Air conditioner load multi-objective optimization cooperative control method and system based on network load interaction

Abstract

The invention relates to an air conditioner load multi-objective optimization cooperative control method and system based on network load interaction, and belongs to the field of load management. The method comprises the following steps: collecting network load interaction data, introducing a dynamic priority classification mechanism based on the network load interaction data, and obtaining a priority control set and a dual-target optimization set; based on the priority control set and the double-target optimization set, a multi-target optimization cooperative control model is built, the multi-target optimization cooperative control model comprises a priority control model and a double-target optimization model, and the air conditioner temperature is regulated and controlled based on the multi-target optimization cooperative control model; and aggregating the air conditioners in the priority control set and the dual-target optimization set to form a virtual energy pool, and adjusting the load of the air conditioners through load translation and power smoothing. According to the method, the power grid load emergency degree, the user sensitivity and the temperature deviation are fused, the priority control set and the dual-target optimization set are divided in real time, and the regulation and control accuracy is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of load management, and in particular relates to a multi-objective optimization collaborative control method and system for air-conditioning load based on grid-load interaction. Background Art

[0002] With accelerating urbanization and surging electricity demand, grid load management faces significant challenges, especially in areas where air conditioning loads account for a significant portion. Air conditioning, as a typical temperature-controlled load, has a significant impact on grid stability due to its on / off and power fluctuations. Traditional grid load management technologies primarily include: Demand response mechanisms: These mechanisms use electricity price incentives or direct controls to guide users to adjust their electricity usage, such as time-of-use pricing and load interruption compensation. Static priority control: These mechanisms prioritize loads based on fixed rules (such as device type and user contract level), prioritizing the removal of low-priority devices. Single-objective optimization: These mechanisms prioritize grid economics (such as peak shaving and valley filling) or user comfort (such as temperature deviation). These existing technologies currently have numerous shortcomings, including: Poor dynamic adaptability: Static priority control cannot dynamically adjust priorities based on real-time grid conditions (such as load urgency) and user behavior (such as the frequency of manual adjustments), resulting in inefficient regulation. They also sacrifice user comfort: Traditional demand response mechanisms often force air conditioning adjustments at the expense of user perceived temperature, leading to user complaints (such as sudden temperature fluctuations and frequent starts and stops). Lack of collaborative optimization: Existing methods often focus on a single objective (such as grid economics) and lack systematic modeling for the dual-objective collaborative optimization of the "grid-user" relationship, making it difficult to balance the interests of both. Risk of sudden load changes: The simultaneous startup and shutdown of cluster air conditioners can easily cause power surges, threatening grid stability (such as voltage sags and frequency fluctuations). Summary of the Invention

[0003] In order to solve the above problems existing in the prior art, the present invention provides a multi-objective optimization collaborative control method and system for air-conditioning load based on grid-load interaction.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A multi-objective optimization and coordinated control method for air conditioning load based on grid-load interaction, wherein the implementation of the multi-objective optimization and coordinated control method for air conditioning load includes the following steps:

[0006] S1: Collect grid-load interaction data, introduce a dynamic priority classification mechanism based on the grid-load interaction data, and obtain a priority control set and a dual-objective optimization set;

[0007] S2: constructing a multi-objective optimization collaborative control model based on the priority control set and the dual-objective optimization set, wherein the multi-objective optimization collaborative control model includes a priority control model and a dual-objective optimization model, and regulating the air conditioning temperature based on the multi-objective optimization collaborative control model;

[0008] S3: Aggregate the air conditioners in the priority control set and the dual-objective optimization set to form a virtual energy pool, and adjust the air conditioner load through load shifting and power smoothing.

[0009] Preferably, the step S1 specifically includes:

[0010] S101: Collecting the grid-load interaction data, where the grid-load interaction data includes user-side data and grid-side data;

[0011] S102: Calculating a priority index based on the network-load interaction data for introducing the dynamic priority classification mechanism;

[0012] S103: Based on the priority index, the priority control set and the dual-objective optimization set are obtained. Preferably, the calculation formula of the priority index in step S102 is: Where P is the priority index, α, β and γ are the temperature deviation weight, user sensitivity weight and grid urgency weight respectively, T real is the true temperature, T set is the set temperature, S is the user sensitivity, L emergency The emergency level of the power grid load.

