What is it about?

Summary: Reconfiguration is one of the most important functions in the distribution network's automation system. Reconfiguration is formulated as an optimization problem with a large number of scenarios, which demands high central processing unit time to check all of them. Therefore, it is necessary to utilize a high-efficiency optimization method. In this paper, the minimization of active power losses, total voltage deviations of buses, and maximization of system loading margin are integrated as three objective functions of the proposed reconfiguration model. Also, to improve the voltage profile, reduce power losses, and increase system loading margin, shunt capacitors (SCs) and distributed generations (DGs) are located. Seasonal daily load curves are applied to better simulate networks' real conditions. This paper uses the non-dominated sorting genetic algorithm II, which generates a set of non-dominated solutions. This set includes a wide range of solutions with different weighting coefficients. The multi-criteria decision-making (MCDM) algorithm, as a powerful and flexible decision-making tool, is utilized to select the best solution based on tuning parameters. Also, it is assumed that DGs are wind farms, thus the uncertainty of DGs' power output is taken into the account, and the three-point estimate method (3PEM) is utilized to reduce the number of sub-scenarios generated by 3PEM. Finally, the sub-scenario aggregation method is utilized to extract the value of objective functions in sub-scenarios. The developed model determines the optimal location of SCs and DGs in conjunction with the optimal network reconfiguration. The proposed method is implemented on the typical 33 and 69 bus radial distribution systems.

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Why is it important?

• Proposing a probabilistic multi-objective reconfiguration model in active networks • Determining the optimal location of switches and DGs, considering uncertainties • Minimizing active power losses, voltage deviation, and maximizing loading margin. • Reducing the number of scenarios using 3-point estimate method • Optimizing the model using the NSGA II and the MCDM algorithms

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This page is a summary of: Probabilistic multiobjective reconfiguration considering the optimal location of shunt capacitors and distributed generations in distribution network, International Transactions on Electrical Energy Systems, July 2021, Hindawi Publishing Corporation, DOI: 10.1002/2050-7038.12979.
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