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Master Thesis - Deep Reinforcement Learning for Electricity Market Behaviour Simulation i Finspång

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Are you a master student planning to write your Master Thesis during spring 2020? Join us on our journey into the future #Siemens

Be part of an open and dynamic workplace where professional and personal development is high on the agenda. By making sustainable energy solutions more cost effective, developing new technologies for the future's smart industry and electrifying passenger and freight transport, we make reality of our vision of a sustainable world.

We are now looking for a student to take on the assignment “Deep Reinforcement Learning for Electricity Market Behaviour Simulation.”

Who we are?

Data Analytics in SIT AB, the digitalization transformation performed by Siemens during the recent years has had many consequences. One of the most important ones has been the establishment of processes to collect and maintain useful data in a database format. On the one hand, the extensive maintenance reports database provides the company with useful information about unexpected events, component repair and operation history. On the other hand, multiple sensors placed along the turbine deliver information about thermodynamic parameters and operating parameters as the amount of produced MWh. The data analytics department has been working extensively using this data to automate decision making process for the power plants operators, as well as to provide useful information to other departments within SIT AB. Some examples of these projects have been: creating visualization tools to investigate the operation profile of the turbines, optimizing the compressor washing time, calculating the available capacity using thermodynamics models of the engine or optimizing the power production of the plants according to market and demand forecasts using machine learning. Now we want to go one step beyond, and we want to use all the collected market data from external sources to better understand the behavior of each of the market players.

The assignment:

This master thesis is part of Siemens efforts to develop advanced daily operation strategies that can help the power plants operators to maximize their profit over the whole life cycle of the machine. The final goal of this project is to develop a reinforcement learning agent that is able to simulate the market behavior (competitors bids, price, allocated capacity) and that is able to simulate the effect of changes in the market structures.


The first step of this assignment is to study the market micro and macrostructure of the different electricity markets across Europe. The following questions should be answered:

  • What are the market participants and how these change across REU?
  • What are the trading mechanisms allowed in these different markets?

  • How the price is formed, and which variables affect this formation?

  • What are the financial markets linked with the reality of trading physical energy?

  • How reinforcement learning can be applied to market behavior simulation?

The second step consist of selecting a specific market within the EU region (ie. Nordpool, OMIE etc..) and develop the market simulator. The following steps should be performed:

  • Define the data needs for the simulator (which features do we need, with which granularity, how to handle missing values etc…)
  • Formulate the different reward and penalty functions for the algorithm.

  • Understand and model the effect of transition between states (how the market behavior at previous timestamps affects the current status).

  • Develop and try different strategies for the deep reinforcement learning algorithm (Q-learning, Montecarlo+DP, DQN etc…)

  • Quantify numerically which strategies produce the best result.

Students will be provided with access to all the needed data. They will be working closely with domain experts with strong backgrounds in statistics, data mining, machine learning and mathematical optimization.

Your Profile: 

  • The project is suitable for a student with academic background in energy systems, engineering, computer science, statistics, mathematics or another relevant field.
  • As a student you have strong analytical skills and solid mathematical background.

  • Besides, you are interested in data analytics (especially in prescriptive analytics) and hold good programming skills.

  • We consider meritorious skills the knowledge of machine learning oriented libraries (scikit-learn or caret), data handling libraries (Pandas or tidyverse).

  • We also consider meritorious SQL knowledge.

  • We consider highly meritorious the knowledge of Keras-RL, Tensorforce, Stable baselines or OpenAI baselines.


Do not hesitate - apply today via ref nr 179985 and no later than 2019-11-30.

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