Multi-objective optimization control method for air source heat pump air conditioning system based on improved ant lion algorithm

Abstract

The invention relates to the technical field of multi-objective optimization control, in particular to an improved ant lion algorithm-based multi-objective optimization control method for an air source heat pump air conditioning system, which comprises the following steps of: constructing a triple constraint fitness function; the method has the beneficial effects that the limitation of single target optimization of a traditional control method is broken through by constructing a triple constraint fitness function comprising a system energy consumption item, a comfort item and a system comprehensive penalty item. By dynamically adjusting the correction coefficient, the relation of energy consumption, comfort level and stability can be flexibly balanced according to actual requirements, the extreme situation that comfort level is sacrificed for energy conservation or excessive energy consumption is caused for comfort is avoided, and the optimal comprehensive performance of the system is achieved.

Description

Technical Field

[0001] This invention relates to the field of multi-objective optimization control technology, specifically to a multi-objective optimization control method for an air source heat pump air conditioning system based on an improved antlion algorithm. Background Technology

[0002] With the deepening of the "dual carbon" strategy, energy-saving renovations in the building sector, a key area of ​​energy consumption, have become a crucial link in achieving carbon emission reduction targets. In my country, many public buildings (such as office buildings, shopping malls, and hospitals) were built relatively early, and their associated air conditioning systems generally suffer from aging equipment and outdated control logic, resulting in low energy efficiency and high operating energy consumption. Air source heat pump systems, as a form of clean energy utilization, are widely used in the renovation of air conditioning cooling and heating sources in older buildings due to their high energy efficiency and convenient installation. However, existing air source heat pump systems still face many challenges in actual operation:

[0003] The problem of hydraulic imbalance is prominent: due to design defects, pipe aging or valve damage, the terminal pipe network has always been accompanied by hydraulic imbalance, resulting in insufficient cooling / heating in some areas and energy waste in other areas, which seriously affects the overall operating efficiency of the system.

[0004] Poor adaptability of control strategies: Traditional control methods often use fixed parameters or simple feedback adjustment, which makes it difficult to optimize operating parameters in real time according to changes in the outdoor environment (such as temperature and humidity fluctuations) and fluctuations in terminal load (such as personnel movement and equipment start-up and shutdown), resulting in the unit often operating in an inefficient condition.

[0005] Balancing energy consumption and comfort is difficult: Existing control logic often focuses on a single objective (such as prioritizing energy reduction or only ensuring comfort), lacking coordinated optimization of multiple objectives such as "energy consumption-comfort-system stability", which can easily lead to extreme situations of "sacrificing comfort for energy saving" or "excessive energy consumption for comfort". Summary of the Invention

[0006] The purpose of this invention is to provide a multi-objective optimization control method for air source heat pump air conditioning systems based on an improved antlion algorithm, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a multi-objective optimization control method for an air source heat pump air conditioning system based on an improved antlion algorithm, comprising the following steps:

[0008] Construct a triple-constraint fitness function, the expression of which is: F(X) = w1E total +w2|η unf |+w3Pen;

[0009] Among them, w1, w2, and w3 are correction coefficients, which are adjusted according to the actual system requirements and operating conditions; E total This is the system energy consumption item, reflecting the total energy consumption level of the system; η unf The comfort term reflects the comfort level during system operation; Pen is the system comprehensive penalty term used to constrain system stability; the operating parameters are optimized based on the improved antlion algorithm; the optimal solution obtained by optimization is applied to the actual system, the optimization execution cycle is set, and the global optimal variable is calculated and applied again.

[0010] Preferably, the system energy consumption item E total The expression is:

[0011]

[0012] Where, N hp Number of heat pumps in operation; Q i The heat exchange capacity of a single heat pump is calculated from the data of the dry pipe energy meter. The COP value for the unit under the current environment and set outlet water temperature is obtained through the three-dimensional COP interpolation table provided by the manufacturer; k is the pump power coefficient (kW / Hz). 3 ), f p Set the frequency for the current water pump; n is the number of water pumps in operation; E max This represents the system's historical maximum power.

