly influenced by the NG price. This study shows that the NG infrastructure has the potential to supply low-carbon gases in China, thereby significantly reducing GHG emissions and increasing both China’s short- and long-term gas supply independence.

Optimization of injection-withdrawal schedules for underground gas storage in a multi-block depleted gas reservoir considering operation stability

ABSTRACT Underground gas storage (UGS) is an important facility to overcome the imbalance between natural gas supply and demand. In this paper, an optimization model of injection-withdrawal scheduling for UGS in a depleted gas reservoir is established to find the optimal operating state of the gas storage facility. Faults are widely developed in reservoir, which can divide a full reservoir into several pressure disconnected reservoir blocks (RB). Considering that the unbalanced pressure distribution of reservoir will significantly affect the stable operation of UGS, the optimization model aims to minimize the deviation degree of pressure between RBs under the condition of satisfying the gas injection-withdrawal requirements. The decision variables are the number of operating wells and the flow rate of a single well of each RB. A series of equality and inequality constraints are developed, including maximum inventory of RB, maximum pressure of RB and maximum flow rate of a single well. To verify the validity of the proposed method, the optimization model is applied to an actual UGS in a depleted gas reservoir in China. The GAMS modeling system and DICOPT solver are adopted to solve the optimization problem. The results show that the deviation degree of pressure between RBs of the optimized scheme is about 75% lower than that of the empirical scheme. In the empirical scheme, there is an extremely high-pressure RB with a maximum pressure of 43.98 MPa, which exceeds the pressure limit of 5.38 MPa. However, all RBs meet the pressure requirement in the optimized scheme. Overall, the optimized scheme can effectively reduce the deviation degree of pressure between RBs and avoid the occurrence of extremely high-pressure RB.

Commercial Energy Demand Forecasting in Bangladesh

Although both aggregate and per capita energy consumption in Bangladesh is increasing rapidly, its per capita consumption is still one of the lowest in the world. Bangladesh gradually shifted from petroleum-based energy to domestically sourced natural-gas-based energy sources, which are predicted to run out within next two decades. The present study first identified the determinants of aggregate commercial energy and its three major components of oil, natural gas, and coal demand for Bangladesh using a simultaneous equations framework on an annual database covering a period of 47 years (1972–2018). Next, the study forecast future demand for aggregate commercial energy and its three major components for the period of 2019–2038 under the business-as-usual and ongoing COVID-19 pandemic scenarios with some assumptions. As part of a sensitivity analysis, based on past trends, we also hypothesized four alternative GDP and population growth scenarios and forecast corresponding changes in total energy demand forecast. The results revealed that while GDP and lagged energy demand are the major drivers of energy demand in the country, we did not see strong effects of own- and cross-price elasticities of energy sources, which we attributed to three reasons: subsidized low energy prices, time and cost required to switch between different energy-mix technologies, and suppressed energy demand. The aggregate energy demand is expected to increase by 400% by the end of the forecasting period in 2038 from its existing level in 2018 under the business-as-usual scenario, whereas the effect of COVID-19 could suppress it down to 300%. Under the business-as-usual scenario, the highest increase will occur for coal (3.94-fold), followed by gas (2.64-fold) and oil (2.37-fold). The COVID-19 pandemic will suppress the future demand of all energy sources at variable rates. The ex ante forecasting errors were small, varying within the range of 3.6–3.7% of forecast values. Sensitivity analysis of changes in GDP and population growth rates showed that forecast total energy demand will increase gradually from 3.58% in 2019 to 8.79% by 2038 from original forecast values. Policy recommendations include capacity building of commercial energy sources while ensuring the safety and sustainability of newly proposed coal and nuclear power installations, removing inefficiency of production and distribution of energy and its services, shifting towards renewable and green energy sources (e.g., solar power), and redesigning subsidy policies with market-based approaches.

Research on optimal dispatch method of virtual power plant considering various energy complementary and energy low carbonization

