Optimization and Model-predictive Control for Overload Mitigation in Resilient Power Systems.
dc.contributor.author | Almassalkhi, Mads R. | en_US |
dc.date.accessioned | 2013-09-24T16:03:38Z | |
dc.date.available | NO_RESTRICTION | en_US |
dc.date.available | 2013-09-24T16:03:38Z | |
dc.date.issued | 2013 | en_US |
dc.date.submitted | 2013 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/100049 | |
dc.description.abstract | The National Academy of Engineering named the electric power grid the greatest engineering achievement of the 20th century. However, as recent large-scale power grid failures illustrate, the (electro-mechanical) electric grid is being operated closer and closer to its limits. Specifically, the electric grid of the 20th century is aging and congested. Due to the protracted and cost-intensive nature of upgrading energy infrastructures, major research initiatives are now underway to improve the utility of the existing infrastructure. One important topic is contingency management. Accordingly, this dissertation comprises of practical, yet rigorously justified, feedback control algorithms that are suitable for power system contingency management. The main goals of the algorithms are to prevent or mitigate overloads on network elements (e.g. lines and transformers). In this dissertation, a coupling of energy infrastructures is examined as a method for improving system reliability and a simple cascade mitigation approach highlights the role of model-predictive control and energy storage in improving system response to severe disturbances (e.g. line outages). The ideas of balancing economic and safety criteria are developed and implemented with a receding-horizon model-predictive controller (RHMPC) for electric transmission systems with energy storage and renewables. The novel RHMPC scheme employs a lossy "DC" power flow model and is proven to alleviate conductor temperature overloads and returns the system to an economically optimal state. Finally, an incentive-based distributed predictive-control algorithm is developed to prevent overloads in the distribution network caused by overnight charging of plug-in electric vehicles. In addition, Matlab-based simulations are included to illustrate the performance and behavior of all proposed overload mitigation schemes. The automatic schemes presented in this dissertation are, essentially, "closing the loop'' in contingency management, and will help bring the electric power grid into the 21st century. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | Model Predictive Control | en_US |
dc.subject | Convex Optimization | en_US |
dc.subject | Cascade Mitigation | en_US |
dc.subject | Energy Hubs | en_US |
dc.subject | Energy Systems Modeling | en_US |
dc.subject | Power Systems | en_US |
dc.title | Optimization and Model-predictive Control for Overload Mitigation in Resilient Power Systems. | en_US |
dc.type | Thesis | en_US |
dc.description.thesisdegreename | PhD | en_US |
dc.description.thesisdegreediscipline | Electrical Engineering: Systems | en_US |
dc.description.thesisdegreegrantor | University of Michigan, Horace H. Rackham School of Graduate Studies | en_US |
dc.contributor.committeemember | Hiskens, Ian | en_US |
dc.contributor.committeemember | Cohn, Amy Ellen | en_US |
dc.contributor.committeemember | Hofmann, Heath | en_US |
dc.contributor.committeemember | Sun, Jing | en_US |
dc.contributor.committeemember | Andersson, Goran | en_US |
dc.subject.hlbsecondlevel | Electrical Engineering | en_US |
dc.subject.hlbsecondlevel | Engineering (General) | en_US |
dc.subject.hlbsecondlevel | Industrial and Operations Engineering | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbsecondlevel | Science (General) | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/100049/1/malmassa_1.pdf | |
dc.owningcollname | Dissertations and Theses (Ph.D. and Master's) |
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