[0013] Preferably, the comfort item η unf The expression is:

[0014]

[0015] in, Set the unit's outlet water temperature; This is the design value for the unit's outlet water temperature; η represents the maximum permissible deviation of the designed value from the set value for the outlet water temperature. room The room comfort coefficient is derived from a combination of the obtained indoor temperature setpoint and the actual value.

[0016] Preferably, the expression for the system comprehensive penalty term Pen is:

[0017] Pen=αΦ ΔT +βΦ pred +δλ flow

[0018] Where α, β, and δ are correction coefficients; Φ ΔT For temperature difference deviation; Φ pred For load forecasting; λ flow Penalties are applied to traffic.

[0019] Preferably, the initialization steps for optimizing the running parameters based on the improved antlion algorithm include: setting the population size, the number of ants and antlions, and setting the maximum number of iterations; defining ant parameters:

[0020] XA = [N] hp ,f p ,T out ];

[0021] Where, N hp f is the number of heat pumps in operation. p The frequency of combined operation of the water pumps; T out This refers to the outlet water temperature of the heat pump.

[0022] Define variable boundaries:

[0023] Number of heat pumps: Define the number of pumps in operation as {1, 2, ..., n}; Frequency of combined operation of water pumps: [f min ,f max Heat pump outlet water temperature:

[0024] Pipeline network minimum differential pressure constraint: If the sensor data for the most unfavorable differential pressure is less than the design value, then the constraint ant parameter f is applied. p →f p +1Hz, if the worst-case differential pressure sensor data is within the required range, then constrain the trap range, ant parameter f. p Adjustments in the negative direction are not allowed.

[0025] Comfort emergency constraints: If η room If the value is ≥0.2, then the range of the constraint trap is determined, and the ant parameter N is used. hp , f p Adjustments in the negative direction are not allowed.

[0026] Summer maximum outlet water temperature constraints and winter minimum outlet water temperature constraints: The summer maximum outlet water temperature of the unit should meet the dehumidification requirements, and the winter minimum outlet water temperature should meet the comfort requirements of the terminal equipment.

[0027] Randomly initialize ant and antlion positions: randomly select the number of heat pumps for each antlion; randomly generate the water pump frequency and heat pump outlet water temperature f for each antlion. p ,T out Each ant chooses the same initial position as the antlion.

[0028] Preferably, the fitness evaluation step for optimizing the running parameters based on the improved antlion algorithm is as follows: for each ant and antlion, calculate its fitness value at the current position based on the collected data and the constructed model.

[0029] Preferably, the roulette wheel selection step for optimizing the running parameters based on the improved antlion algorithm is as follows: for each ant, an antlion is selected as its target through roulette wheel selection. The better the fitness of the antlion, the greater the probability of being selected. The antlion with the best fitness is selected as the elite antlion.

[0030] Preferably, the random walk modeling step based on the improved antlion algorithm to optimize the solution of running parameters includes:

[0031] Let the position of the i-th ant at iteration t be:

[0032]

[0033] Define a random function:

[0034]

[0035] r∈[0,1], random number, then random walk function

[0036] X(t)=[0,cumsum(2r(t1)-1,cumsum(2r(t2)-1),…,cumsum(2r(t) n )-1)];

[0037] The value of the i-th ant's random walk in generation t is The maximum value of a random walk in the t-th generation population is Minimum value is

[0038] definition Let be the relative position of the i-th ant during the random walk in generation t, and its calculation formula is as follows:

[0039]

[0040] The ant selects the k-th antlion as its target antlion. The range function for calculating the antlion trap with penalty direction is as follows:

[0041]

[0042] Let d be the position of the k-th antlion in the d-th dimension; The range of values ​​for the d-th dimension of the k-th antlion trap; Pen max This is the highest penalty value in history.

[0043] After the ants randomly wandered around the chosen ant lion, their positions in the trap were as follows:

[0044]

[0045] Meanwhile, the ants randomly move towards the globally optimal elite antlion according to the above process, and obtain the elite antlion's movement value. The ant's position is then updated to The ant will stay at the midpoint between these two locations; this is counted as...

