# Presentation: 259488

**Tool Development for ****Peak Electrical Demand Limiting ****Using Building Thermal Mass**

- Jim Braun and Kyoung-Ho Lee
- Purdue University

**Ray W. Herrick Laborator****ies**
**Purdue University**

**January 2004**

## 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

## 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

## 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

## Facility Layout

**IA, IB - INTERIOR TEST ROOMS                              **

**EA, EB****	****- EAST TEST ROOMS**

**SA, SB - SOUTH TEST ROOMS**

**WA,WB****	****- WEST TEST ROOMS**

## 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

## Test Results - Interior Test Rooms 

## Test Results – All Test Rooms

## Inverse Model Structure

- Ta

- Tz

- Tg

- Tzo

- Qsol,r

- Qg,rad,e

- Qsol,e

- Qg,conv

- Qg,rad,i

- Qsol,f

- Qg,rad,f

- Resistance

- Capacitance

- Ta

- Ta

## 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**

## Effect of Training Length (simulated data, precooling strategy for training and testing)

## Effect of Control Strategy (simulated data, night setup for training and precooling for testing)

## Comparison with Test Results 

## 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

## 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

## 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

## 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

## Peak Load Reduction Potential(South, East, West, and Interior Zones Combined) 

- 20-40%
- Peak-Load
- Reduction
- with
- Precooling

**Precooling **
_**Start-Time**_

## 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

## 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

##  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