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Energies 2017, 10(5), 638; doi:10.3390/en10050638

Bacteria Foraging Reinforcement Learning for Risk-Based Economic Dispatch via Knowledge Transfer

1
School of Electric Power, South China University of Technology, Guangzhou 510640, China
2
Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China
*
Author to whom correspondence should be addressed.
Academic Editor: Josep M. Guerrero
Received: 18 January 2017 / Revised: 9 April 2017 / Accepted: 24 April 2017 / Published: 6 May 2017
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Abstract

This paper proposes a novel bacteria foraging reinforcement learning with knowledge transfer method for risk-based economic dispatch, in which the economic dispatch is integrated with risk assessment theory to represent the uncertainties of active power demand and contingencies during power system operations. Moreover, a multi-agent collaboration is employed to accelerate the convergence of knowledge matrix, which is decomposed into several lower dimension sub-matrices via a knowledge extension, thus the curse of dimension can be effectively avoided. Besides, the convergence rate of bacteria foraging reinforcement learning is increased dramatically through a knowledge transfer after obtaining the optimal knowledge matrices of source tasks in pre-learning. The performance of bacteria foraging reinforcement learning has been thoroughly evaluated on IEEE RTS-79 system. Simulation results demonstrate that it can outperform conventional artificial intelligence algorithms in terms of global convergence and convergence rate. View Full-Text
Keywords: bacteria foraging reinforcement learning; risk-based economic dispatch; knowledge matrix; knowledge transfer bacteria foraging reinforcement learning; risk-based economic dispatch; knowledge matrix; knowledge transfer
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Han, C.; Yang, B.; Bao, T.; Yu, T.; Zhang, X. Bacteria Foraging Reinforcement Learning for Risk-Based Economic Dispatch via Knowledge Transfer. Energies 2017, 10, 638.

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