|Funding Agency||National Science Foundation|
Research Initiation Awards provide support for junior and mid-career faculty at Historically Black Colleges and Universities who are building new research programs or redirecting and rebuilding existing research programs. It is expected that the award helps to further the faculty member's research capability and effectiveness, improves research and teaching at the home institution, and involves undergraduate students in research experiences. The award to the University of the District of Columbia has potential broader impacts in a number of areas. The goal of this project is to develop the concept, model, simulate, and characterize a novel framework for predictive wind speed and wind-penetrated power systems making use of less precise existing models and associated outcomes. This will make important contributions to improving the methodology of probabilistic renewable energy forecasts.
The goal of the research is to develop a novel Bayesian approach to take into account the uncertainties inherent in the wind speed models due to variation among the locations of the wind turbines on a wind farm in a wind-penetrated power system; wind blow angle of attack at the hubs of the turbines in a wind farm; and variation among the distances between multiple dependent correlated wind farms and random loads in a wind-penetrated power system, simultaneously. Bayesian information will result in better decisions while it improves the characterization of wind speeds as it has lower variance in estimates, as well as less bias. The result will be better utilization of wind resources and less reliance on the need for thermal capacity to be in service to compensate for wind variability. Furthermore, the goal is to present the application of proposed approaches to well-known power system problems such as stochastic economic dispatch, linearized AC optimal power flow, and security constraint unit commitment.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.