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

Abstract

The invention discloses a cluster air conditioner load grouping regulation and control method and device based on user comfort preference, and relates to the field of electric power. The method comprises the steps that firstly, cluster air conditioner data of a target area is obtained, cluster air conditioners in the target area are divided into a plurality of air conditioner sets according to the cluster air conditioner data, the user comfort preference of each air conditioner set is determined, and the user comfort preference comprises the air conditioner use temperature and the pre-refrigeration temperature; then a regulation and control target of the target area is determined according to the transformer area load rate in the target area, and a regulation and control model is constructed according to the regulation and control target and the user comfort preference of each air conditioner set; and finally, based on the regulation and control model, a regulation and control strategy of each air conditioner set is obtained, and the regulation and control strategies are used for controlling each air conditioner set to conduct pre-refrigeration or shutdown in different time periods. According to the method, the user experience is improved while load regulation is met.

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 more and more popular, the disordered demand-side load consumption has caused the seasonal peak load to become increasingly prominent, the growth rate of power load is significantly higher than the growth rate of power, the peak-to-valley difference continues to increase, the peak power supply supply and demand situation is tense, and the seasonal and regional peak power supply is tight. Building energy consumption accounts for about 30% of the total energy consumption of the national economy, and air conditioning energy consumption accounts for 50%-70% of the total energy consumption of buildings, that is, air conditioning energy consumption accounts for about 20% of the national economic energy consumption.

[0003] Energy consumption perception and regulation on the demand side of air conditioners is an important direction for promoting power load regulation. Taking refrigeration as an example, the traditional method of regulating air conditioners is mainly to control the start and stop of air conditioners in sections and batches. After the air conditioner stops running, the room temperature will continue to rise, the cooling effect will deteriorate, and the user's comfort requirements will be exceeded, affecting the user's experience. Therefore, there is an urgent need for an air conditioner load regulation solution that takes user comfort into consideration. 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 cluster air conditioning load grouping control method based on user comfort preference is provided, the method comprising:

[0006] Acquire cluster air conditioning data of a target area, divide the cluster air conditioning in the target area into a plurality of air conditioning groups according to the cluster air conditioning data, and determine a user comfort preference of each of the air conditioning groups, wherein the user comfort preference includes an air conditioning use temperature and a pre-cooling temperature;

[0007] Determine the control target of the target area according to the load rate of the station area in the target area, and build a control model according to the control target and the user comfort preference of each of the air conditioning groups;

[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 air-conditioning usage time and air-conditioning usage temperature; 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 of the air-conditioning groups, 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 determining the control target of the target area according to the substation load rate in the target area includes: obtaining the substation load rate of the target area; when the substation load rate of the target area belongs to a 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 a second preset interval, the cluster air conditioner in the target area is load-reduced and controlled, 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 a third preset interval, the cluster air conditioner in the target area is load-limited and controlled, 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 of the air-conditioning groups includes:

[0012]

[0013] Wherein, T0 is the duration of the sub-interval; T is the duration of the control interval; 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 dispatch flag of air conditioning group i in the kth sub-interval, S1 i [k] is 0, indicating that the air conditioner group i is not shut down in subinterval k; [k] is 1, indicating that the air conditioner group i is shut down in subinterval k. i [k] is 0, indicating that the air conditioning group i is not pre-cooled in the sub-interval k, and is 1, indicating that the air conditioning group i is pre-cooled in the sub-interval k; ΔP is the control target, that is, the load reduction power; Δη p To regulate the demand load rate; η p,lim is the load rate adjustable limit; P rate is the rated power of the station; η pis the load factor of the station area; 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 dispatch flag of air conditioning group i in the kth sub-interval, S1 i [k] is 0, indicating that the air conditioner group i is not shut down in subinterval k; [k] is 1, indicating that the air conditioner group i is shut down in subinterval k. i [k] is 0, indicating that the air-conditioning group i is not pre-cooled in the sub-interval k, and is 1, indicating that the air-conditioning group i is pre-cooled in the sub-interval k; T0 is the sub-interval 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 use temperature of each of the air conditioning groups:

[0018]

[0019] in, is the lowest temperature of the air conditioner in air conditioner group i; is the highest temperature of the air conditioner in air conditioner group i; is the temperature of air-conditioning group i after regulation in sub-interval 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, used 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 according to the cluster air-conditioning data, and determine a user comfort preference of each of the air-conditioning groups, wherein the user comfort preference includes an air-conditioning use temperature and a pre-cooling temperature;

[0027] A model building module, 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 of the air conditioning groups;

[0028] A strategy generation module is 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.

