Page 1 OPTIMAL ENERGY MANAGEMENT of HVAC
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OPTIMAL ENERGY MANAGEMENT OF HVAC SYSTEMS BY USING EVOLUTIONARY ALGORITHM
Session Number: CIB T2S5
Authors Fong K F, BEng, MSc, CEng, MCIBSE, MHKIE, MASHRAE Hanby V I, BSc, PhD, CEng, MInstE, MCIBSE Chow T T, PhD, CEng, MIMechE, MCIBSE, FHKIE, MASHRAE
Abstract The available plant and energy simulation packages become robust and user-friendly, and this is the major reason that they are so popular even in the consultancy fields in these few years. In fact, these plant and energy simulation packages can be widely adopted in studying different alternatives of the operation of HVAC and building services systems. Since there are many parameters involved in different equipment and systems, one of the useful areas of studies is to optimize the essential parameters in order to provide a satisfactory solution for design or operation in terms of efficient and effective facilities management. Therefore in this paper, the simulation-optimization approach is proposed for effective energy management of HVAC systems. Due to the complexity of the HVAC systems, which commonly include the refrigeration, water and air side systems, it is necessary to suggest optimal conditions for different operation according to the dynamic cooling load requirements throughout a year. A simulation-EA coupling suite has been developed by using the metaheuristic skill, and the evolutionary algorithm can be effectively used to handle the discrete, non-linear and highly constrained characteristics of the typical HVAC and building services optimization problems. The effectiveness of this simulation-EA coupling suite has been demonstrated through the establishment of the monthly optimal reset scheme of the chilled water supply temperature of a local central chiller plant. Further development of this performance-based optimization suite would be useful in handling a variety of scenarios of the HVAC and building services systems. This would be essential to promote the facilities and energy management in terms of both sustainability and finance.
K.F. Fong is lecturer, Division of Building Science and Technology, City University of Hong Kong, Hong Kong SAR, China; V.I. Hanby is assistant director, Institute of Energy and Sustainable Development, De Montfort University, UK; T.T. Chow is principal lecturer, Division of Building Science and Technology, City University of Hong Kong, Hong Kong SAR, China.
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1. INTRODUCTION
Energy management is one of the essential foci of the sustainable facilities management of building services. Particularly for the HVAC (heating, ventilating and air-conditioning) services installations, a variety of energy management measures can be adopted and implemented. These measures may cost no extra investment and installation, such as using the appropriate set points in the operation of the existing equipment of the refrigeration, water and air side systems. Although automatic control provisions and even computerized building management system (BMS) have been included in the typical HVAC design, the control commissioning works are mainly covered for the full load settings, system start-up and step- down, sequential and interlock control among multiple equipment. However the reset scheme and schedule for some major operating parameters, such as the chilled water supply temperature and space air temperature, have not been clearly mentioned in the control operation manual. During the operation stage of the HVAC systems, control reset would not be effectively applied. The cooling capacities may be over-provided in many occasions, and energy saving cannot be achieved through the existing control provisions. Even the operation engineering staff try to implement the control reset in different seasons and partial load conditions, the appropriate set point values would be inevitably developed through the trial-and-error approach. However due to the changing climatic and loading conditions, when sufficient log data has been acquired and suitable set points can be perceived, this may be only left as the operation guidelines for next year. Therefore it is necessary to have suitably predictive method to provide the optimal reset information for reference purpose, and reset control can be properly adopted as the primary energy management measure for the facilities management of HVAC systems.
In this paper, a simulation-optimization approach is proposed through the combination of plant simulation and metaheuristic-based optimization, in order to determine the required optimal conditions for effective energy management purpose. The effectiveness of this developed model has been demonstrated through the application in a central chiller plant serving for a number of local subway stations.
2. SIMULATION-OPTIMIZATION FOR HVAC AND ENERGY MANAGEMENT PROBLEMS
In the context of the HVAC and building services systems, the simulation-optimization approach has been widely applied. For the optimization problems, a variety of objective functions can be formulated from different aspects, such as year-round energy consumption, life-cycle cost, thermal comfort, compromise between energy consumption and discomfort, plant scheduling, routing and distribution, as reflected in the works of Wright (1986), Kintner- Meyer (1994), Huh (1995), deWit (1995), Taylor (1996), and Fong et al. (2001) respectively. Particularly in the field of building services engineering, life-cycle cost analysis has been increasingly concerned by the building developers, architects and engineers. Since this can help them to foresee beyond the initial investment and installation costs, and to incorporate the running and maintenance costs properly throughout the service life of the systems.
