arXiv subjects

Nico Westerbeck

Publications and source records attributed to Nico Westerbeck.

5 recordsLinked to original sources

arXiv2026

Transmission Topology Optimization using accelerated MapElites

Transmission Topology Optimization has great potential to improve efficiency and flexibility of grid operations through non-costly switching actions, but previous approaches struggle with runtime performance and scalability. In this work, we present an optimization approach that leverages GPU acceleration to speed up computations. In a genetic algorithm setting, topologies are randomly mutated and evaluated in parallel for multiple optimization criteria. Combined with a fully GPU-native DC loadflow solver, there is no CPU-GPU data transfer required in the DC optimization loop. Using a variant of the illumination algorithm MapElites, we efficiently generate a set of diverse candidate solutions on the pareto front. Together with an importing and AC validation step, we present an end-to-end optimization solution that runs in under 15 minutes. The approach is currently under evaluation by operational planning operators in two European TSOs. We furthermore open-source our code at github.com/eliagroup/ToOp.

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arXiv2025

Voltage-sensitive distribution factors for contingency analysis and topology optimization

Topology optimization is a promising approach for mitigating congestion and managing changing grid conditions, but it is computationally challenging and requires approximations. Conventional distribution factors like PTDFs and LODFs, based on DC power flow, fail to capture voltage variations, reactive power, and losses, thereby limiting their use in detailed optimization tasks such as busbar splitting. This paper introduces generalized distribution factors derived from a voltage-sensitive linearization of the full AC power flow equations. The proposed formulation accurately reflects reactive power flows, Ohmic losses, and voltage deviations while remaining computationally efficient. We derive and evaluate generalized PTDFs, LODFs, and topology modification factors using matrix identities. We discuss potential applications including voltage-aware N-1 security analysis and topology optimization with a focus on busbar splitting. Numerical experiments demonstrate close agreement with full AC solutions, significantly outperforming the traditional DC approximation.

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arXiv2025

RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations

Reinforcement learning (RL) can provide adaptive and scalable controllers essential for power grid decarbonization. However, RL methods struggle with power grids' complex dynamics, long-horizon goals, and hard physical constraints. For these reasons, we present RL2Grid, a benchmark designed in collaboration with power system operators to accelerate progress in grid control and foster RL maturity. Built on RTE France's power simulation framework, RL2Grid standardizes tasks, state and action spaces, and reward structures for a systematic evaluation and comparison of RL algorithms. Moreover, we integrate operational heuristics and design safety constraints based on human expertise to ensure alignment with physical requirements. By establishing reference performance metrics for classic RL baselines on RL2Grid's tasks, we highlight the need for novel methods capable of handling real systems and discuss future directions for RL-based grid control.

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arXiv2025

Accelerated DC loadflow solver for topology optimization

We present a massively parallel solver that accelerates DC loadflow computations for power grid topology optimization tasks. Our approach leverages low-rank updates of the Power Transfer Distribution Factors (PTDFs) to represent substation splits, line outages, and reconfigurations without ever refactorizing the system. Furthermore, we implement the core routines on Graphics Processing Units (GPUs), thereby exploiting their high-throughput architecture for linear algebra. A two-level decomposition separates changes in branch topology from changes in nodal injections, enabling additional speed-ups by an in-the-loop brute force search over injection variations at minimal additional cost. We demonstrate billion-loadflow-per-second performance on power grids of varying sizes in workload settings which are typical for gradient-free topology optimization such as Reinforcement Learning or Quality Diversity methods. While adopting the DC approximation sacrifices some accuracy and prohibits the computation of voltage magnitudes, we show that this sacrifice unlocks new scales of computational feasibility, offering a powerful tool for large-scale grid planning and operational topology optimization.

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arXiv2024

Unified algebraic deviation of distribution factors in linear power flow

Distribution factors are indispensable tools in the design and analysis of power transmission grids. Recently, they received a renewed interest in the field of topology optimization, leading to the definition of bus merge and bus split distribution factors. In this article, we introduce a unified derivation of the most relevant distribution factors based on matrix algebraic manipulations. This approach facilitates the generalization to more complex grid modification, in particular simultaneous switching events or bus splits.

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