CN101251291A - Central air conditioning system global optimization energy-saving control method and device based on model - Google Patents

Abstract

The invention provides a method for controlling a global optimization of energy saving for a central air conditioning system based on a model, comprising a control module, a multichannel data acquisition card and control card, a refrigerating unit data collector and a controller, a humidity sensor, a temperature sensor, a flow sensor, a watt transducer, a water pump frequency converter, a fan frequency converter, an RS232/RS485 conversion module and a RS485/RS232 conversion module. The whole system can be operated under the condition of most energy saving according to an operating condition of optimized energy saving of each energy consumption device acquired by calculation of a calculation module by taking energy consumption modules of a refrigerating unit, a water pump and a fan and an ARMA air conditioning load forecasting model. The invention thoroughly concerns an influence of the operating condition of each power device in the system to the energy consumption of the whole system, takes a control strategy based on a forecasted air conditioning load, and performs an online update for the energy consumption modules of key energy consumption devices in the system, thereby greatly improving an effect of optimization of energy saving for the central air conditioning system.

Description

Central air-conditioning system global optimization energy-saving control method and device based on model

Technical field

What the present invention relates to is that a kind of central air-conditioning system is optimized energy-saving control method and device, particularly a kind of central air-conditioning system global optimization energy-saving control method and device based on model.

Background technology

Along with the urban modernization The development of architecture, central air-conditioning system has become an important component part in the Architectural Equipment, therefore building energy consumption also increases considerably simultaneously, this shows that the optimization Energy Saving Control of central air-conditioning system is an important channel of building energy conservation.Owing to be subjected to the restriction of current central air-conditioning system design concept, the design capacity of the most of air conditioning system of China all obviously surpasses the actual load demand of building, in order to reduce the energy dissipation that causes thus, the optimization energy-saving run control of central air-conditioning system is absolutely necessary.

Find through literature search prior art, Chinese patent (application) number is 03249625, name is called central air conditioner system energy-saving control device patent of invention, a kind of central air conditioner system energy-saving control device is disclosed, this invention is mainly regulated the operating condition of cooling water pump, chilled water pump and blower fan of cooling tower by frequency converter according to the user side demand in real time, to realize the Energy Saving Control of central air-conditioning system.But this optimization energy-saving control device is a target with the local energy-conservation of system only, can't realize central air-conditioning system energy conservation object of overall importance, in addition, this device has been ignored the energy consumption influence of refrigeration unit operating condition to whole system, certainly will reduce its Energy Saving Control effect.

Summary of the invention

The objective of the invention is to overcome the deficiency and the defective of prior art, a kind of central air-conditioning system global optimization energy-saving control method and device based on model is provided, can improve device greatly central air-conditioning system is optimized energy-conservation control effect, the realization system is farthest energy-conservation.

The present invention is achieved by the following technical solutions.

Central air-conditioning system global optimization energy-saving control method based on model provided by the invention may further comprise the steps:

The first step is tentatively determined refrigeration unit in the central air-conditioning system according to tune-up data, cooling water pump, and the energy consumption model of chilled water pump and blower fan of cooling tower, and each model parameter is solidificated in the control module;

In second step, control module is obtained refrigeration unit, water pump operation data and outdoor environment temperature and humidity by data acquisition module, and these parameters are preserved successively by the time sequence;

Control module obtains condensation temperature, evaporating temperature, chilled water supply water temperature, chilled water return water temperature and the unit energy consumption of refrigeration unit by the refrigeration unit data acquisition unit, obtain the energy consumption of the total cooling water flow of data acquistion system, total chilled-water flow, each chilled water pump and cooling water pump by multi-channel data acquisition integrated circuit board and sensor, and the temperature and humidity of cooling tower air inlet air, these parameters are preserved one by one by the time sequence, and the minimal data sampling interval can be set at 30 seconds.

In the 3rd step, control module utilizes autoregressive moving average (ARMA) air conditioner load forecast model to obtain the following demand of user side air-conditioning cold flow constantly according to the current of chilled water water-in and water-out temperature and chilled-water flow and historical data sequence; The condensation temperature that control module is current according to refrigeration unit, evaporating temperature, the refrigeration duty rate, cold in-water temperature, condenser cooling water flow and refrigeration unit energy consumption data utilize multiple nonlinear regression method to upgrade the efficiency model of refrigeration unit; Control module is upgraded the water pump energy model according to the current operation flow of water pump with corresponding energy consumption data; The blower fan of cooling tower energy model remains unchanged;

In the 4th step, based on air conditioner load predicted value and the up-to-date energy consumption model of each equipment, control module is determined next optimization energy-saving run operating mode of each equipment constantly according to optimizing energy-conservation condition calculating model;

In the 5th step, the optimization operating condition that control module will obtain by the RS-485 connection is transferred to the adjuster of each equipment, makes each equipment energy-saving run under the situation of system's total energy consumption minimum;

In the 6th step, setting-up time at interval repeated for the 3rd step, the 4th step and the 5th step.

