Air conditioning equipment cluster regulation and control method and system suitable for zero-carbon park

Abstract

The invention discloses an air conditioning equipment cluster regulation and control method and system suitable for a zero-carbon park, and the method comprises the steps: obtaining the operation parameters of all air conditioning equipment in the park, and constructing a load regulation and control model with the net revenue maximization of an aggregator as a target, the model comprehensively considers peak regulation income, carbon market transaction income and user incentive cost based on carbon emission intensity; and the model is solved through a self-adaptive light sensation optimization algorithm, and optimal air conditioning equipment regulation time and temperature setting are output. According to the method, the problems of incomplete carbon emission accounting, single optimization target, slow convergence of a traditional algorithm and the like in the prior art are effectively solved, and collaborative optimization of the zero-carbon park air conditioning equipment cluster in economy and low-carbon property is realized.

Description

Technical Field

[0001] This invention belongs to the field of cluster control technology, and more specifically, relates to a method and system for cluster control of air conditioning equipment suitable for zero-carbon industrial parks. Background Technology

[0002] With the advancement of the "dual carbon" goals, zero-carbon industrial parks, as important carriers of low-carbon development, have seen their energy system optimization and control become a research hotspot. Air conditioning equipment, as one of the main energy-consuming devices in these parks, directly impacts the overall carbon reduction effect through its operating efficiency and carbon emission intensity. Currently, the control of air conditioning equipment largely focuses on single energy efficiency optimization or load peak shaving, lacking a comprehensive consideration of multiple objectives such as carbon emission intensity, carbon market participation, and user incentives. There is an urgent need for a cluster control method and system for air conditioning equipment that comprehensively considers electricity and refrigerant carbon emissions, balances economic efficiency and low-carbon performance, and employs efficient optimization algorithms. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for cluster control of air conditioning equipment in zero-carbon parks, so as to solve the problems of incomplete carbon emission accounting, low optimization efficiency and lack of multi-objective coordination in the existing technology.

[0004] To achieve the above objectives, according to one aspect of the present invention, a method for cluster control of air conditioning equipment suitable for zero-carbon industrial parks is disclosed, comprising the following steps:

[0005] Step 1: Obtain the air conditioning equipment parameters of the zero-carbon park and build an aggregator load control model;

[0006] Step 2: Solve the aggregator load control model using an adaptive light-sensing optimization algorithm.

[0007] Furthermore, in step one, the air conditioning equipment parameters of the zero-carbon park include: the total number of air conditioners. , No. k Taiwan air conditioning equipment refrigerant capacity , No. k Equivalent consumption coefficient of refrigerant in air conditioning equipment , No. k GWP value of refrigerant for air conditioning equipment , , No. k The air conditioning unit is in the first t Air conditioning power at each sampling time Local electricity carbon emission factor .

[0008] Furthermore, in step one, the characteristic is that the air conditioning equipment in the zero-carbon park participates in load regulation through an aggregator; the air conditioning equipment in the zero-carbon park participates in the aggregator's load regulation model. Defined as:

[0009] (1)

[0010] in, This indicates the calculation of the maximum value. This indicates that aggregators benefit from participating in power companies' peak-shaving tasks using clusters of air conditioning equipment in zero-carbon industrial parks. This refers to the revenue generated by aggregators through participating in carbon market trading using clusters of air conditioning equipment in zero-carbon industrial parks. This refers to the incentive paid by the aggregator to users of air conditioning equipment in zero-carbon industrial parks based on the load carbon emission intensity incentive mechanism.

[0011] Furthermore, the specific parameters in the aforementioned model are as follows:

[0012] a. Aggregators benefit from participating in power company peak-shaving tasks using air conditioning equipment clusters in zero-carbon industrial parks. The following formula is used to calculate:

[0013] (2)

[0014] in, For task response power, The peak-shaving unit price corresponds to the duration of regulation, and the unit is yuan / kW;

[0015] b. Revenue generated by aggregators participating in carbon market trading using clusters of air conditioning equipment from zero-carbon industrial parks. The following formula is used to calculate:

[0016] (3)

[0017] in, Indicates the carbon price on the trading day. For free carbon allowances, This represents the total carbon emissions from the air conditioning equipment in the zero-carbon park.

