Abstract
This paper presents an evolutionary neural network (ENN) approach for solving the power system unit commitment problem. The proposed ENN approach combines a genetic algorithm (GA) with a back-propagation neural network (BPNN). The BPNN is first used as a dispatch tool to generate raw unit combinations for each hour temporarily ignoring time-dependent constraints. Then, the proposed decoding algorithm decodes the raw committed schedule of each unit into a feasible one. The GA is then used to find the finally optimal schedule. The most difficult time-dependent minimal uptime/downtime constraints are satisfied throughout the proposed encoding and decoding algorithm. Numerical results from a 10-unit example system indicate the attractive properties of the proposed ENN approach, which are a highly optimal solution and faster rate of computation.
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© 2006 Springer-Verlag Berlin Heidelberg
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Chen, PH., Chen, HC. (2006). Application of Evolutionary Neural Network to Power System Unit Commitment. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_188
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DOI: https://doi.org/10.1007/11760023_188
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
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