Thesis
Decision support tools for renewable rich power systems: a stochastic approach
Washington State University
Master of Science (MS), Washington State University
2014
Handle:
https://hdl.handle.net/2376/102623
Abstract
In this thesis, we introduce a new methodology for stochastic unit commitment. In first part, we will use a new method to forecast solar radiation and wind speed and their resulting electrical power generation futures in a region of interest over a one-day time horizon, that captures the inherent stochasticity in radiative flux and hence generation at the resolution needed for useful prediction. The proposed methodology has three stages: first, a stochastic automaton known as the influence model is used to capture stochastic spatiotemporal propagation of discrete weather states relevant to solar and wind generation (e.g., clear-sky vs. broken-clouds states, low wind speed vs. high wind speed). Then the solar model predicts the possible solar-energy-generation futures based on the radiation-flux trajectories and solar-cell areas/efficiencies, while the wind model predicts the possible wind-energy generation futures based on the physical model of wind turbines. In the second part, we will use the possible renewable energy generation futures to coordinate scheduling and dispatch of traditional units. Our focus in building the scheduling module is to use a stochastic unit commitment algorithm that is practical for implementation in the current transmission-system operational paradigm, rather than to propose a new decisionmaking paradigm. Our perspective is to view stochastic unit commitment as a two-step process, first requiring an identification of critical conventional units whose schedules are dependent on the renewable units' generation futures, and second achieving an optimization of these unit's schedules and hourly economic dispatches.
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Details
- Title
- Decision support tools for renewable rich power systems
- Creators
- Jiayi Jiang
- Contributors
- Sandip Roy (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; [Pullman, Washington] :
- Identifiers
- 99900525382801842
- Language
- English
- Resource Type
- Thesis