Comparison of different methods for OPF problem of multi-objective optimization

Chen, Yuxuan (2021) Comparison of different methods for OPF problem of multi-objective optimization. MSc(R) thesis, Unversity of Glasgow.

Abstract

This paper compares two different models of grid-connected voltage source converter (VSC) based battery energy storage systems (BESSs) – an equivalent circuit model and a current injection model for multi-objective optimization problems. Typical VSC-based BESSs normally consist of a VSC, a connection transformer, and a BESS. This paper presents details of a specific optimal power flow (OPF) named active-reactive-OPF (AR-OPF) that can be used to analyze both active and reactive power flow simultaneously. It is compared with a conventional current injection model using the Newton-Raphson method to complete the calculation process. Case studies take IEEE-30 benchmark system established in MATLAB simulation environment with 3 BESSs at 3 different nodes (nodes 7, 8, and 21) to compare the mentioned models of VSC-based BESSs, for multi-objective OPF problem using mixed integer SOCP (Second Order Cone Programming). In order to show the applicability of the 2 models, result comparisons on the 3 aspects mentioned are generated and discussed. Results show that the VSC-based BESS equivalent circuit model has better performance than the current injection model in all aspects (total power loss, converter power loss, and total energy consumption over 24 hours). The advantage is most obvious in total power loss - the difference rate is 9.205%. Values for converter power loss are closest with only a 0.16% difference ratio and a 3.166% difference for total 24-hour energy consumption.

• Item Type: Thesis (MSc(R))
• Qualification Level: Masters
• Additional Information: Due to copyright issues this thesis is not available for viewing.
• Subjects: T Technology > T Technology (General)
• Colleges/Schools: College of Science and Engineering > School of Engineering
• Supervisor's Name: Jin, Dr. Yang
• Date of Award: 2021
• Depositing User: Enlighten Team
• Unique ID: glathesis:2021-82267
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 10 Jun 2021 14:46
• Last Modified: 14 Jun 2021 09:32
• Thesis DOI: 10.5525/gla.thesis.82267
• URI: https://theses.gla.ac.uk/id/eprint/82267