A demand response regulation method considering air conditioning load heterogeneity

Abstract

The application discloses a demand response regulation method considering air conditioner load heterogeneity, and relates to the technical field of smart grid demand response, comprising: a two-stage combined regulation strategy: in the first stage, a load aggregator evaluates the demand response potential of each cluster on the basis of cluster parameter identification, and formulates a combined scheduling strategy for the clustering station in combination with complementary coupling characteristic analysis between clusters; in the second stage, a micro-element hierarchical sorting priority scheduling method is adopted, the response process is decomposed into micro-element periods by introducing the micro-element method, and the willingness of users and the demand for privacy protection are considered, and the regulation object is determined by a PSME-VESS method. The method used in the application effectively solves the technical problems of heterogeneous air conditioner load cluster scheduling difficulty and low resource utilization, and provides a fine demand response solution for a high-proportion renewable energy power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart grid demand response, and more particularly to a demand response regulation method considering the heterogeneity of air conditioning load. BACKGROUND

[0002] With the transformation of the power system to a high proportion of renewable energy, the regulation potential of flexible loads such as air conditioning load has gradually become a key breakthrough for optimizing power supply and demand balance. However, due to its temporal and spatial distribution characteristics, it has a significant impact on the peak-valley difference and peak regulation pressure of the power grid, making the power supply and demand imbalance problem increasingly prominent.

[0003] The current hot research direction is to rely on advanced information and control technology to tap the regulation potential of individual air conditioning load, improve its adjustable capacity, and achieve a benign and friendly interaction between air conditioning load and the power grid in terms of demand response. However, individual air conditioning load as a small load user cannot reach the minimum access level in the corresponding market. Therefore, it has become a trend to consider the heterogeneous air conditioning load cluster as a whole interest subject to participate in demand response. Thus, the Load Aggregator (LA) has emerged. As a new type of market subject, it aggregates a large number of small and dispersed load resources and participates in power market transactions and demand response projects as a proxy for these load resources. However, the current technology ignores the heterogeneity of air conditioning load, making the generalization model of air conditioning load not suitable for the overall energy use characteristics, making it difficult to accurately model and regulate the aggregated response behavior.

[0004] Therefore, it is inevitable to consider the heterogeneous air conditioning load as a cluster to participate in demand response by dividing the air conditioning load cluster according to heterogeneity. The existing research on air conditioning load participating in demand response has achieved certain results. However, due to the lack of research on the demand response strategy of the heterogeneity and spatial and temporal differences of air conditioning load, and the potential correlation and coupling of the energy use characteristics of heterogeneous load have not been tapped.

[0005] Therefore, how to propose a demand response regulation method considering the heterogeneity of air conditioning load to solve the current situation of difficult cluster scheduling and low resource utilization rate caused by the heterogeneity of air conditioning load in the renewable energy power grid is a problem that needs to be solved by those skilled in the art. SUMMARY

[0006] In view of this, the present application provides a kind of demand response regulation method considering air conditioning load heterogeneity, consider the premise of the heterogeneity and space-time difference of air conditioning load, guarantee the demand response index of accurate response power grid.From LA angle, the hidden correlation and coupling between the energy consumption characteristics of heterogeneous load are deeply researched, and demand response regulation strategy is formulated on this basis, guarantee economic benefit and long-term operation, solve the current situation of low resource utilization and cluster scheduling difficulty caused by air conditioning load heterogeneity in renewable energy power grid, in order to achieve the above purpose, the present application adopts the following technical solutions:

[0007] A kind of demand response regulation method considering air conditioning load heterogeneity, contains two stages, wherein the first stage is mainly to formulate combined scheduling strategy, specifically:

[0008] Air conditioning load cluster is divided by clustering algorithm, and typical daily load curve of load cluster is generated;

[0009] Parameter identification is carried out on air conditioning load cluster by load aggregator;

[0010] Virtual energy storage model is established based on identified parameters to calculate virtual state of charge of each air conditioning load cluster, and demand response potential is evaluated;

[0011] The complementary coupling characteristics between air conditioning load clusters are evaluated by time complementarity analysis model, power complementarity analysis model and pearson coupling coefficient;

[0012] Combined scheduling strategy is formulated according to demand response potential and complementary coupling characteristics between clusters;

[0013] Optionally, the first stage adopts clustering algorithm to divide air conditioning load cluster, and the typical daily load curve of the cluster includes:

[0014] The daily load curve of a plurality of air conditioning load devices is recorded as , Indicates that there are Parameter heterogeneity, Indicates that there are Type heterogeneity;

[0015] Distributed clustering site divides air conditioning load into a plurality of clusters based on the region where air conditioning load is located;

[0016] Distributed clustering site adopts K-shape clustering algorithm to cluster load curve based on air conditioning load cluster information collected in the region , obtains Clustering center curve, that is, form Load cluster, recorded as , each clustering center curve is regarded as the typical daily load curve of the corresponding load cluster.

[0017] Optionally, the first stage of parameter identification of the air conditioning load cluster by the load aggregator includes:

[0018] The operating mode of the air conditioning load is identified, and the load curve is segmented into two states: air conditioning on / off and static / dynamic state.

[0019] By analyzing the static segmentation of the load curve, the static parameters of the air conditioning load are estimated using the constrained regression method.

[0020] A hybrid estimation method based on particle swarm optimization algorithm is used to identify dynamic parameters.

[0021] Optionally, the first phase of assessing demand response potential includes:

[0022] The power released by the air conditioning load is the maximum downward adjustment power:

[0023] ;

[0024] In the formula, This is the maximum downward adjustment power; for The air conditioner's operating power at any given time; This is the minimum operating power for the air conditioning load;

[0025] The demand response potential of a single air conditioning load is:

[0026] ;

[0027] ;

[0028] In the formula: and They are respectively Time of the first Total energy storage and current power consumption of each air conditioning system; for Time of the first Indoor temperature of the air conditioning system Let t be the maximum indoor temperature of the air conditioning system. Let t be the minimum indoor temperature of the air conditioning system at time t;

[0029] When the air conditioning load receives a charging command (i.e., a power increase command) from the load aggregator, the air conditioning load lowers the set temperature, making the difference between the room temperature and the set temperature positive. The air conditioning compressor speed increases to its maximum, and the air conditioning cooling capacity is at its maximum. At this time, the power change of the air conditioning load is the maximum upward adjustment power.

[0030] ;

[0031] wherein, is the maximum upward adjustment power; is is the air conditioning operation power at time t; is the maximum operation power of the air conditioning load;

[0032] The demand response potential of the single air conditioning load is:

[0033] .

