Stochastic Optimization Method for the Operation of Integrated Energy Systems Under High Renewable Energy Penetration

Integrated energy system with high renewable en-ergy penetration is an effective way to reduce carbon emissions and improve energy efficiency. However, the increase in system complexity, as well as the randomness and intermittency of wind power, have brought difficulties to the optimal scheduling of system. In order to address this challenge, this paper proposes a stochastic optimization method for integrated electricity and heat system (IEHS) based on hierarchical optimization strategy. Firstly, thermoelectric decoupling is performed for the integrated energy system, and then establish thermal network balance model and optimal power flow model of power system. Considering the randomness of wind power output, the quasi-Monte Carlo method is introduced to solve economic and stable operating parameters; Subsequently, the bacterial foraging optimization algorithm (BFOA) with better global search ability is used as the solution algorithm of the model; Numerical simulations show that the proposed method can significantly improve the system's ability to resist random interference and operate economically.