Set interval greater than equaling 5 minutes.

For realizing control method of the present invention, the invention provides a kind of central air-conditioning system global optimization energy-saving control device based on model, comprising: control module, the multi-channel data acquisition integrated circuit board, multichannel control integrated circuit board, refrigeration unit data acquisition unit, the refrigeration unit controller, humidity sensor, temperature sensor, flow sensor, watt transducer, pump variable frequency device, fan frequency converter, the RS232/RS485 modular converter, the RS485/RS232 modular converter.

Humidity sensor and plurality of temperature sensor, the data signal line of several flow sensors and several watt transducers links to each other with the signal input port of multi-channel data acquisition integrated circuit board respectively, several pump variable frequency devices link to each other with the signal output port of multichannel control integrated circuit board respectively with several fan frequency converters, the multi-channel data acquisition integrated circuit board links to each other with the RS232 serial ports of control module with the RS232/RS485 modular converter by the RS485/RS232 modular converter respectively with multichannel control integrated circuit board, humidity sensor and a temperature sensor place near the outdoor cooling tower air inlet, a temperature sensor is installed in import of central air-conditioning system chilled water water collector and outlet respectively, in the water collector import flow sensor is installed simultaneously, a flow sensor is installed in every chilled water pump and cooling water pump import respectively, the electrical input of while every chilled water pump and cooling water pump is installed a watt transducer respectively, the refrigeration unit condenser cooling water advances, a temperature sensor is installed in outlet respectively, the signal input part of refrigeration unit data acquisition unit links to each other with each detecting sensor of refrigeration unit, its communication interface is connected with the RS485/RS232 modular converter, the signal output part of refrigeration unit controller links to each other with each controller of refrigeration unit, and its communication interface links to each other with the RS232/RS485 modular converter.

Beneficial effect of the present invention: the present invention at first supplies according to the handpiece Water Chilling Units chilled water, the historical cold of the using when historical data of backwater temperature difference and chilled-water flow obtains pursuing of air-conditioning and construction user, utilize the following air conditioner load constantly of autoregressive moving-average model (ARMA) prediction, (mainly wrap up refrigeration unit according to each power-equipment of central air-conditioning system then, cooling water pump, chilled water pump and blower fan of cooling tower) energy model, with system's total energy consumption minimum is that optimization aim obtains the following energy-saving run operating mode of each equipment constantly, comprise: refrigeration unit operation evaporating temperature, the chilled water pump flow, cooling water pump flow and blower fan of cooling tower air quantity, these are optimized energy-saving run operating modes and are transferred to each equipment adjuster by the RS-485 connection, make energy-saving run under each equipment and safe operating mode of meeting consumers' demand minimum at system's total energy consumption.The present invention has improved device greatly central air-conditioning system has been optimized energy-conservation control effect owing to adopt global optimization Energy Saving Control model, and the realization system is farthest energy-conservation.

Description of drawings

Fig. 1 is the structural representation of apparatus of the present invention embodiment.

Fig. 2 is the inventive method embodiment flow chart.

The specific embodiment

Below in conjunction with accompanying drawing technical scheme of the present invention is described in further detail.

As shown in Figure 1 and Figure 2, apparatus of the present invention embodiment comprises control module 1, multi-channel data acquisition integrated circuit board 2, multichannel control integrated circuit board 3, refrigeration unit data acquisition unit 4, refrigeration unit controller 5, humidity sensor 6, temperature sensor 7, flow sensor 8, watt transducer 9, pump variable frequency device 10, fan frequency converter 11, RS232/RS485 modular converter 12, RS485/RS232 modular converter 13.

Humidity sensor 6 and plurality of temperature sensor 7, the data signal line of several flow sensors 8 and several watt transducers 9 links to each other with the signal input port of multi-channel data acquisition integrated circuit board 2 respectively, several pump variable frequency devices 10 link to each other with the signal output port of multichannel control integrated circuit board 3 respectively with several fan frequency converters 11, multi-channel data acquisition integrated circuit board 2 links to each other with the RS232 serial ports of control module 1 with RS232/RS485 modular converter 12 by RS485/RS232 modular converter 13 respectively with multichannel control integrated circuit board 3, humidity sensor 6 and a temperature sensor 7 place near the outdoor cooling tower air inlet, a temperature sensor 7 is installed in import of central air-conditioning system chilled water water collector and outlet respectively, in the water collector import flow sensor 8 is installed simultaneously, a flow sensor 8 is installed in every chilled water pump and cooling water pump import respectively, the electrical input of while every chilled water pump and cooling water pump is installed a watt transducer 9 respectively, the refrigeration unit condenser cooling water advances, a temperature sensor 7 is installed in outlet respectively, the signal input part of refrigeration unit data acquisition unit 4 links to each other with each detecting sensor of refrigeration unit, its communication interface is connected with RS485/RS232 modular converter 13, the signal output part of refrigeration unit controller 5 links to each other with each controller of refrigeration unit, and its communication interface links to each other with RS232/RS485 modular converter 12.