Recently, simulation-optimization approach has been also adopted for decision making process, as demonstrated in the works of Dasgupta (1997), Wright et al. (2002) and Fong et al. (2003), as well as from the viewpoint of the building developers (Kennett 2001). Usually multi-criterion optimization approach would be applied, together with the posteriori preference articulation approach, a number of possible scenarios would be provided for the decision makers and engineering operators as an essential information source of reference. For simulation of different performance and scenarios of the HVAC systems, a variety of plant simulation models and software have been reported in the aforesaid research works. One major characteristic among those plant simulation tools is component-based. Each part or equipment is modeled as a component, which is defined by a series of mathematical expressions for the related input and output parameters. Then linkage can be developed between any two components in order to provide the path of data flow and manipulation.
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After building up the entire plant or system with the corresponding components, the results would be generated through a number of iteration processes until the criterion of convergence is satisfied.
3. EVOLUTIONARY ALGORITHMS FOR OPTIMIZATION
Common optimization problems of HVAC design and operation often have discrete, non- linear and highly constrained characteristics in the search space. Owing to this, there is continuous advancement of the optimization methods in last two decades, so that the local optimum can be effectively prevented and global optimum can be efficiently identified. Wright and Hanby (1987) applied the direct search method, but there might be inaccuracy when constraints were encountered. Wright (1993) used the successive approximation method for improvement. Hanby and Angelov (2000) adopted univariate search method, a gradient- based technique, to carry out plant design optimization based on the dynamic performance from building/plant simulation. Recent years, there are growing applications of evolutionary algorithm (EA) in handling different optimization problems. Giraud-Moreau and Lafon (2002) have highlighted that EA is very suitable to handle the complex mechanical design problems since no derivative information is required. For instance, Simpson et al (1994) applied for the optimization of pipework networks. For EA in general, there are three major paradigms – evolution strategy (ES), evolutionary programming (EP) and genetic algorithm (GA) (Bäck et al. 2000, Beyer 2001). Wright (1996) used genetic algorithm (GA) for the HVAC optimization studies in sizing. Huang and Lam (1997) used GA for optimizing the controller performance in HVAC systems. Sakamoto et al (1999) examined the application of GA to optimize the operation schedule for a district heating and cooling plant. Asiedu et al (2000) focused on the application of GA for the duct system design. Wright et al (2002) even applied MOGA (multi- objective genetic algorithm) search method to identify the optimal pay-off characteristic between building energy cost and occupant thermal discomfort. Although many optimization works have been effectively handled by GA, the effectiveness of other paradigms in EA, such as EP and ES, has not been thoroughly studied and reported.
EA is a probabilistic algorithm and developed from the Darwinian paradigm of evolution, which is often viewed analogous to optimization (Beyer 2001). Owing to the dynamic and stochastic nature of EA, the primary aim of analysis is to make predictions on the temporal behaviour of the evolutionary system, i.e. the inter-relationship between EA and fitness function. For EA, the essential steps are derived from the fundamental principles of recombination, mutation and selection of the Darwinian evolution throughout generation. EA can be summarized in the evolutionary loop as shown in Figure 1.
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Initialization
Evaluation (+ Constraint Handling Technique) Mutation
Selection
Recombination
N Max Epoch?
Y
End
Figure 1. Typical Evolutionary Loop of Evolutionary Algorithm
The key genetic operators of EA are recombination, mutation and selection, as highlighted in Figure 1. For a complete optimization algorithm, it starts from the initialization process that would generate the required number of individuals, and this becomes the first population for next process - evaluation. In evaluation, the fitness of each individual within the population would be evaluated by the objective function and if any, the constraint functions. In order to effectively prioritize the fitness of the individuals, suitable constraint handling technique would be applied. Then in the selection process, the required number of individuals with the best fitness would be selected for the ongoing recombination and mutation processes, until the preset number of epoch or the stopping rule has been achieved.
For the three major paradigms GA, EP and ES of EA, their major discrepancies can be perceived from the data representation, importance of recombination and mutation, and the approach of selection. Generally for GA, the representation of the individuals is binary, recombination (or crossover) is essential, mutation has less importance, and selection is stochastic. For EP, the representation is real-valued, mutation is essential, no recombination, and the selection is stochastic. For ES, the representation is also real-valued, both mutation and recombination are important, but the selection is deterministic. In this paper, the paradigm of EP is applied as the optimization method linking with the plant simulation model.
