Air conditioning control method based on virtual power plant

Abstract

The present invention discloses an air conditioning control method based on a virtual power plant. By calculating the cumulative value of the suitability of the currently verified multi-type power distribution mode for each air conditioner in the target control load area pointed to by the data obtained in step L1, and taking the mode with the maximum cumulative value of suitability as the current proposed mode for the target control load area and executing it, virtualized optimization control of multi-type power inputs in the load area is achieved. The control process takes into account the actual situation of the currently available resources on the input side, as well as the influence of different multi-type power distribution available modes on the input side on the flexibility of control of air conditioners with different working conditions and different performances on the load side, so that the scheduling of multi-type power resources on the input side is more reasonable, and the flexible control on the load side is more accurate and imperceptible.

Description

Technical Field

[0001] The present invention relates to the technical field of energy scheduling and air conditioning control, and in particular to an air conditioning control method based on a virtual power plant. Background Art

[0002] A virtual power plant is an advanced energy management system that integrates decentralized power generation resources (such as solar energy, wind energy), energy storage systems, controllable loads, industrial equipment, etc. through information and communication technologies and software systems to form a virtualized power production and dispatching system. These decentralized energy resources and loads are coordinated and managed through the Internet, intelligent control and data analysis technologies to achieve more efficient energy production, distribution and consumption.

[0003] In the flexible control scenario with air conditioners as loads, it is expected that within a specified period of time, the air conditioner temperature can be adjusted "without feeling" in a short period of time with the purpose of reducing or accurately controlling the power consumption of the air conditioner. However, in this scenario, the current focus is on how to achieve precise flexible control of the air conditioner at the load end, such as how to achieve precise flexible control of air conditioners with different working conditions and different performances under the same type of multi-power input, but the multi-type power supply itself is not considered, such as the influence of different input proportions of each type of power supply on the precision of flexible control of air conditioners with different working conditions and different performances. Therefore, only measures are taken at the load end, while the influencing factor of the multi-type power supply itself at the input end is ignored, resulting in the current inaccurate flexible control of air conditioners, which has room for improvement. Summary of the invention

[0004] The present invention provides an air conditioning control method based on a virtual power plant, which takes into account the influence of multiple types of power supplies at the input end on the flexible adjustment accuracy of each air conditioner in different working conditions and with different performances, and improves the flexible control accuracy of the same air conditioner under different types of power input conditions. To achieve this purpose, the present invention adopts the following technical solutions:

[0005] A method for controlling an air conditioner based on a virtual power plant is provided, the steps comprising:

[0006] L1, after the monitoring and automatic control module of the virtual power plant monitors and parses the flexible control requirements, it uses the flexible control requirements as acquisition conditions to obtain the data required for control, including historical flexible control basis data and historical distribution data of multiple types of power sources;

[0007] L2, calculate the available multi-type power distribution modes for the target control load area, and then verify the applicability of each mode to the historical target actual control temperature of each air conditioner pointed to by the data obtained in step L1, and use the mode with the maximum applicability accumulated value for the target control load area where the air conditioner is located as the proposed mode and execute it.

[0008] Preferably, in step L1, the method for acquiring data required for management and control comprises the steps of:

[0009] L11, selecting the air conditioners having the same flexible control requirements from the historical control record table;

[0010] L12, matching the Bloom filter forward nodes bound to the load area where each of the screened air conditioners is located, and then further matching the Bloom filter reverse nodes associated with the forward nodes according to the forward and reverse relationships of the nodes;

[0011] L13, obtaining the historical flexible control basis data generated during the historical control period for the air conditioner screened in step L11 under the flexible control requirements from the forward node matched in step L12, and obtaining the multi-type power supply historical distribution data for regulating the load area where the air conditioner is located during the same historical control period from the reverse node having a positive and negative relationship with the forward node.

[0012] Preferably, the historical flexible control basis data includes: load characteristic data collected and calculated by polling, response feedback data, and historical target actual control temperature calculated by approximation analysis during the historical control period with the same flexible control requirement parsed in step L1, taking into account the working conditions and performance of the air conditioner itself;

[0013] The multi-type power supply historical allocation data includes: a historical power supply proportion characteristic value of each power supply among the multi-type power supplies allocated to the load area where the air conditioner as the control object is located during the historical control period with the flexible control requirement;

[0014] The historical flexible control basis data generated during each historical control period of performing flexible control on the air conditioner is stored as a separate data set at the forward node of the Bloom filter bound to the load area where the air conditioner is located;

[0015] The monitoring and automatic control module monitors a corresponding load area.

[0016] Preferably, the method for approximating and analyzing and calculating the actual controlled temperature of the historical target comprises the steps of:

[0017] S1, after obtaining or issuing the target temperature for flexible regulation of the specified air conditioner, determine the temperature range for flexible regulation in the current stage, and then poll, collect and calculate the load characteristic data and response feedback data of the specified air conditioner according to the designed data feature amplification structure;

[0018] S2, based on the data collected and calculated in step S1, draw a second fitting curve between the wind speed prediction value and the power fluctuation value to find the power abnormal fluctuation point;

[0019] S3, determining whether the number of the power abnormal fluctuation points found during the load characteristic data collection period according to the temperature range is greater than a preset number threshold,

[0020] If yes, in the flexible control of the designated air conditioner in the next stage which is continuous with the current stage, the range of the temperature interval is narrowed, and the power adjustment amplitude of the designated air conditioner per unit time is reduced, and then the process goes to step S4;

[0021] If not, continue to flexibly adjust the designated air conditioner according to the width of the temperature interval determined in step S1;

[0022] S4, determining whether the reduced width of the temperature interval is smaller than a preset width threshold,

[0023] If so, the upper limit value of the temperature range narrowed down for the next stage is used as the target actual control temperature;

[0024] If not, after the flexible control enters the next stage, the next stage is used as the current stage in step S1, and then the process returns to step S1.

[0025] Preferably, in step S1, the load characteristic data collected by polling includes: a power fluctuation value collected at each wind speed prediction time point in each polling period under the temperature range;

[0026] The response feedback data collected and calculated by polling include: the sudden change of wind pressure at the air outlet of each air duct section during each polling period, the air density detected by each air duct section air density sensor in the multi-stage air duct section air density sensor in the pipe, and the predicted wind speed value of the initial air outlet of the air conditioner at each air outlet wind speed prediction time point in each polling;

[0027] Each of the outlet wind speed prediction time points in one polling is the moment when the wind pressure sensor arranged at the air outlet of each air duct section in the data feature amplification structure detects a sudden change in wind pressure.

[0028] Preferably, in step S2, the method for finding the abnormal power fluctuation point comprises the steps of:

[0029] C1, extracting the wind speed prediction abnormal value from each wind speed prediction value calculated in each polling under the temperature range and adding it to the wind speed abnormal value set, and then extracting the element value from the wind speed abnormal value set;

[0030] C2, using the element value extracted in step C1 as the independent variable of a second fitting function for fitting the second fitting curve, and solving the y value of the second fitting function;

[0031] C3, judging whether the y value deviates from the maximum allowable value of power fluctuation of the corresponding polling,

[0032] If yes, the number of power abnormal fluctuation points of the flexible control for the designated air conditioner is counted by "1";

[0033] If not, go to step C4;

[0034] C4, judging whether the wind speed abnormal value set has been extracted as an "empty set",

[0035] If so, the output power abnormal fluctuation point counting result is obtained;

[0036] If not, return to step C1.

[0037] Preferably, in step C3, the method for determining whether the y value deviates from the maximum allowable value of power fluctuation of the corresponding secondary polling comprises the steps of:

[0038] C31, obtaining the air conditioning power of the designated air conditioner collected each time in the same polling as the element value used to solve the y value;

[0039] C32, calculating the difference between the maximum air conditioning power and the minimum air conditioning power obtained in step C31 as the maximum allowable value of the power fluctuation as a basis for deviation judgment;

[0040] C33, judging whether the absolute value of the difference between the y value and the maximum allowable value of power fluctuation is greater than a preset threshold,

[0041] If yes, it is determined that the y value deviates from the maximum allowable value of power fluctuation;

[0042] If not, go to step C4.

