Research Initiation Award: Predictive Models for Wind-Penetrated Power Systems Using Bayesian Approach

研究启动奖为历史悠久的黑人学院和大学的初级和职业生涯中期教师提供支持，这些教师正在建立新的研究计划或重新定向和重建现有的研究计划。预计该奖项有助于进一步提高教师的研究能力和有效性，改善所在机构的研究和教学，并让本科生参与研究经验。授予哥伦比亚特区大学的奖项可能在许多领域产生更广泛的影响。该项目的目标是利用不太精确的现有模型和相关结果，为预测风速和风能穿透的电力系统开发一个新的概念、模型、模拟和特征。该研究的目标是开发一种新的贝叶斯方法，以同时考虑穿透风电系统中风电场上风力机位置的变化、风电场风力机中心处的风吹角以及多个相关风电场之间的距离与穿透风电系统中随机负荷之间的距离变化而导致的风速模型中固有的不确定性。贝叶斯信息将带来更好的决策，同时它改善了风速的特征，因为它具有较低的估计方差，以及较少的偏差。其结果将是更好地利用风能资源，并减少对热力发电能力的依赖，以补偿风能的变化。此外，目标是展示所提出的方法在诸如随机经济调度、线性交流最优潮流和安全约束机组组合等著名电力系统问题中的应用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.