Voltage-based load recognition and its integration into an application use case

Abstract

The expansion of renewable energies, the growing number of consumers, e.g., in e-mobility, and their connection to low voltage grids pose new challenges for grid operation, particularly regarding voltage control and energy losses. In this context, comprehensive knowledge about the surrounding grid facilitates the intelligent control of photovoltaic (PV)-battery systems, creating a need to observe grid participants. In that regard, a previously developed load recognition method is successfully applied in a use case to recognize two electric vehicles and a heat pump in a simulative grid environment, yielding an accuracy of around 96%. Considering to couple that method with a subsequent control algorithm, an approach is developed to estimate a virtual voltage signal, which represents the grid voltage without the impact of control actions. It is validated that this enables the effective application of load recognition in control conditions. Finally, it is demonstrated how to integrate the load recognition method into a control setup using the virtual voltage concept. The corresponding control algorithm manages the power flow between a PV-battery system and the grid in conjunction with a static Q(U) algorithm. It leverages the information about active loads with the objectives of maintaining a stable and balanced grid voltage and optimizing energy consumption in the example use case. Thereby, the voltage deviation is reduced by around 26% and the grid unbalance by approximately 38%, compared to only using the Q(U) algorithm without a battery. It can be concluded that the load recognition method can successfully gather information from the surroundings of a grid node even in control applications.