A Hierarchical Simulation Model for Spatial-Temporal Electric Load Forecasting: Case Study in CENTROSUR

Abstract

This article proposes a Spatial Load Forecasting (SLF) model applied to an electric distribution system for the short, medium and long term horizon, which includes the estimation of the magnitude and geographic location of electricity demand energy from new consumers.

The proposed model is a hybrid trendingsimulation method and using a hierarchical structure: bottom-up for aggregate data and analyze, and, top-down for load growth allocate to sub-areas and micro-areas. The small area approach is combined with spatial-time series regression models and trend analysis in large regions. Local, proximity and surround factors are used to create preference maps. For each region, it allocates the region´s growth, based on the preference map values and cellular automaton technique, and merges the land use with the load curve data, to calculate the small area loads.

We test the SLF model with CENTROSUR´s electric distribution system; the result is a map of electric load density that shows the areas where new consumers are most likely to be allocated, thereby providing information on where, how much, and when electric demand will change, in sufficient detail and with the required accuracy.