[0013] Preferably, the step S2 specifically includes:

[0014] S201: Acquire a priority control adjustment value, and construct the priority control model based on the priority control adjustment value and the priority control set;

[0015] S202: Acquire predicted temperature parameters and construct the dual-objective optimization model according to the dual-objective optimization set.

[0016] Preferably, the construction of the priority control model in step S201 specifically includes:

[0017] A user comfort deviation is obtained, where the user comfort deviation is the difference between the actual temperature and the set temperature. The priority control adjustment value is calculated according to the user comfort deviation, and the priority control model is constructed.

[0018] Preferably, the calculation formula of the priority control adjustment value is T adj =T set +δ·L emergncy +ε·ΔT comfort , where T adj is the priority control adjustment value, δ and ε are dynamic weight coefficients, △T comfort The user comfort deviation is determined, and the temperature of the priority-controlled centralized air conditioner is adjusted according to the priority-controlled adjustment value.

[0019] Preferably, the acquisition of the predicted temperature parameter in step S202 specifically includes:

[0020] Obtain the air conditioning cooling power of the dual-objective optimization centralized air conditioning and the regional height and regional area of the corresponding area, perform spatiotemporal temperature field prediction and obtain the predicted temperature parameters. The mathematical description of the predicted temperature parameters is: Among them, T pred (x,y,t+Δt) is the predicted temperature parameter after time Δt, T real is the true temperature, △T out is the outdoor temperature change after △t, I is the air conditioning efficiency coefficient, Q AC is the cooling power of the air conditioner, △t is the prediction time step, ρ is the air density, c is the specific heat capacity of air, H is the regional height, Area is the regional area, φ is the thermal diffusion coefficient, is the second-order derivative of temperature in the X direction, is the second-order derivative of temperature in the Y direction.

[0021] Preferably, the construction of the dual-objective optimization model in step S202 specifically includes:

[0022] The dual-objective optimization model is constructed based on the predicted temperature parameters and the temperature of the dual-objective optimization centralized air conditioner is adjusted. The mathematical description is: Among them, n is the number of central air conditioners in dual-objective optimization, ΔP i is the power change of air conditioner i during the adjustment process, J is the stage electricity price, Δt adj To control the duration, S i is the user sensitivity of the area corresponding to air conditioner i, [T min ,T max ] are the preset upper and lower thresholds.

[0023] Preferably, the step S3 specifically includes:

[0024] S301: performing load shifting by cooling and storing energy, that is, cooling in advance when the grid load is low to store cold energy;

[0025] S302: suppressing off-peak start and stop of air conditioners through cluster power fluctuations, eliminating sudden load changes, and achieving power smoothing.

[0026] A multi-objective optimization collaborative control system for air conditioning load based on grid-load interaction, used to implement the multi-objective optimization collaborative control method for air conditioning load described above, comprising a priority division module, a collaborative control module, and a virtual adjustment module;

[0027] The priority classification module is used to collect grid-load interaction data, introduce a dynamic priority classification mechanism based on the grid-load interaction data, and obtain a priority control set and a dual-objective optimization set;

[0028] The collaborative control module is used to construct a multi-objective optimization collaborative control model based on the priority control set and the dual-objective optimization set, the multi-objective optimization collaborative control model including a priority control model and a dual-objective optimization model, and to control the air conditioning temperature based on the multi-objective optimization collaborative control model;

[0029] The virtual adjustment module is used to aggregate the air conditioners in the priority control set and the dual-objective optimization set to form a virtual energy pool, and adjust the air conditioner load through load shifting and power smoothing.

[0030] The beneficial effects of the present invention are:

[0031] (1) High-value control objects are accurately identified through a dynamic priority classification mechanism, and grid demand is quickly responded to with minimal user-perceived cost. At the same time, efficiency is improved through hierarchical control. The priority control set relieves grid pressure through rapid temperature adjustment, buying time for the refined control of the dual-objective optimization set and avoiding "one-size-fits-all" global control.

