A method for air conditioning load aggregation considering differences in user thermal comfort

Abstract

The invention discloses an air conditioning load aggregation method considering the difference of thermal comfort of users, which belongs to the technical field of air conditioning load aggregation and solves the problem of how to accurately and effectively tap the scheduling potential of each type of air conditioning user, establishes a second-order ETP model of air conditioning load taking into account the changes of indoor air temperature and solid temperature; establishes a thermal comfort interval model taking into account environmental parameters and human body parameters; according to the human body parameters in the thermal comfort interval model, ISODATA clustering is used to divide user groups with different thermal tolerance characteristics; based on the cluster control of air conditioning load with the difference of thermal comfort interval, the thermal comfort threshold and room temperature tolerance range of each type of user group are used as the start and stop constraints of air conditioning; the invention establishes an air conditioning load aggregation model taking into account the difference of thermal comfort of users based on the thermal tolerance characteristics of a large number of air conditioning load users, which can optimize the control of the aggregated air conditioning load and achieve the effect of smoothing the fluctuation of renewable energy and shaving the peak and filling the valley.

Description

Technical Field

[0001] The invention belongs to the technical field of air conditioning load aggregation, and relates to an air conditioning load aggregation method taking into account differences in thermal comfort of users. Background Art

[0002] With the transformation of the power system towards clean and low-carbon, new energy represented by wind power and photovoltaics has been vigorously developed, and its proportion in the power grid is increasing. In order to solve the problem of wind and solar power abandonment under the high penetration rate of new energy, on the basis of traditional units, it is necessary to fully mobilize demand-side resources and improve the absorption capacity of the power grid. Air-conditioning load has become a high-quality flexible load resource due to its environmental thermal energy storage characteristics, controllable characteristics, and large load ratio. Direct load control is the main way for air-conditioning load to participate in demand-side regulation. Researchers often use the preset temperature range as the start and stop constraint of air-conditioning load, but the user's thermal comfort is not only affected by the indoor temperature, but also by other environmental factors and human factors. It is difficult to measure the user's real thermal comfort experience by only considering the indoor temperature.

[0003] In this regard, some literature considers the defect of only using the preset temperature range as the start-stop constraint of air-conditioning load. For example, the literature "Peak Shaving Method of Variable Frequency Air-conditioning Group under Demand Response" (Yang Jiru, Shi Kun, Cui Xiuqing, et al. Automation of Electric Power Systems, 2018, 42(24):44-52.) adopts an air-conditioning load control strategy based on the thermal comfort interval model, models the thermal comfort index as the penalty term of the objective function, and incorporates multiple uncertain factors into the relevant problems for analysis. However, the methods of air-conditioning load participation in demand-side regulation and control considering the thermal comfort interval model are in the initial stage at home and abroad, and the existing theories have some shortcomings in the application of air-conditioning users participating in demand-side response. Some methods only consider the thermal comfort of one group of people, without taking into account the diversity of user groups. For example, in the document “Optimizing the regulation of aggregated thermostatically controlled loads by jointly considering consumer comfort and tracking error” (YANG JE, LIU TY, WANG HB, et al. [J] . Energies, 2019, 12 (9): 1757), different user groups have different thermal tolerance and different requirements for thermal comfort temperature ranges. If unified scheduling is implemented, it will directly affect the enthusiasm of users to participate in the response. Summary of the invention

[0004] The technical solution of the present invention is used to solve the problem of how to accurately and effectively tap the scheduling potential of each type of air-conditioning user.

[0005] The present invention solves the above technical problems through the following technical solutions:

[0006] An air conditioning load aggregation method considering differences in user thermal comfort includes the following steps:

[0007] S1. Establish a second-order ETP model of air conditioning load taking into account the changes in indoor air temperature and solid temperature;

[0008] S2. Establish a thermal comfort interval model that comprehensively considers environmental parameters and human parameters to quantify the user's thermal comfort experience;

[0009] S3. Based on the human body parameters in the thermal comfort interval model, ISODATA clustering is used to divide user groups with different thermal tolerance characteristics;

[0010] S4. Cluster control of air conditioning load based on thermal comfort interval differences, using the thermal comfort threshold and room temperature tolerance range of each user group as the start and stop constraints of the air conditioner.

[0011] Furthermore, the method for establishing the second-order ETP model of air conditioning load taking into account the changes in indoor air temperature and solid temperature described in step S1 is specifically as follows:

[0012] S11. Based on the equivalent circuit composed of resistors, capacitors and other circuit elements, the heat transfer and energy balance process between the air-conditioned room and the outside world is simulated, and the second-order ETP model used to describe the changes in indoor air temperature and solid temperature is obtained as follows:

[0013] (1)

[0014] In formula (1), , , are the indoor air temperature, indoor solid temperature and outdoor air temperature at time t respectively; and are the equivalent heat capacity of indoor air and the equivalent heat capacity of indoor solid respectively; and They are the equivalent thermal resistance of indoor air and the equivalent thermal resistance of indoor solid respectively; The working power of the air conditioner;

[0015] S12. When the air conditioner is running, its power is the rated power. Under the cooling model, the relationship between its operating power and temperature is:

[0016] (2)

[0017] In formula (2), is the air conditioning energy efficiency ratio; is the rated power of the air conditioner; For air conditioning Working power at all times; and They are the upper and lower limits of the operating temperature, respectively, and are related to the set temperature The relationship between them is:

[0018] (3)

[0019] In formula (3), Provide dead zone for temperature control of air conditioner;

[0020] S13. If the air-conditioning unit is composed of N air conditioners, the total power consumption of the air-conditioning unit at time t is:

[0021] (4)

[0022] In formula (4), is the power consumption value of the nth air-conditioning device at time t.

