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Overview

Dr Behzad Kazemtabrizi

Associate Professor, Director of Education


Affiliations
AffiliationTelephone
Associate Professor, Director of Education in the Department of Engineering+44 (0) 191 33 41734

Biography

I am an Associate Professor at Durham University, where my research focuses on electrical power systems optimisation, reliability assessment, and the integration of renewable energy resources into modern grids. I received my PhD in Electrical Engineering from the University of Glasgow in 2011. My research work covers areas such as hybrid AC/DC network modeling, wind farm reliability, microgrid control, and data-driven forecasting—addressing key challenges in energy transition and grid modernisation.

I have contributed to the development of computational tools for hybrid AC/DC grids and HVDC transmission, aimed at improving the robustness and efficiency of next-generation transmission networks. My research on wind farm reliability supports asset management and condition-based maintenance strategies that help reduce operational costs and enhance performance. In addition, I work on microgrid control and distributed energy resource management to enable secure, real-time integration of renewables.

Collaboration is central to my approach. I work closely with statisticians, computer scientists, and industry partners to deliver practical solutions such as AI-driven wind forecasting, stochastic optimisation for energy planning, and resilient grid architectures. My goal is to ensure that these innovations inform industry practices and contribute to the transformation of energy systems toward greater reliability, flexibility, and sustainability.

Recently, I have been incorporating machine learning and data-driven methods into power system studies, including work on frequency stability and unit commitment using machine learning techniques, as well as forecasting and optimisation for renewable integration. I have also been increasingly focused on on applications of physics-informed neural networks (PINNs) for developing fast-acting surrogate models to solve computationally intensive planning problems in power systems, such as Optimal Power Flow (OPF) and Unit Commitment.

I am always happy to hear from potential collaborators in industry and academia, as well as prospective PhD candidates interested in applied mathematical/computational problems in power systems.

Alongside my research, I have served as Director of Education in the Department of Engineering since 2023, where I am committed to enhancing the student learning experience and supporting curriculum development.

Research interests

  • Advanced Power Systems Modelling
  • Decision Making under Uncertainty
  • Flexibility and Resiliency in Future Power Systems
  • Optimal Power Flows
  • Artificial Intelligence and Machine Learning applied to Power Systems
  • Reliability Evaluation of Power Systems
  • Renewable Energy Integration (Wind Power)
  • Power System Dynamics

Publications

Authored book

Chapter in book

Conference Paper

Journal Article

Presentation

Supervision students