Distribution network planning method: Integration of a recurrent neural network model for the prediction of scenarios

Abstract

The integration of new types of consumers and prosumers into distribution networks presents significant challenges for network planners, especially given the uncertainties of future expansions. This paper introduces a method for distribution network planning that integrates a Long-Short Term Memory (LSTM) model with a confidence interval threshold for long-term scenario predictions. The proposed forecasting model considers datasets collected from monitoring systems which include aggregated demand and self-consumption from photovoltaic sources. The use of the LSTM model enables a more accurate prediction of expansions, as it captures nonlinear patterns in time series data while taking into account the inherent characteristics of non-stationary time series data. The proposed method was tested on an existing radial medium voltage distribution network in Spain, taking into account the operational thermal limits for lines and substation time series measurements. The proposed planning approach also considers asset costs, active planning solutions, and time-series load flow analysis. The results obtained indicate that the use of time-based forecasts from aggregated generation and demand is a more realistic solution for flexible planning solutions when they are compared to traditional strategies.