In contrast to building energy conversion equipment, less improvement has been achieved in thermal energy distribution, storage and control systems in terms of energy efficiency and peak load reduction potential. Cooling of commercial buildings contributes significantly to the peak demand placed on an electrical utility grid and time-of-use electricity rates are designed to encourage shifting of electrical loads to off-peak periods at night and on weekends. Buildings can respond to these pricing signals by shifting cooling-related thermal loads either by precooling the building’s massive structure (passive storage) or by using active thermal energy storage systems such as ice storage. Recent theoretical and experimental work showed that the simultaneous utilization of active and passive building thermal storage inventory can save significant amounts of utility costs to the building operator, yet increased electrical energy consumption may result. The article investigates the relationship between cost savings and energy consumption associated with conventional control, minimal cost and minimal energy control, while accounting for variations in fan power consumption, chiller capacity, chiller coefficient-of-performance, and part-load performance. The model-based predictive building controller is employed to either minimize electricity cost including a target demand charge or electrical energy consumption. This work shows that buildings can be operated in a demand-responsive fashion to substantially reduce utility costs with marginal increases in overall energy consumption. In the case of energy optimal control, the reference control was replicated, i.e., if only energy consumption is of concern, neither active nor passive building thermal storage should be utilized. On the other hand, cost optimal control suggests strongly utilizing both thermal storage inventories.

1.
Energy Information Administration
(EIA/DOE), 2002, Annual Energy Review 2002. U.S. Department of Energy. URL: www.eia.doe.gov/emeu/aer/enduse.htmlwww.eia.doe.gov/emeu/aer/enduse.html. October 2003.
2.
Arthur
D. Little
, Inc., 1999,
Guide for Evaluation of Energy Savings Potential
. Prepared for the Office of Building Technology, State and Community Programs (BTS), U.S. Department of Energy.
3.
American Refrigeration Institute
(ARI) 1999, Statistical Profile of the Air-Conditioning,
Refrigeration, and Heating Industry
. p.
28
, Arlington, VA.
4.
American Standard
, Inc., 1999,
EarthWise Today
, Vol.
24
, p.
3
.
LaCrosse
, Wisconsin.
5.
National Energy Technology Laboratory
(NETL/DOE) 2003, Federal Assistance Solicitation for Energy Efficient Building Equipment and Envelope Technologies Round IV. PS No. DE-PS26-03NT41635. p.
4
. U.S. Department of Energy.
6.
Braun
,
J. E.
, 1990, “
Reducing Energy Costs and Peak Electrical Demand through Optimal Control of Building Thermal Mass
,”
ASHRAE Trans.
0001-2505,
96
(
2
), pp.
876
888
.
7.
Rabl
,
A.
, and
Norford
,
L. K.
, 1991, “
Peak Load Reduction by Preconditioning Buildings at Night
,”
Int. J. Energy Res.
,
15
, pp.
781
798
.
8.
Conniff
,
J. P.
, 1991, “
Strategies for Reducing Peak Air-Conditioning Loads by Using Heat Storage in the Building Structure
,”
ASHRAE Trans.
0001-2505,
97
(
1
), pp.
704
709
.
9.
Andresen
,
I.
and
Brandemuehl
,
M. J.
, 1992, “
Heat Storage in Building Thermal Mass: A Parametric Study
,”
ASHRAE Trans.
0001-2505,
98
(
1
), pp.
910
918
.
10.
Morris
,
F. B.
,
Braun
,
J. E.
, and
Treado
,
S. J.
, 1994, “
Experimental and Simulated Performance of Optimal Control of Building Thermal Storage
,”
ASHRAE Trans.
0001-2505,
100
(
1
), pp.
402
414
.
11.
Keeney
,
K. R.
and
Braun
,
J. E.
, 1996, “
A Simplified Method for Determining Optimal Cooling Control Strategies for Thermal Storage in Building Mass
,”
HVAC&R Res.
1078-9669,
2
(
1
), pp.
59
78
.
12.
Keeney
,
K. R.
and
Braun
,
J. E.
1997, “
Application of Building Precooling to Reduce Peak Cooling Requirements
,”
ASHRAE Trans.
0001-2505,
103
(
1
), pp.
463
469
.
13.
Mozer
,
M. C.
,
Vidmar
,
L.
, and
Dodier
,
R. H.
, 1997, “
The Neurothermostat: Predictive Optimal Control of Residential Heating Systems
,”
Advances in Neural Information Processing Systems
,
MIT Press
. Cambridge, MA.
14.
Chen
,
T. Y.
, 2001, “
Real-time Predictive Supervisory Operation of Building Thermal Systems with Thermal Mass
,”
Energy Build.
0378-7788,
33
, pp.
141
150
.
15.
Braun
,
J. E.
,
Montgomery
,
K. W.
, and
Chaturvedi
,
N.
, 2001, “
Evaluating the Performance of Building Thermal Mass Control Strategies
,”
HVAC&R Res.
1078-9669,
7
(
4
), pp.
403
428
.
16.
Braun
,
J. E.
,
Lawrence
,
T. M.
,
Klaassen
,
C. J.
, and
House
,
J. M.
, 2002, “
Demonstration of Load Shifting and Peak Load Reduction with Control of Building Thermal Mass
,”
Proceedings of the 2002 ACEEE Conference on Energy Efficiency in Buildings
, Monterey, CA.
17.
