A method and device for quantitatively evaluating the adjustable capacity of air conditioning load

Abstract

The present application discloses a method and device for quantitatively evaluating the adjustable capacity of air-conditioning load, wherein the method includes: constructing a multidimensional vector of full-information air-conditioning; executing a loop step to determine whether the number of Gaussian mixture components in the Bayesian information volume reaches a preset value, if not, returning to execute the loop step, if reaching the preset value, executing the following steps: using the parameters corresponding to the minimum value in the Bayesian information volume as the parameters of the Gaussian mixture model to obtain the Gaussian mixture model; calculating the adjustable capacity of the air-conditioning load according to the Gaussian mixture model. The present application utilizes data that is easy to collect and observe, greatly reducing the difficulty of the power system in collecting air-conditioning state parameters and room physical parameters, and realizing the quantitative evaluation of the adjustable capacity of large-scale air-conditioning loads with high evaluation accuracy.

Description

Technical Field

[0001] The present application relates to the field of power systems, and in particular to a method and device for quantitatively evaluating the adjustable capacity of air-conditioning loads. Background Art

[0002] With the development of Internet of Things technology, the load resources on the demand side of the power system can be regulated to provide flexible and adjustable capacity for the power system. Among the load resources on the demand side of the power system, air conditioning is a relatively important flexible load, because the power consumption of air conditioning accounts for nearly 50% of the total power consumption of the power system, and the adjustable capacity is huge; and the short-term regulation of air conditioning will not affect the normal production and life of users, and has broad application prospects. In order to utilize the air conditioning load resources, the power system dispatching department needs to evaluate the total adjustable capacity of the air conditioning load cluster before dispatching, so as to formulate a dispatching plan to ensure the safe and stable operation of the power system.

[0003] There are two main quantitative evaluation methods for the adjustable capacity of air-conditioning loads: the first is to collect the total power consumption of the air-conditioning load cluster, and directly control the air-conditioning load power consumption to achieve peak shaving and valley filling when the system is short of power. However, this method will undoubtedly have a great impact on the user's comfort; the second is to comprehensively collect the parameters of each air-conditioning load and the parameters of the room in which it is located, including air-conditioning operating power, air-conditioning energy efficiency ratio, indoor temperature, outdoor temperature, user-set temperature, user-allowed adjustable temperature, room specific heat capacity, room thermal resistance, etc. This method can ensure user comfort during the load control process by constructing a load control model. However, this method requires comprehensive and accurate collection of a large amount of data, especially parameters such as room specific heat capacity and room thermal resistance, which involve multi-dimensional factors such as building structure, materials, and door and window areas. There are no available sensors for direct collection of parameters such as room specific heat capacity and room thermal resistance, making the second method difficult to implement in actual power systems.

[0004] Therefore, the above-mentioned technical problems existing in the related technology need to be solved urgently. Summary of the invention

[0005] The present application aims to solve one of the technical problems in the related art. To this end, the embodiment of the present application provides a method and device for quantitatively evaluating the adjustable capacity of air-conditioning loads, which can greatly reduce the difficulty of the power system in collecting air-conditioning state parameters and room physical parameters, and realize the quantitative evaluation of the adjustable capacity of large-scale air-conditioning loads.

[0006] According to one aspect of an embodiment of the present application, a method for quantitatively evaluating the adjustable capacity of an air conditioning load is provided, the method comprising:

[0007] Constructing a multidimensional vector of a full-information air conditioner, wherein the full-information air conditioner includes an air conditioner for which specific heat capacity and room thermal resistance parameters of a room corresponding to the air conditioner have been obtained;

[0008] Execute the loop step: initialize the parameters of the expectation maximization algorithm, and obtain the full information air conditioning set according to the parameters of the expectation maximization algorithm and the multi-dimensional vector calculation of the full information air conditioning;

[0009] Expanding the full information conditioning set using latent variables to obtain a complete data set;

[0010] Calculating the log-likelihood function of the complete data set, and solving the parameters corresponding to the maximization of the log-likelihood function;

[0011] Solving the Bayesian information of the complete data set according to the parameters corresponding to the maximization of the log-likelihood function;

[0012] Determine whether the number of Gaussian mixture components in the Bayesian information reaches a preset value. If not, return to execute the loop step. If it reaches the preset value, execute the following steps:

[0013] Using the parameters corresponding to the minimum value in the Bayesian information as the parameters of the Gaussian mixture model to obtain the Gaussian mixture model;

[0014] The adjustable capacity of the non-full-information air conditioner and the total adjustable capacity of the air conditioning load cluster are calculated according to the Gaussian mixture model, wherein the non-full-information air conditioner includes air conditioners other than the full-information air conditioner, and the air conditioning load cluster includes clusters of non-full-information air conditioners and full-information air conditioners.

