Premeditated generic energy storage model for sources rating selection in grid applications

• Published in: International journal of electrical power & energy systems Vol. 157; p. 109837
• Main Authors: Aharon, Ilan, Shmaryahu, Aaron, Sitbon, Moshe, Dagan, Kfir Jack, Baimel, Dmitry, Amar, Nissim
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2024
• Subjects: Battery model; Energy storage devices; Grid-connected sources; Li-FePo4 battery; Sizing; Battery model; Energy storage devices; Grid-connected sources; Li-FePo4 battery; Sizing
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2024.109837

•Battery modeling allows accurate prediction of the parameters in long-term processes improving decision-making.•The generic modeling algorithm combines three methods in one model: the experimental database, the equivalent circuit, and the analytical equations.•The model input signals are power demand and environment temperature corresponding to power system simulations such as sizing procedures.•The generic model could easily be utilized for any ESS (with similar technology) by a few experimental procedures. The lengthy process of sizing and optimizing hybrid energy sources requires an accurate battery model. This paper presents a generic new energy storage system model premeditated to solve the optimization problem of the sizing procedure. The model comprises several methods, a lookup table, an equivalent battery circuit, and analytical equations. The database is created offline based on experimental results achieved under various conditions. In the first step, the model receives an external vector of signals comprising load power demand, instantaneous generated energy, and ambient temperature. Then, the algorithm predicts the impact of the load on the battery parameters by either interpolation or extrapolation. The results are utilized at an equivalent circuit that supplies the basic parameters and the battery constraints. Next, analytical methods reveal the more advanced parameters such as charge, supplied and remaining energy, etc. The results show that the proposed dynamic battery model can predict the battery states through all operating zones and under different battery conditions. The benchmark results present higher accuracy than other available models. The proposed model was employed in a sizing procedure to verify the model’s accuracy. It was shown that the new model estimates the required source rating more precisely than standard models. Since the suggested algorithm is based on actual battery curves, it can be utilized for all types of batteries by reentering the data of any other battery.