To address the challenges posed by the direct integration of large-scale wind and solar power into the grid for peak-shaving, this paper proposes a short-term optimization scheduling model for hydro–wind–solar multi-energy complementary systems, aiming to minimize the peak–valley difference of system residual load. The model generates and reduces wind and solar output scenarios using Latin Hypercube Sampling and K-means clustering methods, capturing the uncertainty of renewable energy generation. Based on this, a new improved algorithm, Tent–Gaussian Enterprise Development Optimization (TGED), is introduced by incorporating chaotic initialization and Gaussian random walk mechanisms, which enhance the optimization capability and solution accuracy of the traditional enterprise development optimization algorithm. In a practical case study of a certain hydropower station, the TGED algorithm outperforms other benchmark algorithms in terms of solution accuracy and convergence performance, reducing the residual load peak–valley difference by over 600 MW. This effectively mitigates the volatility of wind and solar power output and significantly enhances system stability. The TGED algorithm demonstrates strong applicability in complex scheduling environments and provides valuable insights for large-scale renewable energy integration and short-term optimization scheduling of hydro–wind–solar complementary systems.