[0015] In one embodiment, the initialization of the parameters of the expectation-maximization algorithm includes:

[0016] Initialize the number of iterations and expectation-maximization algorithm parameters.

[0017] In one embodiment, the calculation formula of the multi-dimensional vector of the full information air conditioner is:

[0018]

[0019] Wherein, X is the multidimensional vector of the full-information air conditioner, P is the operating power of the air conditioner, is the indoor temperature.

[0020] In one embodiment, the method of expanding the full information conditioning set using hidden variables to obtain a complete data set includes:

[0021] Using K-dimensional latent variables Expand the full information air conditioning set, where the hidden variable It complies with the following formula:

[0022]

[0023] Wherein, K is the dimension of the latent variable.

[0024] In one embodiment, after obtaining the complete data set, the method further includes:

[0025] Calculate prior probabilities and log-likelihood functions;

[0026] Obtain the parameters corresponding to the maximization of the log-likelihood function.

[0027] In one embodiment, obtaining the parameters corresponding to maximizing the log-likelihood function includes:

[0028] Get the expression of the log-likelihood function:

[0029]

[0030] The parameters corresponding to the maximization of the log-likelihood function are expressed as:

[0031]

[0032] The variables of the parameters corresponding to the maximum log-likelihood function Taking the derivative and equal to 0, it is expressed as:

[0033]

[0034] Find the first The optimal parameters corresponding to the iteration are compared with the The maximum value of the log-likelihood function corresponding to the jth iteration and the previous (jth) iteration, that is, and ,Will and If , then let , and obtain the parameters corresponding to the maximization of the log-likelihood function.

[0035] In one embodiment, the adjustable capacity of the non-full information air conditioner is calculated according to the Gaussian mixture model, and the calculation formula is:

[0036]

[0037] Among them, the vector is the multidimensional vector of non-full information air conditioning in the load cluster.

[0038] In one embodiment, the total adjustable capacity of the air conditioning load cluster is calculated according to the Gaussian mixture model, and the calculation formula is:

[0039]

[0040] Wherein, P is the total adjustable capacity of the air conditioning load cluster.

[0041] According to one aspect of an embodiment of the present application, a load cell control device is provided, which is used to execute a quantitative evaluation method for the adjustable capacity of an air-conditioning load as described in the previous embodiment, and the device includes:

[0042] System power supply module, used to supply power to the entire device via a switch;

[0043] A current sensing and signal input 1 module is used to input the measured air conditioning load current parameter value into the load cell control device;

[0044] The voltage sensing and signal input 2 module is used to input the measured air conditioning load voltage parameter value into the load cell control device;

[0045] Remote signal and photoelectric isolation 1 module, used to receive signal instructions from the load cluster control device and send them to the load single control device;

[0046] Indoor temperature acquisition module, used to input the real-time temperature value in the room into the load unit control device;

[0047] The input keyboard panel and the set temperature input module are used to input the user set temperature value and the user allowed adjustable temperature into the load unit control device;

[0048] Control output and photoelectric isolation 2 module, used to send the control signal from the load unit control device to the air conditioning load equipment;

[0049] A data storage module is used to store historical data, and stores a set of data at preset intervals. The stored data includes indoor temperature, user-set temperature value, user-allowed adjustable temperature, air-conditioning load current value, air-conditioning load voltage value, and air-conditioning load power value;

[0050] The display module is used to display the current indoor temperature, the user-set temperature value, the user-allowed adjustable temperature, the air-conditioning load current value, the air-conditioning load voltage value, and the air-conditioning load power value.

