The invention discloses a distributed energy agent regulation and control method and
system based on deep
reinforcement learning, and the method comprises the following steps: collecting distributed energy operation data and network constraint data in a micro-grid or park power distribution network, and carrying out the preprocessing; constructing a
reinforcement learning environment
state representation vector; configuring a distributed energy agent set; obtaining a disturbance
antigen fingerprint entry set, and generating an immunoaffinity
score sequence; generating a normal regulation and control action
instruction set, combining and issuing to execute or enter a perturbation sampling process; generating a feasible action domain and a perturbation action candidate set, and executing exploration control contract
verification; a target perturbation action is selected and issued for execution or a corrected perturbation action is generated and issued for execution; and updating the deep
reinforcement learning strategy network. According to the method, deep reinforcement learning and disturbance
immune mechanisms are adopted, distributed energy
intelligent agent cooperative regulation and control are achieved, and the method has the advantages of being high in safety, high in adaptability and good in stability.