Abstract: To efficiently solve the security constrained economic dispatch problem in a high-proportional renewable energy integrated power system, a security-constrained economic dispatch based on a proximal policy optimization algorithm is proposed. First, a dispatch model of the power system is established based on the AC power flow. The Markov reward process of the dispatch model under the framework of deep reinforcement learning is developed. Subsequently, the reward function mechanism of the proximal policy optimization algorithm is designed to guide the agents to generate a dispatching plan that satisfies both the power flow requirements and the N-1 security constraints. Next, the incorporating mechanism of the dispatching model with the proximal policy optimization algorithm is figured out, establishing a generation and extraction of the training samples as well as the training mechanism for the value network and the policy network. Finally, the effectiveness and adaptability of the proposed method are validated by using the standard IEEE 30-node and IEEE 118-node test systems.