Cluster air conditioning load grouping control method and device based on user comfort preference

Abstract

The present invention discloses a cluster air conditioning load grouping control method and device based on user comfort preference, which relates to the field of electric power. The method includes: first obtaining cluster air conditioning data of a target area, dividing the cluster air conditioning in the target area into multiple air conditioning groups according to the cluster air conditioning data, and determining the user comfort preference of each air conditioning group, wherein the user comfort preference includes the air conditioning use temperature and the pre-cooling temperature; then determining the control target of the target area according to the load rate of the substation in the target area, and constructing a control model according to the control target and the user comfort preference of each air conditioning group; finally, obtaining a control strategy for each air conditioning group based on the control model, and the control strategy is used to control each air conditioning group to perform pre-cooling or shut down in different time periods. The above method improves user experience while meeting load control requirements.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a cluster air-conditioning load grouping control method and device based on user comfort preference. Background Art

[0002] As people's living standards continue to improve and high-power electrical equipment becomes increasingly prevalent, the disordered demand-side load consumption has led to increasingly prominent seasonal peak loads. The growth rate of power load has significantly exceeded the growth rate of power consumption, and the peak-to-valley difference continues to widen. The supply and demand situation during peak power supply is tense, and seasonal and regional peak power supply is tight. Building energy consumption accounts for approximately 30% of the total national energy consumption, and air conditioning energy consumption accounts for 50%-70% of the total building energy consumption, meaning that air conditioning energy consumption accounts for approximately 20% of the national energy consumption.

[0003] Sensing and regulating energy consumption on the demand side of air conditioning is a key driver of power load regulation. Taking cooling as an example, traditional air conditioning control methods primarily rely on starting and stopping air conditioners in stages and batches. After the air conditioner stops operating, the room temperature continues to rise, reducing cooling efficiency, exceeding user comfort requirements and impacting the user experience. Therefore, an air conditioning load control solution that considers user comfort is urgently needed. Summary of the Invention

[0004] In view of this, the present application provides a cluster air conditioning load grouping control method and device based on user comfort preference, the main purpose of which is to solve the technical problem that traditional air conditioning control methods do not take user comfort into consideration.

[0005] According to a first aspect of the present invention, a method for grouping and controlling cluster air conditioning loads based on user comfort preferences is provided, the method comprising:

[0006] Acquiring cluster air conditioning data for a target area, dividing the cluster air conditioning in the target area into a plurality of air conditioning groups based on the cluster air conditioning data, and determining a user comfort preference for each of the air conditioning groups, wherein the user comfort preference includes an air conditioning operating temperature and a pre-cooling temperature;

[0007] Determining a control target for the target area according to the load rate of the substations in the target area, and constructing a control model according to the control target and the user comfort preference of each air-conditioning group;

[0008] A control strategy for each of the air-conditioning groups is obtained based on the control model, and the control strategy is used to control each of the air-conditioning groups to perform pre-cooling or shut down in different time periods.

[0009] Optionally, the cluster air-conditioning data includes the air-conditioning usage time and the air-conditioning usage temperature; the cluster air-conditioning in the target area is divided into multiple air-conditioning groups according to the cluster air-conditioning data, and the user comfort preference of each air-conditioning group is determined, including: clustering the cluster air-conditioning in the target area using the air-conditioning usage time and the air-conditioning usage temperature as clustering indicators to obtain multiple air-conditioning groups; determining the pre-cooling temperature of each air-conditioning group according to the air-conditioning usage time and the air-conditioning usage temperature of each air-conditioning group.

[0010] Optionally, the control target of the target area is load reduction power, and the control target of the target area is determined based on the substation load rate in the target area, including: obtaining the substation load rate of the target area; when the substation load rate of the target area belongs to the first preset interval, no control is performed, and the load reduction power is zero; when the substation load rate of the target area belongs to the second preset interval, the cluster air conditioner in the target area is load-reduced, and the load reduction power is calculated based on the control demand load and the substation rated power; when the substation load rate of the target area belongs to the third preset interval, the cluster air conditioner in the target area is load-limited, and the load reduction power is calculated based on the substation load rate, the load rate controllable limit and the substation rated power.

[0011] Optionally, constructing a control model according to the control target and the user comfort preference of each air-conditioning group includes:

[0012]

[0013] Wherein, T0 is the sub-interval duration; T is the control interval duration; N is the number of the air conditioning groups; is the coefficient of variation; P i tol is the total power of the host of air conditioning group i; P i s The power added by air conditioning group i during pre-cooling; S1 i [k] and S2 i [k] is the scheduling flag of air conditioning group i in the kth subinterval, S1 i [k] is 0, indicating that the air conditioning group i is not shut down in the subinterval k; [k] is 1, indicating that the air conditioning group i is shut down in the subinterval k. S2 i [k] is 0, indicating that the air-conditioning group i is not pre-cooled in the subinterval k; [k] is 1, indicating that the air-conditioning group i is pre-cooled in the subinterval k; ΔP is the control target, i.e., the load reduction power; Δη p To regulate the demand load rate; η p,lim P is the load rate adjustable limit; rate is the rated power of the substation; η pis the load rate of the substation; m i is the number of air conditioners included in air conditioner group i; is the unit average power of air conditioning group i; i is the energy consumption coefficient of air conditioning group i; is the pre-cooling temperature of air-conditioning group i.

