Xueping Du’s research while affiliated with Xi'an Jiaotong University and other places

Underground gas storage (UGS) is an important part of the natural gas industry. Its peaking characteristics and storage capacity are critical to national energy supply and energy security. Compared with the mature gas market in Europe, the construction of UGSs in China faces many challenges. This study is expected to provide references for China UGS through a detailed analysis and calculations of the European natural gas market and underground storage. First, the current status and development of the European gas market were summarized. And the situation of primary energy structure, gas utilization, and UGSs construction was compared between the EU and China. Second, the working gas volume of UGSs in Europe was analyzed from the perspective of its relationship with total consumption and peak‐shaving volume. Finally, the simulation and prediction of the working curve of the energy storage facilities and the reasonable ratio of the energy storage to load were solved by the numerical fitting method. Meanwhile, the recommendations for China's natural gas market and UGSs were provided. It is appropriate to set the planning target of China UGS capacity to load at 5% of total consumption and the actual peaking gas volume will be 14.4 billion cubic meters by 2030.

With the rapid growth of natural gas consumption in China, the monthly peak shaving and security issues of the gas supply have become increasingly prominent. In view of this situation, the monthly fluctuation law of natural gas consumption in China must be studied to guide gas storage peak shaving and gas supply planning. In this study, the concepts of the gas year (statistical method of breaking through the calendar year to gas year and reflecting the complete gas consumption cycle), typical year (the year that had the representative load curve of China), and some fluctuation characteristics parameters were applied to study the monthly fluctuation law of natural gas consumption in China. Furthermore, according to the monthly statistical data of China, including natural gas demand, power generation, crude steel output, refined copper output, the monthly average temperature in the typical city, the relationship between influencing factors and natural gas demand was analyzed by the gray relative correlation method. Based on the stepwise regression Cobb–Douglas (C–D) production function, the natural gas demand of China in the gas year of 2030/31 was predicted. The research results can be used for energy planning, statistics, peak shaving of gas storage, liquefied natural gas trade, and can also be used as a reference for energy big data analysis and refined management.