Intelligent solar energy integration in Wageningen university campus (MSc)
Both European and Dutch climate policies have dictated that serious efforts should be made to reduce carbon emissions and increase the share of renewable energy in order to mitigate climate change and meet the targets of Paris climate agreement . This has led to a rapid development and application of renewable energy technologies across all sectors. In the Netherlands, there is a clear objective towards an increased electronification and sustainability in the agriculture sector, according to the Dutch climate agreement in 2019 . Digital solutions and data-driven approaches can play a key role in enabling accurate decision support for the large scale integration of renewable energy technologies.
 The Paris Agreement, 2015, https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement
 National Climate Agreement (The Netherlands), 2019, https://www.klimaatakkoord.nl/documenten/publicaties/2019/06/28/national-climate-agreement-the-netherlands
Renewable energy generation and energy efficiency are key drivers to reduce CO2 emissions in the built environment3. Buildings are the largest energy consumers in the EU; they account for 40% of the total energy consumption and 36% of the CO2 emissions4. In the Netherlands, an increasing deployment of solar Photovoltaic (PV) energy systems (e.g., on land and on buildings’ rooftops) has been noticed over the past few years. Wageningen university has also been paying an increasing attention to sustainable energy transition and renewable energy integration in buildings and farming applications; the WUR facilities in Lelystad are an example.
This thesis will look at the technical potential of local solar PV energy production at Wageningen university campus using data science, GIS maps, modeling and simulation approaches. The final aim is to develop a data-driven framework to assess: i) the potential of solar PV energy production at different buildings located in Wageningen university campus and ii) the self-consumption and self-sufficiency ratios of solar energy in those buildings. Further, the thesis will identify the opportunities and barriers to PV adoption at WU campus and provide recommendations to decision makers.
The work in this master thesis entails:
- To collect data, analyze and create an overview of the available and potential for solar PV panels installation in different buildings’ rooftops (or other suitable locations) in Wageningen university campus.
To develop an algorithm that:
- assesses energy generation potential of those PV systems using a data-driven approach.
- evaluates the self-consumption and self-sufficiency of those buildings (i.e., electricity demand that can be fulfilled by integrated PV systems).
- To identify the opportunities and barriers to PV adoption at Wageningen university campus and provide recommendations to decision makers.
- Luthander, Rasmus, et al. "Photovoltaic self-consumption in buildings: A review." Applied energy 142 (2015): 80-94
- Kucuksari, Sadik, et al. "An Integrated GIS, optimization and simulation framework for optimal PV size and location in campus area environments." Applied Energy 113 (2014): 1601-1613
- Litjens, G. B. M. A., et al. "A spatio-temporal city-scale assessment of residential photovoltaic power integration scenarios." Solar Energy 174 (2018): 1185-1197
Requirements: Programming in Python, basic data analytics and GIS maps, interest about sustainable energy transition.
Key words: Data science / Software engineering for Sustainability
Dr. Tarek Alskaif ( firstname.lastname@example.org)
3) European Parliament, Directive on energy efficiency 2012 EU, (2012) 1–56.
4) European Commission, Energy Performance of Buildings, (2014).