[0029] According to a third aspect of the present invention, there is provided a storage medium storing a computer program, wherein 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, there is provided a computer device 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 conditioning load grouping control method and device based on user comfort preference, firstly obtain the cluster air conditioning data of the target area, divide the cluster air conditioning in the target area into multiple air conditioning groups according to the cluster air conditioning data, and determine 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 determine the control target of the target area according to the load rate of the station area in the target area, and build a control model according to the control target and the user comfort preference of each air conditioning group; finally, obtain the control strategy of 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 groups the cluster air conditioning through the cluster air conditioning data, and determines the user comfort preference of each air conditioning group, wherein different air conditioning groups correspond to different user comfort preferences. Then, according to the control target and the user comfort preference of each air conditioning group, the pre-cooling temperature and the pre-cooling and shut-down time of the air conditioning group are controlled, and after the air conditioning is shut down, even if the temperature rises, it will not exceed the user's comfort requirements, thereby satisfying the 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 schematic diagram of the structure 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 combination with embodiments. It should be noted that, in the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. In the various embodiments shown below, the first, second and various digital numbers are only used to distinguish for the convenience of description and are not used to limit the scope of the embodiments of the present 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. Acquire cluster air-conditioning data of a target area, divide the cluster air-conditioning in the target area into a plurality of air-conditioning groups according to the cluster air-conditioning data, and determine a user comfort preference of each of the air-conditioning groups, wherein the user comfort preference includes an air-conditioning use 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, 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, and 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 the pre-cooling and shutdown method to group and rotate the cluster air conditioners. Specifically, the cluster air conditioners can be grouped by clustering to obtain multiple air conditioner groups, and the user comfort preference corresponding to each air conditioner group is determined according to the cluster air conditioner data corresponding to each air conditioner group. The user comfort preference can be the air conditioner use temperature and the pre-cooling temperature. Among them, the pre-cooling temperature can be determined according to the cooling temperature range preferred by the user. For example, if the user's preferred cooling temperature is high and the cooling time is long, it means that the user has a high requirement for comfort, so a lower pre-cooling temperature can be set (for example, pre-cooling is performed by reducing 3 degrees relative to the normal temperature preferred by the user), so as 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's preferred cooling temperature is high and the cooling time is short, it means that the user has a low requirement for comfort, so a higher pre-cooling temperature can be set (for example, pre-cooling is performed by reducing 1 degree relative to the normal temperature preferred by the user), so as 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 are different, so it is necessary to determine the control target of the target area based on the load rate of the substation corresponding to the target area and the preset load rate threshold. The control target can be characterized by power. For example, if the load rate threshold is 90% and the substation load rate of the target area is 95%, it can be determined that the load rate that needs to be regulated is 5%. At this time, the power that needs to be regulated can be calculated based on the product of the rated load rate of the substation and the load rate that needs to be regulated, that is, the control target.

[0047] The air conditioning use temperature and pre-cooling temperature corresponding to each air conditioning group are different, and the number of cluster air conditioners in each air conditioning group is also different. Accordingly, the power consumed by pre-cooling and the power saved by shutting down different air conditioning groups are also different. When different time periods and different air conditioning groups implement different pre-cooling or shutdown strategies, the load control effect is also different. Therefore, based on the air conditioning use temperature, pre-cooling temperature and control target of the target area of ​​each air conditioning group, a control model can be determined, and a control strategy can be generated through the control model to control each air conditioning group to perform pre-cooling or shut down at different time periods, so as to reduce the load while meeting the user's comfort needs.

