Air conditioner credible adjustable capability quantification method and system based on clustering analysis

Abstract

The invention relates to the technical field of power distribution systems, in particular to an air conditioner credible adjustable capability quantification method and system based on clustering analysis, and the method comprises the steps: obtaining the operation parameters of each air conditioner in a target regulation and control region, and calculating the adjustable power and adjustable power of each air conditioner according to the parameters; air conditioners are clustered, a plurality of air conditioner clusters are obtained, statistical analysis is conducted on parameters of each cluster, and uncertainty statistical characteristics are obtained; based on the statistical characteristics, a probability model of the adjustable capacity of the air conditioner clusters is established, and probability distribution of the up-adjustable capacity and the down-adjustable capacity of all the clusters is calculated; calculating the credibility adjustable capability according to a preset credibility level, and aggregating to obtain the overall credibility adjustable capability of the target area; finally, actual constraint factors of air conditioner adjustment are considered, a calculation result is corrected, and through clustering analysis and probability modeling, the adjustable capacity of the air conditioner can be accurately quantified, and a more accurate adjustment capacity quantification result is provided.

Description

Technical Field

[0001] This invention relates to the field of power distribution system technology, and in particular to a method and system for quantifying the reliable adjustability of air conditioning based on cluster analysis. Background Technology

[0002] Air conditioning regulation capability refers to the ability of an air conditioner to adjust its cooling or heating power according to changes in the external environment or grid demand. Air conditioners have a certain thermal inertia, meaning that they can change their power in a short period of time without significantly affecting the indoor temperature. This gives air conditioners the potential to be used as a demand response resource. In smart grids, air conditioners can be used as demand-side management resources to help balance grid load by adjusting their power output. Therefore, quantifying the reliable and adjustable capabilities of air conditioners is particularly important for grid dispatch.

[0003] Currently, some methods have attempted to quantify the adjustability of air conditioners using historical operating data. These methods typically estimate adjustability based on the power and temperature data of the air conditioner through statistical analysis or regression models. However, most of these existing methods rely on static data and ignore the dynamic changes of the air conditioner during actual adjustment, resulting in significant deviations in the calculated adjustability. Furthermore, many methods do not incorporate confidence analysis and lack quantification of the air conditioner's adjustability at different confidence levels, which may expose the scheduling system to high uncertainty risks.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a method and system for quantifying the reliability and adjustability of air conditioning based on cluster analysis, thereby effectively solving the problems in the background technology.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a method for quantifying the reliable and adjustable capability of air conditioners based on cluster analysis, comprising the following steps: Obtain the operating parameters of each air conditioner within the target control area, and determine the adjustable power and adjustable power of each air conditioner based on the operating parameters; Based on the aforementioned operational parameters, the air conditioners are clustered to obtain multiple air conditioner clusters. Statistical analysis is then performed on the air conditioner parameters within each cluster to obtain statistical features that characterize the uncertainty of the air conditioner clusters. Based on the aforementioned statistical characteristics, a probabilistic model of the adjustable capability of each air conditioning cluster is established to obtain the probability distribution characteristics of the adjustable capability and the adjustable capability of each air conditioning cluster. Based on the probability distribution characteristics, the reliable up-adjustable capability and reliable down-adjustable capability of each air conditioning cluster are calculated according to a preset reliability level, and the reliable adjustable capability of each air conditioning cluster is aggregated to obtain the reliable adjustable capability of the air conditioning in the target control area. Based on the actual constraints of air conditioning participation in regulation, the reliable adjustability of air conditioning in the target regulation area obtained by aggregation is corrected to obtain the corrected reliable adjustability of air conditioning resources in the target regulation area.

[0007] Furthermore, determining the adjustable power and adjustable power of each air conditioner based on relevant operating parameters includes: Based on the historical power and temperature data of air conditioners, a first-order equivalent thermal parameter model is established to identify the thermal resistance and heat capacity of each air conditioner. The adjustable power is defined as the power increase of the air conditioner when switching from standby mode to rated operating mode; The adjustable power is defined as the power reduction achieved when the air conditioner switches from its rated operating state to standby state.

[0008] Furthermore, the parameter identification of the first-order equivalent thermal parameter model specifically includes: Two steady-state operating points with zero indoor temperature change rate are selected from the historical operating data of the air conditioner. The thermal resistance is calculated based on the difference in indoor temperature and the difference in air conditioning cooling capacity between the two steady-state operating points. The heat capacity is calculated based on the thermal resistance and the thermal time constant derived from the historical temperature curve.

[0009] Furthermore, the clustering of air conditioners based on the aforementioned operational parameters includes: Each air conditioner's thermal resistance, thermal capacity, set temperature, rated electrical power, and rated cooling power are used to construct a state vector and then normalized. The weights of each parameter in the state vector are determined using the analytic hierarchy process (AHP). Based on the weighted Euclidean distance, the improved K-means++ algorithm is used to cluster the normalized state vectors to obtain multiple air conditioner clusters.

[0010] Furthermore, the statistical analysis of the air conditioning parameters within each air conditioning cluster yields statistical characteristics, including the expected value and variance of each parameter within each air conditioning cluster.

[0011] Furthermore, the establishment of the probabilistic model for the adjustability of the air conditioning cluster specifically includes: Based on the error propagation principle, the expected value and variance of the start-up time, standby time and duty cycle of the air conditioning cluster under steady-state operation are calculated according to the expected value and variance of the parameters within each air conditioning cluster. Based on the probability distribution of the duty cycle, the expected value and variance of the up-adjustment capability and down-adjustment capability of the air conditioning cluster are calculated, and a probability distribution is established based on the normal distribution assumption.

