Optimizing the technical and economic value of energy storage systems in LV networks for DNO applications

M. H. Roos, D. A.M. Geldtmeijer, H. P. Nguyen, J. Morren, J. G. Slootweg

Research output: Contribution to journalArticleAcademicpeer-review

21 Citations (Scopus)
149 Downloads (Pure)

Abstract

Electrical energy production from renewable energy sources and electrification of consumer energy demand are developments in the ongoing energy transition. These developments urge the demand for flexibility in low voltage distribution networks, on the one hand caused by the intermittency of renewable energy sources, and on the other hand by the high power demand of battery electric vehicles and heat pumps. One of the foremost ways to create flexibility is by using energy storage systems. This paper proposes a method to first optimize the siting, power and capacity rating, technology, and operation of energy storage systems based on the technical and economic value. Secondly the method can be used to make cost- and time-based network planning decisions between network upgrades and network upgrade deferral by energy storage systems. To demonstrate the proposed method, study cases are analyzed of five low voltage distribution networks with different penetrations of photovoltaics, heat pumps and battery electric vehicles. The optimal energy storage systems in the study cases are: flow batteries sited at over 50% of the cable length with a high capacity rating per euro. With the current state of energy storage system development, network upgrade deferral is up to 61% cheaper than network upgrades in the study cases. The energy storage systems can offer additional value by reducing the peak loading of the medium voltage grid which is not taken into account in this research.

Original languageEnglish
Pages (from-to)207-216
Number of pages10
JournalSustainable Energy, Grids and Networks
Volume16
DOIs
Publication statusPublished - 1 Dec 2018

Keywords

  • Energy storage
  • Network upgrade deferral
  • Optimization
  • Power system planning

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