ABSTRACT
Climate change, the scarcity of fossil fuels, advances in clean energy, and volatility of crude oil prices have led to the recognition of clean energy as a viable alternative to dirty energy. This paper investigates the multifractal scaling behavior and efficiency of green finance markets, as well as traditional markets such as gold, crude oil, and natural gas between 1 January 2018, and 9 March 2023. To test the serial dependency (autocorrelation) and the efficient market hypothesis, in its weak form, we employed the Lo and Mackinlay test and the DFA method. The empirical findings showed that returns data series exhibit signs of (in)efficiency. Additionally, there is a negative autocorrelation among the crude oil market, the Clean Energy Fuels Index, the Global Clean Energy Index, the gold market, and the natural gas market. Arbitration strategies can be used to obtain abnormal returns, but caution should be exercised as prices may increase above their actual market value and reduce the profitability of trading. This work contributes to the body of knowledge on sustainable finance by teaching investors how to use predictive strategies on the future values of their investments.
ABSTRACT
The global economy is moving into a new era characterized by digital and green development. To examine the impact of digital industrialization development on the energy supply chain, in relation to the sustainable development of China's energy security, we discuss the nonlinear impact and transmission mechanism of digital industrialization on the supply chain of the energy industry using a panel threshold regression model based on sample data on the development of the provincial natural gas industry in China from 2006 to 2020. We found that there are multiple threshold effects of digital industrialization level development on energy supply chain length, and the results are statistically significant, i.e., digital industrialization development positively contributes to natural gas supply chain length after digital industrialization is raised to or crosses the critical threshold. Meanwhile, the heterogeneity analysis results show that there are differences in the impact of digital industrialization on the energy supply chain from sub-sectors, regional development differences, and different development periods. Therefore, we provide some factual support and experience for achieving the construction goal of "Digital China" and accelerating the digital reform of the energy supply chain as well as transforming and upgrading the economic structure.
ABSTRACT
In this article, proportional‐integral (PI) control to ensure stable operation of a steam turbine in a natural gas combined cycle power plant is investigated, since active power control is very important due to the constantly changing power flow differences between supply and demand in power systems. For this purpose, an approach combining stability and optimization in PI control of a steam turbine in a natural gas combined cycle power plant is proposed. First, the regions of the PI controller, which will stabilize this power plant system in closed loop, are obtained by parameter space approach method. In the next step of this article, it is aimed to find the best parameter values of the PI controller, which stabilizes the system in the parameter space, with artificial intelligence‐based control and metaheuristic optimization. Through parameter space approach, the proposed optimization algorithms limit the search space to a stable region. The controller parameters are examined with Particle Swarm Optimization based PI, artificial bee colony based PI, genetic algorithm based PI, gray wolf optimization based PI, equilibrium optimization based PI, atom search optimization based PI, coronavirus herd immunity optimization based PI, and adaptive neuro‐fuzzy inference system based PI (ANFIS‐PI) algorithms. The optimized PI controller parameters are applied to the system model, and the transient responses performances of the system output signals are compared. Comparison results of all these methods based on parameter space approach that guarantee stability for this power plant system are presented. According to the results, ANFIS‐ PI controller is better than other methods.
