Tuning and Testing an Online Feedback Optimization Controller to Provide Curative Distribution Grid Flexibility

TL;DR

The paper addresses disaggregating curative flexibility requests from the transmission grid into a heterogeneous set of FPUs in a distribution grid using Online Feedback Optimization (OFO). It presents two optimization formulations—a Cost Approach and a Constraint Approach—and analyzes how to tune the OFO controller via the matrix $G$ to achieve fast and reliable convergence under voltage constraints and external disturbances. Through experiments on a real distribution-grid laboratory, the authors demonstrate that the Constraint Approach yields faster convergence, lower losses, and better interaction with other grid controllers, while also providing a mechanism to signal infeasibility. The work validates OFO as a robust, scalable tool for distributing curative actions in real grids and offers practical guidance on problem formulation and controller tuning.

Abstract

Due to more volatile generation, flexibility will become more important in transmission grids. One potential source of this flexibility can be distribution grids. A flexibility request from the transmission grid to a distribution grid then needs to be split up onto the different flexibility providing units (FPU) in the distribution grid. One potential way to do this is Online Feedback Optimization (OFO). OFO is a new control method that steers power systems to the optimal solution of an optimization problem using minimal model information and computation power. This paper will show how to choose the optimization problem and how to tune the OFO controller. Afterward, we test the resulting controller on a real distribution grid laboratory and show its performance, its interaction with other controllers in the grid, and how it copes with disturbances. Overall, the paper makes a clear recommendation on how to phrase the optimization problem and tune the OFO controller. Furthermore, it experimentally verifies that an OFO controller is a powerful tool to disaggregate flexibility requests onto FPUs while satisfying operational constraints inside the flexibility providing distribution grid.

Figures (8)

• Figure 1: Cost for redispatch in Germany. The data is taken from redispatch_cost.
• Figure 2: Laboratory setup used for the experimental validation. PV inverter I is connected to bus 3, PV inverter II is connected to bus 1, and the BESS inverter is connected to bus 2. The figure is taken from klein2023providing.
• Figure 3: Block diagram of an OFO controller. The OFO controller iteratively changes $u$ until the solution of the optimization problem is reached. The figure is adapted from klein2023providing.
• Figure 4: Extremely slow convergence of the cost approach with $G=I$.
• Figure 5: Fast convergence of the cost approach due to a tuned $G$ matrix.