[0012] Furthermore, the specific implementation of calculating the reliability up-adjustment capability and reliability down-adjustment capability of each air conditioning cluster according to a preset reliability level includes: For each air conditioning cluster, the expected value and standard deviation of each air conditioning cluster are determined based on the probability distribution characteristics of its up-adjustment capability and down-adjustment capability. Based on the mathematical expectation, standard deviation, and the standard normal distribution quantiles corresponding to the selected confidence level, the confidence up-adjustment capability boundary value and the confidence down-adjustment capability boundary value at the confidence level are calculated.

[0013] The calculation of the reliable up-adjustment capability and reliable down-adjustment capability of each air conditioning cluster according to a preset reliability level specifically includes: Furthermore, based on the expected value and variance of the adjustable capacity and the adjustable capacity, and combined with the standard normal distribution quantiles, the adjustable capacity boundary value at a specified confidence level is calculated. The preset confidence level includes at least 50%, 80%, 90%, 95%, 99%, and 100%.

[0014] Furthermore, the actual constraint factors include at least one of the following: breach of contract coefficient, repeated adjustment coefficient, and communication delay coefficient.

[0015] Furthermore, the modification specifically includes: Calculate the breach of contract coefficient, repeated adjustment coefficient, and communication delay coefficient based on the external temperature, historical adjustment count, communication packet loss rate, and latency, respectively. The adjustment response capability index is obtained by combining the breach of contract coefficient, the repeated adjustment coefficient, and the communication delay coefficient. The credible adjustability is reduced by the adjustment response capability index to obtain the corrected credible adjustability.

[0016] The present invention also includes a system for quantifying the reliable adjustability of air conditioners based on cluster analysis, the system comprising: The parameter acquisition and identification module is used to acquire the operation-related parameters of each air conditioner within the target control area, and determine the adjustable power and adjustable power of each air conditioner based on the operation-related parameters. The clustering and statistical analysis module is used to cluster the air conditioners based on the operation-related parameters to obtain multiple air conditioner clusters, and to perform statistical analysis on the air conditioner parameters within each air conditioner cluster to obtain statistical features that characterize the uncertainty of the air conditioner clusters. The probability modeling module is used to establish a probability model of the adjustable capability of the air conditioning cluster based on the statistical characteristics, and to obtain the probability distribution characteristics of the adjustable capability and adjustable capability of each air conditioning cluster. The reliable capability calculation and aggregation module is used to calculate the reliable up-adjustable capability and reliable down-adjustable capability of each air conditioning cluster according to a preset reliability level based on the probability distribution characteristics, and to aggregate the reliable adjustable capability of each air conditioning cluster to obtain the reliable adjustable capability of the air conditioners in the target control area. The correction module is used to correct the reliable adjustability of the air conditioners in the target control area based on the actual constraints of the air conditioners participating in the regulation, so as to obtain the corrected reliable adjustability of the air conditioner resources in the target control area.

[0017] The beneficial effects of this invention are as follows: By combining cluster analysis and probabilistic models, the adjustability of air conditioners can be accurately quantified, and the credible adjustability boundary can be calculated based on different confidence levels. This effectively solves the problems of inaccurate assessment of air conditioner adjustability, poor dynamics, and lack of credibility calculation in existing technologies. Compared with traditional methods, this invention considers the physical characteristics of air conditioners such as thermal resistance and heat capacity, and uses the error propagation principle for probabilistic modeling, making the quantification results more accurate and able to reflect the true adjustability of air conditioners under different operating conditions.

[0018] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 The flowchart shows the method for quantifying the reliability and adjustability of air conditioners based on cluster analysis. Figure 2 This is a temperature curve for a typical summer day. Figure 3 A reliable and up-adjustable capacity curve for typical intraday air conditioning resources in summer; Figure 4 A reliable curve showing the adjustable capacity of air conditioning resources during a typical summer day. Figure 5This is a schematic diagram of the structure of an air conditioning reliability and adjustability quantification system based on cluster analysis. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0023] Example 1: like Figures 1 to 4 As shown, this application provides a method for quantifying the reliability and adjustability of air conditioning based on cluster analysis. The method includes: S10: Obtain the operating parameters of each air conditioner within the target control area, and determine the adjustable power and adjustable power of each air conditioner based on the operating parameters; S20: Based on the relevant operating parameters, the air conditioners are clustered to obtain multiple air conditioner clusters, and the air conditioner parameters within each air conditioner cluster are statistically analyzed to obtain statistical characteristics that characterize the uncertainty of the air conditioner clusters. S30: Based on statistical characteristics, establish a probabilistic model of the adjustable capacity of air conditioning clusters, and obtain the probability distribution characteristics of the adjustable capacity and adjustable capacity of each air conditioning cluster. S40: Based on the probability distribution characteristics, calculate the reliable up-adjustable capability and reliable down-adjustable capability of each air conditioning cluster according to the preset reliability level, and aggregate the reliable adjustable capability of each air conditioning cluster to obtain the reliable adjustable capability of the air conditioning in the target control area. S50: Based on the actual constraints of air conditioning participation in regulation, the reliable adjustability of air conditioning in the target control area obtained by aggregation is corrected to obtain the corrected reliable adjustability of air conditioning resources in the target control area.

