HP2C-DT: High-Precision, High-Performance Digital Twin

Eigenverlag des Österreichischen Verbandes für Elektrotechnik

Authors: Eduardo Prieto-Araujo, Francesca Rossi, Juan Carlos Olives Camps, Élia Mateu Barriendos, Soufiane El Yaagoubi, Marcel Garrobé Fonollosa, Joan Gabriel Bergas-Jané, Vinícius Lacerda, Eduardo Iraola, Mauro García Lorenzo, Francesc Lordan, Rosa M. Badia

This article presents the development of a High-Performance Computing-enabled Digital Twin (HPC-DT) as an innovative solution designed to significantly enhance real-time power system management for grid operators. It begins by providing a comprehensive overview of the HPC-DT architecture, detailing its key components and the technical methodology for integrating the digital twin with actual power systems. Following the architectural discussion, the article introduces a suite of specialized tools, many of which leverage the computational power of HPC systems, to illustrate the practical capabilities of the proposed concept. The energy transition towards 100% renewable energy systems necessitates innovative tools to support power system operators in managing networks, particularly in light of the challenges posed by replacing conventional synchronous generation with power electronics (PE)-interfaced renewable energy systems.

Conference/Journal Publication

Authors: Andrés E. Quintero, Vinícius A. Lacerda, Oriol Gomis-Bellmunt, Moisés J. B. B. Davi, Mario Oleskovicz

This paper investigates how grid-forming (GFM) and grid-following (GFL) control strategies in inverter-based resources (IBRs) influence line distance and differential protection in converter-dominated transmission systems. A modified IEEE 39-bus system is evaluated with GFM and GFL units equipped with low-voltage ride-through logic, current limiting, and positive- or negative-sequence prioritization. Distance protection is implemented with a mho characteristic, while line differential protection uses an alpha-plane approach. Results show that phase-to-ground loops in distance protection can substantially overestimate the fault location near the Zone-1 reach. For line differential protection, external faults may cause the operating point to briefly enter the trip region of the alpha-plane, even for the healthy-phase in ABG faults under GFL control and during the initial moments of the fault, demanding strong external security measures.

Conference/Journal Publication

Authors: Moisés J. B. B. Davi, Vinícius A. Lacerda, Andrés Quintero, Felipe V. Lopes, Oriol Gomis-Bellmunt, Mário Oleskovicz

As power systems rapidly transition to a landscape dominated by inverter-based resources (IBRs) with grid-forming (GFM) controls, existing protection philosophies face growing challenges. This paper presents a case study-based analysis of how distinct GFM control modes, dictated by grid codes, affect the reliability of conventional distance protection. A 14-bus, 400 kV network dominated by GFM IBRs was modeled in MATLAB/Simulink, incorporating four fault ride-through (FRT) strategies aligned with recent literature. Several distance protection strategies (self-, cross-, and memory-polarized Mho relays, as well as zero- and negative-sequence polarized quadrilateral characteristics) were assessed. The findings reveal that grid code requirements significantly influence the dependability and security of protection, with virtual-admittance and dual-sequence reactive current injection FRT strategies providing the most consistent performance. In particular, the zero-sequence polarized quadrilateral characteristic exhibited near-perfect dependability for faults involving ground across all evaluated IBR control modes.

Conference/Journal Publication

Authors: Ferriol Falip Torras, Francesca Rossi, Alexandre Gracia Calvo, Eduardo Prieto Araujo

This paper focuses on applying data visualization and analysis techniques to explore and identify patterns in large datasets of power system operating points. The work addresses a database containing an extensive set of operating points of the power grid, randomly sampled within its feasible operating space, with system stability assessed for each point. The methodology includes data preprocessing, dimensionality reduction using PCA, t-SNE, and UMAP techniques, clustering methods (k-means and DBSCAN), and visualization to extract meaningful insights. The main objective is to determine which factors have the most relevant influence on grid stability, providing a solid foundation for future research directions or potential improvements in grid operation and management. The case study uses the NREL-118 system, consisting of 118 buses, 53 generators (including 18 converter-interfaced generators), operating under different control modes.