[0048] The cluster air conditioner load grouping control method provided in this embodiment first obtains the cluster air conditioner data of the target area, divides the cluster air conditioners in the target area into multiple air conditioner groups according to the cluster air conditioner data, and determines the user comfort preference of each air conditioner group, wherein the user comfort preference includes the air conditioner use temperature and the pre-cooling temperature; then determines the control target of the target area according to the load rate of the station area in the target area, and constructs a control model according to the control target and the user comfort preference of each air conditioner group; finally, obtains the control strategy of each air conditioner group based on the control model, and the control strategy is used to control each air conditioner group to perform pre-cooling or shutdown in different time periods. The above method groups the cluster air conditioners through the 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, thereby satisfying the 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 according to the cluster air conditioning data, and the user comfort preference of each air conditioning group is determined, which 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 according to 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 c-means clustering algorithm. Specifically, the cluster air conditioners in the target area can be clustered by 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 preference 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 the air conditioner group is pre-cooled. 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 is pre-cooled, 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. Determining the control target of the target area according to the substation load rate in the target area can be achieved in the following ways: 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 controlled, 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 controlled, 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, which can be specifically load reduction power. The control target is determined according to the load rate of the station area of ​​the target area and the preset load rate threshold. When the station area load rate belongs to the first preset interval, no load control is performed. For example, if the load rate threshold is 90%, when the station area load rate of the target area is less than or equal to 90%, there is no need to perform load control on the target area. When the station area load rate belongs to the second preset interval, the target area is load-reduced. For example, when the station area load rate is between 90% and 100%, the load reduction power required to be regulated in the target area is calculated. The load reduction power can be calculated based on the load rate of the control demand and the rated power of the station area, where the load rate of the control demand can be set according to demand. When the station area load rate belongs to the second preset interval, the target area is load-limited. For example, when the station area load rate of the target area exceeds 100%, the corresponding load reduction power is calculated by the station area load rate, the load rate adjustable limit and the rated power of the station area, and the load of the target area is limited.

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

[0054]

[0055] Wherein, ΔP is the control target, that is, 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 station; η p is the load factor of the station.

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

[0057]

[0058] Formula (2) is used to express that the power reduction required for regulating the cluster air conditioner in the target area needs to meet the regulation target ΔP. Wherein, T0 is the sub-interval duration; T is the regulation 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 dispatch flag of air conditioning group i in the kth sub-interval, S1 i [k] is 0, indicating that the air conditioner group i is not shut down in subinterval k; [k] is 1, indicating that the air conditioner group i is shut down in subinterval k. i [k] is 0, indicating that the air conditioning group i is not pre-cooled in the sub-interval k, and is 1, indicating that the air conditioning group i is pre-cooled in the sub-interval k; ΔP is the control target, that is, 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 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. Determine the first constraint condition according to the control interval:

[0060]

[0061] The first constraint is used to constrain 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 sub-interval, 0 means that air-conditioning group i does not participate in regulation, and 1 means that air-conditioning group i participates in regulation; T0 is the sub-interval duration; T is the regulation interval duration; N is the number of air-conditioning groups.

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

[0063]

[0064] in, is the lowest temperature of the air conditioner in air conditioner group i; is the highest temperature of the air conditioner in air conditioner group i; is the temperature of air-conditioning group i after regulation in sub-interval 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 when entering the main control sub-interval, shut down the pre-cooling air-conditioning group, 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 by a genetic algorithm so that the generated control strategy satisfies both the control objectives and the constraints, and is optimized by an optimization function. When the peak-shaving benefit in the optimization function reaches the maximum, or the genetic algorithm reaches a preset number of iterations, the optimal solution of the control model is obtained and the control strategy is generated.

[0071] In a specific embodiment, based on the room temperature control at 26°C, 1,500 air conditioners are involved in the control in the target area, and the control interval is 2 hours. The air conditioners in the target area are clustered and grouped based on the cluster air conditioner data, and a combination 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 is the lowest. When the first group participates in the control, 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 / unit 237 762 501 Expected load reduction / kW 260.7 1447.8 1012.1 Pre-cooling temperature / ℃ 3 2 1

[0074] Based on the grouping situation in Table 1, the cluster air conditioners are grouped and rotated. According to the current operation of the power grid and the status of the air conditioner equipment, one of the pre-cooling, shutdown or normal state is selected for control. The control cycle of 2 hours is divided into six sub-intervals, and the control time corresponding to each sub-interval is 20 minutes. Among them, the first sub-interval is the start auxiliary control sub-interval (the first auxiliary control sub-interval), the second sub-interval to the fifth sub-interval are the main control sub-intervals, and the sixth sub-interval is the end auxiliary control sub-interval (the second auxiliary control sub-interval). Based on the load rate of the substation, the current power grid control target of 1600kW·h is calculated. The control strategy obtained by genetic algorithm optimization, the actual control power of the air conditioner is 1638.37.22kW·h, and the total income of the air conditioner users participating in the peak load regulation is 1906.66 yuan. During the control period, the control of the cluster air conditioner load group is shown in Table 2.

