An Enterprise Control Assessment Method for Variable Energy Resource-Induced Power System Imbalances--Part II: Parametric Sensitivity Analysis
Name
Youcef_An enterprise.pdf
Size
572.06 KB
Format
Adobe PDF
Checksum (MD5)
b2ea922b09c1ee88ff0b9a0f41200e56
Author(s)
Muzhikyan, Aramazd
Farid, Amro M.
Youcef-Toumi, Kamal
Date Issued
March 2015
Journal
IEEE Transactions on Industrial Electronics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Muzhikyan, Aramazd, Amro M. Farid, and Kamal Youcef-Toumi. “An Enterprise Control Assessment Method for Variable Energy Resource-Induced Power System Imbalances--Part II: Parametric Sensitivity Analysis.” IEEE Trans. Ind. Electron. 62, no. 4 (April 2015): 2459–2467.
Version
Author's final manuscript
Abstract
In recent years, renewable energy has developed to address energy security and climate change drivers. However, as energy resources, they possess a variable and uncertain nature that significantly complicates grid balancing operations. As a result, an extensive academic and industrial literature has developed to determine how much such variable energy resources (VERs) may be integrated and how to best mitigate their impacts. While certainly insightful with the context of their application, many integration studies have methodological limitations because they are case specific, address a single control function of the power grid balancing operations, and are often not validated by simulation. The prequel to this paper presented a holistic method for the assessment of power grid imbalances induced by VERs based upon the concept of enterprise control. This paper now systematically studies these power grid imbalances in terms of five independent variables: 1) day-ahead market time step; 2) real-time market time step; 3) VER normalized variability; 4) normalized day-ahead VER forecast error; and 5) normalized short-term VER forecast error. The systematic study elucidates the impacts of these variables and provides significant insights as to how planners should address these independent variables in the future.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TIE.2015.2395380
