Robust Optimal Control Method for Air Conditioning Chilled Water System Considering Measurement Uncertainty

Abstract

The present application relates to a robust optimization control method for an air-conditioning cooling water system taking into account measurement uncertainty. It is applicable to the fields of building energy conservation and intelligent building control technology. The technical solution includes: obtaining the sensor bias and measurement noise of each measurement parameter with uncertainty in the air-conditioning cooling water system, and calculating the measurement error distribution of the sensor corresponding to each measurement parameter; based on the measurement value and the measurement error distribution, generating multiple possible true value scenarios; based on the possible true value scenarios, combined with the mathematical expectation form of the measurement parameters, solving and calculating to obtain the cooling water outlet temperature of each chiller of the air-conditioning cooling station at the current control moment; the mathematical expectation form of the measurement parameters is to use the robust optimization theory to express the optimization control task of the air-conditioning cooling water system; the optimization control task of the air-conditioning cooling water system includes the objective function of the optimization control problem, the heat rejection constraint and the energy conservation constraint, and the objective function is to minimize the total energy consumption of the air-conditioning cooling water system.

Description

Technical Field

[0001] The present invention relates to a robust optimization control method for an air-conditioning cooling water system taking measurement uncertainty into consideration, and is applicable to the technical fields of building energy conservation and intelligent building control. Background Art

[0002] During the operation of public buildings, central air conditioning systems consume approximately 50%-60% of their energy, exceeding the total energy consumption of systems like lighting and elevators, making them the largest energy consumer during the building's operational phase. The cooling water system is a crucial component of central air conditioning systems. Improving the operating efficiency of air conditioning cooling water systems is crucial for reducing overall energy consumption and offers a powerful entry point for achieving energy conservation and low-carbon development in public buildings. Regulating air conditioning cooling water systems through rational optimization and control methods, thereby reducing system operating energy consumption while meeting the demands of various terminal loads, is a research hotspot in this field.

[0003] Optimizing and controlling air conditioning cooling water systems relies on accurate measurement of key operating parameters. Due to factors such as sensor bias and measurement noise, measurement uncertainty is unavoidable during actual cooling water system operation, often leading to a certain deviation between the input and actual operating parameter values. However, existing optimization and control methods for cooling water systems rarely consider the impact of measurement uncertainty on optimization results. This not only affects system efficiency but, in severe cases, can even impact system safety and stability. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: in view of the above-mentioned problems, a robust optimization control method for an air-conditioning cooling water system is provided which takes measurement uncertainty into consideration.

[0005] The technical solution adopted by the present invention is: a robust optimization control method for an air conditioning cooling water system considering measurement uncertainty, characterized by comprising:

[0006] Obtain the sensor bias and measurement noise of each measurement parameter with uncertainty in the air conditioning cooling water system, and calculate the measurement error distribution of the sensor corresponding to each measurement parameter;

[0007] Based on the measurement value of each sensor at the current control moment and the measurement error distribution of each sensor, a plurality of possible true value scenarios are generated by the adaptive Monte Carlo method, and the possible true value scenarios include the possible true value of each measurement parameter;

[0008] Based on possible truth value scenarios and the mathematical expectation form of the measurement parameters, the cooling water outlet temperature of each chiller in the air-conditioning cooling station at the current control time is calculated;

[0009] The mathematical expectation form of the measurement parameters is used to express the optimization control task of the air conditioning cooling water system using robust optimization theory;

[0010] The optimization control task of the air-conditioning cooling water system includes the objective function of the optimization control problem, the heat rejection constraint and the energy conservation constraint, and the objective function is to minimize the total energy consumption of the air-conditioning cooling water system.

