Optimal Sizing of Battery Energy Storage Characteristics in Radial Distribution Network based on Contrasting Investment and Net Present Values Approach

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

Abstract

This paper studies economic evaluation based on net present value (NPV) of the optimal sizing of battery energy storage (BES) to shave peak load in the radial distributed system. A study case in streetlights system is conducted as representing the radial system which drives peak load. Since there are various types of BES characteristics, while each type has different characteristics such as energy density, storage cost, and lifetime, the optimal BES configuration becomes complicated. Due to this complexity, to design a BES system to shave peak load, optimization algorithm, namely Crazy-PSO is utilized. The factors affecting the NPV and BES configuration are investigated such as BES lifetime, BES replacement cost due to run out of the lifetime, investment, saving cost, and quantity of BES configuration. Moreover, this paper's essence is to point out the contrast between BES optimization in investment and NPV objective functions.

Original languageEnglish
Title of host publicationInternational Conference on Innovative Smart Grid Technologies, ISGT Asia 2018
EditorsQuan Hao, Anurag Sharma
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages121-126
Number of pages6
ISBN (Electronic)9781538642917
DOIs
Publication statusPublished - 18 Sept 2018
Externally publishedYes
Event2018 International Conference on Innovative Smart Grid Technologies, ISGT Asia 2018 - Singapore, Singapore
Duration: 22 May 201825 May 2018

Publication series

NameInternational Conference on Innovative Smart Grid Technologies, ISGT Asia 2018

Conference

Conference2018 International Conference on Innovative Smart Grid Technologies, ISGT Asia 2018
Country/TerritorySingapore
CitySingapore
Period22/05/1825/05/18

Keywords

  • Batteries
  • energy storage
  • industrial economics
  • particle swarm optimization
  • power systems

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