[0018] c. Response incentives paid by aggregators to users of air conditioning equipment in zero-carbon parks based on load carbon emission intensity incentive mechanisms. It can be obtained by calculation using the following formula:

[0019] (4)

[0020] in, For the first k The excitation coefficient of the air conditioner For the first kThe total response power of the air conditioner during the control process. No. k The total response time of the air conditioner during the control process. , This represents the total number of air conditioners;

[0021] Furthermore, the first k Excitation coefficient of air conditioner It can be obtained by calculation using the following formula:

[0022] (5)

[0023] in, For the first k The excitation coefficient of the air conditioning equipment. This is the incentive coefficient; The incentive was reduced accordingly; , , These represent the boundaries for low, medium, and high carbon emission levels, respectively. For the first k The average carbon emission intensity of a single air conditioning unit is calculated using the following formula:

[0024] (6)

[0025] in, and The first k The air conditioning unit is in the first t Carbon emissions generated during each sampling period The sampling duration was evaluated for the air conditioning equipment.

[0026] Furthermore, the total carbon emissions of air conditioning equipment in zero-carbon parks It can be obtained by calculation using the following formula:

[0027] (7)

[0028] in, For the first k Total carbon emissions generated by air conditioning equipment, in units of ; For the first k Taiwanese air conditioning equipment in the past T Carbon emissions generated by electricity consumption during a sampling period, in units of ; For the first k Carbon emissions from refrigerant in a single air conditioning unit. ;

[0029] in, It can be calculated using the following formula:

[0030] (8)

[0031] in, For the first k Taiwanese air conditioning equipment in the past T Carbon emissions generated by electricity consumption during a sampling period, in units of , For the first k Air conditioning equipment in t Air conditioning power consumption at each sampling time As a carbon emission factor for electricity, , The sampling time step for the air conditioner's electrical power;

[0032] It can be calculated using the following formula:

[0033] (9)

[0034] in, For the first k Taiwanese air conditioning equipment in the past T Refrigerant consumption during each sampling period, in m³ 3 , For the first k The GWP value of the refrigerant for this air conditioning unit, in units of ;No. k Taiwanese air conditioning equipment in the past T Refrigerant consumption during each sampling period It can be calculated using the following formula:

[0035] (10)

[0036] in, For the first k The equivalent consumption coefficient of refrigerant in a Taiwanese air conditioning unit. For the first k The refrigerant capacity of an air conditioning unit is expressed in cubic meters (m³). 3 ;

[0037] Furthermore, in step two, the specific calculation steps for solving formula (1) using the adaptive light-sensing optimization algorithm and then performing aggregate load regulation are as follows:

[0038] Step 1: Initialize relevant parameters, including the total number of individual photons. Reference light sensing radius and maximum light-sensing radius Learning rate Control coefficient Maximum number of iterations ;

[0039] Step 2: Define iteration count labels Set the initial value to ;

[0040] Step 3: Initialize the spatial vector position of each individual photon, i.e.:

[0041] (11)

[0042] in, and They represent the first k The air conditioning unit is in the first s The control time and optimal temperature settings for the next iteration. , ;

[0043] Step 4: Substitute the spatial vector position of each photon into formula (1) to calculate the position of each photon. The loss function value at the next iteration, i.e., the light intensity. , ;

[0044] Step 5: Calculate the first step using the following formula. m The photon individual in the first s The light-sensing radius at the next iteration ,Right now

[0045] (12)

[0046] in, and They represent the first s The maximum and minimum loss function values ​​at the nth iteration;

[0047] Step 6: Determine the first step using the following formula. s During the nth iteration j Whether the individual photon is in the first... m The neighborhood of each individual photon:

[0048] (13)

[0049] in, Indicates the calculation of the 2-norm;

