from    
to    
search  

 


清华大学材料科学与工程研究院《材料科学论坛》:Design and Properties of Hybrid...
清华大学材料科学与工程研究院《材料科学论坛》:Photoalignment for liquid crystals
Pseudo-criticality and its implication for the lost conformality
Implications of superadditive algebras in large N field theories for holography
报告题目:
Embracing Low Inertia in Power System Frequency Control: A Dynamic Droop Approach
 报告人:
Dr. Enrique Mallada
Johns Hopkins University
报告时间:
2019-08-02 10:00
报告地点:
西主楼2-212
主办单位:
电机系
  简介:

Abstract: 

The transition into renewable energy sources -with limited or no inertia- is seen as potentially threatening to classical methods for achieving grid synchronization. A widely embraced approach to mitigate this problem is to mimic inertial response using grid-connected inverters. That is, introduce virtual inertia to restore the stiffness that the system used to enjoy. In this talk, we seek to challenge this approach and advocate towards taking advantage of the system’s low inertia to restore frequency steady-state without incurring in excessive control efforts. With this aim in mind, we develop an analysis and design framework for inverter-based frequency control. We define several performance metrics of practical relevance for power engineers and systematically evaluate the performance of standard control strategies, such as virtual inertia and droop control, in the presence of power disturbances and measurement noise. Our analysis unveils the relatively limited role of inertia on improving performance as well as the inability of droop control to improve performance without incurring in large steady-state control efforts. To solve this problem, we propose a novel dynamic droop control (iDroop) for grid-connected inverters -exploiting classical lead/lag compensation from control theory- that can significantly outperform existing solutions with comparable control efforts.


今日相关信息
Design of Novel Supramolecular Analyt...
 
同类别相关信息
国有企业党的建设工作研讨会----暨习近...
预见未来第三期“科技照护生命”新型医学...
会议预告|清华大学主办的第七届高电压工...
Phase-field Modeling of Ferroic Het...
Perspectives of Deep Learning for P...
学术活动