Ice storage control method and system based on hierarchical multi-objective optimization

Abstract

The invention discloses an ice storage control method and system based on hierarchical multi-objective optimization, and the method comprises the steps: building a double-layer ice storage system control model, and carrying out the optimization of the working states of all equipment in an ice storage system through taking the operation cost, the carbon emission and the energy consumption of the ice storage system as optimization objectives; compared with traditional ice storage for optimizing the cold release amount and the ice storage amount, the energy consumption is low, and a better effect can be achieved; meanwhile, a penalty function is introduced into a lower-layer ice storage system control model, the switching frequency of a dual-working-condition refrigeration host is reduced, and the system stability is improved. In addition, the equipment in the ice storage system is optimized by utilizing the double-layer ice storage system control model, the calculation process is simple and convenient, and the feasibility is higher; in order to ensure the effectiveness and the accuracy of system optimization control, the mathematical model of the equipment is continuously updated and perfected in the process of optimizing the target so as to adapt to the dynamically changing environment and requirements.

Description

Technical Field

[0001] The present invention belongs to the technical field of ice thermal storage, and in particular relates to an ice thermal storage control method and system based on hierarchical multi-objective optimization. Background Art

[0002] As energy crisis and environmental problems become increasingly serious, how to improve the energy efficiency of refrigeration systems and reduce carbon emissions has become an urgent problem to be solved. Traditional air-conditioning systems focus on meeting user needs during operation, but often ignore energy consumption and environmental impact. In recent years, ice storage air-conditioning systems have been widely used because they can store cold energy when electricity prices are low and release cold energy when electricity prices are high. However, in actual applications, ice storage air-conditioning systems still face the following challenges:

[0003] 1. In the optimization control of ice storage air conditioning systems, some studies have introduced electricity price information and electricity carbon emission factors to achieve the goal of minimum operating cost and minimum carbon emission by using a single-layer model. However, this approach has limitations when dealing with complex systems. It usually ignores the differences in performance characteristics of different chillers and the impact of load distribution on overall energy efficiency. In addition, in practical applications, due to frequent changes in system parameters and external environment, single-layer models are difficult to adapt and adjust in real time.

[0004] 2. In the optimization control of ice storage air conditioning systems, the lowest system operating cost is often pursued qualitatively, and the system stability is ignored. The given operating strategy will frequently switch equipment, which will also lead to increased equipment damage, reduced efficiency and increased energy consumption.

[0005] 3. In the optimization control of ice storage air conditioning system, establishing the mathematical model of the equipment is the basis for achieving effective control and optimization. In existing research, the changes in the on-site environment and the gradual degradation of the performance of the equipment as the service life increases are often ignored. The mathematical model of the original equipment may no longer be accurate, which leads to the deviation of the optimization control results of the ice storage air conditioning system from the actual situation. Summary of the invention

[0006] The object of the present invention is to provide an ice storage control method and system based on hierarchical multi-objective optimization.

[0007] In a first aspect, the present invention provides an ice storage control method based on hierarchical multi-objective optimization, which comprises the following steps:

[0008] Step 1: Taking the minimum operating cost and the minimum carbon emission of the ice storage system as the optimization target of the upper ice storage system control model, the objective function and constraint conditions of the upper ice storage system control model are constructed according to the ice storage melting amount and the power consumption of the refrigeration host;

[0009] Step 2: Solve the control model of the upper ice storage system to obtain the optimal hourly ice melting amount of the ice storage system and cooling capacity ;

[0010] Step 3: construct energy consumption models of the dual-condition refrigeration host, water pump system and cooling tower in the ice storage system respectively, and extract training samples of the mathematical models of the dual-condition refrigeration host, water pump system and cooling tower respectively through the sliding window method, and when the distribution difference of the training samples at the current moment and the previous moment reaches the preset condition, use the training samples at the current moment to update the parameters of the energy consumption model;

[0011] Step 4: Take the energy consumption of the ice storage system as the optimization target of the control model of the lower ice storage system to build the control model of the lower ice storage system, introduce the penalty function of the dual-condition refrigeration host switching, and build the objective function and constraint conditions of the control model of the lower ice storage system; the constraint conditions of the control model of the lower ice storage system include the optimal cooling capacity obtained by the control model of the upper ice storage system .