[0043] Preferably, under the "if" judgment condition in step S3, the method for reducing the range according to the temperature interval and reducing the power adjustment amplitude comprises the steps of:

[0044] S31, calculating the ratio of the number of the power abnormal fluctuation points found to the number of the wind speed prediction values ​​remaining after extracting the wind speed prediction abnormal values ​​when drawing the second fitting curve as a correction coefficient ;

[0045] S32, calculating the increment of the temperature range or the power adjustment range in the next stage ;

[0046] S33, taking the upper limit of the temperature interval of the current stage as the lower limit of the temperature interval of the next stage, and using the lower limit and the increment representing the temperature interval The sum of the values ​​is the upper limit of the temperature range for the next stage to determine the temperature range for the next stage, or the power adjustment range is represented by As the power adjustment range for the designated air conditioner in the next stage.

[0047] Preferably, the historical power supply proportion characteristic value of each of the multiple types of power supplies is: the historical power supply proportion of each of the multiple types of power supplies allocated to the load area where the regulated object is located during the historical regulation period and the influence weight of each power supply on the flexibility of regulation of the regulated object.

[0048] Preferably, in step L2, the method for verifying the applicability of each mode to the historical target actual control temperature of the air conditioner comprises the steps of:

[0049] L21, using the calculated power supply proportion of each type of power source recorded in the mode currently available in the target control load area as the dependent variable of the sub-curve fitting function for fitting the corresponding sub-curve in the first fitting curve corresponding to the air conditioner, and reversely solving the independent variable of each sub-curve fitting function;

[0050] L22, performing weighted summation on the values ​​of different independent variables solved for the same air conditioner with corresponding influence weights, and using the weighted summation value as the historical target predicted control temperature for controlling the air conditioner using the currently verified mode;

[0051] L23, searching for the multi-type power supply historical allocation data for the load area where the air conditioner is located that has the minimum mode deviation from the currently verified mode, and then obtaining the historical target actual control temperature of the air conditioner under the conditions of the multi-type power supply historical allocation data found;

[0052] L24, judging whether the second absolute value of the difference between the historical target predicted control temperature calculated in step L22 and the historical target actual control temperature found in step L23 is less than a preset second difference absolute value threshold,

[0053] If yes, it is determined that the currently verified mode is applicable to the air conditioner, and "1" is accumulated to the applicability value of the currently verified mode for the target control load area where the air conditioner is located;

[0054] If not, it is determined that the currently verified mode is not applicable to the air conditioner.

[0055] The present invention has the following beneficial effects:

[0056] 1. By verifying the applicability of each available multi-type power distribution mode in the target control load area to the historical target actual control temperature of the control object (air conditioner), the most suitable multi-type power distribution mode for precise and flexible regulation of each air conditioner in the target control load area is screened out and executed. The influence of the multi-type power supply at the input end on the flexible regulation accuracy of each air conditioner in different working conditions and different performance is taken into account, so that the flexible regulation of each air conditioner in the target control load area is more accurate.

[0057] 2. By constructing a Bloom filter forward node separately for each load area, and constructing a Bloom filter reverse node with a positive and negative relationship with the forward node based on the monitoring relationship between the monitoring and automatic control module and the load area, the historical flexible control generated by the control object in the load area during each historical control period is based on the data and the module. The historical distribution data of multiple types of power sources generated by the load area during each historical control period are associated with each other, which makes it convenient for the module to quickly obtain the data required for the control based on the virtual power plant based on the flexible control requirements monitored and parsed, thereby greatly improving the efficiency of control.

[0058] 3. By calculating the cumulative value of the suitability of the currently verified multi-type power distribution mode for each air conditioner in the target control load area pointed to by the data obtained in step L1, and taking the mode with the maximum cumulative value of suitability as the current proposed mode for the target control load area and executing it, the one-to-one correspondence between the monitoring and automation control module and the load area is realized to realize the virtual optimization control of the multi-type power input of the load area. The control process takes into account the actual situation of the currently available resources on the input side, and the influence of different multi-type power distribution modes on the input side on the flexibility of the control of air conditioners with different working conditions and different performances on the load side, so that the scheduling of multi-type power resources on the input side is more reasonable, and the flexible control of the load side is more precise and imperceptible.

[0059] 4. By analyzing the flexible control process data of the previous stage of the specified air conditioner under different working conditions and different performances, the feature amplification of the abnormal data points of the previous stage of flexible control is realized by using the data feature amplification structure; by drawing the second fitting curve of the wind speed prediction value and the power fluctuation value, the abnormal power fluctuation point in the previous stage of flexible control is quickly and accurately found; then The calculation is used to adjust the temperature range and power adjustment range of the next stage of flexible control, so that the flexible control of the specified air conditioner in the next stage is more flexible and less noticeable.

[0060] 5. By finding the points of abnormal power fluctuation, it is verified whether the abnormal wind speed forecast values ​​found in each polling period under the temperature range are caused by the power fluctuation of flexible control in the corresponding polling, which reduces the possibility of errors in the adjustment of flexible control strategies and avoids unnecessary adjustments of flexible control strategies.

[0061] 6. When it is determined that the flexible control of the specified air conditioner is abnormal at the current stage, the calculation As an update result of the increment of the temperature range or the power adjustment range for flexible control of the designated air conditioner in the next stage, the flexible control strategy of the designated air conditioner in the next stage is adjusted, making the flexible control of the designated air conditioner more flexible and less noticeable. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0063] Figure 1 is a diagram of implementation steps of an air conditioning control method based on a virtual power plant provided in an embodiment of the present invention;

[0064] Figure 2 is a schematic diagram of the data feature amplification structure designed in this embodiment;

[0065] Figure 3 It is a point map of the predicted wind speed value of the initial air outlet of the air conditioner predicted at the predicted time point of the air outlet wind speed of each level of the air duct section in a number of polls within a temperature range in the same xy axis plane coordinate system;

[0066] Figure 4 is an example diagram of the second fitting curve of wind speed prediction value and power fluctuation value;

[0067] Figure 5 It is a structural block diagram of energy dispatch management of virtual power plants. DETAILED DESCRIPTION

[0068] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0069] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on this patent; in order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0070] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "inner", "outer", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as a limitation on this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0071] In the description of the present invention, unless otherwise clearly specified and limited, if the term "connection" or the like appears to indicate the connection relationship between components, the term should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two components or the interaction relationship between two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0072] The air conditioning control method based on the virtual power plant provided in the embodiment of the present invention is as follows: Figure 1 As shown, the steps include:

[0073] L1, after the monitoring and automatic control module of the virtual power plant monitors and analyzes the flexible control requirements, it uses the analyzed flexible control requirements as the acquisition conditions to obtain the data required for control, including historical flexible control basis data and historical distribution data of multiple types of power sources;

[0074] In this embodiment, Figure 5As shown in , a separate monitoring and automatic control module is used to monitor the flexible control instructions issued by the system for the control object in the corresponding load area, that is, each load area has an independent monitoring and automatic control module. The flexible control instruction contains flexible control requirements for the control object. The flexible control requirement is, for example, to adjust the temperature of a specified air conditioner working in a specific working condition and having specific performance as the control object from 23°C to 26°C. After the monitoring and automatic control module monitors the flexible control instruction issued by the system to a specified air conditioner in the corresponding load area it is responsible for monitoring, it parses the content of the flexible control instruction. For example, after parsing the flexible control requirement for the air conditioner a in the A load area to be adjusted from the current 23°C to 26°C, it starts to obtain the data required for the air conditioning control based on the virtual power plant provided in this embodiment, including historical flexible control basis data and multi-type power supply historical allocation data with the same flexible control requirements as parsed.

[0075] The following is a detailed description of the method for obtaining the data required for control in step L1:

[0076] The method for obtaining the data required for control includes the following steps:

[0077] L11, select the air conditioners with the same flexible control requirements from the historical control record table;

[0078] For example, the historical control record table records: Figure 5 The air conditioner 1 in the load area A shown in FIG has been regulated twice with the same flexible regulation requirement parsed in step L1, and the air conditioner 3 has been regulated once with the same flexible regulation requirement parsed in step L1, and Figure 5 If the air conditioner 6 in the load area B has been controlled three times with the same flexible control requirement analyzed in step L1, then the air conditioners 1, 3, and 6 are selected from the historical control record table as the data acquisition objects required for control.

