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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

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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

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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

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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

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Facility Layout IA, IB - INTERIOR TEST ROOMS EA, EB- EAST TEST ROOMS SA, SB - SOUTH TEST ROOMS WA,WB- WEST TEST ROOMS

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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

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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

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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

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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

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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

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

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

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Comparison with Test Results

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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

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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

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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

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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

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Peak Load Reduction Potential (South, East, West, and Interior Zones Combined) 20-40% Peak-Load Reduction with Precooling Start-Time

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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

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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

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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