[0034] Optionally, the first stage further comprises:

[0035] When the number of the air conditioning load aggregation cluster is N, the adjustable potential of the air conditioning cluster is as follows:

[0036] (1) When the target set temperature is increased, the maximum downward adjustment power of the aggregated air conditioning cluster is:

[0037] ;

[0038] wherein, is the maximum downward adjustment power of the single cluster, and N is the number of the air conditioning load aggregation cluster.

[0039] The minimum sustainable time of the minimum operation power of the cluster is:

[0040] ;

[0041] wherein, is the minimum sustainable time of the minimum operation power of the cluster at time t, and n represents the number of the cluster;

[0042] The maximum positive demand response potential of the cluster is:

[0043] ;

[0044] (2) When the target set temperature is decreased, the maximum upward adjustment power of the aggregated air conditioning cluster is:

[0045] ;

[0046] wherein, is the maximum upward adjustment power of the single cluster, and N is the number of the air conditioning load aggregation cluster.

[0047] The minimum sustainable time of the maximum operation power of the cluster is:

[0048]

[0049] wherein, is the minimum sustainable time for the maximum operating power of the cluster at time t, and n represents the number of clusters;

[0050] The maximum reverse demand response potential of the cluster is:

[0051] .

[0052] Optionally, the first stage evaluates the complementary coupling characteristics between the air conditioning load clusters through a time complementarity analysis model, a power complementarity analysis model, and a Pearson coupling coefficient, and the method comprises the following steps:

[0053] The time complementarity analysis model is calculated by the difference curve variance;

[0054] The power complementarity analysis model is calculated by the difference degree of the average power level of different load curves;

[0055] The Pearson coupling coefficient is calculated by calculating the Pearson correlation coefficient between two load curves.

[0056] Optionally, the first stage formulates a combined scheduling strategy according to the demand response potential and the complementary coupling characteristics between the clusters, and the method comprises the following steps:

[0057] The income of the load aggregator formulating the combined scheduling strategy is represented as:

[0058] ;

[0059] In the formula, is the total income of the load aggregator obtained from the top-level scheduling center after scheduling, is the cost compensated by the load aggregator when scheduling the air conditioning load;

[0060] The combined strategy formulation aims to minimize the controlled frequency and maximize the utilization of the demand response potential, and when selecting the clusters participating in the combination, the heterogeneous load clusters with strong associated coupling are concentratedly responded, and the heterogeneous load clusters with time domain complementarity are dispersedly responded, and the combination target is represented as:

[0061] ;

[0062] ;

[0063] ;

[0064] In the formula, represents the scheduling time; represents the combined demand response potential; represents the coupling coefficient inside the combination; represents Combination in The complementary characteristic coefficient at time; For the first The combination in Always consider the equivalent potential value of complementary coupling characteristics; for The equivalent potential value of the G combination at time; There are gn combinations in The net difference between coupling and complementarity at any given moment. There are gn combinations in The coupling complementarity coefficient at time step.

[0065] Optionally, the first phase may also include:

[0066] In order to take into account the controlled frequency of the cluster, after selecting the air conditioning load cluster in the G combination, the combined scheduling strategy selects the cluster with the smallest actual demand response potential from the clusters whose actual demand response potential is greater than the remaining unresponded power, and replaces the cluster with the smallest actual demand response potential in the G combination with the cluster with the smaller number of control times, thus obtaining the final G combination.

[0067] In the objective function of the load aggregator, its incentive price needs to be calculated based on the price before combination. According to the known attributes of the cluster in the G combination, its type is divided into civil load and non-civil load. The dispatchable potential of non-civil load is much greater than that of residential load, and different subsidy prices are set.

[0068] The objective function of the load aggregator is expressed as:

[0069] ;

[0070] In the formula, These are the unit demand response compensation prices for LA to residential and non-residential loads, respectively. The unit incentive paid by the upper-level dispatch center to LA; Indicated as in The power component of the residential air conditioning load cluster participating in demand response in the G combination at any given time; express The power component of the non-residential air conditioning load cluster in the G combination at any given time participates in demand response.

[0071] Optionally, a demand response control method that takes into account the heterogeneity of air conditioning loads includes two stages. The second stage mainly includes: determining the air conditioning load clusters that need to be controlled according to the combined scheduling strategy; and in the control execution stage, prioritizing scheduling by combining user willingness and PSME-VESS hierarchical sorting rules.

[0072] Optionally, the second phase, which combines user willingness and PSME-VESS hierarchical ranking rules for priority scheduling, includes:

[0073] (1) Grouping: Grouping the air conditioning system according to the user willingness degree level collected by the intelligent control device, setting the user willingness degree level, and dividing into k groups from high to low;

[0074] (2) Clustering: Clustering each group based on the thermal parameter RC value and the characteristic temperature difference QR value, taking the RC value and the QR value as characteristic attributes, adopting the K-means clustering method, and clustering the air conditioners into n aggregated groups according to the principle that the RC value and the QR value are similar, when , the response duration of the power grid is decomposed into a meta-process with a duration of , the minimum sustainable duration is , the response duration of the power grid is , and a type with a relatively large QR value is controlled first; when , a type with a relatively small RC value and a large QR value is controlled first.

[0075] (3) Inter-class sorting: After grouping and clustering the air conditioning load, there are k*n classes in total, and the classes in the group are sorted, and the air conditioning system class with a relatively fast response and a large response amount is selected for control first.

[0076] (4) In-class sorting: In the air conditioning load in the same class, the sorting is performed from high to low based on the curve index, and the user with a relatively large demand response potential is controlled first, and after the sorting is completed, the demand response meta-process is controlled according to the sorting queue until the demand response amount in the period reaches the demand of the power grid.

[0077] According to the above technical solution, compared with the prior art, the present application provides a demand response regulation method considering air conditioning load heterogeneity, which has the following beneficial effects:

[0078] The present application provides a demand response regulation method considering air conditioning load heterogeneity, which includes two stages: in the first stage, a clustering algorithm is used to divide the air conditioning load cluster, and a typical daily load curve of the load cluster is generated; the parameters of the air conditioning load cluster are identified through load aggregation; a virtual energy storage model is established based on the identified parameters to calculate the virtual state of charge of each air conditioning load cluster, and the demand response potential is evaluated; the complementary coupling characteristics between the air conditioning load clusters are evaluated through a time complementarity analysis model, a power complementarity analysis model and a Pearson coupling coefficient; a combined scheduling strategy is developed according to the demand response potential and the complementary coupling characteristics between the clusters; in the second stage, the air conditioning load cluster that needs to be regulated is determined according to the combined scheduling strategy, and in the regulation execution stage, the priority scheduling is performed in combination with the user willingness degree and the PSME-VESS hierarchical sorting rule.

