Natural Gas Prices Research Articles

Techno-Economic and CO2 Emissions Analysis of the Molten Carbonate Fuel Cell Integration in a DRI Production Plant for the Decarbonization of the Steel Industry

Developing low-carbon systems for the steel industry is increasingly considered necessary for contributing to the targets of CO2 emission reduction requested by modern environmental policies. This work focuses on a process for decarbonizing primary steel production by integrating an MCFC system in a DRI production plant based on the Energiron ZR process configuration. An MCFC system can be used to remove CO2 from the flue gases while producing electricity and reducing the net electric consumption of the DRI plant. The reference scenario, the carbon capture case, and an additional hydrogen-based H-DRI system using high temperature electrolysis are simulated using Aspen Plus to evaluate and compare their energy and environmental performance. The results show that even considering the additional power consumption of the complete carbon capture and separation system, the overall electrical consumption of the carbon capture case is decreased by 78 % and the direct CO2 emissions by up to 95 %. Furthermore, reduced electricity consumption reduces scope 2 emissions, increasing the sustainability of the process in the steel industry. On the other hand, the H-DRI case decreases primary energy consumption by 24 % but significantly increase electricity requirement; thus, it represents a better future solution when cheap low-CO2 electricity is available. The plant configurations are compared economically by retrieving investment and operational costs from the open literature to estimate the levelized cost of DRI and the cost of CO2 avoided. The results show that installing the MCFC and the anode purification system increases the investment costs by 38 % compared to a conventional plant. However, the lower electricity consumption and carbon tax expenditure lead to a comparable final DRI product cost. Contrarily, due to the electrolyzer high investment and operational costs, the additional marginal DRI cost of the H-DRI case is 31 % higher. Finally, the sensitivity analysis on the main variable costs shows that, independently from the electricity and natural gas price, the carbon capture case is economically competitive to the reference scenario, considering a carbon tax of 60 €/tCO2 or higher. At the same time, the H-DRI solution requires low electricity prices to be competitive. In conclusion, even if, in the long term, the objective is to completely replace the use of fossil fuels with renewable energy, in the short term, the implementation of MCFC in the DRI plant can significantly reduce the environmental impact of primary steel production without significant economic penalization.

The dynamic impact of network attention on natural resources prices in pre-and post-Russian-Ukrainian war

Natural resources prices have been greatly affected by the Russian-Ukrainian war. Investors also pay special attention to the price of natural gas and oil. This paper introduces the Thermal optimal path (TOP) method and the Long Short-Term Memory (LSTM) model to investigate the dynamic lead-lag relationship between investor attention and natural resources prices for crude oil and natural gas.We find that during the war, the ability of investor attention to lead the prices of crude oil and natural gas become significantly stronger. Adding investor attention to the model significantly improves price prediction in the full sample. In addition, after the sample period is segmented according to the TOP method, the forecasting performance is greatly improved compared to the full sample. These findings indicate that traders and companies can incorporate investor attention into price predictions to further improve their trading strategies.

An optimisation method for planning and operating nearshore island power and natural gas energy systems

As the development and use of offshore islands increase, so do the diversity of their energy needs, the cost of utilising the mainland for resource replenishment, and the instability of island energy systems. To address this challenge, this paper introduces an innovative system integrating fossil and renewable energy tailored for these islands, considering seasonal variations and external energy fluctuations. Leveraging a P-graph, an optimisation framework is designed to assess different optimised solutions. A comprehensive analysis is also performed to compare the solutions, while sensitivity assessments evaluate factors including electricity and gas price fluctuations, renewable energy availability, and natural gas demand. Additionally, the carbon emissions resulting from different power generation scenarios are visually represented and thoroughly analysed. The results support two key findings: 1) the cost of combined energy supply is lowest when wind energy accounts for 59 % of the total energy, mainland grid connection accounts for 3 %, fossil fuel energy accounts for 28 %, and natural gas accounts for 10 % of the energy mix; and 2) the price of electricity and natural gas supplied to the islands and the variability in the energy sources of wind and natural gas can significantly affect the cost of the energy system.

