Research on Time-of-Use Pricing Optimization and Load Adjustment in Distributed Energy Systems

To address the challenges of integrating high proportions of renewable energy into the grid amid the energy transition, this paper proposes a time-of-use pricing-based electricity optimization model for distributed wind-solar hybrid systems. The model aims to guide load demand through price signals, achieving synergistic optimization of efficient energy consumption and economical system operation. To address the limitations of traditional single-objective optimization, this study constructs a multi-objective optimization framework centered on maximizing wind power consumption and minimizing operational costs. A hybrid optimization strategy integrating the hierarchical evolution algorithm (HEA) and tabu search (TS) is designed to enhance global search capability and convergence efficiency. The model comprehensively considers key conditions such as power balance constraints, dynamic characteristics of energy storage systems, and equipment operational limitations, establishing a systematic power output and multi-objective optimization function. Simulation results demonstrate that the proposed method effectively smooths load curves, enhances grid stability, and significantly improves renewable energy utilization rates. This research offers an innovative solution for balancing renewable energy integration with economic efficiency within electricity market environments, validating the practical application potential of time-of-use pricing strategies in advancing energy internet development.