Planning optimization and stochastic analysis of RE-DGs for techno-economic benefit maximization in distribution networks

Zia Ullah*, M. R. Elkadeem, Shaorong Wang, S. W. Sharshir, Muhammad Azam

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

In practical distribution networks of long-distance feeders, load's expansion and seasonal load variations seriously affect the system performance. In the context of the present advancements in D.G.s, especially generating from the renewable energy (RE) sources along with the recent support of policy-makers, it is smart consideration to transform the long-distance conventional distribution networks into the optimal DG-integrated systems for the augmentation of the technical and economic benefits jointly. In this article, a novel methodology, namely Hybrid-PPSO-GSA, is proposed and applied to maximize the techno-economic benefits via an optimal RE-DG integrated system. The proposed methodology focused on the optimal placement and sizing of RE-DG into the distribution system, considering the stochastic analysis of RE-DGs such as W.T. and P.V. with multiple objectives. The Hybrid PPSO-GSA was tested on standard 69-bus systems and particularly implemented on 94-bus long-distance practical distribution feeder located in Portuguese; for power loss reduction, voltage profile, and stability enhancement. Apart from the reduction in the power loss and voltage improvement, the energy loss can be improved jointly, resulting in the coupled techno-economic contribution of the DG-integrated system. Simulations are conducted for various scenarios, and the obtained results demonstrate the merits of the proposed scheme.

Original languageEnglish
Article number100210
JournalInternet of Things (Netherlands)
Volume11
DOIs
StatePublished - Sep 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 Elsevier B.V.

Keywords

  • Distributed generator
  • Distribution system
  • Energy loss minimization
  • Load variation
  • Planning optimization
  • Renewable energy sources

ASJC Scopus subject areas

  • Software
  • Computer Science (miscellaneous)
  • Information Systems
  • Engineering (miscellaneous)
  • Hardware and Architecture
  • Computer Science Applications
  • Artificial Intelligence
  • Management of Technology and Innovation

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