Conceptual design and life-cycle environmental and economic assessment of low-carbon cement manufacturing processes

Cement manufacturing, a fundamental process industry characterized by high CO2 emissions, sees over 58% of its emissions stemming from the limestone calcination process. However, this significant source of CO2 has not yet been adequately addressed by most existing cement decarbonization processes. As such, systematic environmental and economic comparisons between different cement processes are essential. This paper proposes a new cement process (NCP) that uses natural gas to react with limestone to reduce CO2 emissions and simultaneously produce high-value-added chemical products (methanol). To explore strategies for further improving the performance of the CO2 emission reduction, two processes are developed, integrating the organic Rankine cycle (ORC) and carbon capture and storage (CCS) units with the traditional cement process (TCP) and the NCP, namely TCP-ORC-CCS and NCP-ORC-CCS respectively. Life cycle assessment (LCA) and economic analysis were performed on the TCP, NCP, TCP-ORC-CCS, and NCP-ORC-CCS processes, revealing their advantages and limitations in different scenarios. LCA adopts the "cradle to gate" system boundary, which includes the whole cement process from ore mining to clinker generation and other processes used for carbon reduction. The LCA results show that four cement processes exhibit different environmental performances when choosing different allocation methods. Under economic allocation, the global warming potential (GWP) of TCP, NCP, TCP-ORC-CCS, and NCP-ORC-CCS are 933, 300, 432, and 194 kgCO2eq/ton·clinker, respectively. The comprehensive analysis indicates that NCP has the best comprehensive economic and environmental performance when natural gas and methanol prices are 332 and 319 $/ton, respectively. The price fluctuation of natural gas and methanol is found to greatly impact the levelized cost of clinker (LCOC) of NCP through sensitivity analysis. NCP is economically preferred over TCP when the price of methanol (MeOH) is at least $76 above approximately two-thirds of the price of methane (CH4). Similarly, NCP-ORC-CCS is more advantageous than TCP-ORC-CCS when the price of MeOH is at least $87 above nearly two-thirds of the price of CH4. This study provides a comprehensive analysis for low-carbon cement process design to achieve CO2 emissions reduction.

Determining co-movements of tomato prices in the United States and macroeconomic variables in Mexico for 2023

Objective: To analyze the co-movements of macroeconomic variables in Mexico and prices of Mexican tomato exports and to estimate the prices of Mexican tomatoes in American and Canadian supply markets based on Mexican macroeconomic variables. Design/Methodology/Approach: The research was conducted using Pearson's coefficient—calculating the standard scores for X and Y. We determined the co-movements of Mexican tomato market prices and Mexico’s GDP, the Interbank Equilibrium Interest Rate (IEIR), natural gas prices, and consumer inflation. Econometric techniques were thus combined with agricultural sector variables as a reliable precedent of the relation intensity between said variables. Results: The coefficient of determination showed an acceptable degree of linear relationship between the market prices of Mexican tomatoes in different cities and the selected macroeconomic variables, with an average correlation of 20%. We concluded that the variables are not entirely independent since they show a weak linear relationship between them. Study limitations/implications: It is crucial to conduct studies to determine whether the coefficients of determination support linearity or independence between the evaluated macroeconomic variables. Findings/Conclusions: Econometric techniques were combined with agricultural sector variables as a reliable precedent of the relation intensity between said variables. The coefficient of determination showed an acceptable degree of linear relationship between the market prices of tomatoes in different cities and the selected macroeconomic variables. We recommend the creation of a price forecasting model.

How electricity and natural gas prices affect banking systemic risk

This paper analyzes the impact of energy market conditions, specifically electricity and natural gas prices, on systemic risk in the Euro Area. We adopt the CoVaR methodology introduced by Tobias and Brunnermeier (2016) to analyze shifts in the system’s value at risk, incorporating considerations for electricity and natural gas prices. Our findings reveal that energy-related variables influence systemic risk to a similar extent as other state variables, such as interbank spreads and market volatility. Notably, we find that electricity prices have a more pronounced impact on banks’ risk compared to natural gas prices. Moreover, we observe that the ΔCoVaR serves as a reliable measure of systemic stress conditions when compared to the CISS index established by the ECB. Specifically, the ΔCoVaR developed in this paper, based on data up to the year 2021, anticipates the stress in the system that the CISS index captures in 2022.