Described refrigeration unit data acquisition unit 4 is mainly used in condensation temperature, evaporating temperature and the unit energy consumption of gathering refrigeration unit, and by RS485/RS232 modular converter 13 these data is sent in the control module 1, and preserves by the time sequence; Plurality of temperature sensor 7 is respectively applied for the confession of monitoring chilled water, return water temperature, cooling water confession, return water temperature and cooling tower air intake air themperature, humidity sensor 6 is used to monitor cooling tower air intake air humidity, discharge when flow sensor 8 is used to monitor water pump operation, watt transducer 9 is used to monitor the operation energy consumption of water pump; The signal of temperature sensor 7, flow sensor 8 and watt transducer 9 is gathered by multi-channel data acquisition integrated circuit board 2, is admitted in the control module 1 by RS485/RS232 modular converter 13 then, and preserves by the time sequence.The data message that 1 pair of refrigeration unit data acquisition unit 4 of control module and multi-channel data acquisition integrated circuit board 2 collect is handled, according to the following air-conditioning cold flow demand constantly of these data prediction user sides, energy consumption model to refrigeration unit and water pump upgrades simultaneously, then based on the predicted value of air-conditioning cold flow demand and each plant capacity model, determine next optimization energy-saving run operating mode of each equipment constantly by the energy-conservation condition calculating model of the optimization in the control module 1, be transferred to multichannel control integrated circuit board 3 by RS232/RS485 modular converter 12 again.Multichannel control integrated circuit board 3 will be optimized the adjuster that operating condition is transferred to corresponding device again, make each equipment energy-saving safety operating under the situation of system's total energy consumption minimum.

As shown in Figure 2, the inventive method implementing procedure specifies as follows:

At first, tentatively determine refrigeration unit in the central air-conditioning system according to tune-up data, cooling water pump, the energy consumption model of chilled water pump and blower fan of cooling tower, and each model parameter is solidificated in the control module;

Secondly, obtain condensation temperature, evaporating temperature, chilled water supply water temperature, chilled water return water temperature and the unit energy consumption of refrigeration unit by refrigeration unit data acquisition unit 4; According to the cooling water flow that is installed in the flow sensor 8 acquisition condensers on each cooling water water pump, obtain the condenser cooling water inflow temperature by the temperature sensor 7 that is installed in the condenser cooling water import, obtain the operation flow of water pump and the energy consumption of correspondence by the water flow sensor 8 and the watt transducer 9 that are installed on the water pump.These data are all sent into control module 1.

Obtain data by control module 1 by multi-channel data acquisition integrated circuit board 2 and related sensor again, advance according to the chilled water water collector, the historical data of leaving water temperature and chilled-water flow utilizes autoregressive moving average (ARMA) air conditioner load forecast model to obtain the following demand of user side air-conditioning cold flow constantly, the condensation temperature current according to refrigeration unit, evaporating temperature, the refrigeration duty rate, cold in-water temperature, condenser cooling water flow and refrigeration unit energy consumption data utilize multiple nonlinear regression method to upgrade the efficiency model of refrigeration unit, the operation flow current according to water pump upgrades the water pump energy model with corresponding energy consumption data, the data that the blower fan of cooling tower energy model provides according to product sample and the data of field test are determined, do not carry out online updating.

Then,, determine next optimization energy-saving run operating mode of each equipment constantly by the energy-conservation condition calculating model of the optimization in the control module 1 based on predicted value and each plant capacity model of air-conditioning cold flow demand,

At last, will optimize the adjuster that operating condition is transferred to each equipment, make each equipment energy-saving run under the situation of system's total energy consumption minimum by the RS-485 connection.

In the present embodiment, the energy-conservation condition calculating model description of the optimization in the control module 1 is as follows:

$F\begin{array}{c}\left(\begin{array}{c}{t}_{e,1},t_{e,2},\·\·\·t_{e,M};G_{\mathrm{cw},1},G_{\mathrm{cw},2},\·\·\·,G_{\mathrm{cw},N};\\ G_{\mathrm{ew},1},G_{\mathrm{ew},2},\·\·\·,{G_{\mathrm{ew},K};G}_{\mathrm{ta},1},G_{\mathrm{ta},2},\·\·\·,G_{\mathrm{ta},L};\end{array}\right)=\end{array}\min \left(\begin{array}{c}\underset{i=1}{\overset{M}{\mathrm{\Σ}}}{E}_{\mathrm{chiller},i}+\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{E}_{\mathrm{cp},j}+\\ \underset{k=1}{\overset{K}{\mathrm{\Σ}}}{E}_{\mathrm{ep},k}+\underset{n=1}{\overset{J}{\mathrm{\Σ}}}{E}_{\mathrm{tf},n}\end{array}\right)---\left(1\right)$

The constraint correlation:

C _te，min≤t _e，i≤C _te，max i∈[1，M] (1a)

C _Gcw，min≤G _cw，j≤C _Gcw，max j∈[1，N] (1b)

