renewable energy sources has significantly increased the scale and complexity of power systems, often resulting in computation times ranging from tens of minutes to several hours in large-scale systems with thousands of constraints, along with potential degradation in solution accuracy due to dimensionality-induced convergence issues. To address these challenges, this paper presents a multi-layer model reconstruction algorithm via objective consensus for power systems integrated with renewable energy sources. First, dispatch resources are clustered into homogeneous groups through a correlation-based consensus analysis. For each group, a cost aggregation method generates compact cost parameters that replace the original cost parameters. These aggregated costs are incorporated into the initial model, facilitating model reconstruction and optimization. Then, the algorithm uses hierarchical optimization theory to divide the initial model into multi-layer and multi-type models for fast solving. Simulation studies demonstrate that the algorithm effectively reduces both the dimensionality and complexity of the dispatch optimization problem and decreases computation time compared to state-of-the-art methods while maintaining the solution accuracy, validating its effectiveness and superiority for renewable-integrated power systems dispatch optimization.