[0046] The update of the number of operating units utilizes discretization based on domain constraints for further processing. After discretization... replace Updated to

[0047] Preferably, the step of updating the antlion position by optimizing the solution of running parameters based on the improved antlion algorithm is as follows: after all ants have moved, the fitness values ​​of all ants are recalculated, and the antlion fitness values ​​calculated previously are combined to select the better individuals as antlions. If the original antlion is not selected, it is deleted, and elite antlions are selected again.

[0048] Preferably, the iterative and termination steps for optimizing the operating parameters based on the improved antlion algorithm are as follows: after reaching the maximum number of iterations or satisfying the convergence condition, output the global optimal solution: the optimal number of heat pumps to be turned on, the frequency of joint operation of water pumps, and the set temperature of the unit outlet water.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] This invention proposes a multi-objective optimization control method for air-source heat pump air conditioning systems based on an improved antlion algorithm. By constructing a triple-constraint fitness function that includes system energy consumption, comfort, and overall system penalty terms, it overcomes the limitations of traditional single-objective optimization methods. Through dynamic adjustment of correction coefficients, the relationship between energy consumption, comfort, and stability can be flexibly balanced according to actual needs, avoiding extreme situations such as "sacrificing comfort for energy saving" or "excessive energy consumption for comfort," thus achieving optimal overall system performance.

[0051] A dynamic constraint mechanism is introduced to address key hydraulic imbalance factors such as minimum differential pressure in the pipe network and the most unfavorable loop conditions. By constraining the adjustment direction of the pump frequency (e.g., forcibly increasing the frequency when the most unfavorable differential pressure is insufficient), the hydraulic imbalance in the terminal pipe network is effectively improved, reducing insufficient cooling / heating or energy waste in some areas. Simultaneously, temperature difference deviation and flow matching penalties in the system's comprehensive penalty items further constrain system operating parameters to maintain them within a reasonable range, enhancing overall stability.

[0052] Based on the improved Antlion algorithm, the optimization process can dynamically optimize the number of heat pumps in operation, pump frequency, and outlet water temperature in real time by combining outdoor environmental parameters and terminal load fluctuations. Compared with traditional fixed parameter or simple feedback regulation, this method can quickly respond to changes in operating conditions, ensuring that the unit always operates in the high-efficiency range, and solving the problem of poor adaptability of traditional control strategies.

[0053] The improved antlion algorithm enhances the convergence speed and solution accuracy of the optimization process by introducing elite antlion guidance, random walk dynamic constraints, and discretization of the number of heat pumps, enabling it to find the global optimum within a finite number of iterations. Furthermore, the correction coefficients can be flexibly adjusted according to different building types (such as office buildings, shopping malls, and hospitals) and operational requirements, making it adaptable to any air-source heat pump system. This results in low engineering application costs and high feasibility. Attached Figure Description

[0054] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the present invention clear and complete, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only some, not all, embodiments of the present invention, and are merely illustrative of the embodiments of the present invention. They are not intended to limit the embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Please provide a technical solution: a multi-objective optimization control method for an air source heat pump air conditioning system based on an improved antlion algorithm, comprising the following steps:

[0057] 1. Construct a triple-constraint fitness function. The expression for the triple-constraint fitness function is:

[0058] F(X) = w1E total +w2|η unf |+w3Pen

[0059] Among them, w1, w2, and w3 are correction coefficients, which can be adjusted according to the actual system requirements and operating conditions; E total This is the system energy consumption item, reflecting the total energy consumption level of the system; η unf The comfort term reflects the comfort level during system operation; Pen is the system comprehensive penalty term, used to constrain system stability. The formulas for each variable are as follows:

[0060]

[0061] N hpNumber of heat pumps in operation;

[0062] Q i The heat exchange capacity of a single heat pump is calculated from the data of the dry pipe energy meter.