[0012] Step 5. Solve the control model of the lower ice storage system to obtain the optimal hourly operating parameters of the dual-condition refrigeration host, water pump system and cooling tower; control the dual-condition refrigeration host, water pump system and cooling tower to perform ice storage or cold release tasks according to the hourly operating parameters.

[0013] Preferably, in step 4, the penalty function The expression is:

[0014]

[0015] in, The number of times the dual-mode cooling host is turned on; The number of times the dual-mode cooling host is shut down; is the single operation time of the dual-mode refrigeration host; run is the shutdown sequence number; is the preset penalty function coefficient.

[0016] The objective function f5 of the control model of the lower ice storage system is:

[0017]

[0018] in, Energy consumption of dual-mode refrigeration host; is the energy consumption of the cooling tower; W 水泵 is the energy consumption of the pump system.

[0019] Preferably, in step 3, the energy consumption model of the dual-mode refrigeration host is:

[0020]

[0021] in, It is the rated cooling capacity of the dual-mode refrigeration host; is the operating load rate of the dual-condition refrigeration host; cop2 is the actual operating energy efficiency of the dual-condition refrigeration host, and its expression is:

[0022]

[0023] Among them, P j is the least squares fitting coefficient; t c is the cooling water temperature.

[0024] Preferably, the energy consumption model of the cooling tower is:

[0025]

[0026] Among them, m a is the cooling tower air volume; are the least squares fitting coefficients of cooling tower fans.

[0027] Preferably, in step 3, the water pump system includes a freezing pump and a cooling pump, and the energy consumption model of the water pump system is:

[0028]

[0029] Among them, m cw is the flow rate of the water pump; are the least squares fitting coefficients of the water pump respectively.

[0030] Preferably, in step 1, the objective function The expression is:

[0031]

[0032] in, For operating expenses; is carbon emissions; a and b are operating costs and carbon emissions The weight of

[0033] Operating costsf1 and carbon emissions The expression is:

[0034] f 1 = ∑ i =1 24 [ W system ( i ) + W Accumulation ( i )] · E

[0035] f 2 = ∑ i =1 24 [ W system ( i ) + W Accumulation ( i )] · E F

[0036] in, Daily operating expenses; for carbon emissions; The power consumption of the refrigeration host at the i-th moment; For the The amount of ice storage and ice melting at the moment; E is System electricity price at the moment; E F For the The carbon emission factor of the system at that moment.

[0037] The constraints of the upper ice storage system control model are:

[0038]

[0039] in, The power consumption of the refrigeration host at the i-th moment; For the The cooling capacity of the refrigeration host at the moment; cop1 is the estimated operating energy efficiency of the refrigeration host; For the The amount of ice storage and ice melting at each moment; For the The ice storage tank releases coldness at all times; is the cold release pipeline transport coefficient; For the Total cooling capacity of ice storage system at each moment; and They are the maximum cooling capacity and minimum cooling capacity of the refrigeration host respectively; and They are the maximum and minimum cooling capacity of the ice storage tank respectively.

[0040] Preferably, in step 4, the constraint conditions of the control model of the lower ice storage system are:

[0041]

[0042] in, Energy consumption of dual-mode refrigeration host; is the optimal cooling capacity output by the upper ice storage system control model; cop2 is the actual operating energy efficiency of the dual-condition refrigeration host; It is the rated cooling capacity of the dual-mode refrigeration host; is the operating load rate of the dual-mode refrigeration host; m w is the cooling water flow rate; is the heat released by the cooling tower; c is the specific heat capacity of water; ∆t 冷却 is the cooling water temperature difference; m c is the chilled water flow rate; ∆t 冷冻 is the chilled water temperature difference; is the saturated air enthalpy; is the enthalpy of outdoor air; is the cooling tower heat and humidity relationship fitting coefficient; m a is the cooling tower air volume.

