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RSS FeedsEnergies, Vol. 13, Pages 1544: Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm (Energies)


26 march 2020 04:00:32

Energies, Vol. 13, Pages 1544: Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm (Energies)

This paper describes the algorithm for optimal distribution network reconfiguration using the combination of a heuristic approach and genetic algorithms. Although similar approaches have been developed so far, they usually had issues with poor convergence rate and long computational time, and were often applicable only to the small scale distribution networks. Unlike these approaches, the algorithm described in this paper brings a number of uniqueness and improvements that allow its application to the distribution networks of real size with a high degree of topology complexity. The optimal distribution network reconfiguration is formulated for the two different objective functions: minimization of total power/energy losses and minimization of network loading index. In doing so, the algorithm maintains the radial structure of the distribution network through the entire process and assures the fulfilment of various physical and operational network constraints. With a few minor modifications in the heuristic part of the algorithm, it can be adapted to the problem of determining the distribution network optimal structure in order to equalize the network voltage profile. The proposed algorithm was applied to a variety of standard distribution network test cases, and the results show the high quality and accuracy of the proposed approach, together with a remarkably short execution time. Digg Facebook Google StumbleUpon Twitter
18 viewsCategory: Biophysics, Biotechnology, Physics
Energies, Vol. 13, Pages 1533: Examination of the Spillover Effects among Natural Gas and Wholesale Electricity Markets Using Their Futures with Different Maturities and Spot Prices (Energies)
Energies, Vol. 13, Pages 1551: Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation (Energies)
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