[0032] (2) The user comfort is ensured through dynamic compensation of comfort deviation, which is significantly better than the traditional forced temperature control strategy. The change of air conditioning power is optimized through the prediction of spatiotemporal temperature field to ensure the balance between the stage electricity price cost and the user sensitivity weighted comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0034] Figure 1 This is a flowchart of the steps of a multi-objective optimization collaborative control method for air-conditioning load based on grid-load interaction of the present invention. DETAILED DESCRIPTION

[0035] In order to better understand the present invention, various aspects of the present invention will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present invention and are not intended to limit the scope of the present invention in any way. Throughout the specification, the expression "and / or" includes any and all combinations of one or more of the associated listed items. As used herein, the terms "roughly", "approximately" and similar terms are used as terms to indicate approximate values, rather than as terms to indicate degree, and are intended to illustrate inherent deviations in measurements or calculated values that will be recognized by those of ordinary skill in the art. In addition, in the present invention, the order in which the steps are described does not necessarily represent the order in which these processes occur in actual operation, unless otherwise specified or can be derived from the context.

[0036] It should also be understood that expressions such as "comprises," "including," "having," "includes," and / or "comprising" are open rather than closed expressions in this specification, indicating the presence of the stated features, elements, and / or components, but do not exclude the presence of one or more other features, elements, components, and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features rather than just the individual elements in the list. In addition, when describing embodiments of the present invention, "may" is used to mean "one or more embodiments of the present invention." And, the term "exemplary" is intended to refer to an example or illustration.

[0037] Unless otherwise defined, all terms used herein (including engineering terms and scientific and technological terms) have the same meaning as commonly understood by those skilled in the art to which the present invention pertains. It should also be understood that, unless otherwise expressly stated in the present invention, words defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0038] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0039] Example 1:

[0040] See also Figure 1 , a multi-objective optimization collaborative control method for air conditioning load based on grid-load interaction, including:

[0041] S1: Collect grid-load interaction data, introduce a dynamic priority classification mechanism based on the grid-load interaction data, and obtain a priority control set and a dual-objective optimization set;

[0042] S2: constructing a multi-objective optimization collaborative control model based on the priority control set and the dual-objective optimization set, wherein the multi-objective optimization collaborative control model includes a priority control model and a dual-objective optimization model, and regulating the air conditioning temperature based on the multi-objective optimization collaborative control model;

[0043] S3: Aggregate the air conditioners in the priority control set and the dual-objective optimization set to form a virtual energy pool, and adjust the air conditioner load through load shifting and power smoothing.

[0044] In this embodiment, grid-load interaction data is collected, a dynamic priority classification mechanism is introduced based on the grid-load interaction data, and a priority control set and a dual-objective optimization set are obtained. This can be specifically implemented by the following steps:

[0045] S101: Collecting the grid-load interaction data, which includes user-side data and grid-side data. The user-side data includes the real-time status of the air conditioner and user sensitivity (the ratio of the number of manual temperature adjustments by the user to the total number of temperature adjustments in the past seven days). The real-time status of the air conditioner includes the actual temperature, set temperature, operating mode, etc. The grid-side data includes the urgency of the grid load and the stage electricity price, etc.

[0046] S102: Calculate the priority index based on the network-load interaction data to introduce the dynamic priority classification mechanism. The calculation formula is: Where P is the priority index, α, β and γ are the temperature deviation weight, user sensitivity weight and grid urgency weight respectively, with typical values of 0.5, 0.3 and 0.2 respectively. real is the true temperature, T set is the set temperature, S is the user sensitivity, L emergency is the grid load urgency (the value range is [0.1], the higher the value, the greater the grid pressure);

[0047] S103: Based on the priority index, the priority control set and the dual-objective optimization set are obtained. That is, multiple air conditioners with higher priority indexes are selected to form the priority control set, and the temperature setting is adjusted first to temporarily relieve the pressure on the power grid and leave time for the implementation of subsequent treatment measures. The remaining air conditioners form the dual-objective optimization set. Example: There are 2 air conditioners in an office, the power grid load rate is extremely high (power grid load urgency = 1), air conditioner X: T real -T set =3℃, S=0.2 (low sensitivity), then P X =0.5×3 / 5+0.3×(1-0.2)+0.2×1=0.74; Air Conditioner Y: T real -T set =1℃, S=0.6 (high sensitivity), then P Y =0.5×1 / 5+0.3×(1-0.6)+0.2×1=0.445. At this time, air conditioner X is included in the priority control set to prioritize temperature setting, and air conditioner Y is included in the dual-objective optimization set.