Chaturvedi
,
N.
and
Braun
,
J. E.
, 2002, “
An Inverse Gray-Box Model for Transient Building Load Prediction
,”
HVAC&R Res.
1078-9669,
8
(
1
), pp.
73
100
.
18.
Henze
,
G. P.
,
Krarti
,
M.
, and
Brandemuehl
,
M. J.
, 1997, “
A Simulation Environment for the Analysis of Ice Storage Controls
,”
HVAC&R Res.
1078-9669,
3
(
2
), pp.
128
148
.
19.
Henze
,
G. P.
,
Dodier
,
R. H.
, and
Krarti
,
M.
, 1997, “
Development of a Predictive Optimal Controller for Thermal Energy Storage Systems
,”
HVAC&R Res.
1078-9669,
3
(
3
), pp.
233
264
.
20.
Henze
,
G. P.
, and
Krarti
,
M.
, 1998, “
Ice Storage System Controls for the Reduction of Operating Costs and Energy
,”
J. Sol. Energy Eng.
0199-6231,
120
(
4
), pp.
275
281
.
21.
Henze
,
G. P.
, and
Krarti
,
M.
, 1999, “
The Impact of Forecasting Uncertainty on the Performance of a Predictive Optimal Controller for Thermal Energy Storage Systems
,”
ASHRAE Trans.
0001-2505,
105
(
2
), pp.
553
561
.
22.
Krarti
,
M.
,
Brandemuehl
,
M. J.
, and
Henze
,
J. P.
, 1995, “
Final Project Report for ASHRAE 809-RP: Evaluation of Optimal Control for Ice Storage Systems
,” ASHRAE Report, American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Atlanta, Georgia.
23.
Krarti
,
M.
,
Henze
,
G. P.
,
Bell
,
D.
,
Kreider
,
J. F.
,
Brandemuehl
,
M. J.
, and
Norford
,
L. K.
, 1997, “
Model Based Optimizer Systems With TES: Final Report
,” JCEM Technical Report TR/97/15,
University of Colorado
, Boulder, CO.
24.
Krarti
,
M.
,
Henze
,
G. P.
, and
Bell
,
D.
, 1999, “
Planning Horizon for a Predictive Optimal Controller for Thermal Energy Storage Systems
,”
ASHRAE Trans.
0001-2505,
105
(
2
), pp.
543
552
.
25.
Marken
,
A. V.
, 1997, “
Control of Thermal Energy Storage Systems for Cost-Effectiveness
,” Internal Report,
University of Colorado
, Boulder, CO.
26.
Bell
,
D.
, 1998, “
Evaluation of Optimal Controls for Ice-Based Thermal Energy Storage Systems
,” M.S. Thesis, University of Colorado, Boulder, CO.
27.
Massie
,
D.
, 1998, “
Optimal Neural Network-Based Controller for Ice Storage Systems
,” Ph.D. dissertation, University of Colorado, Boulder, CO.
28.
Drees
,
K. H.
and
Braun
,
J. E.
, 1996, “
Development and Evaluation of a Rule-Based Control Strategy for Ice Storage Systems
,”
HVAC&R Res.
1078-9669,
2
(
4
), pp.
312
336
.
29.
Kintner-Meyer
,
M.
and
Emery
,
A. F.
, 1995, “
Optimal Control of an HVAC System Using Cold Storage and Building Thermal Capacitance
,”
Energy Build.
0378-7788,
23
, pp.
19
31
.
30.
Braun
,
J. E.
, 2003, “
Load Control Using Building Thermal Mass
,”
J. Sol. Energy Eng.
0199-6231,
125
(
3
), pp.
292
301
.
31.
Henze
,
G. P.
,
Felsmann
,
C.
, and
Knabe
,
G.
, 2004, “
Evaluation of Optimal Control for Active and Passive Building Thermal Storage
,”
Int. J. Therm. Sci.
1290-0729,
43
(
2
), pp.
173
183
.
32.
Henze
,
G. P.
,
Kalz
,
D.
,
Felsmann
,
C.
, and
Knabe
,
G.
, 2004, “
Impact of Forecasting Accuracy on Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory
,”
HVAC&R Res.
1078-9669,
10
(
2
), pp.
153
178
.
33.
Liu
,
S.
and
Henze
,
G. P.
, 2004, “
Impact of Modeling Accuracy on Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory
,”
ASHRAE Trans.
0001-2505, Technical Paper 4683,
110
(
1
), pp.
151
163
.
34.
Henze
,
G. P.
,
Kalz
,
D.
,
Liu
,
S.
, and
Felsmann
,
C.
, 2005, “
Experimental Analysis of Model-Based Predictive Optimal Control for Active and Passive Building Thermal Storage Inventory
.”
HVAC&R Res.
1078-9669, in print.
35.
Carrier Corporation
(2003) 30GTN,GTR Air-Cooled Reciprocating Liquid Chillers with ComfortLink™ Controls, available at http://www.xpedio.carrier.com/idc/groups/public/documents/techlit/30gtn-5pd.pdfhttp://www.xpedio.carrier.com/idc/groups/public/documents/techlit/30gtn-5pd.pdf
36.
DOE
(1980) “
DOE-2 Reference Manual, Part 1
,” Version 2.1. Lawrence Berkeley Laboratory.
37.
ASHRAE Handbook of Applications
(2003) Chapter 41: Supervisory Control Strategies and Optimization,
ASHRAE
, Atlanta, GA, p.
41.37
.
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