[0051] According to one aspect of an embodiment of the present application, a load cluster control device is provided, which is used to execute a quantitative evaluation method for adjustable capacity of air-conditioning loads described in the previous embodiment, and the device includes:

[0052] System power supply, used to supply power to the entire device through the switch;

[0053] Display module, used to display the total number of air conditioners in the load cluster, the total power value of the air conditioners, the total adjustable capacity of the air conditioners, and the ambient temperature value;

[0054] Ambient temperature acquisition module, used to input the real-time temperature value of the outdoor environment into the load cluster control device;

[0055] The input keyboard panel and the load cluster information input module are used to input the total number of air conditioners in the load cluster, the number of full-information air conditioners, the energy efficiency ratio of the full-information air conditioners, the specific heat capacity of the room where the full-information air conditioners are located, and the thermal resistance of the room where the full-information air conditioners are located into the load cluster control device;

[0056] A data storage module is used to store historical data. A set of data is stored at a preset time interval. The stored data includes the total number of air conditioners in the load cluster, the total power value of the air conditioners, the total adjustable capacity of the air conditioners, and the ambient temperature value;

[0057] The load cluster control device receives data from the load single control device through the remote signal and the photoelectric isolation module, and the load cluster control device sends a signal instruction to the load single control device through the remote signal and the photoelectric isolation module.

[0058] The beneficial effects of an elevator master control program upgrade method provided in an embodiment of the present application are as follows: the present application uses a method and device for quantitatively evaluating the adjustable capacity of air-conditioning loads, utilizes data that is easy to collect and observe, greatly reduces the difficulty of the power system in collecting air-conditioning status parameters and room physical parameters, and realizes quantitative evaluation of the adjustable capacity of large-scale air-conditioning loads with high evaluation accuracy.

[0059] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0061] Figure 1 A flowchart of a method for quantitatively evaluating the adjustable capacity of air-conditioning load provided in an embodiment of the present application;

[0062] Figure 2 A temperature diagram provided for an embodiment of the present application;

[0063] Figure 3 The air conditioner provided in the embodiment of the present application complies with the total power line graph of cluster operation;

[0064] Figure 4A corresponding plot of the indoor and outdoor temperature difference and the air conditioner operating power provided in the embodiment of the present application;

[0065] Figure 5 A graph showing the relationship between the number of Gaussian mixture components and the amount of Bayesian information provided in the embodiment of the present application;

[0066] Figure 6 Another corresponding plot of the indoor and outdoor temperature difference and the air conditioner operating power provided in the embodiment of the present application;

[0067] Figure 7 A schematic diagram of the structure of a load cell control device provided in an embodiment of the present application;

[0068] Figure 8 A schematic diagram of the structure of a load cluster control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0069] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0070] The terms "first", "second", "third" and "fourth" etc. in the specification, claims and drawings of the present application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0071] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0072] With the development of Internet of Things technology, the load resources on the demand side of the power system can be regulated to provide flexible and adjustable capacity for the power system. Among the load resources on the demand side of the power system, air conditioning is a relatively important flexible load, because the power consumption of air conditioning accounts for nearly 50% of the total power consumption of the power system, and the adjustable capacity is huge; and the short-term regulation of air conditioning will not affect the normal production and life of users, and has broad application prospects. In order to utilize the air conditioning load resources, the power system dispatching department needs to evaluate the total adjustable capacity of the air conditioning load cluster before dispatching, so as to formulate a dispatching plan to ensure the safe and stable operation of the power system.

[0073] There are two main quantitative evaluation methods for the adjustable capacity of air-conditioning loads: the first is to collect the total power consumption of the air-conditioning load cluster, and directly control the air-conditioning load power consumption to achieve peak shaving and valley filling when the system is short of power. However, this method will undoubtedly have a great impact on the user's comfort; the second is to comprehensively collect the parameters of each air-conditioning load and the parameters of the room in which it is located, including air-conditioning operating power, air-conditioning energy efficiency ratio, indoor temperature, outdoor temperature, user-set temperature, user-allowed adjustable temperature, room specific heat capacity, room thermal resistance, etc. This method can ensure user comfort during the load control process by constructing a load control model. However, this method requires comprehensive and accurate collection of a large amount of data, especially parameters such as room specific heat capacity and room thermal resistance, which involve multi-dimensional factors such as building structure, materials, and door and window areas. There are no available sensors for direct collection of parameters such as room specific heat capacity and room thermal resistance, making the second method difficult to implement in actual power systems.