[0014] Optionally, the method further includes: setting a control interval according to the load peak duration, and dividing the control interval into a plurality of sub-intervals; and determining a first constraint condition according to the control interval:

[0015]

[0016] Among them, S1 i [k] and S2 i [k] is the scheduling flag of air conditioning group i in the kth subinterval, S1 i [k] is 0, indicating that the air conditioning group i is not shut down in the subinterval k; [k] is 1, indicating that the air conditioning group i is shut down in the subinterval k. S2 i [k] is 0, indicating that the air-conditioning group i is not pre-cooled in the subinterval k; [k] is 1, indicating that the air-conditioning group i is pre-cooled in the subinterval k; T0 is the subinterval duration; T is the control interval duration; N is the number of the air-conditioning groups.

[0017] Optionally, the method further includes: determining a second constraint condition according to the air conditioning usage temperature of each air conditioning group:

[0018]

[0019] in, is the lowest operating temperature of the air conditioner in air conditioner group i; is the maximum operating temperature of the air conditioner in air conditioner group i; is the temperature of air-conditioning group i after adjustment in subinterval k; is the set temperature of air conditioning group i during normal operation; t tem is the temperature rise coefficient after the air conditioner is turned off.

[0020] Optionally, obtaining a control strategy for each air-conditioning group based on the control model includes:

[0021] Construct an optimization function based on peak-shaving benefits:

[0022]

[0023] Wherein, F is the peak shaving benefit; ρ is the compensation unit price of the peak shaving amount;

[0024] Based on the optimization function, the control model is solved using a genetic algorithm to obtain the control strategy.

[0025] According to a second aspect of the present invention, a cluster air conditioning load grouping control device based on user comfort preference is provided, the device comprising:

[0026] a grouping module, configured to obtain cluster air conditioning data for a target area, divide the cluster air conditioning units in the target area into a plurality of air conditioning groups based on the cluster air conditioning data, and determine a user comfort preference for each of the air conditioning groups, wherein the user comfort preference includes an air conditioning operating temperature and a pre-cooling temperature;

[0027] A model building module is used to determine the control target of the target area according to the load rate of the substation in the target area, and to build a control model according to the control target and the user comfort preference of each air-conditioning group;

[0028] A strategy generation module is used to obtain a control strategy for each of the air-conditioning groups based on the control model, wherein the control strategy is used to control each of the air-conditioning groups to perform pre-cooling or shut down in different time periods.

[0029] According to a third aspect of the present invention, a storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the above-mentioned cluster air conditioning load grouping control method is implemented.

[0030] According to a fourth aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned cluster air conditioning load grouping control method when executing the program.

[0031] The present invention provides a cluster air conditioner load grouping and control method and device based on user comfort preferences. The method first obtains cluster air conditioner data for a target area, divides the cluster air conditioners in the target area into multiple air conditioner groups based on the cluster air conditioner data, and determines the user comfort preferences for each air conditioner group, where the user comfort preferences include the air conditioner operating temperature and pre-cooling temperature. A control target for the target area is then determined based on the load factor of the substations within the target area. A control model is constructed based on the control target and the user comfort preferences of each air conditioner group. Finally, a control strategy for each air conditioner group is derived based on the control model. The control strategy is used to control each air conditioner group to perform pre-cooling or shut down during different time periods. The method groups cluster air conditioners based on the cluster air conditioner data and determines the user comfort preferences for each air conditioner group, where different air conditioner groups correspond to different user comfort preferences. The pre-cooling temperature, pre-cooling temperature, and pre-cooling and shut-down durations of the air conditioner groups are then controlled based on the control target and the user comfort preferences of each air conditioner group. After the air conditioner is shut down, even if the temperature rises, it will not exceed the user's comfort requirements, thereby satisfying load control while improving the user experience.

[0032] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0034] Figure 1 A schematic flow chart of a cluster air conditioning load grouping control method based on user comfort preference provided by an embodiment of the present invention is shown;

[0035] Figure 2 shows a load curve before and after regulation provided by an embodiment of the present invention;

[0036] Figure 3 Shows a temperature curve before and after regulation provided by an embodiment of the present invention;

[0037] Figure 4 A schematic diagram of the structure of a cluster air conditioning load grouping control device based on user comfort preference provided by an embodiment of the present invention is shown;

[0038] Figure 5 A structural diagram of a computer device for implementing a cluster air conditioning load grouping control method based on user comfort preference provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0039] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of this application can be combined with each other. In the various embodiments shown below, the first, second, and various numerical numbers are only used for the convenience of description and are not intended to limit the scope of the embodiments of this application.

