arXiv subjects

Ferran Bohigas-Daranas

Publications and source records attributed to Ferran Bohigas-Daranas.

5 recordsLinked to original sources

arXiv2026

Distribution Network Reconfiguration: A Reproducible Benchmark of Heuristic, Metaheuristic and Mathematical Methods

Distribution network reconfiguration (DNR) has been studied for five decades, yet published algorithms are rarely compared under identical assumptions: authors report results on different test systems, with different power-flow models, and almost never release code. This paper re-implements eight representative DNR algorithms in a single open-source Python framework and benchmarks them on five networks under identical conditions. The algorithms span three of the four established paradigms: heuristics (loop cutting, branch exchange, greedy minimum spanning tree, exhaustive search), metaheuristics (a genetic algorithm and a selective binary particle swarm optimizer) and mathematical programming (a mixed-integer quadratic program built on a convex, simplified DistFlow power-flow model). We report power losses, voltage profile, and, as a hardware-independent cost metric, the number of power-flow evaluations required for convergence. On the 196-bus Simbench urban network, we show that a dynamic DNR policy can save more than 26% of the system losses, weighed against circuit-breaker maintenance cost. All implementations, test cases, and result scripts are released to allow direct extension and replication

eess.SY↗

arXiv2026

Unified Heterogeneous Graph Neural Network solver for Power Flow, Optimal Power Flow and State Estimation

Power Flow (PF), Optimal Power Flow (OPF), and State Estimation (SE) are fundamental problems in power system analysis, but solving them is computationally expensive. Graph Neural Networks (GNNs) have been proposed as fast surrogates, yet existing solvers are trained for a single problem at a time, producing narrow models that must be rebuilt for each new task. We propose a more general approach: a single Heterogeneous Residual Gated Graph Convolutional Network that solves all three problems with one shared backbone. Rather than learning one mapping, the model learns a reusable representation of how the network behaves, from which PF, OPF, and SE can each be estimated. Trained jointly on the three problems across diverse topologies and loading conditions, and evaluated on the IEEE 14-bus and 118-bus systems, the shared model matches the accuracy of task-specific GNN solvers and stays robust on unseen loading levels and topologies. These results show that a single model can capture the basic operation of a power network and serve several analysis tasks at once, a first step toward a foundation model for power systems.

eess.SY↗

arXiv2026

Cybersecurity in Power Grids: Standards and Research Challenges

This paper examines Smart Grid cybersecurity, emphasizing the critical distinctions between IT and OT environments. It analyzes grid architecture, substation threats, and key international standards, specifically IEC 62351, IEC 62443, and ISO 27001. Finally, it overviews latest research trends, including AI-driven threat detection.

cs.CR↗

arXiv2026

Use of the Common Information Model (IEC 61970) for Standardized Data Exchange in HVDC Digital Twin Systems

Digital Twins (DTw) are emerging as transformative tools for High Voltage Direct Current (HVDC) transmission systems, enabling real-time monitoring, predictive maintenance, and operational optimization. However, the effectiveness of these virtual replicas fundamentally depends on seamless, reliable data exchange with their physical counterparts and any other support system. The current landscape of HVDC DTw implementations reveals a critical challenge: the absence of standardized data exchange protocols leads to vendor lock-in, interoperability issues between systems from different vendors. The contribution of this paper is to propose the standardization of Common Information Model (CIM) defined in IEC 61970 standard family, as the data exchange methodology in HVDC DTws, which is exemplified with the Cigre B4 example, as well as connected to the IEC 61850 standard for real time station control, via the harmonization guidelines from the IEC 62361-102 technical report.

eess.SY↗

arXiv2026

Power Flow Feasibility Assessment Using Variational Graph Autoencoders

Data-driven methods, including graph neural networks, have been studied for accelerating power flow calculations in recent years, but very little attention has been paid to the solution feasibility, which can be obtained by traditional solvers. This paper presents a Variational Graph Autoencoder (VGAE) that detects the power flow solution feasibility, using the IEEE 118-bus case, to assess the validity of the solutions provided by AI-driven solvers.

cs.LG↗