[0075] Table 2

[0076]

[0077] In the beginning of the auxiliary control sub-interval, i.e., the first sub-interval, since no air conditioner meets the comfort requirements of the shutdown strategy, a group of air conditioners is selected to execute only the pre-cooling strategy, which increases the load power by 74.24kW. However, in the following second interval, the cluster air conditioners that have undergone the pre-cooling strategy have met the comfort requirements of the shutdown strategy, so the shutdown strategy is implemented for the group of air conditioners, resulting in a load reduction of 1012.1kW. At the same time, in order to ensure the continuous reduction of the load during the main control period, the second group of air conditioners continues to implement the pre-cooling strategy, which increases the load power by 127.97kW; similarly, in the third to fifth sub-intervals, the load reductions are 1447.8kW, 1272.8kW, and 1447.8kW, respectively, and the load increases are 143.59kW, 127.97kW, and 52.3kW, respectively; in the ending auxiliary control sub-interval, i.e., the sixth sub-interval, since no load reduction is required in the next stage, no pre-cooling operation is performed in this stage, so only a load reduction of 260.70kW is generated. 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 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] The cluster air conditioner load grouping control method provided in this embodiment 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 pre-cooling and shutdown time 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 requirement, thereby satisfying the load control while improving the user experience.

[0079] Further, as Figure 1 The specific implementation of the method shown in the 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 may be used 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 according to the cluster air conditioning data, and determine a user comfort preference of each of the air conditioning groups, wherein the user comfort preference includes an air conditioning use temperature and a pre-cooling temperature;

[0081] A model building module 32, which can be used to determine the control target of the target area according to the load rate of the station area in the target area, and build a control model according to the control target and the user comfort preference of each of the air conditioning groups;

[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 according to the cluster air-conditioning data, and the user comfort preference of each of the air-conditioning groups 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; determine 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.

[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 building 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 according to 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 according to the substation load rate, the load rate adjustable 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 duration of the sub-interval; T is the duration of the control interval; 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 dispatch flag of air conditioning group i in the kth sub-interval, S1 i [k] is 0, indicating that the air conditioner group i is not shut down in subinterval k; [k] is 1, indicating that the air conditioner group i is shut down in subinterval k. i [k] is 0, indicating that the air conditioning group i is not pre-cooled in the sub-interval k, and is 1, indicating that the air conditioning group i is pre-cooled in the sub-interval k; ΔP is the control target, that is, 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 station; η p is the load factor of the station area; 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 can also be used to set a control interval according to the load peak duration, and divide the control interval into multiple 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 dispatch flag of air conditioning group i in the kth sub-interval, S1 i [k] is 0, indicating that the air conditioner group i is not shut down in subinterval k; [k] is 1, indicating that the air conditioner group i is shut down in subinterval k. i [k] is 0, indicating that the air-conditioning group i is not pre-cooled in the sub-interval k, and is 1, indicating that the air-conditioning group i is pre-cooled in the sub-interval k; T0 is the sub-interval 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 can also be used to determine the second constraint condition according to the air conditioning use temperature of each air conditioning group:

[0093]

[0094] in, is the lowest temperature of the air conditioner in air conditioner group i; is the highest temperature of the air conditioner in air conditioner group i; is the temperature of air-conditioning group i after regulation in sub-interval 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, a control strategy for each air conditioning group is obtained based on the control model. The strategy generation module 33 can be specifically used to construct an optimization function based on the peak load 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 the embodiment also 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, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a 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 the figure is as follows: Figure 5 As shown, this embodiment also provides a computer device for cluster air conditioning load grouping control, which can be a personal computer, a server, a smart phone, a tablet computer, a smart watch, or other network devices, etc. The computer device includes a storage medium and a processor; the storage medium is used to store computer programs and an operating system; the processor is used to execute the computer program to implement the above 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 also 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 appreciate 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 above-mentioned computer device hardware and the software resources to be identified, and supports the operation of the information processing program and other software and / or programs to be identified. The network communication module is used to realize the communication between the components inside the storage medium, and the communication 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 prior art, it can improve the user experience while meeting the load regulation.