[0079] L12, matches the Bloom filter forward node bound to the load area where each air conditioner is located, and then further matches the Bloom filter reverse node associated with the forward node according to the forward and reverse relationship of the node;

[0080] It should be noted here that Figure 5Each load area shown in is bound to a corresponding forward node in the Bloom filter, and the monitoring and automatic control module for monitoring the load area is bound to a corresponding reverse node in the Bloom filter, and there is a positive and reverse relationship between the two nodes. Step L11, after filtering out the air conditioners with the same flexible control requirements from the historical control record table, according to the attribution relationship between the air conditioner and the load area where it is located, it is possible to quickly identify which load area the air conditioner is located in. For example, the attribution relationship can be represented by a code, such as qyA001, where "001" represents the air conditioner numbered 001, and "qyA" represents load area A. After the monitoring and automatic control module parses that the object to be flexibly controlled is the air conditioner numbered 001, it can quickly match the load area where the air conditioner is located as "qyA", that is, load area A, according to its unique attribution relationship, that is, "qyA001". Then, according to the binding relationship between the load area and the forward node of the Bloom filter, its unique forward node can be quickly matched. The matching method can refer to the matching method of the attribution relationship, which will not be repeated. Then, according to the forward and reverse relationships between the forward nodes and the reverse nodes, the reverse nodes associated with the forward nodes bound to the load area can be further matched. The matching method can also refer to the matching method of the attribution relationship, but a different encoding method is used, which will not be repeated here.

[0081] It should be noted here that the purpose of binding the corresponding Bloom filter forward node to each load area is to provide the same flexible control basis data storage space for each control object in the same load area; the purpose of binding the reverse node to the forward node is to provide independent data storage space for the monitoring and automatic control module for the load area. Then, using the point mapping function of the Bloom filter, the forward and reverse relationships between the forward nodes and the reverse nodes are constructed, and the historical flexible control basis data generated by the control objects in the load area during each historical control period and the historical distribution data of multiple types of power sources generated by the module during each historical control period are related, so that the module can quickly obtain the data required for the control based on the virtual power plant based on the flexible control requirements monitored and parsed, which greatly improves the efficiency of control.

[0082] L13, obtain the historical flexible control basis data generated during the historical control period for the air conditioner screened in step L11 under the same flexible control requirements analyzed in step L1 from the forward node matched in step L12, and obtain the multi-type power supply historical distribution data for regulating the load area where the air conditioner is located during the same historical control period from the matched reverse node having a positive and reverse relationship with the forward node.

[0083] For example, the above-mentioned air conditioners 1, 3, and 6 are regulated 2 times, 1 time, and 3 times respectively according to the same flexible control requirement history parsed in step L1. Since air conditioners 1 and 3 are in load area A, assuming that the forward node of the Bloom filter bound to load area A is JA, the historical flexible control basis data generated by air conditioners 1 and 3 during each historical control period under the flexible control requirement is obtained from the forward node JA. Among them, air conditioner 1 is regulated 2 times under the flexible control requirement, so the historical flexible control basis data generated by air conditioner 1 during the two historical control periods are obtained from the forward node JA. Similarly, the historical flexible control basis data generated by air conditioner 3 during the historical control period under the flexible control requirement is obtained from the forward node JA. Air conditioner 6 is in load area B. Assuming that the forward node of the Bloom filter bound to load area B is JB, the historical flexible control basis data generated by air conditioner 6 during each of the three historical control periods under the flexible control requirement is obtained from the forward node JB.

[0084] In addition, assuming that the reverse node having a positive and negative relationship with the forward node JA is J'-A, the multi-type power supply historical distribution data for regulating the load area A during each historical regulation of the air conditioners 1 and 3 is obtained from J'-A. Assuming that the reverse node having a positive and negative relationship with the forward node JB is J'-B, the multi-type power supply historical distribution data for regulating the load area B during each historical regulation of the air conditioner 6 is obtained from J'-B.

[0085] The following is an explanation of the data content of historical flexible control basis data and multi-type power supply historical allocation data:

[0086] In this embodiment, the historical flexible control basis data includes: taking into account the working conditions and performance of the air conditioner itself, during the historical control period with the same flexible control requirements parsed in step L1, the load characteristic data and response feedback data are collected and calculated by polling, and the historical target actual control temperature is calculated by approximation analysis;

[0087] The historical allocation data of multiple types of power sources include: the historical power supply proportion characteristic value of each type of power source among the multiple types of power sources allocated to the load area where the control object is located during the historical control period with the same flexible control requirement parsed in step L1.

[0088] In order to facilitate the accurate acquisition of data required for control, in this embodiment, the historical flexible control basis data generated during each historical control period when the air conditioner performs flexible control is stored as a separate data set at the forward node of the Bloom filter bound to the load area where the air conditioner is located, that is, multiple data sets are stored at each forward node, and each data set stores the historical flexible control basis data generated by a specified air conditioner in the load area bound to the forward node during the corresponding historical control period.

[0089] The following specifically describes the calculation method of the historical target actual control temperature as the historical flexible control basis data and the historical power supply proportion characteristic value of each type of power supply in the multi-type power supply as the historical allocation data of the multi-type power supply.

[0090] First, the calculation method of the actual control temperature of the historical target is explained, which specifically includes the following steps:

[0091] S1, after obtaining or issuing the target temperature for flexible regulation of the specified air conditioner, determine the temperature range based on the current stage of flexible regulation, and then Figure 2 The data feature amplification structure of the example polls and collects and calculates the load feature data and response feedback data of the specified air conditioner;

[0092] It should be noted here that there are two methods to determine the target temperature for the specified air conditioner, one is direct acquisition, and the other is direct issuance. Direct issuance means that the control terminal directly sends the target temperature to the specified air conditioner, such as instructing the controlled specified air conditioners to uniformly adjust the target temperature of flexible regulation to 26°C. Direct acquisition means that in each flexible regulation of the specified air conditioner, according to the response feedback data of the specified air conditioner to each regulation, an actual target temperature that meets the flexible regulation requirements is calculated. This actual target temperature takes into account the different control response feedback conditions of the specified air conditioners with different working conditions and different performances to the same flexible regulation requirements, that is, the actual target temperatures of different specified air conditioners with different working conditions and different performances are different under the same flexible regulation requirements. For example, there are currently 3 specified air conditioners 1-3 with different working conditions and different performances. The current temperatures of the 3 air conditioners are assumed to be 23°C, and the initial target temperatures set by the control terminal for the 3 air conditioners are all 26°C. However, suppose that in the process of flexibly regulating the designated air conditioner 1 from 23°C to 26°C, and in the process of continuing to regulate from 25°C to 26°C, a large number of or serious abnormalities appear in the response feedback data of the regulation, and the "insensitive" regulation in the flexible regulation requirement can no longer be met. At this time, for the designated air conditioner 1, 25°C is more suitable as the actual regulation target temperature for flexibly regulating it from 23°C and taking 26°C as the initial regulation target temperature. The actual regulation target temperature of 25°C here is the regulation target temperature that the designated air conditioner 1 needs to obtain under the working condition and performance condition in step S1. It should be pointed out that the actual regulation target temperature of different designated air conditioners is related to the given flexible regulation requirement and its response feedback effect to the flexible regulation requirement. The flexible regulation requirement includes the limiting condition of flexibly regulating the current air conditioner temperature to the initial regulation target temperature, and also includes other requirements; the response feedback effect is related to the working condition and performance of the designated air conditioner itself. Therefore, it is a complicated process to calculate the actual regulation target temperature of the designated air conditioner under different working conditions and performance under different flexible regulation requirements. In this embodiment, the method for calculating the actual regulation target temperature is not within the scope of the rights protection required by the present invention, so it is not explained in detail.

