Natural Gas Prices Research Articles

Thermoeconomic simulation of a complex energy system for performance analysis and prediction in deep peak shaving

The establishment of a cost model for complex energy systems based on thermoeconomics can provide technical and economic evaluation indicators for complex energy systems. At the same time, to integrate more renewable energy into the grid, complex energy systems must participate in deep peak shaving. To evaluate the technical-economic performance of complex energy systems involved in deep peak shaving, a novel thermoeconomic cost construction method is proposed based on the production structure diagram, using the gas-steam combined cycle system as an example. these can effectively avoid the derivation error of high-dimensional models, improve the modeling and calculation speed, and obtain the variation trend of system thermoeconomics cost under load changes and operating parameter changes. The results show that the external and internal factor can change the power generator thermoeconomic cost. the power generation cost of the system increases with increasing natural gas price and environmental temperature When the compressor pressure ratio increases, the power generation cost of the system also increases. At 100% load, the power generation cost reaches its lowest value when the exhaust temperature is equal to 615?C.

What Drives Interval-valued Natural Gas Prices?

What Drives Interval-valued Natural Gas Prices?

Forecasting Natural Gas Prices With Deep Learning and Compressed Sensing Denoising

Forecasting Natural Gas Prices With Deep Learning and Compressed Sensing Denoising

A sequential real options analysis for renewable power-to-hydrogen plants for Germany and California

The novelty of large-scale green hydrogen technology with its numerous applications, and the competition with well-entrenched conventional technologies, still makes it difficult to operate renewable power-to-hydrogen (P2H) plants profitably. In this study, sequential investments in the P2H plants are investigated using a Real Options approach. The analysis addresses the question of what the optimal timing is for sequential investments in P2H modules that enable hydrogen feed-in into the gas grid in Germany and California, both pioneers in the sustainable energy transition. Choosing this approach, the uncertain and largely irreversible character of the P2H investments is tackled. Since the focus is on substituting natural gas by adding green hydrogen to the gas grid, the underlying asset considered as stochastic is the price of natural gas. It is modeled by applying a geometric Brownian motion process. The analysis conducted reveals that for both California and Germany polymer electrolyte membrane (PEM) electrolyzer systems are uneconomical in the foreseeable future, at least based on the assumptions made in the case studies. The required natural gas price level that would enable economic viability of gas grid feed-in exceeds the level of today's natural gas prices many times over. Without policy support (e.g., subsidies or quotas for green hydrogen, sufficiently high carbon tax), or an increased willingness to pay for green fuel on the customers' side, it is unlikely that the required minimum hydrogen price level for profitable business will be reached any time soon.

Economic Analysis of Gas Turbine Using to Increase Efficiency of the Organic Rankine Cycle

In this research, a modified organic Rankine cycle (ORC) system has been presented and examined. This system incorporates a gas turbine as an additional subsystem to boost the enthalpy of geothermal brine. The primary objective of this study is to perform an economic evaluation of the modified ORC system, wherein a gas turbine is utilized to enhance the quality of geothermal steam. The suggested modified ORC system is particularly well-suited for areas abundant in geothermal resources with low to medium temperatures. It offers a more effective utilization of such resources, resulting in improved efficiency. The study considered 10 different working fluids and 8 types of gas turbines used to heat the geothermal water brine witch, the temperature vary of which varies between 80–130 °C. Various flue gas temperatures behind the heat exchanger, as well as temperatures of the return of the geothermal water to the injection hole, were examined. Based on that, 990 variations of configuration have been analyzed. The research showed that the lowest simple payback time (SPBT) values were achieved for the SGT-800 gas turbine and the working fluid R1336mzz(Z), for example, for an electricity price equal 200 USD/MWh and a natural gas price equal to 0.4 USD/hg, resulting in a SPBT value of 1.45 years. Additionally, for this variant, the dependence of SPBT on the price of electricity and the depth of the geothermal well was calculated; assuming the depth of the geothermal well is 2000 m, SPBT changes depending on the adopted gas prices and so for 150 USD/MWh it is 2.2 years, while at the price of 100 USD/MWh it is 5.5 years. It can be concluded that a decrease in SPBT is observed with an increase in the price of electricity and a decrease in the depth of the geothermal well. The findings of this study can help us to better understand the need to utilize low and medium temperature geothermal heat by using combined cycles (including gas turbines), also from an economic point of view.