With uncertainty comes opportunity: Repurposing coal assets to create new beginnings in the U.S.

Coal power plants are retiring in the United States due to a sharp decline in natural gas prices along with an aging fleet, the rising cost of coal, and decarbonization goals. In the last five years alone (2019–2023), more than 58 gigawatts (GW) of coal power capacity retired in the United States, while almost 30 percent of the remaining 194 GW of operating capacity is expected to retire by 2040. The cessation of operations at an existing power plant can have important impacts on the local economy, including job loss and a potential reduction in total economic output. Repurposing these assets effectively, including conversion into other power generation technologies, industrial manufacturing facilities, or commercial buildings, among others, can at least partially offset any negative economic impacts. This paper provides a review of ongoing and planned repurposing activities related to former coal power plants (not including repurposing related to fuel-switching from coal to natural gas) being pursued by utilities across the United States and discusses the costs, benefits, and challenges presented by types of repurposing assets or prospects.22Some of the concepts/metrics described have standard language and the authors have tried to stick to the use of these terms for consistency. The review shows that the repurposing type and capacity of the new projects is typically unrelated to the asset type and capacity being retired. The decision to repurpose a retiring power plant or unit is a result of a combination of factors that include, among others, decommissioning costs, land availability, and financial and regulatory incentives directed towards a clean and just energy transition. A list of current planned, in-process (under construction), or completed repurposing projects for energy and non-energy alternatives is presented, including several reference examples of repurposing from fuels other than coal. Relevant factors to consider in repurposing existing retiring assets, including the relevance of incentives for retiring assets in a just energy transition, are identified and described.

Sudden stop: Supply and demand shocks in the German natural gas market

SummaryWe use a structural vector autoregressive (SVAR) model to study the German natural gas market and investigate the impact of the 2022 Russian supply stop on the German economy. Combining conventional and narrative sign restrictions, we find that gas supply and demand shocks have large and persistent price effects, while output effects tend to be moderate. The 2022 natural gas price spike was driven by adverse supply shocks and positive storage demand shocks, as Germany filled its inventories before the winter. Counterfactual simulations of an embargo on natural gas imports from Russia indicate similar positive price and negative output effects compared with what we observe in the data.

Environmental and economic assessment of industrial excess heat recovery collaborations through 4th generation district heating systems

The external use of excess heat from industrial processes is an important factor in the energy transition and 4th generation district heating is an enabling technology. This paper presents a calculation tool for assessing the economic feasibility and the environmental impact of collaborations between potential industrial sources and users. The tool supports the sizing of district heating pipe diameters, pumps, heat pumps, and heat storage systems. A distinctive feature of the model is the possibility of calculating carbon and blue water footprints, alongside economic indicators, for both existing stand-alone systems and potential collaborative configurations. Excess heat recovery from water-cooled condensers of refrigeration systems is evaluated for two case studies involving the space heating of offices in a food logistics hub and the heating of a greenhouse from a frozen pizza factory, respectively. Only the second collaboration is profitable with the baseline price of natural gas (0.04 €/kWh) and electricity (0.12 €/kWh), provided that the distance between the source and the user is less than 2 km. For higher gas prices, distances of approximately 8 km would be viable. However, for source-user distances above 5 km, the water footprint of the collaboration would be higher than that of stand-alone systems.

