Open AccessInvestigating the impact of missing data imputation techniques on battery energy management system
Author(s) -
Pazhoohesh Mehdi,
Allahham Adib,
Das Ronnie,
Walker Sara
Publication year - 2021
Publication title -
iet smart grid
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.612
H-Index - 11
ISSN - 2515-2947
DOI - 10.1049/stg2.12011
Subject(s) - imputation (statistics) , missing data , computer science , smart grid , smart meter , energy management system , computer data storage , energy storage , data mining , database , energy management , engineering , power (physics) , energy (signal processing) , electrical engineering , statistics , physics , mathematics , quantum mechanics , operating system , machine learning
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 .