Henry Hub monthly natural gas price forecasting using CEEMDAN–Bagging–HHO–SVR

As a clean fossil energy source, natural gas plays a crucial role in the global energy transition. Forecasting natural gas prices is an important area of research. This paper aims at developing a novel hybrid model that contributes to the prediction of natural gas prices. We develop a novel hybrid model that combines the “Decomposition Algorithm” (CEEMDAN), “Ensemble Algorithm” (Bagging), “Optimization Algorithm” (HHO), and “Forecasting model” (SVR). The hybrid model is used for monthly Henry Hub natural gas forecasting. To avoid the problem of data leakage caused by decomposing the whole time series, we propose a rolling decomposition algorithm. In addition, we analyzed the factors affecting Henry Hub natural gas prices for multivariate forecasting. Experimental results indicate that the proposed model is more effective than the traditional model at predicting natural gas prices.

The Energy Commodities Price and The Energy Sector Index: Evidence from Indonesia Stock Exchange

Purpose: This study aims to determine the effect and correlation of daily and weekly returns on commodity prices on the IDX Energi index on the Indonesia Stock Exchange. Theoretical Framework: Commodities are goods in the form of raw materials or raw materials that have a selling value. Energy commodities are mining products in the form of extracted fossils which can be used as energy sources. Stock price indices can be divided into several categories, namely stock market indices, sectoral stock price indices and individual stock prices. The stock market index is a representation of the total listed stock prices based on certain criteria and methods and is evaluated periodically. Method: The research period used was January 25 2021 to July 30 2023. The analysis technique used was multiple regression and Dynamic Conditional Correlation Generalized AutoRegressive Conditional Heteroskedasticity. Results: The results showed that the daily and weekly returns from energy commodities had a significant positive effect on the return on the IDX Energy index. Weekly returns on commodity prices can predict returns on the IDX Energi index better than daily returns. Energy commodities are also correlated with the IDX Energi index, but the dynamic correlation pattern of natural gas price returns to the IDX Energi index is different from the dynamic correlation pattern between world oil and coal prices. In general, returns on oil, natural gas and coal prices have an effect and are correlated with returns on the IDX Energi index. Conclusions: The research results can be used as a decision making in investing, especially investing in companies that are included in the IDX Energi sector. The weekly return of the IDX Energi index can be predicted through returns on energy commodity prices, but the contribution is still small. Subsequent research can add new variables that can affect the movement or return of the IDX Energi index, besides that it can examine with a longer sample period.

Effect of geopolitical risk on resources prices in the global and Russian-Ukrainian context: A novel Bayesian structural model

This study evaluated the impact of geopolitical risk (GPR) on natural resources (NRs) by employing monthly from January 1990 to January 2023 and daily data from 3rd January 2022 to 31st March 2022. The outcomes using wavelet QQ and novel Bayesian structural model reveal that GPR has a more profound impact on oil prices (in a quantile range of 0.4–0.8), natural gas prices (range of quartiles from 0.4 to 0.8) and gold prices (in a range of lower to upper quantiles) exhibiting that they are more sensitive to geopolitical uncertainties at all levels. The effects of geopolitical risk are also prominent on palm oil in lower to upper quantile (0.2–0.7) and urea in medium or upper quantile, showing that higher uncertainty drives their prices. In contrast, the response of iron ore (IO) was observed only in upper quantiles (0.7–0.8). The results of the causal analysis reveal that the Russia-Ukraine conflict positively impact NRs prices and that there was a rapid divergence from counterfactual projections. Our findings bear significant implications for sample economies highlighting the alternatives to cope with the GPR and its consequences for natural resource prices.