[0048] In this embodiment, a multi-objective optimization collaborative control model is constructed based on the priority control set and the dual-objective optimization set, which can be specifically implemented by the following steps:

[0049] S201: Acquire a priority control adjustment value, and construct the priority control model based on the priority control adjustment value and the priority control set:

[0050] Obtain the user comfort deviation, which is the difference between the actual temperature and the set temperature. Calculate the priority control adjustment value based on the user comfort deviation and construct the priority control model. The calculation formula is T adj =T set +δ·L emergency +ε·ΔT comfort , where T adj is the priority control adjustment value, δ and ε are dynamic weight coefficients, △T comfort The user comfort deviation is used to adjust the temperature of the priority control centralized air conditioner according to the priority control adjustment value, thereby alleviating the pressure on the power grid and reducing the impact on the user's body feeling. Example: Power grid load surge (L emergency =1), but the actual temperature of the user's room is consistent with the set value (△T comfort =0℃), at this time T adj =26+0.5×1+0.3×0=26.5℃. The air conditioner in this room automatically increases the temperature by 0.5℃, which not only relieves the pressure on the power grid but also does not affect the user's physical experience.

[0051] S202: After adjusting the priority-controlled central air-conditioning temperature, obtaining predicted temperature parameters and constructing the dual-objective optimization model according to the dual-objective optimization set;

[0052] S202-1: Obtain the air conditioning cooling power of the dual-objective optimization centralized air conditioning and the regional height (such as room height) and regional area of the corresponding area, perform spatiotemporal temperature field prediction and obtain predicted temperature parameters. The mathematical description of the predicted temperature parameters is: Among them, T pred (x,y,t+Δt) is the predicted temperature parameter after time Δt, T real is the true temperature, △T out is the outdoor temperature change after △t, I is the air conditioning efficiency coefficient, which is calibrated by the equipment parameters and the unit is m 2 ℃ / J, Q AC is the cooling power of the air conditioner in watts, △t is the prediction time step, ρ is the air density, c is the specific heat capacity of air, H is the region height, Area is the region area, φ is the thermal diffusion coefficient in m 2 / s, is the second-order derivative of temperature in the X direction, in °C / m 2 , is the second-order derivative of the temperature in the Y direction; Example: Predict the temperature of an office 30 minutes later, initial condition: T real =28℃, △T out = +3°C, I = 1.2 × e - 6 m 2 ℃ / J, QAC = 3000W (air conditioner running at full speed), △t = 1800 seconds (30 minutes), room size: 10m × 8m × 3m (Area = 80m 2 , H = 3m), the temperature gradient of the west wall is detected: Then the room temperature is predicted to drop to 28+0.7×3+[(1.2e -6 ×3000×1800) / (1.225×1005×3×80)]-0.02×1800×0.2=22.92°C, and over-cooling may occur.

[0053] S202-2: Construct the dual-objective optimization model based on the predicted temperature parameters and adjust the temperature of the dual-objective optimization centralized air conditioner to balance the economic efficiency of the power grid and user comfort. The mathematical description is: Among them, n is the number of central air conditioners in dual-objective optimization, ΔP i is the power change of air conditioner i during the adjustment process, J is the stage electricity price, Δt adj To control the duration, S i is the user sensitivity of the area corresponding to air conditioner i, [T min ,T max ] are the preset upper and lower thresholds.

[0054] In this embodiment, the air conditioners in the priority control set and the dual-objective optimization set are aggregated to form a virtual energy pool, and the air conditioner load is adjusted through load shifting and power smoothing. Specifically, this can be implemented by the following steps:

[0055] S301: Load shifting is performed by cooling and energy storage, i.e., cooling the room in advance when the grid load is low to store cooling capacity. For example, the air conditioner is set to 22°C (lower than the daytime setting) 2 hours in advance; the compressor is turned off during peak hours, and the room temperature is maintained solely by the pre-stored cooling capacity.

[0056] S302: Suppressing the peak start and stop of air conditioners by cluster power fluctuations, eliminating load mutations, and achieving power smoothing. The mathematical description of cluster power fluctuation suppression is: D≤0.3×(1-L emergency )×D total , where D is the number of air conditioners that can be started and stopped at the same time, D total is the total number of air conditioners, L emergency For example, if there are 100 air conditioners in an area and L = 0.8, the number of air conditioners that can be started and stopped simultaneously is D ≤ 0.3 × (1 - 0.8) × 100 = 6. This prevents all 100 air conditioners from restarting at the same time and causing a grid shock.