[0079] The present application provides a demand response regulation method considering air conditioner load heterogeneity, aiming at the problems of cluster scheduling difficulty, low resource utilization rate and the like caused by the heterogeneity and space-time dispersion of air conditioner load, a two-stage combined regulation strategy is provided: in the first stage, the load aggregator evaluates the demand response potential of each cluster on the basis of cluster parameter identification, and combined with the complementary coupling characteristic analysis between clusters, a combined scheduling strategy for the clustering site is formulated; in the second stage, a priority sorting of micro element virtual energy storage (PSME-VESS) method is used, the response process is decomposed into micro element periods by introducing the micro element method, and the user willingness degree and privacy protection demand are considered, and the regulation object is determined by the PSME-VESS method. The method used by the present application effectively solves the technical problems of heterogeneous air conditioner load cluster scheduling difficulty and low resource utilization rate, and provides a fine demand response solution for high proportion of renewable energy power grid. BRIEF DESCRIPTION OF DRAWINGS

[0080] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description only relate to the embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0081] Figure 1 The distributed regulation framework of the heterogeneous load provided by the present application is shown in the schematic diagram.

[0082] Figure 2 The cluster scheduling mechanism of the heterogeneous load provided by the present application is shown in the schematic diagram.

[0083] Figure 3 The virtual energy storage model of the variable frequency air conditioner provided by the present application is shown in the schematic diagram.

[0084] Figure 4 The two-stage regulation strategy provided by the present application is shown in the schematic diagram.

[0085] Figure 5 The minimum control time length based on the micro element method provided by the present application is shown in the schematic diagram.

[0086] Figure 6 The demand response result diagram of the three scenarios provided by the present application is shown in the schematic diagram.

[0087] Figure 7 The response rate comparison diagram provided by the present application is shown in the schematic diagram.

[0088] Figure 8 The control frequency comparison diagram provided by the present application is shown in the schematic diagram.

[0089] Figure 9 The PSME-VESS and VEPS demand response result graph provided by the present application.

[0090] Figure 10 The user willingness and control frequency contrast graph provided by the present application.

[0091] Figure 11 The PSME-VESS and VEPS demand response power result graph provided by the present application. DETAILED DESCRIPTION

[0092] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0093] The embodiment of the present application discloses a demand response regulation method considering air conditioner load heterogeneity, comprising:

[0094] The first stage adopts a clustering algorithm to divide air conditioner load clusters and generate typical daily load curves of the load clusters;

[0095] The air conditioner load clusters are identified by load aggregation;

[0096] A virtual energy storage model is established based on the identified parameters to calculate the virtual state of charge of each air conditioner load cluster and evaluate the demand response potential;

[0097] The complementary coupling characteristics between the air conditioner load clusters are evaluated through a time complementarity analysis model, a power complementarity analysis model and a Pearson coupling coefficient;

[0098] A combined scheduling strategy is formulated according to the demand response potential and the complementary coupling characteristics between the clusters;

[0099] The second stage determines the air conditioner load clusters that need to be regulated according to the combined scheduling strategy. In the regulation execution stage, the priority scheduling is performed in combination with the user willingness degree and the PSME-VESS hierarchical sorting rule.

[0100] In the specific embodiment, a demand response regulation method considering air conditioner load heterogeneity is shown in Figure 1 The framework changes the traditional unified control method to participate in the demand side regulation of the power grid in a diversified scheduling combination. The framework is divided into four layers: an upper scheduling center, an LA, a load cluster clustering site and the lowest user load resource.

[0101] The topmost scheduling center is in a core decision-making position, and its decision-making process is closely related to the multi-microgrid alliance, the distribution network and the LA, which is responsible for the overall integration of the power generation and power consumption demand information of the LA, multi-microgrid and distribution network, and realizes the optimal allocation of energy. The middle layer LA manages the controllable air conditioning load resources in the region, which is the key hub of the whole framework. The LA closely cooperates with the top scheduling center, receives the scheduling instructions and scheduling demand information issued based on the collaborative optimization model. The LA formulates a combined scheduling strategy for the cluster sites according to these data and the instructions of the top scheduling center. And according to the actual execution situation and various abnormal problems, the subsequent decision is adjusted and optimized. The third layer of the cluster site is responsible for aggregating the demand response potential of a large number of dispersed heterogeneous flexible loads and forming a cluster. First, the user data is classified by the region where the user is located, and the historical air conditioning load data of the user is obtained, the data is cleaned and preprocessed, and a local clustering model is constructed for clustering. Then the cluster division scheme formed by clustering is transmitted to the LA, and the control strategy for specific user load is formulated according to the combined scheduling plan issued by the LA.

[0102] After the cluster site clusters the air conditioning load cluster using the improved AC-GMM algorithm, the cluster center is used as the representative of the load of the cluster, and the energy consumption characteristics of the load cluster are displayed. The improved AC-GMM clustering method classifies the amplitude by introducing a sliding window, which extracts key amplitude features from each window on the load curve, focuses on the extreme value, load change, peak value occurrence time and daily average load characteristics in the window, and then classifies the load curve using the GMM algorithm. In order to better schedule and optimize heterogeneous load resources, based on the above-mentioned distributed regulation and control framework of heterogeneous load clustering, the heterogeneous load cluster scheduling mechanism is obtained as shown in Figure 2 .

[0103] In a specific embodiment, a demand response regulation method considering the heterogeneity of air conditioning load is provided, and the specific steps are as follows:

[0104] Step one: the daily load curve of a plurality of air conditioning load devices is denoted as , denotes that there are parameter heterogeneity characteristics, denotes that there are type heterogeneity characteristics.

[0105] Based on the region where the aggregated air conditioning load is located, the air conditioning load is divided into a plurality of clusters .

[0106] The distributed clustering site uses the K-shape clustering algorithm to cluster the load curve based on the air conditioning load cluster information collected by the region, and obtains The cluster centroid curves represent the formation of n load clusters, denoted as [missing information]. Each cluster center curve is considered to correspond to the typical daily load curve of that load cluster. Based on the typical load curves of each cluster, LA formulates the optimal combination strategy to maximize the utilization of the air conditioning load demand response potential while meeting the demand response power requirements.

[0107] Specifically, the air conditioner described in this embodiment is a variable frequency air conditioner, and its load uses a first-order thermodynamic ETP model, while the load electrical parameter model is a physical model of the variable frequency air conditioner compressor module.

[0108] The expression for the change in room temperature derived from the first-order ETP model is as follows:

[0109] (1);

[0110] In the formula, For time The cooling capacity; For cooling power conversion efficiency; Represents the equivalent heat capacity of the room; Represents the equivalent thermal resistance of the room; It is time The indoor temperature; It is time outdoor temperature, for The indoor temperature of the section.