[0107] Those skilled in the art will appreciate 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 necessary for implementing the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the description of the implementation scenario, or can be changed accordingly and located in one or more devices different from the present implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0108] The above serial numbers of this application are only for description and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of this application, but this application is not limited to them, and any changes that can be thought of by technicians in this field should fall within the scope of protection of this application.

Claims

1. A cluster air conditioning load grouping control method based on user comfort preference, characterized in that: The method comprises: Acquire cluster air conditioning data of a target area, divide the cluster air conditioning in the target area into a plurality of air conditioning groups according to the cluster air conditioning data, and determine a user comfort preference of each of the air conditioning groups, wherein the user comfort preference includes an air conditioning use temperature and a pre-cooling temperature; Determine the control target of the target area according to the load rate of the station area in the target area, and build a control model according to the control target and the user comfort preference of each of the air conditioning groups; 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.

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, comprises: Clustering the clustered air conditioners in the target area by 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 of the air conditioning groups is determined according to the air conditioning use time and the air conditioning use temperature of each of the air conditioning groups.

3. The method according to claim 1, characterized in that 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 interval, 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 regulated, and the load reduction power is calculated according to the regulation demand load and the rated power of the area; When the area load rate of the target area belongs to the third preset interval, the cluster air conditioner in the target area is load limited and regulated, and the load reduction power is calculated based on the area load rate, the load rate adjustable limit and the area rated power.

4. The method according to claim 3, characterized in that: The constructing of the control model according to the control target and the user comfort preference of each air conditioning group includes: Wherein, T0 is the duration of the sub-interval; T is the duration of the control interval; 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 dispatch flag of air conditioning group i in the kth sub-interval, S1 i [k] is 0, indicating that the air conditioner group i is not shut down in subinterval k; [k] is 1, indicating that the air conditioner group i is shut down in subinterval k. i [k] is 0, indicating that the air conditioning group i is not pre-cooled in the sub-interval k, and is 1, indicating that the air conditioning group i is pre-cooled in the sub-interval k; ΔP is the control target, that is, the load reduction power; Δη p To regulate the demand load rate; η p,lim is the load rate adjustable limit; P rate is the rated power of the station; η p is the load factor of the station area; 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.

5. The method according to claim 4, 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: Among them, S1 i [k] and S2 i [k] is the dispatch flag of air conditioning group i in the kth sub-interval, S1 i [k] is 0, indicating that the air conditioner group i is not shut down in subinterval k; [k] is 1, indicating that the air conditioner group i is shut down in subinterval k. i [k] is 0, indicating that the air-conditioning group i is not pre-cooled in the sub-interval k, and is 1, indicating that the air-conditioning group i is pre-cooled in the sub-interval k; T0 is the sub-interval duration; T is the control interval duration; N is the number of the air-conditioning groups.

6. The method according to claim 5, characterized in that The method further includes: determining a second constraint condition according to the air conditioning use temperature of each of the air conditioning groups: in, is the lowest temperature of the air conditioner in air conditioner group i; is the highest temperature of the air conditioner in air conditioner group i; is the temperature of air-conditioning group i after regulation in sub-interval 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.

7. The method according to claim 6, 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: Wherein, F is the peak load-shaving benefit; ρ is the compensation unit price of the peak load-shaving amount; Based on the optimization function, the control model is solved using a genetic algorithm to obtain the control strategy.

8. A cluster air conditioning load grouping control device based on user comfort preference, characterized in that: The device comprises: A grouping module, used 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 according to the cluster air-conditioning data, and determine a user comfort preference of each of the air-conditioning groups, wherein the user comfort preference includes an air-conditioning use temperature and a pre-cooling temperature; A model building module, 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 of the air conditioning groups; A strategy generation module is 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.

9. 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 7 are implemented.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, 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 7 are implemented.

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