Abstract
To achieve frequency regulation, energy-storage systems (ESSs) are systems that monitor and maintain the grid frequency. In South Korea, the total installed capacity of battery ESSs (BESSs) is 376 MW, and these have been employed to achieve frequency regulation since 2015. When the frequency of a power grid is input, accurately estimating the state of charge (SOC) of a battery is difficult because it charges or discharges quickly according to the frequency regulation algorithm. If the SOC of a battery cannot be estimated, the battery can be used in either a high SOC or low SOC. This makes the battery unstable and reduces the safety of the ESS system. Therefore, it is important to precisely estimate the SOC. This paper proposes a technique to estimate the SOC in the test pattern of a frequency regulation ESS using extended Kalman filters. In addition, unlike the conventional extended Kalman filter input with a fixed-error covariance, the SOC is estimated using an adaptive extended Kalman filter (AEKF) whose error covariance is updated according to the input data. Noise is likely to exist in the environment of frequency regulation ESSs, and this makes battery-state estimation more difficult. Therefore, significant noise has been added to the frequency regulation test pattern, and this study compares and verifies the estimation performance of the proposed AEKF and a conventional extended Kalman filter using measurement data with severe noise.
Highlights
Battery energy-storage systems (BESSs) are systems that can realize power savings and increased energy efficiency by supplying electric power to users at specified times
When significant noise is generated in the operation data of the BESS outputted through the frequency regulation (FR) ESS algorithm, it is verified that the state of charge (SOC) can be accurately estimated using
The FR ESS algorithm was designed and the FR test pattern of the ESS was analyzed on the date on which the ESS usage was close to the annual average, and the frequency on the date to verify the SOC estimation performance of the extended Kalman filter (EKF) and adaptive extended Kalman filter (AEKF) in the test pattern
Summary
Battery energy-storage systems (BESSs) are systems that can realize power savings and increased energy efficiency by supplying electric power to users at specified times. A low SOC indicates that the oxidation reaction occurs at the anode electrode, and the lithium ion moves to the cathode and the reduction reaction occurs [17,18] At this time, when the battery is used at an SOC that is too high or too low, the battery may encounter stress due to structural instability, and an instantaneous externally generated current may result in over-voltage or over-discharge conditions [19,20]. When significant noise is generated in the operation data of the BESS outputted through the FR ESS algorithm, it is verified that the SOC can be accurately estimated using. Kalman (AEKF),Algorithm which automatically updates the noise covariance in the algorithm according
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