Abstract: With growing demands for energy efficiency and carbon reduction in power grid operation, addressing the shortcomings of traditional scheduling approaches has become increasingly urgent. A hierarchical carbon trading mechanism is integrated with user-side responsiveness to construct a demand-side response model under carbon constraints. To enhance the solution process, the white shark optimizer is improved through chaotic initialization, directional update, and cosine mutation strategies, mitigating issues such as limited convergence accuracy and susceptibility to local optima. An optimal scheduling model for the active distribution network is formulated, targeting the minimization of operational costs. The refined algorithm is employed to solve the model, with simulation outcomes demonstrating its effectiveness and accuracy. Experimental results indicate that the proposed method significantly improves energy scheduling performance while supporting low-carbon objectives, and holds potential for broader application in smart grids incorporating renewable energy sources.

Key words: active distribution network, improved white shark optimization algorithm, carbon trading,  demand response