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Online Feedback Optimization for Load Sharing of Gas Compressors
We want to apply the novel class of Online Feedback Optimization Controllers to minimise the curtailment of renewable generation. The advantage of these controllers is that they do not track a reference point, but rather drive the system to an optimal point defined by an optimization problem.
Keywords: Online Feedback Optimization
Control Systems
Optimization
Gas Compressors
We want to apply the novel class of Online Feedback Optimization Controllers to the load sharing problem of multiple gas compressors. Gas compressors have an important role in the energy infrastructure today and they are likely to stay relevant in the future in the transportation and distribution of hydrogen. The advantage of Online Feedback Optimization Controller is that they do not track a reference point, but rather drive the system to an optimal point defined by an optimization problem.
These controllers use optimization algorithms and put them to use in a closed-loop control system. This enables us to steer the system to an optimal point while taking constraints of the system into account (maximum pressure, max power, etc). These novel controllers have already shown their superiority in power systems operation. Now, we want to use them to determine the optimal setpoints of multiple gas compressors which as a group have to supply a predefined pressure in the gas grid.
A suitable model will be implemented and the control approach of Online Feedback Optimization will be compared with the state of the art, for example real-time optimization.
We want to apply the novel class of Online Feedback Optimization Controllers to the load sharing problem of multiple gas compressors. Gas compressors have an important role in the energy infrastructure today and they are likely to stay relevant in the future in the transportation and distribution of hydrogen. The advantage of Online Feedback Optimization Controller is that they do not track a reference point, but rather drive the system to an optimal point defined by an optimization problem. These controllers use optimization algorithms and put them to use in a closed-loop control system. This enables us to steer the system to an optimal point while taking constraints of the system into account (maximum pressure, max power, etc). These novel controllers have already shown their superiority in power systems operation. Now, we want to use them to determine the optimal setpoints of multiple gas compressors which as a group have to supply a predefined pressure in the gas grid. A suitable model will be implemented and the control approach of Online Feedback Optimization will be compared with the state of the art, for example real-time optimization.
1. The student will understand the Online Feedback Optimization method and its advantages
2. The student will study the literature on load sharing problems for gas compressors
3. The student will implement a model for the chosen application
4. The student will implement an Online Feedback Optimization Controller
5. The student will analyze the resulting control system and compare it to the state of the art
6. The student will write a written final report and document all the project code
**Corona Disclaimer:** This project could be done in person at the Automatic Control Laboratory, hybrid, or completely remotely depending on the current ETH rules. Most importantly, we can change between these forms whenever needed.
The project can be adapted on the run if new interesting research directions arise.
Finally, if the results are promising they can be turned into a publication.
1. The student will understand the Online Feedback Optimization method and its advantages 2. The student will study the literature on load sharing problems for gas compressors 3. The student will implement a model for the chosen application 4. The student will implement an Online Feedback Optimization Controller 5. The student will analyze the resulting control system and compare it to the state of the art 6. The student will write a written final report and document all the project code
**Corona Disclaimer:** This project could be done in person at the Automatic Control Laboratory, hybrid, or completely remotely depending on the current ETH rules. Most importantly, we can change between these forms whenever needed.
The project can be adapted on the run if new interesting research directions arise.
Finally, if the results are promising they can be turned into a publication.