CN106766450A - Refrigeration heat pump system least energy consumption optimal control device and control method - Google Patents

Abstract

The present invention provides a kind of refrigeration heat pump system least energy consumption optimal control device, and the control device receives the least energy consumption optimal control signal, and controls refrigeration heat pump system according to the least energy consumption optimal control.Also provide a kind of control method of utilization refrigeration heat pump system least energy consumption optimal control device simultaneously.Effect of the present invention can be achieved on the least energy consumption optimal control of refrigeration heat pump system, to reach the saving energy consumption under different cooling conditions.The method makes the change of the good following condition of refrigeration heat pump system and is adjusted, relatively conventional unity loop control, and energy-saving effect is up to more than 18% during 100% workload demand.

Description

Refrigeration heat pump system minimum energy consumption optimization control device and control method

technical field

The invention relates to a minimum energy consumption optimization control device and a control method of a refrigeration heat pump system, belonging to the field of optimization control of an air conditioning system.

Background technique

With the continuous improvement of living standards, people's requirements for indoor comfort continue to increase, resulting in a rapid increase in air-conditioning energy consumption. According to data, the proportion of building energy consumption to the total energy consumption of social commodities has risen from 10% in 1978 to about 25%, and will continue to increase, and finally reach about 35%. About 60%-70% of the electricity load in public buildings in summer is consumed by the central air-conditioning system, of which 50%-60% is used for cooling of refrigeration units, and 20%-30% is used for the transmission and distribution of chilled water pumps and cooling water pumps. It can be seen that Reducing the energy consumption of air-conditioning and refrigeration equipment is very necessary for building energy conservation.

At present, a large number of computer simulation techniques are used in the performance simulation of refrigeration systems, product optimization design and control strategy analysis, and the establishment of mathematical models is the core of simulation. The establishment of the traditional mathematical model of refrigeration system components is based on the steady state, without considering the influence of the system's working state on individual components, and without considering the energy dynamic balance of the system during the entire cycle.

At present, there are three commonly used refrigeration methods, namely, vapor compression type, steam injection type and absorption type refrigeration. The above-mentioned refrigeration methods all directly consume electric energy or heat energy. Through the analysis of theoretical refrigeration cycle and actual energy consumption, it is concluded that the compression refrigeration method has a high cooling capacity per unit energy consumption, but there is a problem of low partial load energy efficiency. The classic single-loop control structure of the refrigeration system is shown in Figure 1. In traditional refrigeration system control, the working point setting values of the four circuits of compressor, chilled water system, cooling water system and electronic expansion valve are given by the operator based on experience, and will no longer change with changes in working conditions. The method cannot automatically change the input of the system under changing working conditions, cannot dynamically match system changes and load demands, and cannot achieve effective energy saving. In Fig. 1, the refrigeration load controller 2 controls the compressor 3, the condensing pressure controller 4 controls the cooling water pump and the condenser 6, the superheat controller 12 controls the electronic expansion valve 7 and the evaporator 9, and the chilled water pump controller 13 controls the chilled water pump, The cooling load measurement 10 determines the load demand, the terminal load 11 determines the terminal load, and the cooling tower 5 adopts fixed frequency control. The control strategy of the traditional refrigeration heat pump monitoring system is single-loop control without optimization, and the setting value of the loop control operating point is determined by the operator. Based on empirical setting, without optimization, the set value of the working point cannot follow the change of the cooling condition, and the minimum energy consumption control cannot be achieved, and the energy saving effect is poor.

Contents of the invention

The object of the present invention is to provide a minimum energy consumption optimization control device and control method for a refrigeration heat pump system to solve the energy consumption problem of the refrigeration heat pump system under variable working conditions.

In order to achieve the above object, the present invention provides a minimum energy consumption optimization control device for a refrigeration heat pump system, the device is connected to the refrigeration heat pump system, wherein: the device includes a frequency converter for a compressor, a frequency converter for a cooling water pump, a frequency converter for a chilled water pump, a device A fixed-point optimal controller, the output end of the compressor frequency converter is connected to the compressor of the refrigeration heat pump system, the output end of the cooling water pump frequency converter is connected to the cooling water pump of the refrigeration heat pump system, and the output end of the chilled water pump frequency converter is connected to the chilled water pump of the refrigeration heat pump system The set point optimal controller includes a hardware module KMD5831 building controller and control software WinControl, the set point optimal controller receives the working status signal of the refrigeration heat pump system, obtains the control signal through WinControl programming calculation, and outputs the control signal to the set The frequency converter of the compressor is used to form an optimal control device for the minimum energy consumption of the refrigeration heat pump system. .

