A review of grid-connected hybrid energy storage systems: Sizing configurations, control strategies, and future directions
Ziyu Fang, Jonathan Shek, Wei Sun
Abstract
As the installed capacity of renewable energy continues to grow, energy storage systems (ESSs) play a vital role in integrating intermittent energy sources and maintaining grid stability and reliability. However, individual ESS technologies face inherent limitations in energy and power density, response time, round-trip efficiency, and lifespan. Hybrid energy storage systems (HESSs) address these challenges by leveraging the complementary advantages of different ESSs, thereby improving both energy- and power-oriented performance while ensuring the safe and efficient operation of storage components. Despite their potential, existing literature lacks comprehensive reviews and critical discussions on HESS applications in large-scale grid integration. This study conducts an in-depth review of grid-connected HESSs, emphasizing capacity sizing, control strategies, and future research directions. Various sizing optimization methods and control strategies are systematically evaluated, with a focus on their strengths, limitations, and applicability. Search-based methods, particularly heuristic approaches, exhibit strong capabilities in addressing nonlinear multi-objective optimization problems for HESS sizing, while intelligent control strategies with adaptive parameter tuning enable efficient real-time power sharing under dynamic operating conditions. Based on the review findings and identified research gaps, this paper advocates for the development of multi-objective economic optimization models and advanced power management systems, providing valuable insights to guide future advancements in grid-integrated HESS technologies. • Multi-objective optimization improves HESS economic viability and enhances cost-effectiveness in grid applications. • Predictive and optimization-based control enhances PMS adaptability in dynamic grid conditions. • Machine learning improves control precision, optimizing HESS efficiency and performance. • Refining cost-effective frameworks and power-sharing mechanisms boosts HESS commercial feasibility and deployment.