公开(公告)号：US20230297842A1

公开(公告)日：2023-09-21

申请号：US18124251

申请日：2023-03-21

Applicant: SHANDONG UNIVERSITY

Inventor： Shuai LIU ,  Xiaowen WANG ,  Haoran ZHAO ,  Bo SUN ,  Lantao XING ,  Xian LI ,  Ruiqi WANG

IPC: G06N3/092 ,  H02J3/46

CPC classification number: G06N3/092 ,  H02J3/46

Abstract: A method for event-triggered distributed reinforcement learning for unit commitment optimization and dispatch to solve the waste problem of unit resources includes obtaining a unit commitment optimization and dispatch model, constructing a fixed action set under preset constraint conditions, and selecting optimal power of each unit; transforming constraint conditions into projection constraints, and projecting the virtual generation power to a corresponding constraint range, to obtain actual generation power of each unit within the constraint range; calculating corresponding rewards based on cost under actual generation power of each unit without bandwidth constraints, and updating local Q values of each unit in a Q table according to Q-learning algorithms, to obtain an optimal action of each unit without bandwidth constraints; and under the constraint conditions of considering bandwidths, obtaining an optimal solution, meeting limited bandwidth constraint conditions, to the unit commitment optimization and dispatch problem.