Virtual power plant (VPP), as a new generation of intelligent control technology integrating multi-link resources of “source-load-storage”, can break regional restrictions and realize energy interconnection and sharing in wide area. In order to utilize multi-energy synthetically and efficiently and promote low carbon energy development, the natural gas production and consumption system is built and reasonably regulated, which is composed of carbon capture unit, power to gas (P2G), manure treatment system (MTS), combined heat and power (CHP) unit and gas fired boiler (GB). P2G uses the captured CO2 as raw material to meet the natural gas demand of CHP and GB together with MTS. In order to deal with the difference of “source load” resources of VPP in the same region and solve the problem of power real-time balance caused by the fluctuation of distributed renewable energy (DRE), the multi-system and multi-unit coordinated operation framework is constructed. Based on the shiftable load characteristics of carbon capture power consumption and flue gas treatment power consumption, the fluctuation of renewable energy output power can be stabilized, and the flexible utilization of wind turbine and photovoltaic can be indirectly realized. Internal energy mutual benefit between VPP regions is priced independently to encourage multiple regions of VPP to participate in the joint scheduling. Aiming at maximization of the operation benefit, a VPP optimal scheduling model of energy sharing and interaction among multiple regions is established, and a coordinated optimal scheduling strategy of distributed energy multiple regions in VPP is proposed. In view of the characteristics of the above model, such as high-dimensional, nonlinear and difficult to solve, the populational variation basis vector is introduced into the compound differential evolution algorithm to solve the model. The simulation results show that the proposed model and method can achieve the coordinated and optimal dispatch of power and heat energy between regions in multiple regions VPP, improve the capacity of renewable energy power utilization, reduce the power curtailment cost of WT and PV by 82%, reduce the carbon emission of VPP, increase the carbon trading revenue by 75%, and increase the operation income of VPP by 9%.

Ground Subsidence Prediction Model and Parameter Analysis for Underground Gas Storage in Horizontal Salt Caverns

Due to a great demand of natural gas or oil storage in these years, horizontal caverns were proposed to fully use bedded salt formations of China. Under the same geological and operating conditions, the horizontal cavern would shrink more than traditional pear-shaped cavern, which might bring larger ground subsidence and affect the safety of storage facilities. A new prediction model was proposed in this paper for the time-dependent ground subsidence above horizontal caverns. The proposed model considered the impurity of bedded salt formations and simplified the horizontal cavern to an ideal cylinder. The shape of the subsidence trough was determined by the probabilistic integration method, and corresponding calculation formulas for the tilt, curvature, horizontal displacement, and horizontal strain were derived. Based on the assumption that the subsidence volume at the ground was proportional to the reduced volume of horizontal cavern, a formula for the reduced volume over time was established. FLAC3D was introduced to simulate the ground subsidence, and the results show that the proposed prediction model agreed well with the simulation results. Finally, the proposed prediction model was used to analyze the impacts of different stratigraphic parameters and design parameters. The results mainly show that, as the draw angle increases, the subsidence trough becomes deeper and narrower; as the depth of the cavern increases, the maximum subsidence first increases and then decreases, and the subsidence trough gradually becomes round; with the increase of the purity, the subsidence gradually decreases; with the increase of the creep properties and the stress exponential constant, the maximum subsidence first increases rapidly and then slowly approaches the limit; increasing the brine extraction velocity can shorten the cavern construction period and then reduce excessive ground subsidence; the subsidence decreases nonlinearly with the increase of internal pressure; with the increase of the cross section diameter and length of the horizontal cavern, the subsidence presents a significant nonlinear increase. In addition, unlike the traditional pear-shaped cavern, under the same conditions, the ground subsidence above the horizontal cavern according to this newly proposed model is much larger, and the ground subsidence contour line is no longer a standard circle. The findings of this study can help for better understanding of the prediction of ground subsidence above salt caverns and also provide a reference for the design and construction. However, the proposed prediction method is ideal and theoretical and should be further improved by engineering practice in the future.

