Load frequency control framework of renewable integrated power systems in multi scenario disturbance conditions with the proximal policy optimization

Abstract

Abstract The rapid adoption of renewable energy sources has radically changed the dynamic qualities of the modern power system, making load frequency control (LFC) problem worse due to unforeseeable generation, reduced system inertia, and the constant changes in disturbances. This paper presents a control framework based only on Proximal Policy Optimisation (PPO) for secondary frequency management in renewable-integrated power systems subjected to realistic multi scenario disruptions. In contrast to traditional gain-tuned or deterministic intelligent controllers, the suggested method immediately acquires an effective frequency regulation policy via policy gradient reinforcement learning, devoid of supplementary heuristic tuning. The controller is carefully evaluated under four pertinent operating conditions namely renewable intermittency, long cycle disturbances, oscillatory conditions of stress and a realistic contingency with renewable outages, night peak loads and gradual recovery. The performance is compared to GA-PI, TS-Fuzzy-PI, GA-Fuzzy-PI and advanced deep reinforcement learning-based controllers DDPG, TD3 and SAC on the basis of frequency domain and time-domain stability. The proposed PPO architecture shows a high improvement in the frequency control performance. In Case 1, the Integral Square Error (ISE) is reduced to 0.45196, as opposed to approximately 1060 with benchmark controllers. At the presence of high cyclic variations (Case 2), the ISE reduces to 0.031126, but the mean amplitude change of the frequency is smaller, 0.00043351 Hz, compared to approximately 0.3125 Hz in other methods. In the realistic contingency (Case 4), an Integral Squared Error (ISE) of 0.10953 and a constrained overshoot of 0.023203 Hz are attained. The findings validate that PPO provides exceptionally resilient, adaptable, and cost-effective frequency stabilisation for renewable-dominated power systems under actual dynamic stress circumstances.