[0040] In one embodiment, Figure 1 As shown, a cluster air conditioning load grouping control method based on user comfort preference is provided, comprising the following steps:

[0041] 101. Obtain cluster air conditioning data of a target area, divide the cluster air conditioning in the target area into multiple air conditioning groups based on the cluster air conditioning data, and determine a user comfort preference for each of the air conditioning groups, wherein the user comfort preference includes an air conditioning operating temperature and a pre-cooling temperature.

[0042] In this embodiment, since the load rate of each substation is different, load control can be performed on a substation basis, that is, the target area can be the area corresponding to any substation. For the cluster air conditioners in the target area, the cluster air conditioners in the area are grouped according to the cluster air conditioner data. The cluster air conditioner data may include the air conditioner usage time, the air conditioner usage temperature, etc. The cluster air conditioner data can reflect the user's preference for comfort during the use of the air conditioner, such as the user's preferred temperature range, the preferred air conditioner cooling temperature, the length of the cooling time, etc. It should be noted that the cooling temperature in this embodiment is relative to the user's air conditioner usage temperature.

[0043] This embodiment uses a pre-cooling and shutdown method to group and rotate the cluster air conditioners. Specifically, the cluster air conditioners can be grouped to obtain multiple air conditioner groups by clustering, and the user comfort preference corresponding to each air conditioner group is determined based on the cluster air conditioner data corresponding to each air conditioner group. The user comfort preference can be the air conditioner operating temperature and the pre-cooling temperature. The pre-cooling temperature can be determined based on the user's preferred cooling temperature range. For example, if the user prefers a high cooling temperature and a long cooling time, it means that the user has a high comfort requirement. Therefore, a lower pre-cooling temperature can be set (for example, pre-cooling is performed by reducing the temperature by 3 degrees relative to the user's preferred normal temperature) to ensure that when the air conditioner is shut down, the temperature rise will not exceed the temperature range required by the user. If the user prefers a high cooling temperature and a short cooling time, it means that the user has a low comfort requirement. Therefore, a higher pre-cooling temperature can be set (for example, pre-cooling is performed by reducing the temperature by 1 degree relative to the user's preferred normal temperature) to reduce power consumption.

[0044] 102. Determine a control target for the target area according to the load rate of the substations in the target area, and construct a control model according to the control target and the user comfort preference of each of the air-conditioning groups.

[0045] 103. Obtain a control strategy for each of the air-conditioning groups based on the control model, where the control strategy is used to control each of the air-conditioning groups to perform pre-cooling or shut down in different time periods.

[0046] In this embodiment, the load rates of different substations vary. Therefore, the control target for the target area needs to be determined based on the load rate of the substation corresponding to the target area and a preset load rate threshold. The control target can be represented by power. For example, if the load rate threshold is 90% and the substation load rate of the target area is 95%, the load rate to be controlled can be determined to be 5%. In this case, the power to be controlled, i.e., the control target, can be calculated by multiplying the rated load rate of the substation by the load rate to be controlled.

[0047] Each AC group has a different operating temperature and pre-cooling temperature, and each group has a different number of clustered AC units. Consequently, different groups consume different amounts of pre-cooling power and save different amounts of power when shutting down. Different pre-cooling or shutdown strategies for different groups at different times and in different periods of time also have different load control effects. Therefore, a control model can be determined based on each group's operating temperature, pre-cooling temperature, and the control target for the target area. This control model can then be used to generate a control strategy to control each group's pre-cooling or shutdown at different times, thereby reducing load while meeting user comfort requirements.

[0048] The cluster air conditioner load grouping control method provided in this embodiment first obtains cluster air conditioner data for a target area, divides the cluster air conditioners in the target area into multiple air conditioner groups based on the cluster air conditioner data, and determines the user comfort preferences for each air conditioner group, where the user comfort preferences include the air conditioner operating temperature and pre-cooling temperature. A control target for the target area is then determined based on the load factor of the substations within the target area, and a control model is constructed based on the control target and the user comfort preferences of each air conditioner group. Finally, a control strategy for each air conditioner group is derived based on the control model, which is used to control each air conditioner group to perform pre-cooling or shut down during different time periods. The above method groups cluster air conditioners based on the cluster air conditioner data and determines the user comfort preferences for each air conditioner group, where different air conditioner groups correspond to different user comfort preferences. The pre-cooling temperature, pre-cooling temperature, and pre-cooling and shut-down durations of the air conditioner groups are then controlled based on the control target and the user comfort preferences of each air conditioner group. After the air conditioner is shut down, even if the temperature rises again, it will not exceed the user's comfort requirements, thereby satisfying load control while improving the user experience.