C _Gew，min≤G _ew，k≤C _Gew，max k∈[1，K] (1c)

C _Gta，min≤G _ta，n≤C _Gta，max n∈[1，J] (1d)

$\underset{i=1}{\overset{K}{\mathrm{\Σ}}}[E_{\mathrm{chiller},i}\·{\mathrm{COP}}_t(t_{e,i},t_{c,i},r_i)]\≥Q_{\mathrm{demand}}---\left(1e\right)$

${\mathrm{COP}}_i(t_{e,i},t_{c,i},r_i)=\frac{r_t}{(\frac{t_{c,i}+273.2}{t_{e,i}+273.2}-1)\·r_t+a_{1,i}\frac{t_{c,i}+273.2}{t_{e,i}+273.2}-a_{2,i}},i\∈[1,M]---\left(1f\right)$

$\begin{array}{cc}{t}_{c,i}=t_{c,\mathrm{wE},i}+\frac{E_{\mathrm{chiller},i}[1+{\mathrm{COP}}_i(t_{e,i},t_{c,i},r_i)]}{a_{3,i}+a_{4,i}\·G_{c,w,i}+a_{5,i}\·{G_{c,w,i}}^2}& i\∈[i,M]\end{array}---\left(1g\right)$

$\begin{array}{cc}{t}_{c,\mathrm{wL},i}=t_{c,\mathrm{wE},i}+\frac{E_{\mathrm{chiller},i}[1+{\mathrm{COP}}_i(t_{e,i},t_{c,i},r_i)]}{c_w{G}_{c,w,i}}& i\∈[i,M]\end{array}---\left(1h\right)$

$\begin{array}{cc}{G}_{c,w,i}=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{G}_{\mathrm{cp},j}/M& i\∈[1,M]\end{array}---\left(1i\right)$

$\begin{array}{cc}{t}_{c,\mathrm{wE},i}=\underset{n=1}{\overset{J}{\mathrm{\Σ}}}(G_{\mathrm{tw},n}\·t_{\mathrm{twL},n})/\underset{n=1}{\overset{J}{\mathrm{\Σ}}}{G}_{\mathrm{tw},n}& i\∈[1,M]\end{array}---\left(1j\right)$

$\begin{array}{cc}{t}_{\mathrm{twL},n}=t_{\mathrm{twE},n}-\frac{(b_{0,n}+b_{1,n}\left(\frac{G_{\mathrm{ta},n}}{G_{\mathrm{tw},n}}\right)+b_{2,n}{\left(\frac{G_{\mathrm{ta},n}}{G_{\mathrm{tw},n}}\right)}^2)\·(h_{\mathrm{as}}-h_a)}{c_w{G}_{\mathrm{tw},n}}& n\∈[1,J]\end{array}---\left(1k\right)$

$\begin{array}{cc}{G}_{\mathrm{tw},n}=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{G}_{\mathrm{cp},j}/J& n\∈[1,M]\end{array}---\left(1l\right)$

$\begin{array}{cc}{t}_{\mathrm{twE},n}=\underset{i=1}{\overset{M}{\mathrm{\Σ}}}(G_{c,w,i}\·t_{c,\mathrm{wL},i})/\underset{i=1}{\overset{M}{\mathrm{\Σ}}}\left(G_{c,w,i}\right)& i\∈[1,M]\end{array}---\left(1m\right)$

E _cp，j＝c _0，j+c _1，j·G _cp，j+c _2，j·G _cp，j ² j∈[1，N] (1n)

E _ep，k＝d _0，k+d _1，k·G _ep，k+d _2，k·G _ep，k ² k∈[1，K] (1o)

E _tf，n＝b _3，n+b _4，n·G _ta，n+b _5，n·G _ta，n ² n∈[1，J] (1p)

In the formula (1)～(1p),

a _{1, i}, a _{2, i}, a _{3, i}, a _{4, i}, a _{5, i}---i platform refrigeration unit energy model coefficient, i ∈ [1, M];

b _{0, n}, b _{1, n}, b _{2, n}, b _{3, n}, b _{4, n}, b _{5, n}---n platform cooling tower energy model coefficient, n ∈ [1, J];

c _{0, j}, c _{1, j}, c _{2, j}---j platform cooling water pump energy model coefficient, j ∈ [1, N];

d _{0, k}, d _{1, d}, d _{2, k}---k platform chilled water pump energy model coefficient, k ∈ [1, K];

c _w---water specific heat, J/kg ℃;

C _{Te, min}---refrigeration unit evaporating temperature lower limit, ℃;

C _{Te, max}---refrigeration unit evaporating temperature higher limit, ℃;

C _{Gew, min}---chilled water pump flux lower limit value, kg/s;

C _{Gew, max}---chilled water pump flow higher limit, kg/s;

C _{Gcw, min}---cooling water pump flux lower limit value, kg/s;

C _{Gcw, max}---cooling water pump flow higher limit, kg/s;

C _{Gta, min}---blower fan of cooling tower air quantity lower limit, kg/s;

