Rider optimization algorithm for optimal DG allocation in radial distribution network

Mansur Khasanov, Salah Kamel, Hany M. Hasanien, Ahmed Al-Durra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

Over the last few years, with a growing interest in energy security and climate change, the integration of renewable energy sources (RES) and energy efficiency, including power loss minimization, are the two pillars of sustainable energy solutions. This paper proposes an application for a recent optimization technique called Rider Optimization Algorithm (ROA). The ROA is inspired by a group of riders. The ROA is applied for determining the optimal allocation of Photovoltaic (PV) and Wind turbine (WT) based distributed generation (DG) units with the aim of minimizing the total power losses. In this regard, the most suitable nodes to place the DGs are identified using Power Loss Sensitivity Factor (PLSF) in order to reduce the search space, then ROA is applied to identify their optimal locations and sizes. The developed technique has been tested on the standard 33-node system. The results obtained by the developed technique are compared with those obtained by other well-known algorithms.

Original languageBritish English
Title of host publication2020 2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages138-143
Number of pages6
ISBN (Electronic)9781728166117
DOIs
StatePublished - 15 Sep 2020
Event2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020 - Bangkok, Thailand
Duration: 15 Sep 202018 Sep 2020

Publication series

Name2020 2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020

Conference

Conference2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020
Country/TerritoryThailand
CityBangkok
Period15/09/2018/09/20

Keywords

  • Distributed Generation
  • Power Loss Sensitivity Factor
  • Rider Optimization Algorithm
  • Total Power Loss

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