A Two-Stage Robust Scheduling Optimization of an Energy Hub With Multiple Chance Constraints

An energy hub (EH) can abstract a multi-energy system into multiple input-output ports and is often employed to characterize the coupling relationship between different energy sources. However, the existing studies mainly focus on the modeling and capacity optimization of the internal components of the EH, and lack of research on the energy scheduling problem with renewable energy (RE) uncertainty at the input of the EH model. Moreover, the varied operating conditions of the internal components are mostly ignored in the traditional EH models, and the energy conversion coefficients are usually set to fixed values. This also leads to the inferior accuracy of the energy scheduling for the internal components. Thus, a two-stage energy scheduling stochastic algorithm is proposed for the EH system containing RE. First, a new EH system containing RE is constructed, and the conversion coefficients models of the EH internal components are established based on the segmented linearization method. Then, a two-stage EH economic scheduling model including strict energy balance equations is developed. In addition, combining Bonferroni's inequality and conditional value-at-risk, the scheduling model is reconstructed to obtain an easily handled conservative approximation. Finally, the various optimization results of the energy dispatch at the input side and individual components of EH are obtained. The results are expected to provide a reference for the cost reduction and efficiency increase of EH operation under a high percentage of RE penetration.