Precision Regulation Strategy for Regional Source, Grid, and Load Friendly Interaction Based on Double-Layer Sensitivity Model

Abstract

The regional source–grid–load friendly interaction and precision regulation of adjustable resources with high sensitivity provide important support for the development of a new power system, improving its regulation ability and efficiency and promoting the safe, efficient, and reliable operation. Therefore, a precision regulation strategy for a regional source–grid–load friendly interaction based on a double-layer sensitivity model is proposed in this paper. Firstly, from the two perspectives of power grid-application requirements and power node-adjustable resources, the overall implementation process of the double-layer sensitivity analysis method is described, and the layered logic architecture of the precision regulation platform for regional source–grid–load friendly interaction is constructed in order to realize and apply the proposed double-layer sensitivity model and precision-regulation strategy. Secondly, the first-layer power transmission line-sensitivity analysis model is proposed for the power grid side to obtain the power transmission line-sensitivity matrix. A fine-grained second-layer adjustable resources sensitivity-analysis model is further established for the diversified adjustable resource aggregated under the power node to complete the quantitative ranking of adjustable resource-regulation sensitivity. Then, the application process of double-layer sensitivity analysis results is designed in detail, and the regulation sequence and regulation quantity of source and load are defined. Finally, taking a power system with seven lines, five power nodes, two power sources, and seven types of adjustable resources as an example, the effectiveness and rationality of the proposed model and strategy are verified. The results indicate that the proposed precise regulation strategy can accurately select high-sensitivity power nodes and effectively sort high-sensitivity adjustable resources when solving the multiple scenarios’ application needs of power grids.