基于强化学习的分布式光伏运维资源动态调度

Aiming at the problem that the scheduling plan was difficult to be implemented due to the influence of dynamic factors in the scheduling process of distributed photovoltaic operation and maintenance resources, a dynamic scheduling method of distributed photovoltaic operation and maintenance resources based on reinforcement learning was proposed. In this method, the priority of operation and maintenance task was adjusted synchronously by constructing dynamic scheduling rules. A dynamic scheduling model was established to minimize the completion cost and time of the new plan. Q-learning was used to solve the model. Through the experimental comparison, Q-Learning algorithm had a fast solving speed and good algorithm stability, which was more suitable for solving dynamic scheduling problems. The proposed dynamic scheduling method of distributed photovoltaic operation and maintenance resources could cope with the influence of dynamic factors in the process of distributed photovoltaic operation and maintenance, and improve the service quality.