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
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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    4OR quarterly journal of the Belgian, French and Italian Operations Research Societies 01/2014; To Appear. · 0.92 Impact Factor
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    ABSTRACT: Unit Commitment Problem (UCP) is a nonlinear mixed integer optimization problem used in the scheduling operation of power system generating units subjected to demand and reserve requirement constraints for achieving minimum operating cost. The task of the UC problem is to determine the on/off state of the generating units at every hour interval of the planning period for optimally transmitting the load and reserve among the committed units. The importance for the necessity of a more effective optimal solution to the UCP problem is increasing with the regularly varying demand. Hereby, we propose a hybrid approach which solves the unit commitment problem subjected to necessary constraints and gives the optimal commitment of the units. The possible combination of demand and their corresponding optimal generation schedule can be determined by the PSO algorithm. Being a global optimization technique, Evolutionary Programming (EP) for solving Unit Commitment Problem, operates on a method, which encodes each unit's operating schedule with respect to up/down time. When the demand over a time horizon is given as input to the network it successfully gives the schedule of each unit's commitment that satisfies the demands of all the periods and results in minimum total cost. Because hybridization is dominating, this approach for solving the unit commitment problem is more effective.
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    ABSTRACT: The aim of the current study is to probe the potentials of ensemble bio-inspired approaches to handle the deficiencies associated with designing large scale power systems. Ensemble computing has been proven to be a very promising paradigm. The fundamental motivation behind designing such bio-inspired optimization models lies in the fact that interactions among different sole optimizers can afford much better income as compared with an individual optimizer. To do so, the authors propose an optimization technique called ensemble mutable smart bee algorithm (E-MSBA) which is based on the aggregation of several independent low-level optimizers. Here, each low-level unit of the proposed ensemble framework uses mutable smart bee algorithm (MSBA) for optimization procedure. The main provocations behind selecting MSBAs of different properties as components of ensemble are twofold. On the one hand, MSBA proved its capability for handling multimodal constraint problems. On the other hand, based on different experiments, it was demonstrated that MSBA can find the optimum solution with a relatively low computational cost. In this study, the authors intend to indicate that the proposed ensemble paradigm can efficiently optimize the operating parameters of a large scale power system which includes different mechanical components. To this end, E-MSBA and some rival methods are taken into account for the optimization procedure. The obtained results reveal that E-MSBA inherits some positive features of the MSBA algorithm. Additionally, it is observed that the ensembling approach enables the proposed method to effectively tackle the flaws associated with optimization of large scale problems.
    Journal of Computational Science 01/2013; 5(2). DOI:10.1016/j.jocs.2013.10.007 · 1.57 Impact Factor

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May 20, 2014