EP2253897B1 - Method and system for controlling a plurality of refrigerating machines - Google Patents

Method and system for controlling a plurality of refrigerating machines Download PDF

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Publication number
EP2253897B1
EP2253897B1 EP10162899.8A EP10162899A EP2253897B1 EP 2253897 B1 EP2253897 B1 EP 2253897B1 EP 10162899 A EP10162899 A EP 10162899A EP 2253897 B1 EP2253897 B1 EP 2253897B1
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EP
European Patent Office
Prior art keywords
refrigerating
refrigerating machines
plr
function
temperature
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Not-in-force
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EP10162899.8A
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German (de)
English (en)
French (fr)
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EP2253897A1 (en
Inventor
Michele Albieri
Alessandro Beghi
Marco Bertinato
Luca Cecchinato
Mirco Rampazzo
Alessandro Zen
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Rhoss SpA
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Rhoss SpA
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Priority claimed from ITBO2009A000313A external-priority patent/IT1394189B1/it
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Priority to PL10162899T priority Critical patent/PL2253897T3/pl
Publication of EP2253897A1 publication Critical patent/EP2253897A1/en
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Publication of EP2253897B1 publication Critical patent/EP2253897B1/en
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Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
    • F25B—REFRIGERATION MACHINES, PLANTS OR SYSTEMS; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS
    • F25B49/00—Arrangement or mounting of control or safety devices
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/46—Improving electric energy efficiency or saving
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/50—Control or safety arrangements characterised by user interfaces or communication
    • F24F11/54—Control or safety arrangements characterised by user interfaces or communication using one central controller connected to several sub-controllers
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
    • F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
    • F25D17/00—Arrangements for circulating cooling fluids; Arrangements for circulating gas, e.g. air, within refrigerated spaces
    • F25D17/02—Arrangements for circulating cooling fluids; Arrangements for circulating gas, e.g. air, within refrigerated spaces for circulating liquids, e.g. brine
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2140/00—Control inputs relating to system states
    • F24F2140/20—Heat-exchange fluid temperature
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
    • F25B—REFRIGERATION MACHINES, PLANTS OR SYSTEMS; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS
    • F25B2400/00—General features or devices for refrigeration machines, plants or systems, combined heating and refrigeration systems or heat-pump systems, i.e. not limited to a particular subgroup of F25B
    • F25B2400/06—Several compression cycles arranged in parallel