[0043] Preferably, the solution method of the upper ice storage system control model and the lower ice storage system control model adopts NSGA-II algorithm.

[0044] Preferably, in step three, the method for determining whether the distribution difference reaches the preset condition is: using the KS test to detect the distribution of training samples at the current moment and the previous moment, if it is detected that the change in the feature distribution exceeds the preset threshold D, the distribution difference reaches the preset condition.

[0045] In the second aspect, the present invention provides an ice storage system based on hierarchical multi-objective optimization, which includes a dual-condition refrigeration main unit, an ice storage device, a water pump system, a cooling tower and a controller; the water pump system includes a freezing pump and a cooling pump; the controller is used to execute the above-mentioned ice storage control method based on hierarchical multi-objective optimization; in the cold release state, the controller controls the ice storage device to melt ice according to the obtained optimal ice melting amount, and the melted cooling water is delivered to the air conditioner or other cooling system through the cooling pump; in the cold storage state, the controller controls the freezing pump to deliver the chilled water to the ice storage device according to the obtained optimal working parameters, and controls the cooling pump to deliver the cooling water generated by the dual-condition refrigeration main unit to the cooling tower for heat dissipation.

[0046] The present invention has the following beneficial effects:

[0047] 1. The present invention establishes a double-layer ice storage system control model, and optimizes the working state of each device in the ice storage system with operating costs and carbon emissions, and the energy consumption of the ice storage system as optimization targets. Compared with the traditional ice storage system for optimizing the cold release and ice storage, the present invention has lower energy consumption and can achieve better results. At the same time, the present invention introduces a penalty function in the control model of the lower ice storage system to reduce the number of switching times of the dual-mode refrigeration host and improve the system stability.

[0048] 2. The present invention utilizes a double-layer ice storage system control model to optimize each device in the ice storage system. The calculation process is relatively simple and has higher feasibility. At the same time, in order to ensure the effectiveness and accuracy of the system optimization control, the present invention continuously updates and improves the mathematical model of the equipment during the optimization process to adapt to the dynamically changing environment and needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is the overall flow chart of the present invention. DETAILED DESCRIPTION

[0050] The present invention will be further described below in conjunction with the accompanying drawings.

[0051] like Figure 1 As shown, an ice storage control method and system based on hierarchical multi-objective optimization includes the following steps:

[0052] Step 1: Construct the objective function and constraints of the upper ice storage system control model

[0053] 1-1. In order to achieve the real-time host cooling capacity and cooling capacity of the system with the lowest electricity cost and the lowest carbon emissions, the lowest operating cost and the lowest carbon emissions are taken as the optimization goals, and the corresponding optimization functions are established respectively:

[0054] f 1 = ∑ i =1 24 [ W system ( i ) + W Accumulation ( i )] · E

[0055] f 2 = ∑ i =1 24 [ W system ( i ) + W Accumulation ( i )] · E F

[0056] in, is the operating cost, in ten thousand yuan per day; is carbon emissions, in tons / day; is the power consumption of the refrigeration host at the i-th moment, in units of ; For the Ice storage and melting amount at the moment, in units of ; E is the The system electricity price at the moment, in yuan; E F For the The system carbon emission factor at that moment, in tons / kWh.

[0057] According to operating costs and carbon emissions The objective function of the upper ice storage system control model is:

[0058]

[0059] Where a and b are the operating costs, respectively. and carbon emissions The weight of .

[0060] The values ​​of weights a and b change dynamically during operation; reducing operating costs (increasing a) during peak electricity consumption periods and reducing carbon emissions (increasing b) during off-peak periods.

[0061] 1-2. The constraints of the upper ice storage system control model in the optimization process are:

[0062]

[0063] in, For the The cooling capacity of the main cooling unit at the moment, in units of ; For the The amount of cold released by the ice storage tank at any given moment, in units of ; cop1 is the estimated operating energy efficiency of the refrigeration host; is the cold release pipeline transport coefficient; For the Total cooling capacity of ice storage system at each moment; and They are the maximum cooling capacity and minimum cooling capacity of the refrigeration host respectively; and They are the maximum and minimum cooling capacity of the ice storage tank respectively.