[0049] Furthermore, in order to fully illustrate the implementation process of this embodiment, the specific implementation methods of the above embodiment are refined and expanded below. Specifically, in one embodiment, the cluster air conditioning data includes the air conditioning usage time and the air conditioning usage temperature; the cluster air conditioning in the target area is divided into multiple air conditioning groups based on the cluster air conditioning data, and the user comfort preference of each air conditioning group is determined. This can be achieved in the following way: clustering the cluster air conditioning in the target area using the air conditioning usage time and the air conditioning usage temperature as clustering indicators to obtain multiple air conditioning groups; determining the pre-cooling temperature of each air conditioning group based on the air conditioning usage time and the air conditioning usage temperature of each air conditioning group.

[0050] In the above embodiment, cluster air conditioners can be clustered by the c-means clustering algorithm. Specifically, the cluster air conditioners in the target area can be clustered using the air conditioner usage time and the air conditioner usage temperature as clustering indicators to obtain multiple air conditioner groups with user comfort preference information. User comfort preferences can be characterized by the air conditioner usage temperature and the pre-cooling temperature. Among them, the pre-cooling temperature is the temperature at which the air conditioner group is controlled to be lower than the normal working temperature when pre-cooling is performed on the air conditioner group. For example, the normal working temperature of the air conditioner group is 26°C, and the pre-cooling temperature can be set to 3°C, 2°C and 1°C. Accordingly, when the air conditioner group is pre-cooling, the corresponding cooling temperature is 23°C, 24°C and 25°C. Among them, the pre-cooling temperature is determined according to the user comfort preference of the air conditioner group to ensure that after the air conditioner group performs pre-cooling, the user's temperature requirements are met when the room temperature rises during the shutdown period.

[0051] In one embodiment, the control target of the target area is load reduction power. The control target of the target area is determined according to the substation load rate in the target area, which can be achieved in the following way: obtaining the substation load rate of the target area; when the substation load rate of the target area belongs to the first preset interval, no control is performed, and the load reduction power is zero; when the substation load rate of the target area belongs to the second preset interval, the cluster air conditioner in the target area is load-reduced, and the load reduction power is calculated based on the control demand load and the substation rated power; when the substation load rate of the target area belongs to the third preset interval, the cluster air conditioner in the target area is load-limited, and the load reduction power is calculated based on the substation load rate, the load rate adjustable limit and the substation rated power.

[0052] In the above embodiment, the load control target of the target area can be characterized by power, specifically load reduction power. The control target is determined based on the substation load rate of the target area and a preset load rate threshold. When the substation load rate falls within the first preset range, no load control is performed. For example, if the load rate threshold is 90%, then when the substation load rate of the target area is less than or equal to 90%, no load control is required for the target area. When the substation load rate falls within the second preset range, load reduction control is performed on the target area. For example, when the substation load rate is between 90% and 100%, the load reduction power required for the target area is calculated. The load reduction power can be calculated based on the control demand load rate and the substation rated power, where the control demand load rate can be set according to demand. When the substation load rate falls within the second preset range, load restriction control is performed on the target area. For example, when the substation load rate of the target area exceeds 100%, the corresponding load reduction power is calculated based on the substation load rate, the load rate controllable limit, and the substation rated power, thereby limiting the load of the target area.

[0053] Specifically, the load reduction power in the target area can be calculated using formula (1):

[0054]

[0055] Wherein, ΔP is the control target, i.e., the load reduction power; Δη p To regulate the demand load rate; η p,lim P is the load rate adjustable limit; rate is the rated power of the substation; η p is the load factor of the area.

[0056] In one embodiment, a control model is constructed according to the control target and the user comfort preference of each air-conditioning group, including:

[0057]

[0058] Formula (2) is used to express that the power reduction required to control the cluster air conditioners in the target area needs to meet the control target ΔP. Wherein, T0 is the sub-interval duration; T is the control interval duration; N is the number of air conditioner groups; is the coefficient of variation; P i tol is the total power of the host of air conditioning group i; P i s The power added by air conditioning group i during pre-cooling; S1 i [k] and S2 i [k] is the scheduling flag of air conditioning group i in the kth subinterval, S1 i [k] is 0, indicating that the air conditioning group i is not shut down in the subinterval k; [k] is 1, indicating that the air conditioning group i is shut down in the subinterval k. S2 i [k] is 0, indicating that the air conditioning group i is not pre-cooled in the subinterval k, and is 1, indicating that the air conditioning group i is pre-cooled in the subinterval k; ΔP is the control target, i.e., the load reduction power; m i is the number of air conditioners included in air conditioner group i; is the unit average power of air conditioning group i; i is the energy consumption coefficient of air conditioning group i; is the pre-cooling temperature of air-conditioning group i.