[0050] If the above is true, then it indicates that in the first... s During the nth iteration, the 1st j The photon individual in the first mWithin the neighborhood of the first individual photon; otherwise, it indicates the first... s During the nth iteration j The photon individual is not in the first m Within the neighborhood of the first photon individual; for the first... s During the nth iteration, at the... m The set of photons in the neighborhood of a given photon is denoted as . ;

[0051] Step 7: Calculate the first step using the following formula. s During the nth iteration, at the... m Within the neighborhood of the nth photon individual p The photon individual to the first m The light intensity of each photon individual ,Right now

[0052] (14)

[0053] in, Indicates exponential calculation. This indicates absolute value calculation;

[0054] Step 8: Update the following formula s At the +1st iteration, the... m Spatial vector of an individual photon:

[0055] (15)

[0056] Step 9: Determine Does it exceed the limit? If so, then perform element restriction processing.

[0057] Step 10: Determine if the current iteration count is equal to the maximum iteration count. If not, then proceed to step 4.

[0058] Step 11: Output the optimal air conditioning equipment control time and temperature settings.

[0059] According to another aspect of the present invention, a cluster control system for air conditioning equipment suitable for zero-carbon industrial parks is provided, comprising:

[0060] Air conditioning equipment data acquisition controller, used to collect air conditioning equipment parameters in zero-carbon parks;

[0061] The control controller is used to solve the load control model of the aggregator.

[0062] Furthermore, the air conditioning equipment data acquisition controller is mainly used to collect the following data: the total number of air conditioners. , No. k Taiwan air conditioning equipment refrigerant capacity , No.k Equivalent consumption coefficient of refrigerant in air conditioning equipment , No. k GWP value of refrigerant for air conditioning equipment , , No. k The air conditioning unit is in the first t Air conditioning power at each sampling time Local electricity carbon emission factor .

[0063] Furthermore, the control controller mainly performs the following calculation process:

[0064] The air conditioning equipment in the zero-carbon industrial park is used in the load control model of the aggregator.

[0065] (16)

[0066] in, This indicates the calculation of the maximum value. This indicates that aggregators benefit from participating in power companies' peak-shaving tasks using clusters of air conditioning equipment in zero-carbon industrial parks. This refers to the revenue generated by aggregators through participating in carbon market trading using clusters of air conditioning equipment in zero-carbon industrial parks. This refers to the incentive paid by the aggregator to users of air conditioning equipment in zero-carbon industrial parks based on the load carbon emission intensity incentive mechanism.

[0067] Furthermore, the specific steps for solving the aggregator load control model using the adaptive light-sensing optimization algorithm are as follows:

[0068] Step 1: Initialize relevant parameters, including the total number of individual photons. Reference light sensing radius and maximum light-sensing radius Learning rate Control coefficient Maximum number of iterations ;

[0069] Step 2: Define iteration count labels Set the initial value to ;

[0070] Step 3: Initialize the spatial vector position of each individual photon, i.e.:

[0071] (17)

[0072] in, and They represent the first k The air conditioning unit is in the first s The control time and optimal temperature settings for the next iteration. , ;

[0073] Step 4: Substitute the spatial vector position of each photon into formula (1) to calculate the position of each photon. The loss function value at the next iteration, i.e., the light intensity. , ;

[0074] Step 5: Calculate the first step using the following formula. m The photon individual in the first s The light-sensing radius at the next iteration ,Right now

[0075] (18)

[0076] in, and They represent the first s The maximum and minimum loss function values ​​at the nth iteration;

[0077] Step 6: Determine the first step using the following formula. s During the nth iteration j Whether the individual photon is in the first... m The neighborhood of each individual photon:

[0078] (19)

[0079] in, Indicates the calculation of the 2-norm;

[0080] If the above is true, then it indicates that in the first... s During the nth iteration, the 1st j The photon individual in the first m Within the neighborhood of the first individual photon; otherwise, it indicates the first... s During the nth iteration j The photon individual is not in the first m Within the neighborhood of the first photon individual; for the first... s During the nth iteration, at the... m The set of photons in the neighborhood of a given photon is denoted as . ;