C _{Gta, max}---blower fan of cooling tower air quantity higher limit, kg/s;

COP _i---the i platform refrigeration unit coefficient of performance, i ∈ [1, M];

E _{Chiller, i}---i platform refrigeration unit energy consumption, i ∈ [1, M], kW;

E _{Ep, k}---k platform chilled water pump energy consumption, k ∈ [1, K], kW;

E _{Cp, j}---j platform cooling water pump energy consumption, j ∈ [1, N], kW;

E _{Tf, n}---n platform blower fan of cooling tower energy consumption, j ∈ [1, N], kW;

G _{Ew, k}---k platform chilled water pump flow, k ∈ [1, K], kg/s;

G _{Cw, j}---j platform cooling water pump flow, j ∈ [1, N], kg/s;

G _{Ta, n}---n platform blower fan of cooling tower air quantity, n ∈ [1, J], kg/s;

G _{Tw, n}---n platform cooling tower water flow, n ∈ [1, J], kg/s;

G _{C, w, i}---i platform refrigeration unit cooling water flow, i ∈ [1, M], kg/s;

h _a---outdoor air enthalpy, kJ/kg;

h _{A, s}---outdoor saturated air enthalpy, kJ/kg;

J---cooling tower operation platform number;

K---chilled water pump operation platform number;

M---refrigeration unit operation platform number;

N---cooling water pump operation platform number;

Q _Demand---air-conditioning cold flow demand, kW;

r _i---i platform refrigeration unit refrigeration duty rate, the i.e. actual refrigerating capacity of refrigeration machine refrigerating capacity ratio nominal with it;

t _{E, i}---i platform refrigeration unit operation evaporating temperature, i ∈ [1, M], ℃;

t _{C, i}---i platform refrigeration unit operation condensation temperature, i ∈ [1, M], ℃;

t _{C, wE, i}---i platform refrigeration unit cooling water inlet temperature, i ∈ [1, M], ℃;

t _{C, wL, i}---i platform refrigeration unit cooling water outlet temperature, i ∈ [1, M], ℃;

t _{TwL, n}---n platform cooling tower leaving water temperature, n ∈ [1, J], ℃;

t _{TwE, n}---n platform cooling tower inflow temperature, n ∈ [1, J], ℃;

Formula (1) is for optimizing the overall goal function of energy-conservation model, formula (1a), (1b), (1c) and (1d) in the parameter upper limit value and lower limit value of respectively optimizing determine according to actual conditions, total cooling amount of the refrigeration unit of formula (1e) expression central air-conditioning system must be greater than the cold demand of using of user side, formula (1f) is the efficiency model of refrigeration unit, the coefficient a in the formula _{1, i}And a _{2, i}According to the condensation temperature of refrigeration unit under a series of operating conditions, evaporating temperature, refrigeration duty rate and unit energy consumption data are returned by nonlinear multivariable and obtain, formula (1g) and (1h) be refrigeration unit condensation temperature computation model, the coefficient a in the formula _{3, i}, a _{4, i}And a _{5, i}Obtain by the nonlinear multivariable recurrence according to the condensation temperature of refrigeration unit under a series of operating conditions and corresponding cold in-water temperature, condenser cooling water flow, evaporating temperature, refrigeration duty rate and unit energy consumption data, formula (1i) expression is by the cooling water flow equalization of the refrigeration unit condenser of every unlatching, formula (1k) is a cooling tower leaving water temperature model, the coefficient b in the formula _{0, n}, b _{1, n}, b _{2, n}Under a series of ambient air temperature and damp condition, cooling tower water-in and water-out temperature, cooling water flow and blower fan of cooling tower air quantity data are returned by nonlinear multivariable and obtain, formula (1n), (1o) and (1p) be respectively the energy model of cooling water pump, chilled water pump and blower fan of cooling tower, all coefficient c in the water pump energy model _{0, j}, c _{1, j}, c _{2, j}, d _{0, k}, d _{1, k}And d _{2, k}Obtain all coefficient b in the blower fan of cooling tower energy model according to a series of operation flow of water pump and corresponding energy consumption by the nonlinear multivariable recurrence _{3, n}, b _{4, n}, b _{5, n}Obtain by the nonlinear multivariable recurrence according to a series of operation air quantity of blower fan of cooling tower and corresponding energy consumption, according to actual project situation, reduce the cost of investment of control system, the data that all coefficients are provided by the corresponding product sample in the blower fan of cooling tower energy model and the data fitting of field test are determined.Air-conditioning cold flow demand Q _DemandCalculated by the air conditioner load forecast model, the air conditioner load model adopts autoregressive moving-average model (ARMA) method.

The present invention is with refrigeration unit, the energy consumption model of water pump and blower fan and ARMA air conditioner load forecast model are the basis, calculate the optimization energy-saving run operating mode of each energy consumption equipment according to computation model, whole system is moved under the most energy-conservation situation, the present invention has considered the influence of the operating condition of each power-equipment of system to the whole system energy consumption comprehensively, substitute current " local optimum is energy-conservation " with " global optimization is energy-conservation ", control strategy is a foundation with the air conditioner load of prediction, energy consumption model to system core energy consumption equipment refrigeration unit and water pump carries out online updating simultaneously, has improved central air-conditioning system greatly and has optimized energy-saving effect.