ABSTRACT
Natural gas will play more and more important role in the sustainable low-carbon development mode characterized by low energy consumption, low pollution and low emission. It has been and will continue to be the focus of attention. The 28th World Gas Conference (WGC2022) was held on May 23-27, 2022 in Daegu, South Korea. The conference summarized the progress of world natural gas in the past four years, analyzed and judged the future development trend, and reached seven consensuses: (1) Natural gas is not only a transitional fuel, but also a basic fuel for future development. (2) Supply and demand value chain of natural gas has high flexibility and adaptability, and supply diversification has become a development advantage. (3) With the effect of the rapid increase of oil and gas price, the reversal of natural gas to coal has intensified the rapid growth of global carbon emissions. (4) Structural tension is emerging in the global LNG market, and the number of long-term agreement contracts will show an increasing trend. (5) The coordinated development of natural gas and hydrogen will accelerate the arrival of the low-carbon era. (6) Methane monitoring and leakage measurement technology in the natural gas industry will become the next important innovation. (7) Governments of various countries have continuously raised the minimum level of underground gas storage, and successively issued incentive policies to increase gas reserves and production. Based on the experience, the following suggestions are put forward for the development of China's natural gas: (1) Continue to highlight the important position of the natural gas industry, increase exploration and development, and improve supply capacity and voice;(2) To adapt to the new setup of international natural gas supply caused by the COVID-19 and the conflict between Russia and Ukraine, and to formulate overall strategies for natural gas import and export trade;(3) Attach importance to LNG business, scientifically arrange the construction of LNG import supporting facilities, and take the initiative to cooperate with natural gas resource countries;(4) The whole industrial chain of natural gas and hydrogen business should be planned and deployed together, and hydrogen and natural gas infrastructure construction should be linked up effectively;(5) Increase policy support, strengthen infrastructure construction such as underground gas storage and LNG terminal, reserve more energy to develop confidence, and build a strong defense line for energy security. © 2022 Natural Gas Industry Journal Agency. All rights reserved.
ABSTRACT
The present study investigates the response of natural gas consumption to temperature on the basis of observations during heating season (middle November–middle March) for the period 2002–2021 in Beijing, China, and then estimates temperature-related changes in the gas consumption under future scenarios by using climate model simulations from the Coupled Model Intercomparison Project Phase 6. Observational evidence suggests that the daily natural gas consumption normalized by gross domestic product is linearly correlated with the daily average temperature during heating season in the past two decades in Beijing. Hence, a linear regression model is built to estimate temperature-related changes in the natural gas consumption under future scenarios. Corresponding to a rising trend in the temperature, the natural gas consumption shows a decrease trend during 2015–2100 under both the SSP245 and the SSP585 scenarios. In particular, the temperature would increase rapidly from early 2040s to the end of 21st century under the SSP585 scenario, leading to an obvious reduction in the natural gas consumption for heating in Beijing. Relative to that in the present day (1995–2014), the natural gas consumption would show a reduction of approximately 9% (±4%) at the end of 21st century (2091–2100) under the SSP245 scenario and approximately 22% (±7%) under the SSP585 scenario.
ABSTRACT
Natural gas is acting as a transitional fuel source as the world shifts toward fully renewable energy. Recently, Europe has confirmed this continued usage of natural gas, with the European Union voting in favor of calling natural gas a "green" or "sustainable" source of energy despite some pushback. However, there is one caveat: a further transition toward biogas or green hydrogen and other renewable gases by 2035. This is a good sign that natural gas power generation units will have a solid future in the energy mix of tomorrow.
ABSTRACT
Natural gas load forecasting provides decision‐making support for natural gas dispatch and management, pipeline network construction, pricing, and sustainable energy development. To explain the uncertainty and volatility in natural gas load forecasting, this study predicts the natural gas load volatility. As the natural gas load volatility has the time‐series features, along with long‐term memory, volatility aggregation, asymmetry, and nonnormality, this study proposes a natural gas load volatility prediction model by combining generalized autoregressive conditional heteroscedasticity (GARCH) family models, XGBoost algorithm, and long short‐term memory (LSTM) network. The model first takes the GARCH family models parameters of sliding estimation and meteorological factors as the influencing factors of volatility, and then it screens these influencing factors through the extreme gradient boosting (XGBoost) algorithm. Finally, the selected important features are input into the LSTM network to predict the volatility, and the 90% confidence interval of the volatility is calculated. Compared with a variety of single and combined models, the model proposed in this study has an average reduction of 45.404% in the evaluation index of mean squared error. The experimental results show that the model proposed in this study has a good performance and accuracy in predicting the volatility of natural gas load.