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Transient Constraint Handling in Feedback Optimization of Power Systems
Feedback optimization is a novel method for designing controllers that drive systems to the steady-state solutions of optimization problems rather than to pre-determined setpoints. The goal of this project is to add the ability to enforce transient constraints in the context of power systems.
Keywords: Feedback Optimization, Power Systems, Reference Governors, Constrained Control
The standard approach for operating a complex system is to determine a steady-state setpoint offline (usually by solving an optimization problem based on a model of the system) then design a feedback controller to stabilize that setpoint. This approach can lead to suboptimal operation, especially when the model is inaccurate, or when the optimal operating point depends on unmeasured disturbances, e.g., loads in the power grid.
Feedback optimization (sometimes also called autonomous/online optimization) tries to make the operation of the system more robust by incorporating feedback from the system into the optimization process. In feedback optimization, the goal is to design a controller that steers a dynamical system to a optimal steady-state while respecting both state and input constraints. Replacing the (potentially inaccurate) model with measurements provides robustness against modelling errors and unmeasured disturbances. Moreover, this feedback allows the system to quickly adapt to changes in time-varying conditions.
Previous work in the area has focused on the stability of feedback optimization (under timescale separation assumptions), input and asymptotic output constraint satisfaction, and applications in power systems. This project aims to develop methods for enforcing state constraints during transients by combining feedback optimization with ideas from reference governors and demonstrate the results on a power systems example.
The standard approach for operating a complex system is to determine a steady-state setpoint offline (usually by solving an optimization problem based on a model of the system) then design a feedback controller to stabilize that setpoint. This approach can lead to suboptimal operation, especially when the model is inaccurate, or when the optimal operating point depends on unmeasured disturbances, e.g., loads in the power grid.
Feedback optimization (sometimes also called autonomous/online optimization) tries to make the operation of the system more robust by incorporating feedback from the system into the optimization process. In feedback optimization, the goal is to design a controller that steers a dynamical system to a optimal steady-state while respecting both state and input constraints. Replacing the (potentially inaccurate) model with measurements provides robustness against modelling errors and unmeasured disturbances. Moreover, this feedback allows the system to quickly adapt to changes in time-varying conditions.
Previous work in the area has focused on the stability of feedback optimization (under timescale separation assumptions), input and asymptotic output constraint satisfaction, and applications in power systems. This project aims to develop methods for enforcing state constraints during transients by combining feedback optimization with ideas from reference governors and demonstrate the results on a power systems example.
- Learn about feedback optimization and explicit reference governors;
- Develop a suitable power system model and associated feedback optimization problem;
- Design a feedback optimization control law and demonstrate its effectiveness in simulation;
- (Stretch) Prove constraint satisfaction and closed-loop stability.
**Publication:** If the final results are promising they can potentially be turned into a publication
**Corona Disclaimer** This project can be done in person at the Automatic Control Lab, hybrid, or completely remotely. Most importantly, we can change between these forms as needed.
**Qualifications**
We are looking for a highly motivated student with some or all of the following.
- Experience with power systems is helpful but not necessary;
- Some familiarity with: Optimization and Control Systems;
- Proficiency in simulation of dynamical systems (MATLAB/Simulink, Python etc.)
- Proficiency in English.
- Learn about feedback optimization and explicit reference governors; - Develop a suitable power system model and associated feedback optimization problem; - Design a feedback optimization control law and demonstrate its effectiveness in simulation; - (Stretch) Prove constraint satisfaction and closed-loop stability.
**Publication:** If the final results are promising they can potentially be turned into a publication
**Corona Disclaimer** This project can be done in person at the Automatic Control Lab, hybrid, or completely remotely. Most importantly, we can change between these forms as needed.
**Qualifications** We are looking for a highly motivated student with some or all of the following. - Experience with power systems is helpful but not necessary; - Some familiarity with: Optimization and Control Systems; - Proficiency in simulation of dynamical systems (MATLAB/Simulink, Python etc.) - Proficiency in English.
Please send your resume/CV (including lists of relevant publications/projects) and transcript of records in PDF format via email to dliaomc@ethz.ch.
Please send your resume/CV (including lists of relevant publications/projects) and transcript of records in PDF format via email to dliaomc@ethz.ch.