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

研究启动奖:使用贝叶斯方法的风穿透电力系统预测模型

基本信息

项目摘要

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.
研究启动奖为传统黑人学院和大学的初级和中期职业教师提供支持,他们正在建立新的研究项目或重新指导和重建现有的研究项目。期望该奖项有助于提高教师的研究能力和效率,改善所在机构的研究和教学,并使本科生参与研究经验。授予哥伦比亚特区大学的这一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,开发预测风速和风力穿透电力系统的新框架的概念、模型、模拟和表征。这将对改进可再生能源概率预测方法做出重要贡献。该研究的目标是开发一种新的贝叶斯方法,以考虑由于风力发电厂风力涡轮机位置的变化而导致的风速模型固有的不确定性;风电场中涡轮机轮毂处的风吹攻角;以及多个相互依赖的风电场之间的距离变化以及风电系统中随机负荷之间的距离变化。贝叶斯信息将导致更好的决策,同时它改进了风速的表征,因为它具有更低的估计方差,以及更少的偏差。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,目标是将所提出的方法应用于众所周知的电力系统问题,如随机经济调度、线性化交流最优潮流和安全约束单元承诺。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(12)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
An interleaved non‐isolated high gain soft switching DC–DC converter with small input current ripple
一种具有小输入电流纹波的交错非隔离高增益软开关 DC-DC 转换器
  • DOI:
    10.1049/pel2.12425
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    2
  • 作者:
    Abbasian, Sohrab;Farsijani, Mohammad;Tavakoli Bina, Mohammad;Abrishamifar, Adib;Hosseini, Arya;Shahirinia, Amir
  • 通讯作者:
    Shahirinia, Amir
Optimal placement of STATCOM using a reduced computational burden by minimum number of monitoring units based on area of vulnerability
根据脆弱区域使用最少数量的监控单元来减少计算负担,从而优化 STATCOM 的布置
  • DOI:
    10.1049/gtd2.12804
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jalalat, Hamed;Liasi, Sahand;Bina, Mohammad Tavakoli;Shahirinia, Amir
  • 通讯作者:
    Shahirinia, Amir
A Nonisolated Common-Ground High Step-Up Soft-Switching DC–DC Converter With Single Active Switch
  • DOI:
    10.1109/tie.2022.3198262
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    7.7
  • 作者:
    Sohrab Abbasian;Mohammad Farsijani;M. Tavakoli Bina;A. Shahirinia
  • 通讯作者:
    Sohrab Abbasian;Mohammad Farsijani;M. Tavakoli Bina;A. Shahirinia
Distributed Dynamic State Estimation Considering Packet Losses in Interconnected Smart Grid Subsystems: Linear Matrix Inequality Approach
  • DOI:
    10.1109/access.2019.2949995
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    3.9
  • 作者:
    M. Rana;A. Shahirinia
  • 通讯作者:
    M. Rana;A. Shahirinia
Optimal location of voltage sags monitors by determining the vulnerable area of network buses
通过确定网络总线的脆弱区域来确定电压暂降监控器的最佳位置
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Hamed Jalalat Mohammad Tavakoli Bina Amir Shahirinia
  • 通讯作者:
    Hamed Jalalat Mohammad Tavakoli Bina Amir Shahirinia
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Amir Hossein Shahirinia其他文献

Amir Hossein Shahirinia的其他文献

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