Weather data-based load profile modeling for grid expansion planning

Abstract

Grid expansion planning requires detailed information regarding profiles of load and generation at all grid nodes considered in order to determine the resulting loading of system components. However, these profiles can have an extremely aggregated character in the high voltage grids, which can make it difficult to estimate the proper simultaneity between generation and load, especially if future scenarios are considered. In this paper, a new modeling approach for building synthetic load profiles for analysis of future high voltage grid expansion requirements is proposed. It is based on an autoregressive process, whereby climatic data are used to represent the correlation of the power infeed at each grid node with regard to the weather conditions over the area of the high voltage grid considered. A validation is performed using the data measured. Thus, a high number of profiles are calculated and the mean values are compared to the profiles measured. The validation shows good congruency.