[0111] Since the compressor speed is determined by the temperature difference between the indoor temperature sensed by the air conditioning load and the user's temperature setting, the compressor operating frequency under air conditioning load can be expressed as:

[0112] (2);

[0113] In the formula, For air conditioner compressors Frequency of time; This refers to the minimum operating frequency of the air conditioner compressor. This refers to the maximum operating frequency of the air conditioner compressor. This is the minimum temperature difference between the indoor temperature and the set temperature in the air-conditioned room. This represents the maximum temperature difference between the indoor temperature and the set temperature in the air-conditioned room. It is a constant.

[0114] There is a specific correlation between the compressor's operating power, cooling capacity, energy efficiency ratio, and frequency variation, and the fitting function expression is as follows.

[0115] (3);

[0116] (4);

[0117] (5);

[0118] In the formula, For air conditioner compressor Operating power at any given time; For air conditioner compressor Cooling capacity at any given time; For air conditioner compressor The energy efficiency ratio at time t; a, b, c, d, and e are all constant coefficients of the function.

[0119] Step 2: Construct a generalized model that accurately reflects the actual characteristics of the air conditioning load, explore the common features of the cluster, reduce the dependence on a large amount of individual data, and identify model parameters based on load data.

[0120] (1) The basis of parameter identification is the identification of the operating mode of the air conditioning load. Therefore, the load curve is segmented into two states: air conditioning on / off and static / dynamic state. (This is consistent with...) The segment of the curve is considered the off state of the air conditioner, while the remaining segment is considered the on state. The separation between static and dynamic states is determined by the following factors:

[0121] (8);

[0122] In the formula, For air conditioning Operating power at any given time; for The outdoor ambient temperature at any given time. The segment with the first segment is considered a static state, while the other segments are considered dynamic states.

[0123] (2) By analyzing the static segmentation of the load curve, the static parameters of the air conditioning load are obtained, namely the temperature response slope and the setpoint temperature. Considering that the setpoint temperature may vary due to different ambient temperatures, this embodiment proposes to use a constrained regression method to estimate the static parameters. For the on-off-off state segment... The objective function and its constraints for all points are shown below:

[0124] (9);

[0125] In the formula, and Consider them as decision variables; It is a constant; and These are the upper and lower limits of the setpoint temperature, respectively. and These represent the upper and lower limits of the temperature response slope determined by the building materials.

[0126] (3) Dynamic parameter identification is based on the dynamic segmentation of the load curve to identify the heat capacity. This is a crucial dynamic parameter. However, since the dynamic air conditioning load characteristics, which are partly determined by changes in indoor temperature, are difficult to obtain in this embodiment, the heat capacity... The estimation difficulty increases. Therefore, this embodiment adopts a hybrid estimation method based on particle swarm optimization algorithm. First, the initial value, minimum value, and maximum value of heat capacity are determined by the following formula.

[0127] (10);

[0128] In the formula, The specific heat capacity of air; air density; Represents the height of the building; Represents building area, Representative Buildings area, Representative Buildings The height.

[0129] (4) The PSO algorithm is used for iterative optimization, with the root mean square error and setpoint temperature set as the fitness indices of the algorithm, to obtain the optimal estimate of heat capacity. In summary, parameter identification of load data can help calculate the demand response potential of load clusters. By defining the demand response potential of air conditioning loads, it can be seen that the demand response potential is determined by six main factors, which can be summarized as follows: ,in, These are known parameters determined by the load aggregator and participants.

[0130] Step 3: In this embodiment, the demand response potential of the air conditioning load specifically refers to the actual ability of the air conditioning load resources to participate in demand response. Different indoor temperatures, ambient temperatures, and set temperatures will all lead to changes in the demand response potential. Therefore, accurately assessing the demand response potential can provide a key basis for the formulation of demand response strategies. When the air conditioning load receives a discharge command from the LA, i.e., a power reduction command, the air conditioning load increases the set temperature, making the difference between the room temperature and the set temperature negative. The air conditioning compressor speed drops to the minimum, and the air conditioning cooling capacity is minimized. At this time, the power released by the air conditioning load is the maximum downward adjustment power:

[0131] (11);

[0132] In the formula, This is the maximum downward adjustment power; for The air conditioner's operating power at any given time; The minimum operating power of the air conditioning load.

[0133] The demand response potential of the single air conditioning load is:

[0134] (12);

[0135] When the air conditioning load receives the charging instruction, i.e. the power increasing instruction, issued by the LA, the air conditioning load reduces the set temperature, so that the difference between the room temperature and the set temperature is a positive value, the air conditioning compressor speed rises to the highest, and the air conditioning refrigerating capacity is the largest. At this time, the power of the air conditioning load change is the maximum upward adjustment power:

[0136] (13);

[0137] In the formula, is the maximum upward adjustment power; is the air conditioning operating power at time t; is the maximum operating power of the air conditioning load.

[0138] The demand response potential of the single air conditioning load is:

[0139] (14).

[0140] Step four: when the air conditioning load is processed in clusters, the heterogeneity and spatio-temporal difference of the air conditioning load need to be considered, so the demand response potential of each type of load is evaluated after the load cluster division is implemented. When the number of aggregated air conditioning clusters is N, the maximum adjustable power of the cluster and the steady-state power can be directly represented by multiplying the single adjustable potential data. Therefore, the adjustable potential of the air conditioning cluster is calculated as follows:

[0141] (1) When the target set temperature is increased, the maximum downward adjustment power of the aggregated air conditioning cluster is:

[0142] (15);

[0143] In the formula, is the maximum downward adjustment power of a single cluster, and N is the number of aggregated air conditioning clusters.

[0144] The minimum operating power of the cluster works for the minimum sustainable time:

[0145] (16);

[0146] In the formula, is the minimum sustainable time of the minimum operating power of the cluster at time t, and n represents the number of clusters; ​

[0147] The maximum positive demand response potential of this cluster is:

[0148] (17);

[0149] (2) When the target set temperature decreases, the maximum upward adjustment power of the aggregated air conditioning cluster is:

[0150] (18);

[0151] In the formula, N represents the maximum upward adjustment power of a single cluster, and N is the number of air conditioning load aggregation clusters.

[0152] The minimum duration during which the cluster can operate at maximum power is:

[0153] (19);

[0154] In the formula, The minimum possible duration for the cluster to operate at its maximum power at time t, where n represents the number of clusters;

[0155] The maximum reverse demand response potential of this cluster is:

[0156] (20).