[0064] Step 2: Use the NSGA-II algorithm to solve the control model of the upper ice storage system. The NSGA-II algorithm can effectively solve the control model of the ice storage system and optimize its operation strategy. The specific process of solving the control model of the upper ice storage system is as follows:

[0065] 2-1. A set of solutions (initial population) is randomly generated according to the constraints. Each individual in the population represents a control scheme for the ice storage system, including the hourly ice melting amount. and cooling capacity .

[0066] 2-2. Select operation

[0067] (a) Perform non-dominated sorting on the individuals in the population and identify different non-dominated hierarchies (Pareto ranks); each individual is assigned a rank and crowding distance.

[0068] (b) Calculate the crowding degree of each individual to maintain the diversity of the population.

[0069] (c) Use tournament selection or roulette wheel selection to select suitable individuals as parents. Individuals with lower (better) non-dominated ranks are preferred, while considering crowding.

[0070] 2-3. Crossover and mutation operations

[0071] The selected parent generation is crossovered to generate offspring. In this embodiment, simulated binary crossover (SBX) is used to combine the characteristics of the parent generation. The offspring after crossover is mutated to increase the diversity of solutions. In this embodiment, polynomial mutation or Gaussian mutation method is used.

[0072] 2-4. Generating subpopulations

[0073] The parent population and the child population are merged, and the non-dominated sorting and crowding degree calculation are performed again; suitable individuals are selected from the merged population to form a new population for the next iteration. Individuals with low non-dominated levels and high crowding degrees are preferred.

[0074] 2-5. Iterative Evolution

[0075] The process of selection, crossover, mutation and generation of subpopulations is repeated, and evolution is performed iteratively until the maximum number of iterations N is reached.

[0076] 2-6. Obtaining the optimal solution set

[0077] After the iteration is completed, the non-dominated solution set of the last generation of population is output, which is the Pareto frontier. These solutions represent the hourly ice melting amount of different ice storage systems. and cooling capacity . According to specific needs, select the appropriate solution from the Pareto frontier for application.

[0078] Step 3: Melt ice in actual ice storage system and cooling capacity The chiller's operating efficiency Cop is 5.0; operating costs and carbon emissions The weights are 0.7 and 0.3 respectively; the maximum and minimum cooling capacity of a single refrigeration host and 2500kW and 500kW respectively; the maximum cooling capacity of the ice storage tank is 5600kW; the initial ice storage capacity is 40000kW; the NSGA-II algorithm is used to calculate the optimal hourly ice melting capacity of ice storage and cooling capacity The solution is to obtain the system carbon emission factor of 0.5703 tons / kWh, and the cooling capacity of the optimal refrigeration host at different times As shown in Table 1, the electricity prices at different times are shown in Table 2.

[0079] Table 1 Refrigeration capacity at different times

[0080] time 1 2 3 4 5 6 7 8 Load (kW) 0 0 0 0 0 0 0 0 time 9 10 11 12 13 14 15 16 Load (kW) 9400 6000 6000 6000 6000 5000 5000 5000 time 17 18 19 20 21 22 23 24 Load (kW) 4000 3000 0 0 0 0 0 0

[0081] Table 2 Electricity prices at different times

[0082] time 1 2 3 4 5 6 7 8 Load (yuan / kW) 0.2293 0.2293 0.2293 0.2293 0.2293 0.2293 0.2293 0.6000 time 9 10 11 12 13 14 15 16 Load (yuan / kW) 1.0262 1.0262 0.7661 0.7661 1.0262 0.7661 0.7661 1.0262 time 17 18 19 20 21 22 23 24 Load (yuan / kW) 1.0262 1.0262 1.0262 1.0262 0.6000 0.6000 0.6000 0.2293

[0083] It can be seen from Tables 1 and 2 that the ice storage system meets the actual demand when the cooling capacity is zero when the electricity price is low.

