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Faster evolutionary algorithm based optimal power flow using incremental variables
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DOI：10.1016/j.ijepes.2013.07.019.png)
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Abstract
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This paper proposes an efficient approach for evolutionary algorithm based Optimal Power Flow (OPF). The main drawback of evolutionary based OPF is the excessive execution time due to large number of power flows required in the solution process. The proposed Efficient Evolutionary Algorithm (EEA) uses the concept of incremental power flow model, based on sensitivities. With this, the number of power flows are reduced substantially, resulting in solution speed up. The original advantages of the evolutionary algorithms, like: the ability to handle discontinuities, complex non-linearities in the objective function, discrete variables, and multi-objective optimization, are still available in the proposed approach. The OPF solution is obtained with single objectives (fuel cost, loss, voltage stability index) and multiple objective (fuel cost and voltage stability index). The potential of the proposed approach is tested on IEEE 30, 118 and 300 bus systems, and the results obtained with proposed EEA are compared with other evolutionary algorithms. The proposed approach is generic one and can be used with any evolutionary algorithm based OPF. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Enhanced genetic algorithm
Evolutionary algorithms
Linear programming
Multi-objective optimization
Optimal power flow
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