Analysis of nuclear microreactor efficacy with hydrogen production methods

A major issue with the economic validity of nuclear power technologies is their rate of return on investment. Since these systems have high expected initial capital costs, it is difficult to instill investment confidence against the backdrop of untested construction and operation. Nonetheless, emerging nuclear technologies such as microreactors remain promising as their output is carbon-free and the high outlet temperature associated with many microreactor concepts enables them to generate process heat that can power industrial processes, thereby widening their versatility beyond electricity generation. The pairing of these systems with higher value commodities, such as hydrogen, could potentially improve the economic viability of microreactors. Hydrogen production has become a subject of great interest in recent years for numerous applications such as for transportation, metal refining, and fertilizers. With a profitable microreactor-powered hydrogen production system, the price dependency of these applications and downstream commodities on the volatile natural gas prices can be reduced. In this work, the pairing of a microreactor with natural gas reforming (NGR) and high-temperature electrolysis (HTE) was modeled and it was found that a 10 MWth to 20 MWth microreactor could become economically viable through revenue from hydrogen production. The technology-agnostic microreactor energy source paired with the NGR and HTE plants was able to generate achievable principal loan values that were above $4.5 M/MWth for a 15 MWth reactor over a 20-year period. The cost of a first-of-a-kind microreactor according to available estimates exceeded the average achievable loan values for the HTE system while the NGR system was able to achieve these estimates within 21 years. The pairing of HTE with autothermal reforming was also investigated and it was found to be uncompetitive as compared with NGR and HTE.

Economic Evaluation of a 1 MWel Capacity Power-to-Biomethane System

Power-to-biomethane (bio-P2M) is a novel technology that combines the long-term storage of periodically available renewable energy sources (RES) and the upgrading of biogas. This article introduces a complex economic analysis of a 1 megawatt electric (MWel) capacity bio-P2M system based on economic characteristics considered to be typical in practice. The evaluation includes an investment analysis to present the basic scenario, a sensitivity analysis and a unit cost calculation to show the economic viability, the cost structure and the possible reserves of the synthetic natural gas (SNG) as a final output. The risk analysis is executed using Monte Carlo simulation, and the final results are the mean and standard deviation of the outputs, distribution functions and probabilities. Our results show that a significant state subsidy would be needed to boost competitiveness either in terms of investment costs (44% in our calculation) or in technology development to improve technological effectiveness. Another important competitiveness issue is the full utilization of the plant and the lowest possible price for the electricity used. If both cannot be optimized at the same time, then the first one is more important. Natural gas prices and the full utilization of waste heat might result in smaller changes.

Exploring the influence of the geopolitical risks on the natural resource price volatility and correlation: Evidence from DCC-MIDAS-X model

This paper uses the expanded dynamic conditional correlation mixed data sampling (DCC-MIDAS-X) model to examine the impact of low-frequency geopolitical risks (GPR) on high-frequency volatility and the correlation of natural resource prices. The empirical results show that GPR and its sub-indices can significantly affect the volatility and correlation of natural resources. The increase in Geopolitical Threats (GPRT) significantly affects the long-run volatility of European Union Allowance (EUA) and Brent crude oil prices. Geopolitical Acts (GPRA) mainly affect the long-run volatility of natural gas prices. In addition, the long-run correlation coefficient, long-run correlation coefficient indicates a competitive relationship between natural resources. Finally, increased GPR and GPRT can significantly reduce the long-run correlation between natural resources. Based on its conclusions, this research makes policy suggestions for businesses and the government. Investors should effectively identify different types of geopolitical risks and their influences to reduce losses and optimize investments. Governments could adjust their policies appropriately to avoid economic shocks due to natural resource volatility caused by geopolitical risks and ensure a stable supply of natural resources.