Claims (10)

1, a kind of central air-conditioning system global optimization energy-saving control method based on model may further comprise the steps:

The first step is tentatively determined refrigeration unit in the central air-conditioning system according to tune-up data, cooling water pump, and the energy consumption model of chilled water pump and blower fan of cooling tower, and each model parameter is solidificated in the control module;

Second step, control module obtains condensation temperature, evaporating temperature, chilled water supply water temperature, chilled water return water temperature and the unit energy consumption of refrigeration unit by the refrigeration unit data acquisition unit, obtain the energy consumption of the total cooling water flow of data acquistion system, total chilled-water flow, each chilled water pump and cooling water pump by multi-channel data acquisition integrated circuit board and sensor, and the temperature and humidity of cooling tower air inlet air, these parameters are preserved one by one by the time sequence;

In the 3rd step, control module utilizes autoregressive moving average air conditioner load forecast model to obtain the following demand of user side air-conditioning cold flow constantly according to the current of chilled water water-in and water-out temperature and chilled-water flow and historical data sequence; The condensation temperature that control module is current according to refrigeration unit, evaporating temperature, the refrigeration duty rate, cold in-water temperature, condenser cooling water flow and refrigeration unit energy consumption data utilize multiple nonlinear regression method to upgrade the efficiency model of refrigeration unit; Control module is upgraded the water pump energy model according to the current operation flow of water pump with corresponding energy consumption data; The blower fan of cooling tower energy model remains unchanged;

In the 4th step, based on air conditioner load predicted value and the up-to-date energy consumption model of each equipment, control module is determined next optimization energy-saving run operating mode of each equipment constantly according to optimizing energy-conservation condition calculating model;

In the 5th step, the optimization operating condition that control module will obtain by the RS-485 connection is transferred to the adjuster of each equipment, makes each equipment energy-saving run under the situation of system's total energy consumption minimum;

In the 6th step, setting-up time at interval repeated for the 3rd step, the 4th step and the 5th step.

2, the central air-conditioning system global optimization energy-saving control method based on model according to claim 1 is characterized in that, in second step, the minimal data sampling interval is set at 30 seconds.

3, the central air-conditioning system global optimization energy-saving control method based on model according to claim 1 is characterized in that, in the 4th step, described optimization energy-saving run condition calculating model is as follows:

$F\begin{array}{c}\left(\begin{array}{c}{t}_{e,1},t_{e,2},\·\·\·t_{e,M};G_{\mathrm{cw},1},G_{\mathrm{cw},2},\·\·\·,G_{\mathrm{cw},N};\\ G_{\mathrm{ew},1},G_{\mathrm{ew},2},\·\·\·,{G_{\mathrm{ew},K};G}_{\mathrm{ta},1},G_{\mathrm{ta},2},\·\·\·,G_{\mathrm{ta},L};\end{array}\right)=\end{array}\min \left(\begin{array}{c}\underset{i=1}{\overset{M}{\mathrm{\Σ}}}{E}_{\mathrm{chiller},i}+\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{E}_{\mathrm{cp},j}+\\ \underset{k=1}{\overset{K}{\mathrm{\Σ}}}{E}_{\mathrm{ep},k}+\underset{n=1}{\overset{J}{\mathrm{\Σ}}}{E}_{\mathrm{tf},n}\end{array}\right)$

The constraint correlation:

C _te，min≤t _e，i≤C _te，max i∈[1，M]

C _Gcw，min≤G _cw，j≤C _Gcw，max j∈[1，N]

C _Gew，min≤G _ew，k≤C _Gew，max k∈[1，K]

C _Gta，min≤G _ta，n≤C _Gta，max n∈[1，J]

$\underset{i=1}{\overset{K}{\mathrm{\Σ}}}[E_{\mathrm{chiller},i}\·{\mathrm{COP}}_t(t_{e,i},t_{c,i},r_i)]\≥Q_{\mathrm{demand}}$

${\mathrm{COP}}_i(t_{e,i},t_{c,i},r_i)=\frac{r_t}{(\frac{t_{c,i}+273.2}{t_{e,i}+273.2}-1)\·r_t+a_{1,i}\frac{t_{c,i}+273.2}{t_{e,i}+273.2}-a_{2,i}},i\∈[1,M]$

$\begin{array}{cc}{t}_{c,i}=t_{c,\mathrm{wE},i}+\frac{E_{\mathrm{chiller},i}[1+{\mathrm{COP}}_i(t_{e,i},t_{c,i},r_i)]}{a_{3,i}+a_{4,i}\·G_{c,w,i}+a_{5,i}\·{G_{c,w,i}}^2}& i\∈[i,M]\end{array}$

