Thesis
Energy savings and volt/var optimization using intelligent control
Washington State University
Master of Science (MS), Washington State University
2013
Handle:
https://hdl.handle.net/2376/100872
Abstract
A major part of the smart grid development is focused on the distribution system. In order to utilize the smart grid to its fullest potential, many improvements to the existing system are necessary. In order to demonstrate the smart grid concepts and to quantify the benefits, the Unites States Department of Energy invested extensively in the numerous Smart Grid Demonstration projects. In the Pullman Smart Grid Demonstration project, one of the research activities relates to volt/VAr control. As part of this project, the smart switches installed on the Pullman distribution feeders are analyzed and modeled into SynerGEE software tool for power flow computation with higher accuracy. In addition to expanding the distribution model to reflect the smart devices installed, comparative analysis for two volt/VAr control methods based on the power factor rule and 60-40 rule are studied. Even though these volt/VAr control methods achieve their respective goals and also contribute to energy savings, these methods do not consider savings a primary goal. In this thesis, an intelligent control algorithm to maximize system energy savings is presented. The developed algorithm uses particle swarm optimization v to determine the optimal capacitor states and voltage regulator tap settings to achieve volt/VAr control and maximal energy savings. The developed method is validated against an enumerative search strategy. Both of these control algorithms are implemented and tested on the IEEE 13 bus distribution system.
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Details
- Title
- Energy savings and volt/var optimization using intelligent control
- Creators
- Griet Devriese
- Contributors
- Anurag K. Srivastava (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
- 99900525030501842
- Language
- English
- Resource Type
- Thesis