[0157] Step 5: To fully tap the demand response potential of air conditioning loads, the variable frequency air conditioning load is treated as a virtual energy storage (VES) model to participate in the demand-side market. This model can quickly calculate the demand response power of the air conditioning system. By regulating the power changes of the air conditioning load in the short term, its effect is equivalent to the charging and discharging process of traditional energy storage. The variable frequency air conditioning virtual energy storage model is as follows: Figure 3 As shown.

[0158] The relationship between the system's virtual state-of-charge (VSOC), current room temperature, and temperature adjustability margin is as follows:

[0159] (6);

[0160] In the formula: and They are respectively Time of the first Total energy storage and current power consumption of each air conditioning system; for Time of the first Indoor temperature of the air conditioning system Let t be the maximum indoor temperature of the air conditioning system. Let t be the minimum indoor temperature of the air conditioning system at time t.

[0161] Taking discharge as an example, according to equations (1) and (6), the VSOC change of the air conditioning system is as follows:

[0162] (7);

[0163] In the formula: Let t be the outdoor temperature of the air conditioning system. Let t+1 be the maximum indoor temperature of the air conditioning system. Let t+1 be the maximum indoor temperature of the air conditioning system.

[0164] As shown in the equation, the charging and discharging speed of virtual energy storage is related to the thermal parameter RC and the characteristic temperature difference QR. Air conditioning systems with a smaller thermal parameter RC can respond more quickly to the grid's regulation needs and are suitable for priority dispatch. Air conditioning systems with a larger characteristic temperature difference QR can provide a larger load regulation capacity and are suitable for priority dispatch.

[0165] Step Six: In order to fully tap the demand response potential of air conditioning loads, in addition to considering variable frequency air conditioning loads as virtual energy storage models to participate in the demand-side market, it is also necessary to consider the demand response potential and energy consumption characteristics of different load clusters, and rationally allocate the demand response time of load clusters, thereby improving the stability and economy of power system operation.

[0166] (1) The cluster center represents the generalized energy consumption characteristics of the cluster. Therefore, based on the complementarity and coupling of the cluster center curve, the complementary coupling characteristics between heterogeneous load clusters can be evaluated.

[0167] (twenty one);

[0168] (twenty two);

[0169] In the formula, Indicates the first Each cluster center is located at Power value at any given time; Indicates the first The power values ​​of each cluster center at time t; Indicates the first Each cluster center is located at The difference in time; Indicates the average difference; This represents the variance of the difference.

[0170] (2) Power complementarity reflects the degree of difference in average power levels between different load curves. It can be calculated using the following formula:

[0171] (23);

[0172] wherein, denotes the average power of the th cluster center; denotes the average power of the th cluster center; denotes the maximum power in all cluster center curves.

[0173] (3) In the same time period, the load curves with the same fluctuation frequently appearing in the trend have coupling. When the change trend of the load curves is similar, there is strong coupling between the two load curves; on the contrary, if one load curve rises while the other falls, they may have complementarity. By calculating the Pearson correlation coefficient between the two load curves, the linear coupling degree between them is quantified. In summary, the resident air conditioning cluster and the central air conditioning cluster with strong correlation coupling characteristics can be centrally responded.

[0174] Step seven: Based on the complementary coupling characteristic analysis of demand response potential evaluation, the two-stage combined regulation strategy for heterogeneous air conditioning load clusters is realized. First, the complementary coupling characteristics between the heterogeneous air conditioning load clusters are analyzed. Second, considering the air conditioning parameter privacy problem of user load, the static and dynamic parameters of air conditioning load are identified by parameter identification method, and then on the basis of air conditioning load parameters, the demand response potential evaluation model of heterogeneous air conditioning load single and cluster is established. Finally, according to the above information, the air conditioning load regulation strategy is formulated. In the regulation strategy, the first stage is mainly aimed at the interaction between LA and cluster site. LA formulates a combined scheduling strategy considering the characteristics and demand response potential of heterogeneous load clusters. The second stage is the PSME-VESS method, which aims to guide the distributed cluster site to control the user air conditioner based on the user willingness degree and the virtual energy storage VSOC state of single air conditioner, Figure 4 is a schematic diagram of the two-stage combined regulation strategy.

[0175] (1) When the air conditioning load participates in demand response in the form of demand side resources, the user signs a contract with LA, and LA pays the demand response compensation fee to the user. After completing the demand response, the upper dispatching center encourages the completed part according to the completion of the LA scheduling target. Therefore, the income of LA formulating the combined scheduling strategy is represented as:

[0176] (24);

[0177] wherein, is the total income obtained by the aggregator from the top-level dispatching center after the dispatch is completed, is the compensation cost of the aggregator when scheduling the air conditioning load.

[0178] (2) The strategy is to minimize the controlled frequency and maximize the utilization of demand response potential, and when selecting the participating combination cluster, it is preferred to concentrate on responding to heterogeneous load clusters with strong coupling, and to disperse responding to heterogeneous load clusters with time domain complementarity. The combination target can be represented as follows:

[0179] (25);

[0180] (26);

[0181] (27);

[0182] In the formula, indicates the scheduling time; indicates the demand response potential of the combination; indicates the coupling coefficient within the combination; indicates the complementary characteristic coefficient of the combination at the time; is the equivalent potential value of the nth combination at the time considering the complementary coupling characteristics; is the equivalent potential value of the Gth combination at the time, is the net difference between the coupling and complementarity of the gn combinations at the time, is the coupling and complementary coefficient of the gn combinations at the time.

[0183] (3) The above steps aim to maximize the utilization of demand response potential while considering complementary coupling characteristics, and accordingly select clusters that meet the combination target. Then, in order to consider the controlled frequency of the cluster, the combination scheduling strategy also needs to replace the cluster with the smallest real demand response potential in the combination with a cluster with smaller control frequency from the clusters whose real demand response potential is greater than the remaining unresponsive power after the air conditioning load cluster in the combination is selected.

[0184] (4) At the same time, the incentive price of the LA target function still needs to be calculated according to the price before the combination. According to the known attributes of the clusters in the combination, their types are divided into civil loads and non-civil loads, and the schedulable potential of non-civil loads is usually much larger than that of residential loads, so different subsidy prices need to be set. Therefore, the target function of the LA can be represented in detail as follows: ​​​​

[0185] (28);

[0186] wherein, are the unit demand response compensation price of LA for civil and non-civil loads, respectively; is the unit incentive paid by the upper dispatching center to LA; represents the power part of the civil air conditioning load cluster participating in demand response in the G combination at time represents the power part of the non-civil air conditioning load cluster participating in demand response in the G combination at time represents represents the power part of the non-civil air conditioning load cluster participating in demand response in the G combination at time

[0187] Specifically, the objective function is constrained as follows:

[0188] (1) Demand response constraint: in order to ensure that the total cluster response load meets the target load demand in each time period, the constraint is as follows:

[0189] (29);

[0190] wherein, is the target load demand at time is the response load demand of the cluster at time

[0191] (2) Response load power constraint:

[0192] (30);

[0193] wherein, is the maximum response load demand of the cluster at time

[0194] (3) Continuous dispatching times constraint: in order to ensure that the continuous dispatching times of each cluster do not exceed the maximum continuous times, and to avoid the temperature exceeding the maximum acceptable temperature, the constraint is as follows:

[0195] (31);

[0196] wherein, is the continuous dispatching times of k cluster, is the maximum continuous times.