$\begin{array}{cc}{t}_{c,\mathrm{wL},i}=t_{c,\mathrm{wE},i}+\frac{E_{\mathrm{chiller},i}[1+{\mathrm{COP}}_i(t_{e,i},t_{c,i},r_i)]}{c_w{G}_{c,w,i}}& i\∈[i,M]\end{array}$

$\begin{array}{cc}{G}_{c,w,i}=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{G}_{\mathrm{cp},j}/M& i\∈[1,M]\end{array}$

$\begin{array}{cc}{t}_{c,\mathrm{wE},i}=\underset{n=1}{\overset{J}{\mathrm{\Σ}}}(G_{\mathrm{tw},n}\·t_{\mathrm{twL},n})/\underset{n=1}{\overset{J}{\mathrm{\Σ}}}{G}_{\mathrm{tw},n}& i\∈[1,M]\end{array}$

$\begin{array}{cc}{t}_{\mathrm{twL},n}=t_{\mathrm{twE},n}-\frac{(b_{0,n}+b_{1,n}\left(\frac{G_{\mathrm{ta},n}}{G_{\mathrm{tw},n}}\right)+b_{2,n}{\left(\frac{G_{\mathrm{ta},n}}{G_{\mathrm{tw},n}}\right)}^2)\·(h_{\mathrm{as}}-h_a)}{c_w{G}_{\mathrm{tw},n}}& n\∈[1,J]\end{array}$

$\begin{array}{cc}{G}_{\mathrm{tw},n}=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{G}_{\mathrm{cp},j}/J& n\∈[1,M]\end{array}$

$\begin{array}{cc}{t}_{\mathrm{twE},n}=\underset{i=1}{\overset{M}{\mathrm{\Σ}}}(G_{c,w,i}\·t_{c,\mathrm{wL},i})/\underset{i=1}{\overset{M}{\mathrm{\Σ}}}\left(G_{c,w,i}\right)& i\∈[1,M]\end{array}$

E _cp，j＝c _0，j+c _1，j·G _cp，j+c _2，j·G _cp，j ² j∈[1，N]

E _ep，k＝d _0，k+d _1，k·G _ep，k+d _2，k·G _ep，k ² k∈[1，K]

E _tf，n＝b _3，n+b _4，n·G _ta，n+b _5，n·G _ta，n ² n∈[1，J]

In the formula,

a _{1, i}, a _{2, i}, a _{3, i}, a _{4, i}, a _{5, i}---i platform refrigeration unit energy model coefficient, i ∈ [1, M];

b _{0, n}, b _{1, n}, b _{2, n}, b _{3, n}, b _{4, n}, b _{5, n}---n platform cooling tower energy model coefficient, n ∈ [1, J];

c _{0, j}, c _{1, j}, c _{2, j}---j platform cooling water pump energy model coefficient, j ∈ [1, N];

d _{0, k}, d _{1, k}, d _{2, k}---k platform chilled water pump energy model coefficient, k ∈ [1, K];

c _w---water specific heat, J/kg ℃;

C _{Te, min}---refrigeration unit evaporating temperature lower limit, ℃;

C _{Te, max}---refrigeration unit evaporating temperature higher limit, ℃;

C _{Gew, min}---chilled water pump flux lower limit value, kg/s;

C _{Gew, max}---chilled water pump flow higher limit, kg/s;

C _{Gcw, min}---cooling water pump flux lower limit value, kg/s;

C _{Gcw, max}---cooling water pump flow higher limit, kg/s;

C _{Gta, min}---blower fan of cooling tower air quantity lower limit, kg/s;

C _{Gta, max}---blower fan of cooling tower air quantity higher limit, kg/s;

COP _i---the i platform refrigeration unit coefficient of performance, i ∈ [1, M];

E _{Chiller, i}---i platform refrigeration unit energy consumption, i ∈ [1, M], kW;

E _{Ep, k}---k platform chilled water pump energy consumption, k ∈ [1, K], kW;

E _{Cp, j}---j platform cooling water pump energy consumption, j ∈ [1, N], kW;

E _{Tf, n}---n platform blower fan of cooling tower energy consumption, j ∈ [1, N], kW;

G _{Ew, k}---k platform chilled water pump flow, k ∈ [1, K], kg/s;

G _{Cw, j}---j platform cooling water pump flow, j ∈ [1, N], kg/s;

G _{Ta, n}---n platform blower fan of cooling tower air quantity, n ∈ [1, J], kg/s;

C _{Tw, n}---n platform cooling tower water flow, n ∈ [1, J], kg/s;

G _{C, w, i}---i platform refrigeration unit cooling water flow, i ∈ [1, M], kg/s;

h _a---outdoor air enthalpy, kJ/kg;

h _{A, s}---outdoor saturated air enthalpy, kJ/kg;

J---cooling tower operation platform number;

K---chilled water pump operation platform number;

M---refrigeration unit operation platform number;

N---cooling water pump operation platform number;

