Research area/discipline: Simulation Modelling, Data Science for Sustainability
Prerequisites: Programming in Python (INF-22306) and/or Data Science Concepts (INF-34306)
Over the past decades, global power demand has increased significantly across all sectors. At the same time, both European and Dutch national policies have dictated that serious efforts should be made to reduce carbon emissions and increase the share of Renewable Energy Sources (RES) in order to mitigate climate change. In the Netherlands, this has led to a rapid movement toward electrification and renewable energy sources deployment across all sectors including the agriculture sector1 .
However, the largescale integration of intermittent RES (e.g., wind and solar) puts challenges on the operation of the power network and requires flexibility from end-users to both handle the rapid move towards electrification and match demand and renewable energy supply locally and intelligently. In this regard, Information and Communication Technologies (ICT) and Demand Response (DR) algorithms could play an essential role in solving this problem from different perspectives. This thesis will look at the flexibility potential of electric greenhouses in the agriculture sector using ICT-enabled and DR solutions.
The work in this master thesis entails:
To collect data, analyze and create an overview of the electricity demand pattern in the agriculture sector in the Netherlands (i.e., with focus on greenhouses).
- To assess the flexibility potential of electric assets in greenhouses.
- To design an ICT-based framework and develop a DR algorithm that enable the implementation of electric demand flexibility, taking into consideration the interest of different stakeholders.
Programming in Python, basic data analytics and modeling techniques, interest about sustainability, energy transition and smart grids.
- Palensky, Peter, and Dietmar Dietrich. "Demand side management: Demand response, intelligent energy systems, and smart loads." IEEE transactions on industrial informatics 7.3 (2011): 381-388.
- Good, Nicholas, Keith A. Ellis, and Pierluigi Mancarella. "Review and classification of barriers and enablers of demand response in the smart grid." Renewable and Sustainable Energy Reviews 72 (2017): 57-72.
For more information:
For making an appointment to discuss the thesis topic, please send an email to: Dr.Ir. Tarek AlSkaif | Assistant Professor | Information Technology group (INF) | Wageningen University & Research (WUR) | https://www.wur.nl/en/Persons/Tarek-dr.-T-Tarek-Alskaif.htm | firstname.lastname@example.org
1) National Climate Agreement (The Netherlands), 2019, https://www.klimaatakkoord.nl/documenten/publicaties/2019/06/28/national-climate-agreement-the-netherlands