Directional connectedness between the electricity prices and natural gas prices: evidence from Alberta’s electricity market

Purpose This study aims to assess volatility spillovers and directional connectedness between electricity (EPs) and natural gas prices (GPs) in the Canadian electricity market, based on a hydrothermal power generation market strongly dependent on exogenous variables such as fossil fuel prices and climatology factors. Design/methodology/approach The methodology is divided into two stages. First, a quantile vector autoregression model is used to evaluate the direction and magnitude of the influence between natural gas and electricity prices through different quantiles of their distributions. Second, a cross-quantilogram is estimated to measure the directional predictability between these prices. The data set consists of daily electricity and natural gas prices between January 2015 and December 2023. Findings The main finding shows that electricity prices are pure shock receivers of volatility from natural gas prices for the different quantiles. In this way, natural gas price fluctuations explain 0.20%, 0.98% and 22.72% of electricity price volatility for the 10th, 50th and 90th quantiles, respectively. On the other hand, a significant and positive correlation is observed in the high quantiles of the electricity prices for any natural gas price value. Originality/value The study described the risk to the electricity market caused by nonrenewable source price fluctuations and provided evidence for designing regulatory policies to reduce its exposure in Alberta, Canada. It also allows us to understand the importance of natural gas in the energy transition process and define it as the fundamental determinant of the electricity market dynamic.

Examining the factors of fertilizer pricing

AbstractA strong surge in fertilizer prices in 2021 led farmers and policymakers to question the factors that drive fertilizer pricing. This study examines the relationship among natural gas, corn, and fertilizer prices, finding five structural breaks from 1997 to 2022. The pass‐through rates of natural gas and corn price changes to fertilizer prices show significant shifts after the passage of the biofuel mandate, with those changes fading as the market matured. Meanwhile, natural gas price changes are the main contributing factor in the latest period. However, the linkage among these prices continues to evolve and is likely to diminish over time.

Auctions to reveal consumers’ willingness to pay for low-carbon hydrogen projects: combining lessons from renewables and natural-gas industries

Abstract Low-carbon hydrogen is expected to play a key role in realizing net-zero and sustainable development plans. Nonetheless, there is a gap between the cost of producing low-carbon hydrogen and its potential users’ willingness to pay for such hydrogen. To implement support for the development of the industry, we propose using low-carbon hydrogen long-term agreements allocated through auction mechanisms. The objectives are 2-fold: (i) matching supply and demand volumes considering the time horizon and geographical delivery point specification and (ii) allocating the subsidy. This perspective article innovates by proposing a reference price indexed to liquid to natural-gas prices, which is the main product that low-carbon hydrogen aims to substitute. The premium and the production cost are defined through a double-sided auction. This aims to minimize the public policy funds required to incentivize the low-carbon hydrogen market while facilitating long-term agreements and mitigating price risks that may hinder investment.

Technological evolution of large-scale blue hydrogen production toward the U.S. Hydrogen Energy Earthshot

Hydrogen potentially has a crucial role in the U.S. transition to a net-zero emissions economy. Learning from large-scale hydrogen projects will boost technological evolution and innovation toward the U.S. Hydrogen Energy Earthshot. We apply experience curves to estimate the evolving costs of blue hydrogen production and to further examine the economic effect on technological evolution of the Inflation Reduction Act’s tax credits for carbon sequestration and clean hydrogen. Learning-by-doing alone can decrease the production cost of blue hydrogen. Without tax incentives, however, it is hard for blue hydrogen production to reach the cost target of $1/kg H2. Here we show that the breakeven cumulative production capacity required for gas-based blue hydrogen to reach the $1/kg H2 target highly depends on tax credit, natural gas price, inflation rate, and learning rates. We make recommendations for hydrogen hub development and for accelerating technological progress toward the Hydrogen Energy Earthshot.

Multi-step Natural Gas Price Forecasting using Ensemble Empirical Mode Decomposition and Long Short-Term Memory Hybrid Model

Multi-step Natural Gas Price Forecasting using Ensemble Empirical Mode Decomposition and Long Short-Term Memory Hybrid Model

Effects of adverse geopolitical risk on price bubbles contagion from European natural gas prices to international energy prices

