Advanced Primary Frequency Regulation Optimization in Wind Storage Systems with DC Integration Using Double Deep Q-Networks

Abstract

With the gradual increase in wind power installation capacity, the proportion of traditional synchronous generators driven by fossil fuel is gradually declining. Due to the fact that wind turbines are connected to the grid through power electronic converters, which decouple rotor speeds from the system frequency and reduce system inertia levels, inadequate inertia levels can pose a threat to frequency stability when disturbances occur. To address this issue, this paper proposes a frequency regulation optimization strategy for the direct current (DC) transmission of a wind storage system. This strategy incorporates virtual inertia control and virtual droop control to adjust wind power output based on frequency deviation and rate of change. Fuzzy logic control is employed for energy storage, adaptively adjusting active power based on frequency deviation and the rate of change. Additionally, under the context of multi-DC transmission in renewable energy systems, an optimization strategy for proportion and integration (PI) parameters of the frequency limit controller (FLC) is proposed. Considering frequency deviation and DC regulation power simultaneously, the double deep Q-network (DDQN) algorithm is adopted in the simulation model to attain the optimal parameters of FLC. Simulation results conducted using MATLAB/Simulink 2022a indicate that this strategy increases the lowest frequency by 0.28 Hz and decreases the response time by 1.04 s compared with the non-optimized strategy.