[0093] In step S1, it is assumed that the target temperature for flexible regulation of the designated air conditioner obtained or issued is 26°C, and the current temperature of the designated air conditioner is assumed to be 23°C. According to the pre-established rule for determining the temperature range based on flexible regulation, it is assumed that the distance between the current temperature of the air conditioner and the target temperature, that is, the difference between the target temperature such as 26°C and the current temperature such as 23°C is greater than or equal to 3°C, and the initial increase of the temperature range is 0.5°C, then it is clear that the first temperature range based on flexible regulation of the designated air conditioner is [23°C, 23.5°C].

[0094] The following is designed according to Figure 2The data feature amplification structure shown is used to illustrate the method of polling, collecting and calculating the load feature data of the specified air conditioner and the response feedback data:

[0095] Figure 2 The data feature amplification structure shown in FIG. 1 is installed at the initial air outlet 10 of the air conditioner. The initial air outlet 10 of the air conditioner is one of the air outlets distributed along the length direction of the air conditioner, preferably a section of the distributed air outlets arranged at the far left or far right of the air conditioner. The initial air outlet 10 is tightly pressed against and sealed with the initial air inlet 20 of the data feature amplification structure. The initial air inlet 20 is Figure 2 The air inlet of the first air duct segment 1 in the air inlet cross section of the first air duct segment 1 is the same as the air outlet cross section of the initial air outlet 10, so as not to change the wind speed of the wind entering from the initial air outlet 10 to the initial air inlet 20 of the first air duct segment 1, so that the acceleration object of the first wind accelerator 11 arranged in the first air duct segment 1 retains the wind speed characteristics when it comes out of the initial air outlet 10, thereby reducing the subsequent calculation amount of the response feedback data.

[0096] The first air duct section 1 is provided with a first wind accelerator 11 and a first air density sensor 13. The first wind accelerator 11 is used to accelerate the incoming wind with a set power for a fixed time and then Figure 2 The air is discharged from the first air duct section outlet 12 shown in the figure; the air outlet cross-sectional area of ​​the first air duct section outlet 12 of the first air duct section 1 is smaller than the air inlet cross-sectional area of ​​the initial air inlet 20 of the first air duct section 1. The purpose of such a setting is to perform secondary acceleration on the wind accelerated by the first wind accelerator 11 with the reduced first air duct section outlet 12, and further amplify the wind pressure characteristics hitting the first wind pressure sensor set at the first air duct section outlet 12. The first air density sensor 13, the first wind accelerator 11 and the first air duct section outlet 12 have a first distance and a second distance respectively, and the first distance is greater than the second distance; the first air density sensor 13 is used to detect the air density in the first air duct section 1;

[0097] The remaining air duct sections after the first air duct section are sealed and connected end to end. The air inlet cross-section, air outlet cross-section, and air duct section length are the same. This is to ensure that the air discharged after acceleration in the previous air duct section has a basically the same diffusion environment after entering the next air duct section, so as to reduce the calculation complexity of the subsequent wind speed prediction value and response feedback value. Each of the remaining air duct sections is provided with several sets of sensor combinations along its length, and the spacing between the two sensor combinations is the same. The sensor combinations include Figure 2The air duct segment air density sensor 30 and the air duct segment wind accelerator 40 shown in the figure are located at a distance from the air duct segment wind accelerator 40 to the air outlet of the air duct segment where it is located, which is smaller than the distance between the air duct segment air density sensor 30 in the combination and the air outlet of the air duct segment where it is located. The air duct segment air density sensor 30 is used to detect the air density in the air duct segment; the air duct segment wind accelerator 40 is used to accelerate the wind with the same set power and fixed duration as the air duct segment wind accelerator in the upper-level air duct segment when the deviation between the air density detected by the air duct segment air density sensor 30 in the combination and the air density detected by the air duct segment air density sensor 30 in the upper-level air duct segment is smaller than the preset deviation range, and the purpose is also to reduce the calculation complexity of the wind speed prediction value and the response feedback value, thereby reducing the complexity of the flexible control of the air conditioner.

[0098] In step S1, the load characteristic data collected by polling includes: the power fluctuation value collected at each wind speed prediction time point in each polling period according to the temperature range.

[0099] The duration of a polling cycle is related to the number of wind speed prediction time points, that is, the number of air duct sections in the designed data feature amplification structure. Figure 2 The data feature amplification structure shown in the figure includes four duct segments, namely Figure 2 The first air duct section 1, the second air duct section 2, the third air duct section 3 and the fourth air duct section 4 in the air duct. The time point when the first wind accelerator 11 in the first air duct section 1 performs wind acceleration is determined as the starting time point of the current polling, and the air diffusion allowable time of the second air duct section 2, the third air duct section 3 and the fourth air duct section 4 is set to 5s, then the duration of one polling is 15s, that is, the acceleration frequency of the first wind accelerator 11 in the first air duct section 1 is once every 15s. It should be emphasized that the response feedback effect of the flexible control is dynamically changing according to the width of the temperature interval (that is, the interval range), in order to improve the insensitivity and accuracy of the flexible control. Therefore, different temperature intervals usually have different polling times.

[0100] In this embodiment, the wind speed prediction time points in one polling include Figure 2 The moment when the wind pressure sensor installed at the outlet of each air duct section in the data feature amplification structure shown in the figure detects a sudden change in wind pressure. Sudden wind pressure refers to the wind pressure detected by the wind pressure sensor installed at the outlet of the air duct section after the air conditioning air is accelerated by the wind accelerator in the corresponding air duct section. For example, Figure 2The conditioned air in the first air duct section 1 is accelerated by the first wind accelerator 11 and then enters the second air duct section 2. After entering, within the set air diffusion allowable time of 5s, if the density deviation (preferably the absolute value of the difference between the second air density and the first air density) between the second air density detected by the air duct section air density sensor in a certain sensor combination in the second air duct section 2 and the first air density detected by the first air density sensor 13 in the first air duct section 1 at the acceleration moment of the first wind accelerator 11 is less than the preset density deviation, at this time, the air duct section wind accelerator combined with the air duct section air density sensor that monitors the second air density accelerates the incoming air according to the same set power and fixed time as the first wind accelerator 11, and then discharges it at the air outlet of the second air duct section, and the wind pressure sensor arranged at the air outlet of the second air duct section detects the wind pressure, which is a sudden change in wind pressure, and the moment when the sudden change in wind pressure is detected is the time for the wind pressure sensor to detect the sudden change in wind pressure. Figure 2 The time point at which the air outlet wind speed is predicted for the initial air outlet 10 of the air conditioner shown in FIG.

[0101] At each wind speed forecast point in a poll, not only Figure 2 The air outlet speed of the initial air outlet 10 of the air conditioner shown in the figure is also collected, and the power fluctuation value of the specified air conditioner at a certain temperature point in the current temperature range is also collected.

[0102] The following describes the meaning of the power fluctuation value and the calculation method of the wind speed forecast value at each wind speed forecast time point in a poll:

[0103] The power fluctuation value refers to the difference between the air conditioning work power at two time points. In this embodiment, it is the absolute value of the difference between the second air conditioning power collected at the wind speed prediction time point of the next air duct section in one polling and the first air conditioning power collected at the wind speed prediction time point of the previous air duct section. Figure 2 The second air conditioning power of the designated air conditioner collected at the second air outlet wind speed prediction time point of the second air duct segment 2 is p2, and its upper-level air duct segment is the first air duct segment 1. In this polling, the first air conditioning power of the designated air conditioner collected at the first air outlet wind speed prediction time point of the first air duct segment 1 is p1. Then the power fluctuation value at the second air outlet wind speed prediction time point is the absolute value of the difference between p2 and p1.

[0104] The wind speed prediction at each polling time point is as follows: Figure 2 The method of calculating the air velocity of the initial air outlet 10 of the air conditioner is expressed by the following formula (1):

[0105]

[0106] In formula (1), Indicated in In the next poll, the first The outlet wind speed of the air conditioner initial air outlet predicted at the corresponding outlet wind speed prediction time point of the level duct section;

[0107] Indicated in In the second round of polling, The air density detected by the air density sensor of the air duct section in the level air duct section and the air duct section wind accelerator that performs the wind acceleration action to form a sensor combination;

[0108] Indicated in In the next poll, set The sudden change in wind pressure detected by the wind pressure sensor at the air outlet of the stage air duct section;

[0109] The accelerated wind speed of the wind accelerator in the duct section that performs the wind acceleration action.