PurposeThe recent Russia–Ukraine conflict highlights the geopolitical importance of natural gas, especially in Europe. In this light, this study examines the impact of the Russia–Ukraine conflict on the spread of price bubbles from European natural gas to international energy prices.Design/methodology/approachThe Generalized Supremum Augmented Dickey-Fuller (GSADF) test is employed to detect the occurrence of price bubble episodes while the Dynamic Logit Model is used to examine price bubble contagion between the two markets. Further, a tri-variate VAR model is used to examine the determinants of the price bubble.FindingsThe findings reveal multiple bubble episodes in both European natural gas and international energy prices. Further, evidence of bilateral contagion between European natural gas and the international energy market is found. In addition, the Russia–Ukraine conflict triggers price bubble episodes in both markets. Finally, a counterfactual analysis suggests that the conflict increases the bubble contagion from the European natural gas market to the international energy market by about 40%. These findings imply that the Russia–Ukraine conflict is a significant driver of high upside risks to bubble occurrence and subsequent contagion to both European natural gas and international energy prices.Originality/valueTo the best of our knowledge, this study contributes new empirical evidence that the Russian–Ukrainian conflict significantly impacts the spread of price bubbles from the European natural gas market to international energy markets.

Towards sustainable regional energy solutions: An optimized operational model for integrated energy systems with price-responsive planning

With dynamic changes in the energy market and continuous advances in smart energy technology, regional integrated energy systems (RIESs) have emerged as a vital direction in the evolution of energy systems. They are increasingly gaining significance in the realm of energy supply (ES) and demand response initiatives. In this context, this article introduces a novel optimal operational model for an integrated energy system (IES) that incorporates energy price responsiveness. The paper commences by delivering a comprehensive overview of the typical structure of an IES, presenting detailed models for each system module, encompassing the electrical energy supplier, heating, and cooling components. The proposed system model comprises three distinct subsystems, each catering to specific energy requirements. The primary emphasis is on devising an efficient planning scheme tailored to the energy consumption patterns and operational aspects of the IES. Through the presented model, there is substantial potential for significantly reducing the overall system cost without inducing a noteworthy upsurge in environmental pollution. Moreover, the energy efficiency of the system can experience considerable enhancement. The desired optimization problem has been solved using the proposed multi-objective Horse Herd Optimization Algorithm (MOHHOA). Additionally, in this strategy, Pareto front and fuzzy selection are employed to make better decisions among all Pareto solutions. The optimization strategy devised in this research not only enables the integrated operation of the IES but also ensures its compatibility with ongoing energy development initiatives. The proposed method yields a diverse set of Pareto solutions across the objective space, providing various trade-offs between economic, environmental, and reliability considerations. Utilizing MOHHOA, the algorithm efficiently balances exploration and exploitation during optimization, resulting in informed decision-making. In optimal planning, Scenario 2 exhibits lower operational costs (8.2 % reduction) and a dramatic improvement in LESP (1.24 %–1.176 %) compared to Scenario 1. Sensitivity analysis reveals an 11.2 % increase in environmental costs in Scenario 1 with a 36 % rise in electricity prices. Conversely, in Scenario 2, a 10 % reduction in natural gas prices leads to a 24.5 % increase in system running costs. These findings underscore the significance of considering energy price fluctuations for optimizing integrated energy system performance.

Фінансові перешкоди впровадженню теплових насосів в централізованому теплопостачанні

The use of powerful industrial heat pumps (HP) is a fairly effective means of rational and environmentally friendly use of energy resources both in individual households and in district heating (DH), as well as in production processes in various industries. Powerful HPs are used in Ukraine much less than in developed European countries. In fact, these are isolated cases. The post-war reconstruction of destroyed cities with their district heating systems gives Ukraine a unique chance to use modern energy-efficient technologies, including HP. As a result, efficiency will improve and DH competitiveness will increase due to the low cost of thermal energy for consumers. Almost all developed countries of the world have government financial support for the HP implementation. The purpose of this study is to determine the level and form of government financial support for projects to implement HP in DHs of Ukraine. Financial modeling of the implementation of heat pump plants shown that they are more expediently to be used in the mode of generating thermal energy than in the mode of regulating the electric load of power systems. It is shown that the projects of heat pump plants, which use the heat of flue gases as a source of low-potential heat (LPH), are the most economically attractive and practically do not require government financial support. Projects of heat pump plants, which use air, ventilation emissions, waste water, soil and groundwater, sea, rivers, and waste heat of technological processes as LPH, are not financially attractive without government financial support, and some of them are even unprofitable. The most appropriate comprehensive financial support is tax incentives and interest compensation on the loan. Sensitivity analysis showed that projects for the implementation of heat pump plants may be the most sensitive to the amount of generated thermal energy, the price of natural gas and electricity. Keywords: heat pumps, low-potential heat sources, district heating, financial obstacles, tax incentives, grants.