Definitions

  • the present invention relates to a method and a system for controlling a plurality of refrigerating machines of an air-conditioning plant.
  • the present invention is advantageously, but not exclusively intended for use to control domestic HVAC (Heating, Ventilation and Air-Conditioning) plants that comprise a plurality of refrigerating machines consisting of fluid coolers and/or hydronic heat pumps connected in parallel, to which the following description specifically refers but without any loss of generality.
  • HVAC Heating, Ventilation and Air-Conditioning
  • Air-conditioning plants are known in the prior art (see for example patent document US-A-4463574 ) that comprise a plurality of refrigerating machines connected in parallel and one or more convection units, for example fan coil units or ordinary water radiators, appropriately arranged within a building in which the air-conditioning is to be controlled and hydraulically connected to the refrigerating machines via a hydronic circuit, within which a service fluid flows, said fluid consisting of a water-based coolant, and said circuit having a delivery duct within which the service fluid flows from the refrigerating machines to the convection units and a return duct within which the service fluid flows in the opposite direction.
  • convection units for example fan coil units or ordinary water radiators
  • Said air-conditioning plant comprises a plurality of pumps, each of which is connected to a respective refrigerating machine to force the flow of the service fluid through said machine when it is on, and at least an additional pump arranged on the delivery duct to distribute a constant flow of the service fluid to the convection units.
  • the hydronic circuit typically comprises a bypass duct connecting the delivery duct, upstream of the distribution pump, directly to the return duct so as to disconnect, in terms of the flow of service fluid, the part of the plant with the refrigerating machines from that with the convection units.
  • each refrigerating machine is capable of part load operation, i.e. it can deliver cooling capacity according to a plurality of capacity steps.
  • the air-conditioning plant comprises a control system to control the operation of the refrigerating machines.
  • Control systems are known in the prior art that comprise a temperature sensor to measure the delivery temperature or return temperature of the service fluid and a control unit to control switching on and/or part load operation of the refrigerating machines so that the measured temperature follows a preset setpoint that is the same throughout the plant.
  • each refrigerating machine has a level of efficiency, expressed as the ratio between the cooling capacity delivered and the electric power consumed, which varies with the percentage of part load operation and which has a maximum value for a percentage of part load operation that is typically less than 100% and depends on the number of active compressors in the refrigerating machine, the architecture of the hydronic circuit and the machine's control logic.
  • the refrigerating machines may differ from one another and thus have a maximum efficiency value for different percentages of part load operation.
  • the object of the present invention is to provide a method for controlling a plurality of refrigerating machines of an air-conditioning plant and to provide a relative control system, which overcome the inconveniences described above and are, at the same time, easy and economic to produce.
  • number 1 generally indicates an air-conditioning plant, as a whole, which comprises a plurality of refrigerating machines 2 connected in parallel and one or more convection units 3 consisting, for example, of fan coil units and connected to the refrigerating machines 2 via a hydronic circuit 4, within which a service fluid consisting of a water-based coolant flows.
  • the hydronic circuit 4 comprises a main delivery duct 5 within which the service fluid flows from the refrigerating machines 2 to the convection units 3 and a main return duct 6 within which the service fluid flows in the opposite direction.
  • the plant 1 comprises a plurality of pumps 7, each of which is connected to a respective refrigerating machine 2 to force the flow of the service fluid through said machine 2 when the latter is on, at least one additional pump 8 arranged on the delivery duct 5 to distribute a constant flow of the service fluid to the convection units 3.
  • the hydronic circuit 4 also comprises a bypass duct 9 connecting a point 5a on the delivery duct 5 upstream of the pump 8 and a point 6a on the return duct 6 to disconnect, in terms of the service fluid flow, the part of the plant with the refrigerating machines 2 from that with the convection units 3.
  • the plant 1 comprises a storage tank 10 arranged on the delivery duct 5 downstream of the bypass duct 9 to generate a thermal inertia in the hydronic circuit 4, slowing the dynamics of the plant 1 so as to prevent any undesirable oscillations in the control valves (not illustrated) of the convection units 3.
  • the presence of the storage tank is, however, optional.
  • Each of the refrigerating machines 2 is capable of part load operation, i.e. it is able to deliver cooling capacity according to a plurality of capacity steps.
  • each refrigerating machine 2 comprises several compressors of a known type which can be switched on in an increasing number.
  • the plant 1 comprises a control system 11 for controlling the operation of the refrigerating machines 2.
  • the control system 11 implements the method for controlling an air-conditioning plant of the invention, as described below.
  • the control system 11 comprises temperature sensing means, which are arranged along the hydronic circuit 4 and comprise a temperature sensor 12 to measure a temperature TDLV of the service fluid in the delivery duct 5 upstream of the bypass duct 9, a temperature sensor 13 to measure a temperature TRET of the service fluid in the return duct 6 and a sensor 14 to measure a temperature TLIN of the service fluid in the delivery duct 5 downstream of the bypass duct 9, and in particular downstream of the storage tank 10.
  • the control system 11 comprises a conventional flow rate sensor 30 to measure the mass flow rate of the hydronic circuit 4 in a point of the return duct 6 downstream of the bypass duct 9.
  • the letter m is used to indicate said mass flow rate.
  • the control system 11 also comprises control means structured on two levels, and in particular high-level control means and low-level control means.