4. SIMULATION-OPTIMIZATION BY USING SIMULATION-EA COUPLING SUITE
For the coupling of the optimization codes with the simulation programs, it has been advocated by Wetter (2001), with the development of GenOpt, which is an optimization platform for minimizing an objective function that is evaluated by an external simulation program. From the latest user manual (Wetter 2003), there are algorithms for one- dimensional and multi-dimensional optimization, as well as the algorithms for parametric runs. A variety of optimization methods have been included on this GenOpt platform, however EA or other metaheuristic approaches have not been included in this latest version. Therefore in this paper, a simulation-EA coupling suite has been developed to perceive the effectiveness of the EA optimization methodology. This simulation-EA coupling suite contains the plant simulation module and EA module, as well as the coupling linkages. This optimization suite would provide the fitness values of the “parents” through the plant simulation module, then direct the results to EA module for evaluation, selection and variation. The "offsprings" would
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be therefore produced for the plant simulation module, and the loop continues until the required stopping rule has been satisfied. The conceptual relationship of this simulation-EA coupling suite is illustrated in Figure 2.
“Offsprings” from EA to plant simulation model
Evolutionary Plant Simulation Algorithm Model
“Parents” with optimized results back to EA for continual evolutionary processes
Figure 2. Conceptual Relationship of Simulation-EA Approach
5. APPLICATION IN CENTRAL CHILLER PLANT OF SUBWAY STATIONS
5.1 System Description of Central Chiller Plant
The developed simulation-EA coupling suite has been used to study a central chiller plant that provides district chilled water supply for five subway stations in Hong Kong. TRNSYS has been used to develop the entire chiller plant with total cooling capacity of 6000 TR. The whole simulation plant model consists of 6 numbers of 1000 TR water-cooled chillers, the associated chilled water pumps, the corresponding heat rejection equipment (including condenser water pumps, heat exchangers and sea water pumps), the differential pressure bypass circuit, the air handling units (AHUs), the associated outdoor air and exhaust air fans, as well as the free cooling fans. In order to respond to different cooling loads throughout a year, the plant model has included the control for automatic staging of chillers and associated pumps, and air side free cooling mode in case of suitable outdoor air enthalpy. The central chiller plant on the assembly panel of the interfacing program IISiBat of the TRNSYS is shown in Figure 3.
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Figure 3. Central Chiller Plant on the Assembly Panel in IISiBat of TRNSYS
5.2 Details of Simulation-EA Coupling Suite for Monthly Reset of Chilled Water Supply Temperature
This simulation-EA coupling suite has been developed by linking the plant simulation software TRNSYS with the EA written in MATLAB, both under the Windows environment of a conventional Pentium III personal computer. For the EA module of the coupling suite, the paradigm of EP is applied, real-valued parameters can be directly used, and no recombination process is involved. In the process of selection and mutation, the approach of elitism has been adopted. The individual with the best fitness, i.e. the minimum monthly energy consumption, would be selected and maintained for next epoch without any change, while the rest would be varied with a mutation factor. In this study, the population of individuals for each epoch is 10, and the maximum number of epoch is 15.
For the plant simulation module, TRNSYS has been used to build up the central chiller plant model, by including all the equipment components, and the related input and output parameters. In this application, the chilled water supply temperature set point of the central chillers has been identified for optimization. Since in the existing plant operation, this temperature is just constantly set at 7.2 °C throughout the year, and no reference information is available for temperature reset purpose. In order to achieve more effective energy management, the total energy consumption of all the HVAC equipment involved should be minimized for optimizing the chilled water supply temperature. The analysis is based on monthly basis, so the objective function is the monthly energy consumption, which is directly resulted from the number and efficiency of equipment in operation to tackle the hourly cooling load within that month. The equipment components include the chillers, chilled water pumps, condenser water pumps, sea water pumps, AHU fans and cooling coils, outdoor air fans, exhaust air fans, and free cooling fans.
