Day-ahead economic dispatch of integrated wind-PV-thermal-storage systems considering forecast errors and incentive-penalty tiered carbon trading

This study proposes innovative solutions for power system decarbonization. This study developed a mixed Gaussian model to characterize renewable forecast errors and designed an enhanced tiered carbon pricing mechanism with incentive features for negative emissions. A stochastic optimization model integrating carbon capture with hybrid renewable-thermal-storage systems was formulated to minimize total costs. Case studies using CPLEX solver demonstrated: (1) The new pricing mechanism achieved 34.14 tons additional emission reductions vs conventional approaches; (2) Carbon capture integration reduced operating costs by 9.47% and emissions by 28.7% while improving flexibility; (3) The proposed uncertainty management method cut reserve costs by 27.4% and total costs by 1.77%, with only 3.42% reduced extreme scenario coverage. These solutions effectively coordinate carbon markets with renewable integration.