arXiv subjects

Nils Bornhorst

Publications and source records attributed to Nils Bornhorst.

3 recordsLinked to original sources

arXiv2026

OptiQU: Coordinated Multi-Level Voltage and Reactive Power Control for Enhanced Voltage Quality and Secure Grid Operation

Modern low-voltage (LV) distribution grids face rising shares of photovoltaic generation and high-power loads such as heat pumps and electric vehicle charging stations. Due to high simultaneity, voltage constraints often become binding before thermal limits, triggering costly conventional grid reinforcement measures. Existing voltage and reactive power control in LV grids - e.g., fixed cos($ϕ$) or Q(V) control of distributed generators, on-load tap-changing distribution transformers, and line voltage regulators - is typically applied locally and independently, leaving reactive power flexibility potential unused. This paper presents OptiQU, a coordinated voltage and reactive power control concept for medium-voltage (MV) and LV distribution grids, combining centralised optimisation with decentralised local control and fallback strategies. The approach coordinates operational targets and setpoints across MV and LV (e.g., DER reactive power and substation equipment) to mitigate voltage violations and curtailment and to increase hosting capacity, while enabling robust operation under limited communication. The concepts are being evaluated using representative MV/LV models in simulation and lab environments and will be validated in field tests with two German DSOs. Based on existing research, the coordinated approach is expected to increase the exploitable flexibility for upstream voltage and reactive power control. The planned evaluation will quantify this potential and investigate trade-offs between performance, communication effort, and resilience.

eess.SY↗

arXiv2025

A Computationally Efficient Method for Solving Mixed-Integer AC Optimal Power Flow Problems

Stepwise controllable devices, such as switched capacitors or stepwise controllable loads and generators, transform the nonconvex AC optimal power flow (AC-OPF) problem into a nonconvex mixed-integer (MI) programming problem which is generally hard to solve optimally. Existing methods for solving MI-AC-OPF problems usually suffer from either limited accuracy or computational intractability, making them impractical for real-world applications. To address these challenges, we propose an efficient iterative deflation approach providing high-quality approximate solutions. In each iteration, a continuously relaxed version of the MI-AC-OPF problem is solved and one candidate integer value is systematically eliminated based on the evaluation of a simple power flow result. The computational complexity of the proposed algorithm grows linearly with the number of integer optimization variables, ensuring scalability. Simulations demonstrate that the proposed approach achieves significant improvements in solution accuracy compared to a state-of-the-art approach. Thus, the proposed method is promising for solving practical MI-AC-OPF problems.

math.OC↗

arXiv2019

Distribution System Monitoring for Smart Power Grids with Distributed Generation Using Artificial Neural Networks

The increasing number of distributed generators connected to distribution grids requires a reliable monitoring of such grids. Economic considerations prevent a full observation of distribution grids with direct measurements. First approaches using a limited number of measurements to monitor such grids exist, some of which use artificial neural networks (ANN). The current ANN-based approaches, however, are limited to static topologies, only estimate voltage magnitudes, do not work properly when confronted with a high amount of distributed generation and often yield inaccurate results. These strong limitations have prevented a true applicability of ANN for distribution grid monitoring. The objective of this paper is to overcome the limitations of existing approaches. We do that by presenting an ANN-based scheme, which advances the state-of-the-art in several ways: Our scheme can cope with a very low number of measurements, far less than is traditionally required by the state-of-the-art weighted least squares state estimation (WLS SE). It can estimate both voltage magnitudes and line loadings with high precision and includes different switching states as inputs. Our contribution consists of a method to generate useful training data by using a scenario generator and a number of hyperparameters that define the ANN architecture. Both can be used for different grids even with a high amount of distributed generation. Simulations are performed with an elaborate evaluation approach on a real distribution grid and a CIGRE benchmark grid both with a high amount of distributed generation from photovoltaics and wind energy converters. They demonstrate that the proposed ANN scheme clearly outperforms state-of-the-art ANN schemes and WLS SE under normal operating conditions and different situations such as gross measurement errors when comparing voltage magnitude and line magnitude estimation errors.

cs.CE↗