[0084] Step 5. Construct mathematical models of the dual-mode refrigeration host, water pump system and cooling tower in the ice storage system respectively. In the ice storage system, establishing the mathematical model of the equipment is the basis for effective control and optimization. As the on-site environment changes and the equipment becomes longer due to its service life, the mathematical model of the characteristic curve of the equipment changes. Therefore, the equipment needs to continuously update the coefficients in the mathematical model to adapt to the dynamically changing environment and needs. The sample size used to fit the ice storage mathematical model is extracted through the sliding window method to ensure that the model only uses data from the most recent period of time, thereby improving its adaptability. Select a suitable window based on the dynamic characteristics of the system and the frequency of data changes, and update the sliding window each time a new sample is received. Use the KS test (Kolmogorov-Smirnov test) method to compare whether the distribution of the samples at the previous moment is significantly different from that of the samples at the current moment; set the threshold D, and the expression of the threshold D is:

[0085]

[0086] Among them, F n (x) is the distribution of the overall sample; F0(x) is the distribution of the given sample.

[0087] If it is detected that the change of the characteristic distribution exceeds the threshold D, the equipment model in the ice storage system is updated.

[0088] The mathematical models of each device in the ice storage system obtained by sample fitting are as follows:

[0089] 5-1. Dual-mode refrigeration host

[0090] The mathematical model for constructing a dual-condition refrigeration host is:

[0091]

[0092] in, Energy consumption of dual-mode refrigeration host; It is the rated cooling capacity of the dual-mode refrigeration host; is the operating load rate of the dual-condition refrigeration host; cop2 is the actual operating energy efficiency of the dual-condition refrigeration host, and its expression is:

[0093]

[0094] Among them, P j is the least squares fitting coefficient; t c is the cooling water temperature in °C.

[0095] In this embodiment, the rated cooling capacity is 2500kW; the obtained least squares fitting coefficients are shown in Table 3.

[0096] Table 3 Least squares fitting coefficients

[0097] <![CDATA[P0]]> <![CDATA[P1]]> <![CDATA[P2]]> <![CDATA[P3]]> <![CDATA[P4]]> <![CDATA[P5]]> <![CDATA[P6]]> <![CDATA[P7]]> <![CDATA[P8]]> <![CDATA[P9]]> 11.1 54.9 -0.593 -61.1 -1.1276 0.0088 1.056 -0.0011 0.000027 13.9

[0098] 5-2. Cooling tower

[0099] When the cooling tower structure is determined, its heat exchange efficiency can be determined by the air volume and water volume of the cooling tower. The efficiency of the cooling tower and the ratio of the cooling water mass flow rate and the air mass flow rate of the cooling tower are fitted into a polynomial curve.

[0100]

[0101] in, is the energy consumption of the cooling tower, in kW; m a is the cooling tower air volume, in m 3 / h; are the least squares fitting coefficients of cooling tower fans.

[0102] In this embodiment, the least squares fitting coefficients are shown in Table 4.

[0103] Table 4 Least squares fitting coefficients

[0104] <![CDATA[I0]]> <![CDATA[I1]]> <![CDATA[I2]]> <![CDATA[I3]]> -0.05029 0.00456 -0.000133 6.87×E-06

[0105] 5-3. Water pump system

[0106] The water pump system provides power for the circulation of water and refrigerant in the ice storage system. The water pump system includes a freezing pump and a cooling pump. The energy consumption model of the water pump system is as follows:

[0107]

[0108] Among them, W 水泵 is the energy consumption of the water pump system; m cw is the flow rate of the water pump; are the least squares fitting coefficients of the water pump respectively.