Conference/Journal Publication

Authors: Juan Carlos Olives-Camps, Francesca Rossi, Eduardo Prieto-Araujo

This study proposes a tool embedded in a Digital Twin (DT) of the power system to identify potential undesirable interactions that may emerge within the system. The approach assumes that an up-to-date and accurate digital model of the power grid is available, but only black box models of the IBRs connected to the grid are accessible. The methodology consists of two stages: firstly, data-driven techniques are utilised to determine the equivalent impedance model of the electrical network and that of the IBR connected to that same bus. Then, the impedance models are employed to ascertain the stability of the system within the bandwidth permitted by these models. Equivalent models are obtained by running multiple EMT simulations in parallel within the DT. The Positive Mode Damping technique is used to determine interactions, based on the network's and converter's impedance models.

IEEE Access, vol. 12, pp. 135913-135928, 2024

Authors: Àlex Tudoras-Miravet, Esteban González-Iakl, Oriol Gomis-Bellmunt, Eduardo Prieto-Araujo

This paper proposes an approach that leverages the inclusion of physical constraints into the loss function using a penalty factor and the utilization of bounds of optimization variables in the activation functions to enhance the generalization performance of tuned neural networks. The results indicate that this method significantly improves the success rate and computational speed gains of AC-optimal power flow (AC-OPF) calculations, especially when forward predictions are employed as warm-start points. The PINN models are trained using accurate AC-OPF solutions from slow high-precision interior-point solvers across several power system scenarios. The proposed PINN model offers a promising solution for adapting neural networks to diverse scenarios of a physical problem and provides a robust methodology for successfully addressing optimal power flow (OPF) problems in power systems.

Journal Publication

Authors: Carlos Collados-Rodriguez, Daniel Westerman Spier, Marc Cheah-Mane, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt

This paper analyses frequency dynamics in modern power systems with a high penetration of converter-based generation. A fundamental analysis of the frequency dynamics is performed to identify the limitations and challenges when the converter penetration is increased. The voltage-source behaviour is found as an essential characteristic of converters to improve the initial frequency derivative of Synchronous Generators (SGs). A detailed small-signal analysis, based on the system's eigenvalues, participation factors and mode shapes, is then performed in a reduced system for different converter penetrations, showing that the flexibility of grid-forming (GFOR) converters as well as the system's inertia reduction may lead to have a more controllable system frequency. First-order frequency responses can be programmed for high converter penetrations, when GFOR operation can impose their dominance over SGs. These results have been validated in the IEEE 118-bus system simulated in PSCAD.

Journal Publication

Authors: Francesca Rossi, Mauro Garcia Lorenzo, Eduardo Iraola de Acevedo, Elia Mateu Barriendos, Vinicius Albernaz Lacerda, Francesc Lordan-Gomis, Rosa Badia, Eduardo Prieto-Araujo

The increasing penetration of inverter-based resources (IBRs) is fundamentally reshaping power system dynamics and creating new challenges for stability assessment. Data-driven approaches, and in particular machine learning models, require large and representative datasets that capture how system stability varies across a wide range of operating conditions and control settings. This paper presents an open-source, high-performance computing framework for the systematic generation of such datasets. The proposed tool defines a scalable operating space for large-scale power systems, explores it through an adaptive sampling strategy guided by sensitivity analysis, and performs small-signal stability assessments to populate a high-information-content dataset. The framework efficiently targets regions near the stability margin while maintaining broad coverage of feasible operating conditions. The workflow is fully implemented in Python and designed for parallel execution. The resulting tool enables the creation of high-quality datasets that support data-driven stability studies in modern power systems with high IBR penetration.

Journal Publication

Authors: Francesca Rossi, Juan Carlos Olives-Camps, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt

This study proposes a control strategy to ensure the safe operation of modern power systems with high penetration of inverter-based resources (IBRs) within an optimal operation framework. The objective is to obtain operating points that satisfy the optimality conditions of a predefined problem while guaranteeing small-signal stability. The methodology consists of two stages. First, an offline analysis of a set of operating points is performed to derive a data-driven regression-based expression that captures a damping-based stability index as a function of the operating conditions. Second, an Online Feedback Optimization (OFO) controller is employed to drive the system toward an optimal operating point while maintaining a secure distance from the instability region. The proposed strategy is evaluated on an academic test case based on a modified version of the IEEE 9-bus system, in which synchronous generators are replaced by IBRs operating under both grid-following and grid-forming control modes. The results demonstrate the effectiveness of the method and are discussed in detail.

