A framework for evaluating energy security in China: Empirical analysis of forecasting and assessment based on energy consumption

• Published in: Energy (Oxford) 2021-11, Vol.234, p.121314, Article 121314
• Main Authors: Peng, Cheng, Chen, Heng, Lin, Chaoran, Guo, Shuang, Yang, Zhi, Chen, Ke
• Format: Article
• Language: English
• Subjects: Batteries; Clean energy; Compressed air; Empirical analysis; Energy consumption; Energy security; Energy storage; Energy storage system with large-scale batteries; Evaluation; Hybrid systems; Mathematical models; Optimal non-linear metabolic grey model; Optimization; Rechargeable batteries; Renewable energy; Risk assessment; Risk assessment model; Security; Storage batteries; Sustainable development
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2021.121314

To safeguard energy security and achieve sustainable development in the future, China's decision makers are focusing on the identification and assessment of energy security risks. In this study, we proposed a framework that combines the grey model and risk assessment model systematically to forecast and evaluate the energy security in China. Empirical results show that the curve of China's coal consumption would be a decreasing tendency within 2020–2029, however, other energy consumption would have an increasing trend in the future. Although the increase of China's oil products consumption from 2020 to 2029 is found as 142.76 million tons of equivalent, the curve of the data increases at a diminishing rate. Also, the risks of China's energy security in 2018 and 2022 are ‘moderate’ and ‘light’, respectively. According to the empirical results, we develop a hybrid system for energy storage based on the large-scale batteries to improve the efficiency of comprehensive utilization for renewable and clean energy. Research results also illustrate that the hybrid system of energy storage with large-scale batteries, involving the iron-based aqueous redox flow batteries (IBA-RFBs) and the compressed air energy storage (CAES), can not only improve the efficiency of energy storage, but also create more added value. •The ONMGM (1,1) model was adopted to forecasting the energy consumption in China.•The genetic algorithm was applied to solve the non-linear optimization issue.•A framework of prediction and assessment for energy security risk.•The hybrid system of energy storage is proposed.•This hybrid system can improve the efficiency of energy storage.