ASHRAE IJHVAC 17-1-2011 HVAC&R RESEARCH An International Journal of Heating Ventilating Air-Conditioning and Refrigerating Research.pdf

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1、 EditorialNet-zero-energy technology for building retrofitAbout one to two million buildings are being newly constructed in the United States every year. However,there are approximately 110 million existing buildings. Even when each of the new buildings woulduse net-zero-energy technology, it will t

2、ake decades to achieve significant impact on the overall energyconsumption for the entire building stock. A much more productive approach for achieving building energyefficiency is to focus on the retrofit of existing buildingsthis is where the research challenges and thebusiness opportunities lie.

3、Most existing buildings were not designed to meet net-zero-energy requirementsnortooeasilyaccommodate suchrenovations andtheimplementation ofrespective measures during retrofitpresents a huge challenge.This is where research comes in. It is a very rewarding and high impact research task to develop a

4、ndimplement the concepts that allow for the cost effective conversion of existing buildings into net-zero-energy structures. A prerequisite is incredible creativity, out-of-the-box thinking, and a multi-disciplinaryapproach.Practical research efforts have to address all aspects of buildings, includi

5、ng the envelope, heating,cooling and ventilation technologies, indoor air quality, controls, occupant comfort and operation andcommissioning.HVACsoft-computing or control techniques, such as neural networks, fuzzy logic, genetic algorithms; and onthe fusion or hybrid of hard- and soft-control techni

6、ques. Thus, it is to be noted that the terminology“hard” and “soft” computing/control has nothing to do with the “hardware” and “software” that is beinggenerally used. Part I of a two-part series focuses on hard-control strategies, and Part II focuses on soft-andfusion-controlinadditiontosomefutured

7、irectionsinHVAC accepted November 1, 2010D. Subbaram Naidu, PhD, is Director, School of Engineering. Craig G. Rieger, PhD, is ICIS Distinctive Signature Lead.is to prevent the spread of any chemical or biolog-ical species from any point where these species arereleased to the rest of the building. Th

8、e primaryprofessional organization responsible for all activ-ities of HVAC process control, such as proportional-integral-derivative (PID) control; and supervisory or cen-tralized control (CC) with a model-based method(physical model, gray-box model, black-box model,hybrid model, etc.).Further, vari

9、ous optimization techniques werediscussed in this review by Wang and Ma (2008).From the perspective of the topics on energy,comfort, and control, a review was conducted inDounis and Caraiscos (2009) of the work ini-tially on conventional (optimal, predictive, andadaptive) control schemes and then th

10、e state-of-the art intelligent (neural, fuzzy, neuro-fuzzy,proportional-integral (PI)-fuzzy, adaptive fuzzyproportional-derivative (PD) and PID) control sys-tems for improving the efficiency and indoor en-vironment in buildings, with a particular empha-sis on multi-agent control systems (MACS) withs

11、imulations using TRNSYS/MATLAB software.Also, see the previous literature review worksby Dexter (1988), Kelly (1988), and Sane et al.(2006).Overview and terminology: hard control(HC) and soft control (SC)There are various ways of conducting anoverview, such as a chronologicalor topicaloverview. The

12、main purpose of this overview isto provide the reader with a summary of the re-cent results on the topic of control techniques forHVAC the focus is on1. HC, such as basic controls involving PID con-trol,optimalcontrol(AndersonandMoore1990;Naidu 2003; Lewis et al. 2008), nonlinear con-trol (Kristic e

13、t al. 1995), robust or H control(Zhou and Doyle 1998), and adaptive control(Tao 2003);2. SC, involving neural networks (NNs), fuzzylogic (FL), genetic algorithms (GAs), andother evolutionary methods (Jang et al. 1997;Tsoukalas and Uhrig 1997; Nguyen et al. 2003;KarrayandDeSilva2004;Konar2005;Kasabov

14、2007; Sumathi et al. 2008); and3. hybrid control resulting from the fusion of SCand HC to achieve a better performance (Ovaskaet al. 2002; Tettamanzi and Tomassini 2001;Konar2005;Kasabov2007;Sumathietal.2008).It is to be noted that the new terminology, the“hard” in HC and “soft” in SC, has been used

15、 re-cently in the control systems community (Ovaska etal. 2002; Karray and De Silva 2004) and has noth-ing to do with the “hardware” and “software” thatis generally used.Modeling, testing, and validationModelingAgenericpiping/processandinstrumentationdi-agram (P heat pumps andairflowductwork,calledt

16、hedistributionsubsystem;and the subsystem consisting of the environmen-tal zones; the whole configuration is also called amulti-zone space heating (MZSH) system (Zaheer-Uddin et al. 1993; Saboksayr et al. 1995). Here,using the principles of energy conservation and bal-ance, a seventh-order, bilinear