[0024] Specifically, such as Figure 1As shown, firstly, the operating parameters of each air conditioner within the target control area are obtained, including historical power and temperature data. Using this data, a first-order equivalent thermal parameter model is established to identify the thermal resistance and heat capacity of each air conditioner. Furthermore, the adjustable power and adjustable power of the air conditioner are calculated. Specifically, the adjustable power is defined as the power increase when the air conditioner switches from standby to rated operation, while the adjustable power is defined as the power decrease when switching from rated operation to standby. Next, based on these operating parameters, the air conditioners are clustered to obtain multiple air conditioner clusters. Statistical analysis is then performed on the air conditioner parameters within each cluster to obtain statistical characteristics representing the uncertainty of the air conditioner cluster. This is achieved by constructing a state vector from the thermal resistance, heat capacity, set temperature, rated power, and rated cooling power of each air conditioner and normalizing it. A weighted Euclidean distance and an improved K-m method are then used. The EANSI++ algorithm is used for clustering to ensure the accuracy of the clustering results. Furthermore, based on the aforementioned statistical characteristics, a probabilistic model of the adjustable capacity of air conditioning clusters is established. The expected value and variance of the duty cycle, adjustable capacity, and adjustable capacity of the air conditioning cluster are calculated, and a normal distribution is assumed to be followed to construct the corresponding probability distribution. This probabilistic model provides accurate quantification of the adjustable capacity for each air conditioning cluster. Then, based on the probability distribution characteristics of each air conditioning cluster, the boundary values ​​of the credible adjustable capacity and credible adjustable capacity are calculated according to a preset confidence level, thereby obtaining the credible adjustable capacity of each cluster. The credible adjustable capacity of all air conditioning units within the region is then aggregated. Finally, based on the actual constraints of air conditioning participation in regulation, the aggregated credible adjustable capacity is corrected to obtain the corrected credible adjustable capacity of the air conditioning units, thus better reflecting the needs of actual scheduling scenarios.

[0025] By combining cluster analysis and probabilistic models, this invention can accurately quantify the adjustability of air conditioners and calculate the reliable adjustability boundary based on different confidence levels. This effectively addresses the problems of inaccurate air conditioner adjustability assessment, poor dynamics, and lack of reliability calculation in existing technologies. Compared to traditional methods, this invention considers the physical characteristics of air conditioners, such as thermal resistance and heat capacity, and utilizes the error propagation principle for probabilistic modeling, resulting in more accurate quantification results that reflect the true adjustability of air conditioners under different operating conditions. Furthermore, by introducing cluster analysis, air conditioners are divided into different clusters according to their operating characteristics, further improving the detailed description of their adjustability. The clustered air conditioner clusters effectively reflect different adjustability characteristics, making the calculation of adjustability more targeted. Simultaneously, considering the constraints of air conditioner adjustment in actual operation, such as default, adjustment fatigue, and communication delays, the proposed correction mechanism further optimizes the adjustment results, ensuring that the dispatching system can reliably utilize air conditioning resources in practical applications, thereby improving the flexibility and stability of the power grid. Through these innovations, this invention not only improves the accuracy and reliability of air conditioner adjustability quantification but also provides more reliable data support for power grid dispatching, effectively promoting the optimization and development of smart grids and demand response systems.

[0026] As a preferred embodiment of the above, in step S10, determining the adjustable power and adjustable power of each air conditioner based on operating parameters includes: Based on the historical power and temperature data of air conditioners, a first-order equivalent thermal parameter model is established to identify the thermal resistance and heat capacity of each air conditioner. Adjustable power is defined as the power increase of an air conditioner when switching from standby mode to rated operating mode; Adjustable power is defined as the power reduction of an air conditioner when it switches from rated operation to standby mode.

[0027] As a preferred embodiment of the above, the parameter identification of the first-order equivalent thermal parameter model specifically includes: Two steady-state operating points with zero indoor temperature change rate were selected from the historical operating data of the air conditioner. The thermal resistance was calculated based on the difference in indoor temperature and the difference in air conditioning cooling capacity between the two steady-state operating points. The heat capacity is calculated based on the thermal resistance and the thermal time constant derived from historical temperature curves.

[0028] Specifically, the steps for S10 are as follows: S11: Based on the power and temperature curves in the historical operating data of air conditioners, a method for calculating thermal resistance and heat capacity based on historical operating data is proposed. This involves collecting data on the ambient temperature and power consumption of the air conditioner during operation, and calculating the cooling capacity provided by the air conditioner, taking refrigeration as an example, according to its inherent properties. in, Let i be the cooling power of air conditioner i at time t; Let be the electrical power of air conditioner i at time t; The performance coefficient of air conditioner i can be determined from the air conditioner's nameplate.

[0029] Establish a first-order equivalent thermal model for the air conditioner, specifically as follows: in, The heat capacity of the environment in which the air conditioner i is located; The thermal resistance of the environment in which the air conditioner i is located; , These are the internal and external temperatures of air conditioner i at time t, respectively; This indicates the on / off state of the air conditioner; a value of 1 indicates it is on, and a value of 0 indicates it is in standby mode.

[0030] The first-order equivalent thermal model established by the above equation is mathematically a first-order linear differential equation, and the homogeneous solution of this equation is in exponential form. Therefore, the thermal time constant of the environment in which air conditioner i is located is defined. ,in It can be obtained by inferring the indoor temperature change curve from the historical operating data of the air conditioner.