[0074] In order to solve the above problems, this application proposes a quantitative evaluation method for the adjustable capacity of air-conditioning loads. The solution proposed in this application is to collect simple and easy-to-obtain parameters (i.e., air-conditioning operating power, indoor temperature) in the face of an air-conditioning load cluster (assuming that the total amount of air-conditioning loads is N); then randomly select an air-conditioner from these N air-conditioning loads (the proportion of the total amount N can be only one percent or one thousandth), and the power company personnel will collect the specific heat capacity, room thermal resistance and other parameters of the room corresponding to this air-conditioner (this air-conditioner is called a "full-information air-conditioner", and other air-conditioners are called "non-full-information air-conditioners"). This application can use this full-information air-conditioner to quantitatively evaluate the total adjustable capacity of a cluster containing N air-conditioning loads. Therefore, the technology of this application can greatly reduce the number and difficulty of parameter collection, and the accuracy of the example evaluation reaches more than 98.5%, which fully meets the accuracy requirements of the power system for load control.

[0075] Next, an embodiment is used to specifically illustrate a method and device for quantitatively evaluating the adjustable capacity of air-conditioning loads of the present application.

[0076] Figure 1 A flowchart of a method for quantitatively evaluating the adjustable capacity of air conditioning load provided in an embodiment of the present application. Figure 1 As shown, a method for quantitatively evaluating the adjustable capacity of air conditioning load provided in an embodiment of the present application includes the following steps:

[0077] S101. Construct a multi-dimensional vector of full information air conditioning.

[0078] The full-information air conditioner includes an air conditioner that has acquired the specific heat capacity and room thermal resistance parameters of the room corresponding to the air conditioner.

[0079] Optionally, the calculation formula of the multi-dimensional vector of the full information air conditioner in this embodiment is:

[0080]

[0081] Wherein, x is the multidimensional vector of the full-information air conditioner, P is the operating power of the air conditioner, is the indoor temperature.

[0082] Based on the above formula, we can The set of full information air conditioners is expressed as:

[0083]

[0084] For any vector x in set X, the joint probability density function can be expressed as:

[0085]

[0086] Where d is the dimension of vector x; u is the dimensional mean vector of vector x, and D is the covariance matrix of vector x, expressed as:

[0087]

[0088] Based on the multidimensional joint probability density function, a Gaussian mixture model can be constructed, which is expressed as:

[0089]

[0090] in, is the Gaussian mixture component number, the initial value is set to 1; is the mixture coefficient corresponding to the kth Gaussian mixture component, and is the parameter corresponding to the kth Gaussian mixture component.

[0091] S102, executing a loop step: initializing the parameters of the expectation-maximization algorithm, and obtaining a full-information air-conditioning set according to the parameters of the expectation-maximization algorithm and the multi-dimensional vector calculation of the full-information air-conditioning.

[0092] In this embodiment, the parameters of the expectation maximization algorithm are initialized, including: initializing the number of iterations and expectation-maximization algorithm parameters.

[0093] S103, using latent variables to expand the full information conditioning set to obtain a complete data set.

[0094] In this embodiment, the use of latent variables to expand the full information conditioning set to obtain a complete data set includes: using K-dimensional latent variables Expand the full information air conditioning set, where the hidden variable It complies with the following formula:

[0095]

[0096] Wherein, K is the dimension of the latent variable.

[0097] Optionally, after obtaining the complete data set in step S103, the method further includes: calculating the prior probability and the log-likelihood function; and obtaining the parameters corresponding to the maximization of the log-likelihood function. The method for obtaining the parameters corresponding to the maximization of the log-likelihood function may be to obtain the expression of the log-likelihood function:

[0098]

[0099] The parameters corresponding to the maximization of the log-likelihood function are expressed as:

[0100]

[0101] The variables of the parameters corresponding to the maximum log-likelihood function Taking the derivative and equal to 0, it is expressed as:

[0102]

[0103] Find the first The optimal parameters corresponding to the iteration are compared with the The maximum value of the log-likelihood function corresponding to the jth iteration and the previous (jth) iteration, that is, and ,Will and If , then let , and obtain the parameters corresponding to the maximization of the log-likelihood function, where The optimal parameters corresponding to the iterations are:

[0104]

[0105] Among them, j is the number of iterations.

[0106] S104, calculating the log-likelihood function of the complete data set, and solving the parameters corresponding to the maximization of the log-likelihood function.

[0107] S105 . Calculate the Bayesian information of the complete data set according to the parameters corresponding to the maximization of the log-likelihood function.

[0108] It should be noted that Bayesian information is to use subjective probability to estimate some unknown states under incomplete intelligence, then use the Bayesian formula to correct the probability of occurrence, and finally use the expected value and corrected probability to make the optimal decision variable.

