A Hybrid Neural Network and Simulated Annealing Approach to the Unit Commitment Problem

Machine Learning Research Centre, School of Computer Science, QUT, Brisbane, Qld 4001, Australia; Department of Electrical Engineering, University of Roorkee, Roorkee, UP 247667, India
08/2000; DOI: 10.1016/S0045-7906(99)00037-3
Source: OAI

ABSTRACT In this paper, the authors present an approach combining the feedforward neural network and the simulated annealing method to solve unit commitment, a mixed integer combinatorial optimisation problem in power system. The artificial neural network (ANN) is used to determine the discrete variables corresponding to the state of each unit at each time interval. The simulated annealing method is used to generate the continuous variables corresponding to the power output of each unit and the production cost. The type of neural network used in this method is a multi-layer perceptron trained by the back-propagation algorithm. A set of load profiles as inputs and the corresponding unit-commitment schedules as outputs (satisfying the minimum up-down, spinning reserve and crew constraints) are utilized to train the network. A method to generate the training patterns is also presented. The experimental result demonstrates that the proposed approach can solve unit commitment in a reduced computational time with an optimum generation schedule.

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May 20, 2014