At the same time, it also provides a control method utilizing the minimum energy consumption optimization control device of the refrigeration heat pump system.

Effect of the present invention is:

First, the set-point controller established according to the total energy consumption model of the refrigeration system developed takes into account the four input variables of the system compressor frequency, chilled water pump frequency, cooling water pump frequency, electronic expansion valve opening and the system internal The relationship between the state variables evaporating pressure, condensing pressure and degree of superheat is used to comprehensively study some dead angle problems in energy-saving optimization of refrigeration systems.

Second, the optimal control scheme for minimum energy consumption is designed using a layered control structure. This method is well adapted to the energy-saving requirements of the refrigeration system under variable operating conditions. Compared with conventional control, it has good tracking performance and dynamic control accuracy within within ±5%.

Thirdly, the optimization objective is solved by using the pattern search algorithm combined with the external penalty function, which effectively solves the problem of calculating the derivative of the nonlinear energy consumption objective function.

Fourth, when the working conditions of the refrigeration system change, the model parameter self-adaptive method is used to optimize the set values of each control loop online. The actual control results show that this method makes the refrigeration heat pump system adjust well following the changes in working conditions. Compared with the traditional single-loop control, the energy-saving effect reaches more than 18% when the load demand is 100%.

Description of drawings

Figure 1 is a traditional refrigeration system single-loop control structure diagram;

Fig. 2 overall plan diagram of optimal control of refrigeration system of the present invention;

Figure 3 is a schematic diagram of layered optimization control of the refrigeration system of the present invention;

Fig. 4 self-adaptive structural diagram of refrigeration system model parameters of the present invention;

Fig. 5 is the minimum energy consumption control flowchart of the refrigeration heat pump system of the present invention;

Fig. 6 is in conjunction with the flowchart of the pattern search method of external penalty function;

Fig. 7 is a structural diagram of the refrigeration system set point optimization control of the present invention.

In the picture:

1. Set point optimal controller 2. Cooling load controller 3. Compressor 4. Condensing pressure controller 5. Cooling tower 6. Condenser 7. Expansion valve 8. Evaporating pressure controller 9. Evaporator 10. Refrigeration Load measurement 11. Terminal load 12. Evaporator superheat controller 13. Chilled water pump controller 14. Dynamic controller 15. Refrigeration unit 16. Adaptive layer 17. Optimization layer 18. Dynamic control layer 19. Optimal controller 20. Energy consumption function evaluation 21. Model parameter adaptation 22. Parameter adaptation 23. Linear dynamic estimation 24. Steady-state model 25. Low-pass filter 26. Cooling water pump 27. Chilled water pump 28. Compressor inverter 29. Cooling water pump inverter 30. Chilled water pump frequency converter

specific implementation plan

The minimum energy consumption optimization control device and control method of the refrigeration heat pump system of the present invention will be described in conjunction with the accompanying drawings.

The minimum energy consumption optimization control device of the refrigeration heat pump system of the present invention is connected with the refrigeration heat pump system, and the device includes a compressor frequency converter 28, a cooling water pump frequency converter 29, a chilled water pump frequency converter 30, and a set point optimal controller 1. The output end of the compressor inverter 28 is connected to the compressor of the refrigeration heat pump system. 3. The output end of the cooling water pump inverter 29 is connected to the cooling water pump 26 of the refrigeration heat pump system, and the output end of the chilled water pump inverter 30) is connected to the refrigeration unit of the refrigeration heat pump system. Water pump 27; the set point optimal controller 1 includes a hardware module KMD5831 building controller and control software WinControl, the set point optimal controller 1 receives the working state signal of the refrigeration heat pump system, and obtains the control signal through WinControl programming calculation, and controls The signal is output to the compressor frequency converter 28 to form a minimum energy consumption optimization control device for the refrigeration heat pump system.