[0031] Select two steady-state operating points from the historical operating data of the air conditioner. , This refers to the case where the rate of change of indoor temperature is 0. When two points are close in time, the outside temperature can be considered constant. By substituting the indoor temperature and cooling capacity of the two steady-state operating points into the first-order equivalent thermal model and rearranging, the formula for calculating thermal resistance can be obtained: in, This represents the difference in indoor temperature under two steady-state conditions. , , These are the steady-state operating points. , Indoor temperature; This represents the difference in cooling capacity between two steady-state conditions. , , These are the steady-state operating points. , The cooling capacity.

[0032] Subsequently, the heat capacity of the air-conditioned environment was calculated based on the thermal time constant: S12: Air conditioners in residential loads operate at rated power. Under rated operation, the standby power is very low and negligible, therefore it consumes no power in standby mode. When power needs to be increased, the air conditioner, which was originally operating normally and in standby mode, can switch to operating mode to provide the increased power. Therefore, the power change from standby power to maximum power is defined as the increased power. When power needs to be decreased, the air conditioner, which was originally operating normally and in full power mode, can switch to standby mode to provide the decreased power. Therefore, the power change from maximum power to standby power is defined as the decreased power. Based on the above characteristics, the increased power that each air conditioner can provide is denoted as... Let the reduced power that each air conditioner can provide be recorded as . Generally, there are: In this embodiment, step S20, clustering the air conditioners based on operational parameters, includes: Each air conditioner's thermal resistance, thermal capacity, set temperature, rated electrical power, and rated cooling power are used to construct a state vector and then normalized. The weights of each parameter in the state vector are determined using the analytic hierarchy process (AHP). Based on the weighted Euclidean distance, the improved K-means++ algorithm is used to cluster the normalized state vectors to obtain multiple air conditioner clusters.

[0033] As a preferred embodiment of the above, statistical analysis is performed on the air conditioning parameters within each air conditioning cluster, and the obtained statistical characteristics include the mathematical expectation and variance of each parameter within each air conditioning cluster.

[0034] Specifically, the steps for S20 are as follows: S21: Compile the parameters for each air conditioner, adding the set temperature to the parameters obtained in S10. The thermal resistance of the i-th air conditioner The heat capacity of the i-th air conditioner The rated power of the i-th air conditioner The rated cooling capacity of the i-th air conditioner Together, we can derive the state vector of each air conditioner. And normalize the state vector: in, Let l be the normalized value of the l-th element in the state vector of the i-th air conditioner. , respectively corresponding to the state vector ; Let l be the value of the l-th element in the state vector of the i-th air conditioner; It is a very small positive number.

[0035] After normalization, the standard state vector of the air conditioner is obtained. ,in , , , , These are the normalized thermal resistance, thermal capacity, rated electrical power, rated cooling power, and set temperature of the i-th air conditioner.

[0036] S22: The weighting analysis of the five elements of the air conditioner state vector is performed using the analytic hierarchy process (AHP) to establish a judgment matrix: in, For the judgment matrix; This is a criterion based on commonly used values ​​1-9, used to determine the importance of the i-th element compared to the j-th element. 1 indicates equal importance, 3, 5, 7, and 9 indicate progressively greater importance, and 2, 4, 6, and 8 are intermediate importance values. The matrix elements satisfy the following criteria: .

[0037] Subsequently, the weight values ​​of the five elements were determined using the eigenvalue method, and the judgment matrix was solved. Eigenvector corresponding to the largest eigenvalue And normalize to obtain the weight values: in, Let l be the weight value corresponding to the l-th element in the air conditioner's state vector. Finally, we obtain the element weight vector of the air conditioner's state vector. and satisfy And the weight value is positive.

[0038] S23: An improved K-means++ method considering parameter weights is used to cluster air conditioners, and a standard state vector for the air conditioners is defined. With cluster center Weighted Euclidean distance: in, The weighted Euclidean distance between the standard state vector and the cluster center is used; the smaller the distance, the higher the similarity. K is the number of cluster centers; Let be the cluster center vector of the k-th cluster, and , , , , , These are the center thermal resistance, center thermal capacity, center rated electrical power, center rated cooling power, and center set temperature of the cluster, respectively.

[0039] From the set of N standard state vectors corresponding to all N air conditioners A sample is randomly selected as the first initial cluster center. Subsequently, when the number of selected centers is t ( When ), for each standard state vector Calculate its shortest distance to all currently selected centers. And select the next center from the standard set of state vectors with the following probabilities: Subsequently, the selected standard state vectors are used as the new initial cluster centers. Repeat the above steps until K initial cluster centers are obtained. .

[0040] For each standard state vector Calculate the weighted Euclidean distance from the i-th air conditioner to each cluster center, and assign it to the cluster with the smallest distance; specifically, the cluster number to which the i-th air conditioner belongs is: For each cluster, the set of sample indices for that cluster is defined as follows: And update the cluster center of the k-th cluster to the sample mean within the cluster: in, The number of standard state vectors contained in the k-th cluster. Repeat the above operation until either of the following conditions is met: (1) the cluster numbers of two adjacent iterations (2) The cluster centers change less than the threshold between two adjacent iterations. (3) The number of iterations reaches the upper limit. After stopping the iteration, output the cluster number of each air conditioner. Cluster centers of each cluster And the cluster centers of each cluster in the state vector space. .