17、, state-space modelfor a two-zone space heating system was developedwitha114seven state variables: boiler temperature, temper-ature of the evaporator for heat pump 1, tem-perature of the evaporator for heat pump 2, tem-perature of the condenser coil for heat pump 1,temperature of the condenser coil

18、for heat pump2, zone 1 temperature, and zone 2 temperature;a114three output variables: boiler temperature, zone 1temperature, and zone 2 temperature,a114fivecontrol(input)variables:airflowrateforzone1controller1,airflowrateforzone2controller2,boilerfuelfiringratecontroller3,inputenergyforheatpump1vi

19、acontroller4,andinputenergyforheat pump 2 via controller 5, essentially groupedinto three controllers.In another detailed study by Zaheer-Uddin andZheng (1994), as many as 328 nonlinear, time-varying equations were developed for the dynamicmodels for a two-zone variable airflow volume(VAV) system in

20、 terms of subsystem models for en-vironmental zones, cooling and dehumidifier coil,variable air flow rates in the duct, fan motor, andchiller and storage tank, described by nine controlinput variablessix dampers (for zone 1, zone 2,fan, outdoor air, exhaust air, and recirculating air),fan and chille

21、r energy inputs, and mass flow rate ofchilledwatertocontrolthetemperaturesandhumid-ity ratios in the two zones, discharge air conditions,chilled water temperature, outdoor and supply air-flow rates, static pressure in the duct system, andfan speed. The transient analysis of the open-loopsystem perfo

22、rmed on the linearized system showedthe dynamics of the overall VAV system being com-posed a slow phenomenon due to the chiller-coil-zone thermal subsystem and a fast phenomenonduetothefan-airflowsubsystem.Thisinteractionofslowand fast phenomena gives risetoan interestingDownloaded by T surprisingly

23、, no significant work hasbeen done (except in Dexter 1988; Zaheer-Uddinand Patel 1995; Zaheer-Uddin and Zheng 2000) inthis direction to apply the SPaTS methodology toHVAC Kokotovicet al. 1986; Naidu 1988, 2002).Distributed-parameter modelIn modeling most HVAC the testing method validationwas perform

24、ed using two types of tests: open-looptests (without a real controller) and closed-looptests, showing good agreement between the mea-sured and emulated testing methods for heating, fancoil, and chilled ceiling applications.Dedicated software for HVAC see, for example, BLAST(building loads analysis a

25、nd system thermodynam-ics;MATLABandSIMULINKareregisteredtrade-marks of The Mathworks, Inc., Natick, MA, USA),originally developed by University of Illinois at Ur-bana Champaign (UIUC), Urbana, IL (Blast-UIUC1983). BLAST features are now being incorporatedinto the DOE-2 of EnergyPlus. DOE-2 is a com-

26、puter program that predicts energy usage and costsof a building given the description of the buildingand its HVAChttp:/). Also, see other programssuch as HVACSIM+ (HVAC Simulation; HVAC-SIM1986)andTRNSYS(TransientEnergySystemSimulation Tool; TRNSYS 1996). It is to be notedthat BLAST and DOE-2 have m

27、ore or less mergedto form EnergyPlus (E+), combining the best fea-tures and capabilities of BLAST and DOE-2, andit is now more widely used among researchers andpractitionersintheHVACZaheer-UddinandZheng2000; Wang and Ma 2008). The function of the lo-cal controller is to ensure stability first and th

28、ento ensure good set-point tracking, where the super-visory control is in charge of coordinating variouslocal controllers for the various subsystems and, atthe same time, maintain the overall operation of theentire HVAC hence, it is best used as a firstcut for tuning PID controllers. There are a num

29、-ber of excellent books on this subject (see Astromand Hagglund 1995, 2006; ODwyer 2003; Visioli2006).Early investigations using basic single-inputsingle-output (SISO) control methods, suchas PID controllers, faced the problem of tuningthe KP, KI, and KD parameters in addition tothe inability to tak

30、e into account the interactionsbetween the various loops (Nesler and Stoecker1984; Nesler 1986). A four-level HVAC the next (third) level taking careof temperature and relative humidity of the airsupplied by the HVAC the second levelassuming the responsibility of maintaining thedesired performance o

31、f the plant actuators andlocal control loops; and, finally, the lowest (first) orlocal level taking care of maintaining the desiredcontroller setting based on the system model.Problems associated with discrete-time simulationofanHVAC Hartman 1980;Kaya et al. 1982) was to find optimal control poli-ci

32、es to minimize the overall energy expenditure andsimultaneouslycontrolroomtemperature,roomhu-midity, and outside wind velocity to keep the roomatacomfortzone,asrecommendedbyASHRAE.Acomparativestudywasmadewithconventionalcon-trol methods consisting of a heating/cooling ther-mostat and humidistat, cla