  • the high-level control means comprise a supervision unit 15, for example a PC configured to implement a load estimation module 16 suitable to provide an estimation of the thermal load PLE of the hydronic circuit 4 as a function of the temperatures TDLV, TRET and TLIN measured by the sensors 12, 13, 14 and 30 and an optimization module 17 suitable to determine operating state values ST i and part load ratios PLR i to set for the refrigerating machines 2 and such as to enable the refrigerating machines 2 to deliver an overall cooling capacity that satisfies the estimated thermal load PLE with minimum electric power consumption.
  • a supervision unit 15 for example a PC configured to implement a load estimation module 16 suitable to provide an estimation of the thermal load PLE of the hydronic circuit 4 as a function of the temperatures TDLV, TRET and TLIN measured by the sensors 12, 13, 14 and 30
  • an optimization module 17 suitable to determine operating state values ST i and part load ratios PLR i to set for the refrigerating machines 2 and such as to enable the refrigerating machines 2 to deliver an overall cooling capacity that
  • part load refers to a ratio between the cooling capacity requested of the n th refrigerating machine 2 at a certain point of operation and the maximum nominal cooling capacity (PCmax i ) of the n th refrigerating machine 2.
  • the operating state ST i of the n th refrigerating machine can be "on” or "off”.
  • the optimization module 17 provides N operating states ST i and N part load ratios PLR i , as illustrated in figure 1 .
  • the supervision unit 15 is, moreover, configured to implement a calculation module 18 suitable to determine, for each refrigerating machine 2, a respective machine delivery temperature setpoint TSET i as a function of the estimated thermal load PLE, the temperature TDLV and the part load ratio PLR i set for said refrigerating machine 2.
  • the low-level control means comprise control means 19 to control the switching on and part load operation of the refrigerating machines 2 as a function of the respective operating states ST i and of the respective setpoints TSET i , set by the supervision unit 15.
  • control means 19 are suitable, in general, to control the switching on and part load operation of the refrigerating machines 2 as a function of the respective operating states ST i and of the respective part load ratios PLR i .
  • control means 19 comprise a plurality of local controllers 20, each of which is connected to a respective refrigerating machine 2 to control the switching on of the refrigerating machine 2 as a function of the set operating state ST i and control the part load operation of the refrigerating machine 2 as a function of the set setpoint TSET i , and thus as a function of the set part load ratio PLR i .
  • each local controller 20 comprises a respective temperature sensor (not illustrated) to measure the local delivery temperature, i.e.
  • each local controller 20 is of a known type and is therefore not described in further detail.
  • the load estimation module 16 determines the estimated thermal load PLE by processing the temperatures TDLV, TRET and TLIN using a state observer.
  • the state observer is applied to a dynamic model of the plant 1 in state space form.
  • the temperatures TDLV, TRET and TLIN are sampled and the dynamic model is shown in state space at discrete points in time.
  • the state observer is a Luenberger observer.
  • the state observer is a Kalman filter.
  • the load estimation module 16 filters the estimated thermal load PLE, before supplying it to the subsequent calculation modules, through a lowpass filter, that is not described, in order to reduce the effects of compressor switching operations (on and off).
  • the optimization module 17 determines the operating states ST i and part load ratios PLR i by minimizing an objective function OBJ defined as the sum of at least a first and a second term.
  • the first term depends on a difference between the estimated thermal load PLE and a sum of the cooling capacities PC i delivered by all the refrigerating machines 2.
  • the second term depends on a sum of the electric power PE i consumed by all the refrigerating machines 2 at the respective cooling capacities PC i .
  • the penalty coefficient hc is between 5 and 25.
  • the penalty exponent kc is between 0.5 and 2.
  • the penalty coefficient he is between 0.5 and 7.
  • the penalty exponent ke is between 0.5 and 3.
  • Each cooling capacity PC i is defined by the product of a maximum cooling capacity (PCmaxi) PCmax i that can be delivered by the respective refrigerating machine 2 multiplied by the part load PLR i associated with the refrigerating machine 2 and each electric power PE i is defined by the product of a maximum nominal electric power PEmax i of the respective refrigerating machine 2 multiplied by a fraction of electric power Z i corresponding to the part load PLR 1 associated with the refrigerating machine 2.
  • the fraction of electric power Z i is extracted from a curve expressing a ratio between the electric power PE i consumed and the maximum electric power PEmax i of the refrigerating machine 2 when the set part load ratio PLR i changes.
  • Figure 2 illustrates an example of a curve expressing the electric power ratio as a function of the part load ratio PLR and of two temperature values Tair of the air outside the refrigerating machine (20°C and 35°C). Said curve, indicated in the following description as Z(Tair,PLR) has been constructed on the basis of the manufacturer's data for the refrigerating machine 2.
  • OBJ is a function of the part load ratios PLR i .
  • the operating state ST i can be derived from the value of the corresponding part load ratio PLR i as follows:
  • the minimization of the function OBJ(PLRi) thus returns the best solution of the set of unknown values made up of the plurality of part load ratios PLR i and operating states ST i .
  • the optimization module 17 implements a multi-phase optimization algorithm consisting of a multi-phase genetic algorithm acting on individuals defined by potential solutions for operating states ST i and part load ratios PLR i and having an fitness index of the individuals defined on the basis of the objective function OBJ(PLR 1 ).
  • the multi-phase genetic algorithms are of a known type. For this reason only the aspects of the genetic algorithm that affect the invention are described here.
  • the set of unknown values for which the best solution is to be found i.e. all the part load ratios PLR i and operating states ST i , is encoded in binary format.
  • Each phase of the genetic algorithm acts on an initial population of solutions (individuals) split into random solutions and the best solutions generated by the previous phase. The first phase is clearly only initialized with random solutions.
  • the population of each phase contains the same number of individuals N s .
  • the individuals are recombined to provide a new generation using several operators (reproduction, crossover, mutation, etc.).