[0109] S106, determining whether the number of Gaussian mixture components in the Bayesian information volume reaches a preset value, if not, returning to execute the loop step, if reaching the preset value, executing the following steps.

[0110] S107. Using the parameters corresponding to the minimum value in the Bayesian information as the parameters of the Gaussian mixture model to obtain the Gaussian mixture model.

[0111] It should be noted that the Gaussian mixture model uses the Gaussian probability density function (normal distribution curve) to accurately quantify things. It is a model that decomposes things into several Gaussian probability density functions (normal distribution curves).

[0112] S108. Calculate the adjustable capacity of the non-full-information air conditioner and the total adjustable capacity of the air conditioning load cluster according to the Gaussian mixture model.

[0113] The non-full-information air conditioner includes air conditioners other than the full-information air conditioner, and the air conditioner load cluster includes clusters of non-full-information air conditioners and full-information air conditioners.

[0114] Specifically, this embodiment calculates the adjustable capacity of the non-full information air conditioner according to the Gaussian mixture model, and the calculation formula is:

[0115]

[0116] Among them, the vector is the multidimensional vector of non-full information air conditioning in the load cluster.

[0117] Optionally, before calculating the adjustable capacity of the non-full information conditioning using the Gaussian mixture model, the parameters based on the full information conditioning can also be calculated to calculate the adjustable capacity expectation of the th component in the Gaussian mixture model, and the formula is as follows:

[0118]

[0119] In the above formula, the vector is the full-information air conditioner in the load cluster, and the number is indivual.

[0120] Specifically, this embodiment calculates the total adjustable capacity of the air conditioning load cluster according to the Gaussian mixture model, and the calculation formula is:

[0121]

[0122] Wherein, P is the total adjustable capacity of the air conditioning load cluster.

[0123] The beneficial effects of the upgrade method of the elevator master control program provided by the embodiment of the present application are as follows: the present application uses a quantitative evaluation method and device for the adjustable capacity of air-conditioning load, utilizes data that is easy to collect and observe, greatly reduces the difficulty of the power system in collecting air-conditioning state parameters and room physical parameters, and realizes quantitative evaluation of the adjustable capacity of large-scale air-conditioning loads with high evaluation accuracy.

[0124] In order to better illustrate the technical effects and implementation methods of the present application, a quantitative evaluation method for the adjustable capacity of air-conditioning load proposed in the present application is applied to an actual project test of a power system, as follows:

[0125] Figure 2 This is a temperature diagram provided in the embodiment of the present application. Figure 2 As shown, the minimum outdoor temperature is 29 degrees Celsius, the maximum outdoor temperature is 39 degrees Celsius, and the outdoor temperature changes with time.

[0126] Figure 3 The air conditioner provided in the embodiment of the present application complies with the total power line graph of cluster operation. Figure 5 As shown in the figure, the number of air-conditioning loads N is 100,000 units, and the user-set temperature values ​​are distributed between 18 and 27°C. Before the air-conditioning load is adjusted, the indoor temperature of the room where the air-conditioning is located is approximately equal to the set temperature. The user allows the adjustable temperature to be ±2°C of the set temperature value, and the operating power of the individual air-conditioning loads is distributed between 0 and 7 kilowatts.

[0127] Full information air conditioner quantity For 1,000 units, the energy efficiency ratio of full information air conditioners Distributed between 2.6 and 3.6, the specific heat capacity of the room where the full-information air conditioner is located The thermal resistance of the room where the full-information air conditioner is located is distributed between 30 and 300 kJ / ℃ Distributed between 4.4~83.3℃ / kW.

[0128] Adjust start time , adjust the end time , evaluate the temperature deviation allowed by the user The adjustable capacity of the air conditioning load cluster within the range. At 12:30, the multidimensional vector of these 1,000 air conditioners It can be expressed as Figure 4 The two-dimensional space points shown.

[0129] The above completes parameter collection. The following is the evaluation result of S308-S318.

[0130] like Figure 5 As shown in the figure, the minimum value of Bayesian information BIC appears at 3, and the corresponding parameter K=3 is used as the final number of Gaussian mixture components.

[0131] The corresponding Gaussian mixture model of full information air conditioning is as follows Figure 6 shown. Figure 6 The adjustable capabilities of the three components in the Gaussian mixture model are expected to be kilowatt, kilowatt, Based on this, the total adjustable capacity of the air conditioning load cluster is 31.20 MW, and the actual total adjustable capacity is 31.68 MW. The evaluation accuracy of this method is as high as 98.48%.