[0081] Step 7: Calculate the first step using the following formula. s During the nth iteration, at the... m Within the neighborhood of the nth photon individual p The photon individual to the first m The light intensity of each photon individual ,Right now

[0082] (20)

[0083] in, Indicates exponential calculation. This indicates absolute value calculation;

[0084] Step 8: Update the following formula s At the +1st iteration, the... m Spatial vector of an individual photon:

[0085] (twenty one)

[0086] Step 9: Determine Does it exceed the limit? If so, then perform element restriction processing.

[0087] Step 10: Determine if the current iteration count is equal to the maximum iteration count. If not, then proceed to step 4.

[0088] Step 11: Output the optimal air conditioning equipment control time and temperature settings.

[0089] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0090] Compared with existing technologies, this invention achieves synergistic optimization of economic and carbon emission reduction benefits by constructing a multi-objective aggregator control model that integrates power peak shaving, carbon trading, and user incentives; it innovatively incorporates refrigerant leakage carbon emissions into the accounting system, making carbon accounting more comprehensive and accurate; the dynamic incentive mechanism based on carbon emission intensity design can intelligently guide low-carbon equipment to respond first; and it adopts an adaptive light-sensing optimization algorithm, which effectively solves the problems of traditional algorithms easily getting trapped in local optima and slow convergence when solving high-dimensional nonlinear models, significantly improving the real-time performance and global optimization capability of the control strategy. Attached Figure Description

[0091] Figure 1 This is a diagram showing the equipment connection of an air conditioning equipment cluster control system suitable for zero-carbon industrial parks, provided by an embodiment of the present invention.

[0092] Figure 2 This is a schematic diagram of an aggregator load control process provided in an embodiment of the present invention;

[0093] Figure 3 This is a schematic diagram of the process of solving the aggregate load control model using an adaptive light-sensing optimization algorithm provided in an embodiment of the present invention. Detailed Implementation

[0094] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0095] like Figure 1 The diagram shown is a connection diagram of an air conditioning equipment cluster control system suitable for zero-carbon industrial parks, provided by an embodiment of the present invention, including:

[0096] Air conditioning equipment data acquisition controller, used to collect air conditioning equipment parameters in zero-carbon parks;

[0097] The control controller is used to solve the load control model of the aggregator.

[0098] like Figure 2 As shown in the figure, an embodiment of the present invention provides a schematic diagram of an aggregator load control process, which includes the following steps:

[0099] Step A1: Obtain the air conditioning equipment parameters for the zero-carbon park, including: the total number of air conditioners. , No. k Taiwan air conditioning equipment refrigerant capacity , No. k Equivalent consumption coefficient of refrigerant in air conditioning equipment , No. k GWP value of refrigerant for air conditioning equipment , , No. k The air conditioning unit is in the first t Air conditioning power at each sampling time Local electricity carbon emission factor ;

[0100] Step A2: Construct a load control model for air conditioning equipment in a zero-carbon industrial park, involving aggregators. Defined as:

[0101] (1)

[0102] in, This indicates the calculation of the maximum value. This indicates that aggregators benefit from participating in power companies' peak-shaving tasks using clusters of air conditioning equipment in zero-carbon industrial parks. This refers to the revenue generated by aggregators through participating in carbon market trading using clusters of air conditioning equipment in zero-carbon industrial parks. This indicates the response incentive paid by the aggregator to users of air conditioning equipment in zero-carbon industrial parks based on the load carbon emission intensity incentive mechanism;

[0103] Step A3: Solve the load control model using an adaptive light-sensing optimization algorithm;

[0104] Step A4: Output the air conditioning equipment control strategy.

