Investigating the impact of missing data imputation techniques on battery energy management system
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-04-01
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| Series: | IET Smart Grid |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/stg2.12011 |
| _version_ | 1811319005887070208 |
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| author | Mehdi Pazhoohesh Adib Allahham Ronnie Das Sara Walker |
| author_facet | Mehdi Pazhoohesh Adib Allahham Ronnie Das Sara Walker |
| author_sort | Mehdi Pazhoohesh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T12:34:45Z |
| format | Article |
| id | doaj.art-a7a8e11ec44d44a796c277a03d0da384 |
| institution | Directory Open Access Journal |
| issn | 2515-2947 |
| language | English |
| last_indexed | 2024-04-13T12:34:45Z |
| publishDate | 2021-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Smart Grid |
| spelling | doaj.art-a7a8e11ec44d44a796c277a03d0da3842022-12-22T02:46:42ZengWileyIET Smart Grid2515-29472021-04-014216217510.1049/stg2.12011Investigating the impact of missing data imputation techniques on battery energy management systemMehdi Pazhoohesh0Adib Allahham1Ronnie Das2Sara Walker3Faculty of Computing, Engineering and Media, School of Engineering and Sustainable Development De Montfort University Leicester UKFaculty of Engineering Middle East University (MEU) Amman JordanNewcastle University Business School Newcastle University Newcastle upon Tyne UKFaculty of Science, Agriculture and Engineering, School of Engineering Newcastle University Newcastle upon Tyne UKAbstract 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.https://doi.org/10.1049/stg2.12011battery management systemsbattery powered vehiclesbattery storage plantsdistribution networksenergy management systemsenergy storage |
| spellingShingle | Mehdi Pazhoohesh Adib Allahham Ronnie Das Sara Walker Investigating the impact of missing data imputation techniques on battery energy management system IET Smart Grid battery management systems battery powered vehicles battery storage plants distribution networks energy management systems energy storage |
| title | Investigating the impact of missing data imputation techniques on battery energy management system |
| title_full | Investigating the impact of missing data imputation techniques on battery energy management system |
| title_fullStr | Investigating the impact of missing data imputation techniques on battery energy management system |
| title_full_unstemmed | Investigating the impact of missing data imputation techniques on battery energy management system |
| title_short | Investigating the impact of missing data imputation techniques on battery energy management system |
| title_sort | investigating the impact of missing data imputation techniques on battery energy management system |
| topic | battery management systems battery powered vehicles battery storage plants distribution networks energy management systems energy storage |
| url | https://doi.org/10.1049/stg2.12011 |
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