Open AccessThe distributed economic dispatch of smart grid based on deep reinforcement learning
Author(s) -
Fu Yang,
Guo Xiaoyan,
Mi Yang,
Yuan Minghan,
Ge Xiaolin,
Su Xiangjing,
Li Zhenkun
Publication year - 2021
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12206
Subject(s) - reinforcement learning , economic dispatch , computer science , smart grid , process (computing) , node (physics) , mathematical optimization , grid , function (biology) , sequence (biology) , distributed computing , artificial intelligence , electric power system , power (physics) , engineering , physics , geometry , mathematics , quantum mechanics , electrical engineering , structural engineering , genetics , evolutionary biology , biology , operating system
In order to solve the problems of inefficient, inflexible and insecure for traditional centralized algorithm in the process of optimization dispatch, and with the application of artificial intelligence technology to smart grids, the novel distributed solution is proposed by using the deep reinforcement learning and the consensus theory to optimize the economic dispatch. Firstly, the optimal commitment sequence of massive units is realized through constructing deep reinforcement learning model. Secondly, the optimal unit output and efficient economic dispatch can be obtained by utilizing the improved consensus algorithm together with Adam's algorithm. Finally, simulation results of IEEE‐14 and IEEE‐162 node systems may demonstrate the effectiveness of the proposed solution for the smart grids with complex network structures, which can not only solve the problem of massive data processing, but also it may reduce the dependence on the exact objective function when dealing with extremely complex load distribution scenes and distributed powers.