An Optimization Scheduling Method for Integrated Energy Microgrid Based on Heavy-Ball Acceleration Distributed Optimization

The utilization of distributed renewable energy sources such as photovoltaic and wind power is characterized by uncertainty and volatility, posing significant challenges to the reliability and stability of integrated energy microgrids. Compared to centralized optimization methods, distributed optimization methods exhibit lower computational and communication burdens, along with enhanced flexibility, reliability, and scalability. This paper introduces heavy-ball acceleration technology into the traditional Alternating Direction Method of Multipliers (ADMM) algorithm. This approach not only ensures the stability of node voltages but also reduces network losses and accelerates the convergence speed of the algorithm. Firstly, the second-order cone relaxation technique is utilized to convexify the optimization problem of the integrated energy microgrid model, thereby improving the solution accuracy and enabling direct solution by the ADMM algorithm. Subsequently, an ADMM algorithm model incorporating heavy-ball acceleration technology is proposed for real-time optimization scheduling and power allocation in the integrated energy microgrid. Finally, simulation calculations are performed based on an improved IEEE 33-node integrated energy microgrid system, with an analysis conducted on the voltage stability of each node, network losses, and model convergence. The simulation results demonstrate that the improved algorithm can reduce network losses and improve the solution accuracy and convergence speed of the system while maintaining the basic stability of node voltages. This has certain guiding significance for practical engineering applications.