Applications of Optimization and Artificial Intelligence in Power Grids
A Special Issue of Energies (ISSN 1996-1073) belonging to the section "F5: Artificial Intelligence and Smart Energy".
Deadline for manuscript submissions: 5 November 2026 | Viewed by 549
Editors
Interests: artificial intelligence for power systems; large language models and foundation models for grid operation; load forecasting; power-grid optimization; smart-grid cybersecurity; carbon-aware energy management; reinforcement learning for energy systems
Interests: low-carbon transformation of new power systems; smart grid cybersecurity; power-system optimization; AI and data-driven methods for smart grids; electricity markets and demand response
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Special Issue Information
Dear Colleagues,
Power grids are undergoing a profound transformation with the large-scale integration of renewable generation, distributed energy resources, energy storage, flexible loads, electric vehicles, digital substations, and increasingly dynamic electricity markets. These developments are greatly improving the flexibility and intelligence of modern power systems, but they also make grid operation, control, protection, and planning more complex, uncertain, and strongly coupled across physical and cyber layers. Traditional model-based methods remain essential, yet they are increasingly challenged by high-dimensional decision spaces, nonlinearity, variability, data heterogeneity, and stricter requirements for security, resilience, and real-time response. In this context, optimization and artificial intelligence have emerged as powerful tools for enabling more accurate forecasting, adaptive control, efficient scheduling, automated decision-making, and secure operation in next-generation power grids.
This Special Issue aims to present and disseminate recent advances in the theory, methods, and practical applications of optimization and artificial intelligence in power grids. It seeks high-quality contributions that bridge methodological innovation with realistic engineering problems and field-relevant constraints. Topics of interest for publication include, but are not limited to, the following:
- Optimal power flow, unit commitment, economic dispatch, restoration, and grid planning under uncertainty;
- Load, renewable generation, and electricity price forecasting using data-driven, hybrid, and physics-informed approaches;
- Energy management for microgrids, active distribution networks, virtual power plants, energy storage systems, and electric vehicles;
- Reinforcement learning and multi-agent learning for wind farm control, demand response, voltage regulation, and autonomous grid operation;
- Fault detection, fault diagnosis, event classification, and predictive maintenance for power-system equipment and infrastructures;
- AI-enabled detection, localization, and mitigation of cyber-physical attacks, including false data injection, denial-of-service, and stealth attacks;
- Data-driven stability assessment, contingency analysis, resilience enhancement, and secure operation of smart grids;
- Graph learning, explainable AI, federated learning, and large language model assisted applications for grid monitoring, diagnostics, and decision support;
- Digital twins, benchmark systems, hardware-in-the-loop platforms, and real-world case studies for validating AI and optimization methods in power applications.
This Special Issue welcomes both theoretical and application-oriented studies that advance reliable, scalable, secure, and practical solutions for modern power grids.
Dr. Guolong Liu
Prof. Dr. Gaoqi Liang
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- power grids
- power system optimization
- artificial intelligence
- reinforcement learning
- optimal power flow
- energy management
- renewable energy integration
- cyber-physical security
- grid resilience
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