A recent research trend is driven to increase the monitoring and control capabilities of low voltage networks. This paper describes a probabilistic forecasting methodology based on kernel density estimation and that makes use of distributed computing techniques to create a highly scalable forecasting system for LV networks. The results show that the proposed algorithm outperforms three benchmark models (one for point forecast and two for probabilistic forecasts) and demonstrate the applicability of the distributed in-memory computing solution for a practical operational scenario. The ultimate goal is to integrate information about net-load forecasts in power flow optimization frameworks for low voltage networks in order to solve technical constraints with the available home energy management system flexibility.

This study presents the functional model that provides net-load forecasts for each low-voltage (LV) node (including PV generation and self-consumption), developed for the UPGRID (real proven solutions to enable active demand and distributed generation flexible integration, through a fully controllable low-voltage and medium-voltage distribution grid) framework project. Several tests scenarios were simulated and the results regarding forecast accuracy and computational performance are given. Results demonstrate the applicability of the distribution in memory solution in a practical operational scenario, offering a highly scalable forecasting system for LV networks. Based on forecasts and available real-time information, an architecture for preventive control of LV grids is built upon chronological analysis capabilities of DPlan. An illustration on how such capabilities are used in the context of the foreseen UPGRID preventive control framework is provided.

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