[0197] ​​​​​Step eight: determine and execute the specific regulated object sequence by using the PSME-VESS method. After the LA issues a response instruction, the distributed cluster site first evaluates the air conditioning load demand response potential in the jurisdiction based on the air conditioning model parameters identified from historical operation data parameters, then formulates a control strategy considering the user's willingness degree on the basis of the user response willingness data collected by the intelligent control device in response to the grid demand, and finally relies on the intelligent control device to implement the strategy execution.

[0198] The distributed cluster site relies on hardware device support for the air conditioning group control process, and needs to install an air conditioning device controller on each terminal air conditioning device. The controller sets four response levels of yellow, purple, red and black. Users independently select the corresponding response level to participate in grid regulation according to their enthusiasm and willingness to participate in demand response. The response levels here have a clear gradient of participation willingness, from high to low, yellow, purple, red, and black.

[0199] This embodiment assumes that before the LA issues a demand response instruction, the user air conditioning load cluster responsible by the distributed cluster site has reached a stable state, and the average room temperature is the initial set temperature of the air conditioner . After the demand response starts, the user air conditioning set temperature is raised to the maximum set temperature in the comfortable temperature range, and the room temperature needs a certain time from the current room temperature to the set temperature, and the sustainable duration of air conditioning load is:

[0200] (32);

[0201] In the formula, is the maximum set temperature in the user comfort range; is the minimum power of the air conditioner; is the building heat capacity, thermal resistance and operating efficiency obtained by parameter identification.

[0202] The minimum sustainable duration is expressed as:

[0203] (33);

[0204] When , the demand response potential of the air conditioning cluster is the current air conditioning load power minus the minimum operating power. When , the air conditioning cluster cannot complete the response demand of the grid within . Therefore, the idea of infinitesimal method in physics is introduced, and the response duration of the grid is divided into processes with a duration of . When The control strategies of all the meta-processes are combined to obtain an air conditioner control strategy that satisfies the power grid scheduling requirement. The minimum control time length based on the micro-element method is shown in Figure 5 .

[0205] In specific embodiments, the first stage of the strategy is based on the demand response potential, the controlled frequency of the cluster, and the complementary coupling characteristics between clusters to develop a cluster combination scheduling strategy. The second stage is a PSME-VESS scheduling method that decomposes the response period into micro-element units with a time length of . In each micro-cloud unit, the user participation is quantified according to the response level (yellow = 0.99, purple = 0.66, red = 0.33, black = 0) in the group, and then the classes are sorted in ascending order of R, C, and in descending order of QR. Finally, the classes are sorted according to the demand response potential of the individual.

[0206] In specific embodiments, to verify the effectiveness of the heterogeneous air conditioner load combination control strategy that considers the complementary coupling double characteristics of the air conditioner load cluster and the demand response potential, the following three scenarios are set up for comparison:

[0207] Scenario 1: Quantum particle swarm optimization combination control strategy;

[0208] Scenario 2: Combination scheduling model considering only the demand response potential;

[0209] Scenario 3: Combination control strategy considering the double characteristics and the demand response potential.

[0210] (1) This embodiment simulates under the same demand response scheduling target condition, and schedules the heterogeneous air conditioner load cluster with different strategies. The demand response simulation results of the three scenarios are shown in Figure 6 , and the simulation data is shown in Table 1.

[0211] Table 1 Data comparison results

[0212]

[0213] From Figure 6 ​It can be seen that the response degree of the three scenario demand responses is the same, and the response rate is 99.94%. However, as can be seen from the specific data shown in Table 1, scenario 3, i.e., the regulation strategy proposed in the application, exhibits significant comprehensive advantages in the example analysis. Scenario 3 performs best in terms of demand response cost, with a cost value of 15533.71 yuan, which is 14.78% and 14.99% lower than that of the other two scenarios, respectively, reflecting its efficiency in resource allocation and economic optimization. Compared with the other two scenarios, the dispatching frequency of non-residential loads in scenario 3 is significantly reduced, and on the basis of meeting the demand response target, the resident load with lower compensation price is maximized, and this differentiated regulation strategy can effectively reduce the overall energy consumption cost according to the power consumption characteristics and economic demand of different types of loads. Although the solution time of scenario 3 is slightly higher than that of scenario 2, its solution efficiency is still better than that of the quantum particle swarm strategy, indicating that the strategy proposed in the embodiment achieves a reasonable trade-off between solution speed and dispatching combination optimization. In addition, the dispatching frequency of scenario 3 is 49 times, which is 22.22% less than that of scenario 2, reflecting that its decision-making process increases the consideration of complementary coupling characteristics, which is conducive to optimizing energy distribution and improving overall energy utilization efficiency and reducing energy waste. Although the dispatching frequency of scenario 3 is slightly higher than that of scenario 1, in combination with the cost and time index analysis, its comprehensive performance is still more practical.

[0214] In summary, the combined regulation strategy proposed in the embodiment effectively balances the economic efficiency, timeliness and system stability demand by analyzing the complementary coupling characteristics and considering the demand response potential while ensuring the response rate, providing a more practical solution for dispatching optimization of the power system.

[0215] (2) To highlight the competitiveness of each strategy under different dispatching scales, five groups of different scale demand response powers are generated to form five scenarios, and the cost change trend of the three scenarios under each scenario is compared from the response rate and economic benefit two dimensions. The corresponding LA economic benefit of different scenarios, i.e., the total cost of the dispatching center incentive minus the demand response compensation, is shown in Table 2, and the response rate is shown in Table 3. Figure 7

[0216] Table 2 LA economic benefit

[0217]

[0218] As shown in Table 2, the embodiment represented by scenario 3 exhibits significant economic advantages in each scenario. Taking scenario 5 as an example, the income of scenario 3 reaches 49187.58 yuan, which is 2.54% and 2.90% higher than that of the other two scenarios, respectively. Further observation of the income growth trend shows that as the scenario scale increases, the income of scenario 3 gradually expands, which confirms its effectiveness in large-scale scenarios. Figure 7 ​It can be seen that the response rate of scenario 3 is generally higher than that of the other two scenarios. This advantage comes from the synergistic effect of two aspects: one is to prioritize the dispatch of civilian loads; the second is to maximize the virtual energy storage potential of air conditioning load for demand response, thereby improving the response rate and maximizing the benefits while controlling the cost.