The control method of the present invention using the minimum energy consumption optimization control device of the refrigeration heat pump system, the control method adopts hierarchical optimization control through WinControl software, and the hierarchical optimization control includes an adaptive layer 16, an optimization layer 17 and a dynamic control layer 18 , realize the minimum energy consumption control of the refrigeration heat pump system through the minimum energy consumption optimization control device of the refrigeration heat pump system, including the following steps:

1) In the set point optimal controller 1, the compressor energy consumption model of the refrigeration heat pump system is established through WinControl software programming:

In the formula, W _k —compressor energy consumption (kW) Q _e —evaporator cooling capacity (kW)

h _ci —enthalpy value of condenser inlet (kJ/kg) h _co —enthalpy value of condenser outlet (kJ/kg)

h _{eo —Evaporator} outlet enthalpy (kJ/kg) P _e —Evaporation pressure (kPa)

P _c —condensing pressure (kPa)

Cooling water pump energy consumption model:

In the formula, m _{clw —cooling} water mass flow rate (kg/s) m _a —cooling tower air mass flow rate (kg/s)

c _p,w — specific heat capacity of cooling water (kJ/kg·℃) T _clwo — outlet temperature of cooling water (℃)

T _amb —outdoor air temperature (℃) Q _{clw —cooling} tower load (kW)

a ₁ , a ₂ , a ₃ —fitting coefficients related to cooling tower structure

Chilled water pump energy consumption model:

In the formula, k _2,chw , k _{2,cho —the} relationship coefficient between chilled water mass flow rate and frequency

T _e —refrigerant temperature of evaporator (°C) T _{chwi —refrigerant} temperature of chilled water (°C)

α _e — heat transfer coefficient between refrigerant and chilled water m _e — heat transfer index

f _chw — Chilled water pump frequency (Hz)

Combined construction of the overall energy consumption model of the refrigeration heat pump system;

2) According to the overall energy consumption model and the minimum energy consumption evaluation 20 of the self-adaptive layer 16, the minimum energy consumption model is established and the constraint conditions are determined to be realized in the set point optimal controller 1 through WinControl software programming;

Minimum energy consumption model:

In the formula, W _k —compressor energy consumption (kW) W _{chw —chilled} water pump energy consumption (kW)

W _{clw —cooling} water pump energy consumption (kW)

The constraints are: state variable limit: Ex _ss +b _x ≤ 0

Control signal limit: Fu _ss (x _ss ,v _ss )+b _u ≤0

Cooling capacity limitation: Q _e = Q _e,o

Superheat limitation: T _sh ＝ T _sh,min (Q _e )

Among them, x _ss =[P _e P _c T _sh ] ^T is the state variable, T _sh is the degree of superheat,

b _x ＝[-P _c,max P _e,min 0] ^T , P _c,max is the maximum value of condensing pressure, P _e,min is the minimum value of evaporation pressure, u _ss ＝[f _k f _clw f _chw ] ^T , f _k is the compressor frequency, f _clw is the cooling water pump frequency, f _chw is the chilled water pump frequency, is the system disturbance, b _u ＝[0 -f _clw,max 0 -f _chw,max 0 -f _k,max ] ^T , f _clw,max is the maximum operating frequency of the cooling water pump, f _chw,max is the operating frequency of the chilled water pump The maximum value, f _k,max is the maximum operating frequency of the compressor, Q _e,o is the cooling demand, T _sh,min (Q _e ) is the minimum superheat,

3) Based on the model adaptive control 21 of the adaptive layer 16, the parameter adaptive control of the minimum energy consumption model is completed in the set point optimal controller 1 through WinControl software programming.

4) According to the optimization controller 19 of the optimization layer 17, in the set-point optimal controller 1, the external penalty function of the minimum energy consumption model is established by WinControl software programming to complete the minimum energy consumption from constrained optimization to unconstrained optimization Convert, and solve the minimum energy consumption value and the state quantity value under the minimum energy consumption according to the pattern search algorithm.

5) According to the dynamic control layer 18, the state value under the minimum energy consumption is used as the input of the dynamic controller 14 of the dynamic control layer 18.

6) The output signal of the dynamic controller 14 is connected to the compressor frequency converter 28, the cooling water pump frequency converter 29, the chilled water pump frequency converter 30 and the electronic expansion valve 7 in the minimum energy consumption optimization control device.

7) The minimum energy consumption optimization control device outputs signals to the refrigeration unit 15, thereby completing the minimum energy consumption optimization control.

When the working condition of the refrigeration heat pump system changes, the model parameter adaptation 21 will adaptively adjust the coefficient of the minimum energy consumption function evaluation 20 . The minimum energy consumption function model constructed by the minimum energy consumption function evaluation 20 is related to the state quantity of the refrigeration heat pump system.

The optimization controller 19 of the optimization layer 17 adopts a pattern search algorithm combined with an external penalty function.

The minimum energy consumption model is a mechanism-based minimum energy consumption model, and the compressor energy consumption model, cooling water pump energy consumption model and Chilled water pump energy consumption model.

