Test and evaluation of energy saving potentials in a complex building central chilling system using genetic algorithm

Abstract

This article presents the test and evaluation of energy saving potentials of complex building central chilling systems using genetic algorithm (GA). The evaluation was conducted based on a simulated virtual system representing the actual complex central chilling system in a super high-rise building in Hong Kong. GA was used as the optimisation tool to search for globally optimal control settings. The simulated virtual system was used as the test platform and acted as the performance predictor in estimating the overall system performance and responses to the changes of control settings. The test results show that about 4.54—5.06% daily energy in the system studied can be saved by using optimal control settings, as compared to that using the conventional control settings. The results also show that the evaluation process by using the simulated virtual system and the GA optimiser is time costly due to strong dynamic effects of the simulated virtual system and the optimisation principle of GA.

Practical applications: This article presents an approach for evaluating energy saving potentials of complex central chilling systems using GA. This approach can be used to identify maximum energy saving potentials in air-conditioning systems by optimising the major control variables simultaneously. The results can be used to guide the development of advanced control and optimisation strategies suitable for practical applications.

Get full access to this article

View all access options for this article.

References

Lu L. , Cai WJ , Xie LH , Li SJ , Soh YC HVAC system optimization - in-building section. Energy; Buildings 2005; 37(1): 11-22.

Ma ZJ , Wang SW Building energy research in Hong Kong: A review. Renewable and Sustainable Energy Reviews 2009; 13(8): 1870-83.

Ashrae 2007 ASHRAE handbook - HVAC applications. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., 2007.

Rishel JB Control of variable speed pumps for HVAC water systems. ASHRAE Transactions 2003; 109(1): 380-9.

Ma ZJ , Wang SW Energy efficient control of variable speed pumps in complex building central air-conditioning systems. Energy and Buildings 2009; 41(2): 197-205.

Tillack L. , Rishel JB Proper control of HVAC variable speed pumps. ASHRAE Journal 1998; 40(11): 41-6.

Braun JE A general control algorithm for cooling towers in cooling plants with electric and/or gas-driven chillers. HVAC&R Research 2007; 13(4): 581-98.

Ma ZJ , Wang SW , Xu XH , Xiao F. A supervisory control strategy for building cooling water systems for practical and real time applications. Energy Conversion and Management 2008; 49(8): 2324-36.

Chang YC , Lin FA , Lin CH Optimal chiller sequencing by branch and bound method for saving energy. Energy Conversion and Management 2005; 46(12-14): 2158-72.

10.

Sun J. , Reddy A. Optimal control of building HVAC&R systems using complete simulation-based sequential quadratic programming (CSB-SQP). Building and Environment 2005; 40(5): 657-69.

11.

Wang SW , Ma ZJ Supervisory and optimal control of building HVAC systems: A review. HVAC&R Research 2008; 14(1): 3-32.

12.

Koeppel EA , Klein SA , Mitchell JW , Flake BA Optimal supervisory control of an absorption chiller system. HVAC&R Research 1995; 1(4): 325-42.

13.

Olson RT , Liebman S. Optimization of a chilled water plant using sequential quadratic programming . Engineering Optimization 1990; 15: 171-91.

14.

Gibson GL A supervisory controller for optimization of building central cooling systems . ASHRAE Transactions 1997; 103(1): 486-93.

15.

Chow TT , Zhang GQ , Lin Z. , Song CL Global optimization of absorption chiller system by genetic algorithm and neural network. Energy and Buildings 2002; 34(1): 103-9.

16.

Wang SW , Jin XQ Model-based optimal control of VAV air-conditioning system using genetic algorithm . Building and Environment 2000; 35(6): 471-87.

17.

Nassif N. , Kajl S. , Sabourin R. Optimization of HVAC control system strategy using two-objective genetic algorithm . HVAC&R Research 2005; 11(3): 459-86.

18.

Mossolly M. , Ghali K. , Ghaddar N. Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm. Energy 2009; 34(1): 58-66.

19.

Huang W. , Lam HN Using genetic algorithms to optimize controller parameters for HVAC systems . Energy and Buildings 1997; 26(3): 277-82.

20.

Carroll DL FORTRAN genetic algorithm (GA) driver. Version 1.7a. Available at: http://cuaerospace.com/carroll/ga.html (accessed February 2003).

21.

Wang SW Dynamic simulation of a building central chilling system and evaluation of EMCS on-line control strategies. Building and Environment 1998; 33(1): 1-20.

22.

Haves P. , Norford LK , DeSimone M. A standard simulation test bed for the evaluation of control algorithms and strategies. ASHRAE Transactions 1998; 104(1A): 460-73.

23.

Sud I. Control strategies for minimum energy usage. ASHRAE Transactions 1984; 90(2A): 247-77.

24.

Klein SA , Hughes PJ , Theilacker JC , Beckman WA , Eberlein ME , Morrison GL , Cooper PI , Karman VD , Clark DR , Duffie NA , Pawelski MJ , Braun JE , Freeman TL , Utzinger DM , Evans BL , Mitchel JC , Duffie JA , Kummer JP , Beekman DM , Brandemuehl MJ , Urban RE , Oonk RL , Arny MD TRNSYS, a Transient Simulation Program. USA: University of Wisconsin-Madison, Solar Energy Laboratory. Version 13.1, 1990.

25.

Braun JE , Klein SA , Mitchell JM Effectiveness models for cooling towers and cooling coils. ASHRAE Transactions 1989; 95(2): 164-74.

26.

EnergyPlus. Version 1.2.3. Available at: http://www.eere.energy.gov/buildings/energyplus/ (accessed December 2005).