Techno-economic assessment of integrated NH3-power co-production with CCS and energy storage in an LNG regasification terminal

Energy efficiency and carbon capture and storage (CCS) are two key levers to attain global warming targets. Integration of various industrial and energy processes as well as complementary use of fuels with low carbon intensity such as natural gas with renewable sources will enable to mitigate environmental impacts in a cost competitive manner. This study proposes one such opportunity through integration of LNG cold exergy utilization for liquid N2 energy storage during regasification coupled to a novel NH3-electricity co-production scheme with CCS. A techno-economic benchmarking between standalone NH3 production process with net electricity imports (Case A), an integrated co-production solution (Case B) and non-integrated co-production plant with a standard combined cycle for power generation (Case C) is performed. Base economic assumptions are a natural gas price of 6.5 €/ton, CO2 tax of 100 €/ton, NH3 selling price of 400 €/ton and electricity average price of 80 €/MWh. At varying power generation capacity factors CFEl ranging from 30 to 50%, the electricity price premium required by the co-production schemes to break even with Case A is 2.5–9.9 €/MWh lower for Case B compared to Case C, with a premium as low as 12.9 €/MWh at 50% CFEl for the former when 2 days' worth of energy storage capacity is constructed. Thus, integration improves economic competitiveness for short storage times, applicable to regions with reliable year-round solar resources. However, the competitive advantage disappears when storage periods longer than one week are considered, such as power systems with high wind shares or seasonal solar availability.

Nigeria’s Inflation in the Era of Liquefied Natural Gas Exports: Insights from 2000-2021 ARDL Cointegration Analysis

This paper presents a comprehensive analysis of the impact of Liquefied Natural Gas (LNG) exports on inflation in Nigeria over the period 2000–2021. Utilizing the Autoregressive Distributed Lag (ARDL) bound co-integration approach, the study investigated both long-term and short-term relationships between LNG exports, natural gas prices, crude oil prices, and inflation in Nigeria. The Augmented Dickey-Fuller (ADF) unit root test indicated a mixed order of integration for the variables, suggesting the appropriateness of the ARDL cointegration method. The empirical results revealed the presence of a long-term relationship among these factors. In the long term, LNG exports, natural gas prices, and crude oil prices exhibited varying effects on inflation. While natural gas prices and crude oil prices had a statistically significant adverse impact on inflation at a 5% significance level, LNG exports did not show statistical significance. In the short term, LNG exports displayed both a negative and statistically insignificant influence, whereas crude oil prices had a positive and statistically significant effect. Natural gas prices, on the other hand, did not impact inflation significantly in the short term. The Granger causality tests revealed that there is no causal relationship between LNG exports and inflation in Nigeria. Although the influence of LNG exports on inflation lacks statistical significance, it is recommended that the government continue its commitment to the LNG sector, focusing on infrastructure and technology investments to enhance competitiveness and efficiency. Expanding into new markets and diversifying the LNG portfolio will ensure a steady revenue stream while prioritising economic diversification beyond the oil and gas sector

Innovative synthetic natural gas production from biomass and renewable hydrogen: Evaluation and optimization with sustainability perspective

