基于DDPG的新型配电网调频功率分配仿真与控制研究OA

In the new power system,the generation method and operating characteristics of new energy are significantly different from traditional synchronous machines,and the safe and stable operation of frequency faces new challenges and opportunities.When there is a large frequency fluctuation caused by high-power disturbances in the power grid,in order to adjust the system frequency,it is necessary to manually adjust the output of the units in a timely manner.The rationality of power allocation of frequency modulation units is of great significance to energy utilization efficiency and power grid stability.Based on the deep deterministic policy gradient(DDPG)algorithm in deep reinforcement learning,a reward function was designed based on the frequency deviation and regional control error of the regional system.Multiple scenarios were set to train the intelligent agent,and scene weights were assigned according to the disturbance size.The weighted power allocation factor of the general policy set was output,ultimately achieving a reasonable allocation of unit frequency modulation power.A joint simulation platform was established based on Python calling PSS/E,and the 44 node IEEE standard power system model in the Nordic region was used as a simulation example to verify the feasibility of using the joint simulation platform to generate optimization strategy results.