80220302 (Optimization Method in Modern Power Systems)

Course Name: Optimization Method in Modern Power Systems

Course Number: 80220302

Program: Graduate program

Type: Elective

Credits: 2

Term Offered: Spring

Prerequisite(s): Operation Research, Linear Algebra, Calculus, Computer Programming

Instructor(s): Libao Shi


Libao Shi and Zhaoyang Dong, Computational Intelligence in Power Systems, Research Signpost, 2010.


Hans-Paul Schwefel, Evolution and Optimum Seeking, New York: Wiley & Sons, 1995

Eiben and Smith, Introduction to Evolutionary Computing, Springer, 2003.

Haupt and Haupt, Practical Genetic Algorithms, John Wiley, 2004.

Dorigo and Stützle, Ant Colony Optimization, MIT Press, 2004.

A. J. Wood and B. F. Wollenberg, Power generation, Operation and Control, 2nd ed., New York: John Wiley and Sons, 1996.

Course Description:

     The course aims to study the advanced theories and computational methods in power system optimization as an elective course. And this course emphasizes the strong combination of the theory and practice. This course will emphatically introduce some cutting-edge developments and achievements in power system optimization as well as teaching of the basic theory and general computational methods. Particularly the case studies mainly focus on the solutions of the real problems of power systems. The task of this course is to make the students master the basic knowledge of power system optimization and understand the way and methods to solve the power system optimization problem via each teaching section and the all kinds of teaching measures and methods. With this course, it is helpful to cultivate and train students how to solve the power system optimization problem efficiently. Also it will lay a necessary foundation for being engaged in the related work in power system in the future.

Course Objectives and Outcomes:

     Numbers in brackets are linked to department educational outcomes.

1.Provide students a major research and development experience in power system optimization. [5]

2.Master techniques of evolutionary computation as well as the traditional optimization methods for various optimization and learning problems. [1, 3]

3.Students will be aware of the process of scientific research and paper writing. [1, 3, 7, 11]

4.Recognize the changing nature of power system characteristics, and understand how to complete engineering practice. [2, 5, 8, 11]

5.Master the modelling process, solution design process and the need for physical thinking, assumption and simplification to decide on a solution. [1, 2, 3]

6.Student will understand current and emerging issues in the power system operation and control. [5]

7.Use tools such as Matlab, Excel and power system analysis software to complete their design. [11]

Course Topics:

1.Introduction of NP-hard problem

2.Introduction of the traditional mathematical programming

3.Introduction of global optimization method

4.Genetic algorithms and its application

5.Evolution strategies and its application

6.Evolutionary programming and its application

7.Ant colony optimization and its application

8.Simulated annealing and its application

9.Genetic algorithm for optimal reconfiguration of radial distribution systems

10.     Evolutionary programming for optimal power flow of power systems

11.     Ant colony optimization for unit commitment

Experiment(s): Numerical experiments, which is the entitled as projects as follows.


Excel Practice with Linear Programming

 Understand and utilize Excel tool to solve basic linear programming problems

Basic Optimization with Global Optimization Methods

 Understand and utilize global optimization algorithms to solve basic parameter optimization problems

Application of power system planning and operation with EAs

 Understand and utilize evolutionary algorithms to solve power system planning and optimal operation problems

Team work research

 Discuss evolutionary algorithms and its application with different group

Course Assessment:

       The following way can be applied to assess the points.

     Two projects with specified topics. 50 points

     A in-class presentation to survey the state-of-the-art of global optimization methods. 30 points.

     Four numerical experiments. 20 points.