Open Access

ARTICLE

Optimal Location and Sizing of Distributed Generator via Improved Multi-Objective Particle Swarm Optimization in Active Distribution Network Considering Multi-Resource

Guobin He^*, Rui Su, Jinxin Yang, Yuanping Huang, Huanlin Chen, Donghui Zhang, Cangtao Yang, Wenwen Li

Dali Power Supply Bureau of Yunnan Power Grid Co., Ltd., Dali, 671099, China

* Corresponding Author: Guobin He. Email:

(This article belongs to the Special Issue: Fault Diagnosis and State Evaluation of New Power Grid)

Energy Engineering 2023, 120(9), 2133-2154. https://doi.org/10.32604/ee.2023.029007

Received 23 January 2023; Accepted 13 April 2023; Issue published 03 August 2023

Abstract

In the framework of vigorous promotion of low-carbon power system growth as well as economic globalization, multi-resource penetration in active distribution networks has been advancing fiercely. In particular, distributed generation (DG) based on renewable energy is critical for active distribution network operation enhancement. To comprehensively analyze the accessing impact of DG in distribution networks from various parts, this paper establishes an optimal DG location and sizing planning model based on active power losses, voltage profile, pollution emissions, and the economics of DG costs as well as meteorological conditions. Subsequently, multi-objective particle swarm optimization (MOPSO) is applied to obtain the optimal Pareto front. Besides, for the sake of avoiding the influence of the subjective setting of the weight coefficient, the decision method based on a modified ideal point is applied to execute a Pareto front decision. Finally, simulation tests based on IEEE33 and IEEE69 nodes are designed. The experimental results show that MOPSO can achieve wider and more uniform Pareto front distribution. In the IEEE33 node test system, power loss, and voltage deviation decreased by 52.23%, and 38.89%, respectively, while taking the economy into account. In the IEEE69 test system, the three indexes decreased by 19.67%, and 58.96%, respectively.

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Keywords

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