Modern power systems are experiencing larger fluctuations and more uncertainties caused by increased penetration of renewable energy sources (RESs) and power electronics equipment. Therefore, fast and accurate corrective control actions in real time are needed to ensure the system security and economics. This paper presents a novel method to derive real-time alternating current (AC) optimal power flow (OPF) solutions considering the uncertainties including varying renewable energy and topology changes by using state-of-the-art deep reinforcement learning (DRL) algorithm, which can effectively assist grid operators in making rapid and effective real-time decisions. The presented DRL-based approach first adopts a supervised-learning method from deep learning to generate good initial weights for neural networks, and then the proximal policy optimization (PPO) algorithm is applied to train and test the artificial intelligence (AI) agents for stable and robust performance. An ancillary classifier is designed to identify the feasibility of the AC OPF problem. Case studies conducted on the Illinois 200-bus system with wind generation variation and N - 1 topology changes validate the effectiveness of the proposed method and demonstrate its great potential in promoting sustainable energy integration into the power system.