[0011] The mathematical expectation form of the measurement parameters includes:

[0012]

[0013] Where J is the total energy consumption of the air conditioning cooling water system; N s is the number of possible true value scenarios; N chi 、N cwp and N ct Respectively represent the number of chillers, cooling water pumps and cooling towers in the air conditioning cooling water system; P chi,i 、P cwp,i and P ct,i Represent the energy consumption functions of the i-th chiller, cooling water pump and cooling tower respectively; T cwr,i and T cws,i are the cooling water outlet temperature and return water temperature of the i-th chiller respectively; m cw,i is the cooling water flow rate flowing through the i-th cooling water pump; and RH s are the possible true values ​​of outdoor dry-bulb temperature and relative humidity under the sth possible true value scenario; Q cl is the terminal cooling load; Q ct,i is the heat rejection of the i-th cooling tower; c p is the specific heat capacity of water at constant pressure.

[0014] The step of obtaining sensor deviation and measurement noise of each measurement parameter with uncertainty in the air-conditioning cooling water system and calculating the measurement error distribution of the sensor corresponding to each measurement parameter includes:

[0015]

[0016] Among them, e j is the measurement error distribution of the sensor corresponding to the jth measurement parameter; μ j is the sensor bias of the sensor corresponding to the jth measurement parameter; are the measurement noises of the sensors corresponding to the j-th measurement parameters.

[0017] The method generates multiple possible true value scenarios based on the measurement values ​​of each sensor and the measurement error distribution of each sensor at the current control moment through the adaptive Monte Carlo method, including:

[0018] Get the measurement value of each sensor at the current control moment;

[0019] Perform multiple random samplings from the measurement error distribution corresponding to each measurement parameter to generate a set of possible true values ​​of each measurement parameter;

[0020] Determine whether all possible truth value sets have met the convergence conditions. If not, continue sampling to generate possible truth value sets.

[0021] The convergence conditions include:

[0022]

[0023] in, is the mean vector of each measurement parameter in the i-th possible true value set; is the variance of each measurement parameter in the i-th possible true value set; STD is the standard deviation calculation function; h is the number of all possible true value sets at present; ε1 and ε2 are the corresponding thresholds.

[0024] The measured parameters include outdoor dry-bulb temperature and outdoor relative humidity.

[0025] A robust optimization control system for an air-conditioning cooling water system taking measurement uncertainty into account is characterized by comprising:

[0026] The error calculation module is used to obtain the sensor deviation and measurement noise of each measurement parameter with uncertainty in the air conditioning cooling water system, and calculate the measurement error distribution of the sensor corresponding to each measurement parameter;

[0027] A scenario generation module is used to generate multiple possible true value scenarios based on the measurement values ​​of each sensor and the measurement error distribution of each sensor at the current control moment through an adaptive Monte Carlo method. The possible true value scenarios include possible true values ​​of each measurement parameter;

[0028] The system optimization module is used to calculate the cooling water outlet temperature of each chiller in the air conditioning cooling station at the current control time based on possible true value scenarios and the mathematical expectation form of the measurement parameters;

[0029] The optimization control task of the air-conditioning cooling water system includes the objective function of the optimization control problem, the heat rejection constraint and the energy conservation constraint, and the objective function is to minimize the total energy consumption of the air-conditioning cooling water system.

[0030] A storage medium stores a computer program that can be executed by a processor, characterized in that when the computer program is executed, the steps of the robust optimization control method for an air-conditioning cooling water system considering measurement uncertainty are implemented.

[0031] An electronic device has a memory and a processor, wherein the memory stores a computer program that can be executed by the processor, and is characterized in that when the computer program is executed, the steps of the robust optimization control method of the air-conditioning cooling water system considering measurement uncertainty are implemented.

[0032] The beneficial effects of the present invention are as follows: the present invention uses adaptive Monte Carlo simulation to generate a series of possible true value scenarios of uncertainty parameters, thereby simulating and estimating the distribution law of measurement errors. The present invention uses robust optimization theory to comprehensively consider various possible true value scenarios of uncertainty parameters, and calculates the optimal control scheme under measurement uncertainty conditions from the perspective of mathematical expectation. The robust optimization control method for air-conditioning cooling water systems considering measurement uncertainty proposed in the present invention can effectively reduce the impact of measurement uncertainty on optimization results, not only ensuring the safe and reliable operation of air-conditioning cooling water systems under measurement uncertainty environments, but also improving the operating efficiency of the system.