[0105] like Figure 3 The diagram shown is a flowchart illustrating an adaptive light-sensing optimization algorithm for solving an aggregater load control model, according to an embodiment of the present invention. The flowchart includes the following steps:

[0106] Step B1: Initialize relevant parameters, including the total number of individual photons. Reference light sensing radius and maximum light-sensing radius Learning rate Control coefficient Maximum number of iterations ;

[0107] Step B2: Define iteration count labels Set the initial value to ;

[0108] Step B3: Initialize the spatial vector position of each individual photon, i.e.:

[0109] (2)

[0110] in, and They represent the first k The air conditioning unit is in the first s The control time and optimal temperature settings for the next iteration. , ;

[0111] Step B4: Substitute the spatial vector position of each photon into formula (1) to calculate the position of each photon in the formula. The loss function value at the next iteration, i.e., the light intensity. , ;

[0112] Step B5: Calculate the following formula m The photon individual in the first s The light-sensing radius at the next iteration ,Right now

[0113] (3)

[0114] in, and They represent the first s The maximum and minimum loss function values ​​at the nth iteration;

[0115] Step B6: Determine the first step using the following formula. s During the nth iteration j Whether the individual photon is in the first... m The neighborhood of each individual photon:

[0116] (4)

[0117] in, Indicates the calculation of the 2-norm;

[0118] If the above is true, then it indicates that in the first... s During the nth iteration, the 1st j The photon individual in the first m Within the neighborhood of the first individual photon; otherwise, it indicates the first... s During the nth iteration j The photon individual is not in the first m Within the neighborhood of the first photon individual; for the first... s During the nth iteration, at the... m The set of photons in the neighborhood of a given photon is denoted as . ;

[0119] Step B7: Calculate the following formula s During the nth iteration, at the... m Within the neighborhood of the nth photon individual p The photon individual to the first m The light intensity of each photon individual ,Right now

[0120] (5)

[0121] in, Indicates exponential calculation. This indicates absolute value calculation;

[0122] Step B8: Update the following formula s At the +1st iteration, the... m Spatial vector of an individual photon:

[0123] (6)

[0124] Step B9: Determine Does it exceed the limit? If so, then perform element restriction processing.

[0125] Step B10: Determine if the current iteration count is equal to the maximum iteration count. If not, then proceed to step B4.

[0126] Step B11: Output the optimal air conditioning equipment control time and temperature settings.

[0127] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for cluster control of air conditioning equipment suitable for zero-carbon industrial parks, characterized in that, Includes the following steps: Step 1: Obtain the air conditioning equipment parameters of the zero-carbon park and build an aggregator load control model; Step 2: Solve the aggregator load control model using an adaptive light-sensing optimization algorithm; The air conditioning equipment in the zero-carbon park participates in load regulation through aggregators, and the air conditioning equipment in the zero-carbon park participates in the aggregator's load regulation model. Defined as: （1） in, This indicates the calculation of the maximum value. This indicates that aggregators benefit from participating in power companies' peak-shaving tasks by utilizing clusters of air conditioning equipment in zero-carbon industrial parks. This refers to the revenue generated by aggregators through participating in carbon market trading using clusters of air conditioning equipment in zero-carbon industrial parks. This refers to the incentive paid by the aggregator to users of air conditioning equipment in zero-carbon industrial parks based on the load carbon emission intensity incentive mechanism.

2. The method according to claim 1, characterized in that, Obtaining the air conditioning equipment parameters for the zero-carbon park includes: the total number of air conditioners. , No. k Taiwan air conditioning equipment refrigerant capacity , No. k Equivalent consumption coefficient of refrigerant in air conditioning equipment , No. k GWP value of refrigerant for air conditioning equipment , , No. k The air conditioning unit is in the first t Air conditioning power at each sampling time Local electricity carbon emission factor .

