Extended mixed integer quadratic programming for simultaneous distributed generation location and network reconfiguration





active distribution networks, distribution system reconfiguration, distributed generation, mixed-integer quadratic programming, power loss


Introduction. To minimise power loss, maintain the voltage within the acceptable range, and improve power quality in power distribution networks, reconfiguration and optimal distributed generation placement are presented. Power flow analysis and advanced optimization techniques that can handle significant combinatorial problems must be used in distribution network reconfiguration investigations. The optimization approach to be used depends on the size of the distribution network. Our methodology simultaneously addresses two nonlinear discrete optimization problems to construct an intelligent algorithm to identify the best solution. The proposed work is novel in that it the Extended Mixed-Integer Quadratic Programming (EMIQP) technique, a deterministic approach for determining the topology that will effectively minimize power losses in the distribution system by strategically sizing and positioning Distributed Generation (DG) while taking network reconfiguration into account. Using an efficient Quadratic Mixed Integer Programming (QMIP) solver (IBM ®), the resulting optimization problem has a quadratic form. To ascertain the range and impact of various variables, our methodology outperforms cutting-edge algorithms described in the literature in terms of the obtained power loss reduction, according to extensive numerical validation carried out on typical IEEE 33- and 69-bus systems at three different load factors. Practical value. Examining the effectiveness of concurrent reconfiguration and DG allocation versus sole reconfiguration is done using test cases. According to the findings, network reconfiguration along with the installation of a distributed generator in the proper location, at the proper size, with the proper loss level, and with a higher profile, is effective.


Author Biographies

Y. Tami, University of Medea


K. Sebaa, University of Medea

Professor, LSEA

M. Lahdeb, University of Laghouat

Doctor, LaACoSE, Electriques

O. Usta, Istanbul Technical University

Professor, Department of Electrical Engineering

H. Nouri, University of the West of England

Professor, Department of Engineering, Design and Mathematics


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How to Cite

Tami, Y., Sebaa, K., Lahdeb, M., Usta, O., & Nouri, H. (2023). Extended mixed integer quadratic programming for simultaneous distributed generation location and network reconfiguration. Electrical Engineering & Electromechanics, (2), 93–100. https://doi.org/10.20998/2074-272X.2023.2.14



Power Stations, Grids and Systems