33、iming energy savings of38.5% with an optimal control strategy. A similartreatment was given by Nizet et al. (1984) by usingthe airflow rate as a control variable for a simplifiedmodeltominimizethetotalenergycostandthermalcomfort penalty using discretization of the originalcontinuous optimal control

34、problem, and by solvingthe resulting problem using a conjugate gradientmethod (Fletcher 1980) to realize energy savings of12% to 30% compared to the case without using theconjugate method.AcombinationofPID,feed-forward,andoptimalcontrol for regulating the temperature within a ther-mal space was deve

35、loped in Cherchas et al. (1985)to maintain a set-point value with a linear cost func-tion.Inparticular,amathematicalmodelforasingleenvironmentalspacewasdevelopedintermsofbothexact and simplified equations, using the principlesofconservationofmass,humidity,andenergy(Bor-resen1981).Themodelhadasthetwo

36、statevariablesDownloaded by T Naidu 2003), yielding lower operatingcosts compared to the previous work in Cherchaset al. (1985). An optimal control strategy using thePontryagin maximum principle (Pontryagin et al.1962) was applied to a heat pump system, whenthestoragecapabilitywasavailableandtime-of

37、-dayenergy incentives were offered by electrical utilitycompany, using a single-zone model and a simplespace load and cost function to be minimized asthe cost of purchasing electrical energy (Rink et al.1988),yieldingextremaltrajectorieswithonebang-bang interval and one singular interval. Also see t

38、herelated works by Le et al. (1987) and Zaheer-Uddin(1989) for a more accurate single-zone model andZaheer-Uddin(1991)fordigitalcontrol.Withaper-formancecriteriontomaximizehumancomfortandto minimize the energy and operating costs, a fuzzyoptimal controller was developed by Shoureshi andRahmani (1989

39、).Continuing developments: 19901999Agroupofresearchers(Houseetal.1991)devel-oped an optimal control methodology for a repre-sentativeHVAC Koko-tovic et al. 1986; Naidu 2002). A reduced-order fastmodel was retained by neglecting the slow subsys-tem, although in the normal SPaTS literature, thefast su

40、bsystem is neglected while retaining the slowsubsystem and an optimal tracking control was de-signed for set-point changes. Two techniques of dif-ferential dynamic programming (DDP) and nonlin-ear programming (NLP) were compared as appliedto three cases of optimal control of an HVAC and the mass flo

41、w rate of chilledwater as the control variable for the heating case;discharge air temperature, discharge air humidityratio, and their set-points as the state variables;and input energy to chiller, the mass flow rate ofchilled water, and electric heater energy as the threecontrol variables. Simulatio

42、ns using the numericalsearch (gradient) method (Mufti 1970) for opti-mal control showed smoother and rapid responsesin discharge air temperature and air humidity ra-tio, while tracking performance showed significantimprovement.Recent developments: 20002010Forthetime-scheduledoperationofanHVAC Naidu

43、1988). A similar ap-proach by Zaheer-Uddin and Zheng (2001), involv-ing multi-stage optimization and SPaTS methodol-ogy, was developed for a single-zone space heating(SZSH) system with different operating strategiesof constant volume (CV; where zone temperaturealone is modulated), VAV (where airflow

44、 rate ismodulated) and a more general VAV called gen-eral variable-air-volume (VAVN) (air-supply tem-perature and flow rate are continuously modulated),and with three-mode building time-scheduled oper-ation. The results showed that the VAVN strategyoffered a operating costs savings of 25% comparedto

45、 the CV strategy.A simple second-order model was used for theHVACNNs are used to predict system dynamics; uncon-trollable loads and SDP are used to solve the HVACunit subproblems with a thermal load described bya single-state Markov chain; and heuristics are usedto obtain feasible solutions. A mathe

46、matical basisfor a complete simulation-based SQP (CSB-SQP)methodology was developed and applied to a sim-ple model arising in determining the optimal con-trol for the operation of HVAC the proposed method, com-pared with model-based predictive optimal control,was implemented on a full-scale laborato

47、ry facility(Liu and Henze 2006a, 2006b). A simulated rein-forcement learning consists of two phases: a simu-lated learning phase (where a learning controller istrained by a simulator without using the actual re-sponse) and an implemented learning phase (wherethelearningcontrollerisexpectedlearnandim

48、provewhile in direct contact with the environment). Thehybrid approach was validated by an experimentalstudy, and it was found that the hybrid approachachieved cost savings of 8.3% compared to the caseusing measured data, while the quality of the simu-lator remained a key disadvantage. A simple near

49、-optimal method was developed by Braun (2007) forcontrolling the charging and discharging thermalstorage systems having real-time pricing (RTP) bydetermining the effective on-peak and off-peak pe-riods. The performance was measured relative to abenchmark optimization problem, resulting in an-nual costs of about 2% of the costs with optimalcontrol with the additional features of low hard-ware costs and a simplified controller architecture.AnothersurveybySaneetal.(2006)ofliteraturefo-cused