[0110] For example, in the first poll, Figure 2 The first-level duct section in the data feature amplification structure shown is Figure 2 The predicted time point of the outlet wind speed of the first air duct section 1 is Figure 3 In At the time point, the wind pressure sensor installed at the air outlet of the first air duct section 1 detects a sudden change in wind pressure of The first wind accelerator 11 disposed in the first air duct section 1 generates a sudden change in wind pressure. At the acceleration moment, the air density detected by the first air density sensor 13 in the first air duct section 1 is The first wind accelerator 11 has an air density of The air passes through Accelerate to sudden wind pressure At this time, Time point, according to ,right Figure 2 The predicted wind speed at the initial air outlet 20 of the air conditioner shown in . Figure 3 In , , They are Figure 2 The wind speed prediction time points of the second air duct segment 2, the third air duct segment 3 and the fourth air duct segment 4 in the data feature amplification structure shown in FIG. 1 are respectively corresponding to the wind speed prediction values ​​at the first polling. , , .

[0111] In step S1, the response feedback data collected by polling include: the sudden change wind pressure at the air outlet of each air duct section during each polling period, and the air density detected by the air density sensor of each air duct section in the multi-stage air duct section air density sensor in the duct; the response feedback data calculated by polling include: the wind speed prediction value of the initial air outlet of the air conditioner at the wind speed prediction time point in each polling, and each wind speed prediction time point in a polling is the moment when the wind pressure sensor set at the air outlet of each air duct section in the data feature amplification structure detects the sudden change wind pressure. In each polling, the method for predicting the wind speed prediction value of the initial air outlet of the air conditioner based on the response feedback data collected by polling associated with each air duct section has been described in detail in the above content and will not be repeated here.

[0112] After polling, collecting and calculating the load characteristic data and response feedback data of the specified air conditioner, Figure 1 As shown, the method for approximating and analyzing and calculating the actual control temperature of the historical target provided in this embodiment is transferred into the steps:

[0113] S2, based on the data collected and calculated in step S1, draw a second fitting curve between the wind speed prediction value and the power fluctuation value to find the power abnormal fluctuation point;

[0114] The following specifically describes a method for drawing a second fitting curve of a wind speed prediction value and a power fluctuation value of a specified air conditioner, which specifically includes the following steps:

[0115] B1, extract the wind speed prediction abnormal value from each wind speed prediction value calculated in each polling under the temperature range and add it to the wind speed abnormal value set, and extract each remaining wind speed prediction value to form a The data pairs expressed are , They represent the first The first stage of the duct section The wind speed forecast and power fluctuation values ​​in the next poll. Specifically, For the The first stage of the duct section The second air conditioning power collected at the outlet wind speed prediction time point in the second polling and the The first stage of the duct section The absolute value of the difference in the first air conditioner power collected at the outlet wind speed prediction time point in the second polling;

[0116] For example, in the temperature range of [23℃, 23.5℃], a total of 5 polls are performed to collect and calculate the load characteristic data and response feedback data of the specified air conditioner. In these 5 polls, assuming that in the first poll, based on Figure 2 The wind speed prediction value calculated from the response feedback data of the first air duct section 1 shown in Similarly, in the second to fifth polls, , , , , assuming , , , The values ​​of are 1, 1.5, 1.8, 1.2, and 2.8 respectively. Then 2.8 is the identified wind speed forecast anomaly because it has the largest deviation from the average of these five values. Then 2.8 is added to the wind speed anomaly set. The data pairs are , , , ,in Indicates the first-level duct section, i.e. Figure 2 The second air conditioning power collected at the predicted wind speed time point of the first air duct section 1 in the first polling (the moment when the first wind pressure sensor 13 arranged at the air outlet of the first air duct section 1 detects the sudden change of wind pressure in the first polling) and the wind acceleration moment of the 0th level air duct section in the first polling (i.e. Figure 2 The first air duct segment 1 is the first-level air duct segment that is arranged at the front in the data feature amplification structure. When calculating the power fluctuation value for the first-level air duct segment, the first air conditioning power is preferably the air conditioning power collected for the designated air conditioner by the first air duct segment 1 in the first polling when the first wind accelerator 11 performs the wind acceleration action. The calculation method is the same as , no further details will be given. For each level of air duct section arranged behind the first air duct section 1, For the The first stage of the duct section The second air conditioning power collected at the outlet wind speed prediction time point in the second polling and the The first stage of the duct section The absolute value of the difference in the first air conditioning power collected at the outlet wind speed prediction time point in the second polling. For example, in the first polling, the outlet wind speed prediction time point of the first-level air duct section is the moment when the first wind pressure sensor set at its outlet detects a sudden change in wind pressure, and the outlet wind speed prediction time point of the second-level air duct section, i.e., the second air duct section 2, is the moment when the second wind pressure sensor 21 set at its outlet detects a sudden change in wind pressure. , The second air conditioning power of the designated air conditioner at the moment when the second air pressure sensor 21 disposed at the air outlet of the second air duct section 2 detects a sudden change in air pressure in the first polling; The first air-conditioning power of the designated air conditioner at the time when the first air pressure sensor disposed at the air outlet of the first air duct section 1 detects a sudden change in wind pressure in the first polling.

[0117] B2, the wind speed prediction value and power fluctuation value are respectively the horizontal axis coordinate and the vertical axis coordinate of the xy axis coordinate system, and the The data pairs are plotted in the xy axis coordinate system, and the plotted data points are fitted into a second fitting curve by a second fitting function. The second fitting curve is shown in Figure 4 shown.

[0118] It should be noted here that in each polling, each air duct segment has a corresponding wind speed prediction value and power fluctuation value. When drawing the second fitting curve for each temperature interval, it depends on the wind speed prediction value and power fluctuation value calculated by each air duct segment during each polling period under the temperature interval. In addition, the second fitting function used for curve fitting is determined according to the variation characteristics between the power fluctuation value and the wind speed prediction value, for example, a binary quadratic function or a high-order equation may be used. Since the method for fitting the curve is not within the scope of the invention, it is not described in detail.

[0119] After completing the curve fitting, in step S2, the method for finding the abnormal power fluctuation point specifically includes the following steps:

[0120] C1, extracts element values ​​from the wind speed anomaly value set, and the element values ​​are wind speed prediction anomalies;

[0121] C2, using the element value extracted in step C1 as the independent variable of the second fitting function, solving the y value of the second fitting function;

[0122] C3, determine whether the y-axis deviates from the maximum allowable value of power fluctuation of the corresponding polling,

[0123] If yes, the number of abnormal power fluctuation points of the flexible control for the specified air conditioner is counted by "1";

[0124] If not, go to step C4;

[0125] C4, determine whether the wind speed outlier value set has been extracted as an "empty set",

[0126] If so, the output power abnormal fluctuation point counting result is obtained;

[0127] If not, return to step C1.

[0128] In step C3, the method for determining whether the y value deviates from the maximum allowable value of power fluctuation of the corresponding polling includes the following steps:

[0129] C31, obtaining the air conditioning power of the designated air conditioner collected each time in the same polling as the element value used to solve the y value; the collection time of the air conditioning power of the designated air conditioner in each polling has been described in detail in the above content and will not be repeated here;

[0130] C32, calculating the difference between the maximum air conditioning power and the minimum air conditioning power obtained in step C31 as the maximum allowable value of power fluctuation for deviation judgment;

[0131] C33, judging whether the absolute value of the difference between the y value and the maximum allowable value of power fluctuation is greater than a preset threshold,

[0132] If yes, then it is determined that the y value deviates from the maximum permissible value of power fluctuation;

[0133] If not, go to step C4.

[0134] After finding the abnormal power fluctuation point in step S2, the method for approximating and analyzing and calculating the actual control temperature of the historical target provided in this embodiment proceeds to step:

[0135] S3, judging whether the number of power abnormal fluctuation points found in the load characteristic collection period under the temperature range determined in step S1 is greater than a preset number threshold,

[0136] If yes, in the flexible control of the designated air conditioner in the next stage which is continuous with the current stage, the range of the temperature range is narrowed, and the power adjustment amplitude of the designated air conditioner per unit time is reduced, and then the process goes to step S4;

[0137] If not, the designated air conditioner continues to be flexibly adjusted according to the width of the temperature interval determined in step S1.