Synthetic natural gas has a promising potential to contribute in sustainable supply of the natural gas and mitigating the intermittence issue of renewable energy sources. This study presents a novel system for synthetic natural gas production from biomass and renewable electrolytic hydrogen. A novel biomass and renewable electricity driven synthetic natural gas production system is presented. Hydrogen, an important input of the system, is supplied by biomass gasification and water electrolysis driven by renewable energy. Integrated system of photovoltaic panels and wind turbines supplies the required electricity of the system. An oxygen-steam mix is utilized as the gasification agent. An ion transport membrane is employed for oxygen production as an alternative to an expensive and complicated air separation unit. A comprehensive thermodynamic and economic analysis of the system is carried out. All equipment and ancillary costs are considered. Since solid carbon generation can lead to deactivation of the methanation reactor catalysts, the potential for solid carbon generation is considered for practicality. Multi-objective optimizations of different scenarios, are performed to determine the optimal economical and technical performance of the system. The optimization results indicate that the minimum cost for producing synthetic natural gas is 0.28 $/kWh. Furthermore, the heating value of synthetic natural gas is 41,500 kJ/kg, which closely approximates that of natural gas. Additionally, the price of the produced synthetic natural gas is lower than the price of natural gas in certain countries. In Sweden and the Netherlands, synthetic natural gas costs are notably 36% and 10% lower than conventional natural gas. However, in Germany, Austria, and Denmark, synthetic natural gas prices closely align with conventional natural gas rates, reflecting regional market variations.

ВЛИЯНИЕ САНКЦИЙ НА ЕВРОПЕЙСКИЙ ЭНЕРГЕТИЧЕСКИЙ РЫНОК И ЭКОНОМИКУ РОССИИ

This article reviews the views of prominent Russian economic figures on the imposed economic sanctions, as well as their foreign colleagues. An analysis is made of the benefits of sanctions against Russia for the main participants in the “Western coalition”. Using the example of the natural gas market, the inappropriateness of blocking energy supplies from Russia is shown. The financial losses from the unfinished Nord Stream 2 project are presented, and how instability in interstate relations between Russia and the EU affected the price of natural gas on the European energy market is considered. In conclusion, conclusions are presented and prospects for economic development for Russia under conditions of sanctions pressure are outlined.

Economic policy uncertainty and natural resources commodity prices: A comparative analysis of pre- and post-pandemic quantile trends in China

This study examines the relationship between natural resource commodity prices and economic policy uncertainty in China, both before and after the COVID-19 pandemic. This study uses an innovative Quantile Autoregressive Distributed Lag (ARDL) model to uncover both short- and long-term linkages among variables of interest. This study finds that there is a quantile relationship between these variables, with economic policy uncertainty having a negative impact on gold and natural gas prices during and after the pandemic. In contrast, during the pre-pandemic era, economic policy uncertainty had a negative impact on gold and natural gas prices but not on oil prices. These findings have significant policy implications, particularly in the context of the pandemic and its effects on the global economy.

Local Coal for Power Generation in Pakistan

PREFACE
 Coal contributes significantly to global energy supplies. In 2021, coal was the second-largest energy source consumed globally (Chart 1). Over the years, coal demand has increased substantially from 2.6 billion tons in 1980 to 5.5 billion tons in 2021 (Chart 2). Because of environmental concerns and the increasing trend towards renewables, its share declined in the United States and many European countries, decreasing global consumption in 2014 and onwards. But the trend reversed in 2020. It is because of the Russia-Ukraine war leading to the worldwide energy crisis that the demand for coal has increased.
 According to the International Energy Agency (IEA) forecast, coal demand is expected to exceed the previous coal demand record of 2014 in the next few years. The United States and many European countries are shifting back to coal as it is still one of the cheapest energy sources.
 The primary coal-consuming sector is electricity generation. High natural gas prices have increased reliance on coal for generating power. Coal consumption in electricity generation is expected to grow by more than 2%. If the gas prices continue to increase in 2023 or onwards, dependence on coal will remain, and demand will surge further. Besides gas prices, coal prospects will depend on the transition speed towards renewable energy sources.
 PAKISTAN COAL POTENTIAL
 Pakistan has 186 billion tons of coal reserves, primarily located in the province of Sindh (Chart 4). Only Thar desert (10,000 sq. km) contains the world’s 7th largest coal reserves of about 175 billion tons (Chart 5), equivalent to 50 billion tons of oil equivalent (more than Saudi Arabia and Iran’s oil reserves) and 2000 trillion cubic feet of gas (68 times more than Pakistan’s total gas reserves). Thar Block-II alone contains 2 billion tons of lignite reserves, of which 1.57 billion tons are exploitable. This Thar Block-II can produce 5,000MW of electricity for 50 years, while the total Thar reserves can sustain 100,000MW for over two centuries . Most of the coal in Pakistan is lignite (with more moisture content, up to 50%).