[0033] The robust optimization control method proposed in this paper, which accounts for measurement uncertainty, improves the operating efficiency of air conditioning cooling water systems in the presence of inaccurate sensor measurements. By simulating and estimating the possible true values ​​of the sensor measurement process, this method reduces the impact of measurement uncertainty on the optimal control solution and reduces energy waste caused by sensor measurement deviations. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Flowchart of an embodiment of the present invention.

[0035] Figure 2 Schematic diagram of an air conditioning cooling water system in an embodiment of the present invention.

[0036] Figure 3 These are the measured values ​​of outdoor dry-bulb temperature and relative humidity under two working conditions in the embodiment of the present invention.

[0037] Figure 4 Result comparison of cooling water return temperature in the embodiment of the present invention.

[0038] Figure 5 The following is a comparison of the energy consumption of the system in the embodiment of the present invention. DETAILED DESCRIPTION

[0039] Example 1: Figure 1 As shown, this embodiment is a robust optimization control method for an air conditioning cooling water system considering measurement uncertainty, which specifically includes the following steps:

[0040] S1. Obtain the sensor bias and measurement noise of each measurement parameter in the air conditioning cooling water system that has uncertainty and is associated with the heat rejection of the cooling tower in the system, and calculate the measurement error distribution of the sensor corresponding to each measurement parameter.

[0041] In this embodiment, there are two uncertain measurement parameters, including outdoor dry-bulb temperature and outdoor relative humidity. The measurement error distribution of the sensor corresponding to each measurement parameter is shown in the following formula:

[0042]

[0043] Where e1 and e2 are the measurement error distributions of outdoor dry-bulb temperature and outdoor relative humidity, respectively; μ1 and μ2 are the sensor biases of outdoor dry-bulb temperature and outdoor relative humidity, respectively. The sensor bias is determined by a calibration method, which introduces a set of redundant reference sensors with higher accuracy. By comparing the measurement differences of the same physical quantity between the sensor to be calibrated and the reference sensors, the sensor bias is calculated. The measurement noise corresponding to the outdoor dry-bulb temperature and outdoor relative humidity, respectively, is determined by the following equations:

[0044]

[0045] Where, E1 and E2 are the sensor accuracies indicated in the sensor manuals for the outdoor dry-bulb temperature and outdoor relative humidity, respectively.

[0046] S2. Based on the measurement value of each sensor at the current control moment and the measurement error distribution of each sensor, a plurality of possible true value scenarios are generated by an adaptive Monte Carlo method. The possible true value scenarios include possible true values ​​of each measurement parameter.

[0047] S2-1. Set the number of measurement parameters N msr = 2 and the number of samples in a single set of possible truth values ​​N batch =10.

[0048] S2-2, obtain the actual measurement value θ of the sensor corresponding to each measurement parameter at the control moment msr,j .

[0049] S2-3. Perform multiple random samplings from the measurement error distribution corresponding to each measurement parameter to generate a set of possible true values ​​of each measurement parameter.

[0050] From the measurement error distribution e j Random sampling is performed and the possible true value of each measurement parameter is determined according to the following formula:

[0051] θ PT,j =θ msr,j -e j

[0052] Among them, θ PT,j is the possible true value of the jth measurement parameter.

[0053] The above sampling is repeated N timesbatch times, generating a set of possible truth values, as shown below:

[0054]

[0055] in, is the possible true value set of the hth measurement parameter, and a single set contains N batch = 10 possible truth value scenarios.

[0056] S2-4: Determine whether all possible true value sets currently collected meet the convergence condition. If so, the sampling process ends and proceeds to step S3. If not, return to step S2-3 and perform sampling again.