Journal Publication

Authors: Alexandre Gràcia-Calvo, Francesca Rossi, Eduardo Iraola, Juan Carlos Olives-Camps, Eduardo Prieto-Araujo

Modern power networks face increasing vulnerability to cascading failures due to high complexity and the growing penetration of intermittent resources, necessitating rigorous security assessment beyond the conventional N-1 criterion. Current approaches often struggle to achieve the computational tractability required for exhaustive N-2 contingency analysis integrated with complex stability evaluations like small-signal stability. Addressing this computational bottleneck and the limitations of deterministic screening, this paper presents a scalable methodology for the vulnerability assessment of modern power networks, integrating N-2 contingency analysis with small-signal stability evaluation. To prioritize critical components, we propose a probabilistic Risk Index (Ri) that weights the deterministic severity of a contingency (including optimal power flow divergence, islanding, and oscillatory instability) by the failure frequency of the involved elements based on reliability data. The proposed framework is implemented using High-Performance Computing (HPC) techniques through the PyCOMPSs parallel programming library, orchestrating optimal power flow simulations (VeraGrid) and small-signal analysis (STAMP) to enable the exhaustive exploration of massive contingency sets. The methodology is validated on the IEEE 118-bus test system, processing more than 57000 scenarios to identify components prone to triggering cascading failures. Results demonstrate that the risk-based approach effectively isolates critical assets that deterministic N-1 criteria often overlook.

Journal Publication

Authors: Ferran Bohigas-Daranas, Oriol Gomis-Bellmunt, Eduardo Prieto-Araujo

This paper presents a critical and practical approach to the evolution of distribution network reconfiguration algorithms, tracing their development from foundational heuristic methods introduced in 1975 to contemporary state-of-the-art techniques. The article systematically reviews seven different methodologies, including classical heuristic algorithms (Merlin, Baran, and others), advanced meta-heuristic methodologies (particle swarm optimization (PSO) and genetic algorithms), and purely mathematical approaches (MILP-based), analyzing their theoretical foundations, implementation strategies, computational complexity, and performance metrics based on extensive literature review and our own empirical testing. Each methodology is assessed through standardized test systems, considering multiple objectives such as power loss minimization and voltage profile improvement. The comparative analysis reveals the strengths and limitations of each approach under various network conditions and operational constraints. Furthermore, this work provides significant value to the research community by offering an open-source repository containing documented implementations of all reviewed algorithms. This resource facilitates accessibility for newcomers to the field, promotes reproducible research, and accelerates the development of next-generation distribution network optimization solutions. The repository includes comprehensive documentation, test cases, and performance benchmarks.

Conference/Journal Publication

Authors: Ferran Bohigas-Daranas, Arkadiusz Burek, Oriol Gomis-Bellmunt, Eduardo Prieto-Araujo

Digital twins 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 digital twin implementations reveals a critical challenge: the absence of standardized data exchange protocols leads to vendor lock-in, interoperability issues between data provided by different vendors, and increased lifecycle costs. This paper argues for the adoption of standardized data exchange methodologies in HVDC digital twins and examines the suitability of the Common Information Model (CIM) defined in IEC 61970 as a foundation for this standardization, an established and proven data structure for electrical systems that also includes HVDC elements.

Future Generation Computing Systems journal

Authors: Eduardo Iraola, Mauro García-Lorenzo, Francesc Lordan-Gomis, Francesca Rossi, Eduardo Prieto-Araujo, Rosa Maria Badia

Digital twins are transforming the way we monitor, analyze, and control physical systems, but designing architectures that balance real-time responsiveness with heavy computational demands remains a challenge. Cloud-based solutions often struggle with latency and resource constraints, while edge-based approaches lack the processing power for complex simulations and data-driven optimizations. To address this problem, we propose the High-Precision High-Performance Computer-enabled Digital Twin (HP2C-DT) reference architecture, which integrates High-Performance Computing (HPC) into the computing continuum. Unlike traditional setups that use HPC only for offline simulations, HP2C-DT makes it an active part of digital twin workflows, dynamically assigning tasks to edge, cloud, or HPC resources based on urgency and computational needs. Furthermore, to bridge the gap between theory and practice, we introduce the HP2C-DT framework, a working implementation that uses COMPSs for seamless workload distribution across diverse infrastructures. We test it in a power grid use case, showing how it reduces communication bandwidth by an order of magnitude through edge-side data aggregation, improves response times by up to 2x via dynamic offloading, and maintains near-ideal strong scaling for compute-intensive workflows across a practical range of resources. These results demonstrate how an HPC-driven approach can push digital twins beyond their current limitations, making them smarter, faster, and more capable of handling real-world complexity.