[0059] In one embodiment, the cluster air conditioner control in the target area needs to satisfy not only formula (2) but also constraint conditions. The construction of constraint conditions includes: setting a control interval according to the load peak duration, and dividing the control interval into multiple sub-intervals. The first constraint condition is determined based on the control interval:

[0060]

[0061] The first constraint is used to prevent the same air conditioning group from being pre-cooled or shut down in two consecutive sub-intervals, and from being pre-cooled and shut down in the same sub-interval. i [k] is the scheduling flag of air-conditioning group i in the kth subinterval, 0 indicates that air-conditioning group i does not participate in regulation, and 1 indicates that air-conditioning group i participates in regulation; T0 is the subinterval duration; T is the regulation interval duration; N is the number of air-conditioning groups.

[0062] And, according to the air-conditioning operating temperature of each air-conditioning group, determine the second constraint condition:

[0063]

[0064] in, is the lowest operating temperature of the air conditioner in air conditioner group i; is the maximum operating temperature of the air conditioner in air conditioner group i; is the temperature of air-conditioning group i after adjustment in subinterval k; is the set temperature of air conditioning group i during normal operation; t tem is the temperature rise coefficient after the air conditioner is turned off.

[0065] In one embodiment, a control strategy for each air-conditioning group is obtained based on the control model, which can be specifically achieved in the following manner: an optimization function is constructed based on the peak-shaving benefit, and then based on the optimization function, a genetic algorithm is used to find the optimal solution for the control model to obtain a control strategy to control each air-conditioning group to perform pre-cooling or shutdown in different sub-intervals.

[0066] The expression of the optimization function is as follows:

[0067]

[0068] Wherein, F is the peak shaving benefit; ρ is the compensation unit price of the peak shaving amount.

[0069] In the above embodiment, the multiple sub-intervals of the control interval include a first auxiliary control sub-interval, multiple main control sub-intervals, and a second auxiliary control sub-interval. The main control sub-interval corresponds to the duration of the load peak period. A first auxiliary control sub-interval is set before the load peak period to enter the load control in advance, control some air-conditioning groups to perform pre-cooling, and then shut down the pre-cooling air-conditioning groups when entering the main control sub-interval, so that part of the load can be shared before the peak period. A second auxiliary control sub-interval is also set after the load peak period to delay the load control for a period of time, so that the load control can transition smoothly and avoid load rebound caused by all air-conditioning groups resuming normal operation at the same time when the load control ends.

[0070] In the above embodiment, the control model is solved using a genetic algorithm, resulting in a control strategy that satisfies both the control objectives and the constraints. This strategy is then optimized using an optimization function. When the peak-shaving benefit in the optimization function reaches a maximum, or the genetic algorithm reaches a preset number of iterations, the optimal solution to the control model is obtained, and the control strategy is generated.

[0071] In a specific embodiment, based on the room temperature being controlled at 26°C, 1,500 air conditioners are involved in the regulation in the target area, and the regulation interval is 2 hours. The air conditioners in the target area are clustered and grouped based on the clustered air conditioner data, and a combined strategy of "pre-cooling + shutdown" is set according to the clustering results to control the air conditioners in groups. Among them, the multiple air conditioner groups obtained by clustering are shown in Table 1. The first group of users has the highest comfort requirements, the second group is second, and the third group has the lowest. When the first group participates in the regulation, the reduction amount that can be provided is less than that of the second and third groups. The pre-cooling temperatures of the first to third groups are 3°C, 2°C, and 1°C, respectively.

[0072] Table 1

[0073] Group 1 Group 2 Group 3 Number of air conditioners / units 237 762 501 Expected load reduction / kW 260.7 1447.8 1012.1 Pre-cooling temperature / ℃ 3 2 1

[0074] Based on the groupings shown in Table 1, cluster air conditioners are grouped and rotated. Control is performed in one of the following states: pre-cooling, shutdown, or normal, depending on the current grid operation and the status of the air conditioners. The 2-hour control cycle is divided into six subintervals, each with a 20-minute control duration. The first subinterval is the start of the auxiliary control subinterval (the first auxiliary control subinterval), the second through fifth subintervals are the main control subintervals, and the sixth subinterval is the end of the auxiliary control subinterval (the second auxiliary control subinterval). Based on the load factor of the substation, the current grid control target of 1600 kW·h is calculated. Using the control strategy optimized by the genetic algorithm, the actual control power of the air conditioners is 1638.3722 kW·h. The total revenue for air conditioner users participating in peak load regulation is 1906.66 yuan. The load grouping and control of cluster air conditioners during the control period are shown in Table 2.

