4.7 Article

A case study on the behaviour of residential battery energy storage systems during network demand peaks

期刊

RENEWABLE ENERGY
卷 180, 期 -, 页码 712-724

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2021.08.107

关键词

Battery energy storage systems; Peak demand reduction; Battery system response; Virtual power plants; Battery coordination; Distribution network support

资金

  1. Australian Research Council [ARC IH180100020]
  2. Australian Government Research Training Program (RTP) Scholarship
  3. Next Generation PhD top-up Scholarship

向作者/读者索取更多资源

This study analyzed the actual performance of 15 residential batteries deployed in Australia during network peak demand periods, showing that some batteries performed lower than simulated expectations and did not fully reduce grid import and export.
Over the last decade, the electricity sector has seen a significant increase in the number of residential battery systems, as well as increasing interest in using them to reduce demand during network peaks. Although there is an abundance of literature assessing this ability using modelled residential batteries, there is a lack of detailed assessment using deployed residential batteries. This paper analyses 1-min resolution data from 15 non-coordinated residential batteries deployed in Australia across 6 network peak demand periods. A novel metric was used to quantify errors in BESS load-following, which occurred when the batteries did not completely mitigate grid import and export even when they had sufficient energy capacity and rated power. On average the 15 batteries discharged around 25% of their rated power during network demand peaks, whereas those that load-followed discharged around 40%. Despite the small sample size, these results suggest that the outcomes from modelled batteries represent the ideal upper bound and the actual performance of some batteries is likely to be lower. This there is a need for more research into the actual operation of deployed batteries, and what this means for the current modelled findings regarding their ability to reduce demand during network peaks. (c) 2021 Elsevier Ltd. All rights reserved.

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