[0109] Step 6: Construct the control model of the lower ice storage system

[0110] The objective function and constraint conditions of the lower ice storage system are constructed based on the mathematical model of the dual-condition refrigeration host, water pump system and cooling tower in the ice storage system. Since the specifications and energy efficiency of the dual-condition refrigeration host in the ice storage system are different, the problem of frequent host switching often occurs during optimal control. The penalty function is introduced in the lower mathematical model to avoid undesirable behaviors such as frequent switching by adding additional costs. The penalty function design reduces the number of host switching while ensuring efficient operation, thereby improving the equipment life and overall stability. The expression is:

[0111]

[0112] in, The number of times the dual-mode cooling host is turned on; The number of times the dual-mode cooling host is shut down; is the running time of the dual-mode refrigeration host; run is the ordinal number of shutdown; is the preset penalty function coefficient.

[0113] According to the control model of the lower ice storage system, the energy consumption and penalty function of the ice storage system are Constructing the objective function The expression is:

[0114]

[0115] The constraints of the control model of the lower ice storage system are:

[0116]

[0117] in, The optimal cooling capacity output by the control model for the upper ice storage system; The heat released by the cooling tower, in kW; m w is the cooling water flow rate, in m 3 / h;m c is the chilled water flow rate, in m 3 / h; c is the specific heat capacity of water, unit is J·(kg·C) -1 ;∆t 冷却 is the cooling water temperature difference, in °C; ∆t 冷冻 Chilled water temperature difference, in °C; is the saturated air enthalpy; is the enthalpy of outdoor air; is the cooling tower heat and humidity relationship fitting coefficient.

[0118] In this embodiment, the cooling tower heat and humidity relationship fitting coefficient They are 3.2, 1.11 and 1.12 respectively.

[0119] Step 7: Use the NSGA-II algorithm to solve the control model of the lower ice storage system, obtain the optimal number and frequency of each device used hourly, and complete the ice storage and release process of the ice storage system.

Claims

1. An ice storage control method based on hierarchical multi-objective optimization, characterized in that: The following steps are involved: Step 1: Taking the minimum operating cost and the minimum carbon emission of the ice storage system as the optimization target of the upper ice storage system control model, the objective function and constraint conditions of the upper ice storage system control model are constructed according to the ice storage melting amount and the power consumption of the refrigeration host; Step 2: Solve the control model of the upper ice storage system to obtain the optimal hourly ice melting amount of the ice storage system and cooling capacity ; Step 3: construct energy consumption models of the dual-condition refrigeration host, water pump system and cooling tower in the ice storage system respectively, and extract training samples of the mathematical models of the dual-condition refrigeration host, water pump system and cooling tower respectively through the sliding window method, and when the distribution difference of the training samples at the current moment and the previous moment reaches the preset condition, use the training samples at the current moment to update the parameters of the energy consumption model; Step 4: Taking the energy consumption of the ice storage system as the optimization target of the control model of the lower ice storage system, the control model of the lower ice storage system is constructed, and the penalty function of the dual-condition refrigeration host switching is introduced to construct the objective function and constraint conditions of the control model of the lower ice storage system; The constraints of the lower ice storage system control model include the optimal cooling capacity obtained by the upper ice storage system control model ; Step 5. Solve the control model of the lower ice storage system to obtain the optimal hourly operating parameters of the dual-condition refrigeration host, water pump system and cooling tower; control the dual-condition refrigeration host, water pump system and cooling tower to perform ice storage or cold release tasks according to the hourly operating parameters.

2. The ice storage control method based on hierarchical multi-objective optimization according to claim 1 is characterized in that: In step 4, the penalty function The expression is: ； in, The number of times the dual-mode cooling host is turned on; The number of times the dual-mode cooling host is shut down; It is the single operation time of the dual-mode refrigeration host; is the preset penalty function coefficient; The objective function f5 of the control model of the lower ice storage system is: ； in, Energy consumption of dual-mode refrigeration host; is the energy consumption of the cooling tower; W 水泵 is the energy consumption of the pump system.

3. The ice storage control method based on hierarchical multi-objective optimization according to claim 2 is characterized in that: In the step 3, the energy consumption model of the dual-mode refrigeration host is: ； in, It is the rated cooling capacity of the dual-mode refrigeration host; is the operating load rate of the dual-condition refrigeration host; cop2 is the actual operating energy efficiency of the dual-condition refrigeration host, and its expression is: ； Among them, P j is the least squares fitting coefficient; t c is the cooling water temperature.