WORLD AND TURKEY’S NATURAL GAS DEMAND SCENARIO

Although the global energy demand varies from country to country, it is constantly increasing on a global scale. As per IEA’s projections, the usage of two energy sources will increase (renewable with 12% and natural gas with 28%) in the global energy demand until 2040. In the study, 48 number of experts/managers (Decision Makers-DM) working in the energy sector were interviewed to establish/determine 10 main criteria and 43 sub-criteria used in demand scenarios. In the study, fuzzy multi-objective mathematical model (by using fuzzy AHP, and fuzzy TOPSIS) is developed to calculate World's and Turkey’s natural gas demand under high and low demand scenarios. By the help of model, the usage of natural gas amount in World by regions between 2020 and 2030 is calculated. In Scenario-High it will increase by approx. 26 % between 2020 and 2030 and reached 4.800 bcm in 2040. In Scenario-Low it will increase by approx. 5 % from 2020 to 2030 and reached 4.000 bcm in 2030. It is the only fossil fuel expected to grow beyond 2030 since it is clean energy source. In Scenario-High natural gas demand by region is calculated/projected as follows: in 2030 North America 1250 bcm, Central and South America 250 bcm, Europe 650 bcm, Middle East 750 bcm, Eurasia 650 bcm, Asia Pacific 1250 bcm. In the study, under the high demand scenario it has been calculated that the usage of natural gas in Turkey will increase by 52% between 2020 and 2030 and reach approximately 76 bcm, and in the low demand scenario Turkey's total natural gas demand will decrease by approximately 9% and reach approximately 45 bcm. In the study by using Fuzzy TOPSIS method, 10 number of sectors are examined and “Energy sector” was the first and “Industry sector” was the second in the ranking of the sectors in terms of global and Turkey’s natural gas demand scenario. In the study, the usage of natural gas is the only fossil resource that is expected to increase in the global energy mix among fossil fuels in 2030. This is due to high reserve amount of natural gas, i.e. global conventional natural gas reserves with 206 trillion m3 and unconventional unexplored natural gas reserves with 354 trillion m3, and as well as being a clean and environmental-friendly energy source. Since it is a clean fossil fuel and it pollutes nature &amp; air much less than other fossil fuels and has a minimum greenhouse gas emission amount compared to other fossil sources.

The dynamics and elasticities on the U.S. natural gas market. A Bayesian Structural VAR analysis

Natural gas is an important source of energy in the global economy, hence understanding the drivers of its prices is of significant interest for economic agents. This paper investigates the role of structural shocks for the dynamics of the U.S. natural gas market within the Bayesian Structural Vector Autoregression framework applied by Baumeister and Hamilton (2019a, AER), to the crude oil market. This approach provides clear intuition for the identification strategy and allows us to correctly estimate the short-term price elasticity of natural gas supply and demand. Our results indicate that the former is low, whereas the latter is higher than the average estimate in the literature. We also show that market specific demand shocks explain a dominant fraction of natural gas prices variability, while the contribution of supply, aggregate economic activity and inventory shocks is important only during specific market events such as the recent outbreak of the COVID-19 pandemic. Finally, we illustrate how changes in supply in the era of shale gas revolution contributed to the dynamics of natural gas prices.

Sectoral and temporal changes in natural gas demand in the United States

This research explores the temporal and spatial variation of sectoral natural gas demand in the United States. Fixed-effects econometric methods with robust identification approaches are the empirical techniques configured in the study using monthly data between 2001 and 2015. The results reveal the inelastic price responses at state, regional, and national levels across natural gas consumption sectors. The implication is that the efficacy of price based policies can differ in the natural gas consumption sectors across spatial scales. The statistically significant weather impacts in terms of heating degree days (HDD) and cooling degree days (CDD) revealed in this analysis are consistent with the extant energy demand literature, where higher HDD stimulates greater consumption of natural gas in the residential sector while CDD appears to increase natural gas consumption for electricity generation.

Dynamic stochastic joint expansion planning of power systems, natural gas networks, and electrical and natural gas storage

Over the last decades, electricity generation from natural gas has substantially increased, mostly driven by low natural gas prices due to fracturing and lower extraction costs. The geographic distance between natural gas resources and load centers calls for a holistic tool for joint expansion of power systems and natural gas networks. In this paper, a Dynamic Stochastic Joint Expansion Planning (DSJEP) of power systems and natural gas networks is proposed to minimize the investment and operational costs of power and natural gas systems. Electrical and natural gas storage (ENGS) are considered as an option for decision-makers in the DSJEP problem. The proposed approach takes into account long-term uncertainties in natural gas prices and electric and natural gas demands through scenario realizations. In dynamic planning, more scenario needs more time for computation; therefore, scenario reduction is implemented to eschew unnecessary scenarios. The proposed formulation is implemented on a four-bus electricity system with a five-node natural gas network. To demonstrate the efficiency and scalability of the proposed approach, it is also tested on the IEEE 118-bus system with a 14-node natural gas network. The numerical results demonstrate that ENGS can reduce the total investment cost, up to 52% in the test cases, and operational cost, up to 3%. In this paper, co-planning of power and natural gas systems considering natural gas and electrical storage is represented. Also, electrical and natural gas load growth uncertainties are taken into account to model the real situations. The purpose of the model is to minimize investing and operational costs.

