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.
Highlights
In recent years, energy storage systems (ESS) have become an important pillar of future smart grids, thanks to their ability to carry out energy arbitrage while providing a range of ancillary services to the network [1,2,3,4]
The standard deviation (SD) distance results observed within the general missing pattern (GP) indicates that regression and forward filling techniques produced the smallest distance compared to other techniques within this specific missing pattern group
Peak demand management through energy storage and shifting energy load is perceived to be an important area of investigation for ensuring low operational cost and the uptake of carbon efficient storage technologies [39,40]
Summary
Energy storage systems (ESS) have become an important pillar of future smart grids, thanks to their ability to carry out energy arbitrage while providing a range of ancillary services to the network [1,2,3,4]. The energy management system (EMS) of the BESS plays a key role in enabling the BESS to provide designated services or functions required. A typical energy management system requires supportive data acquisition systems in enabling different actors working to update the control unit about their current operation conditions. Data driven knowledge acquired from an electricity network, including BESS data, can be used for monitoring and forecasting electricity usage [7,8,9]. These data are valuable in identifying electricity consumption patterns and role of BESS as a peak demand support mechanism, especially when developing smart city infrastructure [10,11]. Failure in accurate network demand data capturing framework
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