Paid MASTERs Thesis Considering Uncertain Electrical Power Surplus in Model Predictive Control of Smart Homes

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BEST Bioenergy and Sustainable Technologies GmbH is the competence center in Austria that conducts precompetitive industryrelated research in the field of bioenergy and biobased economy.

At BEST we work as a team to actively shape the transition to a sustainable and climate friendly bioeconomy.

Your talent and your passion for research and science are explicitly needed with us. Collaborate with us on interesting and promising tasks and develop your scientific creativity. Join us on our approach to a more environmentally and climate friendly future!

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Tasks

To dedicated students (m/w/d) of electrical / mechanical engineering information and computer engineering or related disciplines we offer the opportunity to write a paid Masters thesis. The thesis will be conducted in cooperation with the Institute of Automation and Control Graz University of Technology.

Smart energy management systems for homes increasingly need to consider multiple energy sectors i.e. electricity heating and cooling in order to reach their maximum potential. For example surplus electricity from photovoltaics (PV) often is stored as heat in a thermal buffer by driving a heat pump or a heating rod. Varying electricity prices already provided by certain tariffs (e.g. aWATTar) and uncertainties in PV yield however make deciding on the best starting time and operating strategy difficult. We at BEST have developed a modular framework for energy management applications which answers such questions by formulating and solving an optimization problem. However this optimization takes time and systems often need to react quickly to changes in surplus electricity to avoid unwanted import from or export to the electricity grid.

Your tasks

• Investigate existing solutions and typical setpoints for power inverters (minimum / maximum battery charging power) and heating rods / heat pumps
• Deduce setpoints for lowlevel controllers based on optimization results that guarantee optimal behaviour even when only optimizing at a low sampling rate using e.g. stochastic optimization
• Test your algorithms in simulations and implement them on a real system

Requirements

Your interests

• Studies in electrical mechanical or computer engineering or physics
• Ideally with some background in control engineering
• Programming experience with MATLAB Python or (ideally) Julia
• Study admission for an Austrian university EU visa residence permit

Benefits

Your benefits

• Integration into a dedicated team
• Perspective of participation in followup projects after successful completion
• Financial compensation based on student staff salary scheme
• Provision of a work place (remote work from home also possible)

In the spirit of diversity BEST would especially welcome applications from women.

Please also have a look at our homepage for 2 other master theses and one PhD position at our competence area Automation and Control.