Abstract
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.
Highlights
The installation of smart grid equipment, such as smart meters (SM) and data concentrators, contributes to increase the monitoring and control capabilities of low-voltage (LV) grids
A new paradigm is the preventive control of distribution grids, where a key input is information about net-load forecasts in each node of the distribution grid, which is used to run power flows to detect potential technical problems in the pre-defined time horizon (e.g. 24 h ahead)
Chronological power-flow analysis can be used to provide a new level of awareness to the LV dispatch operators, while allowing a proactive management of potential grid problems
Summary
The installation of smart grid equipment, such as smart meters (SM) and data concentrators, contributes to increase the monitoring and control capabilities of low-voltage (LV) grids. A new paradigm is the preventive control of distribution grids, where a key input is information about net-load forecasts in each node of the distribution grid, which is used to run power flows to detect potential technical problems in the pre-defined time horizon (e.g. 24 h ahead). When technical problems are detected, a set of control rules and/or automatic optimisation algorithms can be applied to derive preventive control actions that mitigate the foreseen technical problems. This approach can be complementary to the real-time control and avoid expensive control actions such as load or renewable energy curtailment. The UPGRID project developed this framework to be tested in the Portuguese demo [1]
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