Spyros Chatzivasileiadis

Technical University of Denmark

Denmark

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Professors in Energy Engineering

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Articles (20)

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2024

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2024

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2024

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2023

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2023

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2023

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2023

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2023

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2023

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2023

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2023

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2023

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2022

Collaborators (13)

Pierre Pinson

Technical University of Denmark

DENMARK

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Ali Mehrizi‐Sani

UNITED STATES

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Christian Becker

Hamburg University of Technology

GERMANY

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Panagiotis Papadopoulos

University Of Strathclyde

UNITED KINGDOM

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Rahul Nellikkath

Technical University of Denmark

DENMARK

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Hjörtur Jóhannsson

Technical University of Denmark

DENMARK

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Federico Milano

Professor

Dublin City University

IRELAND

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Samuel Chevalier

Technical University of Denmark

DENMARK

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Baosen Zhang

University of Washington

UNITED STATES

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Davood Babazadeh

Senior Scientist & Lecturer

Hamburg University of Technology

GERMANY

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Jochen Stiasny

Technical University of Denmark

DENMARK

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Petros Aristidou

CYPRUS

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Mathaios Panteli

CYPRUS

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Spyros Chatzivasileiadis

Research Interests

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Articles (20)

Physics-Informed Neural Networks: A plug and play integration into power system dynamic simulations

Physics-informed machine learning for power system dynamics: A framework incorporating trustworthiness

Stochastic quantum power flow for risk assessment in power systems

A dataset generation toolbox for dynamic security assessment: On the role of the security boundary

PINNSim: A simulator for power system dynamics based on Physics-Informed Neural Networks

Physics-informed neural networks for phase locked loop transient stability assessment

A hybrid quantum–classical algorithm for mixed-integer optimization in power systems

Bayesian Physics-informed Neural Networks for system identification of inverter-dominated power systems

Optimal Design of Volt/VAR Control Rules of Inverters using Deep Learning

Can Machine Learning Help Keep the System Secure?: Power Systems and Change Addressing the Increasing Complexity and Uncertainty During the Energy Transition

On Machine Learning-Based Techniques for Future Sustainable and Resilient Energy Systems

Physics-informed neural networks for time-domain simulations: Accuracy, computational cost, and flexibility

Decentralized model-free loss minimization in distribution grids with the use of inverters

A novel decentralized inverter control algorithm for loss minimization and LVRT improvement

Learning Active Constraints to Efficiently Solve Linear Bilevel Problems: Application to the Generator Strategic Bidding Problem

Micro-flexibility: Challenges for power system modeling and control

Deep Learning for Scalable Optimal Design of Incremental Volt/VAR Control Rules

Network-Aware Flexibility Requests for Distribution-Level Flexibility Markets

Optimal Design of Volt/VAR Control Rules for Inverter-Interfaced Distributed Energy Resources

Modeling the AC power flow equations with optimally compact neural networks: Application to unit commitment

Collaborators (13)

Pierre Pinson

Ali Mehrizi‐Sani

Christian Becker

Panagiotis Papadopoulos

Rahul Nellikkath

Hjörtur Jóhannsson

Federico Milano

Samuel Chevalier

Baosen Zhang

Davood Babazadeh

Jochen Stiasny

Petros Aristidou

Mathaios Panteli

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