[0057] In this embodiment, the convergence judgment condition is that the following two equations are satisfied simultaneously:

[0058]

[0059] in, is the mean vector of each measurement parameter in the i-th possible true value set; is the variance of each measurement parameter in the i-th possible true value set; STD is the standard deviation calculation function; h is the number of all possible true value sets at present; ε1 and ε2 are the corresponding thresholds.

[0060] S3. Based on the possible true value scenario and combined with the mathematical expectation form of the measurement parameters, the cooling water outlet temperature of each chiller of the air-conditioning cooling station at the current control time is calculated.

[0061] In this embodiment, the mathematical expectation form of the measurement parameters is used to express the optimization control task of the air-conditioning cooling water system using robust optimization theory. The optimization control task of the air-conditioning cooling water system includes the objective function of the optimization control problem, the heat rejection constraint, and the energy conservation constraint.

[0062] In this embodiment, the objective function is to minimize the total energy consumption of the air conditioning cooling water system (chiller, cooling water pump and cooling tower); the heat rejection constraint is that the total heat rejection of the cooling tower should be equal to the sum of the total operating energy consumption of the chiller and the terminal cooling load.

[0063] The optimization control task of the air conditioning cooling water system in this example is as follows:

[0064]

[0065]

[0066] Q ct,i (T db ,RH)=c p mcw,i (T cws,i -T cwr,i ),i=1,...,N ct

[0067] Among them, J is the total energy consumption of the air conditioning cooling water system, N chi 、N cwp and N ct Respectively represent the number of chillers, cooling water pumps and cooling towers in the air conditioning cooling water system, P chi,i 、P cwp,i and P ct,i Represent the energy consumption functions of the i-th chiller, cooling water pump and cooling tower, T cwr,i and T cws,i are the cooling water outlet temperature and return water temperature of the i-th chiller, m cw,i is the cooling water flow rate flowing through the i-th cooling water pump, T db and RH represent outdoor dry bulb temperature and relative humidity respectively (the two most important measurement parameters for air conditioning cooling water systems), Q cl is the terminal cooling load, Q ct,i is the heat rejection of the i-th cooling tower, c p is the specific heat capacity of water at constant pressure.

[0068] In this embodiment, the energy consumption functions of the chiller, cooling water pump, and cooling tower are as follows:

[0069]

[0070] Among them, Q nom is the rated cooling capacity of the chiller, COP nom is the rated performance coefficient of the chiller, Q chi is the actual cooling capacity of the refrigerator, T chw,out is the chilled water supply temperature of the chiller, T cw,in is the return water temperature of the chiller cooling water, a1-a3, b1-b6 are the undetermined coefficients of the chiller, η cwp is the pump efficiency, SG is the specific gravity of the conveyed fluid (take SG = 1), m cwp is the cooling water pump flow rate, f cwp is the operating frequency of the cooling water pump, f rated is the rated operating frequency of the water pump and fan, h1-h3 is the coefficient to be determined for the cooling water pump, P ct,rated is the rated power of the cooling tower fan, f ct is the operating frequency of the cooling tower fan, and d1-d4 are the undetermined coefficients of the cooling tower fan.

[0071] The heat rejection function of the cooling tower in this embodiment is as follows:

[0072]

[0073] Among them, m ct,air is the air supply volume of the cooling tower fan, m ct,cw,in is the cooling water inlet flow rate of the cooling tower, T ct,cw,in is the cooling water inlet temperature of the cooling tower, c1-c3 is the heat transfer coefficient of the cooling tower, T wb is the outdoor wet-bulb temperature, which is calculated from the outdoor dry-bulb temperature and outdoor relative humidity.

