Show simple item record

Optimization and Model-predictive Control for Overload Mitigation in Resilient Power Systems.

dc.contributor.authorAlmassalkhi, Mads R.en_US
dc.date.accessioned2013-09-24T16:03:38Z
dc.date.availableNO_RESTRICTIONen_US
dc.date.available2013-09-24T16:03:38Z
dc.date.issued2013en_US
dc.date.submitted2013en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/100049
dc.description.abstractThe 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.isoen_USen_US
dc.subjectModel Predictive Controlen_US
dc.subjectConvex Optimizationen_US
dc.subjectCascade Mitigationen_US
dc.subjectEnergy Hubsen_US
dc.subjectEnergy Systems Modelingen_US
dc.subjectPower Systemsen_US
dc.titleOptimization and Model-predictive Control for Overload Mitigation in Resilient Power Systems.en_US
dc.typeThesisen_US
dc.description.thesisdegreenamePhDen_US
dc.description.thesisdegreedisciplineElectrical Engineering: Systemsen_US
dc.description.thesisdegreegrantorUniversity of Michigan, Horace H. Rackham School of Graduate Studiesen_US
dc.contributor.committeememberHiskens, Ianen_US
dc.contributor.committeememberCohn, Amy Ellenen_US
dc.contributor.committeememberHofmann, Heathen_US
dc.contributor.committeememberSun, Jingen_US
dc.contributor.committeememberAndersson, Goranen_US
dc.subject.hlbsecondlevelElectrical Engineeringen_US
dc.subject.hlbsecondlevelEngineering (General)en_US
dc.subject.hlbsecondlevelIndustrial and Operations Engineeringen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbsecondlevelScience (General)en_US
dc.subject.hlbtoplevelEngineeringen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/100049/1/malmassa_1.pdf
dc.owningcollnameDissertations and Theses (Ph.D. and Master's)


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.