Challenges of LPG Supply, Distribution &amp; Pricing in Growing Market: Bangladesh Case Study

The prime energy scarcity is nowadays a mostly the debated issue in Bangladesh in the awaken of spreading energy and fuel crisis. Pipeline natural gas support only 6 % domestic consumption of total population by 16 % of total produced gas which is declined mode. The claim of natural gas is on the continuous increase to meet domestic needs in the city, suburban areas together with industrial, commercial, power generation and diverse usage against the scarcity of gas. The demand for natural gas is on the continuous increase to meet domestic needs in the urban, suburban areas alongside industrial, commercial, power generation and diverse usage against the scarcity of gas. In this situation, LPG becomes a popular alternative energy source to reduce the natural gas crisis along with providing clean fuel in rustic areas. LPG is using by about 81% in its total amount for domestic purposes. At present in 2018 LPG demand in Bangladesh is approximate 750,000 MT and in future 2025 it would be 250, 0000 MT with growth rate 25%, 17 %, 14%, 15 %, 8%, 20% and 17% respectively. Now a day 12 KG LPG cylinders price is BDT 1150-1200 because of the variety of market barriers, government VAT and higher transaction costs which is not affordable for the middle as well as a low-income family though Bangladesh government fixed the price every month. LPG could assume an increasingly significant job in giving cooking energy to low-middle pay family units in Bangladesh if an appropriate advertising structure and circulation plan are masterminded. The aim of this project is to find out the challenges of LPG supply, distribution, and pricing in the growing Bangladesh market and to make a well planned and details road map so that LPG companies of Bangladesh can serve 70% of the domestic cooking fuel through LPG by 2025.

Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model

Accompanying the trend of low-carbon energy consumption, natural gas has ushered in the energy transition era’s development. However, rapid growth has thrown off the balance of urban natural gas supply and demand, resulting in gas shortages in many Chinese cities for several consecutive years. This work proposes a novel model for short-term load forecasting that combined the decomposition-fusion technique with a replacement data function, feature selection, and a diversified Stacking ensemble learning model. First, fast ensemble empirical mode decomposition is used to disintegrate the original complex nonstationary time series data into several modes. To ensure accurate information and computational efficiency while preventing excessive decomposition, the Pearson coefficient is used to fuse highly correlated patterns further. Second, hybrid feature engineering is used to select high contribution candidate input variables. Finally, K-Flod cross-validation is performed in each base-learner to enhance generalization capability during the training process. The empirical results prove that the base-learners’ capabilities and discrepancy will significantly impact the model (MAE = 167.409, MAPE = 3.125, RMSE = 234.654). Even if different types of city data are used, the proposed model’s effectiveness and robustness in gas load forecasting is not weakened, and decomposition-fusion technology can boost the model’s effectiveness. However, it cannot play a decisive role; the ensemble learning approach can integrate the ascendancy of the single model while effectively reducing the risk of falling into a local minimum. The developed model has good application prospects in natural gas dispatch and control systems as it outperforms alternative models in prediction accuracy, adaptability, stability, and generalization ability.

Analysis of the optimal spatial distribution of natural gas under ‘transition from coal to gas’ in China

China has been attempting to realise green sustainable economic development. Thus, China has proposed and begun to implement the ‘switching from coal to gas’ policy to realise the energy structure transition but neglected to consider seasonal natural gas demand fluctuations, gas supply shortages, the backward gas transportation and storage infrastructure. This paper constructs a model of China’s natural gas distribution system by incorporating these four factors: ① supply factors including domestic natural gas fields, liquid natural gas receiving stations, internationally piped gas sources, ② demand factors including domestic regional demands, ③ real natural gas transportation pipeline systems in the country, and ④ the pricing mechanism of natural gas. Optimal spatial natural gas distribution with maximum social welfare under the three conditions are simulated, respectively: the ① fixed price system, ② market pricing mechanism, and ③ seasonal fluctuations. The simulation results indicate substantial changes would occur in China’s natural gas system during the switch from coal to gas, and the natural gas infrastructure requires further improvement. This paper also provides references for the natural gas storage facility location selection in China.

A closed-loop integration of scheduling and control for hydraulic fracturing using offset-free model predictive control