[0075] Table 2

[0076]

[0077] In the first subinterval of the auxiliary control, since no air conditioners met the comfort requirements for the shutdown strategy, a group of air conditioners was selected to implement only the pre-cooling strategy, resulting in a load increase of 74.24 kW. However, in the second subinterval, the cluster air conditioners that had already met the comfort requirements for the shutdown strategy after using the pre-cooling strategy were shut down, resulting in a load reduction of 1012.1 kW. To ensure continuous load reduction during the main control period, the second group of air conditioners continued to implement the pre-cooling strategy, resulting in a load increase of 127.97 kW. Similarly, in the third, fifth, and fifth subintervals, the load reductions were 1447.8 kW, 1272.8 kW, and 1447.8 kW, respectively, while the load increases were 143.59 kW, 127.97 kW, and 52.3 kW, respectively. In the sixth subinterval of the auxiliary control, since no load reduction was required in the next phase, no pre-cooling operation was performed, resulting in a load reduction of only 260.70 kW. Among them, the load curve and temperature curve before and after the cluster air conditioner is grouped and controlled are as follows: Figure 2 and Figure 3 As shown. Figure 2 and Figure 3 It can be seen from the figure that the cluster air conditioning load grouping control method provided in this embodiment can effectively reduce the load during peak hours and meet the user's demand for temperature.

[0078] This embodiment provides a cluster air conditioner load grouping control method that groups cluster air conditioners using cluster air conditioner data and determines the user comfort preferences for each air conditioner group. Different air conditioner groups correspond to different user comfort preferences. The pre-cooling temperature, as well as the pre-cooling and shutdown times, are then controlled based on the control target and each air conditioner group's user comfort preferences. After the air conditioner is shut down, even if the temperature rises, it will not exceed the user's comfort requirements. This improves the user experience while meeting load control requirements.

[0079] Further, as Figure 1 The specific implementation of the method shown in this embodiment provides a cluster air conditioning load grouping control device based on user comfort preference, such as Figure 4 As shown, the device includes: a grouping module 31, a model building module 32, and a strategy generating module 33.

[0080] The grouping module 31 is configured to obtain cluster air conditioning data of a target area, divide the cluster air conditioning in the target area into a plurality of air conditioning groups based on the cluster air conditioning data, and determine a user comfort preference for each of the air conditioning groups, wherein the user comfort preference includes an air conditioning operating temperature and a pre-cooling temperature;

[0081] A model building module 32 is configured to determine a control target for the target area according to the load rate of the substations in the target area, and to build a control model according to the control target and the user comfort preference of each air conditioning group;

[0082] The strategy generation module 33 can be used to obtain a control strategy for each of the air-conditioning groups based on the control model, and the control strategy is used to control each of the air-conditioning groups to perform pre-cooling or shut down in different time periods.

[0083] In a specific application scenario, the cluster air-conditioning data includes the air-conditioning usage time and the air-conditioning usage temperature; the cluster air-conditioning in the target area is divided into multiple air-conditioning groups based on the cluster air-conditioning data, and the user comfort preference of each air-conditioning group is determined. The grouping module 31 can be specifically used to cluster the cluster air-conditioning in the target area using the air-conditioning usage time and the air-conditioning usage temperature as clustering indicators to obtain multiple air-conditioning groups; the pre-cooling temperature of each air-conditioning group is determined based on the air-conditioning usage time and the air-conditioning usage temperature of each air-conditioning group.

[0084] In a specific application scenario, the control target of the target area is the load reduction power, and the control target of the target area is determined according to the substation load rate in the target area. The model construction module 32 can be specifically used to obtain the substation load rate of the target area; when the substation load rate of the target area belongs to the first preset interval, no control is performed, and the load reduction power is zero; when the substation load rate of the target area belongs to the second preset interval, the cluster air conditioner in the target area is load-reduced, and the load reduction power is calculated based on the control demand load and the substation rated power; when the substation load rate of the target area belongs to the third preset interval, the cluster air conditioner in the target area is load-limited, and the load reduction power is calculated based on the substation load rate, the load rate controllable limit and the substation rated power.

[0085] In a specific application scenario, the control model is constructed according to the control target and the user comfort preference of each air-conditioning group. The control model constructed by the model construction module 32 includes:

[0086]

[0087] Wherein, T0 is the sub-interval duration; T is the control interval duration; N is the number of the air conditioning groups; is the coefficient of variation; P i tol is the total power of the host of air conditioning group i; P i s The power added by air conditioning group i during pre-cooling; S1 i [k] and S2i [k] is the scheduling flag of air conditioning group i in the kth subinterval, S1 i [k] is 0, indicating that the air conditioning group i is not shut down in the subinterval k; [k] is 1, indicating that the air conditioning group i is shut down in the subinterval k. S2 i [k] is 0, indicating that the air-conditioning group i is not pre-cooled in the subinterval k; [k] is 1, indicating that the air-conditioning group i is pre-cooled in the subinterval k; ΔP is the control target, i.e., the load reduction power; Δη p To regulate the demand load rate; η p , lim P is the load rate adjustable limit; rate is the rated power of the substation; η p is the load rate of the substation; m i is the number of air conditioners included in air conditioner group i; is the unit average power of air conditioning group i; i is the energy consumption coefficient of air conditioning group i; is the pre-cooling temperature of air-conditioning group i.

