| dc.identifier.uri | http://hdl.handle.net/11401/76493 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.abstract | Optimized planning of power devices and power generators is essential for saving energy and reducing costs in smart grid. Many of these optimization problems share a common characteristic and seek to optimize accumulated rewards or costs within a finite time planning horizon. For this reason, some of those problems can be formulated as finite horizon optimal planning problems. This dissertation performs detailed investigation on two representative finite horizon optimal planning problems: the Unit Commitment problem (UCP) and the Voltage and Reactive Power Control (VVC) Problem. UCP is an important optimal power planning problems in electric grids. The purpose of UCP is to determine when to start up and shut down power generator units and how to dispatch committed units to meet electricity demands, ancillary services requirements, and security constraints in order to minimize total operational costs. This dissertation improves the traditional lagrangian relaxation (LR) approach and analyzes the effectiveness of using parallel computing for solving large-scale unit commitment problems with wind power penetration. Additionally, we investigate the potential of combining parallel computing with a rolling horizon scheme to improve the solution quality when a large amount of wind power is present. One of the objectives of VVC is to determine the proper status of capacitor banks and transformer tap positions in a power distribution system to minimize daily power losses or the daily power consumption. In this dissertation, we propose to use an approximate stochastic annealing (ASA) algorithm for solving VVC problems. We also propose a lagrangian relaxation dynamic programming (LR-DP) algorithm for solving VVC problems with operation limits on power devices to obtain upper and lower bounds on the performance of the optimal solution. The performance of the ASA algorithm is illustrated on a well-known PG&E 69-bus distribution network. Our testing results indicate that the ASA algorithm may yield solutions very close to the optimum within a moderate amount of computational time. This dissertation also discusses the convergence properties of the ASA algorithm for VVC problems. | |
| dcterms.available | 2017-09-20T16:50:25Z | |
| dcterms.contributor | Feinberg, Eugene | en_US |
| dcterms.contributor | Feinberg, Eugene A. | en_US |
| dcterms.contributor | Mitchell, Joseph | en_US |
| dcterms.contributor | Hu, Jiaqiao | en_US |
| dcterms.contributor | Robertazzi, Thomas. | en_US |
| dcterms.creator | Yuan, Eting | |
| dcterms.dateAccepted | 2017-09-20T16:50:25Z | |
| dcterms.dateSubmitted | 2017-09-20T16:50:25Z | |
| dcterms.description | Department of Applied Mathematics and Statistics. | en_US |
| dcterms.extent | 109 pg. | en_US |
| dcterms.format | Monograph | |
| dcterms.format | Application/PDF | en_US |
| dcterms.identifier | http://hdl.handle.net/11401/76493 | |
| dcterms.issued | 2013-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:50:25Z (GMT). No. of bitstreams: 1
Yuan_grad.sunysb_0771E_11553.pdf: 814327 bytes, checksum: 4e9171f2e6c5e0652af08057b456bb10 (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Approximate Stochastic Annealing, Optimization, Smart Grid, Unit Commitment Problem, Voltage Control | |
| dcterms.subject | Operations research | |
| dcterms.title | A Study on Two Optimal Planning Problems in Smart Grid | |
| dcterms.type | Dissertation | |