[0219] (3) In order to better show the changes of different user willingness, the user willingness is quantified, assuming that the quantified user willingness corresponding to the user willingness level of green, yellow, red and black is 0.99, 0.66, 0.33 and 0. Set the demand response demand of the power grid as 2000KW, and the outdoor temperature as 36℃. Through experiments, the user willingness and the controlled frequency of the PSME-VESS method and the virtual energy storage priority sorting (VESPS) method are compared. Different initial set temperatures affect the size of the subsequent virtual energy storage demand response potential, so the initial set temperature is analyzed and compared from 22℃ to 28℃. The control frequency comparison results are shown in Table 3, and the control frequency comparison is shown in Figure 8 .

[0220] Table 3 Control frequency comparison results

[0221]

[0222] From Figure 8 and Table 3, it can be seen that as the set temperature decreases, the average control frequency of the PSME-VESS method is more significant compared to the optimization effect of the VESPS method. The overall average control frequency is reduced by 11.3%, and the user willingness is improved by 4.3%. The demand response results of PSME-VESS and VESPS are shown in Figure 9 , at low initial set temperature, the response rate of the PSME-VESS method is slightly higher than that of the VESPS method. On the one hand, the PSME-VESS method increases the consideration of user willingness difference and air conditioning load heterogeneity compared with the VESPS method, and adopts a hierarchical sorting priority scheduling method to stratify the air conditioning virtual energy storage according to user characteristics, and realizes more accurate priority sorting. On the other hand, combined with the micro-element method, when the air conditioning set temperature is low, the air conditioning virtual energy storage is in a high VSOC state, has a longer controllable time, reduces the control frequency, and improves the user willingness. In summary, the PSME-VESS method proposed in this embodiment optimizes the minimum target control time through the micro-element method, so that at a lower set temperature, it can obtain a higher demand response rate and a lower control frequency than the VESPS method, effectively reducing the calculation amount of LA, and better meeting the demand of the power grid.

[0223] (4) In order to highlight the optimization effect of hierarchical prioritization on user willingness and control frequency, the embodiment sets two scenarios to analyze the effectiveness of hierarchical prioritization, and the scenario conditions are as follows:

[0224] Scenario one: PSME-VESS strategy with sorting;

[0225] Scenario two: NPSME-VESS strategy without sorting.

[0226] The hierarchical prioritization effect comparison data is shown in Table 4, and the comparison results of user willingness and control frequency, PSME-VESS and NPSME-VESS demand response power are shown in Figure 10 , Figure 11

[0227] Table 4 Hierarchical prioritization effect

[0228]

[0229] As can be seen from Table 4, regardless of the control temperature, the performance of the PSME-VESS strategy is always better than that of the NPSME-VESS strategy. By increasing hierarchical prioritization, the average user willingness is increased by 32.82%, and the control frequency is reduced by 13.22%. This is because the PSME-VESS strategy preferentially selects air conditioners with high state of charge, high user willingness and low control frequency for control, so it has good performance in terms of willingness and control frequency, and in the Figure 11 , the demand response rate of scenario one is higher than that of scenario two. In summary, the PSME-VESS strategy can effectively improve the willingness of users participating in demand response and reduce the control frequency of the overall users.

[0230] (5) The embodiment carries out example simulation through MATLAB R2019a software, compares the solving time of the PSME-VESS strategy proposed in the embodiment and the VEPS, NPSME-VESS strategy and the VEPS strategy with hierarchical prioritization method, and the comparison result is shown in Table 5.

[0231] Table 5 Solving time analysis

[0232]

[0233] ​From Table 5, it can be seen that the solving time of the PSME-VESS strategy is 0.0822, which is higher than the VEPS strategy and the NPSME-VESS strategy, but far lower than the VEPS strategy with hierarchical priority sorting. This is because the PSME-VESS strategy combines the micro-element method with hierarchical priority sorting, comprehensively considers user willingness, device characteristic clustering and VSOC sorting, and realizes multi-level optimization from global to local. The VEPS strategy and the NPSME-VESS strategy are faster in calculation because they do not perform hierarchical priority sorting. However, the solving time of the VEPS strategy with hierarchical priority sorting is greater than that of the PSME-VESS strategy. This shows that the PSME-VESS strategy achieves a good balance between optimization effect and calculation efficiency, and is suitable for complex multi-user scenarios. Although the solving time is slightly higher, this is the cost paid for more comprehensive optimization, and the solving time can be further reduced by optimizing the algorithm.

[0234] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed by the embodiments, since it corresponds to the method disclosed by the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0235] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A demand response regulation method considering air conditioning load heterogeneity, characterized in that, The method comprises the following steps: In the first stage, a clustering algorithm is used to divide air conditioning load clusters, and a typical daily load curve of the load cluster is generated; Parameters of the air conditioning load cluster are identified through load aggregation; Virtual energy storage models are established based on the identified parameters to calculate the virtual state of charge of each air conditioning load cluster, and the demand response potential of each load cluster is evaluated; The complementary coupling characteristics between air conditioning load clusters are evaluated through a time complementarity analysis model, a power complementarity analysis model and a Pearson coupling coefficient; A first-stage combined scheduling strategy is formulated according to the demand response potential and the complementary coupling characteristics between clusters; According to the first-stage combined scheduling strategy, the air conditioning load clusters that need to be regulated are determined, and in the second stage, priority scheduling is performed in combination with user willingness and PSME-VESS hierarchical sorting rules during load regulation execution. 2.The demand response regulation method considering air conditioning load heterogeneity according to claim 1, wherein, The first stage uses a clustering algorithm to divide air conditioning load clusters, and generate a typical daily load curve of the cluster, comprising: The daily load curves of several air conditioning load devices are recorded as , indicate that there is a parameter heterogeneous characteristic, indicate that there is a type heterogeneous characteristic; Based on the region where the aggregated air conditioning load is located, the air conditioning load is divided into several clusters ; based on the air conditioning load cluster information collected in the region, the K-shape clustering algorithm is used to cluster the load curve, and a cluster centroid curve is obtained, that is, a load cluster is formed, denoted as Each cluster center curve is regarded as a typical daily load curve of the corresponding load cluster. 3.The demand response regulation method considering air conditioning load heterogeneity according to claim 1, wherein, The parameters of the air conditioning load cluster are identified through load aggregation, comprising: The operating mode of the air conditioning load is identified, and the load curve is segmented and decomposed into two states: the on / off and static / dynamic states of the air conditioner; The static parameters of the air conditioning load are estimated by using a constrained regression method through analysis of the static segments of the load curve; The dynamic parameters are identified by using a hybrid estimation method based on a particle swarm optimization algorithm.