[0088] In a specific application scenario, the model building module 32 may be further configured to set a control interval according to the load peak duration, and divide the control interval into a plurality of sub-intervals;

[0089] Determine the first constraint condition according to the control interval:

[0090]

[0091] Among them, S1 i [k] and S2 i [k] is the scheduling flag of air conditioning group i in the kth subinterval, S1 i [k] is 0, indicating that the air conditioning group i is not shut down in the subinterval k; [k] is 1, indicating that the air conditioning group i is shut down in the subinterval k. S2 i [k] is 0, indicating that the air-conditioning group i is not pre-cooled in the subinterval k; [k] is 1, indicating that the air-conditioning group i is pre-cooled in the subinterval k; T0 is the subinterval duration; T is the control interval duration; N is the number of the air-conditioning groups.

[0092] In a specific application scenario, the model building module 32 may be further configured to determine a second constraint condition based on the air conditioning operating temperature of each air conditioning group:

[0093]

[0094] in, is the lowest operating temperature of the air conditioner in air conditioner group i; is the maximum operating temperature of the air conditioner in air conditioner group i; is the temperature of air-conditioning group i after adjustment in subinterval k; is the set temperature of air conditioning group i during normal operation; t tem is the temperature rise coefficient after the air conditioner is turned off.

[0095] In a specific application scenario, the control strategy of each air-conditioning group is obtained based on the control model. The strategy generation module 33 can be used to construct an optimization function based on the peak-shaving benefit:

[0096]

[0097] Wherein, F is the peak shaving benefit; ρ is the compensation unit price of the peak shaving amount;

[0098] Based on the optimization function, the control model is solved using a genetic algorithm to obtain the control strategy.

[0099] It should be noted that for other corresponding descriptions of the functional units involved in the cluster air conditioning load grouping control device based on user comfort preference provided in this embodiment, please refer to Figure 1 The corresponding description in will not be repeated here.

[0100] Based on the above Figure 1 The method shown in FIG. 1 is a method for performing the above-mentioned operation. Accordingly, this embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned Figure 1 The cluster air conditioning load grouping control method shown.

[0101] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product. The software product to be identified can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0102] Based on the above Figure 1 The method shown, and Figure 4 In order to achieve the above-mentioned purpose, the cluster air conditioning load grouping control device embodiment shown in FIG. Figure 5 As shown, this embodiment also provides a computer device for cluster air conditioning load grouping control, which can be a personal computer, server, smart phone, tablet computer, smart watch, or other network device, etc. The computer device includes a storage medium and a processor; the storage medium is used to store computer programs and operating systems; the processor is used to execute computer programs to achieve the above-mentioned Figure 1 The method shown.

[0103] Optionally, the computer device may further include an internal memory, a communication interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a Wi-Fi module, a display, an input device such as a keyboard, etc. Optionally, the communication interface may further include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a Wi-Fi interface), etc.

[0104] Those skilled in the art will understand that the computer device structure for cluster air conditioning load grouping control provided in this embodiment does not constitute a limitation on the computer device, and may include more or fewer components, or a combination of certain components, or different component arrangements.

[0105] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the computer device hardware and the software resources to be identified, supporting the execution of the information processing program and other software and / or programs to be identified. The network communication module is used to enable communication between components within the storage medium and with other hardware and software in the information processing computer device.

[0106] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus the necessary general hardware platform, or by hardware. By applying the technical solution of the present application, the above method groups cluster air conditioners through cluster air conditioner data, and determines the user comfort preference of each air conditioner group, wherein different air conditioner groups correspond to different user comfort preferences. Then, the pre-cooling temperature of the air conditioner group and the duration of pre-cooling and shutdown are controlled according to the control target and the user comfort preference of each air conditioner group. After the air conditioner is shut down, even if the temperature rises, it will not exceed the user's comfort requirements. Compared with the existing technology, it can improve the user experience while meeting the load regulation.

[0107] Those skilled in the art will understand that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required to implement the present application. Those skilled in the art will understand that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more devices different from the implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0108] The serial numbers of the above application are for descriptive purposes only and do not represent the advantages or disadvantages of the implementation scenarios. The above disclosure only discloses several specific implementation scenarios of the present application, but the present application is not limited thereto. Any changes that can be conceived by those skilled in the art should fall within the scope of protection of the present application.