[0138] Under the "if yes" determination in step S3, the method of narrowing the range according to the temperature range and reducing the power adjustment range includes the following steps:

[0139] S31, calculate the ratio of the number of power abnormal fluctuation points found to the number of remaining wind speed prediction values ​​extracted in step B1 as the correction coefficient ;

[0140] S32, calculating the increment or power adjustment range according to the temperature range in the next stage , the calculation method is expressed by the following formula (2):

[0141]

[0142] Indicates the width of the temperature range of the previous stage that has stage continuity with the next stage or the power adjustment amplitude of the flexible control of the specified air conditioner in the previous stage;

[0143] Indicates that the previous stage The next continuous stage, that is, after completing the flexible regulation of the designated air conditioner according to the temperature range of [23℃, 23.5℃], enters the next stage of continuing to flexibly regulate the designated air conditioner.

[0144] It should be noted here that the width of the temperature interval refers to, for example, for the temperature interval [23℃, 23.5℃], its width is 23.5-23=0.5; the power adjustment range for flexible control of the specified air conditioner is, for example: according to the preset initial power adjustment range, in the process of raising the specified air conditioner from 23℃ to 23.5℃, according to the specified time. For example, if the temperature is 23℃, the power of the specified air conditioner is If the specified time is set to 10s, the power of the specified air conditioner will be adjusted to , Indicates Based on the temperature range.

[0145] S33, taking the upper limit of the temperature interval of the current stage, i.e., the upper limit of the temperature interval of the previous stage, as the lower limit of the temperature interval of the next stage, and using the lower limit and the increment representing the temperature interval The sum of the values ​​is the upper limit of the temperature range for the next stage to determine the temperature range for the next stage, or the power adjustment range is represented by This is used as the power adjustment range for the designated air conditioner in the next stage.

[0146] For example, assuming that the current stage's base temperature interval determined in step S1 is [23°C, 23.5°C], and its upper limit is 23.5°C, so the lower limit of the next stage's base temperature interval is 23.5°C; assuming that the increment representing the base temperature interval is 0.2℃, then the next stage of the basic temperature interval is [23.5℃, 23.7℃]. Assume that at 23.5℃, the power of the specified air conditioner is After calculation in step S32, the power adjustment range in the next stage according to the temperature range is , the power of the specified air conditioner is reduced to Flexible control of air conditioning power is carried out through flexible control.

[0147] It should be noted here that in step S1, the temperature range based on which the flexible control is carried out in the current stage is determined to be the temperature range that is reduced or maintained based on the "if" or "if no" judgment condition in step S3, that is, when entering the next stage indicated in step S3, the next stage is transformed into the current stage of flexible control.

[0148] After the judgment of step S3 is completed, the method of approximating the analysis and calculating the actual control temperature of the historical target is transferred to the step:

[0149] S4, determining whether the width of the reduced temperature interval is less than a preset width threshold,

[0150] If so, the upper limit of the temperature range narrowed down for the next stage is used as the target actual control temperature;

[0151] If not, after the flexible control of the designated air conditioner enters the next stage, the next stage is used as the current stage in step S1, and then the process returns to step S1.

[0152] After the flexible control of the designated air conditioner is completed, the target actual control temperature becomes the historical target actual control temperature.

[0153] The following is an explanation of the calculation method of the historical power supply proportion characteristic value of each type of power supply:

[0154] The characteristic value of the historical power supply proportion of each of the multiple types of power sources is: during the historical regulation period, the historical power supply proportion of each of the multiple types of power sources allocated to the load area where the regulation object is located, and the weighted average of the historical power supply proportions of each power source.

[0155] For example, in Figure 5 During the historical regulation period in which the air conditioner 1 in the load area A is flexibly regulated according to the flexible regulation requirements analyzed in step L1, the monitoring and automatic regulation module a dispatches the input power sources for the load area A, for example, including thermal power, wind power and photovoltaic power, and the power supply proportions are assumed to be 60%, 30% and 10% respectively. During the historical regulation period, it is also assumed that the influence weights of the three types of input power sources, thermal power, wind power and photovoltaic power, on the flexibility of the regulation of the air conditioner 1 in the load area A are respectively , , , then the weighted average value of the multiple types of power modes allocated by the monitoring and automatic control module a during the historical control of the air conditioner 1 in the load area A is . The purpose of solving the weighted average is to verify in step L2 the availability of each available mode of multi-type power distribution to the actual control temperature of the historical target of the control object. It should be noted here that the calculation process of the influence weights of different types of input power supplies on the flexibility of control of the control object is relatively complicated. How to calculate the influence weights is not within the scope of the rights protected by the present invention, so it is not explained in detail. In this embodiment, the influence weights of different types of input power supplies on the flexibility of control of the control object are all set to "1".

[0156] After the data required for control is obtained in step L1, the air conditioning control method based on the virtual power plant provided in this embodiment proceeds to step:

[0157] L2, calculate the available modes of multi-type power distribution for the target control load area, and then verify the applicability of each mode to the historical target actual control temperature of each air conditioner pointed to by the data obtained in step L1;

[0158] In step L2, the available multi-type power distribution mode refers to the multi-type power distribution mode currently available for the target load area. For example, the current control object is Figure 5 Air conditioner 1 in the figure is in load area A, so the current target load area is load area A. After the current target load area is determined, Figure 5 The monitoring and automatic control module a in the load area A will automatically calculate the current available multiple types of power distribution modes. The calculation method is comprehensive. Figure 5 The current load conditions of all loads in the system and the allocated and remaining allocated power resources are comprehensively analyzed. Since how to calculate the currently available multi-type power distribution mode is not within the scope of the invention and does not affect the technical problem to be solved by the invention, it will not be elaborated in detail.

[0159] Assume that the currently available multi-type power distribution modes calculated for load area A include mode a1 and mode a2, where in mode a1, the proportions of thermal, wind and photovoltaic power supplies are 60%, 30% and 10% respectively; in mode a2, the proportions of thermal, wind and photovoltaic power supplies are 50%, 20% and 30% respectively.

[0160] Taking the above assumptions about modes a1 and a2 as an example, the method for verifying the applicability of the historical target actual control temperature of each air conditioner pointed to by the data obtained in step L1 in step L2 is specifically described.

[0161] In step L2, the method for verifying the applicability of each mode to the historical target actual control temperature of each air conditioner pointed to by the data obtained in step L1 specifically includes the following steps:

[0162] L21, using the power supply proportion of each type of power source recorded in the currently available mode of the target control load area as the dependent variable of the sub-curve fitting function for fitting the corresponding sub-curve in the first fitting curve corresponding to each air conditioner, and reversely solving the independent variable of each sub-curve fitting function;

[0163] For step L21, first, a method for drawing a first fitting curve of the historical target actual control temperature and the historical power supply ratio is briefly described as follows:

[0164] With the historical target actual control temperature and the historical power supply ratio as the horizontal axis coordinate value and the vertical axis coordinate value of the xy axis plane coordinate system respectively, each point (historical target actual control temperature, historical power supply ratio) is plotted in the same xy axis plane coordinate system. Figure 5 During a historical regulation period in which the air conditioner 1 in the load area A shown in the figure is flexibly regulated according to the flexible regulation requirements parsed in step L1, the multi-type power supply distribution mode regulated by the monitoring and automatic regulation module a is: the historical power supply proportions of thermal power, wind power and photovoltaic power are 60%, 30% and 10% respectively. During this historical regulation period, the temperature of the air conditioner 1 is assumed to be actually regulated from 23°C to 25.5°C (the target regulation temperature in the flexible regulation requirements is assumed to be 26°C). This 25.5°C is the historical target actual regulation temperature of the air conditioner 1 during the historical regulation period.

