[Crib-list] SPEAKER: Christiane Adcock (Stanford) - Friday, October 7, 2022 - 12:00 PM - 1:00 PM via "ZOOM"

[Shirley Entzminger]

          COMPUTATIONAL RESEARCH in BOSTON and BEYOND Seminar
 			      (CRIBB)


   ZOOM meeting info...

 	https://mit.zoom.us/j/96155042770

 	Meeting ID: 961 5504 2770


====================================

DATE:	Friday, October 7, 2022

TIME:	12:00 PM - 1:00 PM


TITLE:	Hybrid Modeling for Energy System Simulation and Control


SPEAKER:  CHRISTIANE ADCOCK (Stanford)

ABSTRACT:

Simulating and controlling energy systems, such as buildings and wind farms, 
often requires a tradeoff between accuracy and computational speed. I explore 
how hybrid methods can combine the best attributes of each constituent method 
and thereby achieve both accuracy and speed. First, I consider high-fidelity 
simulation of a wind farm. Typically, simulations accurately represent only a 
subset of the following: blade boundary layer dynamics, wake-atmospheric 
boundary layer (ABL) interactions, and turbine-turbine interactions. I develop 
a hybrid between two fluid models, one Reynolds-Averaged Navier Stokes (RANS) 
and one large eddy simulation (LES), to simultaneously capture all three 
effects. I implement this work in a massively parallel flow solver, Nalu-Wind, 
and run simulations on a supercomputer, Eagle. Second, I compare a range of 
model-based, learning-based, and hybrid methods for real-time control of a 
grid-interactive building. The model-based approaches are accurate but require 
slow online optimization. In contrast, the learning-based approaches are rapid 
online but require lengthy offline training. I show that hybrid methods can 
achieve high accuracy while running quickly online and offline. Finally, I 
apply the most promising method from building control, differentiable 
predictive control (DPC), to wind farm control, specifically wake steering for 
power maximization. Typically wake steering methods develop a lookup table that 
maps wind farm conditions, such as incoming wind speed and direction, to the 
yaw angle for each turbine in the farm which maximizes the total wind farm 
power. If any turbines in the farm shutdown, the lookup table leads to 
sub-optimal control; extending the table to account for shutdown would be 
computationally prohibitive.  I show that  DPC can accomplish wake steering 
under turbine shutdown while maintaining low online and offline computational 
cost.

=======================================

For information about the "Computational Research in Boston and Beyond Seminar"
(CRIBB), please visit:

 		https://math.mit.edu/sites/crib/


=================

Shirley A. Entzminger
Administrative Assistant II
Department of Mathematics
Massachusetts Institute of Technology
77 Massachusetts Avenue
Building 2, Room 350A
Cambridge, MA 02139
PHONE: 	(617) 253-4994
E-mail:	daisymae at math.mit.edu
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