By suitable labelling of the parameter to be optimized (i.e. chilled water supply temperature in
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Parents X1, X2, ..., Xn (with corresponding mim monthly energy consumption
Evaluation Determine Xbest based on minimum monthly energy consumption
Evolutionary
Selection For elitism, set Xnew, 1 = Xbest Algorithm Module
Xnew, 2 = Xbest + Mut-factor2 Mutation For the rest of population, : : : Xnew, n = Xbest + Mut-factorn
Offsprings Xnew, 1, Xnew, 2, ..., Xnew, n
Chillers
Condenser Water Chilled Water Hourly Cooling Pumps Pumps Load Plant Simulation AHU Fans Sea Water Pumps Outdoor Air Fans Module Exhaust Air Fans Free Cooling Fans
Hourly Energy Consumption
Monthly Energy Consumption for Each Individual Xnew, 1, Xnew, 2, ..., Xnew, n
Figure 4. Details of Simulation-EA Coupling Suite
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5.3 Results from Simulation-EA Coupling Suite
In Figure 5, the change of the optimized chilled water supply temperature with the epoch of EA is shown. Nearly all months, the chilled water supply temperatures have been converged after epoch 8, this is also supported from the change of minimum monthly energy consumption against the epoch as shown in Figure 6. This shows that the proposed number of epoch is enough to provide the satisfactory and convergent results for this case.
8.1
7.9 Jan Feb 7.7 Mar Apr 7.5 May Jun 7.3 Jul
7.1 Aug Sep 6.9 Oct Nov 6.7 Dec Chilled Water Supply Temperature (degC) Supply Temperature Chilled Water 6.5 123456789101112131415 Epoch
Figure 5. Optimal Chilled Water Supply Temperature Vs Epoch
7.100E+09
Jan 6.600E+09 Feb Mar 6.100E+09 Apr May 5.600E+09 Jun Jul 5.100E+09 Aug Sep 4.600E+09 Oct Nov Monthly Energy Consumption (kJ) Consumption Energy Monthly 4.100E+09 Dec
3.600E+09 123456789101112131415 Epoch
Figure 6. Minimum Monthly Energy Consumption Vs Epoch
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The monthly optimal chilled water supply temperatures have been summarized and illustrated in Figure 7. Compared with the existing constant set point 7.2 °C throughout a year, the general trend is that higher chilled water supply temperatures can be set from October to April (i.e. the autumn, winter and spring in Hong Kong), while lower temperatures are particularly required for May and June, since the weather is still humid that renders to comparatively higher latent load from the outdoor air. For the hottest months from July to September, the original set point 7.2 °C is basically satisfactory, however a higher set point in August would even give further energy saving in that month.
8.1
7.9
7.7
7.5
7.3
7.1
existing6.9 set point 7.2 °C throughout a year 6.7 Chilled Water Supply Temperature (degC)
6.5 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Figure 7. Monthly Optimal Chilled Water Supply Temperature
5.600E+09
5.100E+09
4.600E+09
4.100E+09 Monthly Energy Consumption (kJ)
3.600E+09 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Figure 8. Monthly Energy Consumption of Central Chiller Plant of Subway Stations
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Based on the optimal chilled water supply temperatures in different months, the corresponding monthly energy consumptions of the central chiller plant of the subway stations are summarized in Figure 8. The profile follows the climatic and environmental changes in Hong Kong, with the highest value in August (i.e. the summer) and the lowest value in February (i.e. the winter).
If there is BMS for the HVAC installations, the developed information of the reset scheme can be used as a reference for the operating chilled water supply temperature in different months. The log data from BMS can then be used to review and verify the proposed optimal values. Through this research work, the monthly optimization of chilled water supply temperature has been demonstrated. In effect, the analysis can be down to the weekly optimization, and more comprehensive and frequent information can be provided for chilled water temperature reset, thus better energy management can be achieved.
6. CONCLUSION
In this paper, the simulation-optimization approach has been proposed for sustainable facilities and energy management of HVAC systems, since this is effective in optimizing the operation of different equipment and systems. A simulation-EA coupling suite has been developed with the metaheuristic skill for optimization, and evolutionary algorithm has been applied to handle the discrete, non-linear and highly constrained characteristics of typical HVAC optimization problems. Since the optimization suite is working on the Windows platform, the transferability of this package would be highly enhanced. The effectiveness of this simulation-EA coupling suite has been demonstrated through the development of the monthly reset scheme of the chilled water supply temperature of a local central chiller plant. For further application, the space air temperature is another possible parameter that can be optimized for better energy management. In fact the current performance-based optimization suite can be used to handle a variety of scenarios of the HVAC and building services systems in terms of both sustainability and finance. For the continual improvement of this optimization suite with EA, the direction would include the involvement of the features of ES, such as the recombination operators, deterministic selection operators, and other effective mutation operators. The targets are to let the feasible space be located more effectively, the searching topology be more accurately perceived, and the searching rate be enhanced more efficiently.
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