Investigating the impact of missing data imputation techniques on battery energy management system

Mehdi Pazhoohesh*, Adib Allahham, Ronnie Das, Sara Walker

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Effective control of energy storage system (ESS), supplying an ancillary service to a grid, requires effective and critical calculation of state-of-charge (SoC). Charging and discharging values from battery operations are essential in calculating the efficiency and performance of a storage system. This information can also be a key to understand and forecast peak demand performance. Missing data is a real problem in any operations system, and it appears to be more common within powers systems due to sensor and/or network malfunctioning problems. Missing data imputation techniques have evolved in power systems research using smart meter data, but little research has gone into understanding how missing data can be best handled within storage management systems. This paper builds on a year's worth of charging and discharging data collected from a real 6MW/10MWh lithium-ion storage battery deployed on the distribution network at Leighton Buzzard, UK. Using R Studio version (1.3.959-1) open-source software, eight selected imputation techniques were applied in identifying the best suited technique in replacing various missing data amounts and patterns. Findings from the study open up avenues for discussion and debate in identifying an appropriate imputation technique within the storage management context. The study also provides a pioneering lead in understanding the importance of decomposition in evaluating the right imputation technique.

Original languageEnglish
Pages (from-to)162-175
Number of pages14
JournalIET Smart Grid
Volume4
Issue number2
Early online date15 Feb 2021
DOIs
Publication statusPublished - 1 Apr 2021
Externally publishedYes

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