一种用于高比例可再生能源电力系统频率调节的创新协调控制策略

Abstract As the share of solar and wind energy in power systems increases, the decline of traditional frequency regulation resources results in frequency instability in low-inertia systems. Traditional approaches relying on synchronous generators (SGs) face challenges in providing adequate frequency response, necessitating advanced control technologies for asynchronous units to stabilize frequency. This paper aims to improve system frequency dynamics and proposes an enhanced Dynamic Scheduling Control Strategy (DSCS) integrated with a Deep Reinforcement Learning (DRL) framework to optimize the coordination of frequency responses between SGs and power electronics-interfaced asynchronous resources in hybrid power systems (HyPS). Firstly, a scalable system frequency model of the HyPS with high renewable energy source (RES) penetration is developed, accounting for the frequency support provided by RESs under varying operational conditions. Secondly, the DRL framework is integrated and leverages the frequency dynamics analysis of the Generic System Frequency Response (G-SFR) model to establish the reward mechanism. Lastly, a 36-bus system is employed to evaluate frequency dynamics under various disturbances and renewable penetrations, showing that while the fundamental DSCS scheme maintains the frequency nadir above 49.5 Hz, the proposed method achieves a 3.73 % reduction in RMS frequency deviation through adaptive optimization in simulated daily operation. The proposed method significantly enhances frequency stability in low-inertia systems with high renewable penetration without modifying the reserve capacities of the controlled units, and its further potential is discussed in scenarios involving additional reserve allocation by renewable units.