Energy storage capability of seawater batteries for intermittent power generation systems: Conceptualization and modeling

• Published in: Journal of power sources Vol. 580; p. 233322
• Main Authors: Park, Sanghun, Yoon, Nakyung, Ullah, Zahid, Tarus, Bethwel Kipchirchir, Choi, Byeongwook, Kim, Hoo Hugo, Son, Moon
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2023
• Subjects: Deep learning; Energy storage; Renewable energy; Seawater battery; Deep learning; Renewable energy; Seawater battery; Energy storage
• ISSN: 0378-7753
• DOI: 10.1016/j.jpowsour.2023.233322

The use of renewable energy for power generation is increasing rapidly. However, residual electricity supplied in excess of demand is a global concern. To effectively utilize excess power, storing surplus renewable energy in energy storage systems (ESSs) is important. In this study, a seawater battery (SWB) is proposed as an ESS for intermittent power resources, and its energy storage capability is evaluated. Four charging scenarios that imitate different forms of renewable energy (constant current, solar, tidal, and wind) reveal that SWB is an efficient ESS for intermittent renewable energy sources. Scenario-dependent energy efficiency follows the order: ideal constant current (83.6%) > solar power (80.4%) > tidal power (79.6%) > wind power (79.4%). The ability of two artificial intelligence models is also tested to estimate the potential of SWBs. A novel long short-term memory model outperforms an artificial neural network model, predicting the potential of SWB with a high precision (R2 > 0.99) and an extremely low error rate (<0.18%). Therefore, the conceptualization and modeling of an SWB as an ESS may pave the way for energy storage from and management of intermittent energy sources. The scenario-based research on the energy storage capability of seawater batteries for intermittent power generation systems is experimentally demonstrated and modeled by machine learning algorithms. [Display omitted] •The capability of SWB for renewable energy storage was investigated.•Charging scenarios using solar power had the highest energy storage efficiency.•A deep learning model (LSTM) was trialed to estimate the performance of SWB.•The LSTM model outperformed a neural network in prediction accuracy (R2 > 0.99).