Smart Grid Analytics

AI-Driven Methods, Models, and Practices

Author

Kundan Kumar

Preface

This is a collection of notes and guidelines on Smart Grid Analytics with a focus on AI-driven methods, models, and practices.
The materials consolidate insights from research and professional practice, providing both technical depth and practical preparation for modern smart grid applications.

Each section begins with learning objectives and required references, with additional resources provided throughout the text.
The notes highlight essential methods, provide applied examples, and offer strategies for research and practice. They are not intended to replace textbooks or formal training, but rather to complement them with targeted, application-focused guidance.

The intended audience is expected to be familiar with programming and the following concepts and methods:

  • Energy Systems Foundations
    • fundamentals of power systems and distribution grids
    • grid operations: voltage stability, power flow, and reliability
    • volt-VAR control (VVC) and reactive power management
    • distributed energy resources (DERs), storage, and demand response
  • Optimization & Control
    • optimization techniques (convex, non-convex, gradient-based methods)
    • model predictive control and decision-making under constraints
    • uncertainty modeling and robust control strategies
  • Machine Learning & AI for Energy
    • classical and modern machine learning methods for forecasting and anomaly detection
    • reinforcement learning (RL) and deep reinforcement learning (DRL) for grid optimization
    • federated learning for decentralized energy intelligence
    • physics-informed learning: embedding power system constraints into AI models
  • Next-Generation Intelligence
    • natural language processing (NLP) and large language models (LLMs) for energy decision support
    • AI/LLM design patterns (retrieval-augmented generation, agentic workflows, tool use, safety & alignment)
    • multi-agent optimization and coordination in distributed grids
  • research methodology
    • research methodology and experimental design for cyber-physical systems
    • benchmarking, reproducibility, and evaluation metrics in energy AI

Author

Kundan Kumar - https://kundan-kumarr.github.io/

Citation

Kumar, K. (2025). Smart Grid Analytics: AI-Driven Methods, Models, and Practices. Edition 2025-09.

License

This work is licensed under the MIT License.