Dissertation Announcement for Chunheng Wang
06/16/16 at 12:30 PM
June 8, 2016
Dear faculty, graduate and undergraduate students,
You are cordially invited to my Ph.D. dissertation defense.
Title: PGNME: High Performance Applications in Large-Scale Power System Operations
When: Thursday, June 16, 2016, 12:30pm.
Where: Simrall Hall, Room 228
Candidate: Chunheng Wang
Degree: Ph.D., Electrical and Computer Engineering
Committee:
Dr. Yong Fu
Associate Professor of Electrical and Computer Engineering
(Major Professor)
Dr. Sherif Abdelwahed
Associate Professor of Electrical and Computer Engineering
(Committee Member)
Dr. Masoud Karimi
Associate Professor of Electrical and Computer Engineering
(Committee Member)
Dr. Shantia Yarahmadian
Associate Professor of Mathematics
(Committee Member)
Abstract:
Electrical energy is the basic necessity for the economic development of human societies. In recent decades, the electricity industry is undergoing enormous changes, which has evolved into a large-scale and competitive industry. The integration of volatile renewable energy, and the emergence of transmission switching (TS) techniques bring great challenges to the existing security-constrained unit commitment (SCUC) solution engines. In order to deal with the uncertainty of volatile renewable energy, scenario-based stochastic optimization approach has been widely employed to ensure the reliability and economic of power systems, in which each scenario would represent a possible system situation. Meanwhile, the emergence of TS techniques allow the system operators to change the topology of transmission systems in order to improve economic benefits by mitigating transmission congestion, in which the transmission switching lines can be switched ON/OFF. However, with the introduction of extra scenarios and decision variables, the complexity of the SCUC model will increase dramatically and more computational efforts will be required, which might make the problem difficult to solve and even intractable. Therefore, an advanced solution technique is urgently needed to solve such stochastic SCUC problems and TS-based SCUC problems in an effective and fast way.
In this work, a decomposition framework is presented for the optimal operation of the large-scale power system, which decomposes the original large-size power system optimization problem into smaller-size, tractable subproblems, then, solves these subproblems in a parallel manner with the help of high performance computing techniques. Numerical case studies on a modified IEEE 118-bus system and a practical 1168-bus system demonstrate the effectiveness and efficiency of the proposed approach which will offer the power system a secure and economic operation under various uncertainties and contingencies.
Best Regards,
Chunheng Wang