3. The method according to claim 1, characterized in that, Aggregators benefit from participating in power company peak-shaving tasks using air conditioning equipment clusters in zero-carbon industrial parks. The following formula is used to calculate: （2） in, For task response power, The peak-shaving unit price corresponds to the duration of regulation, and the unit is yuan / kW; Revenue generated by aggregators participating in carbon market trading using clusters of air conditioning equipment in zero-carbon industrial parks The following formula is used to calculate: （3） in, Indicates the carbon price on the trading day. For free carbon allowances, This represents the total carbon emissions from the air conditioning equipment in the zero-carbon park. Aggregators pay response incentives to users of air conditioning equipment in zero-carbon industrial parks based on load carbon emission intensity incentive mechanisms. It can be obtained by calculation using the following formula: （4） in, For the first k The excitation coefficient of the air conditioner For the first k The total response power of the air conditioner during the control process. No. k The total response time of the air conditioner during the control process. , This represents the total number of air conditioners; No. k Excitation coefficient of air conditioner It can be obtained by calculation using the following formula: （5） in, For the first k The excitation coefficient of the air conditioning equipment. This is the incentive coefficient; The incentive was reduced accordingly; , , These represent the boundaries for low, medium, and high carbon emission levels, respectively. For the first k The average carbon emission intensity of a single air conditioning unit is calculated using the following formula: （6） in, and The first k The air conditioning unit is in the first t Carbon emissions generated during each sampling period The sampling duration was evaluated for the air conditioning equipment.

4. The method according to claim 3, characterized in that, Total carbon emissions of air conditioning equipment in zero-carbon parks The following formula is used to calculate: （7） in, For the first k Total carbon emissions generated by air conditioning equipment, in units of ; For the first k Taiwanese air conditioning equipment in the past T Carbon emissions generated by electricity consumption during a sampling period, in units of ; For the first k Carbon emissions from refrigerant in a single air conditioning unit. ; in, It can be calculated using the following formula: （8） in, For the first k Taiwanese air conditioning equipment in the past T Carbon emissions generated by electricity consumption during a sampling period, in units of , For the first k Air conditioning equipment in t Air conditioning power consumption at each sampling time As a carbon emission factor for electricity, , The sampling time step for the air conditioner's electrical power; It can be calculated using the following formula: （9） in, For the first k Taiwanese air conditioning equipment in the past T Refrigerant consumption during each sampling period, in m³ 3 , For the first k The GWP value of the refrigerant for this air conditioning unit, in units of ;No. k Taiwanese air conditioning equipment in the past T Refrigerant consumption during each sampling period It can be calculated using the following formula: （10） in, For the first k The equivalent consumption coefficient of refrigerant in a Taiwanese air conditioning unit. For the first k The refrigerant capacity of an air conditioning unit is expressed in cubic meters (m³). 3 .

5. The method according to claim 1, characterized in that, The specific calculation steps for solving formula (1) using the adaptive light-sensing optimization algorithm and then performing aggregator load regulation are as follows: Step 1: Initialize relevant parameters, including the total number of individual photons. Reference light sensing radius and maximum light-sensing radius Learning rate Control coefficient Maximum number of iterations ; Step 2: Define iteration count labels Set the initial value to ; Step 3: Initialize the spatial vector position of each individual photon, i.e.: （11） in, and They represent the first k The air conditioning unit is in the first s The control time and optimal temperature settings for the next iteration. , ; Step 4: Substitute the spatial vector position of each photon into formula (1) to calculate the position of each photon. The loss function value at the next iteration, i.e., the light intensity. , ; Step 5: Calculate the first step using the following formula. m The photon individual in the first s The light-sensing radius at the next iteration ,Right now （12） in, and They represent the first s The maximum and minimum loss function values ​​at the nth iteration; Step 6: Determine the first step using the following formula. s During the nth iteration j Whether the individual photon is in the first... m The neighborhood of each individual photon: （13） in, Indicates the calculation of the 2-norm; If the above is true, then it indicates that in the first... s During the nth iteration, the 1st j The photon individual in the first m Within the neighborhood of the first individual photon; otherwise, it indicates the first... s During the nth iteration j The photon individual is not in the first m Within the neighborhood of the first photon individual; for the first... s During the nth iteration, at the... m The set of photons in the neighborhood of a given photon is denoted as . ; Step 7: Calculate the first step using the following formula. s During the nth iteration, at the... m Within the neighborhood of the nth photon individual p The photon individual to the first m The light intensity of each photon individual ,Right now （14） in, Indicates exponential calculation. This indicates absolute value calculation; Step 8: Update the following formula s At the +1st iteration, the... m Spatial vector of an individual photon: （15） Step 9: Determine Does it exceed the limit? If so, then perform element restriction processing. Step 10: Determine if the current iteration count is equal to the maximum iteration count. If not, then proceed to step 4. Step 11: Output the optimal air conditioning equipment control time and temperature settings.