[0041] S24: For the set of air conditioners belonging to the k-th cluster. For the five parameters R, C, ... , , Perform statistics to calculate the expected value and variance, and construct a normal distribution function that the five parameters follow within the cluster.

[0042] Within the k-th cluster, for any parameter Calculate its expectation within the k-th cluster: in, This represents the value of parameter X corresponding to the i-th air conditioner. ; Let X be the expectation of the intrinsic parameter X of the k-th cluster; This represents the number of air conditioners in the cluster.

[0043] Subsequently, the variance of the intrinsic parameter X of the k-th cluster is calculated: in, Let X be the variance of the intrinsic parameter X of the k-th cluster.

[0044] In summary, the parameters within the k-th cluster are obtained. Follows a normal distribution .

[0045] Furthermore, after clustering, it is assumed that air conditioning performance is the same within the same cluster, and the energy efficiency ratio of air conditioning resources is the same. Therefore, the energy efficiency ratio of air conditioning resources within the same cluster can be derived as follows: in, Let be the energy efficiency ratio of the air conditioner in the k-th cluster.

[0046] As a preferred embodiment of the above, in step S30, establishing a probabilistic model of the adjustability of the air conditioning cluster specifically includes: Based on the error propagation principle, the expected value and variance of the start-up time, standby time and duty cycle of the air conditioning cluster under steady-state operation are calculated according to the expected value and variance of the parameters within each air conditioning cluster. Based on the probability distribution of duty cycle, the expected value and variance of the up-adjustment and down-adjustment capabilities of the air conditioning cluster are calculated, and a probability distribution is established based on the normal distribution assumption.

[0047] Figure 2 The temperature curve for a typical summer day shows the changes in the external ambient temperature of the target control area over 24 hours, providing key environmental input parameters for subsequent calculations of air conditioning operating time, standby time, and assessment of heat load.

[0048] Specifically, the steps of step S30 are as follows: S31: Calculate the start-up time, standby time, and duty cycle of each air conditioner cluster. During normal operation, the air conditioner has a specific set temperature, and then the indoor temperature fluctuates around the set temperature between certain upper and lower limits. During steady-state operation, one complete start-up and shutdown process of the air conditioner is a complete working cycle. The upper and lower limits of the indoor temperature are shown in the following formula: in, Set the temperature for the air conditioner; , These are the lower and upper boundaries of indoor temperature change, respectively. To address the dead zone of air conditioner temperature regulation, the formula will be... Attached ,Mode From this, we can calculate the operating time and standby time of the air conditioner within one complete working cycle under steady-state operating conditions: in, Set the temperature for air conditioner i; , These are the start-up time and standby time of air conditioner i, respectively; Let t be the ambient temperature during the time period t.

[0049] Under steady-state operation, the air conditioner is always in a complete duty cycle. The proportion of time a single air conditioner is in operation within one duty cycle is called the duty cycle, i.e.: in, Let this be the duty cycle. Considering the sufficiently large number of air conditioners in each cluster, based on the law of large numbers, the adjustable capacity of the air conditioner resources in the k-th cluster is aggregated. The adjustable capacity of the air conditioners in the k-th cluster is: in, The adjustable capacity of the k-th cluster of air conditioners; Let be the rated power of the l-th air conditioner in the k-th cluster; Let K be the number of air conditioners in the k-th cluster. Let be the duty cycle of the k-th cluster of air conditioners.

[0050] Similarly, the adjustable capacity of the k-th cluster of air conditioners is: in, This represents the adjustable capacity of the k-th cluster of air conditioners.

[0051] S32: Perform a probabilistic analysis of the duty cycle. The duty cycle is a function of the operating time and standby time. The formulas for calculating the operating time and standby time include heat capacity, thermal resistance, cooling power, and set temperature. The expected value and variance of these four variables have been obtained above. Considering residents' daily usage habits and the characteristics of household air conditioners, the set temperature is usually a certain integer set by the manufacturer, such as 24℃, 26℃, etc. Therefore, within the same cluster, we assume that the set temperature is the same value. Based on the heat capacity, thermal resistance, and maximum power, we perform a probabilistic analysis of the operating time, standby time, and duty cycle. Using the error propagation formula and making certain approximations, we can obtain the expected value and variance of the duty cycle of all air conditioners within a cluster: Among them, among them, , Let $\begin{bmatrix}$ and $\begin{bmatrix}$ be the expected value and variance of the duty cycle of the $k$-th cluster of air conditioners, respectively. , , , All are intermediate variables, already shown in the formula. The explanation is provided below.

[0052] S33: Probabilistic analysis of the upward and downward adjustment capabilities is performed. Considering that the dispersion of the aggregated air conditioning power is within the allowable range, the probabilistic characteristics of the aggregated power are analytically derived using a first-order Taylor expansion and error propagation formula at the expected power value.

[0053] Subsequently, according to the formula Calculate the expected value and standard deviation of the downmodifiable capacity. The expected value and standard deviation of the downmodifiable capacity for the k-th cluster are: in, Let be the expected downtunable capability of the k-th cluster; Let be the standard deviation of the down-adjustment capability of the k-th cluster; for right Find the partial derivative, and in The value at that location.

[0054] Similarly, according to the formula Calculate the expected value and standard deviation of the upregulation capacity. The expected value and standard deviation of the upregulation capacity within the k-th cluster are: in, Let be the expected upregulation capability of the k-th cluster; Let be the standard deviation of the upregulation capability of the k-th cluster.

