Cognitive Digital Twin for Microgrid: A Real-World Study for Intelligent Energy Management and Optimization

Digital twin technology is a promising solution for achieving optimized microgrid control with enhanced efficiency, reliability, and sustainability. In this paper, we focus on a real-world microgrid in Singapore and develop a cognitive digital twin. Our digital twin consists of a client, located near the physical microgrid for real-time control, and a cloud-based server for running computationally intensive algorithms for energy management and optimization. We design and implement communication architectures to ensure seamless and real-time communication. The functionality and performance of our digital twin are validated through different microgrid operational scenarios. The results show that our digital twin outperforms comparison algorithms significantly and approximates the theoretical optimal with merely a 0.24% difference in operation cost. Overall, we demonstrate the effectiveness of our digital twin in enabling real-time optimization and management of microgrid operations, paving the way for technology adoption in smart grids to achieve improved grid resilience and efficiency.