Optimum flexible maintenance schedule - 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” Optimum flexible maintenance schedule”.
The correlation between electricity demand and the availability of renewable energy (e.g. wind and solar) is particularly relevant to understand how the deployment of renewable generation capacity affects the generation mix of an electricity system. Already, intermittent generation has an impact in the price and stability of the physical grid. Energy experts have been aware that baseload operation is often hard to coordinate with intermittent renewable generation. Because of this difficulty, it is highly important to provide fast dispatchable power units that can ramp up and down as quick as possible to deal with this dynamic market environment. The SGT-800 produced by Siemens is one of these machines.
However, cyclic operation causes higher thermal stresses to the components of the turbine causing shorter maintenance intervals. This maintenance needs become even higher if the power produces want to operate at peak load when the electricity price is much higher than the gas price.
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 model that optimizes the maintenance cost considering the specific operation profile of a determined customer. Instead of providing fixed maintenance intervals, the algorithm should be able to provide different maintenance schedules depending of the operator behavior (peak load operation, baseload operation etc.).
The model should consider the current electricity and gas prices as well as the maintenance cost. Additionally, the output of the algorithm should consider that the amount of produced MWh per period of study must be constant. Therefore, the non-produced power in the maintenance interval should be distributed over the study period in case that the number of maintenances increases.
Students will be provided with access to the entire sensor data needed to develop the efficiency models of the gas units. Also, they will be provided with the efficiency recovery after the various maintenance operations. They will be working closely with domain experts with strong backgrounds in: thermodynamics, reliability analysis, data mining, machine learning and mathematical optimization.
Who are you?
- 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)
- We consider meritorious skills the knowledge of machine learning oriented libraries (scikit-learn or caret), data handling libraries (Pandas or tidy verse) and basic knowledge of mathematical optimization packages as: ROI (R), Pyomo (Python), PuLP (Python), JuMP (Julia) or CVX (Matlab).
Do not hesitate - apply today via siemens.se ref nr 83400 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: 83400
Organisation: Power Generation Services
Experience Level: Student (Not Yet Graduated)
Job Type: Either