A Mixed-Integer Quadratically Constrained Programming Model for Network Reconfiguration in Power Distribution Systems with Distributed Generation and Shunt Capacitors
Main Article Content
Abstract
This paper proposes a formulation based on mixed-integer quadratically constrained programming (MIQCP) for the problem of optimally determining network topology aiming at minimum power loss in electrical distribution grids considering distributed generation and shunt capacitors. The proposed optimization model is derived from the originally nonlinear optimization model by leveraging the modified distribution power flow method that is linear. This optimization model can be effectively solved by standard commercial solvers such as CPLEX. Then, the MIQCP-based formulation is verified on an IEEE 33-bus distribution network and a 190-bus real distribution network in Luc Ngan district, Bac Giang province, Vietnam, in 2021. The effects of the load power level on optimal solutions are analyzed in detail. Furthermore, results of power flow analysis achieved from the modified distribution power flow approach are compared to those from solving nonlinear equations of power flow using the power summation method that gives exact solutions.
Keywords
Mixed-integer quadratically constrained programming (MIQCP), Radial distribution systems, Distributed generation, Minimum power loss, Power flow analysis
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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Smart Grid, vol. 11, no. 4, pp. 2995-3007, Jul. 2020,
https://doi.org/10.1109/TSG.2020.2974922.
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Reconfiguration and capacitor placement
simultaneously for energy loss reduction based on an
improved reconfiguration method, IEEE Trans. Power
Syst., vol. 27, no. 2, pp. 587-595, May 2012,
https://doi.org/10.1109/TPWRS.2011.2167688.
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minimization in distribution networks using
segmented-time feeder reconfiguration and reactive
power control of distributed generators, IEEE Trans.
Power Syst., vol. 31, no. 2, pp. 983-993, Mar. 2016,
https://doi.org/10.1109/TPWRS.2015.2419716.
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radiality constraints for distribution system restoration
and reconfiguration problems, IEEE Trans. Power
Syst., vol. 35, no. 4, pp. 3294-3296, Jul. 2020,
https://doi.org/10.1109/TPWRS.2020.2991356.
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models: basic theory and error analysis, IEEE Trans.
Power Syst., vol. 34, no. 2, pp. 1315-1324, Mar. 2019,
https://doi.org/10.1109/TPWRS.2018.2871182.
[8] J. Yang, N. Zhang, C. Kang, and Q. Xia, A state-
independent linear power flow model with accurate
estimation of voltage magnitude, IEEE Trans. Power
Syst., vol. 32, no. 5, pp. 3607-3617, Sep. 2017,
https://doi.org/10.1109/TPWRS.2016.2638923.
[9] L. Bai, J. Wang, C. Wang, C. Chen, and F. Li,
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congestion management and voltage support, IEEE
Trans. Power Syst., vol. 33, no. 4, pp. 4061-4073, Jul.
2018,
https://doi.org/ 10.1109/TPWRS.2017.2767632.
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distribution system reconfiguration, IEEE Trans.
Power Syst., vol. 27, no. 3, pp. 1407-1413, Aug. 2012,
https://doi.org/10.1109/TPWRS.2012.2184307.
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linear branch flow model for radial distribution
networks and its application to reactive power
optimization and network reconfiguration, IEEE
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May 2021,
https://doi.org/10.1109/TSG.2020.3039984.
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models for radial power distribution grids: a
comparison, TNT Journal of Science and Technology,
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https://doi.org/10.34238/tnu-jst.4665.
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May 2021,
https://doi.org/10.1109/TPWRS.2020.3038030.
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planning, and analysis tools for power systems
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26, no. 1, pp. 12-19, Feb. 2011,
https://doi.org/ 10.1109/TPWRS.2010.2051168.