Advancements in AI-Driven Energy Management and Grid Optimization

The field of energy management and grid optimization is witnessing a significant shift towards the adoption of artificial intelligence (AI) and decentralized technologies. Researchers are exploring innovative approaches to optimize energy management in microgrids, enhance the reliability of power electronics converters, and develop collaborative edge AI solutions for decentralized energy systems. These advancements aim to address pressing challenges such as renewable energy demand forecasting, cyberattack protection, and operational cost optimization. Noteworthy papers in this area include:

  • A study on using AI-based methodologies in energy management systems of microgrids, highlighting the potential of self-healing microgrids and integration with blockchain technology.
  • A proposal for a novel current control approach that integrates reliability requirements into the design framework of power electronics converters.
  • A conceptualization of electric grids as a computing asset, enabling them to optimize, compute, and make data-driven decisions using distributed power electronic converters.

Sources

An Overview of the Prospects and Challenges of Using Artificial Intelligence for Energy Management Systems in Microgrids

Optimal Current Control Strategy for Reliable Power Electronics Converters: Frequency-Domain Approach

Empowering the Grid: Collaborative Edge Artificial Intelligence for Decentralized Energy Systems

DB InfraGO's Automated Dispatching Assistant ADA-PMB

Unlocking Innate Computing Abilities in Electric Grids

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