A Robust Optimization Approach to the Self-scheduling Problem Using Semidefinite Programming
| dc.contributor.author | Landry, Jason Conrad | |
| dc.date.accessioned | 2010-01-21T16:58:46Z | |
| dc.date.available | 2010-01-21T16:58:46Z | |
| dc.date.issued | 2010-01-21T16:58:46Z | |
| dc.date.submitted | 2009 | |
| dc.description.abstract | In deregulated electricity markets, generating companies submit energy bids which are derived from a self-schedule. In this thesis, we propose an improved semidefinite programming-based model for the self-scheduling problem. The model provides the profit-maximizing generation quantities of a single generator over a multi-period horizon and accounts for uncertainty in prices using robust optimization. Within this robust framework, the price information is represented analytically as an ellipsoid. The risk-adversity of the decision maker is taken into account by a scaling parameter. Hence, the focus of the model is directed toward the trade-off between profit and risk. The bounds obtained by the proposed approach are shown to be significantly better than those obtained by a mean-variance approach from the literature. We then apply the proposed model within a branch-and-bound algorithm to improve the quality of the solutions. The resulting solutions are also compared with the mean-variance approach, which is formulated as a mixed-integer quadratic programming problem. The results indicate that the proposed approach produces solutions which are closer to integrality and have lower relative error than the mean-variance approach. | en |
| dc.identifier.uri | http://hdl.handle.net/10012/4961 | |
| dc.language.iso | en | en |
| dc.pending | false | en |
| dc.publisher | University of Waterloo | en |
| dc.subject | Semidefinite Programming | en |
| dc.subject | Robust Optimization | en |
| dc.subject | Self-scheduling problem | en |
| dc.subject | Electricity markets | en |
| dc.subject.program | Management Sciences | en |
| dc.title | A Robust Optimization Approach to the Self-scheduling Problem Using Semidefinite Programming | en |
| dc.type | Master Thesis | en |
| uws-etd.degree | Master of Applied Science | en |
| uws-etd.degree.department | Management Sciences | en |
| uws.peerReviewStatus | Unreviewed | en |
| uws.scholarLevel | Graduate | en |
| uws.typeOfResource | Text | en |