[0063] The COP value for the unit under the current environment and set outlet water temperature is obtained through the three-dimensional COP interpolation table provided by the manufacturer.

[0064] k is the pump power coefficient (kW / Hz) 3 ), f p Set the frequency for the current water pump;

[0065] n is the number of water pumps in operation;

[0066] E max This represents the system's historical maximum power.

[0067]

[0068] Set the unit's outlet water temperature;

[0069] This is the design value for the unit's outlet water temperature;

[0070] This refers to the maximum permissible deviation between the design value and the set value for the outlet water temperature.

[0071] η room The room comfort coefficient is derived by combining the obtained indoor temperature setpoint and actual value.

[0072] Pen=αΦ ΔT +βΦ pred +δλ flow ;

[0073] α, β, and δ are correction coefficients;

[0074] Φ ΔT This is the temperature difference deviation term;

[0075] Φ pred For load forecasting;

[0076] λ flow Penalties are applied to traffic.

[0077] 2. Optimize the solution of running parameters based on the improved antlion algorithm.

[0078] 1) Initialization:

[0079] Set the population size (N for both ants and antlions) and the maximum number of iterations.

[0080] Define ant parameters:

[0081] XA = [N] hp ,f p ,T out ];

[0082] N hp Number of heat pumps in operation;

[0083] f p This refers to the frequency of combined operation of the water pumps;

[0084] T out This refers to the outlet water temperature of the heat pump.

[0085] Define variable boundaries:

[0086] Number of heat pumps: Define the number of units in operation as {1, 2, ..., n}.

[0087] Pump joint operation frequency: [f min ,f max ].

[0088] Heat pump outlet water temperature:

[0089] Pipeline network minimum differential pressure constraint: If the sensor data for the most unfavorable differential pressure is less than the design value, then the constraint ant parameter f is applied. p →f p +1Hz, if the worst-case differential pressure sensor data is within the required range, then constrain the trap range, ant parameter f. p Adjustments in the negative direction are not allowed.

[0090] Comfort emergency constraints: If η room If the value is ≥0.2, then the range of the constraint trap is determined, and the ant parameter N is used. hp , f p Adjustments in the negative direction are not allowed.

[0091] Summer maximum outlet water temperature constraints and winter minimum outlet water temperature constraints: The summer maximum outlet water temperature of the unit should meet the dehumidification requirements, and the winter minimum outlet water temperature should meet the comfort requirements of the terminal equipment.

[0092] Randomly initialize the positions of ants and antlions:

[0093] Randomly select the number of heat pumps for each antlion (discrete data);

[0094] For each antlion, randomly generate the water pump frequency and heat pump outlet water temperature f. p ,T out (Continuous within the boundary);

[0095] Each ant chooses the same initial position as the antlion.

[0096] 2) Assess fitness:

[0097] For each ant and antlion, calculate its fitness value at its current location based on the collected data and the constructed model.

[0098] 3) Roulette options:

[0099] For each ant i, an antlion is selected as its target through a roulette wheel selection process. The better the fitness of the antlion, the greater the probability of it being selected. The antlion with the best fitness is selected as the elite antlion.

[0100] 4) Random walk modeling:

[0101] Let the position of the i-th ant at iteration t be:

[0102]

[0103] Define the random function as follows:

[0104]

[0105] r∈[0,1], random number, then random walk function

[0106] X(t)=[0,cumsum(2r(t1)-1,cumsum(2r(t2)-1),…,cumsum(2r(t) n )-1)];

[0107] The value of the i-th ant's random walk in generation t is

[0108] The maximum value of a random walk in the t-th generation population is Minimum value is

[0109] definition Let be the relative position of the i-th ant during the random walk in generation t, and its calculation formula is as follows:

[0110]

[0111] The ant selects the k-th antlion as its target antlion. The range function for calculating the antlion trap with penalty direction is as follows:

[0112]

[0113] Let d be the position of the k-th antlion in the d-th dimension.

[0114] Let d be the range of values ​​for the k-th antlion trap.

[0115] Pen max This is the highest penalty value in history.

