In the context of deep integration of carbon emission regulation in the power system, the synergistic optimization of carbon cost and dispatch economy becomes the core challenge of the multi-objective scheduling problem. A distributed optimization framework integrating improved multi-objective evolutionary algorithm and deep reinforcement learning strategy is constructed, which systematically integrates Pareto sorting, adaptive perturbation, Actor-Critic decision network and master-slave scheduling mechanism to realize the joint optimization of carbon quota adjustment and real-time power generation strategy for the electric-carbon coupled system. In the IEEE-118 node grid simulation, the constructed model reduces the average scheduling cost by 2.8%, carbon emission by 10.3%, and the number of convergence rounds by 43.0% compared with the NSGA-II algorithm, which demonstrates significant solution efficiency and strategy stability. The system adopts GPU acceleration and asynchronous caching mechanism to maintain 92.6% resource utilization when the number of node concurrency is ≥8. Comparative analysis shows that the fusion algorithm has better solution set distribution and scheduling adaptability in non-convex objective space. This method can provide an engineering-deployable intelligent optimization tool for carbon scheduling for large-scale multi-energy systems, which is of value for dissemination.
Huang et al. (Fri,) studied this question.