Optimization, Learning, and Control for Energy Networks
| dc.contributor.author | Singh, Manish K. | en |
| dc.contributor.committeechair | Kekatos, Vasileios | en |
| dc.contributor.committeemember | Centeno, Virgilio A. | en |
| dc.contributor.committeemember | Gugercin, Serkan | en |
| dc.contributor.committeemember | Williams, Ryan K. | en |
| dc.contributor.committeemember | Liu, Chen-Ching | en |
| dc.contributor.department | Electrical Engineering | en |
| dc.date.accessioned | 2021-07-01T08:00:52Z | en |
| dc.date.available | 2021-07-01T08:00:52Z | en |
| dc.date.issued | 2021-06-30 | en |
| dc.description.abstract | Massive infrastructure networks such as electric power, natural gas, or water systems play a pivotal role in everyday human lives. Development and operation of these networks is extremely capital-intensive. Moreover, security and reliability of these networks is critical. This work identifies and addresses a diverse class of computationally challenging and time-critical problems pertaining to these networks. This dissertation extends the state of the art on three fronts. First, general proofs of uniqueness for network flow problems are presented, thus addressing open problems. Efficient network flow solvers based on energy function minimizations, convex relaxations, and mixed-integer programming are proposed with performance guarantees. Second, a novel approach is developed for sample-efficient training of deep neural networks (DNN) aimed at solving optimal network dispatch problems. The novel feature here is that the DNNs are trained to match not only the minimizers, but also their sensitivities with respect to the optimization problem parameters. Third, control mechanisms are designed that ensure resilient and stable network operation. These novel solutions are bolstered by mathematical guarantees and extensive simulations on benchmark power, water, and natural gas networks. | en |
| dc.description.abstractgeneral | Massive infrastructure networks play a pivotal role in everyday human lives. A minor service disruption occurring locally in electric power, natural gas, or water networks is considered a significant loss. Uncertain demands, equipment failures, regulatory stipulations, and most importantly complicated physical laws render managing these networks an arduous task. Oftentimes, the first principle mathematical models for these networks are well known. Nevertheless, the computations needed in real-time to make spontaneous decisions frequently surpass the available resources. Explicitly identifying such problems, this dissertation extends the state of the art on three fronts: First, efficient models enabling the operators to tractably solve some routinely encountered problems are developed using fundamental and diverse mathematical tools; Second, quickly trainable machine learning based solutions are developed that enable spontaneous decision making while learning offline from sophisticated mathematical programs; and Third, control mechanisms are designed that ensure a safe and autonomous network operation without human intervention. These novel solutions are bolstered by mathematical guarantees and extensive simulations on benchmark power, water, and natural gas networks. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:31853 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/104064 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Network flow | en |
| dc.subject | optimal power flow | en |
| dc.subject | convex relaxation | en |
| dc.subject | mixed-integer programming | en |
| dc.subject | deep learning | en |
| dc.subject | distributed control | en |
| dc.subject | dynamic stability | en |
| dc.title | Optimization, Learning, and Control for Energy Networks | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Electrical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |