Andrea Benigni

Iva Bačić, Jakob Niehues, Philipp C. Böttcher et al.

Dynamic grid stability is traditionally ensured with synchronous generators. Modern grids rely substantially more on inverter-based resources, which require grid-forming control to guarantee adequate system-wide synchronization and stability. Small-signal stability has granted various centralized and decentralized stability certificates - but these have primarily been limited to sufficient criteria only. In this work, we construct a necessary and sufficient small-signal stability criterion for lossless inverter-based power grids with arbitrary topology. We show that asymptotic stability is equivalent to the positive definiteness of a single matrix that combines network topology, operating point, and effective droop gains. We derive graph-theoretic stability criteria based on an augmented cone graph and show that the contribution of graph cycles is typically small, as illustrated for three IEEE test cases. The resulting framework yields decentralized stability criteria, quantifies the conservatism introduced by decentralization, and may support the development of future grid codes.

Christian Wallisch, Andrea Benigni, Carsten Hartmann et al.

Electrical distribution networks are regional, medium- and low-voltage power grids connecting energy sources to individual households and businesses with given power demands. While these networks contain redundant power lines for reliability, they are typically operated in a radial (spanning tree) configuration by opening and closing switches on the lines. The challenge is to find a spanning tree that minimizes the sum of the resistive power losses: The power loss of a line $e$ is its resistance $r(e)$ times the squared current $f(e)^2$ flowing across the line. We study approximation algorithms for this problem, known as Distribution Network Reconfiguration (DNR). We give an $n$-approximation algorithm and, via a new NP-hardness for planar Balanced Connected Partition with a fixed number of parts, show that no $n^{1-\varepsilon}$-approximation is possible even on planar graphs unless P $=$ NP, for any $\varepsilon>0$. Since the approximation hardness holds only if there are many sources, we focus on $k$-DNR with $k$ sources; this is motivated by traditional distribution networks, where oftentimes $k = 1$. For $2$-DNR, we give an approximation lower bound of $Ω(\log^2 n)$ conditioned on P $\neq$ NP. For $1$-DNR, which is equivalent to finding an uncapacitated confluent flow minimizing the squared Euclidean norm, we prove APX-hardness and give an $\mathcal{O}(\sqrt{n})$-approximation for uniform line resistances, answering an open question by Gupta et al. [Math. Program. 2022].

Felix Bonhoff, Thiemo Pesch, Andrea Benigni et al.

Metaheuristic-based global optimization with embedded, long-running simulations is a computationally expensive process. To support various stages of development and execution, a seamless transition from personal computers to distributed clusters is desired, enabling execution across all computational scales. However, existing tool chains are often characterized by rigidity and hardware-bound constraints, which impede scalability and the integration of complex simulations. Bridging this gap, we present a containerized HPC scalable microservice-based framework for genetic algorithms with embedded simulations (CHAMB-GA). The deployment of the framework scales consistently across cloud infrastructure via container orchestration and HPC clusters via batch-scheduled parallel execution. Users provide the GA operators and simulation backend separately. The framework is designed to run these components in a distributed and decoupled manner, mapped to separate hardware. This approach ensures that the fitness evaluation and genetic operations are not managed within the same process and are utilizing distinct parts of the compute infrastructure. A central message broker coordinates asynchronous manager-worker communication between microservices, thereby parallelizing evolutionary operations and fitness evaluations. We demonstrate CHAMB-GA's scalability, portability, and reproducibility, while facilitating the integration of external tools and complex simulations on benchmark and powerflow problems. The capabilities of CHAMB-GA are validated in a two-part approach: (i) a benchmark study demonstrating minimal overhead while scaling to over 3,500 CPU cores, and (ii) a dispatch optimization of High Voltage Direct Current (HVDC) lines in the German transmission grid, showing seamless migration from Kubernetes to SLURM, combined horizontal and vertical scaling, and integration of multi-stage workflows.