期刊
IEEE TRANSACTIONS ON SMART GRID
卷 9, 期 3, 页码 2152-2165出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2016.2607801
关键词
Consensus transfer Q-learning; virtual generation tribe; decentralized generation command dispatch; automatic generation control
资金
- National Key Basic Research Program of China (973 Program) [2013CB228205]
- National Natural Science Foundation of China [51477055]
This paper develops a consensus transfer Q-learning (CTQ) for decentralized generation command dispatch (GCD) of automatic generation control (AGC). A two-layer decentralized GCD based on virtual generation tribe (VGT) is adopted to resolve the course of dimension emerged in large-scale power systems. The leader of VGTs can calculate the generation command of each VGT through exchanging the Q-value matrices with its adjacent VGTs. In addition, a behavior transfer is employed into CTQ to exploit the prior knowledge of source tasks for a new optimization task according to their similarities, such that the algorithm convergence rate can be accelerated and the requirement of AGC period is satisfied. Case studies are carried out to evaluate the performance of CTQ for decentralized GCD on China southern power grid model.
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