Go top
Paper information

Optimal planning in active distribution networks considering nonlinear loads using the MOPSO algorithm in the TOPSIS framework

H. Ebrahimi, S. Rezaeian-Marjani, V. Talavat

International Transactions on Electrical Energy Systems Vol. 30, nº. 3, pp. e12244-1 - e12244-17

Summary:

In recent years, harmonic distortion has sharply increased because of injecting harmonic components caused by using nonlinear loads in the distribution system. Uncontrolled harmonic distortion can bring damage to equipment of the power system, reduce the power system efficiency, and interrupt protection and measurement devices. This parameter can be managed using various resources and control programs in active distribution networks. This paper presents active distribution networks (ADNs) with multiple objective particle swarm optimization (MOPSO) algorithm using the technique for order of preference by similarity to ideal solution (TOPSIS) framework in the presence of nonlinear loads. The presented planning model manages the sample network to minimize the total harmonic distortion (THD) of the system while minimizing the operational and investment costs. Four active management schemes, network reconfiguration, distributed generation (DG) dispatch, demand-side management (DSM), and reactive power compensation are considered for this purpose. Optimal sizing and siting of the energy storage system (ESS) is used to reduce peak load and operational costs. The IEEE 33 node distribution network is used to verify the proposed planning model.


JCR Impact Factor and WoS quartile: 2,860 - Q2 (2020); 2,300 - Q3 (2022)

DOI reference: DOI icon https://doi.org/10.1002/2050-7038.12244

Published on paper: March 2020.

Published on-line: December 2019.



Citation:
H. Ebrahimi, S. Rezaeian-Marjani, V. Talavat, Optimal planning in active distribution networks considering nonlinear loads using the MOPSO algorithm in the TOPSIS framework. International Transactions on Electrical Energy Systems. Vol. 30, nº. 3, pp. e12244-1 - e12244-17, March 2020. [Online: December 2019]


pdf Preview
Request Request the document to be emailed to you.