[0132] Through the above-mentioned renderings and actual data, it can be seen that the quantitative evaluation method for the adjustable capacity of air-conditioning load proposed in this application can greatly reduce the difficulty of the power system in collecting air-conditioning status parameters and room physical parameters, and realize the quantitative evaluation of the adjustable capacity of large-scale air-conditioning loads with high evaluation accuracy.

[0133] In addition, the present application also proposes two devices for executing a quantitative evaluation method of the adjustable capacity of air-conditioning load described in this embodiment.

[0134] Figure 7 This is a schematic diagram of the structure of the load cell control device provided in the embodiment of the present application. Figure 7 As shown, the load cell control device comprises:

[0135] System power supply module, used to supply power to the whole device through the switch; current sensing and signal input 1 module, used to input the measured air-conditioning load current parameter value into the load single control device; voltage sensing and signal input 2 module, used to input the measured air-conditioning load voltage parameter value into the load single control device; remote signal and photoelectric isolation 1 module, used to receive signal instructions from the load cluster control device and send them to the load single control device; indoor temperature acquisition module, used to input the real-time temperature value in the room into the load single control device; input keyboard panel and set temperature input module, used to input the user set temperature value and the user allowed adjustable temperature into the load single control device; control output and photoelectric isolation 2 module, used to send the control signal issued by the load single control device to the air-conditioning load device; data storage module, used to store historical data, store a set of data every preset time, the stored data includes indoor temperature, user set temperature value, user allowed adjustable temperature, air-conditioning load current value, air-conditioning load voltage value, air-conditioning load power value; display module, used to display the current indoor temperature, user set temperature value, user allowed adjustable temperature, air-conditioning load current value, air-conditioning load voltage value, air-conditioning load power value.

[0136] Figure 8 This is a schematic diagram of the structure of the load cluster control device provided in the embodiment of the present application. Figure 8 As shown, the load cluster control device includes: a system power supply for supplying power to the entire device through a switch; a display module for displaying the total number of air conditioners in the load cluster, the total power value of the air conditioners, the total adjustable capacity of the air conditioners, and the ambient temperature value; an ambient temperature acquisition module for inputting the real-time temperature value of the outdoor environment into the load cluster control device; an input keyboard panel and a load cluster information input module for inputting the total number of air conditioners in the load cluster, the number of full-information air conditioners, the energy efficiency ratio of the full-information air conditioners, the specific heat capacity of the room where the full-information air conditioners are located, and the thermal resistance of the room where the full-information air conditioners are located into the load cluster control device; a data storage module for storing historical data, storing a set of data at preset intervals, and the stored data includes the total number of air conditioners in the load cluster, the total power value of the air conditioners, the total adjustable capacity of the air conditioners, and the ambient temperature value; the load cluster control device receives data from the load single control device through the remote signal and the photoelectric isolation module, and the load cluster control device sends a signal instruction to the load single control device through the remote signal and the photoelectric isolation module.

[0137] It should be noted that the specific operations of the above-mentioned load single control device and load cluster control device in executing the method proposed in this embodiment are as follows:

[0138] Step 1: If the total amount of air conditioning load involved in power system regulation is N, then install load cell controllers for the rooms where these N air conditioners are located, and install load cluster controllers for the air conditioning load dispatching department. Turn on the switches of the load cluster controller and all load cell controllers to power all controllers.

[0139] Step 2: Input the total number of air conditioners N and the number of air conditioners with full information in the load cluster through the input keyboard panel of the load cluster controller and the load cluster information input module. , Full information air conditioning energy efficiency ratio , Specific heat capacity of the room where the full information air conditioner is located , Thermal resistance of the room where the full-information air conditioner is located , Adjust start time , Adjust end time The above data is stored in the data storage module and displayed in the display module.

[0140] Step 3: Obtain the outdoor ambient temperature through the ambient temperature acquisition module of the load cluster controller , and stored in the data storage module and displayed in the display module.

[0141] Step 4: Input the user set temperature value through the input keyboard panel and set temperature input module of the load cell controller , User-adjustable temperature The above data is stored in the data storage module and displayed in the display module.

