Scenario probabilistic data-driven two-stage robust optimal operation strategy for regional integrated energy systems considering ladder-type carbon trading

Regional integrated energy system (RIES) is regarded as an innovative approach for integrating resources and promoting sustainable social development. However, the coupling of multiple energy and uncertainties present operational risks for RIES. Hence, a scenario probabilistic data-driven robust optimal operation strategy has been devised to enhance operational performance within RIES with multi-energy coupling, multiple uncertainties, and various carbon sources. Firstly, in the operational framework of RIES, a ladder-type carbon trading system has been instituted, incorporating both reward and punishment mechanisms to mitigate carbon emissions. Subsequently, considering the conventional two-stage robust method, a probabilistic data-driven methodology is introduced to tackle multiple uncertainties. Afterward, an improved column and constraint generation (C&CG) algorithm has been employed for efficiently solving models. Finally, a case study is conducted to validate the suggested strategy. The findings reveal that, in comparison with the two alternative scenarios, the proposed ladder-type carbon trading mechanism exhibits a significant decrease in carbon emissions by 8.26 % and 6.44 %, respectively, while simultaneously mitigating economic losses within the system during uncertain and disrupted conditions. The implementation of the data-driven RO approach effectively balances the system's economic efficiency and robustness. Additionally, the improved C&CG algorithm has achieved a substantial reduction of 78.62 % in computing time compared to its original version. In conclusion, the proposed optimal operating strategy actively contributes to achieving low-carbon, economic, and stable performance in RIES.