| dc.identifier.uri | http://hdl.handle.net/11401/76117 | |
| 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 | This dissertation studies two problems in smart grid, one of which is real-time power flow analysis. Power flow analysis is used to obtain the steady-state voltage phasors for the power system. The ability to perform power flow analysis quickly is essential for the successful implementation of advanced real-time control of transmission systems. We describe a sensor placement algorithm for conducting real-time parallel transmission network power flow computations. In particular, Phasor Measurement Units (PMUs) can be such sensors. Graph partitioning is used to decompose the system into several subsystems and to locate sensors in an efficient way. Power flow calculations are then run in parallel for each area. Test results on the IEEE 118- and 300-bus systems show that the proposed algorithm is faster than the traditional(serial) Newton’s method, and is suitable for real-time applications. Electricity load forecasting is another problem investigated in this dissertation. Electric load forecasting techniques are used by most electric utility companies for operation and planning. Many operational and financial decisions are based on load forecasting, such as reliability analysis, voltage control,unit commitment,security assessment, and in purchasing electric power. We focus on short-term electric load forecasting. For this problem we present two models that predict future electricity demands based on historical hourly load and hourly weather information. A data cleaning scheme is applied to make the models robust. The estimation of the next day load is performed with an Artificial Neural Network (ANN) method and a Modified Statistical Learning method (MSL). We compare the results obtained by ANN and MSL method. Numerical testing shows that both methods provide accurate predictions. | |
| dcterms.abstract | This dissertation studies two problems in smart grid, one of which is real-time power flow analysis. Power flow analysis is used to obtain the steady-state voltage phasors for the power system. The ability to perform power flow analysis quickly is essential for the successful implementation of advanced real-time control of transmission systems. We describe a sensor placement algorithm for conducting real-time parallel transmission network power flow computations. In particular, Phasor Measurement Units (PMUs) can be such sensors. Graph partitioning is used to decompose the system into several subsystems and to locate sensors in an efficient way. Power flow calculations are then run in parallel for each area. Test results on the IEEE 118- and 300-bus systems show that the proposed algorithm is faster than the traditional(serial) Newton’s method, and is suitable for real-time applications. Electricity load forecasting is another problem investigated in this dissertation. Electric load forecasting techniques are used by most electric utility companies for operation and planning. Many operational and financial decisions are based on load forecasting, such as reliability analysis, voltage control,unit commitment,security assessment, and in purchasing electric power. We focus on short-term electric load forecasting. For this problem we present two models that predict future electricity demands based on historical hourly load and hourly weather information. A data cleaning scheme is applied to make the models robust. The estimation of the next day load is performed with an Artificial Neural Network (ANN) method and a Modified Statistical Learning method (MSL). We compare the results obtained by ANN and MSL method. Numerical testing shows that both methods provide accurate predictions. | |
| dcterms.available | 2017-09-20T16:42:24Z | |
| dcterms.contributor | Samulyak, Roman | en_US |
| dcterms.contributor | Feinberg, Eugene | en_US |
| dcterms.contributor | Hu, Jiaqiao | en_US |
| dcterms.contributor | Robertazzi, Thomas. | en_US |
| dcterms.creator | Li, Muqi | |
| dcterms.dateAccepted | 2017-09-20T16:42:24Z | |
| dcterms.dateSubmitted | 2017-09-20T16:42:24Z | |
| dcterms.description | Department of Applied Mathematics and Statistics. | en_US |
| dcterms.extent | 102 pg. | en_US |
| dcterms.format | Application/PDF | en_US |
| dcterms.format | Monograph | |
| dcterms.identifier | http://hdl.handle.net/11401/76117 | |
| dcterms.issued | 2015-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:42:24Z (GMT). No. of bitstreams: 1
Li_grad.sunysb_0771E_12387.pdf: 1018262 bytes, checksum: 74c90b591a9a8389ce020c8ae04ffa16 (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Electrical engineering | |
| dcterms.subject | Load Forecasting, PMU Placement, Power Flow Calculation, Smart Grid | |
| dcterms.title | Real-Time Power Flow Analysis & Short-term Electricity Load Forecasting in Smart Grid | |
| dcterms.type | Dissertation | |
