Sensing and strategies for power distribution system situational awareness
Abstract/Contents
- Abstract
- A major challenge in power systems will be the integration of advanced technologies such as electric vehicles and rooftop photovoltaics. These technologies pose challenges in the system because they lead to highly variable and unpredictable bidirectional power flows. To integrate these technologies successfully, the power distribution systems must have considerable observability and controllability. Complete observability and controllability have been paradigms of the transmission system. Traditionally, power systems observability has relied on standard, synchronized industrial sensing technologies relying on dedicated Supervisory Control and Data Acquisition (SCADA) communication networks. This investment is justified for national security concerns of grid vulnerability. Distribution systems, on the other hand, have had very little investment in sensing and observability below the level of a regional substation. Deploying the transmission system observability paradigm is definitely possible, but is exceedingly expensive due to the sheer number of devices. On the other hand, many new devices and technologies are being deployed at the customer and utility side. Many of these technologies can sense local conditions on the system. Therefore, using the data from such devices along with advanced estimation methods can lead to increased observability. This leads to the overall focus of this dissertation, which addresses a timely and pertinent question: `How can we increase the observability of the distribution system in a cost effective manner?'. To answer this question, this thesis presents the following contributions. (1) A novel sensing modality that dramatically improves the accuracy of low cost line mounted voltage measurements. (2) A framework for distribution system situational awareness, relying on strategically aggregated smart meter forecasts combined with SCADA network data. (3) A family of simple data driven algorithms using this framework for real time network state estimation in the form of topology and outage detection. (4) A generalization of this framework to a larger set of data and applications, resulting in the Department of Energy Sunshot Initiative Supported: Visualization and Analytics for Distributed Energy Resources (VADER) research project.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Sevlian, Raffi Avo |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Rajagopal, Ram |
| Thesis advisor | Rajagopal, Ram |
| Thesis advisor | El Gamal, Abbas A |
| Thesis advisor | Goldsmith, Andrea, 1964- |
| Advisor | El Gamal, Abbas A |
| Advisor | Goldsmith, Andrea, 1964- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Raffi Avo Sevlian. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Raffi Avo Sevlian
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