Slide 1

Introduction toLoad Shifting and Peak Load

Reduction using Building Thermal Mass

Jim BraunPurdue University

Slide 2

Outline

Building Thermal Mass Concept

Strategy Development and Evaluation

Previous Work

Objectives of Current Work

Slide 3

Control of Building Thermal Mass

Precool at night during off-peak hours

Adjust daytime setpoints to control discharge

Cooled structure reduces daytime, on-peak cooling loads

Savings due to » reduced on-peak energy and

demand usage» improved equipment performance» night ventilation

Slide 4

Building Structural Storage Potential

Concrete Floor

Internal Gains~ 4 - 8 Watts/sq. ft.

Thermal Capacity ~ 2 - 4 Watts-Hours/sq. ft. - F

~ 0.25 - 1 hours of storage per 1 degree F temperature change

Slide 5

Load Shifting

Types of StrategiesZo

ne T

empe

ratu

re

Time of Day

Night Setup

Occupied Period Unoccupied Period

On-Peak Period

Demand Limiting

Lower Comfort Limit

Upper Comfort Limit76 F

70 F

Slide 6

Load EffectsC

oolin

g Lo

ads

Time of DayOccupied Period Unoccupied Period

On-Peak Period

Night Setup

Demand Limiting

Load Shifting

Slide 7

Thermal Mass vs. Ice Storage

No additional costs (Structure already exists!!)

Charging constraints due to occupant comfort

Variable storage efficiency due to coupling between building and environment

Initial cost associated with ice tank(s), piping, support equipment, & installation.

No direct comfort constraints Constant storage efficiency with easily

determined “state of charge”

Slide 8

Strategy Development and Evaluation

Evaluate Maximum Savings

Potential

Develop and Evaluate Generic Control Strategies

SimulationControlled Testing –Validate Simulations

& Demonstrate Savings

Laboratory Testing

Evaluate Real-World Savings Potential

Field TestingImplementation

Issues

A Tool to Develop Site-Specific

Control Strategies

A Tool to Evaluate Field Savings

Slide 9

Forward Simulation

System Physical Description

Weather Data& Schedules Utility RatesControl Strategy

Existing Modeling Tool

Performance Estimates

* use to evaluate savings potential & develop simple control strategies *

Slide 10

Inverse Simulation * use to develop site-specific control strategies & evaluate field savings *

System Training Data

SystemModel Structure Parameter Estimation

ModelValidationSystem Test Data

System Model

Weather Data& Schedules

Utility RatesControl Strategy

Performance Estimates

Slide 11

Previous Studies Simulation Work

» Up to 30% HVAC energy & demand cost savings for large commercial buildings (Braun (1990), Synder and Newell (1990), Rabl and Norford (1991), Andresen and Brandemuehl (1992))

» Cost savings very sensitive to control method, system parameters, utility rates, and weather

» Inverse modeling approach for developing and evaluating site-specific control strategies (Chaturvedi and Braun, 2002)

Laboratory Testing» Up to 50% load shifting & peak reduction for a lightweight internal

zone (Conniff (1991), Morris et. al (1994))» Good agreement between measured loads and load predictions

from TRNSYS building model (Morris et. al (1994))» Load shifting and peak load reduction very sensitive to control

strategy

Slide 12

Previous Studies Field Testing – Large Commercial Buildings

» Small load shifting and peak reduction reported by Ruud et al. (1990)

» 100% shedding from 2 pm to 6:30 pm reported by Sukkhbir et al. (1993)

» ~25% peak cooling load reduction for side-by-side tests from 7 am to 6 pm reported by Keeney and Braun (1996)

» Up to 40% HVAC cost savings predicted for large commercial building by Braun et. al (2002)

Field Testing – Small Commercial Buildings» 23% load shifting for small commercial building reported Braun

et. al (2002)

Slide 13

Simulated Load Shifting Cost Savings

(2-to-1 time-of-day rates)

90807060500

10

20

30

40

50

60

% DailyCooling Cost Savings

Average Daily Temperature (F)

Heavy ZoneGood Part-Load

Heavy ZoneFlat Part-Load

Light ZoneFlat Part-Load

Light ZoneBad Part-Load

* high sensitivity to building and plant *

Slide 14

NIST Laboratory Test FacilityZone Air

Handler

Guard Space

Air Handler

Guard Air Spaces

Plenum

Internal Gains

Concrete Slabs

Controlled to emulate internal zone within a multi-story building

Slide 15

Demand-Limiting Test Results

0

500

1000

1500

2000

Cooling Load (Watts)

0 4 8 12 16 20 24Time of Day (hours)

Night Setback

Minimum Demand

Slide 16

Comfort Results – Demand Limiting

Slide 17

Chicago Field Site Description

No Scale

Elevation View

North

OfficeSpace

OfficeSpaceReception

Area

1st Floor

2nd Floor

3rd Floor

4th FloorGroundLevel

Approx. 1280 ft.(390 m)

Approx. 512 ft.(156 m)

Plan View

1.4 million sq. ft., four 900-ton chillers, west of Chicago$0.052/kw-hr on-peak (9 am – 10 pm)

$0.023/kw-hr off-peak, $16.41 per peak kW

Slide 18

Chicago Field Site

Slide 19

Demand-Limiting Case Study

Slide 20

HVAC Energy Cost Case Study Created an inverse model from measured data Used model to develop & evaluate control strategies

Cooling PlantEvaporatorsCondensers

AirHandlingUnit

AirHandlingUnit

BuildingTest Site

Cooling PlantEvaporatorsCondensersEvaporatorsCondensersEvaporatorsCondensers

AirHandlingUnit

AirHandlingUnit

BuildingTest Site

SimulateCosts

EvaluateSavings

Control StrategiesWeather DataUtility Rates

SimulateCosts

EvaluateSavings

Control StrategiesWeather DataUtility Rates

Buildingmodel

AHU model

Plantmodel

Learn Building & Equip. Models

Buildingmodel

AHU model

Plantmodel

Learn Building & Equip. Models

processeddata

AmbientConditions

processeddata

processeddata

AmbientConditionsAmbient

Conditions

Slide 21~ 5% difference in utility costs

113681

46748 43664

73265 70017

120013

0

20000

40000

60000

80000

100000

120000

140000

Actual Predicted

Costs ($)

Energy Demand Total

Model Validation(HVAC utility costs, July 11 - August 8, 1997, field site data)

Slide 22

3-Month Cost Savings for HVAC

StrategyEnergy

Costs($)Demand Costs($)

Total costs($) Savings(%)

Night Setup 90,802 189,034 279,836 0.0Load-Shifting Strategy 72,671 91,372 164,043 41.4

Slide 23

Objectives of Current Work

Demonstrate peak load reduction potential in a medium size commercial building

Further develop and validate inverse modeling tools » a tool for developing site-specific strategies and

evaluating field site savings

Evaluate peak load reduction potential for a small commercial building