[0057] Example 2:

[0058] A multi-objective optimization collaborative control system for air-conditioning load based on grid-load interaction includes a priority division module, a collaborative control module and a virtual adjustment module.

[0059] The priority division module is used to collect grid-load interaction data, introduce a dynamic priority classification mechanism based on the grid-load interaction data, and obtain a priority control set and a dual-objective optimization set, specifically: collecting the grid-load interaction data, the grid-load interaction data includes user-side data and grid-side data, the user-side data includes the real-time status of the air conditioner and user sensitivity, the real-time status of the air conditioner includes the actual temperature, set temperature, operating mode, etc., and the grid-side data includes the urgency of the grid load, stage electricity price, etc.; calculating the priority index based on the grid-load interaction data for introducing the dynamic priority classification mechanism; obtaining the priority control set and the dual-objective optimization set based on the priority index, that is, selecting multiple air conditioners with higher priority indexes to form the priority control set, giving priority to adjusting the temperature setting, temporarily alleviating the pressure on the grid, and leaving time for the implementation of subsequent processing measures. The remaining air conditioners form the dual-objective optimization set.

[0060] The collaborative control module is used to construct a multi-objective optimization collaborative control model based on the priority control set and the dual-objective optimization set. The multi-objective optimization collaborative control model includes a priority control model and a dual-objective optimization model. The air conditioning temperature is controlled based on the multi-objective optimization collaborative control model, specifically: (1) obtaining a priority control adjustment value, and constructing the priority control model based on the priority control adjustment value and the priority control set: obtaining a user comfort deviation, the user comfort deviation being the difference between the actual temperature and the set temperature, calculating the priority control adjustment value based on the user comfort deviation and constructing the priority control model, and adjusting the temperature of the priority control centralized air conditioning according to the priority control adjustment value, thereby alleviating the pressure on the power grid and reducing the impact on the user's body feeling. (2) After regulating the temperature of the priority control centralized air conditioning, obtaining a predicted temperature parameter and constructing the dual-objective optimization model based on the dual-objective optimization set: obtaining the air conditioning cooling power of the dual-objective optimized centralized air conditioning and the regional height and regional area of the corresponding area, performing a spatiotemporal temperature field prediction and obtaining the predicted temperature parameter. Based on the predicted temperature parameter, the dual-objective optimization model is constructed and the temperature of the dual-objective optimized centralized air conditioning is adjusted to balance the power grid economy and user comfort.

[0061] The virtual adjustment module is used to aggregate the air conditioners in the priority control set and the dual-objective optimization set to form a virtual energy pool, and adjust the air conditioner load through load shifting and power smoothing. Specifically, the load shifting is performed through cooling and energy storage, that is, cooling is performed in advance when the grid load is low to store cold energy; and the off-peak start and stop of air conditioners is suppressed through cluster power fluctuations, load mutations are eliminated, and power smoothing is achieved.

[0062] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A multi-objective optimization collaborative control method for air conditioning load based on grid-load interaction, characterized in that: The implementation of the multi-objective optimization coordinated control method for air conditioning load includes the following steps: S1: Collect grid-load interaction data, introduce a dynamic priority classification mechanism based on the grid-load interaction data, and obtain a priority control set and a dual-objective optimization set; S2: constructing a multi-objective optimization collaborative control model based on the priority control set and the dual-objective optimization set, wherein the multi-objective optimization collaborative control model includes a priority control model and a dual-objective optimization model, and regulating the air conditioning temperature based on the multi-objective optimization collaborative control model; S3: Aggregate the air conditioners in the priority control set and the dual-objective optimization set to form a virtual energy pool, and adjust the air conditioner load through load shifting and power smoothing.

2. The multi-objective optimization coordinated control method for air conditioning load according to claim 1 is characterized in that: The step S1 specifically includes: S101: Collecting the grid-load interaction data, where the grid-load interaction data includes user-side data and grid-side data; S102: Calculating a priority index based on the network-load interaction data for introducing the dynamic priority classification mechanism; S103: Obtaining the priority control set and the dual-objective optimization set based on the priority index.