[0142] Step 5: When it is necessary to quantitatively evaluate the adjustable capacity of the air-conditioning load cluster, the load cluster controller will send a data request to the load cell controller through the photoelectric isolation and remote signal module.

[0143] Step 6: The load single controller receives the data request from the load cluster controller through the remote signal and the photoelectric isolation 1 module, and then measures the air conditioning operating parameters and room environment parameters corresponding to the load single controller. Specifically, the load single controller measures the air conditioning operating current through the current sensing and signal input 1 module. , measure the air conditioner operating voltage through the voltage sensing and signal input 2 modules , and using the formula Get the operating power of the air conditioner i connected to this load unit controller ;Measure the indoor temperature of the room where the air conditioner is located through the indoor temperature acquisition module After the load single controller completes the measurement of the air conditioner operating parameters and room environment parameters, it sends the data to the load cluster controller through the photoelectric isolation 1 and the remote signal module. The data sent includes: air conditioner operating power , the indoor temperature of the room where the air conditioner is located , User set temperature value , User-adjustable temperature .

[0144] Step 7: After receiving the data sent by all load cell controllers through the remote signal and the photoelectric isolation module, the load cluster controller starts to evaluate the adjustable capacity of the air conditioning load cluster according to the method described in the previous embodiment.

[0145] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the application is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part described as a larger operation is performed independently.

[0146] In addition, although the present application is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present application. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional techniques of the engineer. Therefore, those skilled in the art can implement the present application set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the attached claims and their equivalents.

[0147] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0148] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0149] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or processing in another suitable manner as necessary, and then stored in a computer memory.

[0150] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0151] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0152] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the claims and their equivalents.

[0153] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A quantitative evaluation method for the adjustable capacity of air conditioning load, characterized in that: The method comprises: Constructing a multidimensional vector of a full-information air conditioner, wherein the full-information air conditioner includes an air conditioner for which specific heat capacity and room thermal resistance parameters of a room corresponding to the air conditioner have been obtained; Execute the loop step: initialize the parameters of the expectation maximization algorithm, and obtain the full information air conditioning set according to the parameters of the expectation maximization algorithm and the multi-dimensional vector calculation of the full information air conditioning; Expanding the full information conditioning set using latent variables to obtain a complete data set; Calculating the log-likelihood function of the complete data set, and solving the parameters corresponding to the maximization of the log-likelihood function; Solving the Bayesian information of the complete data set according to the parameters corresponding to the maximization of the log-likelihood function; Determine whether the number of Gaussian mixture components in the Bayesian information reaches a preset value. If not, return to execute the loop step. If it reaches the preset value, perform the following steps: use the parameter corresponding to the minimum value in the Bayesian information as the parameter of the Gaussian mixture model to obtain the Gaussian mixture model; The adjustable capacity of the non-full-information air conditioner and the total adjustable capacity of the air conditioning load cluster are calculated according to the Gaussian mixture model, wherein the non-full-information air conditioner includes air conditioners other than the full-information air conditioner, and the air conditioning load cluster includes clusters of non-full-information air conditioners and full-information air conditioners.

2. The method for quantitatively evaluating the adjustable capacity of air conditioning load according to claim 1, characterized in that: The parameters of the initialization expectation maximization algorithm include: initialization number of iterations and expectation-maximization algorithm parameters.

3. The method for quantitatively evaluating the adjustable capacity of air conditioning load according to claim 1, characterized in that: The calculation formula of the multidimensional vector of the full information air conditioner is: ， in, i is the air conditioning load number, is the multidimensional vector of the full information air conditioning, I represents the initial moment, For air conditioning i The operating power at the initial moment, is the outdoor ambient temperature, is the room temperature at the initial moment, T is the transpose symbol of the matrix.

4. The method for quantitatively evaluating the adjustable capacity of air conditioning load according to claim 1, characterized in that: The method of using hidden variables to expand the full information conditioning set to obtain a complete data set includes: using K-dimensional hidden variables Expand the full information air conditioning set, where the hidden variable It complies with the following formula: ， Here, K is the number of Gaussian mixture components, that is, the dimension of the latent variable.

5. The method for quantitatively evaluating the adjustable capacity of air conditioning load according to claim 1, characterized in that: After obtaining the complete data set, the method further includes: Calculate prior probability and the log-likelihood function L; Obtain the parameters corresponding to the maximization of the log-likelihood function.