[0074] In this embodiment, the robust optimization theory is used to express the optimization control task of the air conditioning cooling water system as a mathematical expectation form with respect to the outdoor dry-bulb temperature and the outdoor relative humidity, as shown in the following formula:

[0075]

[0076] Among them, N s is the number of possible truth value scenarios, and RH s are the possible true values ​​of the outdoor dry-bulb temperature and relative humidity under the sth possible true value scenario, which are obtained from the possible true value set generated by step S2. τ represents the degree to which the robust optimization method is allowed to violate the constraints during optimization, which is determined by the following formula:

[0077]

[0078] Where χ is the probability that the decision maker allows the robust optimization method to violate the constraints when searching for the optimal solution. is the maximum deviation allowed to violate the constraint.

[0079] In this embodiment, the optimization solution is performed based on the above formula to calculate a set of optimal optimization variables at the control time. In this example, the air conditioning cold station has 5 chillers, 5 fixed-frequency chilled water pumps, 5 variable-frequency cooling water pumps and 5 cooling towers (see Figure 2 ), the optimal optimization variable is the cooling water outlet temperature of each chiller [T cwr,1 ,T cwr,2 ,T cwr,3 ,T cwr,4 ,T cwr,5 ] and output it to the actuator to execute the control action.

[0080] The following is a specific example to illustrate the verification: The main configuration of the air conditioning cooling water system in this embodiment is as follows: the rated cooling capacity of the chiller is 1600kW, the rated COP is 5.71, the rated power of the chilled water pump is 27kW, the rated flow rate is 288m 3 / h, the rated power of the cooling water pump is 40kW and the rated flow rate is 324m 3 / h, the rated heat exchange of the cooling tower is 2194kW, the rated power of the fan is 22kW, and the rated flow rate of the fan is 4200m 3 / min.

[0081] Two measurement uncertainty conditions are set for performance verification: In condition 1, the measured value of the outdoor wet-bulb temperature calculated based on the outdoor dry-bulb temperature and relative humidity is always less than its true value, where the measurement error distribution of the outdoor dry-bulb temperature is N(-1.5, 0.4 2 ), the unit is ℃, the measurement error distribution of relative humidity is N(-4.5,1.9 2 ), the unit is %. In the second working condition, the measured value of the outdoor wet-bulb temperature is always greater than its true value, and the measurement error distribution of the outdoor dry-bulb temperature is N(1.5,0.4 2 ), the measurement error distribution of relative humidity is N(4.5,1.9 2 The measured values ​​of outdoor dry bulb temperature and relative humidity under the two working conditions are as follows: Figure 3 As shown. χ is set to 5%, Set to 100kW.

[0082] Two other control methods were set up for performance comparison: Method 1, a traditional deterministic control method, directly uses the measured values ​​of outdoor dry-bulb temperature and relative humidity as inputs to the optimization control problem, ignoring measurement uncertainty. Method 2, an ideal control method, uses the actual values ​​of outdoor dry-bulb temperature and relative humidity as inputs to evaluate the degree of deviation between the optimized and ideal results obtained by different control methods under measurement uncertainty. The control interval for all three control methods was set to 10 minutes.

[0083] Comparison of cooling water return temperature results under different control methods in working condition 1 Figure 4 As shown in Figure 1, because the measured outdoor wet-bulb temperature in operating condition 1 is always lower than its true value, the traditional deterministic control method overestimates the cooling tower's heat removal capacity, resulting in the cooling water return temperature being consistently high, exceeding 33°C (the upper limit of the cooling water return temperature recommended in the design specification manual) for as much as 84.1% of the time. In contrast, the cooling water return temperature of the method of the present invention only exceeds 33°C for 0.3% of the time, which is close to the level of the ideal control method.

[0084] The comparison of system energy consumption under different control methods in working condition 2 is as follows: Figure 5 As shown in Figure 2, because the measured outdoor wet-bulb temperature in operating condition 2 was consistently greater than its true value, the traditional deterministic control method underestimated the cooling tower's heat removal capacity, resulting in an increase in the cooling tower fan's operating frequency and increased system energy consumption. In contrast, the proposed method reduced operating energy consumption by 4.5%, with a deviation of less than 0.1% from the ideal control method.