Claims

1. A cluster air conditioning load grouping control method based on user comfort preference, characterized in that: The method comprises: Acquiring cluster air conditioning data for a target area, dividing the cluster air conditioning in the target area into a plurality of air conditioning groups based on the cluster air conditioning data, and determining a user comfort preference for each of the air conditioning groups, wherein the user comfort preference includes an air conditioning operating temperature and a pre-cooling temperature; Determining a control target for the target area according to the load rate of the substations in the target area, and constructing a control model according to the control target and the user comfort preference of each air-conditioning group; Obtaining a control strategy for each of the air-conditioning groups based on the control model, wherein the control strategy is used to control each of the air-conditioning groups to perform pre-cooling or shut down in different time periods; The control target of the target area is load reduction power, and determining the control target of the target area according to the load rate of the substation in the target area includes: Obtaining the load rate of the target area; When the load rate of the target area belongs to the first preset range, no regulation is performed and the load reduction power is zero; When the load rate of the target area belongs to the second preset interval, the cluster air conditioner in the target area is load-reduced, and the load reduction power is calculated according to the control demand load and the rated power of the area; When the load rate of the target area falls within a third preset interval, load limiting control is performed on the cluster air conditioner in the target area, and the load reduction power is calculated based on the load rate, the load rate adjustable limit, and the rated power of the area; The constructing of a control model according to the control target and the user comfort preference of each air-conditioning group includes: ； ； ； ； in, is the subinterval duration; is the duration of the control interval; is the number of the air conditioning groups; is the coefficient of deviation; For air conditioning group Total host power; Air conditioning unit for pre-cooling increased power; and For air conditioning group In the The scheduling flag within the subinterval, 0 means air conditioning group In the sub-interval If it is not shut down, it means the air conditioner group In the sub-interval Internal shutdown, 0 means the air conditioning group In the sub-interval There is no pre-cooling inside, and 1 means the air conditioning unit In the sub-interval Internal pre-cooling; The control target is the load power reduction; To regulate the demand load rate; It is the adjustable limit of load factor; is the rated power of the substation; is the load factor of the area; For air conditioning group Number of air conditioners included; For air conditioning group The unit average power; For air conditioning group Energy consumption coefficient; For air conditioning group Pre-cooling temperature.

2. The method according to claim 1, characterized in that The cluster air conditioning data includes air conditioning usage time and air conditioning usage temperature; The step of dividing the cluster air conditioners in the target area into a plurality of air conditioner groups according to the cluster air conditioner data, and determining the user comfort preference of each of the air conditioner groups, includes: Clustering the clustered air conditioners in the target area using the air conditioner usage time and the air conditioner usage temperature as clustering indicators to obtain multiple air conditioner groups; The pre-cooling temperature of each air-conditioning group is determined according to the air-conditioning usage time and the air-conditioning usage temperature of each air-conditioning group.

3. The method according to claim 1, characterized in that The method further includes: setting a control interval according to the load peak duration, and dividing the control interval into a plurality of sub-intervals; Determine the first constraint condition according to the control interval: ； in, and For air conditioning group In the The scheduling flag within the subinterval, 0 means air conditioning group In the sub-interval If it is not shut down, it means the air conditioner group In the sub-interval Internal shutdown, 0 means the air conditioning group In the sub-interval There is no pre-cooling inside, and 1 means the air conditioning unit In the sub-interval Internal pre-cooling; is the subinterval duration; is the duration of the control interval; is the number of the air conditioning groups.

4. The method according to claim 3, characterized in that The method further includes: determining a second constraint condition according to the air conditioning usage temperature of each air conditioning group: ； ； in, For air conditioning group The lowest operating temperature of the air conditioner; For air conditioning group The maximum operating temperature of the air conditioner; For air conditioning group Temperature after regulation in subinterval k; For air conditioning group Set temperature during normal operation; is the temperature rise coefficient after the air conditioner is turned off.

5. The method according to claim 4, characterized in that The obtaining of a control strategy for each air-conditioning group based on the control model includes: Construct an optimization function based on peak-shaving benefits: ； in, F is the peak shaving benefit; The compensation unit price for peak load regulation; Based on the optimization function, the control model is solved using a genetic algorithm to obtain the control strategy.

6. A cluster air conditioning load grouping control device based on user comfort preference, characterized in that: The cluster air conditioning load grouping control device is used to implement the method according to any one of claims 1 to 5, and the device includes: a grouping module, configured to obtain cluster air conditioning data for a target area, divide the cluster air conditioning units in the target area into a plurality of air conditioning groups based on the cluster air conditioning data, and determine a user comfort preference for each of the air conditioning groups, wherein the user comfort preference includes an air conditioning operating temperature and a pre-cooling temperature; A model building module is used to determine the control target of the target area according to the load rate of the substation in the target area, and to build a control model according to the control target and the user comfort preference of each air-conditioning group; A strategy generation module is used to obtain a control strategy for each of the air-conditioning groups based on the control model, wherein the control strategy is used to control each of the air-conditioning groups to perform pre-cooling or shut down in different time periods.

7. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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