An interval mixed-integer non-linear programming model to support regional electric power systems planning with CO2 capture and storage under uncertainty

BackgroundElectric generating capacity expansion has been always an essential way to handle the electricity shortage, meanwhile, greenhouse-gas (GHG) emission, especially CO2, from electric power systems becomes crucial considerations in recent years for the related planners. Therefore, effective approach to dealing with the tradeoff between capacity expansion and carbon emission reduction is much desired.ResultsIn this study, an interval mixed-integer non-linear programming (IMINLP) model was developed to assist regional electric power systems planning under uncertainty. CO2 capture and storage (CCS) technologies had been introduced to the IMINLP model to help reduce carbon emission. The developed IMINLP model could be disassembled into a number of ILP models, then two-step method (TSM) was used to obtain the optimal solutions. A case study was provided for demonstrating applicability of the developed method.ConclusionsThe results indicated that the developed model was capable of providing alternative decisions based on scenario analysis for electricity planning with consideration of CCS technologies. The IMINLP model could provide an effective linkage between carbon sequestration and electric generating capacity expansion with the aim of minimizing system costs.