Drilling Deeper: Non-Linear, Non-Parametric Natural Gas Price and Volatility Forecasting

This paper studies the forecast accuracy and explainability of a battery of dayahead (Henry Hub and Title Transfer Facility (TTF)) natural gas price and volatility models. The results demonstrate the dominance of non-linear, non-parametric models with deep structure relative to various competing model specifications. By employing the explainable artificial intelligence (XAI) approach, we document that the price of natural gas is formed strategically based on crude oil and electricity prices. While the conditional volatility of natural gas returns is driven by long-memory dynamics and crude oil volatility, the informativeness of the electricity predictor has improved over the most recent volatile time period. Although we reveal that predictive non-linear relationships are inherently complex and time-varying, our findings in general support the notion that natural gas, crude oil and electricity are interconnected. Focusing on the periods when markets experienced sharp structural breaks and extreme volatility (e.g., the COVID-19 pandemic and the Russia-Ukraine conflict), we show that deep learning models provide better adaptability and lead to significantly more accurate forecast performance.

Do OPEC+ policies help predict the oil price: A novel news-based predictor

The OPEC+, composed of the Organization of the Petroleum Exporting Countries (OPEC) and non-OPEC oil-producing countries, exerts considerable influence over the global crude oil market. However, existing literature lacks a comprehensive application of this factor in oil price forecasting, primarily due to the complexity of measuring such policy evolutions. To address this research gap, this study develops a news-based OPEC+ policy index based on text mining methods for comprehensive analysis and forecasting of the oil price. First, by crawling and mining news headlines related to OPEC+ production decisions, a dynamic and high-frequency (weekly) OPEC+ policy index is established. Second, the linear and nonlinear relationship between the proposed OPEC+ policy index and the WTI crude oil futures price is thoroughly examined, assessing the potential predictive power of the index in explaining the movements of the crude oil price. Third, the forecasting efficacy of the constructed index on the oil price is rigorously evaluated across eight econometric and machine learning models. Key findings include: (1) The proposed weekly OPEC+ policy index demonstrates strong concordance with OPEC+ production change decisions, exhibiting notable peaks and troughs corresponding to OPEC+ Ministerial Meetings. (2) The relationship analysis demonstrates a strong linear and nonlinear association between the proposed OPEC+ policy index and the crude oil price. (3) For oil price prediction, models incorporating our proposed OPEC+ policy index demonstrate superior performance compared to models without this index. In particular, the index exhibits a more significant predictive effect within three-week forecasting horizons and performs exceptionally well during periods of pandemic and the Russia-Ukraine conflict. In addition, the OPEC+ policy index also exhibits a significant predictive effect on the daily crude oil price and natural gas price, further confirming the robust and powerful forecasting capability of this index within the energy system.

PRICE DYNAMICS OF FOSSIL FUEL COMMODITIES IN THE PERIOD 2014 - 2024

Over the last decade, the dependence on fossil fuels and their prices has been increasingly discussed. This paper aimed to assess the development of fossil fuel prices and their relationship to GDP in the Czech Republic. To achieve this objective, a VAR model and Granger causality test were used. All fossil fuel prices peaked in 2022. The causal relationship with GDP in the Czech Republic was confirmed for natural gas and coal prices. On the other hand, the causal relationship between oil and GDP in the Czech Republic was not confirmed during the period under study. Weak correlations were found with the Covid-19 pandemic, inflation, and unemployment in the EU. The only exception is the correlation between coal and inflation in the EU, which was moderately strong. A limitation of the paper is the length of the period studied, with data collection taking place only once a month.