The functions of the refrigeration heat pump system minimum energy consumption optimization control device and control method of the present invention are realized in the following way:

FIG. 2 is an overall scheme diagram of optimal control of the refrigeration system of the present invention, including a set point optimal controller 1 , a dynamic controller 14 and a refrigeration unit 15 .

Fig. 3 is a schematic diagram of the layered optimization control of the refrigeration system of the present invention, which includes an adaptive layer 16, an optimization layer 17 and a dynamic control layer 18, and the adaptive layer 16 is realized by a model parameter adaptive control 21, and its input is the system working state Quantity, control signal quantity, its output is sent to optimization layer 17, and described optimization layer 17 comprises minimum energy consumption function evaluation 20 and optimization controller 19, the output of adaptive layer 16 and control signal quantity are sent to minimum energy consumption function evaluation 20 In this process, the establishment of the minimum energy consumption model and the determination of the constraint conditions are completed, the output of the minimum energy consumption function evaluation 20 is used as the input of the optimization controller 19, and the optimization controller 19 completes the solution of the minimum energy consumption value and the state value under the minimum energy consumption, and optimizes Controller 19 output ends connect dynamic control layer 18, and described dynamic control layer 18 comprises dynamic controller 14, and the input signal of dynamic controller 14 is the difference value of the output of optimization layer 17 and actual working state quantity, and dynamic controller 14 outputs The signals are respectively connected to compressor frequency converter 28 , expansion valve 7 , cooling water pump frequency converter 29 and chilled water pump frequency converter 30 .

Fig. 4 is the self-adaptive structural diagram of the refrigeration system model parameters of the present invention, which is a typical model parameter self-adaptive control, realizes the model parameter self-adaptation, and overcomes the problem that the model parameters in the refrigeration system energy consumption model will change with the change of the working state, It includes reference model of refrigeration unit 15, parameter self-adaptation 22, linear dynamic estimation 23, steady-state model 24, low-pass filter 25, since the model is based on steady-state model 24, parameter self-adaptation 22 is only accurate in steady state, The most significant dynamic process in the dynamic regime is formed by the heat transfer walls of the evaporator. This dynamic process is simulated with a low-pass filter 25. In order to adapt to the disturbance of the system in the dynamic regime, a linear dynamic estimation 23 is used for the adaptive layer.

Fig. 5 is a flow chart of the minimum energy consumption control of the refrigeration heat pump system of the present invention, including the following steps:

Step 501: start;

Step 502: In the set point optimal controller 1, use WinControl software programming to establish an overall energy consumption model;

The overall energy consumption model includes the establishment of compressor energy consumption models, cooling water pump energy consumption models, and chilled water pump energy consumption models derived from compressor energy conservation equations, cooling water system energy conservation equations, and chilled water system energy conservation equations.

1) The energy consumption calculation method that considers the relationship between the frequency of the compressor and the internal state variables of the system, evaporating pressure, condensing pressure, and degree of superheat.

Compressor mass flow per unit time:

m _k ＝n _k η _vol V _k ρ _ki (P _e ) (1)

In the formula, ρ _{ki ——the} density of gaseous refrigerant, kg/m ³

η _{vol ——The} volumetric efficiency of the compressor

V _k ——Theoretical gas delivery volume, m ³ /h

n _k ——compressor speed, rpm

Among them, the relationship between compressor speed and frequency:

Actual outlet enthalpy of compressor:

In the formula, h _{kois ——Adiabatic} outlet enthalpy value of compressor, kJ/kg

η _k ——Compressor adiabatic efficiency

Since the compressor outlet enthalpy and the condenser inlet enthalpy, the evaporator outlet enthalpy and the compressor inlet enthalpy have little change, they are replaced by the evaporator outlet and condenser inlet enthalpy, respectively. Formula (3) becomes formula (4).

Evaporator cooling capacity:

Q _e ＝m _k (h _eo (P _e )-h _ei (p _e ))＝m _k (h _eo (P _e )-h _co (p _c )) (5)

Considering that the inlet and outlet enthalpy values of the expansion valve are constant, and the enthalpy value at the outlet of the condenser is equal to the enthalpy value at the inlet of the expansion valve, the enthalpy value at the outlet of the expansion valve is equal to the enthalpy value at the inlet of the evaporator, so the enthalpy value at the outlet of the condenser is equal to the enthalpy at the inlet of the evaporator . The ratio of the work done by the compressor to the refrigerating capacity of the evaporator is shown in formula (6).

