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UID:pretalx-amfc2026-NELTYC@modelica.simtek.cc
DTSTART;TZID=CST:20260921T162500
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DESCRIPTION:The high penetration of renewable energy increases grid volatil
 ity\, necessitating flexible demand-side resources such as District Coolin
 g Systems (DCS) with thermal storage. However\, the multi-timescale coupli
 ng of equipment\, pipelines\, and buildings makes it difficult to integrat
 e high-fidelity physical models with optimization. This paper proposes an 
 FMI-based co-simulation framework for day-ahead DCS optimization. A fullch
 ain Modelica model is exported as an FMI 2.0 CoSimulation FMU and coupled 
 with MATLAB optimizers. The main contribution is a standardized FMU-in-the
 -loop workflow for day-ahead mixed-integer nonlinear programming (MINLP) s
 cheduling with discrete unit commitment\, together with a systematic quant
 ification of the accuracy-runtime trade-off induced by FMI communication s
 ettings. Sensitivity analysis identifies a practical configuration for the
  investigated case\, while a comparison of the genetic algorithm (GA) and 
 surrogateopt reveals the solution-quality and computation-time trade-off. 
 Compared with storagepriority control\, the cost-optimization scenario red
 uces operating costs by 0.97%\, the energy-optimization scenario reduces e
 lectricity consumption by 4.58%\, and the carbon-optimization scenario red
 uces CO2 emissions by 4.20%. The physical feasibility of the generated sch
 edules is verified by dynamic FMU simulation.
DTSTAMP:20261004T070623Z
LOCATION:FMI & MBSE (R2001)
SUMMARY:An FMI Co-Simulation Framework for Day-ahead Optimization of Distri
 ct Cooling Systems Based on Modelica - Weidong Ma\, Feng Sha\, Xiang Li\, 
 Rui Gao
URL:https://modelica.simtek.cc/amfc2026/talk/NELTYC/
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