[0165] In the above example, the first fitting curve includes three sub-curves, which are drawn based on the historical power supply ratios of thermal power, wind power and photovoltaic power and the points formed by the same historical target actual control temperature. These three sub-curves are defined as the first sub-curve, the second sub-curve and the third sub-curve. In the first sub-curve, for the historical target actual control temperature of 25.5℃ for air conditioner 1 in load area A, its point is ( ×60%,25.5℃), and in the second and third sub-curves, the points are ( ×30%,25.5℃)、( ×10%,25.5℃).

[0166] The points (historical target actual control temperature, historical power supply ratio) formed during each historical control period of the same air conditioner pointed to by the data required for control obtained in step L1 and controlled according to the flexible control requirements parsed in step L1 are plotted in the same xy-axis plane coordinate system, and then the fitting function is used to fit each point to obtain a first fitting curve with 3 sub-curves. Each air conditioner has a corresponding first fitting curve.

[0167] In step L21, the method of inversely solving the independent variable of each sub-curve fitting function is exemplified as follows:

[0168] The sub-curve fitting function is determined according to the changing characteristics of the point (the actual controlled temperature of the historical target, the historical power supply ratio). For example, a binary quadratic function or a high-order equation can be used as a whole or in sections. The function used for curve fitting of the point (the actual controlled temperature of the historical target, the historical power supply ratio) is not specifically explained.

[0169] As assumed above, the first fitting curve corresponding to air conditioner 1 has three sub-curves, namely the first sub-curve, the second sub-curve and the third sub-curve corresponding to thermal power, wind power and photovoltaic power respectively. It is also assumed that the target control load area calculated in step L2 (assuming Figure 5 The load area A in the current available modes include mode a1 and a2. In mode a1, the proportions of thermal power, wind power and photovoltaic power supply are 60%, 30% and 10% respectively; in mode a2, the proportions of thermal power, wind power and photovoltaic power supply are 50%, 20% and 30% respectively. When verifying the applicability of mode 1 to the actual control temperature of the historical target of air conditioner 1, the method of inversely solving the independent variables of each sub-curve fitting function is:

[0170] With 60%, 30% and 10% as the dependent variables of the first sub-curve fitting function, the second sub-curve fitting function and the third sub-curve fitting function corresponding to the first sub-curve, the second sub-curve and the third sub-curve in the first fitting curve corresponding to air conditioner 1, respectively, the independent variable of each sub-curve fitting function is obtained by reverse calculation, and this independent variable is the historical target predicted control temperature. Similarly, the power supply proportion of each type of power supply in mode 2 is used as the dependent variable of the three sub-curve fitting functions corresponding to air conditioner 1, and the independent variable of each sub-curve fitting function is solved by reverse calculation.

[0171] After completing step L21, the method for verifying the applicability of each mode to the historical target actual control temperature of the air conditioner proceeds to step:

[0172] L22, the values ​​of different independent variables solved for the same air conditioner are weighted and summed with the corresponding influence weights, and the weighted sum value is used as the historical target predicted control temperature for controlling the air conditioner using the currently verified mode;

[0173] Taking the current verification mode as the above mode a1 as an example, for Figure 5For the air conditioner 1 in the load area A shown in , the proportion of thermal power supply in mode a1 is 60%. 60% is used as the dependent variable of the fitting function of the first sub-curve in the first fitting curve corresponding to the air conditioner 1, and the value of the independent variable of the fitting function of the first sub-curve obtained by reverse deduction is assumed to be x1. Similarly, assuming that the proportion of wind power supply in mode a1 is 30%, the value of the independent variable 2 of the second sub-curve fitting function obtained by reverse deduction of the 30% is assumed to be x2; the proportion of photovoltaic power supply in mode a1 is 10%, and the value of the independent variable of the third sub-curve fitting function obtained by reverse deduction of the 10% is assumed to be x3. The influence weights corresponding to the independent variables x1, x2, and x3 are the influence weights of thermal power, wind power, and photovoltaic power supply on the flexibility of the regulation of the air conditioner 1. Therefore, in step L22, the weighted sum is x1× +x2× +x3× This weighted sum value is used as the historical target predicted control temperature (for example, 25.2°C) if the currently verified mode (mode a1 in this example) is used to control the air conditioner (air conditioner 1 in this example).

[0174] L23, finding the multi-type power supply historical distribution data for the load area where the air conditioner is located that has the minimum mode deviation from the currently verified mode, and then obtaining the historical target actual control temperature of the air conditioner under the conditions of the found multi-type power supply historical distribution data;

[0175] For example, assuming that the historical distribution data of multiple types of power sources for the load area where the air conditioner is located includes data d1 and d2 (data d1 and d2 are generated during the historical regulation period under the flexible regulation requirements analyzed in step L1), wherein data d1 records, for example, that the historical power supply proportions of thermal power, wind power, and photovoltaic power are 70%, 20%, and 10%, respectively, and data d2 records, for example, that the historical power supply proportions of thermal power, wind power, and photovoltaic power are 65%, 25%, and 10%, respectively, then the mode deviation is calculated as follows:

[0176] Calculate the first absolute value of the difference between the power supply proportions of the same type of power supplies in pairs in the currently verified mode and the historical distribution data of multiple types of power supplies, and then calculate the sum of the absolute values ​​of the first differences as the mode deviation between the currently verified mode and the historical distribution data of multiple types of power supplies. For example, the mode deviation between the above-mentioned mode a1 and the data d1 is (70%-60%) + (30%-20%) + (10%-10%) = 20%; and the mode deviation between the above-mentioned mode a1 and the data d2 is (65%-60%) + (30%-25%) + (10%-10%) = 10%, then the mode deviation between the data d2 and the currently verified mode a1 is less than the mode deviation between the data d1 and the mode a1.

[0177] Assuming that for air conditioner 1, the multi-type historical allocation data with the minimum mode deviation is the above-mentioned data d2, then the historical target actual control temperature (for example, 25.5°C) obtained by the air conditioner 1 in the corresponding historical control under the multi-type power supply historical allocation mode of data d1 is obtained from the forward node of the Bloom filter.

[0178] Then, proceed to step:

[0179] L24, determine whether the second absolute value of the difference between the historical target predicted control temperature (such as 25.2°C) calculated in step L22 and the historical target actual control temperature (such as 25.5°C) found in step L23 (25.5°C-25.2°C-=0.3°C) is less than the preset second absolute value threshold of the difference (assuming that the threshold is set to 0.2°C),

[0180] If yes, it is determined that the currently verified mode (such as mode a1) is applicable to the air conditioner (such as air conditioner 1), and the applicability value of the currently verified mode (such as mode a1) for the target control load area (such as load area A) where the air conditioner (such as air conditioner 1) is located is accumulated by "1";

[0181] If not, it is determined that the currently verified mode is not applicable to the air conditioner, and the suitability value of the mode for the target control load area where the air conditioner is located is not accumulated.

[0182] In summary, the present invention calculates the cumulative value of the suitability of the currently verified multi-type power distribution mode for the target control load area where each air conditioner is located pointed to by the data obtained in step L1, and uses the mode with the maximum cumulative value of suitability as the current proposed mode for the target control load area and executes it, thereby realizing virtualized optimization and control of multi-type power inputs in the load area. The control process takes into account the actual situation of the currently available resources on the input side, as well as the influence of different available multi-type power distribution modes on the input side on the flexibility of control of air conditioners with different working conditions and different performances on the load side, so that the scheduling of multi-type power resources on the input side is more reasonable, and the flexible control on the load side is more precise and imperceptible.

[0183] In addition, in the flexible control method provided by the present invention, by analyzing the flexible control process data of the previous stage of the specified air conditioner under different working conditions and different performances, such as using the data feature amplification structure, the feature amplification of the abnormal data points of the previous stage of flexible control is achieved; by drawing a second fitting curve of the wind speed prediction value and the power fluctuation value, the power abnormal fluctuation point in the previous stage of flexible control is quickly and accurately found; and then by The calculation is used to adjust the temperature range and power adjustment range of the next stage of flexible control, so that the flexible control of the specified air conditioner in the next stage is more flexible and less noticeable.

