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Page 1 OPTIMAL ENERGY MANAGEMENT of HVAC Page 1 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. Page 2 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. Page 3 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.
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