The derived compressor work equation is:

2) The energy consumption calculation method that considers the relationship between the cooling water frequency and the system's internal state variables evaporation pressure, condensation pressure and superheat.

The relationship between cooling water pump work and frequency is:

W _clw ＝k _1,clw (f _clw ) ³ +k _1,clo (8)

In the formula, f _{clw ——cooling} water pump input frequency, Hz

The relationship between cooling water flow and frequency:

m _clw =k _2,clw f _clw +k _2,clo (9)

Condenser heat transfer coefficient:

In the formula, α _c ——refrigerant and cooling water heat exchange coefficient

m _c —— heat transfer index, value 0.4-0.84

Cooling water outlet temperature:

The cooling water inlet temperature T _clwi is calculated by the cooling tower heat balance equation (12).

In the formula, Q _{clw —cooling} tower load, kW

m _a , m _{clw —mass} flow rate of cooling tower air and cooling water, kg/s

a ₁ , a ₂ , a ₃ —fitting coefficients, related to cooling tower structure

T _amb — the temperature of the outdoor air, °C

Energy balance equation of refrigerant and cooling water on the tube wall of condenser

m _k (h _ci (P _e ,P _c )-h _co (P _c ))＝m _clw c _p,w (T _clwo -T _clwi ) (13)

Organized and available

m _k (h _ci (P _e ,P _c )-h _co (P _c ))-m _clw c _p,w (T _clwo -T _clwi )＝0 (14)

Substituting equations (8)-(12) into equation (14), we can get the energy conservation equation of the condenser

Equation (15) contains the implicit function relationship between cooling water flow and state variables, and the flow m _clw under different state variables can be obtained by solving the equation, and then the corresponding cooling water pump power consumption W _clw can be obtained.

3) The energy consumption calculation method that considers the relationship between the chilled water frequency and the system's internal state variables evaporation pressure, condensation pressure and superheat.

The relationship between chilled water pump work and frequency is:

W _chw ＝k _1,chw (f _chw ) ³ +k _1,cho (16)

In the formula, f _chw —— chilled water pump input frequency, Hz

The relationship between chilled water volume and frequency:

m _chw =k _2,chw f _chw +k _2,cho (17)

Evaporator heat transfer coefficient:

In the formula, α _e ——refrigerant and chilled water heat exchange coefficient, kJ/(kg K)

m _e —— heat transfer index, value 0.4-0.84

Chilled water supply temperature:

In the formula, T _{chwi ——refrigerated} water return temperature

The energy balance equation of refrigerant and chilled water on the tube wall of the evaporator:

Q _e -m _chw c _p,w (T _chwo -T _chwi )＝0 (20)

Put equations (16)-(19) into equation (20) to get the energy conservation equation on the evaporator:

Equation (21) contains the nonlinear functional relationship between chilled water frequency f _chw and state variables, and the frequency f _chw under different state variables can be obtained by solving the equation, and then the corresponding chilled water pump power consumption W _chw can be obtained.

Step 503: In the set point optimal controller 1, use WinControl software programming to establish a minimum energy consumption model, and determine model constraints;

Combining the power consumption of the compressor, the power consumption of the cooling water circulation pump, and the power consumption of the chilled water circulation pump, the total energy consumption model of the refrigeration system is established, and the energy consumption optimization objective function is constructed as in formula (22).

The restrictions are as follows:

State variable limit: Ex _ss +b _x ≤ 0

Control signal limit: Fu _ss (x _ss ,v _ss )+b _u ≤0

Cooling capacity limitation: Q _e = Q _e,o

Superheat limitation: T _sh ＝ T _sh,min (Q _e )

In the model, W _k is the power consumption of the compressor, W _clw is the power consumption of the cooling water pump, and W _chw is the power consumption of the chilled water pump; x _ss = [P _e P _c T _sh ] ^T is used as a state variable, where P _e is the evaporation pressure , P _c is the condensing pressure, T _sh is the degree of superheat of the evaporator, the condensing pressure P _c and the evaporation pressure Pe are free variables, the set value of the superheat T _sh (minimum stable superheat _{) is modified according to the cooling capacity Q e and} Setting; u _ss =[f _k f _clw f _chw ] ^T is the control variable, where f _k is the input frequency of the compressor, f _clw is the input frequency of the cooling water pump, and f _chw is the input frequency of the chilled water pump; System disturbance, where the outdoor ambient temperature T _amb is the main disturbance of the system, _Tw is the system water supply temperature, is the relative humidity, and v is the outdoor wind speed, which is ignored here because of the small impact on the system; Q _e is the cooling capacity requirement of the system.

