AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability

SCOPE:

The growing integration of intermittent renewable energy sources (RES) like solar and wind into
power grids, despite their current relatively low global penetration share, poses significant stability
challenges and necessitates efficient energy storage solutions to ensure grid reliability and cost-
effectiveness. The Hybrid Energy Storage System (HESS), with its unique technical advantages, can
intelligently regulate the power of the power grid in various application scenarios by organically
integrating the characteristics of different types of energy storage devices, thereby addressing grid
power fluctuations accurately and enhancing grid stability. However, realizing the full potential of
HESS is often hindered by high capital and operational costs, demanding substantial improvements in
cost-effectiveness.
The vigorous development of artificial intelligence technology has opened up new paths for solving
the above-mentioned problems. Relying on advanced reinforcement learning, machine learning
algorithms, artificial intelligence can not only achieve high-precision prediction of renewable energy
generation and grid load, but also provide scientific decision-making basis for capacity configuration
and charging-discharging strategy optimization of HESS. Its powerful self-learning and adaptive
capabilities unlock significant potential for enhancing HESS grid support efficiency in diverse
scenarios and optimizing its lifecycle cost-effectiveness. While AI-driven solutions show promise,
current research lacks focus on multi-scale temporal optimization, cross-domain digital twins, policy-
aware economic models, achieving scalability and trust limiting HESS’s real-world impact.

TOPICS:

1. AI-driven Renewable Energy Power Generation and Load Forecasting
2. Dynamic Optimization Mechanism for Power Grid Costs Driven by AI
3. HESS Gain Path for Grid Cost and Stability in Full Life Cycle
4. Intelligent Algorithms Boost Big Data Computing in HESS
5. Digital Twin-Enabled HESS Health Monitoring and Predictive Maintenance
6. Generative AI Optimizes HESS for Efficient Operation in Multiple Scenarios
7. AI empowers HESS to Optimize Multi-time-scale Power Grid Services
8. Flexible Management of HESS for Smart Grid Stability
9. Reinforcement Learning for Real-time Charging-Discharging Scheduling in Smart Substations

10. Blockchain-based Distributed Smart Grid and Energy Trading Mechanisms
11. Innovative Paths for Smart Grid Policy-Aware Economic Models under Carbon Constraints
12. Trustworthy AI for Safety-Critical HESS-Grid Integration

IMPORTANT DATES

 Manuscript submission deadline: 31.03.2026
 Notification of acceptance: 31.05.2026
 Submission of final revised paper: 31.07.2026
 Publication of special issue (tentative): 30.09.2026

Guest Editors:

Jinfeng Wang, Zhengzhou University, China

Farhad Shahnia, Murdoch University, Australia

Sudhakar Kumarasamy, University Malaysia Pahang, Malaysia