[0116] I(t) is the ant trap scaling function. Taking a maximum iteration count of 1000 as an example, the formula is as follows:

[0117]

[0118] After the ants randomly wandered around the chosen ant lion, their positions in the trap were as follows:

[0119]

[0120] Meanwhile, the ants randomly move towards the globally optimal elite antlion according to the above process, and obtain the elite antlion's movement value. The ant's position is then updated to The ant will stay at the midpoint between these two locations.

[0121] Count as

[0122] The update of the number of operating units utilizes discretization based on domain constraints for further processing. After discretization... replace Updated to

[0123] 5) Update Antlion location:

[0124] After all ants have moved, the fitness values ​​of all ants are recalculated. Based on the previously calculated fitness values ​​of the antlions, the N individuals with the better fitness values ​​are selected as antlions. If the original antlions are not selected, they are deleted, and elite antlions are selected again.

[0125] 6) Iteration and Termination:

[0126] Repeat steps 3-5 until the maximum number of iterations is reached or the convergence condition is met, and output the global optimal solution: the optimal number of heat pumps to be turned on, the frequency of combined operation of water pumps, and the set temperature of the unit's outlet water.

[0127] 7) Control execution:

[0128] The optimal solution obtained through optimization is applied to the actual system, the optimization execution cycle is set, and the global optimal variable is recalculated and applied.

[0129] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-objective optimization control method for an air source heat pump air conditioning system based on an improved antlion algorithm, characterized in that: Includes the following steps: Construct a triple-constraint fitness function, the expression of which is: F(X)<w1E total +w2|η unf |+w3Pen; Among them, w1, w2, and w3 are correction coefficients, which are adjusted according to the actual system requirements and operating conditions; E total This is a system energy consumption item, reflecting the total energy consumption level of the system; η unf The comfort term reflects the comfort level during system operation; Pen is the system comprehensive penalty term used to constrain system stability; the operating parameters are optimized based on the improved antlion algorithm; the optimal solution obtained by optimization is applied to the actual system, the optimization execution cycle is set, and the global optimal variable is calculated and applied again.

2. The multi-objective optimization control method for an air source heat pump air conditioning system based on an improved antlion algorithm according to claim 1, characterized in that: The system energy consumption item E total The expression is: Where, N hp Number of heat pumps in operation; Q i The heat exchange capacity of a single heat pump is calculated from the data of the dry pipe energy meter. The COP value for the unit under the current environment and set outlet water temperature is obtained through the three-dimensional COP interpolation table provided by the manufacturer; k is the pump power coefficient (kW / Hz). 3 ), f p Set the frequency for the current water pump; n is the number of water pumps in operation; E max This represents the system's historical maximum power.

3. The multi-objective optimization control method for an air source heat pump air conditioning system based on an improved antlion algorithm according to claim 2, characterized in that: Comfort item η unf The expression is: in, Set the unit's outlet water temperature; This is the design value for the unit's outlet water temperature; η represents the maximum permissible deviation of the designed value from the set value for the outlet water temperature. room The room comfort coefficient is derived from a combination of the obtained indoor temperature setpoint and the actual value.

4. The multi-objective optimization control method for an air source heat pump air conditioning system based on an improved antlion algorithm according to claim 1, characterized in that: The expression for the system's comprehensive penalty term Pen is: Pen=αΦ ΔT +βΦ pred +sl flow Where α, β, and δ are correction coefficients; Φ ΔT For temperature difference deviation; Φ pred For load forecasting; λ flow Penalties are applied to traffic.

