基于分布式多步回溯<em>Q</em>(<em>λ</em>) 学习的复杂电网最优潮流算法

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Abstract As for the problem that usual optimal power flow algorithm can not meet the timely demand of the complex power grid., this paper presents a novel distributed Q(λ) learning algorithm based on complex districted power grid , which deals no auxiliary process with the optimal power flow (OPF) mathematical model and whose internal agent independently undertakes each district’s learning duty with the standard multi-step Q(λ) learning algorithm, and then coordinately cooperate to reach the optimization of the whole system. The result of the application in IEEE118 bus bar demonstrates that the distributed Q(λ) learning algorithm provides a new feasible and effective method to the complex grid OPF problem.

Key words： Optimal power flow Q(λ ) learning multi-objective optimization distributed reinforcement learning

Received: 19 August 2010 Published: 20 March 2014

PACS:	TM761
	TP273

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	Yu Tao
	Liu Jing
	Hu Xibing

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Yu Tao,Liu Jing,Hu Xibing. Optimal Power Flow for Complex Power Grid Using Distributed Multi-Step Backtrack Q(λ) Learning[J]. Transactions of China Electrotechnical Society, 2012, 27(4): 185-192.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2012/V27/I4/185

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