  • the total number of generations N G is preset.
  • the number of individuals N S is between 50 and 300.
  • the number of generations N G is between 400 and 700.
  • the last phase of the genetic algorithm acts on an initial population of solutions, which comprise solutions implementing a method for controlling the refrigerating machines known as a machine saturation strategy (MS) and solutions implementing a method for controlling the refrigerating machines known as a step saturation strategy (SS).
  • MS machine saturation strategy
  • SS step saturation strategy
  • the initial population of the last phase is divided into randomly-generated solutions, the best solutions generated by the previous phase, solutions implementing the machine control strategy, and solutions implementing the step control strategy.
  • the solutions derived from the known control strategies are inoculated so that the known values can be incorporated into the genetic algorithm which can thus rapidly converge towards a sub-optimal, coherent solution.
  • the initial population is divided into the various solutions described above by means of mixing coefficients, for example according to the following logic.
  • the initial population of each phase that differs from the last phase consists of:
  • the initial population of the last phase consists of:
  • the mixing coefficients L, L 1 and L 2 have respective values of between 0 and 1.
  • each of the mixing coefficients L, L 1 and L 2 is between 0.4 and 0.6.
  • the sub-optimal solution in terms of part load ratios PLR i and operating states ST i is re-calculated at periodic intervals according to a supervision period lasting from between 10 and 60 minutes.
  • the optimization module 17 implements a multi-phase particle swarm optimization algorithm, which acts on particles whose positions in a real multi-dimensional space are defined by potential operating state ST i and part load ratio PLR i solutions and has a particle fitness index defined on the basis of the objective function OBJ(PLR i ).
  • PSO particle swarm optimization
  • the set of unknown values for which the best solution is to be found i.e. all the part load ratios PLR i and operating states ST i , are encoded in a known manner.
  • Each phase of the particle swarm algorithm acts on an initial swarm of solutions (particles) divided into random solutions and the best solutions obtained from the previous phase.
  • the first phase is only initialized with random solutions, i.e. particles with random positions in the multi-dimensional space being considered.
  • the swarm of each phase contains the same number of particles N S .
  • the swarm of particles moves and evaluates the objective function OBJ(PLR i ), i.e. the function to be minimized.
  • the number of phases N PH is preset.
  • the number of particles in the swarm N S is also preset.
  • the number of repetitions for each phase, generally indicated by N G is given by the sum of a positive constant and the number of repetitions related to the previous phase, indicated by N G-1 .
  • the number of phases N PH is between 1 and 10.
  • the number of particles N S is between 10 and 100.
  • the number of repetitions N G is between 10 and 250.
  • the last phase of the particle swarm algorithm acts on an initial swarm of solutions, which comprise solutions implementing a machine saturation strategy (MS) to control the refrigerating machines and solutions implementing a step saturation strategy (SS) to control the refrigerating machines.
  • MS machine saturation strategy
  • SS step saturation strategy
  • the initial swarm of the last phase is divided into randomly-generated solutions, the best solutions generated by the previous phase, solutions implementing the machine control strategy, and solutions implementing the step control strategy.
  • the solutions derived from the known control strategies are inoculated so that the known values can be incorporated into the particle swarm algorithm which can thus rapidly converge towards a sub-optimal, coherent solution.
  • the initial swarm is divided into the various types of solutions mentioned above by means of mixing coefficients, for example in the manner described previously for the genetic algorithm.
  • the sub-optimal solution in terms of part load ratios PLR i and operating states ST i is re-calculated at periodic intervals according to a supervision period lasting from between 10 and 60 minutes.
  • the calculation module 18 comprises a temperature setpoint estimation module 21 to calculate, for each refrigerating machine 2, an intermediate setpoint TSETM i as a function of the estimated thermal load PLE, the temperature TDLV and the part load ratio PLR i set for the refrigerating machine 2.
  • the calculation module 18 also comprises: a subtraction module 22 to calculate an error ERR as the difference between a preset plant delivery temperature setpoint TSETP and the measured temperature TLIN; a PID (proportional-integral-derivative) controller 23, which is known in the prior art and is not described in detail here, to determine a correction factor ⁇ TSET as a function of the error ERR; and an addition module 24 to calculate the setpoint TSET i of each refrigerating machine 2 as the sum of the respective intermediate setpoint TSETM i and the correction factor ⁇ TSET.
  • a subtraction module 22 to calculate an error ERR as the difference between a preset plant delivery temperature setpoint TSETP and the measured temperature TLIN
  • PID proportional-integral-derivative
  • the purpose of the PID controller 23 is to reduce inaccuracies in the calculation of the temperature TLIN after long periods of operation of the plant 1 and due to the approximations introduced by the curves Z(Tair,PLR).
  • the calculation module 18 comprises an error estimation module, instead of the subtraction module 22, to calculate the error ERR using a non-linear function of the setpoint TSETP and of the temperature TLIN.
  • the calculation module 18 comprises a combination module, instead of the addition module 24, to calculate the setpoint TSET i using a non-linear function of the intermediate setpoint TSETM i and of the correction factor ⁇ TSET.
  • the main advantage of the method and of the system for controlling a plurality of refrigerating machines of an air-conditioning plant described above is that it allows the refrigerating machines to be used as close as possible to their peak efficiency level while reducing electric power consumption to a minimum, the delivery of overall cooling capacity being equal.
  • the implementation of a multi-phase genetic algorithm at supervision level makes it possible to rapidly converge towards a sub-optimal, coherent solution to the problem of minimization.