[0184] It should be noted that the above specific implementations are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art should understand that various modifications, equivalent substitutions, changes, etc. can be made to the present invention. However, as long as these changes do not deviate from the spirit of the present invention, they should be within the scope of protection of the present invention. In addition, some terms used in the specification and claims of this application are not restrictive, but are only for the convenience of description.

Claims

1. An air conditioning control method based on a virtual power plant, characterized in that the steps include: L1, after the monitoring and automatic control module of the virtual power plant monitors and parses the flexible control requirements, it uses the flexible control requirements as acquisition conditions to obtain the data required for control, including historical flexible control basis data and historical distribution data of multiple types of power sources; L2, calculate the available modes of multi-type power distribution for the target control load area, and then verify the applicability of each mode to the historical target actual control temperature of each air conditioner pointed to by the data obtained in step L1, and use the mode with the maximum applicability accumulated value for the target control load area where the air conditioner is located as the proposed mode and execute it; In step L1, the method for obtaining the data required for management and control includes the following steps: L11, selecting the air conditioners having the same flexible control requirements from the historical control record table; L12, matching the Bloom filter forward nodes bound to the load area where each of the screened air conditioners is located, and then further matching the Bloom filter reverse nodes associated with the forward nodes according to the forward and reverse relationships of the nodes; L13, obtaining the historical flexible control basis data generated during the historical control period for the air conditioner screened in step L11 under the flexible control requirement from the forward node matched in step L12, and obtaining the multi-type power supply historical distribution data for controlling the load area where the air conditioner is located during the same historical control period from the reverse node having a positive and reverse relationship with the forward node; The historical flexible control basis data includes: load characteristic data collected and calculated by polling, response feedback data, and historical target actual control temperature calculated by approximation analysis during the historical control period with the same flexible control requirement parsed in step L1, taking into account the working conditions and performance of the air conditioner itself; The multi-type power supply historical allocation data includes: a historical power supply proportion characteristic value of each power supply among the multi-type power supplies allocated to the load area where the air conditioner as the control object is located during the historical control period with the flexible control requirement; The historical flexible control basis data generated during each historical control period of performing flexible control on the air conditioner is stored as a separate data set at the forward node of the Bloom filter bound to the load area where the air conditioner is located; The monitoring and automatic control module monitors a corresponding load area; In step L2, the method for verifying the applicability of each mode to the historical target actual control temperature of the air conditioner comprises the steps of: L21, using the calculated power supply proportion of each type of power source recorded in the mode currently available in the target control load area as the dependent variable of the sub-curve fitting function for fitting the corresponding sub-curve in the first fitting curve corresponding to the air conditioner, and reversely solving the independent variable of each sub-curve fitting function; L22, performing weighted summation on the values ​​of different independent variables solved for the same air conditioner with corresponding influence weights, and using the weighted summation value as the historical target predicted control temperature for controlling the air conditioner using the currently verified mode; L23, searching for the multi-type power supply historical allocation data for the load area where the air conditioner is located that has the minimum mode deviation from the currently verified mode, and then obtaining the historical target actual control temperature of the air conditioner under the conditions of the multi-type power supply historical allocation data found; L24, judging whether the second absolute value of the difference between the historical target predicted control temperature calculated in step L22 and the historical target actual control temperature found in step L23 is less than a preset second difference absolute value threshold, If yes, it is determined that the currently verified mode is applicable to the air conditioner, and "1" is accumulated to the applicability value of the currently verified mode for the target control load area where the air conditioner is located; If not, it is determined that the currently verified mode is not applicable to the air conditioner.

2. The air conditioning control method based on a virtual power plant according to claim 1, characterized in that: The method for approximating and analyzing and calculating the actual control temperature of the historical target comprises the following steps: S1, after obtaining or issuing the target temperature for flexible regulation of the specified air conditioner, determine the temperature range for flexible regulation in the current stage, and then poll, collect and calculate the load characteristic data and response feedback data of the specified air conditioner according to the designed data feature amplification structure; S2, based on the data collected and calculated in step S1, draw a second fitting curve between the wind speed prediction value and the power fluctuation value to find the power abnormal fluctuation point; S3, determining whether the number of the power abnormal fluctuation points found during the load characteristic data collection period according to the temperature range is greater than a preset number threshold, If yes, in the flexible control of the designated air conditioner in the next stage which is continuous with the current stage, the range of the temperature interval is narrowed, and the power adjustment amplitude of the designated air conditioner per unit time is reduced, and then the process goes to step S4; If not, continue to flexibly adjust the designated air conditioner according to the width of the temperature interval determined in step S1; S4, determining whether the reduced width of the temperature interval is smaller than a preset width threshold, If so, the upper limit value of the temperature range narrowed down for the next stage is used as the target actual control temperature; If not, after the flexible control enters the next stage, the next stage is used as the current stage in step S1, and then the process returns to step S1.

3. The air conditioning control method based on a virtual power plant according to claim 2, characterized in that: In step S1, the load characteristic data collected by polling includes: the power fluctuation value collected at each wind speed prediction time point in each polling period under the temperature range; The response feedback data collected and calculated by polling include: the sudden change of wind pressure at the air outlet of each air duct section during each polling period, the air density detected by each air duct section air density sensor in the multi-stage air duct section air density sensor in the pipe, and the predicted wind speed value of the initial air outlet of the air conditioner at each air outlet wind speed prediction time point in each polling; Each of the outlet wind speed prediction time points in one polling is the moment when the wind pressure sensor arranged at the air outlet of each air duct section in the data feature amplification structure detects a sudden change in wind pressure.

4. The air conditioning control method based on a virtual power plant according to claim 2, characterized in that: In step S2, the method for finding the abnormal power fluctuation point includes the steps of: C1, extracting the wind speed prediction abnormal value from each wind speed prediction value calculated in each polling under the temperature range and adding it to the wind speed abnormal value set, and then extracting the element value from the wind speed abnormal value set; C2, using the element value extracted in step C1 as the independent variable of a second fitting function for fitting the second fitting curve, and solving the y value of the second fitting function; C3, judging whether the y value deviates from the maximum allowable value of power fluctuation of the corresponding polling, If yes, the number of power abnormal fluctuation points of the flexible control for the specified air conditioner is counted and added with "1"; If not, go to step C4; C4, judging whether the wind speed abnormal value set has been extracted as an "empty set", If so, the output power abnormal fluctuation point counting result is obtained; If not, return to step C1.

5. The air conditioning control method based on a virtual power plant according to claim 4, characterized in that: In step C3, the method for determining whether the y value deviates from the maximum allowable value of power fluctuation of the corresponding secondary polling includes the steps of: C31, obtaining the air conditioning power of the designated air conditioner collected each time in the same polling as the element value used to solve the y value; C32, calculating the difference between the maximum air conditioning power and the minimum air conditioning power obtained in step C31 as the maximum allowable value of the power fluctuation as a basis for deviation judgment; C33, judging whether the absolute value of the difference between the y value and the maximum allowable value of power fluctuation is greater than a preset threshold, If yes, it is determined that the y value deviates from the maximum allowable value of power fluctuation; If not, go to step C4.

6. The air conditioning control method based on a virtual power plant according to claim 3, characterized in that: Under the "if" determination condition in step S3, the method for reducing the range according to the temperature interval and reducing the power adjustment amplitude includes the steps of: S31, calculating the ratio of the number of the power abnormal fluctuation points found to the number of the wind speed prediction values ​​remaining after extracting the wind speed prediction abnormal values ​​when drawing the second fitting curve as a correction coefficient ; S32, calculating the increment of the temperature range or the power adjustment range in the next stage ; S33, taking the upper limit of the temperature interval of the current stage as the lower limit of the temperature interval of the next stage, and using the lower limit and the increment representing the temperature interval The sum of the values ​​is the upper limit of the temperature range for the next stage to determine the temperature range for the next stage, or the power adjustment range is represented by As the power adjustment range for the designated air conditioner in the next stage.

7. The air conditioning control method based on a virtual power plant according to claim 1, characterized in that: The historical power supply proportion characteristic value of each of the multiple types of power supplies is: the historical power supply proportion of each of the multiple types of power supplies allocated to the load area where the regulated object is located during the historical regulation period and the influence weight of each power supply on the flexibility of regulation of the regulated object.

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