An inexact two-stage stochastic risk-aversion model for integrated energy system management in Beijing-Tianjin-Hebei, China

Abstract

An inexact two-stage stochastic risk-aversion model was developed in this study for supporting regional energy system planning and management. It can tackle uncertainties described in terms of probability distributions and permits the in-depth analysis of various policy scenarios when the promised policy targets are violated. Moreover, it can help local decision-makers evaluate trade-offs between energy system economy and stability associated with different robust criteria (risk-aversion levels). An actual case study in the Beijing-Tianjin-Hebei (BTH) region, China, was provided for demonstrating the applicability of the developed model. The results indicated that electricity generated by coal-burning (coal-fired power and coal-fired cogeneration) and gas-fired heating would be the main power forms for power and heat supply to get the maximum security. Furthermore, some policy implications can be concluded as follows: (a) more attention should be focused on increasing the local power generation capacity in the BTH region rather than importing electricity from other regions; (b) the introduction of risk aversion into the optimization model is conducive to the development of solar power, biomass power, and gas-fired cogeneration; and (c) renewable energy (e.g., biomass energy and geothermal energy) development in the BTH region still has a long way to go, because it is constrained by resource endowment and geographical location. Generally, the proposed model not only can help decision-makers identify the desired energy system management policies under risk considerations, but also could be viewed as a prime example for energy system planning and management at different regional levels (e.g., city, province, and multiprovince) and used as a reference for energy structure adjustment in other metropolitan groups such as the Yangtze River Delta and Pearl River Delta urban agglomerations.