3. The multi-objective optimization coordinated control method for air conditioning load according to claim 2 is characterized in that: The calculation formula of the priority index in step S102 is: Where P is the priority index, α, β and γ are the temperature deviation weight, user sensitivity weight and grid urgency weight respectively, T real is the true temperature, T set is the set temperature, S is the user sensitivity, L emergency The emergency level of the power grid load.

4. The multi-objective optimization coordinated control method for air conditioning load according to claim 1, characterized in that: The step S2 specifically includes: S201: Acquire a priority control adjustment value, and construct the priority control model based on the priority control adjustment value and the priority control set; S202: Acquire predicted temperature parameters and construct the dual-objective optimization model according to the dual-objective optimization set.

5. The multi-objective optimization coordinated control method for air conditioning load according to claim 4 is characterized in that: The construction of the priority control model in step S201 specifically includes: A user comfort deviation is obtained, where the user comfort deviation is the difference between the actual temperature and the set temperature. The priority control adjustment value is calculated according to the user comfort deviation, and the priority control model is constructed.

6. The multi-objective optimization coordinated control method for air conditioning load according to claim 5, characterized in that: The calculation formula of the priority control adjustment value is T adj =T set +δ·L emergency +ε·ΔT comfort , where T adj is the priority control adjustment value, δ and ε are dynamic weight coefficients, △T comfort The user comfort deviation is determined, and the temperature of the priority-controlled centralized air conditioner is adjusted according to the priority-controlled adjustment value.

7. The multi-objective optimization coordinated control method for air conditioning load according to claim 4, characterized in that: The acquisition of the predicted temperature parameters in step S202 specifically includes: Obtain the air conditioning cooling power of the dual-objective optimization centralized air conditioning and the regional height and regional area of the corresponding area, perform spatiotemporal temperature field prediction and obtain the predicted temperature parameters. The mathematical description of the predicted temperature parameters is: Among them, T pred (x, y, t+Δt) is the predicted temperature parameter after time Δt, T real is the true temperature, △T out is the outdoor temperature change after △t, I is the air conditioning efficiency coefficient, Q AC is the cooling power of the air conditioner, △t is the prediction time step, ρ is the air density, c is the specific heat capacity of air, H is the regional height, Area is the regional area, φ is the thermal diffusion coefficient, is the second-order derivative of temperature in the X direction, is the second-order derivative of temperature in the Y direction.

8. The multi-objective optimization coordinated control method for air conditioning load according to claim 7, characterized in that: The construction of the dual-objective optimization model in step S202 specifically includes: The dual-objective optimization model is constructed based on the predicted temperature parameters and the temperature of the dual-objective optimization centralized air conditioner is adjusted. The mathematical description is: Among them, n is the number of central air conditioners in dual-objective optimization, ΔP i is the power change of air conditioner i during the adjustment process, J is the stage electricity price, Δt adj To control the duration, S i is the user sensitivity of the area corresponding to air conditioner i, [T min ,T max ] are the preset upper and lower thresholds.

9. The multi-objective optimization coordinated control method for air conditioning load according to claim 1, characterized in that: The step S3 specifically includes: S301: performing load shifting by cooling and storing energy, that is, cooling in advance when the grid load is low to store cold energy; S302: suppressing off-peak start and stop of air conditioners through cluster power fluctuations, eliminating sudden load changes, and achieving power smoothing.

10. A multi-objective optimization collaborative control system for air conditioning load based on grid-load interaction, characterized in that: The system is applied to the multi-objective optimization collaborative control method for air conditioning loads as described in any one of claims 1 to 9, comprising a priority division module, a collaborative control module, and a virtual adjustment module; The priority classification module is used to collect grid-load interaction data, introduce a dynamic priority classification mechanism based on the grid-load interaction data, and obtain a priority control set and a dual-objective optimization set; The collaborative control module is used to construct a multi-objective optimization collaborative control model based on the priority control set and the dual-objective optimization set, the multi-objective optimization collaborative control model including a priority control model and a dual-objective optimization model, and to control the air conditioning temperature based on the multi-objective optimization collaborative control model; The virtual adjustment module is used to aggregate the air conditioners in the priority control set and the dual-objective optimization set to form a virtual energy pool, and adjust the air conditioner load through load shifting and power smoothing.

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