To enhance the efficiency of an energy storage system, it is important to predict and estimate the battery state, including the state of charge (SOC) and state of health (SOH). In general, the statistical approaches for predicting the battery state depend on historical data measured via experiments. The statistical methods based on experimental data may not be suitable for practical applications. After reviewing the various methodologies for predicting the battery capacity without measured data, it is found that a joint estimator that estimates the SOC and SOH is needed to compensate for the data shortage. Therefore, this study proposes an integrated model in which the dual extended Kalman filter (DEKF) and autoregressive (AR) model are combined for predicting the SOH via a statistical model in cases where the amount of measured data is insufficient. The DEKF is advantageous for estimating the battery state in real-time and the AR model performs better for predicting the battery state using previous data. Because the DEKF has limited performance for capacity estimation, the multivariate AR model is employed and a health indicator is used to enhance the performance of the prediction model. The results of the multivariate AR model are significantly better than those obtained using a single variable. The mean absolute percentage errors are 1.45% and 0.5183%, respectively.
Renewable energy generation and energy storage systems are considered key technologies for reducing greenhouse gas emissions. Energy system planning and operation requires more accurate forecasts of intermittent renewable energy resources that consider the impact of battery degradation on the system caused by the accumulation of charging and discharging cycles. In this study, a statistical model is presented for forecasting a day-ahead photovoltaic (PV) generation considering solar radiation and weather parameters. In addition, the technical performance of energy storage systems (ESS) should be evaluated by considering battery degradation that occurs during the charge and discharge cycles of the battery. In this study, a battery degradation model based on the data-driven method is used. Based on a suitable forecasting model, ESS scheduling is performed to charge the maximum amount of PV generation and discharge for the self-consumption of the customer load when PV generation ends. Since the battery is highly dependent on operating conditions such as depth of discharge, state of charge and temperature, two different ESS charge and discharge modes are proposed. From the simulation with the battery degradation model using parameters derived from experiments, we show that the battery is degraded along with charging cycles during testing periods. Variations in state of health are observed owing to the different characteristics of the battery according to the ESS operation modes, which are divided into the low and high SOC. Through experimental validation, it is proved that the state of charge (SOC), 0.45 is the optimal threshold that can determine the low and high SOC. Finally, the simulation results lead to the conclusion that the battery degradation in different operation modes should be taken into account to extend the end of life efficiently.
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.
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