Step 504: In the set point optimal controller 1, use WinControl software to program according to the model parameter adaptive control method shown in Figure 4 to realize the adaptive adjustment of the minimum energy consumption parameter;

Figure 4 is a typical model parameter adaptive control diagram, including reference model of refrigeration unit 15, parameter adaptive 22, linear dynamic estimation 23, steady-state model 24, low-pass filter 25, since the model is based on steady-state condition steady-state model 24 , the parameter self-adaptation 22 is accurate only in the steady state, the most significant dynamic process in the dynamic state is formed by the heat transfer wall of the evaporator, this dynamic process is simulated with a low-pass filter 25, in order to adapt to the dynamic state of the system perturbation, using linear motion estimation 23 for the adaptive layer.

Step 505: According to the external penalty function formula 23, use WinControl software programming in the set point optimal controller 1 to realize the conversion of the minimum energy consumption from constrained to unconstrained;

As shown in FIG. 3 , the optimization layer 17 and the optimization controller 19 use an external penalty function to transform the constrained minimum energy consumption optimization in step 503 into an unconstrained optimization, as shown in formula (23).

In the formula, Q _ss =Q _e -Q _e,o , the heat release of the condenser is affected by the external environment, and the change of the ambient temperature must be considered in the optimization problem.

Step 506: use WinControl software programming in the set point optimal controller 1 to realize the pattern search algorithm to solve the minimum energy consumption value and the state value under the minimum energy consumption;

Figure 6 is a flow chart of the pattern search algorithm combined with the external penalty function, which realizes the solution to formula (23). Each iteration of the search process alternately performs axial movement and pattern movement. The purpose of axial movement is to decrease the value of the search function The favorable direction of the mode movement is to accelerate the movement along the favorable direction. The quantities appearing in the flow chart include: state variable: x=[P _e P _c ] ^T , penalty function: F(x _ss ,τ), penalty factor : τ, step size δ ₀ , contraction factor α, and the initial value of τ can no longer be adjusted after selecting a larger appropriate value, so the penalty function becomes F(x), that is, it is only optimized for the state variable x, and the pattern search process includes the following step:

Step 601: start;

Step 602: Given the initial state value and initial constraint conditions, randomly select the initial point x ₀ =(P _e0 ,P _c0 ) of the state variables of evaporation pressure and condensation pressure, step size δ ₀ =1, shrinkage factor α=0.75, penalty Factor τ=5000, allowable error ε>0, k=0, j=1;

Step 604: Assign a value, assign the value of x _k (k=1, 2...) to y for positive axial movement;

Step 604: Calculate penalty function, calculate penalty function F(y+δ _k e _j ) and F(y);

Step 605: compare the penalty function, and judge whether F(y+δ _k e _j )<F(y) is established or not;

Step 606: If F(y+δ _k e _j )<F(y) holds true, continue forward search;

Step 607: If F(y+δ _k e _j )<F(y) is not established, judge whether F(y-δ _k e _j )<F(y) is true or not, if not, directly judge whether the search is completed;

Step 608: If F(y-δ _k e _j )<F(y) holds true, continue the reverse search;

Step 609:: judge whether the axial movement is completed;

Step 6010:: If the axial movement is not completed, continue the axial movement;

Step 6011: If the axial movement is successful, perform a pattern search, and the pattern search base point x _k+1 =y;

Step 6012: compare the penalty function to determine whether F(x _k+1 )<F(x _k ) holds true;

Step 6013: If F(x _k+1 )<F(x _k ) holds, the mode moves, repeating steps 31 to 41;

Step 6014: If F(x _k+1 )<F(x _k ) does not hold, judge whether δ _k <ε holds true or not;

Step 6015: If δ _k <ε is not established, judge whether x _k+1 = x _k is established or not;

Step 6016: If x _k+1 = x _k holds true, change the step size and repeat steps 31 to 43;

Step 6017: If x _k+1 = x _k does not hold, the step size remains unchanged, x _k+1 = x _k , repeat steps 31 to 43;

Step 6018:: the iteration ends, and the optimal solution is obtained;

Step 6019: end.

Step 507: In the set point optimal controller 1, send the state value under the solved minimum energy consumption to the dynamic controller 14;

The state value under the minimum energy consumption obtained by the above mode search is used as the reference input of the dynamic controller 14, and the dynamic controller 14 includes a condensing pressure PID controller 4, an evaporating pressure PID controller 8, an evaporator superheat PID controller 12 and Chilled water pump PID controller 13.

