Power Plant Operation Optimization: Short-term profit operation optimization of combined cycle power plants - Master Thesis Spring 2019
Are you a master student planning to write your Master Thesis during spring 2019?
Join us on our journey into the future #FutureMakers#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” Power Plant Operation Optimization: Short-term profit operation optimization of combined cycle power plants”,
This Master thesis is part of Siemens efforts done to develop decision support algorithms to help the power plant operators in their daily life with the decisions they need to take. The final goal of the project is to develop a mathematical model that maximizes the profit of the power plant if they are selling all their electricity in the spot market.
The model should consider the current electricity spot market price (€/MWh) and the current gas price (€/MWh) to output the optimum decisions (buy gas/sell electricity, use gas storage/sell electricity, do not produce) and how this decision will look like (load level of each gas unit, load level of the steam turbine etc.). The optimization should consider different constraints as for instance: power demand, maximum available capacity, start-up time etc. Previous work has been done within the data analytics department in this field, but only considering the gas turbines of the combined cycle and within a non-liberalized market (no electricity or gas spot prices).
Students will be provided with access to all the needed sensor data to develop the efficiency models of the power plants and to incorporate the constraints of the optimization. They will be working closely with domain experts with strong backgrounds in: thermodynamics, reliability analysis, data mining, machine learning and mathematical optimization.
As a first approach the students will built the model to optimize the operation with true data. Then, if the first stage of the project is completed, the student will study the pre-schedule (unit-commitment) depending on the forecasted demand, forecasted available capacity, day-ahead electricity spot prices and day-ahead gas prices. Predictive models for demand and available capacity will be provided. Day-ahead electricity spot prices and gas-prices will be downloaded from the Market Operator website.
Who are you?
- The project is suitable for one student with academic background in data science, energy systems, industrial engineering, computer science, mathematics or another relevant field.
- As a student you have strong analytical skills and solid mathematical background.
- You are interested in data analytics (especially in prescriptive analytics) and hold good programming skills (preferable: Python, R or Julia)
- power markets economics.
Do not hesitate - apply today via siemens.se ref nr 82817 and no later than 15 November. For questions about the role please contact recruiting manager Erik Ärlebäck on +46 (0)122 81291.
Type of contract:
Fixed term – approximately 6 months.
Compensation type: hourly paid.
Christine Lindström, Unionen, 0122-817 28
Simon Bruneflod, Sveriges Ingenjörer, 0122-842 24
Jan Lundgren, Ledarna, 0122-812 33
Kenth Gustavsson, IF Metall, 0122-815 25
In this recruitment we renounce all calls relating to advertising and recruitment support.
Job ID: 82817
Organisation: Power Generation Services
Experience Level: Student (Not Yet Graduated)
Job Type: Full-time