A Study of a Dual-Carbon Constrained Grid Planning Methodology Based on Load Spatial Distribution Forecasting

Grid planning methods mainly rely on historical load data for grid structure and capacity planning, and predict future power demand only based on the overall load growth trend, which leads to an increase in the active loss rate, so we study the dual-carbon constrained grid planning method based on load spatial distribution prediction. A data center is applied to collect and process data related to grid planning. Based on these basic data, load spatial distribution prediction is performed to quantify the development potential of each power cell and determine the location of load growth. The grid planning model is further constructed under the dual-carbon constraints, and multiple constraints are considered comprehensively. The optimization algorithm is used to solve the constructed grid planning model and generate the optimal grid planning scheme. The experimental results show that the planning method can effectively curb the growth of carbon emissions, significantly reduce the total active loss rate, and provide technical support for the realization of carbon neutrality.