With the global energy shortage problem intensifying, the large-scale consumption of wind power is very important. In order to overcome the difficulty of large-scale wind power consumption, a genetic optimization method for a multi-vector hydrogen storage power generation system is proposed. Study and analyze mathematical models of equivalent state of charge in wind power, hydrogen energy storage systems, and gas storage tanks separately. Establish a joint optimization model with the goal of maximizing local wind power consumption. Combined with various constraints, the genetic algorithm is used to achieve Finding the optimal solution of energy flow. Based on the actual measurement data of a certain area in Northeast China, a case analysis was carried out. By comparing the effects of wind power consumption during the operation of the system, it is verified that the proposed method can effectively reduce the interaction power and maximize the local wind power consumption.