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DTSTART:20260308T030000
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DTSTART;TZID=America/Los_Angeles:20261021T110000
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DESCRIPTION:TBD\n\nPower system models are essential for planning\, analysi
 s\, and operational studies\, but a model that reproduces a measured respo
 nse is not necessarily a meaningful model. This presentation examines what
  makes a power system model meaningful in practice\, drawing on extensive 
 experience in model development\, parameterization\, validation\, field te
 sting\, and application of generic and vendor-specific models.\n\nThe pres
 entation will discuss the role of models\, the information they can reliab
 ly represent\, and their inherent limitations. It will also consider how m
 odeling objectives\, available data\, model structure\, and parameter unce
 rtainty affect the interpretation and use of simulation results. Examples 
 from generation\, inverter-based resources\, and transmission systems will
  illustrate why meaningful modeling requires more than simply matching mea
 surements.\n\nCo-sponsored by: University of California\, Riverside\n\nSpe
 aker(s): Dr. Pouyan Poubeik (IEEE Fellow)\, \n\nVirtual: https://events.vt
 ools.ieee.org/m/579515
LOCATION:Virtual: https://events.vtools.ieee.org/m/579515
ORGANIZER:mail@maxcherubin.com
SEQUENCE:57
SUMMARY: Power System Modeling: What Makes a Model Meaningful
URL;VALUE=URI:https://events.vtools.ieee.org/m/579515
X-ALT-DESC:Description: <br /><p>TBD</p>\n<p class="isSelectedEnd">Power sy
 stem models are essential for planning\, analysis\, and operational studie
 s\, but a model that reproduces a measured response is not necessarily a m
 eaningful model. This presentation examines what makes a power system mode
 l meaningful in practice\, drawing on extensive experience in model develo
 pment\, parameterization\, validation\, field testing\, and application of
  generic and vendor-specific models.</p>\n<p>The presentation will discuss
  the role of models\, the information they can reliably represent\, and th
 eir inherent limitations. It will also consider how modeling objectives\, 
 available data\, model structure\, and parameter uncertainty affect the in
 terpretation and use of simulation results. Examples from generation\, inv
 erter-based resources\, and transmission systems will illustrate why meani
 ngful modeling requires more than simply matching measurements.</p>
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