The increasing penetration of renewable energy introduces more uncertainties and creates more fluctuations in power systems than ever before, which brings great challenges for automatic generation control (AGC). It is necessary for grid operators to develop an advanced AGC strategy to handle fluctuations and uncertainties. AGC dynamic optimization is a sequential decision problem that can be formulated as a discrete-time Markov decision process. Therefore, this article proposes a novel framework based on proximal policy optimization (PPO) reinforcement learning algorithm to optimize power regulation among each AGC generator in advance. Then, the detailed modeling process of reward functions and state and action space designing is presented. The application of the proposed PPO-based AGC dynamic optimization framework is simulated on a modified IEEE 39-bus system and compared with the classical proportional−integral (PI) control strategy and other reinforcement learning algorithms. The results of the case study show that the framework proposed in this article can make the frequency characteristic better satisfy the control performance standard (CPS) under the scenario of large fluctuations in power systems.

This paper proposes a branch-independence-based reliability assessment approach for transmission systems. The approach consists of branch decoupling and state-space partition techniques. By integrating an impact-increment-based reliability index calculation model and the proposed branch decoupling technique, a proportion of sampled contingency states no longer need to be analyzed using the time-consuming optimal power flow (OPF) algorithm. In this way, the technique speeds up the calculation of reliability indices. Since first-order contingency states have a high probability of being sampled, we propose a state-space partition technique to replace first-order contingency state simulation with first-order contingency state enumeration. Consequently, the calculation of reliability indices is further accelerated by avoiding a large amount of repetitive OPF analyses during simulation process without affecting reliability index accuracy. The validity and applicability of our approach are verified using the IEEE 118-bus and IEEE 145-bus systems. Numerical results indicate that the proposed approach can improve computational efficiency without decreasing accuracy.