[0085] It can be seen that the present invention can reduce the impact of measurement uncertainty on the optimization results of traditional control methods of air-conditioning cooling water systems. On the one hand, it ensures the safety and reliability of the operation process of the air-conditioning cooling water system, and on the other hand, it improves the operating efficiency of the air-conditioning cooling water system.

[0086] Example 2: This example is a robust optimization control system for an air-conditioning cooling water system taking into account measurement uncertainty, including: an error calculation module, a scenario generation module, a system optimization module, etc.

[0087] In this embodiment, the error calculation module is used to obtain the sensor deviation and measurement noise of each measurement parameter with uncertainty in the air-conditioning cooling water system, and calculate the measurement error distribution of the sensor corresponding to each measurement parameter.

[0088] In this example, the scenario generation module is used to generate multiple possible true value scenarios based on the measurement values ​​of each sensor and the measurement error distribution of each sensor at the current control moment through the adaptive Monte Carlo method. The possible true value scenarios include the possible true values ​​of each measurement parameter.

[0089] In this embodiment, the system optimization module is used to solve and calculate the cooling water outlet temperature of each chiller in the air-conditioning cold station at the current control moment based on possible true value scenarios and combined with the mathematical expectation form of the measurement parameters.

[0090] In this embodiment, the optimization control task of the air conditioning cooling water system includes the objective function of the optimization control problem, the heat rejection constraint, and the energy conservation constraint. The objective function is to minimize the total energy consumption of the air conditioning cooling water system.

[0091] Example 3: This example is a storage medium on which a computer program that can be executed by a processor is stored. When the computer program is executed, the steps of the robust optimization control method for the air-conditioning cooling water system considering measurement uncertainty in Example 1 are implemented.

[0092] Example 4: An electronic device having a memory and a processor, wherein the memory stores a computer program that can be executed by the processor, and when the computer program is executed, the steps of the robust optimization control method of the air-conditioning cooling water system considering measurement uncertainty in Example 1 are implemented.

[0093] The embodiment described above is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Persons skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent substitution or equivalent transformation falls within the scope of protection of the present invention.

Claims

1. A robust optimization control method for an air conditioning cooling water system considering measurement uncertainty, characterized in that: include: Obtain the sensor bias and measurement noise of each measurement parameter with uncertainty in the air conditioning cooling water system, and calculate the measurement error distribution of the sensor corresponding to each measurement parameter; Based on the measurement value of each sensor at the current control moment and the measurement error distribution of each sensor, a plurality of possible true value scenarios are generated by the adaptive Monte Carlo method, and the possible true value scenarios include the possible true value of each measurement parameter; Based on possible truth value scenarios and the mathematical expectation form of the measurement parameters, the cooling water outlet temperature of each chiller in the air-conditioning cooling station at the current control time is calculated. The mathematical expectation form of the measurement parameters is used to express the optimization control task of the air conditioning cooling water system using robust optimization theory; The optimization control task of the air conditioning cooling water system includes the objective function of the optimization control problem, the heat rejection constraint and the energy conservation constraint. The objective function is to minimize the total energy consumption of the air conditioning cooling water system. The mathematical expectation form of the measurement parameters includes: ； ； ； in, is the total energy consumption of the air conditioning cooling water system; N s is the number of possible truth value scenarios; 、 and Respectively represent the number of chillers, cooling water pumps and cooling towers in the air conditioning cooling water system; 、 and denote the energy consumption functions of the i-th chiller, cooling water pump and cooling tower respectively; and are the cooling water outlet temperature and return water temperature of the i-th chiller respectively; is the cooling water flow rate flowing through the i-th cooling water pump; and are the possible true values ​​of outdoor dry-bulb temperature and relative humidity under the sth possible true value scenario; is the terminal cooling load; is the heat rejection of the i-th cooling tower; is the specific heat capacity of water at constant pressure.