[0055] In this embodiment, in step S40, the reliability up-adjustment capability and reliability down-adjustment capability of each air conditioning cluster are calculated according to a preset reliability level. The specific implementation includes: For each air conditioning cluster, the expected value and standard deviation of each air conditioning cluster are determined based on the probability distribution characteristics of its up-adjustment capability and down-adjustment capability. Based on the expected value, standard deviation, and the standard normal distribution quantiles corresponding to the selected confidence level, the confidence up-adjustment capability boundary value and the confidence down-adjustment capability boundary value at the confidence level are calculated.

[0056] As a preferred embodiment of the above, in step S40, the reliable up-adjustment capability and reliable down-adjustment capability of each air conditioning cluster are calculated according to a preset reliability level, specifically including: Based on the expected and variance of the adjustable and dead capabilities, and combined with the standard normal distribution quantiles, the adjustable capability boundary values ​​at a specified confidence level are calculated. The preset confidence levels include at least 50%, 80%, 90%, 95%, 99%, and 100%.

[0057] Specifically, the steps of step S40 are as follows: S41: Deriving the reliable adjustability of adjustable power up and down. Considering that the number of air conditioners within the same cluster is usually large, according to the central limit theorem, the adjustable power of the aggregated air conditioners within the cluster approximately follows a normal distribution. Therefore, the adjustable capability boundary at different confidence levels can be directly calculated using the probability density function of the normal distribution. The reliable adjustability is defined as: the adjustable capability is divided according to the confidence level of the adjustable capability, and the adjustable capability corresponding to a specific confidence level is obtained. Six confidence levels are selected: 50%, 80%, 90%, 95%, 99%, and 100%, and the reliable adjustable down capability at these six confidence levels is calculated: in, For the k-th cluster, v% is the reliable down-adjustable capability. ; is the lower quantile of the standard normal distribution, and has Furthermore, when the confidence level of the adjustable capability reaches 99.87%, it is considered that the credible adjustable capability is 100%. .

[0058] Similarly, selecting six confidence levels—50%, 80%, 90%, 95%, 99%, and 100%—we calculate the confidence upscalability at these six confidence levels: in, For the k-th cluster, v% is the reliable upregulation capability. ; is the lower quantile of the standard normal distribution, and has Furthermore, when the confidence level of the upscalable capability reaches 99.87%, it is considered that the confidence level of the upscalable capability is 100%. .

[0059] Based on the above analysis, the reliability levels of the adjustable capacity and the adjustable capacity can be obtained, and then the reliability adjustable capacity of the same cluster of air conditioners can be obtained. Subsequently, the adjustable capacities of the same reliability level of each cluster of air conditioners are superimposed to obtain the overall reliability adjustable capacity of all air conditioners in the region. in, , These are the overall reliable down-adjustment capability and the overall reliable up-adjustment capability, respectively. Based on these, the overall reliable adjustability within each time period is calculated, thus yielding the overall time-series reliable down-adjustment capability of the air conditioning system in the region. The overall reliability and upscalability of air conditioning timing within the region. .

[0060] In step S50, the actual constraint factors include at least one of the following: breach of contract coefficient, repeated adjustment coefficient, and communication delay coefficient.

[0061] The specific amendments include: Calculate the breach of contract coefficient, repeated adjustment coefficient, and communication delay coefficient based on the external temperature, historical adjustment count, communication packet loss rate, and latency, respectively. The adjustment response capability index is obtained by combining the breach of contract coefficient, the repeated adjustment coefficient, and the communication delay coefficient. The credible adjustability is reduced by using the adjustment response capability index to obtain the corrected credible adjustability.

[0062] Specifically, the steps of step S50 are as follows: S51: Propose an improved evaluation system for air conditioning regulation response capability and calculate the air conditioning regulation response capability index. First, residents' willingness to use air conditioning is closely related to the ambient temperature. When the ambient temperature exceeds residents' psychological tolerance temperature, their willingness to participate in air conditioning scheduling will decrease, potentially leading to non-compliance. Therefore, the non-compliance coefficient for air conditioning is defined as: in, This is the default coefficient for air conditioners; The default attenuation factor is set to 0.01. The default temperature for a single air conditioner is set at 37°C, which is the threshold temperature for the orange high-temperature warning.

[0063] Secondly, when residential users frequently adjust their air conditioners within a short period, users may experience adjustment fatigue, leading to a decreased willingness to adjust and potentially forcing the air conditioner to stop adjusting. Therefore, the repeated adjustment coefficient for air conditioners is defined as follows: in, This is the repeated adjustment coefficient; The fatigue attenuation coefficient is set to 0.01. This represents the number of times the air conditioner has adjusted settings in the past 3 hours.

[0064] Furthermore, both the dispatching of adjustment commands and the air conditioning's receipt of adjustment commands rely on the communication network. Packet loss and latency in the communication network can prevent the air conditioning from participating in the dispatching process in a timely manner, thus indirectly weakening its adjustability. Therefore, the communication latency coefficient is defined as follows: in, This is the communication delay coefficient; Packet loss rate; Communication delay, measured in seconds; The timeliness tolerance coefficient is set to 10 seconds.

[0065] Based on the above three coefficients, calculate the air conditioner's regulation response capability index: in, This refers to the air conditioner's regulation response capability index.