As the development of shale gas resources has to be accelerated to satisfy the increasing global natural gas demand, many optimization approaches have been developed to handle the complexity associated with the shale gas system and discern the optimal design and operation strategy. However, it is worth mentioning that the essential hydraulic fracturing operation has not been considered as a dynamic process in the previous studies, which may lead to suboptimal solutions. Motivated by this consideration, we first develop an integrated model to simultaneously consider the scheduling and control of hydraulic fracturing operations to enhance the economic performance of the shale gas system. In particular, a reduced-order model is developed and integrated with the scheduling model to reduce the model complexity. To cope with the plant-model mismatch of the reduced-order model, we propose an online integrated framework with two feedback loops. In the outer loop, the integrated model is solved to determine the scheduling decisions and controller references. Then, an offset-free model predictive control system is designed to track the references online in the inner loop. By utilizing the offset-free model predictive control scheme, the tracking performance is enhanced and the plant-model mismatch is compensated for, which helps avoid dramatic changes in the solutions obtained by re-solving the integrated problem online. The effectiveness of the proposed online integrated framework is demonstrated by considering a hypothetical case study based on Marcellus Shale Play. It shows that the undesirable performance degradation induced by the plant-model mismatch can be decreased by the developed offset-free model predictive control system, and the overall economic performance of the shale gas system is improved with the proposed approach.

Natural Gas Price Prediction Using Machine Learning

Natural gas varies with season. In addition, natural gas supply, demand, storage, and imports are important indicators related to natural gas price. There are plenty of methods for analyzing and forecasting natural gas prices and machine learning is increasingly used. Machine learning algorithms can learn from historical relationships and trends in the data and make data-driven predictions or decisions. Here a new model for predicting price for natural gas by using Machine Learning concepts. Here some algorithms have been used to build the proposed model: Random Forest Regression, Linear Regression, Decision Tree, Multilinear Regression. By using the algorithm, a Flask model has been implemented and tested. The results have been discussed and a full comparison between algorithms was conducted. Random forest Regression was selected as best algorithm based on accuracy.

Renewable biosurfactants for energy-efficient storage of methane: An experimental and computational investigation

The continuously increasing demand for natural gas as an available and clean energy source indicates an inevitable transition to develop more promising technologies such as solidified natural gas (SNG) storage. Herein, we present a comprehensive experimental and computational study of the utilization of sunflower oil as a renewable biomass source to design highly efficient promoters based on the properties of sodium dodecyl sulfate (SDS) for methane hydrate formation. The effect of sunflower oil-based promoters (SFOPs) on methane hydrate kinetics was investigated theoretically via molecular dynamics (MD) simulation and experimentally under both dynamic (stirred reactor) and static (in the presence of porous media) conditions. SFOP1 considerably reduced induction time and overall time of the hydrate formation process compared to SDS under both conditions. In addition, SFOP1 significantly enhanced the kinetic constant of hydrate formation by 13.5 times, 3.7 times, and 2.5 times compared to pure water, SFOP2, and SDS systems, respectively, (in dynamic conditions). Moreover, both SFOPs improved the number of moles of gas consumed up to 450 mM in dynamic and 200 mM in static conditions compared to the pure water test. In contrast to the SDS solution, no foam formation was observed in the solution containing SFOPs. The MD results revealed that the SFOPs increased the transfer of methane molecules to the growing hydrate surface, which lead to enhance the kinetics of methane hydrate formation. Besides, more hydrate was formed by the addition of SFOP1 because of the improvement in the hydrogen bonds between water-water molecules in comparison with SFOP2. These findings clearly confirm that sunflower oil can be used for the development of green and more efficient promoters than SDS for methane hydrate formation without foam generation during the process.

Energy strategies of China and their impacts on energy shipping import through the Straits of Malacca and Singapore

PurposeThis study aims at investigating how energy strategies of China impact its energy shipping import through a strategic maritime link, the Straits of Malacca and Singapore (SOMS).Design/methodology/approachVector error-correction modelling (VECM) is applied to examine the key energy strategies of China influencing crude oil and liquefied natural gas (LNG) shipping import via the SOMS. Strategies investigated include oil storage expansions, government-setting targets to motivate domestic gas production, pipeline projects to diversify natural gas import routes and commercial strategies to ensure oil and gas accessibility and cost-effectiveness.FindingsFor the crude oil sector, building up oil storage and diversifying oil import means, routes and sources were found effective to mitigate impacts of consumption surges and price shocks. For the LNG sector, domestic production expansion effectively reduces LNG import. However, pipeline gas import growth is inefficient to relieve LNG shipping import dependency. Furthermore, energy companies have limited flexibility to adjust LNG shipping import volumes via the SOMS even under increased import prices and transport costs.Practical implicationsAs the natural gas demand of China continues expanding, utilisation rates of existing pipeline networks need to be enhanced. Besides, domestic production expansion and diversification of LNG import sources and means are crucial.Originality/valueThis study is among the first in the literature using a quantitative approach to investigate how energy strategies implemented in a nation impact its energy shipping volumes via the SOMS, which is one of the most important maritime links that support 40% of the global trades.