A two-stage optimal dispatch model for wind-storage-carbon capture power plants considering carbon emission and green certificate trading

Within the background of carbon emission trading (CET) and green certificate trading (GCT) mechanisms, the study establishes a two-stage stochastic optimal (TSO) dispatching model for the integrated wind power-energy storage-carbon capture power plant (WP-ES-CCPP) system, designed to mitigate operational costs and address wind power uncertainty. First, a coordinated operational strategy is developed through topological analysis and mathematical modeling of CCPP, enabling synergistic interactions between WP, ES, and CCPP across day-ahead and real-time scheduling horizons. Second, a TSO model is formulated, wherein the day-ahead stage optimizes unit commitment and power allocation based on wind power forecasts to minimize operational expenditures, while the real-time stage employs stochastic optimization to resolve wind power forecast errors through ES dispatch and CCPP power adjustments, thereby reducing redispatch costs. Finally, simulation results demonstrate: 1) Compared with low-carbon economic dispatch, the addition of the GCT mechanism reduces the integrated system costs by 18.7 % and increases wind power consumption by 44.85 MWh, which is conducive to lowering the marginal peak-shaving cost of CCPP. 2) Configuring the solvent storage tanks reduces the total system cost by 630,000 RMB and reduces carbon emission by 2767t. which significantly improves the performance of CCPP. 3) The average cost of the TSO model proposed in this paper decreases by 19.18 %, 5.91 %, 8.89 %, and 3.65 %, respectively, in comparison with the deterministic dispatch, SO, RO, and TRO; and the average carbon emission decreases by 6.23 %, 2.92 %, 3.53 %, and 2.64 %, respectively. It shows that the TSO model has excellent performance in terms of economy and decarbonization.