- Title
- Novel computational methods for wind farm integrated power systems using computational intelligence and advanced computing techinques
- Creator
- Chen, Yingying
- Relation
- University of Newcastle Research Higher Degree Thesis
- Resource Type
- thesis
- Date
- 2014
- Description
- Research Doctorate - Doctor of Philosophy (PhD)
- Description
- Over the last few decades, due to the environmental concerns and the increase of energy demand, wind power has strongly penetrated the electricity power generation industry. Wind power is variable, uncertain, and intermittent. The system planning, operation and control associated with wind farms are therefore considerably different from conventional power systems. Thus, the integration of wind farms has become the biggest challenges for system operators. The research described in this thesis focuses on the novel computational methods used to address the challenges in wind farm integrated power systems. The computational intelligence (CI) methods and advanced computing techniques are researched and utilized to address the system planning, operation and control issues, which mainly involves optimal power flow (OPF), wind farm collector system layout optimization, and power dispatch problems for wind farm integrated power systems. Based on CI and advanced computing techniques, this research proposed a series of new methods, which can effectively overcome the shortcomings of the conventional approaches. Chapter 1 specifies the research objectives and contributions behind this PhD research paper and also outlines the organization of this thesis. Chapter 2 serves as a literature review of the techniques pertinent to the remainder of this thesis. It includes a brief description and discussion on wind power energy in emerging power systems, the advanced computing techniques for modern power systems, and the application of computational intelligence methods in power systems. Chapter 3 presents a computational grid platform for distributed heterogeneous power systems and a cloud computing based information infrastructure for future power systems. Useful guidelines are also drawn for power engineers to construct the practical computing platforms for large-scale power systems. Chapter 4 proposes a multi-constrained OPF (MCOPF) model with advanced differential evolution algorithms. This problem considers discrete control variables, as well as several practical operation constraints, including transient stability constraints, valve-point effects, POZ of generators, and branch flow thermal constraints. Moreover, cloud computing techniques are utilized for the parallelized optimization of this MCOPF problem, followed with simulation cases to demonstrate the practicability of the proposed approaches. Chapter 5 presents a new and efficient collector system layout optimization (CSLO) model for large-scale offshore wind farms, which considers multiple substations and cable types, and focuses on cable topology optimization among wind turbines and substations with the objective to minimize the overall investment and maintenance cost, as well as the levelized power losses cost, while considering the network reliability and operational constraints simultaneously. Chapter 6 proposes an optimal short-term wind farm dispatch model and an efficient method with battery energy system (BESS) for better integration of wind energy into power systems. The effectiveness of the proposed method is tested with wind farm case studies to demonstrate that the optimal plan of battery charging and discharging processes, and wind energy shedding can help reduce the fast intermittency and high fluctuation of wind power to meet the grid requirement. Chapter 7 presents the conclusions and future direction of this research.
- Subject
- wind power; computational intelligence; optimal power flow; wind farms
- Identifier
- http://hdl.handle.net/1959.13/1045384
- Identifier
- uon:14453
- Rights
- Copyright 2014 Yingying Chen
- Language
- eng
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | ATTACHMENT01 | Abstract | 259 KB | Adobe Acrobat PDF | View Details Download | ||
| View Details Download | ATTACHMENT02 | Thesis | 3 MB | Adobe Acrobat PDF | View Details Download |