5. A multi-objective optimization control method for an air source heat pump air conditioning system based on an improved antlion algorithm according to claim 1, characterized in that: The initialization steps for optimizing the running parameters based on the improved antlion algorithm include: setting the population size, the number of ants and antlions, and the maximum number of iterations; defining the ant parameters: XA=[N hp ,f p ,T out ]; Where, N hp Number of heat pumps in operation; f p The frequency of combined operation of the water pumps; T out This refers to the outlet water temperature of the heat pump. Define variable boundaries: Number of heat pumps: Define the number of pumps in operation {1, 2, ..., n}; Combined operation frequency of water pumps: [f min ,f max Heat pump outlet water temperature: Pipeline network minimum differential pressure constraint: If the sensor data for the most unfavorable differential pressure is less than the design value, then the constraint ant parameter f is applied. p →f p +1Hz, if the most unfavorable differential pressure sensor data is within the required range, then constrain the trap range, ant parameter f. p Adjustments in the negative direction are not allowed. Comfort emergency constraints: If η room If the value is ≥0.2, then the range of the constraint trap is determined, and the ant parameter N is used. hp , f p Adjustments in the negative direction are not allowed. Summer maximum outlet water temperature constraints and winter minimum outlet water temperature constraints: The summer maximum outlet water temperature of the unit should meet the dehumidification requirements, and the winter minimum outlet water temperature should meet the comfort requirements of the terminal equipment. Randomly initialize ant and antlion positions: randomly select the number of heat pumps for each antlion; randomly generate the water pump frequency and heat pump outlet water temperature f for each antlion. p ,T out Each ant chooses the same initial position as the antlion.

6. The multi-objective optimization control method for an air source heat pump air conditioning system based on an improved antlion algorithm according to claim 1, characterized in that: The fitness evaluation step for optimizing the running parameters based on the improved antlion algorithm is as follows: for each ant and antlion, calculate its fitness value at the current position based on the collected data and the constructed model.

7. A multi-objective optimization control method for an air source heat pump air conditioning system based on an improved antlion algorithm according to claim 1, characterized in that: The roulette wheel selection step for optimizing the running parameters based on the improved antlion algorithm is as follows: For each ant, an antlion is selected as its target through roulette wheel selection. The better the fitness of the antlion, the greater the probability of it being selected. The antlion with the best fitness is selected as the elite antlion.

8. A multi-objective optimization control method for an air source heat pump air conditioning system based on an improved antlion algorithm according to claim 1, characterized in that: The random walk modeling steps based on the improved antlion algorithm to optimize the solution of running parameters include: Let the position of the i-th ant at iteration t be: Define a random function: r∈[0,1], random number, then random walk function X(t)=[0,cumsum(2r(t1)-1,cumsum(2r(t2)-1),…,cumsum(2r(t n )-1)]; The value of the i-th ant's random walk in generation t is The maximum value of a random walk in the t-th generation population is Minimum value is definition Let be the relative position of the i-th ant during the random walk in generation t, and its calculation formula is as follows: The ant selects the k-th antlion as its target antlion. The range function for calculating the antlion trap with penalty direction is as follows: Let d be the position of the k-th antlion in the d-th dimension; The range of values ​​for the d-th dimension of the k-th antlion trap; Pen max This is the highest penalty value in history. After the ants randomly wandered around the chosen ant lion, their positions in the trap were as follows: Meanwhile, the ants randomly move towards the globally optimal elite antlion according to the above process, and obtain the elite antlion's movement value. The ant's position is then updated to The ant will stay at the midpoint between these two locations; this is counted as... The update of the number of operating units utilizes discretization based on domain constraints for further processing. After discretization... replace Updated to 9. A multi-objective optimization control method for an air source heat pump air conditioning system based on an improved antlion algorithm according to claim 1, characterized in that: The steps for updating the antlion position by optimizing the running parameters based on the improved antlion algorithm are as follows: After all ants have moved, the fitness values ​​of all ants are recalculated. Combining the previously calculated antlion fitness values, the better individuals are selected as antlions. If the original antlion is not selected, it is deleted, and elite antlions are selected again.

10. A multi-objective optimization control method for an air source heat pump air conditioning system based on an improved antlion algorithm according to claim 1, characterized in that: The iterative and termination steps for optimizing the operating parameters based on the improved Antlion algorithm are as follows: after reaching the maximum number of iterations or meeting the convergence condition, output the global optimal solution: the optimal number of heat pumps to be turned on, the frequency of joint operation of water pumps, and the set temperature of the unit's outlet water.

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