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Physics & Mathematics (AREA)
  • Thermal Sciences (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Signal Processing (AREA)
  • Human Computer Interaction (AREA)
  • Feedback Control In General (AREA)
  • Air Conditioning Control Device (AREA)
EP10162899.8A 2009-05-15 2010-05-15 Method and system for controlling a plurality of refrigerating machines Not-in-force EP2253897B1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PL10162899T PL2253897T3 (pl) 2009-05-15 2010-05-15 Sposób i system do sterowania wieloma urządzeniami chłodzącymi

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
ITBO2009A000313A IT1394189B1 (it) 2009-05-15 2009-05-15 Metodo e sistema per controllare una pluralita' di macchine frigorifere di un impianto di climatizzazione
ITBO2010A000225A IT1399586B1 (it) 2009-05-15 2010-04-13 Metodo e sistema per controllare una pluralita' di macchine frigorifere di un impianto di climatizzazione

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Publication Number Publication Date
EP2253897A1 EP2253897A1 (en) 2010-11-24
EP2253897B1 true EP2253897B1 (en) 2014-12-03

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ES (1) ES2531263T3 (it)
IT (1) IT1399586B1 (it)
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JP5447627B1 (ja) * 2012-09-26 2014-03-19 ダイキン工業株式会社 熱源システム制御装置
WO2015171196A1 (en) * 2014-05-05 2015-11-12 Carrier Corporation Virtual flow measurement system
US10317120B2 (en) 2014-05-13 2019-06-11 Mitsubishi Electric Corporation Air conditioning system with indoor and ventilation circuits
GB2553681B (en) 2015-01-07 2019-06-26 Homeserve Plc Flow detection device
JP2016142491A (ja) * 2015-02-04 2016-08-08 株式会社日立製作所 熱源システムの運転管理装置、熱源システムの運転管理方法およびコンピュータプログラム
GB201501935D0 (en) 2015-02-05 2015-03-25 Tooms Moore Consulting Ltd And Trow Consulting Ltd Water flow analysis
JP6566916B2 (ja) * 2016-07-27 2019-08-28 三菱電機ビルテクノサービス株式会社 空調システム及び運転制御方法
US10895412B2 (en) 2018-07-11 2021-01-19 Mitsubishi Electric Research Laboratories, Inc. System and method for power optimizing control of multi-zone heat pumps
CN111415036B (zh) * 2020-03-17 2022-12-06 西安建筑科技大学 一种中央空调系统并联冷机负荷优化分配方法
CN114440419B (zh) * 2021-12-31 2023-10-27 博锐尚格科技股份有限公司 一种冷站二次泵系统的控制方法、装置、设备和存储介质
CN116880163B (zh) * 2023-09-07 2023-12-05 北京英沣特能源技术有限公司 一种智慧数据中心冷源调控方法及系统

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US4210957A (en) * 1978-05-08 1980-07-01 Honeywell Inc. Operating optimization for plural parallel connected chillers
US4463574A (en) * 1982-03-15 1984-08-07 Honeywell Inc. Optimized selection of dissimilar chillers
US4483152A (en) * 1983-07-18 1984-11-20 Butler Manufacturing Company Multiple chiller control method
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US7028768B2 (en) * 2003-08-20 2006-04-18 Itt Manufacturing Enterprises, Inc. Fluid heat exchange control system

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IT1399586B1 (it) 2013-04-26
ES2531263T3 (es) 2015-03-12
ITBO20100225A1 (it) 2010-11-16
PL2253897T3 (pl) 2015-06-30
EP2253897A1 (en) 2010-11-24

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