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Power Distribution Grid Enhancement via Online Feedback Optimization

The rise in residential photovoltaics and other distributed energy sources poses unprecedented challenges for the operation of power distribution grids. When high amounts of active power are injected into the grid by such power sources, the overall power flow is often limited because of voltages reaching their upper acceptable limits. Volt/VAr control aims to raise this power flow limit by controlling the voltage using reactive power. This way, more active power can be transmitted safely without physically reinforcing the grid. In this paper, we use real consumption and generation data on a low-voltage CIGR\'E grid model and an experiment on a real distribution grid feeder to analyze how different Volt/VAr methods can enhance grid capacity, i.e., by how much they can improve the grid's capability to transmit active power without building new lines. We show that droop control enhances the grid but vastly underutilizes the reactive power resources. We discuss how the effectiveness of droop control can be partially improved by employing machine-learning techniques to tune the droop coefficients, but we demonstrate that local control laws are inherently unable to achieve optimal grid enhancement. In contrast, methods that coordinate the use of reactive power resources across the grid, such as Online Feedback Optimization (OFO), can enhance the grid to its full potential. A numerical study performed on data from an entire year using a realistic grid model suggests that OFO can enable another 9\% of maximum active power injections compared to droop control. To achieve that, OFO only requires voltage magnitude measurements, minimal model knowledge, and communication with the reactive power sources. A real-life experiment provides a demonstration of the practical feasibility of the proposed approach and enhanced the grid by another 10.5\% compared to droop control.

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