Energy Sources Integrated with Power Distribution Systems Using Machine Learning Approach

Dear Colleagues,

The rise of distributed energy sources (DES) integrated into urban power distribution systems and energy management is not just an emerging field of research, but a critical step towards revolutionizing our approach to energy distribution and consumption. As we embark on a new era in novel distributed and grid-interactive technologies to achieve system energy efficiency, reliability and reduce energy costs, the potential of machine learning (ML) to transform these sectors is immense. To address these challenges, interdisciplinary research is paramount. An integrated perspective that considers the inter-relationships between power distribution systems, distribution energy sources, transportation systems, building technologies, etc., is necessary to fully harness the digitalization of next-generation urban power distribution and energy systems. Collaborative approaches are key to bridge the gap between theoretical research and practical implementation, ensuring that the benefits of ML are realized in a way that enhances the efficiency and sustainability of transportation and energy systems.

The focus of this Special Issue is on understanding the evolving interplay between power distribution and energy systems, distribution energy sources, transportation systems, building technologies with the integration of ML approaches. This is about creating a cohesive, intelligent energy ecosystem that can adapt to the changing demands of DES-integrated urban power distribution systems and contribute to a more sustainable and resilient future. In this Special Issue, original research articles and reviews are welcome. The research areas may include (but are not limited to) the following:

1. Novel distributed and grid-interactive technologies;
2. Distributed energy sources integrated urban power system;
3. Electric–vehicle grid integration;
4. Smart energy and electric power systems;
5. Energy-efficient transportation systems;
6. Power distribution systems optimization;
7. Smart grid technologies;
8. Power system applications: forecasting, fault diagnosis, energy management, and power quality disturbances detection;
9. Stability assessment and control;
10. Sustainable power and energy systems.

We look forward to receiving your contributions.

Dr. Haichuan Yang
Prof. Dr. Shangce Gao
Dr. Qing Yu
Guest Editors

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• power and energy system
• distributed energy sources
• power distribution systems
• evolutionary computation
• reinforcement learning
• neural networks and learning systems
• complex systems and networks