Recently, the smart microgrid research group of the Shenyang Institute of Automation, Chinese Academy of Sciences has made progress in the field of smart grid optimization and dispatch.
With the increasing global climate change and environmental pollution problems, the cleanliness, safety and sustainability of the power system are increasingly being valued by countries all over the world. Accelerating the transformation of traditional power systems to smart grids is the development trend and emerging research hotspots of future power systems. However, for power systems that are connected to a high proportion of renewable energy, their operation safety and related intelligent optimization scheduling methods are related to research. There is still a breakthrough.
After years of research, the smart microgrid research group first proposed a real-time energy optimization method for microgrids based on deep reinforcement learning technology. This method can realize real-time optimization control of microgrid operation. Compared with the current microgrid operation control method, it does not require predictive modeling of renewable energy output, and shows good adaptability. This work is not only a new exploration and breakthrough in the research of smart grid optimization scheduling, but also provides new research ideas for the future application of artificial intelligence technology to promote the development of smart grids.