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.

This paper presents the 24-hour optimal scheduling algorithm for Energy Storage System (ESS) using load forecasting and renewable energy forecasting in South Korea electricity tariff structure. For load forecasting and renewable energy forecasting, 24-hour multivariate forecasting model combining very-short-term and short-term forecasting models is developed. Then, load and renewable forecasts are input to the optimal ESS scheduling algorithm. The objective of this algorithm is to maximize the customer's profit by energy arbitrage, minimize the peak load to reduce the contract power, and minimize the charging/discharging cycles to lengthen the expected life of ESS. The effectiveness of this algorithm is validated in case study.

Countries are looking for a pathway toward a sustainable transition from fossil-based to less or zero-carbon sources in energy sector in order to reduce its impact from climate change. Among different renewable resources, photovoltaic power system (PV) is considered as one of the most promising technology, which has the biggest potential for increasing renewable energy. However, its profit can be varied depending on operation and management, which possibly causes performance degradation and safety issues due to faults. Therefore, we have collected actual operation data from the PV monitoring system which located in Changwon, Gyeong-nam province during one year. While most of the PV system has security function which is limited to its inverter protection, in this study, based on fault data software program is developed for fault diagnosis in order to increase robustness of the PV system and minimize operation cost. Also, the program comprises machine learning algorithm based on fault data to classify its types of faults. It also presents economic loss of each PV module considering mean time to repair (MTTR) occurred from the event of faults in the PV system. As a result, the program helps faster fault diagnosis of the PV system and decreasing overall operation cost for the system operator.