4. The ice storage control method based on hierarchical multi-objective optimization according to claim 2 is characterized in that: In the step 3, the energy consumption model of the cooling tower is: ； Among them, m a is the cooling tower air volume; are the least squares fitting coefficients of cooling tower fans.

5. The ice storage control method based on hierarchical multi-objective optimization according to claim 2 is characterized in that: In step 3, the water pump system includes a freezing pump and a cooling pump, and the energy consumption model of the water pump system is: ； Among them, m cw is the flow rate of the water pump; are the least squares fitting coefficients of the water pump respectively.

6. The ice storage control method based on hierarchical multi-objective optimization according to claim 1 is characterized in that: In the step 1, the objective function The expression is: ； in, For operating expenses; is carbon emission; a and b are operating costs respectively and carbon emissions The weight of Operating costsf1 and carbon emissions The expression is: ； ； in, Daily operating expenses; for carbon emissions; The power consumption of the refrigeration host at the i-th moment; For the The amount of ice storage and ice melting at the moment; E is System electricity price at the moment; E F For the The carbon emission factor of the system at that moment; The constraints of the upper ice storage system control model are: ； in, The power consumption of the refrigeration host at the i-th moment; For the The cooling capacity of the refrigeration host at the moment; cop1 is the estimated operating energy efficiency of the refrigeration host; For the The amount of ice storage and ice melting at each moment; For the The ice storage tank releases coldness at all times; is the cold release pipeline transport coefficient; For the Total cooling capacity of ice storage system at each moment; and They are the maximum cooling capacity and minimum cooling capacity of the refrigeration host respectively; and They are the maximum and minimum cooling capacity of the ice storage tank respectively.

7. The ice storage control method based on hierarchical multi-objective optimization according to claim 1 is characterized in that: In step 4, the constraints of the control model of the lower ice storage system are: ； in, Energy consumption of dual-mode refrigeration host; is the optimal cooling capacity output by the upper ice storage system control model; cop2 is the actual operating energy efficiency of the dual-condition refrigeration host; It is the rated cooling capacity of the dual-mode refrigeration host; is the operating load rate of the dual-mode refrigeration host; m w is the cooling water flow rate; is the heat released by the cooling tower; c is the specific heat capacity of water; ∆t 冷却 is the cooling water temperature difference; m c is the chilled water flow rate; ∆t 冷冻 is the chilled water temperature difference; is the saturated air enthalpy; is the enthalpy of outdoor air; is the cooling tower heat and humidity relationship fitting coefficient; m a is the cooling tower air volume.

8. The ice storage control method based on hierarchical multi-objective optimization according to claim 1 is characterized in that: The solution method of the upper ice storage system control model and the lower ice storage system control model adopts the NSGA-II algorithm.

9. The ice storage control method based on hierarchical multi-objective optimization according to claim 1, characterized in that: In the step three, the method for judging whether the distribution difference reaches the preset condition is: using the KS test to detect the distribution of the training samples at the current moment and the previous moment, if it is detected that the change in the feature distribution exceeds the preset threshold D, the distribution difference reaches the preset condition.

10. An ice storage system based on hierarchical multi-objective optimization, characterized in that: It includes a dual-mode refrigeration main unit, an ice storage device, a water pump system, a cooling tower and a controller; the water pump system includes a freezing pump and a cooling pump; the controller is used to execute the ice storage control method based on hierarchical multi-objective optimization described in claim 1; in the cooling release state, the controller controls the ice storage device to melt ice according to the obtained optimal ice melting amount, and the melted cooling water is delivered to the air conditioner or other cooling system through the cooling pump; in the cooling storage state, the controller controls the freezing pump to deliver the freezing water to the ice storage device according to the obtained optimal working parameters, and controls the cooling pump to deliver the cooling water generated by the dual-mode refrigeration main unit to the cooling tower for heat dissipation.

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