Interval-parameter bi-level programming for energy system management under uncertainty: Towards a deep-decarbonized and sustainable future in China

Abstract

To reach the goal of carbon-neutral society, synergistic approaches based on mathematical models that are capable of planning energy system associated with complex uncertainty and hierarchical structure are desired. In this study, an interval-parameter bi-level programming (IBP) method that is effective for solving hierarchical decision-making problems with conflict objectives and uncertainty is developed. Then, an IBP based China's energy system model (abbreviated as IBP_CES) is formulated for mainland China during 2021–2050, where the higher-level objective of minimizing carbon dioxide (CO2) emission is given priority, following by the lower-level objective of minimizing system cost. Results from IBP_CES show that, although China's energy supply would still rely heavily on fossil fuels, the share of coal would decrease to 33.9% by 2050. Results also disclose that the national CO2 emission would reach the peak value during 2026-2030 and then decline rapidly due to development of renewable energy and promotion of carbon-treatment technology which is beneficial for government to achieve carbon neutrality goals. In addition, compared to the traditional single-level model, CO2 emission from IBP_CES can decrease by 5.9%, demonstrating that IBP_CES is more superior in energy system decarbonization. The findings can help decision makers to well understand the interactions and tradeoffs between climate mitigation and economic development, and provide effective information on clean, sustainable and deep-decarbonized development strategies.