Aysegül Kahraman

Aysegül Kahraman serves as a Postdoctoral Researcher (Research Fellow) at the Department of Wind and Energy Systems, Technical University of Denmark (DTU), with active projects running through 2028. Her work directly contributes to UN Sustainable Development Goals in renewable energy and climate action through advanced energy systems research.

Her research expertise spans Wind Power integration, Microgrid optimization, and Machine Learning applications for energy systems. She specializes in deploying Reinforcement Learning and Deep Learning techniques to solve critical challenges in load forecasting, home energy management, and multi-energy system operations under uncertainty, with particular focus on transactive control frameworks and EV scheduling.

Analysis of her 2022-2024 publications reveals a strong trend toward AI-driven solutions for renewable energy integration, with consistent emphasis on real-world implementation in microgrids and distribution systems. Her work demonstrates increasing sophistication in handling system constraints while optimizing energy flexibility across multiple domains.

As academic supervision, she currently serves as supervisor for PhD candidate Sundhu, A. A. in the Real-Time Digital Twin for Active Distribution Networks project, building on her own recently completed PhD research at DTU.