Power planning scheme based on simulated annealing algorithm

With the continuous expansion of the scale and complexity of power systems, power planning has become one of the key challenges that need to be solved urgently. This study proposes a simulated annealing algorithm combined with deep learning technology to solve the power planning problem. By introducing a generative adversarial network to generate high-quality initial solutions and using a multi-task learning model to dynamically adjust the weights between optimization objectives, the improved algorithm shows excellent performance in multiple quantitative indicators. Experimental results show that the improved algorithm is superior to the traditional algorithm in terms of total cost (14.75 × 106 yuan), reliability (98.8%), balance error (0.012), and power generation efficiency (86.3%). In addition, it has fast convergence speed (3500 iterations), short calculation time (800 s), and strong robustness (standard deviation 0.6). These results show that the improved algorithm can not only effectively optimize the economy and reliability of the power system but also adapt to the needs of different load growth rates and power types and has important practical application value.