Decision making under uncertainty in energy systems
Event details
| Date | 20.03.2015 |
| Hour | 10:15 |
| Speaker | Alessandra Parisio |
| Location | |
| Category | Conferences - Seminars |
Robust and stochastic predictive approaches to energy management
Abstract: The increase in energy use, dominated by the growth of the human population and the improvement in its lifestyle, is expected to continue in the near future, if no action is taken. Increasing the energy efficiency and reducing the use of energy from non-renewable sources is thus a growing need. Achieving these targets requires effective energy management systems that incorporate several sources of uncertainty in the decision process (e.g., weather conditions, user behavior, renewable generation), and integrate different technologies and information. Huge amounts of information can be integrated by means of ICT solutions and used to take decisions on different sectors, such as buildings management. By doing so, system operation can be optimized, making sure that energy efficiency does not compromise the level of service. In this talk, I will discuss possible robust and stochastic approaches to the design of integrated optimization-based management systems for energy systems, such as buildings and microgrids. The optimization model includes as many details as possible while taking the inherent system uncertainty into account and still obtaining an optimization problem solvable by standard algorithms. The ultimate goal is a practical implementation of the developed algorithms: experimental results provide insights into the performance of the proposed frameworks.
Bio: Alessandra Parisio received the PhD degree in Computer Engineering from the University of Sannio, Italy. As visiting PhD student, she was involved in the research project "Use of weather and occupancy forecasts for optimal building climate control (OptiControl)" at the Swiss Federal Institute of Technology (ETH), Switzerland. She is currently a Post-Doctoral Fellow with the Automatic Control Laboratory, Royal Institute of Technology (KTH), Sweden. Her research interests focus on the areas of manufacturing system simulation and scheduling, optimization of energy systems and stochastic constrained control.
Abstract: The increase in energy use, dominated by the growth of the human population and the improvement in its lifestyle, is expected to continue in the near future, if no action is taken. Increasing the energy efficiency and reducing the use of energy from non-renewable sources is thus a growing need. Achieving these targets requires effective energy management systems that incorporate several sources of uncertainty in the decision process (e.g., weather conditions, user behavior, renewable generation), and integrate different technologies and information. Huge amounts of information can be integrated by means of ICT solutions and used to take decisions on different sectors, such as buildings management. By doing so, system operation can be optimized, making sure that energy efficiency does not compromise the level of service. In this talk, I will discuss possible robust and stochastic approaches to the design of integrated optimization-based management systems for energy systems, such as buildings and microgrids. The optimization model includes as many details as possible while taking the inherent system uncertainty into account and still obtaining an optimization problem solvable by standard algorithms. The ultimate goal is a practical implementation of the developed algorithms: experimental results provide insights into the performance of the proposed frameworks.
Bio: Alessandra Parisio received the PhD degree in Computer Engineering from the University of Sannio, Italy. As visiting PhD student, she was involved in the research project "Use of weather and occupancy forecasts for optimal building climate control (OptiControl)" at the Swiss Federal Institute of Technology (ETH), Switzerland. She is currently a Post-Doctoral Fellow with the Automatic Control Laboratory, Royal Institute of Technology (KTH), Sweden. Her research interests focus on the areas of manufacturing system simulation and scheduling, optimization of energy systems and stochastic constrained control.
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