2. The robust optimization control method for an air conditioning cooling water system considering measurement uncertainty according to claim 1, characterized in that: The step of obtaining sensor deviation and measurement noise of each measurement parameter with uncertainty in the air-conditioning cooling water system and calculating the measurement error distribution of the sensor corresponding to each measurement parameter includes: ； in, is the measurement error distribution of the sensor corresponding to the jth measurement parameter; is the sensor bias of the sensor corresponding to the jth measurement parameter; are the measurement noises of the sensors corresponding to the j-th measurement parameters.

3. The robust optimization control method for an air conditioning cooling water system considering measurement uncertainty according to claim 1, characterized in that: The method generates multiple possible true value scenarios based on the measurement values ​​of each sensor at the current control moment and the measurement error distribution of each sensor by using an adaptive Monte Carlo method, including: obtaining the measurement values ​​of each sensor at the current control moment; Perform multiple random samplings from the measurement error distribution corresponding to each measurement parameter to generate a set of possible true values ​​of each measurement parameter; Determine whether all possible truth value sets have met the convergence conditions. If not, continue sampling to generate possible truth value sets.

4. The robust optimization control method for an air conditioning cooling water system considering measurement uncertainty according to claim 3 is characterized in that: The convergence conditions include: ； ； in, is the mean vector of each measurement parameter in the i-th possible true value set; is the variance of each measurement parameter in the i-th possible true value set; STD is the standard deviation calculation function; h is the number of all possible true value sets at present; and is the corresponding threshold.

5. The robust optimization control method for an air conditioning cooling water system considering measurement uncertainty according to claim 1, characterized in that: The measured parameters include outdoor dry-bulb temperature and outdoor relative humidity.

6. A robust optimization control system for an air conditioning cooling water system taking into account measurement uncertainty, characterized in that: include: The error calculation module is used to obtain the sensor deviation and measurement noise of each measurement parameter with uncertainty in the air conditioning cooling water system, and calculate the measurement error distribution of the sensor corresponding to each measurement parameter; A scenario generation module is used to generate multiple possible true value scenarios based on the measurement values ​​of each sensor and the measurement error distribution of each sensor at the current control moment through an adaptive Monte Carlo method. The possible true value scenarios include possible true values ​​of each measurement parameter; The system optimization module is used to calculate the cooling water outlet temperature of each chiller in the air-conditioning cooling station at the current control time based on possible true value scenarios and the mathematical expectation form of the measurement parameters; The optimization control task of the air conditioning cooling water system includes the objective function of the optimization control problem, the heat rejection constraint and the energy conservation constraint. The objective function is to minimize the total energy consumption of the air conditioning cooling water system. The mathematical expectation form of the measurement parameters includes: ； ； ； in, is the total energy consumption of the air conditioning cooling water system; N s is the number of possible truth value scenarios; 、 and Respectively represent the number of chillers, cooling water pumps and cooling towers in the air conditioning cooling water system; 、 and denote the energy consumption functions of the i-th chiller, cooling water pump and cooling tower respectively; and are the cooling water outlet temperature and return water temperature of the i-th chiller respectively; is the cooling water flow rate flowing through the i-th cooling water pump; and are the possible true values ​​of outdoor dry-bulb temperature and relative humidity under the sth possible true value scenario; is the terminal cooling load; is the heat rejection of the i-th cooling tower; is the specific heat capacity of water at constant pressure.

7. A storage medium having stored thereon a computer program executable by a processor, characterized in that: When the computer program is executed, the steps of the robust optimization control method for an air-conditioning cooling water system considering measurement uncertainty as described in any one of claims 1 to 5 are implemented.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, wherein: When the computer program is executed, the steps of the robust optimization control method for an air-conditioning cooling water system considering measurement uncertainty as described in any one of claims 1 to 5 are implemented.

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