6. A cluster control system for air conditioning equipment in a zero-carbon industrial park, according to any one of claims 1-5, characterized in that, include: Air conditioning equipment data acquisition controller, used to collect air conditioning equipment parameters in zero-carbon parks; The control controller is used to solve the load control model of the aggregator.

7. The system according to claim 6, characterized in that, The air conditioning equipment data acquisition controller is mainly used to collect the following data: the total number of air conditioners. , No. k Taiwan air conditioning equipment refrigerant capacity , No. k Equivalent consumption coefficient of refrigerant in air conditioning equipment , No. k GWP value of refrigerant for air conditioning equipment , , No. k The air conditioning unit is in the first t Air conditioning power at each sampling time Local electricity carbon emission factor .

8. The system according to claim 7, characterized in that, The control controller mainly performs the following calculations: The air conditioning equipment in the zero-carbon industrial park is used in the load control model of the aggregator. （16） in, This indicates the calculation of the maximum value. This indicates that aggregators benefit from participating in power companies' peak-shaving tasks by utilizing clusters of air conditioning equipment in zero-carbon industrial parks. This refers to the revenue generated by aggregators through participating in carbon market trading using clusters of air conditioning equipment in zero-carbon industrial parks. This indicates the response incentive paid by the aggregator to users of air conditioning equipment in zero-carbon industrial parks based on the load carbon emission intensity incentive mechanism; The aggregator load control model is solved using an adaptive light-sensing optimization algorithm. Step 1: Initialize relevant parameters, including the total number of individual photons. Reference light sensing radius and maximum light-sensing radius Learning rate Control coefficient Maximum number of iterations ; Step 2: Define iteration count labels Set the initial value to ; Step 3: Initialize the spatial vector position of each individual photon, i.e.: （17） in, and They represent the first k The air conditioning unit is in the first s The control time and optimal temperature settings for the next iteration. , ; Step 4: Substitute the spatial vector position of each photon into formula (1) to calculate the position of each photon. The loss function value at the next iteration, i.e., the light intensity. , ; Step 5: Calculate the first step using the following formula. m The photon individual in the first s The light-sensing radius at the next iteration ,Right now （18） in, and They represent the first s The maximum and minimum loss function values ​​at the nth iteration; Step 6: Determine the first step using the following formula. s During the nth iteration j Whether the individual photon is in the first... m The neighborhood of each individual photon: （19） in, Indicates the calculation of the 2-norm; If the above is true, then it indicates that in the first... s During the nth iteration, the 1st j The photon individual in the first m Within the neighborhood of the first individual photon; otherwise, it indicates the first... s During the nth iteration j The photon individual is not in the first m Within the neighborhood of the first photon individual; for the first... s During the nth iteration, at the... m The set of photons in the neighborhood of a given photon is denoted as . ; Step 7: Calculate the first step using the following formula. s During the nth iteration, at the... m Within the neighborhood of the nth photon individual p The photon individual to the first m The light intensity of each photon individual ,Right now （20） in, Indicates exponential calculation. This indicates absolute value calculation; Step 8: Update the following formula s At the +1st iteration, the... m Spatial vector of an individual photon: （21） Step 9: Determine Does it exceed the limit? If so, then perform element restriction processing. Step 10: Determine if the current iteration count is equal to the maximum iteration count. If not, then proceed to step 4. Step 11: Output the optimal air conditioning equipment control time and temperature settings.

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