Low-carbon economic multi-objective dispatch of integrated energy system considering the price fluctuation of natural gas and carbon emission accounting

Natural gas is the main energy source and carbon emission source of integrated energy systems (IES). In existing studies, the price of natural gas is generally fixed, and the impact of price fluctuation which may be brought by future liberalization of the terminal side of the natural gas market on the IES is rarely considered. This paper constructs a natural gas price fluctuation model based on particle swarm optimization (PSO) and Dynamic Bayesian networks (DBN) algorithms. It uses the improved epsilon constraint method and fuzzy multi-weight technology to solve the Pareto frontier set considering the system operation cost and carbon emission. The system operation cost is described using Latin Hypercube Sampling (LHS) to predict the stochastic output of the renewable energy source, and a penalty function based on the Predicted Mean Vote (PMV) model to describe the thermal comfort of the user. This is analyzed using the Grey Wolf Optimization (GWO) algorithm. Carbon emissions are calculated using the carbon accounting method, and a ladder penalty mechanism is introduced to define the carbon trading price. Results of the comparison illustrate that the Pareto optimal solution tends to choose less carbon emission, electricity is more economical, and gas is less carbon-intensive in a small IES for end-users when the price of natural gas fluctuates. The impacts of various extents of natural gas price fluctuation for the same load are also discussed.

Energy price shocks and current account balances: Evidence from emerging market and developing economies

This paper investigates the effects of real energy price shocks on current account balances of 45 emerging market and developing economies. The investigation is based on country-specific structural vector autoregression models, where alternative specifications and identification schemes are considered for robustness purposes. The empirical results suggest that 1 % of a positive real oil price shock results in up to 0.11 (0.08) percentage points of a cumulative improvement (deterioration) in current account balances of oil exporters (importers) after five years, whereas 1 % of a positive real natural gas price shock results in up to 0.06 (0.04) percentage points of a cumulative improvement (deterioration) in current account balances of natural gas exporters (importers) after five years. Real coal price shocks result in higher current account balances of oil exporters and natural gas exporters, suggesting substitution of coal with oil and natural gas in such cases. When contributions of alternative real energy prices to the variance of current account balances are compared, real oil price shocks dominate those of real natural gas and real coal prices. The empirical results investigating the effects of oil demand versus oil supply shocks on current account balances suggest that oil demand shocks (rather than oil supply shocks) result in similar reactions of current account balances to real oil price shocks, supporting the view that the effects of oil demand shocks are different from those of oil supply shocks. The results are robust to the consideration of country-specific changes in real GDP and real effective exchange rates.

Equipment Selection for Coupling a Microgrid with a Power-to-Gas System in the Context of Optimal Design and Operation

This study proposes a one-layer deterministic Mixed-Integer Nonlinear Programming to design and schedule a PTG-integrated microgrid. The key contribution is that optimal equipment selection, design, and scheduling, considering the PTG system at the core of the problem, are determined just in a single formulation. Scenarios based on different carbon dioxide taxes and natural gas prices are investigated. Only one wind turbine farm is chosen when the carbon dioxide tax is increased from 50 $/ton to 100 $/ton. On the other hand, when the natural gas price is increased from 1.548 $/m3 to 1.72 $/m3, two wind turbine farms are selected. Solar panel arrays are not chosen in all the scenarios. Generated power by solar panels is not enough for installation despite their much lower carbon dioxide emissions and negligible operational costs. Consequently, the optimal equipment selections may change linked to the natural gas price and carbon dioxide tax.