کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5476564 | 1521418 | 2017 | 31 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Deep transfer Q-learning with virtual leader-follower for supply-demand Stackelberg game of smart grid
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی (عمومی)
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
This paper proposes a novel deep transfer Q-learning (DTQ) associated with a virtual leader-follower pattern for supply-demand Stackelberg game of smart grid. Each generator and load are regarded as an agent of a supplier and a demander, respectively, in which an economic dispatch (ED) and demand response (DR) can be simultaneously solved by DTQ. To maximize the total payoff of all the agents, a virtual leader-follower pattern is employed to achieve a reliable collaboration among the agents. Then, Q-learning with a cooperative swarm is adopted for the knowledge learning for each agent via appropriate explorations and exploitations in an unknown environment. Furthermore, the original extremely large-scale knowledge matrix can be efficiently decomposed into several simplified small-scale knowledge matrices through a binary state-action chain, while the continuous actions can be generated for continuous variables. Lastly, a deep belief network (DBN) is used for knowledge transfer, thus DTQ can effectively exploit the prior knowledge from source tasks so as to rapidly obtain an optimal solution of a new task. Case studies are carried out to evaluate the performance of DTQ for supply-demand Stackelberg game of smart grid on a 94-agent system and a practical Shenzhen power grid of southern China.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy - Volume 133, 15 August 2017, Pages 348-365
Journal: Energy - Volume 133, 15 August 2017, Pages 348-365
نویسندگان
Xiaoshun Zhang, Tao Bao, Tao Yu, Bo Yang, Chuanjia Han,