NDLI: Lagrangian relaxation neural network for unit commitment

Content Provider	IEEE Xplore Digital Library
Author	Luh, P.B. Yajun Wang Xing Zhao
Copyright Year	1999
Description	Author affiliation: Dept. of Electr. & Syst. Eng., Connecticut Univ., Storrs, CT, USA (Luh, P.B.)
Abstract	This paper presents a novel method for unit commitment by synergistically combining Lagrangian relaxation for constraint handling with Hopfield-type recurrent neural networks for fast convergence to the minimum. The key idea is to set up a Hopfield-type network using the negative dual as its energy function. This network is connected to "neuron-based dynamic programming modules" that make full use of the DP structure to solve individual unit subproblems. The overall network is proved to be stable, and the difficulties in handling integer variables, subproblem constraints, and subproblem local minima plaguing current neural network methods are avoided. Unit commitment solutions are thus natural results of network convergence. Software simulation using data sets from Northeast Utilities demonstrates that the results are much better than what has been reported in the neural network literature, and the method can provide near-optimal solutions for practical problems. Furthermore, the method has the potential to be implemented in hardware with much improved quality and speed.
Starting Page	490
Ending Page	495
File Size	662799
Page Count	6
File Format	PDF
ISBN	0780348931
DOI	10.1109/PESW.1999.747504
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-01-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lagrangian functions Neural networks Dynamic programming Hopfield neural networks Constraint optimization H infinity control Systems engineering and theory Recurrent neural networks Convergence Modeling
Content Type	Text
Resource Type	Article

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