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Panorama from San Gorgonio Pass Wind Farm, CA, by Gregg M. Erickson.

Teaching





Power Systems Operation and Control (ECE 5314)


Course Overview

Computational methods related to the management, monitoring, and control of modern and future power systems are at the focus of this course. Advanced methods are currently utilized in the everyday operation of power systems. Building on mathematical optimization, this course provides an overview of solution methods employed in different facets of power system operation ranging from grid monitoring, generation scheduling, real-time adjustments, energy markets, and contingency practices. Having successfully completed this course, the student will be able to: i) Understand the solution methods of economic dispatch and static state estimation and explain the automatic generation control of a multi-area system; ii) Apply the gradient and the Newton’s method to unconstrained nonlinear optimization problems; iii) Apply the Lagrange’s method to the economic dispatch of thermal units; iv) Explain the automatic generation control and carry out a small-signal analysis of a multi-area system; and v) Understand and derive the weighted least-squares state estimation method of an electric power system.


Textbook and Additional References

  1. A. J. Wood, B. F. Wollenberg, and G. B. Sheble, Power Generation, Operation, and Control, 3rd ed., Wiley, 2014.
  2. Gomez-Exposito, A.-J. Conejo, and C. Canizares, Electric Energy Systems: Analysis and Operation, CRC Press, 2009.
  3. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004.
  4. Josh Taylor, Convex Optimization of Power Systems, Cambridge University Press, 2015.

Lectures

  1. Mathematical background
  2. Introduction and generation units
  3. Convex sets and functions
  4. Optimization problems
  5. Duality and Lagrange multipliers
  6. Economic dispatch
  7. Unit commitment
  8. Power flow problem
  9. Network-constrained economic dispatch
  10. Secure operation of power systems
  11. Power system state estimation
  12. Automatic generation control
  13. Gradient descent and Newton's method