Preventive and Corrective Control Actions on Power Systems via Heuristic Optimization Methods with Consecutive Search Space Reduction

Abstract

This paper represents a new methodology to improve the performance of population-based optimization algorithms designed for corrective and preventive control actions enhancing power system dynamic security. Unlike many adaptive approaches employing the parameters such as population size, crossover or mutation rates, the proposed method for the performance improvement is based on the reduction of the search space size. In the method, optimization algorithms run consecutively, while the size of the search space is reduced according to the objective function values attained during the optimization process. In this study, generation rescheduling combined with load curtailment is applied as a preventive control action, whereas load shedding is selected as a corrective control. Each of these control actions is determined through the formulation of a security constrained optimization problem and its solution via population-based optimization algorithms. The proposed methodology is successfully applied to differential evolution, particle swarm optimization, artificial bee colony optimization, and big bang big crunch optimization methods for solving the optimization problems in a 16-generator-68-bus system and the Turkish Transmission System with 750 generators and 2600 buses. It is demonstrated that the proposed method provides better solutions with lesser computational complexity than the ones obtained by using fixed search space sizes.

Keywords:

• corrective control
• generation rescheduling
• heuristic optimization
• load shedding
• power system security
• preventive control

This work is supported by The Scientific and Technical Research Council of Turkey (TUBITAK) project no. 114E157. The authors thank TUBITAK for supporting the project and to the Turkish Electricity Transmission Company TEIAS for providing the model of Turkish power system.

Additional information

Funding

This work is supported by The Scientific and Technical Research Council of Turkey (TUBITAK) project no. 114E157. The authors thank TUBITAK for supporting the project and to the Turkish Electricity Transmission Company TEIAS for providing the model of Turkish power system

Notes on contributors

Cavit Fatih Kucuktezcan

Cavit Fatih Kucuktezcan received the B.Sc., M.Sc., and Ph.D. degrees in electrical engineering from Istanbul Technical University in 2005, 2008, and 2015, respectively. He is currently an assistant professor in the Department of Electrical and Electronics Engineering at Bahcesehir University. His research interest focuses on heuristic optimization and machine learning applications on power systems.

Veysel Murat Istemihan Genc

Veysel Murat Istemihan Genc received the B.Sc. degree in electrical engineering from Istanbul Technical University, the M.Sc. degrees in electrical engineering, systems and control engineering, and systems science and mathematics from Istanbul Technical University, Bogazici University, and Washington University, respectively. He received the D.Sc. degree in 2001 from Washington University. He is currently a professor in the Department of Electrical Engineering at Istanbul Technical University. His research interests include power system dynamics, stability and control.

Osman Kaan Erol

Osman Kaan Erol received the B.Sc. degree in electronics and communication engineering from Istanbul Technical University, Istanbul Turkey in 1991, the M.Sc. and Ph.D. degrees in control and computer engineering from the same university in 2005, and 2001, respectively. He is currently an associate professor in Control and Automation Engineering Department, Istanbul Technical University. His research is in numerical optimization, digital system design and their applications.