Selected work

Representative Papers

Orchestrating Power Grid Studies with Multi-Agent AI and MCP Servers

Jul 14, 2026

This work proposes an automated power system simulation framework based on multi-agent artificial intelligence and the Model Context Protocol (MCP) to address the lack of intelligent coordination and human–machine collaboration in traditional simulation approaches. By introducing the first pypowsybl-MCP interface, the framework enables large language models to invoke power system simulation tools through a standardized protocol, facilitating an interactive, auditable, and scalable multi-agent workflow under human supervision. The platform supports end-to-end automation of simulation configuration, execution, and analysis, integrating quantitative technical metrics with expert feedback for comprehensive evaluation. This approach significantly enhances the intelligence and collaborative efficiency of transmission system operators in power grid studies.

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Non-stationary GEV models for estimating design sea-states in a changing climate. Applications to offshore wind farms along the French coasts

Mar 09, 2026

This study addresses the inadequacy of conventional extreme-event design methods—based on stationarity assumptions—in ensuring the durability of offshore wind structures in France under nonstationary sea conditions driven by climate change. To overcome this limitation, a nonstationary generalized extreme value (GEV) model is developed, integrating CMIP6 multi-model ensemble projections with reanalysis data to quantify future changes in extreme sea states using monthly maxima of significant wave height. The work further introduces a lifetime-equivalent design sea state framework for forward-looking structural design. Results indicate intensified winter extremes and attenuated summer extremes in the Atlantic and English Channel, leading to heightened seasonal contrasts, while trends in the Mediterranean remain uncertain. Overall, design conditions are projected to become more severe, underscoring the necessity of abandoning stationarity assumptions to enhance climate resilience in offshore wind infrastructure.

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Study Design and Demystification of Physics Informed Neural Networks for Power Flow Simulation

Sep 23, 2025

Under energy transition, high renewable penetration and cross-border interconnections intensify grid uncertainty, rendering conventional power flow solvers inadequate for real-time operation, while purely data-driven models lack physical consistency. This paper proposes a physics-informed neural network (PINN) framework for power flow simulation, integrating Kirchhoff’s laws and other physical priors via hybrid modeling—combining MLP/GNN architectures with physics-constrained regularization and unsupervised loss. We introduce a novel four-dimensional evaluation framework (accuracy, physical consistency, industrial deployability, out-of-distribution generalization) and the LIPS benchmark platform. Systematic ablation studies demonstrate that explicit graph-structured modeling and direct optimization of physical equations are critical to reliability. The resulting model achieves high accuracy, strong physical consistency, robust out-of-distribution generalization, and practical industrial deployment potential. Code is fully open-sourced.

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Gaussian process surrogate model to approximate power grid simulators -- An application to the certification of a congestion management controller

Feb 28, 2025

Digitalization of power systems invalidates traditional physical models, and reliance on high-fidelity simulators renders large-scale numerical experiments—such as safety verification—computationally infeasible. Method: We propose an adaptive Gaussian process (GP) surrogate model tailored for non-Gaussian power system simulators. Its core innovation is a residual-driven uncertainty correction mechanism that relaxes GP’s strong assumption of Gaussian output distributions. By jointly modeling the predictive mean and residual uncertainty, the method enhances both approximation accuracy and statistical reliability under non-Gaussian conditions. Contribution/Results: The surrogate enables statistically rigorous safety certification of congestion management controllers, reducing original simulator calls by over 98% while preserving statistical confidence—thereby achieving substantial computational savings.

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Recent publications

Latest Papers

Orchestrating Power Grid Studies with Multi-Agent AI and MCP Servers

Jul 14, 2026

This work proposes an automated power system simulation framework based on multi-agent artificial intelligence and the Model Context Protocol (MCP) to address the lack of intelligent coordination and human–machine collaboration in traditional simulation approaches. By introducing the first pypowsybl-MCP interface, the framework enables large language models to invoke power system simulation tools through a standardized protocol, facilitating an interactive, auditable, and scalable multi-agent workflow under human supervision. The platform supports end-to-end automation of simulation configuration, execution, and analysis, integrating quantitative technical metrics with expert feedback for comprehensive evaluation. This approach significantly enhances the intelligence and collaborative efficiency of transmission system operators in power grid studies.

0 citationsRead paper

Non-stationary GEV models for estimating design sea-states in a changing climate. Applications to offshore wind farms along the French coasts

Mar 09, 2026

This study addresses the inadequacy of conventional extreme-event design methods—based on stationarity assumptions—in ensuring the durability of offshore wind structures in France under nonstationary sea conditions driven by climate change. To overcome this limitation, a nonstationary generalized extreme value (GEV) model is developed, integrating CMIP6 multi-model ensemble projections with reanalysis data to quantify future changes in extreme sea states using monthly maxima of significant wave height. The work further introduces a lifetime-equivalent design sea state framework for forward-looking structural design. Results indicate intensified winter extremes and attenuated summer extremes in the Atlantic and English Channel, leading to heightened seasonal contrasts, while trends in the Mediterranean remain uncertain. Overall, design conditions are projected to become more severe, underscoring the necessity of abandoning stationarity assumptions to enhance climate resilience in offshore wind infrastructure.

0 citationsRead paper

Study Design and Demystification of Physics Informed Neural Networks for Power Flow Simulation

Sep 23, 2025

Under energy transition, high renewable penetration and cross-border interconnections intensify grid uncertainty, rendering conventional power flow solvers inadequate for real-time operation, while purely data-driven models lack physical consistency. This paper proposes a physics-informed neural network (PINN) framework for power flow simulation, integrating Kirchhoff’s laws and other physical priors via hybrid modeling—combining MLP/GNN architectures with physics-constrained regularization and unsupervised loss. We introduce a novel four-dimensional evaluation framework (accuracy, physical consistency, industrial deployability, out-of-distribution generalization) and the LIPS benchmark platform. Systematic ablation studies demonstrate that explicit graph-structured modeling and direct optimization of physical equations are critical to reliability. The resulting model achieves high accuracy, strong physical consistency, robust out-of-distribution generalization, and practical industrial deployment potential. Code is fully open-sourced.

0 citationsRead paper

Gaussian process surrogate model to approximate power grid simulators -- An application to the certification of a congestion management controller

Feb 28, 2025

Digitalization of power systems invalidates traditional physical models, and reliance on high-fidelity simulators renders large-scale numerical experiments—such as safety verification—computationally infeasible. Method: We propose an adaptive Gaussian process (GP) surrogate model tailored for non-Gaussian power system simulators. Its core innovation is a residual-driven uncertainty correction mechanism that relaxes GP’s strong assumption of Gaussian output distributions. By jointly modeling the predictive mean and residual uncertainty, the method enhances both approximation accuracy and statistical reliability under non-Gaussian conditions. Contribution/Results: The surrogate enables statistically rigorous safety certification of congestion management controllers, reducing original simulator calls by over 98% while preserving statistical confidence—thereby achieving substantial computational savings.

0 citationsRead paper