{"id":8918,"date":"2024-09-10T13:06:11","date_gmt":"2024-09-10T11:06:11","guid":{"rendered":"https:\/\/www.uni.lu\/research-en\/?post_type=events&p=8918"},"modified":"2024-09-19T11:26:14","modified_gmt":"2024-09-19T09:26:14","slug":"doctoral-defence-ramin-bahmani","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/research-en\/events\/doctoral-defence-ramin-bahmani\/","title":{"rendered":"Doctoral Defence: Ramin BAHMANI"},"content":{"rendered":"\n
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The Doctoral School in Science and Engineering<\/a> is happy to invite you to Ramin BAHMANI\u2019s defence entitled<\/p>\n\n\n\n

\nModeling and Data Analysis for Flexibility Optimization and Forecasting in Energy Systems<\/h5>\n\n\n\n

Supervisor: Prof. Gilbert FRIDGEN<\/a><\/p>\n\n\n\n

The energy system is undergoing a significant transformation with the increasing penetration of renewable energy sources into the grid. This shift towards renewables introduces volatility due to the intermittent nature of sources like wind and solar power. To maintain power system stability and avoid unnecessary investments in new power generation or transmission infrastructure, stakeholders propose increasing flexibility within the energy system. Multiple energy sectors can contribute to this flexibility by optimizing their operations to balance supply and demand effectively. Enhanced coordination between these sectors can help mitigate the risks associated with the volatility of renewable energy sources. Additionally, data inputs such as time series of electrical load and renewable generation are essential for optimization, making data analytics crucial for enabling this integrated approach to energy management.<\/p>\n\n\n\n

This doctoral thesis comprises six publications that investigate two primary research directions: flexibility optimization and time series analysis. The first four publications focus on developing flexibility optimization algorithms tailored for various energy sectors and market participants, including industrial companies, residential consumers, and electric vehicle (EV) aggregators. Specifically, two papers address industrial demand flexibility optimization, utilizing a generic data model to facilitate seamless data transfer. Another paper proposes a model for EV aggregator flexibility optimization that account for data uncertainty, ensuring reliable operation under uncertain conditions. The forth paper examines flexibility optimization within an energy hub, integrating electricity, gas, and heat as interconnected energy carriers.<\/p>\n\n\n\n

In addition to optimization strategies, the thesis delves into time series analysis, essential for effective flexibility optimization. Two publications in this domain investigate methods to enhance data quality and forecasting. One publication focuses on real-time spatiotemporal time series missing data imputation, offering solutions to manage and recover incomplete datasets crucial for real-time decision-making. The other publication reviews and evaluates various load forecasting methods, providing insights into their applicability and performance in forecasting future energy demands.<\/p>\n\n\n\n

Together, these studies contribute to a comprehensive understanding of how to optimize flexibility across different sectors and how time series analysis can support this optimization.<\/p>\n\n\n\n