Gas Prices Research Articles

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.

Inflation effects of oil and gas prices in the UK: Symmetries and asymmetries

The United Kingdom is among the countries experiencing a cost-of-living crisis believed to be influenced, at least in part, by the dynamics of the international oil and gas markets. To this end, this study aims to achieve to achieve two objectives. Firstly, the dynamic association between the UK's inflation and oil and gas prices is examined. Further, the study examines whether the response of the UK's inflation to energy price dynamics is (a)symmetric. This study adopts wavelet coherency to determine the dynamic co-movement between energy prices and inflation. In addition, the dynamic simulated autoregressive distributed lag model (DS-ARDL) is used to examine the dynamic response of inflation to energy price changes. The estimation results reveal a symmetric response of inflation to gas price shocks. Further, the response of inflation to oil price shocks is asymmetric. Interestingly, the effect of gas price dynamics passes more strongly to inflation than to oil price dynamics. These findings suggest that a more diversified energy mix could help prevent substantial energy price pass-through to inflationary pressures.

Geopolitical risks and business fluctuations in Europe: A sectorial analysis

Should Europe Really Worry about Geopolitical Risks? This study investigates the impact of geopolitical tensions and risks on the economies of the Euro area. In particular, we assess the reaction of key sectors (financial sector: stock market, exchange rate, inflation; energy sector: oil and gas; real sector: economic growth rate) with regard to geopolitical shocks over the period September 2003–March 2024. We measure the reaction of European economies to global geopolitical risks, as well as risks related to geopolitical events and threats. To this end, we adopted the ARDL model and bound tests to estimate the effects of geopolitical risks in the short and long terms. Our findings present two interesting results. First, geopolitical risks can reduce economic growth and provoke depreciation of the euro. Second, geopolitical tensions can increase inflation and put pressure on Brent oil and gas prices in Europe. The European stock market seems more resilient to geopolitical shocks, however. Finally, adverse geopolitical events are also associated with greater economic and political uncertainty in Europe.

Rozwiązania instytucjonalne na rzecz rozwoju konkurencyjności sektora gazu ziemnego w UE

The study explores the subject of sectoral competitiveness on the example of gas sectors in different European countries. Literature review on sectoral competitiveness suggests that the concept has evolved considerably over time and that contemporary definitions of competitiveness span beyond traditional economic factors. Studies on gas sector in this context to date, however, have largely focused on export competitiveness of upstream activities. To address the research gap, a targeted approach is suggested to study and compare competitiveness of the entire national gas sectors, spanning beyond standard productivity measures. A dedicated synthetic indicator is designed to perform a comparative analysis of contemporary gas sectors, taking account of the gas price offered to the industry, institutional performance, infrastructure readiness and job creation potential. Then, the case study is presented and it examines four countries that have liberalized their gas sectors and are reliant on gas imports to a different degree. Results confirm that the application of a synthetic indicator factoring in different aspects of sectoral competitiveness is appropriate to arrive at a holistic view of gas sector’s relative performance. The significance of the institutional setup is also confirmed from the perspective of inducing investment, enhancing market liquidity, as well as creating sustainable jobs in the gas sector.

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.

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.

Statistical assessment of the financial performance of shale-gas wells coupling stochastic and numerical simulation

We present a new methodology to statistically determine the net present value (NPV) and internal rate of return (IRR) as financial estimators of shale gas investments. Our method allows us to forecast, in a fully probabilistic setting, financial performance risk and to understand the importance of the different factors that impact investment. The methodology developed in this study combines, through Monte Carlo simulation, the computational modeling of gas production from shale gas wells with a stochastic simulation of gas price as a geometric Brownian motion (GMB). To illustrate the methodology’s validity, we apply it to an analysis of investments in shale gas wells. Our results show that gas price volatility is a key variable in the performance of an investment of this type, in such a way that at high volatilities, the potential return on an investment in shale gas increases significantly, but so do the risks of economic loss. This finding is consistent with the history of shale gas operations in which huge investment successes coexist with unexpected investment failures.

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.

The Dynamic Cointegration Relationship between International Crude Oil, Natural Gas, and Coal Price

In this study, we conducted an in-depth analysis of the dynamic cointegration relationship between international crude oil, natural gas, and coal price indices from 2009 to 2023, revealing the changes and differences in the cointegration relationship between these three prices during different periods. Utilizing statistical analysis and economic modeling, we found significant cointegration among these energy prices during the initial decade-long observation period, indicating their close interaction in the global energy market influenced by supply and demand fundamentals, macroeconomic conditions, and geopolitical landscapes. However, since 2020, this long-standing stable cointegration relationship has been severely disrupted due to the global spread of the COVID-19 pandemic and escalating geopolitical tensions, leading to a notable increase in volatility and uncertainty in the energy market. Further analysis highlights that, in recent years, with the strengthening of global climate governance and the advancement of the low-carbon transition trend, fossil fuel markets, particularly high-carbon-emitting crude oil and coal markets, have undergone significant adjustments. Meanwhile, the role of natural gas as a transitional clean energy source has become increasingly prominent. The findings of this study have significant implications for energy policy formulation, market risk management, and strategic planning in the energy industry, while providing directions for future research on resilience and adaptability in the transition process of energy systems.

A deep learning approach for fairness-based time of use tariff design

Time of use (TOU) tariffs aim to shape demand by reducing peak demand. This is significant given the increasing pressure on peak energy supply, particularly in Europe, where peak gas prices have dramatically increased bills. This study defines fairness as a reform measure that carries both distributional and transitional effects on different income users and utilities. A flexible fairness-driven TOU tariff design is proposed, which (1) assigns households to different tariffs by income and estimated price responsiveness calculated from a long short-term memory (LSTM)-based counterfactual electricity consumption model and (2) filters potential tariffs to simultaneously lower total costs, distribute cost savings progressively, and maintain constant utility profitability. Using the 2009-10 Irish Smart Metering Trial based on more than 2000 users, under the proposed frameworks (b) and (c) with fairness constraints, no lower-income users have been observed to pay a higher bill than the higher-income users on average. Specifically, lower-income groups can achieve 8% and 10% bill reduction, respectively, under framework (b) partial bill protection and framework (c) full bill protection for the lower-income users. Our fairness-based TOU framework has successfully achieved fairness of distribution and transition, paving the way for a more equitable and sustainable electricity system.

Electric Vehicle Usage Patterns in Multi-Vehicle Households in the US: A Machine Learning Study

Electric vehicles (EVs) play a significant role in reducing carbon emissions. In the US, EVs are mostly owned by multi-vehicle households, and their usage is primarily studied in the context of vehicle miles traveled. This study takes a unique approach by analyzing EV usage through the lens of vehicle choice (between EVs and internal combustion engine vehicles) within multi-vehicle households. A two-step machine-learning framework (clustering and decision trees) is proposed. The framework determines the preferred trip category for EV use and captures the effects of household attributes, driver attributes, built-environment factors, and gas prices on EV use in multi-vehicle households. Results indicate that discretionary trips (accumulated local effect = 0.037) are mostly preferred for EV use. EV preference is more pronounced among households with fewer workers (<2) and lower income levels. These findings are valuable for policymakers and auto manufacturers in targeting specific market segments and promoting EV adoption.