Communication Resource Scheduling of Virtual Power Plant Based on Improved Simulated Annealing Algorithm

In the operation of virtual power plants, the scheduling of communication resources is often limited by transmission bandwidth and latency issues, which directly affect the real-time and accuracy of energy scheduling, and thus affecting the overall operational efficiency of the power plant. To address the communication resource scheduling problem in virtual power plants, this study introduces an optimization strategy based on an improved simulated annealing algorithm. Firstly, the shortcomings of traditional simulated annealing algorithm in virtual power plant communication resource scheduling are analyzed, such as slow convergence speed and susceptibility to getting stuck in local optimal solutions. Next, an adaptive temperature control mechanism and a multi neighborhood search strategy are introduced to improve the search efficiency and global search capability of the algorithm. Finally, the stability and adaptability of the algorithm are further optimized through cross validation. The experimental results show that compared with communication resource scheduling methods such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), the improved simulated annealing algorithm proposed in this study has faster convergence speed and higher resource utilization efficiency in communication resource scheduling of virtual power plants. In terms of convergence speed, the objective function value drops to 22, and the highest resource utilization rate also reaches the range of 85%-90%. The research conclusion shows that the improved simulated annealing algorithm can effectively solve the scheduling problem of communication resources in virtual power plants, ensuring the efficiency and stability of power plant operation.