[0066] S52: Using the air conditioner's adjustment response capability index, correct the overall reliable down-adjustment capability and the overall reliable up-adjustment capability: in, , These are respectively the ability to adjust overall credibility down and the ability to adjust overall credibility up.

[0067] Subsequently, by correcting the overall reliable adjustability within each time period, the corrected overall temporal reliable adjustability of the air conditioning system in the region can be obtained. And the overall time-series reliability and up-adjustment capability of air conditioning in the region .

[0068] Figure 3 The graph shows the reliable up-adjustment capability curve of air conditioning resources calculated using the method of this invention on a typical summer day. The graph uses time as the horizontal axis and power as the vertical axis to show the reliable up-adjustment capability of air conditioning groups at different times (such as different scheduling periods) corresponding to different confidence levels. The curve shows that this method can quantify the upper limit of the adjustment potential that changes over time and includes confidence information, providing a basis for load increase decisions for power grid scheduling.

[0069] Figure 4 This is a reliable curve showing the adjustable capacity of air conditioning resources calculated using the method of this invention during a typical summer day. The curve is compared with... Figure 3 Similarly, the curve demonstrates the reliable load reduction capability of air conditioning groups at different times and confidence levels. This curve quantifies the reliable potential of air conditioning groups to participate in grid load reduction as flexible loads, providing a basis for grid dispatching load reduction decisions.

[0070] The beneficial effects of this invention are compared with those of the prior art: This invention effectively calculates data such as thermal resistance and heat capacity that are difficult to measure directly based on historical data, optimizes the parameter acquisition path, and, considering the differences in physical parameters of air conditioning resources in different environments, clusters air conditioners according to physical characteristics. During the clustering process, the parameters of the normal distribution that each cluster follows are calculated, thereby improving the accuracy of the clustering results. In the process of quantifying the adjustability of air conditioning resources, an improved air conditioning operating condition parameter calculation model considering probability distribution is proposed based on the error propagation principle to calculate the start-up time, standby time, and duty cycle, thereby obtaining the overall temporal reliability and adjustability of air conditioning in the area. This effectively improves the accuracy of adjustability quantification and the system quantifies the reliability of adjustability, which is more in line with the needs of actual scheduling scenarios.

[0071] This invention considers the subjective and objective factors of air conditioning participation in regulation within a region, making the adjustable capacity of air conditioning resources more consistent with actual operating conditions, and giving the quantitative results of adjustable capacity greater practical significance and reference value. Example 2: This invention also includes a system for quantifying the reliable adjustability of air conditioners based on cluster analysis, such as... Figure 5 As shown, the system includes: The parameter acquisition and identification module is used to acquire the operation-related parameters of each air conditioner within the target control area, and determine the adjustable power and adjustable power of each air conditioner based on the operation-related parameters. The clustering and statistical analysis module is used to cluster air conditioners based on relevant operating parameters to obtain multiple air conditioner clusters, and to perform statistical analysis on the air conditioner parameters within each air conditioner cluster to obtain statistical characteristics that characterize the uncertainty of the air conditioner clusters. The probability modeling module is used to establish a probability model of the adjustable capability of air conditioning clusters based on statistical characteristics, and to obtain the probability distribution characteristics of the adjustable capability and adjustable capability of each air conditioning cluster. The trusted capability calculation and aggregation module is used to calculate the trusted up-adjustable capability and trusted down-adjustable capability of each air conditioning cluster based on the probability distribution characteristics and according to the preset trusted level, and to aggregate the trusted adjustable capability of each air conditioning cluster to obtain the trusted adjustable capability of the air conditioning in the target control area. The correction module is used to correct the reliable adjustability of air conditioners within the target control area based on the actual constraints of air conditioner participation in regulation, thereby obtaining the corrected reliable adjustability of air conditioner resources within the target control area.

[0072] The adjustment system described above in this invention can effectively realize the quantitative method of air conditioner reliability and adjustability based on cluster analysis, and the technical effects it can achieve are as described in the above embodiments, and will not be repeated here.

[0073] Similarly, the above-mentioned optimization schemes for the system can also achieve the optimization effects corresponding to the methods in Embodiment 1, which will not be repeated here.

[0074] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and accompanying drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for quantifying the reliable adjustability of air conditioning based on cluster analysis, characterized in that, The method includes: Obtain the operating parameters of each air conditioner within the target control area, and determine the adjustable power and adjustable power of each air conditioner based on the operating parameters; Based on the aforementioned operational parameters, the air conditioners are clustered to obtain multiple air conditioner clusters. Statistical analysis is then performed on the air conditioner parameters within each cluster to obtain statistical features that characterize the uncertainty of the air conditioner clusters. Based on the aforementioned statistical characteristics, a probabilistic model of the adjustable capability of each air conditioning cluster is established to obtain the probability distribution characteristics of the adjustable capability and the adjustable capability of each air conditioning cluster. Based on the probability distribution characteristics, the reliable up-adjustable capability and reliable down-adjustable capability of each air conditioning cluster are calculated according to a preset reliability level, and the reliable adjustable capability of each air conditioning cluster is aggregated to obtain the reliable adjustable capability of the air conditioning in the target control area. Based on the actual constraints of air conditioning participation in regulation, the reliable adjustability of air conditioning in the target regulation area obtained by aggregation is corrected to obtain the corrected reliable adjustability of air conditioning resources in the target regulation area.