Q _Demand---air-conditioning cold flow demand, kW;

r _i---i platform refrigeration unit refrigeration duty rate, the i.e. actual refrigerating capacity of refrigeration machine refrigerating capacity ratio nominal with it;

t _{E, i}---i platform refrigeration unit operation evaporating temperature, i ∈ [1, M], ℃;

t _{C, i}---i platform refrigeration unit operation condensation temperature, i ∈ [1, M], ℃;

t _{C, wE, i}---i platform refrigeration unit cooling water inlet temperature, i ∈ [1, M], ℃;

t _{C, wL, i}---i platform refrigeration unit cooling water outlet temperature, i ∈ [1, M], ℃;

t _{TwL, n}---n platform cooling tower leaving water temperature, n ∈ [1, J], ℃;

t _{TwE, n}---n platform cooling tower inflow temperature, n ∈ [1, J], ℃.

4, the central air-conditioning system global optimization energy-saving control method based on model according to claim 1 is characterized in that, the 6th the step in, setting interval greater than equaling 5 minutes.

5, a kind of central air-conditioning system global optimization energy-saving control device based on model, comprise: control module (1), multi-channel data acquisition integrated circuit board (2), multichannel control integrated circuit board (3), refrigeration unit data acquisition unit (4), refrigeration unit controller (5), humidity sensor (6), temperature sensor (7), flow sensor (8), watt transducer (9), pump variable frequency device (10), fan frequency converter (11), RS232/RS485 modular converter (12), RS485/RS232 modular converter (13), wherein: a humidity sensor (6) and plurality of temperature sensor (7), the data signal line of several flow sensors (8) and several watt transducers (9) links to each other with the signal input port of multi-channel data acquisition integrated circuit board (2) respectively, several pump variable frequency devices (10) link to each other with the signal output port of multichannel control integrated circuit board (3) respectively with several fan frequency converters (11), multi-channel data acquisition integrated circuit board (2) links to each other with the RS232 serial ports of RS232/RS485 modular converter (12) with control module (1) by RS485/RS232 modular converter (13) respectively with multichannel control integrated circuit board (3), a humidity sensor (6) and a temperature sensor (7) place near the outdoor cooling tower air inlet, a temperature sensor (7) is installed in import of central air-conditioning system chilled water water collector and outlet respectively, in the water collector import flow sensor (8) is installed simultaneously, a flow sensor (8) is installed in every chilled water pump and cooling water pump import respectively, the electrical input of while every chilled water pump and cooling water pump is installed a watt transducer (9) respectively, the refrigeration unit condenser cooling water advances, a temperature sensor (7) is installed in outlet respectively, the signal input part of refrigeration unit data acquisition unit (4) links to each other with each detecting sensor of refrigeration unit, its communication interface is connected with RS485/RS232 modular converter (13), the signal output part of refrigeration unit controller (5) links to each other with each controller of refrigeration unit, and its communication interface links to each other with RS232/RS485 modular converter (12).

6, the central air-conditioning system global optimization energy-saving control device based on model according to claim 5, it is characterized in that, described refrigeration unit data acquisition unit (4) is used to gather condensation temperature, evaporating temperature and the unit energy consumption of refrigeration unit, and these data are sent in the control module (1), and preserve by the time sequence by RS485/RS232 modular converter (13).

7, the central air-conditioning system global optimization energy-saving control device based on model according to claim 5, it is characterized in that, described temperature sensor (7) is respectively applied for the confession of monitoring chilled water, return water temperature, cooling water confession, return water temperature and cooling tower air intake air themperature, humidity sensor (6) is used to monitor cooling tower air intake air humidity, the discharge of flow sensor (8) when being used to monitor water pump operation, watt transducer (9) is used to monitor the operation energy consumption of water pump.

8, the central air-conditioning system global optimization energy-saving control device based on model according to claim 5, it is characterized in that, described multi-channel data acquisition integrated circuit board (2) is used for collecting temperature sensor (7), flow sensor (8) and watt transducer (9) data monitored, by S485/RS232 modular converter (13) data are sent in the control module (1) then, and preserved by the time sequence.

9, central air-conditioning system global optimization energy-saving control device based on model according to claim 5, it is characterized in that, described control module (1) is handled the data message that refrigeration unit data acquisition unit (4) and multi-channel data acquisition integrated circuit board (2) collect, according to the following air-conditioning cold flow demand constantly of these data prediction user sides, energy consumption model to refrigeration unit and water pump upgrades simultaneously, then based on the predicted value of air-conditioning cold flow demand and each plant capacity model, determine next optimization energy-saving run operating mode of each equipment constantly by the energy-conservation condition calculating model of optimization wherein, be transferred to multichannel control integrated circuit board (3) by RS232/RS485 modular converter (12) again.

10, according to claim 5 or 9 described central air-conditioning system global optimization energy-saving control devices based on model, it is characterized in that, described multichannel control integrated circuit board (3) will be optimized the adjuster that operating condition is transferred to corresponding device, make each equipment energy-saving safety operating under the situation of system's total energy consumption minimum.

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