ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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Description
Corrective transmission topology control schemes are an essential part of grid operations and are used to improve the reliability of the grid as well as the operational efficiency. However, topology control schemes are frequently established based on the operator's past knowledge of the system as well as other ad-hoc methods. This research presents robust corrective topology control, which is a transmission switching methodology used for system reliability as well as to facilitate renewable integration.
This research presents three topology control (corrective transmission switching) methodologies along with the detailed formulation of robust corrective switching. The robust model can be solved off-line to suggest switching actions that can be used in a dynamic security assessment tool in real-time. The proposed robust topology control algorithm can also generate multiple corrective switching actions for a particular contingency. The solution obtained from the robust topology control algorithm is guaranteed to be feasible for the entire uncertainty set, i.e., a range of system operating states.
Furthermore, this research extends the benefits of robust corrective topology control to renewable resource integration. In recent years, the penetration of renewable resources in electrical power systems has increased. These renewable resources add more complexities to power system operations, due to their intermittent nature. This research presents robust corrective topology control as a congestion management tool to manage power flows and the associated renewable uncertainty. The proposed day-ahead method determines the maximum uncertainty in renewable resources in terms of do-not-exceed limits combined with corrective topology control. The results obtained from the topology control algorithm are tested for system stability and AC feasibility.
The scalability of do-not-exceed limits problem, from a smaller test case to a realistic test case, is also addressed in this research. The do-not-exceed limit problem is simplified by proposing a zonal do-not-exceed limit formulation over a detailed nodal do-not-exceed limit formulation. The simulation results show that the zonal approach is capable of addressing scalability of the do-not-exceed limit problem for a realistic test case.
This research presents three topology control (corrective transmission switching) methodologies along with the detailed formulation of robust corrective switching. The robust model can be solved off-line to suggest switching actions that can be used in a dynamic security assessment tool in real-time. The proposed robust topology control algorithm can also generate multiple corrective switching actions for a particular contingency. The solution obtained from the robust topology control algorithm is guaranteed to be feasible for the entire uncertainty set, i.e., a range of system operating states.
Furthermore, this research extends the benefits of robust corrective topology control to renewable resource integration. In recent years, the penetration of renewable resources in electrical power systems has increased. These renewable resources add more complexities to power system operations, due to their intermittent nature. This research presents robust corrective topology control as a congestion management tool to manage power flows and the associated renewable uncertainty. The proposed day-ahead method determines the maximum uncertainty in renewable resources in terms of do-not-exceed limits combined with corrective topology control. The results obtained from the topology control algorithm are tested for system stability and AC feasibility.
The scalability of do-not-exceed limits problem, from a smaller test case to a realistic test case, is also addressed in this research. The do-not-exceed limit problem is simplified by proposing a zonal do-not-exceed limit formulation over a detailed nodal do-not-exceed limit formulation. The simulation results show that the zonal approach is capable of addressing scalability of the do-not-exceed limit problem for a realistic test case.
ContributorsKorad, Akshay Shashikumar (Author) / Hedman, Kory W (Thesis advisor) / Ayyanar, Raja (Committee member) / Vittal, Vijay (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2015
Description
Electric power system security assessment is one of the most important requirements for operational and resource planning of the bulk power system ensuring safe operation of the power system for all credible contingencies. This deterministic approach usually provides a conservative criterion and can result in expensive bulk system expansion plans or conservative operating limits. Furthermore, with increased penetration of converter-based renewable generation in the electric grid, the dynamics of the grid are changing. In addition, the variability and intermittency associated with the renewable energy sources introduce uncertainty in the electricity grid. Since security margins have direct economic impact on the utilities; more clarity is required regarding the basis on which security decisions are made. The main objective of this work is to provide an approach for risk-based security assessment (RBSA) to define dynamic reliability standards in future electricity grids. RBSA provides a measure of the security of the power system that combines both the likelihood and the consequence of an event.
A novel approach to estimate the impact of transient stability is presented by modeling several important protection systems within the transient stability analysis. A robust operational metric to quantify the impact of transient instability event is proposed that incorporates the effort required to stabilize any transiently unstable event. The effect of converter-interfaced renewable energy injection on system reliability is investigated us-ing RBSA. A robust RBSA diagnostics tool is developed which provides an interactive user interface where the RBSA results and contingency ranking reports can be explored and compared based on specific user inputs without executing time domain simulations or risk calculations, hence providing a fast and robust approach for handling large time domain simulation and risk assessment data. The results show that RBSA can be used effectively in system planning to select security limits. Comparison of RBSA with deterministic methods show that RBSA not only provides less conservative results, it also illustrates the bases on which such security decisions are made. RBSA helps in identifying critical aspects of system reliability that is not possible using the deterministic reliability techniques.
A novel approach to estimate the impact of transient stability is presented by modeling several important protection systems within the transient stability analysis. A robust operational metric to quantify the impact of transient instability event is proposed that incorporates the effort required to stabilize any transiently unstable event. The effect of converter-interfaced renewable energy injection on system reliability is investigated us-ing RBSA. A robust RBSA diagnostics tool is developed which provides an interactive user interface where the RBSA results and contingency ranking reports can be explored and compared based on specific user inputs without executing time domain simulations or risk calculations, hence providing a fast and robust approach for handling large time domain simulation and risk assessment data. The results show that RBSA can be used effectively in system planning to select security limits. Comparison of RBSA with deterministic methods show that RBSA not only provides less conservative results, it also illustrates the bases on which such security decisions are made. RBSA helps in identifying critical aspects of system reliability that is not possible using the deterministic reliability techniques.
ContributorsDatta, Sohom (Author) / Vittal, Vijay (Thesis advisor) / Undrill, John (Committee member) / Heydt, Gerald (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2017