6. A quantitative evaluation method for adjustable air conditioning load capacity according to claim 5, characterized in that: The obtaining of the parameters corresponding to the maximization of the log-likelihood function includes: Get the expression of the log-likelihood function: ， Wherein, K is the number of Gaussian mixture components, is the number of all-information air conditioner sets, is the mixing coefficient corresponding to the kth Gaussian mixture component, and is the parameter corresponding to the kth Gaussian mixture component; The parameters corresponding to the maximization of the log-likelihood function are expressed as: ， The variables of the parameters corresponding to the maximum log-likelihood function Taking the derivative and equal to 0, it is expressed as: ， Find the first The optimal parameters corresponding to the iteration are compared with the The maximum value of the log-likelihood function corresponding to the jth iteration and the jth iteration, that is, and ,Will and If , then let , and obtain the parameters corresponding to the maximization of the log-likelihood function.

7. The method for quantitatively evaluating the adjustable capacity of air conditioning load according to claim 1, characterized in that: The adjustable capacity of the non-full information air conditioner is calculated according to the Gaussian mixture model, and the calculation formula is: ， Among them, the i is the air conditioning load number, represents non-full information conditioning, K is the dimension of the latent variable, and the vector is the multidimensional vector of non-full information air conditioning in the load cluster, For vector The probability of belonging to the probability density function of the kth component in the Gaussian mixture model, is the expected adjustable capacity of the full information conditioning of the kth component in the Gaussian mixture model.

8. The method for quantitatively evaluating the adjustable capacity of air conditioning load according to claim 1, characterized in that: The total adjustable capacity of the air conditioning load cluster is calculated according to the Gaussian mixture model, and the calculation formula is: ， Among them, the is the total adjustable capacity of the air conditioning load cluster, is the number of all-information air conditioner sets, is the number of non-full information conditioning sets, The adjustable capability of the air conditioner i in the full information air conditioner set, It is the adjustable capacity of air conditioner i in the non-full information air conditioner set.

9. A load unit control device, used to execute the method for quantitatively evaluating the adjustable capacity of air-conditioning loads as claimed in claim 1, characterized in that: The device comprises: a system power supply module, used to supply power to the entire device via a switch; A current sensing and signal input 1 module is used to input the measured air conditioning load current parameter value into the load cell control device; The voltage sensing and signal input 2 module is used to input the measured air conditioning load voltage parameter value into the load cell control device; Remote signal and photoelectric isolation 1 module, used to receive signal instructions from the load cluster control device and send them to the load single unit control device; Indoor temperature acquisition module, used to input the real-time temperature value in the room into the load unit control device; The input keyboard panel and the set temperature input module are used to input the user set temperature value and the user allowed adjustable temperature into the load unit control device; Control output and photoelectric isolation 2 module, used to send the control signal from the load unit control device to the air conditioning load equipment; A data storage module is used to store historical data, and stores a set of data at preset intervals. The stored data includes indoor temperature, user-set temperature value, user-allowed adjustable temperature, air-conditioning load current value, air-conditioning load voltage value, and air-conditioning load power value; The display module is used to display the current indoor temperature, the user-set temperature value, the user-allowed adjustable temperature, the air-conditioning load current value, the air-conditioning load voltage value, and the air-conditioning load power value.

10. A load cluster control device, characterized in that: Used to perform a quantitative evaluation method for the adjustable capacity of air-conditioning load as claimed in claim 1, the device comprising: a system power supply for supplying power to the entire device via a switch; Display module, used to display the total number of air conditioners in the load cluster, the total power value of the air conditioners, the total adjustable capacity of the air conditioners, and the ambient temperature value; Ambient temperature acquisition module, used to input the real-time temperature value of the outdoor environment into the load cluster control device; The input keyboard panel and the load cluster information input module are used to input the total number of air conditioners in the load cluster, the number of full-information air conditioners, the energy efficiency ratio of the full-information air conditioners, the specific heat capacity of the room where the full-information air conditioners are located, and the thermal resistance of the room where the full-information air conditioners are located into the load cluster control device; A data storage module is used to store historical data. A set of data is stored at a preset time interval. The stored data includes the total number of air conditioners in the load cluster, the total power value of the air conditioners, the total adjustable capacity of the air conditioners, and the ambient temperature value; The load cluster control device receives data from the load single control device through the remote signal and the photoelectric isolation module, and the load cluster control device sends a signal instruction to the load single control device through the remote signal and the photoelectric isolation module.

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