Search

The security of natural gas supply and demand has always been an important topic, and as a result, analyzing and predicting the supply and demand of natural gas has received considerable attention from researchers. Numerous studies have extensively researched the supply and demand of natural gas. The main purpose of this study is to explore the supply‐demand balance mechanism for natural gas security. A system dynamics model is constructed to assess the supply and demand of natural gas in Sichuan Province, China. A new system dynamics model based on domestic gas, production gas, ecological environment and natural gas supply is proposed. The study considers four scenarios: natural development, economic priority development, environmental priority development and coordinated development. The results show that the rapid economic development has led to an excessive environmental carrying capacity, a larger gap between natural gas supply and demand, a decline in the quality of life of residents, and even affected the energy security of Sichuan Province and China. The findings also show that people's overprotection of the environment can lead to slow economic development, which prevents it from achieving its desired goals. The coordinated development scenario in this paper not only ensures the production of natural gas and the quality of life of residents, but also is an optimal scheme with low energy consumption and pollution. According to the research conclusions, considering the overall situation of China's energy security and the situation in Sichuan Province, the author proposes reforms from the supply and demand sides of natural gas. [ABSTRACT FROM AUTHOR]

With the continuous advancement of urbanization, the contradiction between urban development and environmental resources has become increasingly prominent, and environmental pollution has become increasingly serious. To fundamentally solve the problems of environment, energy, and low carbon, we must rely on the intelligence of energy. This paper aims to study the sustainable development of China's intelligent energy industry based on artificial intelligence and low‐carbon economy. In view of the problems existing in the optimization of power generation industry, this paper uses the annual load, electricity price, weather, and climate data of a southern power grid, uses the statistical variation particle swarm optimization algorithm, uses the historical runoff and rainfall data to optimize it, and studies the analysis methods, characteristics and laws of short‐term load, electricity price and runoff, as well as the uncertain factors affecting their changes. The experimental results show that the predicted price is close to the actual price, and the median error of each period is <1% in statistical analysis, so the forecast value can be used to replace the actual value for scheduling. This method makes full use of the adaptive mutation in the late stage of particle optimization, and introduces the mechanism of particle size selection, which fully ensures the diversity of particles and improves the search ability of particles. [ABSTRACT FROM AUTHOR]