Generative Diffusion Model-based Energy Management in Networked Energy Systems

In recent years, the proliferation of renewable energy sources has heightened the focus on networked energy systems. These systems face significant challenges due to the unpredictable nature of energy generation and consumption, as well as the complexity of managing numerous components and parameters. To address the challenges associated with the time-consuming nature of optimization problems and the expansive solution space, we propose an innovative energy management method based on a generative diffusion model applicable to general networked energy systems. This approach aims to balance energy supply and demand while minimizing transmission costs. The efficacy of this method is validated through evaluations on real-world datasets and simulations, demonstrating a 26.6% cost reduction compared to the state-of-the-art model and a 62.8% decrease in execution time compared to existing optimizers. This research highlights the potential of generative diffusion techniques in networked energy management. Code: https://github.com/gale13/GEM.