A decision support tool for multi-attribute evaluation of demand-side commercial battery storage products

Ziqian Wu, Kaveh Khalilpour*, Raimo P. Hämäläinen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

With the diversification of commercial energy storage technologies, choosing a suitable technology is becoming a complex decision-making process. The complexity is rooted in the many decision criteria such as technology, brand reputation, energy capacity, volume, weight, aging, and warranty among many others. As such, for non-expert users, particularly small households or enterprises, the act of energy storage adoption is becoming growingly cumbersome. To address this problem, this paper introduces a decision support tool for the evaluation of commercial (small-scale) energy storage products. It then identifies the most suitable option(s) based on the users' preferences. For the reasons elaborated in the paper, nine multi-criteria decision-making (MCDM) methodologies have been employed. Altogether, 19 attributes are identified for the evaluation of (battery) energy storage technologies. The decision support tool is developed in the Matlab environment and includes a graphical user interface for easier interaction of non-expert users. For the demonstration, three scenario cases have been studied for users with different preferences. The ranking results clearly show the marked impact of users preferences on the recommended energy storage technologies. This implies that a tool like this can help small users in the selection of their right technology and avoid resource loss due to inappropriate technology selection, which can be neither economical nor sustainable.

Original languageEnglish
Article number101723
Number of pages11
JournalSustainable Energy Technologies and Assessments
Volume50
DOIs
Publication statusPublished - Mar 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • Battery
  • Decision support systems
  • Energy storage
  • Multi-attribute decision-making
  • Multi-criteria
  • Technology screening

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