Optimal control method of regional power grid based on elastic carrying capacity analysis and day-ahead evaluation

Abstract

To achieve the coordinated consumption and control of a high proportion of renewable energy in the current regional power grid while ensuring sufficient safety margins, this paper proposes an optimization control method based on elastic carrying capacity analysis and recent evaluation. Firstly, a cloud-edge-based sub-provincial collaborative intelligent control model is adopted, integrating power industry and Internet of Things (IoT) technology to realize grid state data perception through multiple sensors. Secondly, based on these data, grid assessment, grid vulnerability assessment, and grid mapping elastic potential analysis are completed. On this basis, a multi-scale collaborative intelligent control method for sub-provincial power grid transmission and distribution is then constructed. Finally, taking the Xingyi power grid as the research object, this paper applies the proposed method to improve the safety margin. The experimental results show that after applying the method, with an installed energy penetration rate close to 180%, reaches more than 95%. This indicates that the method proposed in this paper not only improves the consumption efficiency of new energy, but also significantly enhances the security and stability of the regional power grid, providing new ideas and practices for the sustainable development of regional power grids.