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DTSTART:20260308T030000
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DESCRIPTION:The Optimal Power Flow (OPF) problem is one of the most critica
 l and computationally intensive optimization challenges in power systems\,
  often requiring solutions within very short time intervals\, such as five
 -minute market windows. To address this challenge\, we first employ machin
 e learning techniques to approximate the nonlinear power balance equations
  with linear models\, significantly reducing computational complexity. In 
 parallel\, we reformulate the OPF problem using shortest-path methods\, tr
 ansforming it into a graph-based optimization framework. This approach ena
 bles the computation of high-quality solutions without relying on commerci
 al solvers\, while achieving accuracy comparable to state-of-the-art solve
 rs such as Gurobi.\n\nCo-sponsored by: Resilience and Clean Energy Systems
  (RCES)\n\nSpeaker(s): Sajad Fathi Hafshejani\n\nVirtual: https://events.v
 tools.ieee.org/m/538226
LOCATION:Virtual: https://events.vtools.ieee.org/m/538226
ORGANIZER:bli4@ualberta.ca
SEQUENCE:23
SUMMARY:Optimization Algorithms for Solving the Optimal Power Flow (OPF) Pr
 oblem
URL;VALUE=URI:https://events.vtools.ieee.org/m/538226
X-ALT-DESC:Description: <br /><p>The Optimal Power Flow (OPF) problem is on
 e of the most critical and computationally intensive optimization challeng
 es in power systems\, often requiring solutions within very short time int
 ervals\, such as five-minute market windows. To address this challenge\, w
 e first employ machine learning techniques to approximate the nonlinear po
 wer balance equations with linear models\, significantly reducing computat
 ional complexity. In parallel\, we reformulate the OPF problem using short
 est-path methods\, transforming it into a graph-based optimization framewo
 rk. This approach enables the computation of high-quality solutions withou
 t relying on commercial solvers\, while achieving accuracy comparable to s
 tate-of-the-art solvers such as Gurobi.</p>
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