Advancements in large-scale energy storage technologies for power systems

The rapid evolution of renewable energy sources and the increasing demand for sustainable power systems have necessitated the development of efficient and reliable large-scale energy storage technologies. As the backbone of modern power grids, energy storage systems (ESS) play a pivotal role in managing intermittent energy supply, enhancing grid stability, and supporting the integration of renewable energy. This special issue is dedicated to the latest research and developments in the field of large-scale energy storage, focusing on innovative technologies, performance optimisation, safety enhancements, and predictive maintenance strategies that are crucial for the advancement of power systems.

This special issue encompasses a collection of eight scholarly articles that address various aspects of large-scale energy storage. The articles cover a range of topics from electrolyte modifications for low-temperature performance in zinc-ion batteries to fault diagnosis in lithium-ion battery energy storage stations (BESS). They also include predictive models for capacity decay in vanadium redox flow batteries, safety improvements through arc voltage and temperature analysis, and data-driven approaches for predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs). Additionally, the articles explore lithium inventory estimation, surface modification of electrodes in zinc-bromine flow batteries (ZBFBs), and the impact of water on battery performance and safety. These contributions provide a comprehensive view of the current state and future directions of energy storage technologies in the context of power systems.

Jin et al. review various anti-freezing electrolyte modification strategies for low-temperature aqueous zinc-ion batteries (AZIBs), which are promising for energy storage due to their safety and environmental benefits. They highlight the challenges posed by conventional aqueous electrolytes that freeze in sub-zero temperatures, leading to poor electrochemical performance. The authors emphasise the need for further research to optimise these electrolytes for better performance in extreme conditions, providing insights into future directions for developing effective low-temperature AZIBs.

Lin et al. investigate the impact of water on battery performance and safety. It is found that the reaction of water with LiPF₆ in battery electrolytes ultimately causes electrical contact loss and capacity decay. Excess water reduces electrolyte conductivity, increases internal resistance, and affects lithium-ion migration, altering the electrode structure and performance. The presence of water accelerates exothermic reactions, decreasing thermal stability and increasing heat release rates during thermal events. Experimental results also show that internal resistance and self-discharge rates increase with water content, indicating significant impacts on battery performance and safety.

Li et al. analyse the simulation and experimental results of arc voltage and battery surface temperature to validate a model for lithium-ion battery systems, which are critical for electric vehicles and ESSs. They highlight the risks of electric arcs caused by mechanical stress and ageing in battery connections, which can lead to thermal runaway and combustion. Results indicate that arc voltage increases with gap enlargement, and the model's accuracy is confirmed with minimal errors in arc voltage and temperature measurements. The findings emphasise the importance of understanding arc dynamics to improve safety in battery systems.

Li et al. provide a comprehensive overview of fault diagnosis technologies for lithium-ion BESS. It highlights the increasing safety concerns due to frequent accidents in BESS and emphasises the importance of accurate and rapid fault diagnosis to prevent such incidents. The paper categorises various fault diagnosis methods based on fault types, causes, and characteristics and discusses the topologies, data acquisition, and transmission systems relevant to BESS safety. It also outlines future trends in fault diagnosis, including advancements in data acquisition systems, the need for public datasets, and the development of more effective diagnostic methods.

Li et al. present a method for estimating and predicting the state of health (SOH) of lithium batteries using ridge regression and gated recurrent unit models. By analysing the impact of charging/discharging strategies and operational factors on battery SOH, the study utilises the stanford-MIT battery dataset to demonstrate that the proposed method maintains high stability, accuracy, and generalisation across different charging strategies and cycle counts. The method shows potential for practical applications in accurately assessing and forecasting lithium battery health in ESS.

Xie et al. present a data-driven approach for predicting the RUL of LIBs by employing a combination of short-term and long-term models. It utilises a convolutional neural networks-long and short-term memory recurrent neural networks framework to analyse discharge capacity and voltage curves, enabling accurate health indicator predictions. The long-term model iteratively forecasts capacity degradation based on the short-term health indicator, demonstrating robust performance across various battery cycling profiles. The study highlights the importance of feature selection and the effectiveness of deep learning techniques in enhancing battery life predictions.

Chen et al. report a method for estimating lithium inventory in LIBs using incremental capacity analysis, support vector machines (SVM), and particle swarm optimisation (PSO). It emphasises the significance of lithium inventory as an indicator of battery ageing and performance. The study identifies key features related to lithium inventory, establishes correlations between these features and lithium inventory, and optimises the SVM parameters using PSO to enhance estimation accuracy. Experimental validation demonstrates that the proposed PSO-SVM method achieves high precision in lithium inventory estimation, making it effective for battery health management.

Li et al. review recent advancements in the surface modification of carbon-based electrodes for ZBFBs, highlighting their potential for energy storage due to low cost, high energy density, and safety. They discuss various modification strategies, aiming to improve zinc deposition uniformity, increase electrocatalytic activity, and extend battery life. The authors propose future research directions to optimise electrode materials for better efficiency and commercial viability in energy storage applications.

The selected papers for this special issue highlight the significance of large-scale energy storage, offering insights into the cutting-edge research and charting the course for future developments in energy storage technology within the power system landscape.