Thesis subject

Data-driven understanding of trading behaviour in energy markets (MSc)

Europe's energy sector is going through unprecedented challenges mainly caused by the shortage of natural gas imports and a needed shift towards green energy. Indeed, the world of energy as we used to know it will not ever be the same again. If not handled carefully, such challenges can have serious negative economic and societal impacts. Energy markets are essential instruments to guarantee the balance between energy supply and demand in a technically feasible and economically efficient manner. In energy markets, trading by market participants takes place in multi-settlement markets, allowing to trade of energy products with different temporal granularities. This is needed to satisfy diverse stakeholders’ requirements and accommodate the uncertainty in electricity consumption and generation, which is likely to increase in response to climate policies (e.g., large-scale integration of intermittent renewable energy sources).

Short description

Despite the importance of energy markets and the challenges they are facing, the trading behaviour of their market participants is poorly understood. The objective of this thesis project is focused on using data-driven techniques to analyse and understand the trading behaviour of participants in energy markets, such as trading patterns of different market participants in different locations, energy prices development, and market correlation with / impact of external events. A comprehensive data-driven understanding of participants’ trading behaviour in energy markets will play a key role in improving the functionality of energy markets under the energy transition.

Students doing their MSc thesis on this topic will have the opportunity to be in contact with leading scientists and research institutes of energy markets, data science and AI as part of an international research project.

Objectives

  1. Review previous work on the use of data-driven approaches to understand trading behaviour in energy markets
  2. Build a data-driven model to analyse market participants’ trading behaviour in energy markets (e.g., diagnostic analytics)
  3. Understand the trading patterns of energy market participants (e.g., descriptive analytics)

Tasks

The work in this master thesis entails:

  • To collect full-text articles or PDFs from primary studies and literature reviews on data-driven approaches for understanding trading behaviour in energy marketsl
  • To assess the challenges and solutions available in the literature
  • Design and implement a data-driven model
  • Evaluate the effectiveness of the model in the Dutch energy market using actual data

Literature

  • Bichler, M., Buhl, H. U., Knörr, J., Maldonado, F., Schott, P., Waldherr, S., & Weibelzahl, M. (2022). Electricity Markets in a Time of Change: A Call to Arms for Business Research. Schmalenbach Journal of Business Research, 74(1), 77–102. https://doi.org/10.1007/s41471-021-00126-4
  • Uniejewski, B., Marcjasz, G., & Weron, R. (2019). Understanding intraday electricity markets : Variable selection and very short-term price forecasting using LASSO. International Journal of Forecasting, 35(4), 1533–1547. https://doi.org/10.1016/j.ijforecast.2019.02.001

Requirements:

  • Courses: Programming in Python (INF-22306), Big Data (INF-33806) or Machine Learning (FTE-35306)
  • Required skills/knowledge: basic data analytics/machine learning and willingness to learn new data-driven tools, interest in energy markets and sustainable energy transition

Key words: Energy Markets, Data Analytics, Machine Learning, Statistics, Sustainable Energy Transition

Contact person(s)
Dr.ir. Tarek Alskaif (tarek.alskaif@wur.nl)