Thesis
Static load modeling to quantify the benefits of a smart grid
Washington State University
Master of Science (MS), Washington State University
2012
Handle:
https://hdl.handle.net/2376/101514
Abstract
The United States is one of the leaders in the world's electricity consumption, second only to China. This makes the US the number one country in energy consumption per capita. With the rising increase of electric consumption, the Department of Energy has initiated Smart Grid Demonstration projects across the nation in order to explore its potential benefits. A major component of the smart grid is the addition of communication capabilities to devices in the field, allowing utilities to react more quickly to system disturbances. The readily available information also results in a better understanding of the distribution system, where this knowledge can be used to further improve efficiency. Furthermore, a smart grid is often equipped with Distribution Management System (DMS), which allows for centralized control of the entire network. One method of improving efficiency is the Volt-VAr Control (VVC) scheme. By monitoring the voltages along the feeder, it is possible for utilities to minimize the voltage level in order to reduce the power consumed by the system. In addition to the voltage control, system's efficiency can be further maximized by maintaining near unity power factor. This can be achieved by installing switched capacitor banks to provide reactive power as needed. In order to understand the benefits of implementing VVC in the field, the VVC procedure has been simulated using SynerGEE--a third party distribution modeling software. Because the benefit of VVC is highly dependent on the type of load it is applied to, an accurate model of the load should be established. This research will focus on the load modeling aspect of the VVC benefits estimation efforts.
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Details
- Title
- Static load modeling to quantify the benefits of a smart grid
- Creators
- Marsela Jakub-Wood
- Contributors
- Anjan Bose (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; [Pullman, Washington] :
- Identifiers
- 99900525170801842
- Language
- English
- Resource Type
- Thesis