Step 508: The output signal of the dynamic controller 14 is connected to the compressor frequency converter 28, the cooling water pump frequency converter 29, the chilled water pump frequency converter 30 and the electronic expansion valve 7 in the minimum energy consumption optimization control device;

Step 509: The minimum energy consumption optimization control device controls the compressor 3, the electronic expansion valve 7, the cooling water pump 26 and the chilled water pump 27 in the refrigeration heat pump system according to the output signal of the dynamic controller 14, so as to realize the minimum energy consumption optimization control.

Step 5010: end.

In Fig. 7, the black line marks part of the set point optimal controller 1 to complete the functions of the adaptive layer 16 and the optimization layer 17 in Fig. 3, and realize the establishment of the minimum energy consumption model, the minimum energy consumption value and the state quantity under the minimum energy consumption. Solve, and output the minimum energy consumption control signal to the loop controller, the black line marks part of the cooling load controller 2 output signal to the compressor inverter 28 to control the compressor 3, the condensing pressure controller 4 output signal to the cooling water pump inverter 29 control The cooling water pump 26, condenser 6, and evaporating pressure controller 8 output signals to the chilled water pump inverter 30 to control the chilled water pump 27 and the evaporator 9, and the evaporator superheat controller 12 outputs signals to control the electronic expansion valve 7. The black marked part combines Outer layer set point optimization and inner layer dynamic control, cooling load measurement 10 to determine load demand, end load 11 to determine end load, cooling tower 5 adopts constant frequency control, this minimum energy consumption optimization control method is combined with the refrigeration heat pump optimization control device It can realize the optimal control of the minimum energy consumption of the refrigeration heat pump system, and realize the purpose of saving energy consumption.

Claims (6)