4. The demand response regulation method considering air conditioning load heterogeneity according to claim 1, characterized in that, The evaluation of the demand response potential of each load cluster comprises: The power released by the air conditioning load is the maximum downward adjustment power: ； In the formula, This is the maximum downward adjustment power; for The air conditioner's operating power at any given time; This is the minimum operating power for the air conditioning load; The demand response potential of a single air conditioning load is: ； ； In the formula: and They are respectively Time of the first Total energy storage and current power consumption of each air conditioning system; for Time of the first Indoor temperature of the air conditioning system Let t be the maximum indoor temperature of the air conditioning system. Let t be the minimum indoor temperature of the air conditioning system at time t; When the air conditioning load receives a charging instruction, i.e., a power increase instruction, from the load aggregator, the air conditioning load lowers the set temperature, so that the difference between the room temperature and the set temperature is positive, the compressor speed of the air conditioner rises to the highest, and the refrigerating capacity of the air conditioner is maximum, at this time, the power change of the air conditioning load is the maximum upward adjustment power: ； In the formula, is the maximum upward adjustment power; is is the air conditioning running power at the moment; is the maximum running power of the air conditioning load; The demand response potential of a single air conditioning load is: 。 5. The demand response regulation method considering air conditioning load heterogeneity according to claim 4, characterized in that, Further comprising: When the number of air conditioning load aggregation clusters is N, the adjustable potential of the first-stage air conditioning cluster is as follows: (1) When the target set temperature is increased, the maximum downward adjustment power of the aggregated air conditioning cluster is: ； wherein, is the maximum downward adjustment power for a single cluster, N is the number of aggregated clusters of air conditioning loads; The minimum operating power of the cluster works for the minimum sustainable time: ； wherein, is the minimum sustainable time of the minimum operating power of the cluster at time t, and n represents the number of clusters. The maximum positive demand response potential of the cluster is: ； (2) When the target set temperature is decreased, the maximum upward adjustment power of the aggregated air conditioning cluster is: ； wherein, is the maximum up-regulation power for a single cluster, N is the number of aggregated clusters of air conditioning loads; The minimum operating power of the cluster works for the minimum sustainable time: ； wherein, is the minimum sustainable time for the cluster to work at the maximum operating power at time t, and n represents the number of clusters. The maximum negative demand response potential of the cluster is: 。 6. The demand response regulation method considering air conditioning load heterogeneity according to claim 1, characterized in that, The complementary coupling characteristics between air conditioning load clusters are evaluated through a time complementarity analysis model, a power complementarity analysis model and a Pearson coupling coefficient, comprising: The time complementarity analysis model is calculated by the difference curve variance; The power complementarity analysis model is calculated by the difference degree of different load curves at the average power level; The Pearson coupling coefficient is calculated by calculating the Pearson correlation coefficient between two load curves.

7. The demand response regulation method considering air conditioning load heterogeneity according to claim 1, characterized in that, The first-stage combined scheduling strategy is formulated according to the demand response potential and the complementary coupling characteristics between clusters, comprising: The first-stage load aggregator formulates a combined scheduling strategy with a maximum benefit objective function represented as: ； In the formula, is the total income obtained by the load aggregator from the top-level dispatching center after the dispatch is completed, is the cost compensated by the load aggregator when dispatching the air conditioning load; The strategy is formulated to maximize the benefit and maximize the potential of demand response, and when selecting the participating combination of clusters, the heterogeneous load clusters with strong coupling are concentrated for response, and the heterogeneous load clusters with time domain complementarity are dispersed for response. The combination target is represented as: ； ； ； In the formula, Indicates the scheduling period; express The combined demand response potential; express The coupling coefficient within the assembly; express Combination in The complementary characteristic coefficient at time; For the first The combination in Always consider the equivalent potential value of complementary coupling characteristics; for The equivalent potential value of the G combination at time point. There are gn combinations in The net difference between coupling and complementarity at any given moment. There are gn combinations in The coupling complementarity coefficient at time step.

8. The demand response regulation method considering air conditioning load heterogeneity according to claim 7, characterized in that, Also included are: The first-stage combination scheduling strategy, when selecting the optimal response air conditioning load cluster combination, preferentially selects clusters that have not been controlled or have been controlled a small number of times and have large response potential to form the last participating response combination G; In the objective function of the load aggregator, the incentive price needs to be calculated according to the price before the combination, and according to the known attributes of the clusters in the G combination, the types are divided into civil load and non-civil load. The dispatchable potential of non-civil load is much larger than that of residential load, and different subsidy prices are set. The objective function of the first-stage load aggregator is represented as: ； wherein, respectively, are the unit demand response compensation price of LA for civil load and non-civil load; is the unit incentive paid by the upper dispatching center to LA; represents the power part of the civil air conditioning load cluster participating in demand response in the G combination at the moment represents the power part of the non-civil air conditioning load cluster participating in demand response in the G combination at the moment represents represents the power part of the non-civil air conditioning load cluster participating in demand response in the G combination at the moment 9.The demand response regulation method considering air conditioning load heterogeneity according to claim 1, wherein, The priority scheduling by combining the user willingness degree and the PSME-VESS hierarchical sorting rule includes: (1) Grouping: Grouping the air conditioning system according to the user willingness degree level collected by the intelligent control device, setting the user willingness degree level, and dividing it into k groups from high to low; (2) Clustering: based on the thermal parameter RC value, characteristic temperature difference QR value, clustering each group, taking RC value and QR value as characteristic attributes, using K-means clustering method, according to the principle of similar RC value and QR value, clustering the air conditioners into n aggregated groups, when , the response time of the power grid is decomposed into meta-processes with a time length of , the minimum sustainable time length is , the response time of the power grid is , first control the type with relatively large QR value; when , first control the type with relatively small RC value and relatively large QR value; (3) Inter-class sorting: After grouping and clustering the air conditioning load, there are k×n classes in total, and the classes in the group are sorted, and the air conditioning system class with relatively fast response and large response amount is selected for control first; (4) Intra-class sorting: In the same class of air conditioning load, sort from high to low based on the curve index, control the user with relatively large demand response potential first, and after all the sorting is completed, control according to the sorting queue in the demand response process until the demand response amount in the time period reaches the demand of the power grid.

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