2. The method for quantifying the reliable adjustability of air conditioning based on cluster analysis according to claim 1, characterized in that, The process of determining the adjustable power and adjustable power of each air conditioner based on relevant operating parameters includes: Based on the historical power and temperature data of air conditioners, a first-order equivalent thermal parameter model is established to identify the thermal resistance and heat capacity of each air conditioner. The adjustable power is defined as the power increase of the air conditioner when switching from standby mode to rated operating mode; The adjustable power is defined as the power reduction achieved when the air conditioner switches from its rated operating state to standby state.

3. The method for quantifying the reliable adjustability of air conditioning based on cluster analysis according to claim 2, characterized in that, The parameter identification of the first-order equivalent thermal parameter model specifically includes: Two steady-state operating points with zero indoor temperature change rate are selected from the historical operating data of the air conditioner. The thermal resistance is calculated based on the difference in indoor temperature and the difference in air conditioning cooling capacity between the two steady-state operating points. The heat capacity is calculated based on the thermal resistance and the thermal time constant derived from the historical temperature curve.

4. The method for quantifying the reliable adjustability of air conditioning based on cluster analysis according to claim 1, characterized in that, The clustering of air conditioners based on the aforementioned operational parameters includes: Each air conditioner's thermal resistance, thermal capacity, set temperature, rated electrical power, and rated cooling power are used to construct a state vector and then normalized. The weights of each parameter in the state vector are determined using the analytic hierarchy process (AHP). Based on the weighted Euclidean distance, the improved K-means++ algorithm is used to cluster the normalized state vectors to obtain multiple air conditioner clusters.

5. The method for quantifying the reliable adjustability of air conditioning based on cluster analysis according to claim 1, characterized in that, The statistical characteristics obtained by performing statistical analysis on the air conditioning parameters within each air conditioning cluster include the mathematical expectation and variance of each parameter within each air conditioning cluster.

6. The method for quantifying the reliable adjustability of air conditioning based on cluster analysis according to claim 1, characterized in that, The probabilistic model for establishing the adjustability of the air conditioning cluster specifically includes: Based on the error propagation principle, the expected value and variance of the start-up time, standby time and duty cycle of the air conditioning cluster under steady-state operation are calculated according to the expected value and variance of the parameters within each air conditioning cluster. Based on the probability distribution of the duty cycle, the expected value and variance of the up-adjustment capability and down-adjustment capability of the air conditioning cluster are calculated, and a probability distribution is established based on the normal distribution assumption.

7. The method for quantifying the reliable adjustability of air conditioning based on cluster analysis according to claim 1, characterized in that, The calculation of the reliable up-adjustment capability and reliable down-adjustment capability of each air conditioning cluster according to a preset reliability level is specifically implemented in the following ways: For each air conditioning cluster, the expected value and standard deviation of each air conditioning cluster are determined based on the probability distribution characteristics of its up-adjustment capability and down-adjustment capability. Based on the mathematical expectation, standard deviation, and the standard normal distribution quantiles corresponding to the selected confidence level, the confidence up-adjustment capability boundary value and the confidence down-adjustment capability boundary value at the confidence level are calculated.

8. The method for quantifying the reliable adjustability of air conditioning based on cluster analysis according to claim 1, characterized in that, The calculation of the reliable up-adjustment capability and reliable down-adjustment capability of each air conditioning cluster according to a preset reliability level specifically includes: Based on the expected value and variance of the adjustable capacity and the adjustable capacity, and combined with the standard normal distribution quantiles, the adjustable capacity boundary value at a specified confidence level is calculated. The preset confidence level includes at least 50%, 80%, 90%, 95%, 99%, and 100%.

9. The method for quantifying the reliable adjustability of air conditioning based on cluster analysis according to claim 1, characterized in that, The specific modifications include: Calculate the breach of contract coefficient, repeated adjustment coefficient, and communication delay coefficient based on the external temperature, historical adjustment count, communication packet loss rate, and latency, respectively. The adjustment response capability index is obtained by combining the breach of contract coefficient, the repeated adjustment coefficient, and the communication delay coefficient. The credible adjustability is reduced by the adjustment response capability index to obtain the corrected credible adjustability.

10. A system for quantifying the reliable adjustability of air conditioning based on cluster analysis, characterized in that, The system includes: The parameter acquisition and identification module is used to acquire the operation-related parameters of each air conditioner within the target control area, and determine the adjustable power and adjustable power of each air conditioner based on the operation-related parameters. The clustering and statistical analysis module is used to cluster the air conditioners based on the operation-related parameters to obtain multiple air conditioner clusters, and to perform statistical analysis on the air conditioner parameters within each air conditioner cluster to obtain statistical features that characterize the uncertainty of the air conditioner clusters. The probability modeling module is used to establish a probability model of the adjustable capability of the air conditioning cluster based on the statistical characteristics, and to obtain the probability distribution characteristics of the adjustable capability and adjustable capability of each air conditioning cluster. The reliable capability calculation and aggregation module is used to calculate the reliable up-adjustable capability and reliable down-adjustable capability of each air conditioning cluster according to a preset reliability level based on the probability distribution characteristics, and to aggregate the reliable adjustable capability of each air conditioning cluster to obtain the reliable adjustable capability of the air conditioners in the target control area. The correction module is used to correct the reliable adjustability of the air conditioners in the target control area based on the actual constraints of the air conditioners participating in the regulation, so as to obtain the corrected reliable adjustability of the air conditioner resources in the target control area.

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