1. A minimum energy consumption optimization control device for a refrigeration heat pump system, the device is connected to the refrigeration heat pump system, and is characterized in that: the device includes a compressor frequency converter (28), a cooling water pump frequency converter (29), a chilled water pump frequency converter (30), set point optimal controller (1), the output end of the compressor inverter (28) is connected to the compressor (3) of the refrigeration heat pump system, and the output end of the cooling water pump inverter (29) is connected to the refrigeration heat pump system The cooling water pump (26), chilled water pump inverter (30) output end is connected to the chilled water pump (27) of the refrigeration heat pump system; the set point optimal controller (1) includes hardware module KMD5831 building controller and control software WinControl , the set point optimal controller (1) receives the working state signal of the refrigeration heat pump system, and obtains the control signal through WinControl programming calculation, and the control signal is output to the compressor frequency converter (28), forming the optimal control of the minimum energy consumption of the refrigeration heat pump system device. 2. A control method that utilizes the minimum energy consumption optimization control device of the refrigeration heat pump system, the control method adopts layered optimization control by WinControl software, and the layered optimization control includes an adaptive layer (16), an optimization layer (17) and the dynamic control layer (18), realize the minimum energy consumption control of the refrigeration heat pump system through the optimization control device for the minimum energy consumption of the refrigeration heat pump system, including the following steps: 1) Establish the compressor energy consumption model of the refrigeration heat pump system through WinControl software programming in the set point optimal controller (1): ${\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}\frac{{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; o</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; o</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; o</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$ In the formula, W _k —compressor energy consumption (kW) Q _e —evaporator cooling capacity (kW) h _ci —enthalpy value of condenser inlet (kJ/kg) h _co —enthalpy value of condenser outlet (kJ/kg) h _{eo —Evaporator} outlet enthalpy (kJ/kg) P _e —Evaporation pressure (kPa) P _c —condensing pressure (kPa) Cooling water pump energy consumption model: $\frac{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}}{{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; o</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; o</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&lsqb;</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; o</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; w</span>}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; o</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; w</span>}}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; w</span>}}}{{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}<span\; class="notranslate">\; )</span>}^{{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}}}{{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}^{{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$ In the formula, m _{clw —cooling} water mass flow rate (kg/s) m _a —cooling tower air mass flow rate (kg/s) c _p,w — specific heat capacity of cooling water (kJ/kg·℃) T _clwo — outlet temperature of cooling water (℃) T _amb —outdoor air temperature (℃) Q _{clw —cooling} tower load (kW) a ₁ , a ₂ , a ₃ —fitting coefficients related to cooling tower structure Chilled water pump energy consumption model: ${\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; w</span>}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; o</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; w</span>}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; o</span>}}<span\; class="notranslate">\; )</span>}^{{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; w</span>}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; o</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; w</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$ In the formula, k _2,chw , k _{2,cho —the} relationship coefficient between chilled water mass flow rate and frequency T _e —refrigerant temperature of evaporator (°C) T _{chwi —refrigerant} temperature of chilled water (°C) α _e — heat transfer coefficient between refrigerant and chilled water m _e — heat transfer index f _chw — Chilled water pump frequency (Hz) Combined construction of the overall energy consumption model of the refrigeration heat pump system; 2) According to the minimum energy consumption evaluation (20) of the overall energy consumption model and the adaptive layer (16), set up the minimum energy consumption model and determine the constraints in the set point optimal controller (1) by WinControl software programming accomplish; Minimum energy consumption model: $\underset{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}}}{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$ In the formula, W _k —compressor energy consumption (kW) W _{chw —chilled} water pump energy consumption (kW) W _{clw —cooling} water pump energy consumption (kW) The constraints are: state variable limit: Ex _ss +b _x ≤ 0 Control signal limit: Fu _ss (x _ss ,v _ss )+b _u ≤0 Cooling capacity limitation: Q _e = Q _e,o Superheat limitation: T _sh ＝ T _sh,min (Q _e ) Among them, x _ss =[P _e P _c T _sh ] ^T is the state variable, T _sh is the degree of superheat, b _x ＝[-P _c,max P _e,min 0] ^T , P _c,max is the maximum value of condensing pressure, P _e,min is the minimum value of evaporation pressure, u _ss ＝[f _k f _clw f _chw ] ^T , f _k is the compressor frequency, f _clw is the cooling water pump frequency, f _chw is the chilled water pump frequency, is the system disturbance, b _u ＝[0 -f _clw,max 0 -f _chw,max 0 -f _k,max ] ^T , f _clw,max is the maximum operating frequency of the cooling water pump, f _chw,max is the operating frequency of the chilled water pump The maximum value, f _k,max is the maximum operating frequency of the compressor, Q _e,o is the cooling demand, T _sh,min (Q _e ) is the minimum superheat, $\begin{array}{cc}\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; =</span>\left[\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 0</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}\\ <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}\end{array}\right]& \mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; 0</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; 0</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 0</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 0</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; 0</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}\end{array}\right]\end{array}$ 3) According to the model adaptive control (21) of the adaptive layer (16), in the set point optimal controller (1), complete the parameter adaptive control of the minimum energy consumption model by WinControl software programming; 4) According to the optimization controller (19) of the optimization layer (17), the external penalty function of the minimum energy consumption model is established by WinControl software programming in the set point optimal controller (1) to complete the minimum energy consumption from the constraint Convert from optimization to unconstrained optimization, and solve the minimum energy consumption value and the state value under the minimum energy consumption according to the pattern search algorithm; 5) According to the dynamic control layer (18), the state value under the minimum energy consumption is used as the input of the dynamic control layer (18) dynamic controller (14); 6) The output signal of the dynamic controller (14) is connected to the compressor frequency converter (28), the cooling water pump frequency converter (29), the chilled water pump frequency converter (30) and the electronic expansion valve (7); 7) The minimum energy consumption optimization control device outputs signals to the refrigeration unit (15), thereby completing the minimum energy consumption optimization control. 3. The optimal control method for the minimum energy consumption of the refrigeration heat pump system according to claim 2, characterized in that: when the operating conditions of the refrigeration heat pump system change, the model parameter self-adaptation (21) will adaptively adjust the minimum energy consumption function evaluation Coefficient of (20). The minimum energy consumption optimization control method of the refrigeration heat pump system according to claim 2, characterized in that: the minimum energy consumption function model constructed by the minimum energy consumption function evaluation (20) is related to the state quantity of the refrigeration heat pump system. 5. The optimal control method for minimum energy consumption of a refrigeration heat pump system according to claim 2, characterized in that: the optimization controller (19) of the optimization layer (17) adopts a pattern search algorithm combined with an external penalty function. 6. The optimal control method for minimum energy consumption of refrigeration heat pump system according to claim 2, characterized in that: said minimum energy consumption model is a mechanism-based minimum energy consumption model, using compressor energy conservation equation and cooling water system respectively The energy conservation equation and the energy conservation equation of the chilled water system deduce the energy consumption model of the compressor, the energy consumption model of the cooling water pump and the energy consumption model of the chilled water pump.