Tool Development for Peak Electrical Demand Limiting Using
Building Thermal Mass Jim Braun and Kyoung-Ho Lee Purdue University
Ray W. Herrick Laboratories Purdue University January 2004
Slide 2
Project Objectives Further develop and validate inverse
building modeling tool a tool for developing site-specific
strategies and evaluating field site savings Evaluate potential for
demand reduction in a small commercial building structure
Slide 3
Project Approach Develop calibrated forward simulation model
for the Iowa Energy Center (IEC) Use forward simulation to evaluate
model structure and data training requirements for an inverse
building model Train inverse building model using available data
from the IEC Study impact of precooling duration and on-peak period
on peak cooling demand for the IEC
Slide 4
Iowa Energy Center (Energy Resource Station) Well-instrumented
test rooms that are representative of a small commercial building
(east, south, west, and internal zones) No internal thermal mass
(only floor and exterior walls) Data collected in summer of 2001
for both night setup and a precooling strategy
Slide 5
Facility Layout IA, IB - INTERIOR TEST ROOMS EA, EB- EAST TEST
ROOMS SA, SB - SOUTH TEST ROOMS WA,WB- WEST TEST ROOMS
Slide 6
Strategies for 2001 Tests Night Setup Control: Phase I Testing
74 F occupied setpoint (7 am 6 pm) 90 F unoccupied setpoint (6 pm 7
am) Precooling Control Strategy: Phase II Testing 68 F setpoint for
midnight 6 am 74 F setpoint 6 am 6 pm 90 F setpoint for 6 pm
midnight
Slide 7
Test Results - Interior Test Rooms 0 500 1000 1500 2000 2500
3000 3500 4000 13579 11131517192123252729313335373941434547 Hour
Sensible Cooling Load (Btu/hr) Phase I, Interior A: August 10 - 11
Phase II, Interior A: August 19-20, 2001
Slide 8
Test Results All Test Rooms 0 10000 20000 30000 40000 50000
60000 70000 13579 111315171921232527293133353739414345 47 Hour
Sensible Cooling Load (Btu/hr) Phase I, All Rooms: August 10 - 11
Phase II, All Rooms: August 19-20, 2001
Slide 9
Inverse Model Structure TaTa TzTz TgTg T zo Q sol,r Q g,rad,e Q
sol,e Q g,conv Q g,rad,i Q sol,f Q g,rad,f Resistance Capacitance
TaTa TaTa
Slide 10
Model Training Global Search (Systematic Search) Local Search
(Non-Linear Regression) Prediction of Loads (Building Simulation)
Building Model Best R & C Estimated R & C Training Building
Model Inputs Measurements ambient/zone temperature solar radiation
internal gains Outputs cooling loads zone temperatures Testing
Slide 11
Effect of Training Length (simulated data, precooling strategy
for training and testing)
Slide 12
Effect of Control Strategy (simulated data, night setup for
training and precooling for testing)
Slide 13
Comparison with Test Results
Slide 14
Demand-Limiting Control Evaluation Basic Demand-Limiting
Strategy Unoccupied Period: precool at 67 F Occupied, Off-Peak
Period: maintain zone at 69 F Occupied, Demand-Limiting Period:
maintain zone at 69 F until load exceeds target, then operate at
maximum target capacity and allow temperature to float Parametric
Studies Considered individual days (steady-periodic condition)
Determined target that allowed temperature to float between 69 and
76 F within occupied period Varied start times for precooling and
demand-limiting periods
Slide 15
Precooling with Afternoon Demand Limiting (South, East, West,
and Interior Zones Combined) 30% Afternoon Peak-Load Reduction with
No Precooling 69 F 76 F Over Last 6 Hours of Occupancy
Slide 16
No Precooling with Afternoon Demand Limiting (South, East,
West, and Interior Zones Combined) 27% Afternoon Peak-Load
Reduction with No Precooling 69 F 76 F Over Last 6 Hours of
Occupancy
Slide 17
Precooling with All-Day Demand Limiting (South, East, West, and
Interior Zones Combined) 23% Daytime Peak-Load Reduction with
Precooling 69 F 76 F Over 8 Hours of Occupancy
Slide 18
Peak Load Reduction Potential (South, East, West, and Interior
Zones Combined) 20-40% Peak-Load Reduction with Precooling
Start-Time
Slide 19
West Zone Demand-Limiting Results (No Precooling, Afternoon
Demand Limiting) 35% Peak-Load Reduction at End of Day 69 F 76 F
Over Last 3 Hours of Occupancy
Slide 20
Conclusions Afternoon Demand-Limiting 30-40% Peak Load
Reduction with zone temperature adjustments from 69 - 76 F
Precooling has small effect on afternoon peak Potential for large
morning peak with no precooling All-Day Demand-Limiting ~20% Peak
Load Reduction with zone temperature adjustments from 69 - 76 F
Precooling has significant effect
Slide 21
Control of Building Mass in Small Commercial Buildings ??? Peak
load and load shifting potential is very significant Major portion
of the total building stock cooling requirements Implementation
requires automation Packaged equipment with on/off control and
individual thermostats (no EMCS) Very small ratio of
human-to-equipment supervision Potential for automation is high
System simplicity is an asset (1 thermostat per unit) Thermostat
call for cooling is a load measurement Modern thermostats can be
connected to a network and obtain utility and weather
information