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

研究启动奖为历史上黑人学院和大学的初级和中级职业教师提供支持，他们正在建立新的研究项目或重新定向和重建现有的研究项目。预计该奖项将有助于进一步提高教师的研究能力和效率，改善家庭机构的研究和教学，并使本科生参与研究经验。授予哥伦比亚特区大学的奖项在许多领域都有潜在的更广泛的影响。该项目的目标是开发概念，建模，模拟和表征一个新的框架，用于预测风速和风穿透电力系统，利用现有的不太精确的模型和相关的结果。研究的目的是发展一种新的贝叶斯方法，以考虑风速模型中的不确定性，这些不确定性是由于风电场中风力发电机的位置、风电场中风力发电机轮毂处的风吹攻角、风力发电机的风速、风力发电机的风速、风力发电和随机负荷之间的距离变化。贝叶斯信息将导致更好的决策，同时它改善了风速的表征，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力资源，减少对热容量的依赖，以补偿风力的变化。此外，其目标是提出的方法，如随机经济调度，线性化交流最优潮流，和安全约束单元committee.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准的评估支持著名的电力系统问题的应用。

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.