An Integrated Energy System Optimization Method Considering Q Learning Algorithm

In recent years, the frequent natural disasters worldwide and their effects have attracted great attention of the international community. In this context, the traditional reliability research is not enough to support the safe operation of the power grid, and the concept of toughness emerges as the times require. In this paper, the dynamic power flow model of natural gas network is adopted, and the coupling relationship between distribution network reconfiguration in physical layer and information layer is considered. Based on this, Q learning algorithm is introduced to solve the complex problem. The simulation results show that the Q learning algorithm can achieve better convergence while solving the problem. The improved initialization method and the adopted confidence interval upper bound algorithm can significantly improve the computational efficiency and make the results converge to a better solution. Compared with the conventional mixed integer linear programming model, Q learning algorithm has better optimization results.