[0023] Furthermore, the method of establishing a thermal comfort interval model that comprehensively considers environmental parameters and human body parameters in step S2 to quantify the thermal comfort experience of the user is as follows:

[0024] S21. Calculate the predicted average vote number to measure the thermal comfort experience of air-conditioning load users based on indoor temperature, average radiant temperature, air flow velocity, air humidity, human energy metabolism rate and clothing thermal resistance;

[0025] The calculation formula for the predicted average number of votes is:

[0026] (5)

[0027] In formula (5), is the function value for predicting the average number of votes; M is the energy metabolism rate of the human body, which is related to the degree of human activity; W is the mechanical work generated by human activity; is the water vapor pressure of the environment; is the indoor temperature; is the clothing surface temperature; is the clothing coefficient, which is the thermal resistance of clothing Function of is the mean radiant temperature; is the convective heat transfer coefficient; and The functional relationship is:

[0028] (6)

[0029] S22. Considering the differences among individual users, their perception of hot and cold is not exactly the same. The predicted dissatisfaction percentage is used to measure the user's thermal comfort experience.

[0030] The calculation formula for the predicted dissatisfaction percentage is:

[0031] (7)

[0032] In formula (7), is the function value for predicting the percentage of dissatisfaction.

[0033] Furthermore, the method described in step S3 for dividing user groups with different thermal tolerance characteristics by using ISODATA clustering according to the human body parameters in the thermal comfort interval model is as follows:

[0034] S31. Energy metabolism rate based on different users Thermal resistance of clothing , using equations (5) and (7) to calculate the function values ​​of different predicted average votes , the function value of predicting the percentage of dissatisfaction ;

[0035] S32, Order and Minimum, according to formula (5)-formula (7) calculate the different user parameter arrays The most suitable indoor temperature ;

[0036] S33. According to the different tolerances of different users for temperature changes, the upper and lower limits of the tolerable indoor temperature changes are calculated as follows:

[0037] (8)

[0038] In formula (9), and are the upper and lower limits of the temperature that the i-th user can tolerate; and are the upper dead zone and lower dead zone of the tolerable temperature of the i-th user respectively;

[0039] S34. According to formula (8), the room temperature range that the i-th user can tolerate is calculated as:

[0040] (9)

[0041] S35. Use ISODATA clustering to cluster the function values ​​of the predicted dissatisfaction percentages and the tolerable room temperature ranges corresponding to different users.

[0042] Furthermore, the method of clustering the function values ​​of the predicted dissatisfaction percentages and the tolerable room temperature ranges corresponding to different users by using ISODATA clustering in step S35 is specifically as follows:

[0043] S351. Assuming that there are N air-conditioning users, the function value of the predicted dissatisfaction percentage corresponding to each user is and the tolerable room temperature range are clustered into , construct the matrix , , used to store the function value of the predicted dissatisfaction percentage of N air-conditioning users and the tolerable room temperature range;

[0044] S352, Initialization: Select the initial number of clusters K, the minimum number of samples in each cluster , variance parameter , the minimum allowed distance between two clusters , maximum number of iterations ;

[0045] S353, normalize each dimension component in the matrix Y, and the normalization formula is:

[0046] (10)

[0047] In formula (10), and are the mean and standard deviation of the features, is the standardized sample point;

[0048] S354, calculate each standardized sample point With each cluster center The Euclidean distance is:

[0049] (11)

[0050] In formula (11), is the standardized sample point Coordinates in the mth dimension; is the cluster center Coordinates in the mth dimension;

[0051] Each standardized sample point Assign to the nearest cluster center:

[0052] (12)

[0053] In formula (12), represents the jth cluster, Represents sample points The distance to the center of the kth cluster is the smallest;

[0054] S355. For each cluster , calculate the mean of all data points belonging to the cluster as the new cluster center:

[0055] (13)

[0056] In formula (13), is the number of samples in the jth cluster;

[0057] S356: If the change in the cluster center is less than the preset threshold Or if the maximum number of iterations is reached, the iteration is stopped, otherwise go to S354;

[0058] S357, Split cluster and merge cluster

[0059] If a cluster Variance Exceeding the preset threshold , and the number of samples in the cluster is greater than 2 times the minimum number of samples , the cluster is split into two clusters; the new cluster center is determined by:

[0060] (14)

[0061] In formula (14), is the distance moved in the direction of maximum variance; is the unit vector in the direction of maximum variance, and They are the two new cluster centers obtained after the j-th cluster splits;

[0062] If two clusters and Center distance Less than the preset minimum distance threshold , or the number of samples in a cluster is less than the minimum number of samples , then the new cluster center obtained by merging these two clusters is:

[0063] (15)

[0064] In formula (15), and They are represented as the k-th cluster and the center of the k-th cluster respectively;

[0065] S358. Repeat S354 to S357 until a preset number of iterations is reached or the number and shape of clusters are stable.

[0066] Furthermore, the air conditioning load cluster control based on thermal comfort interval differences described in step S4 uses the thermal comfort threshold and room temperature tolerance range of each user group as the start and stop constraints of the air conditioner, as follows:

[0067] S41. After obtaining the information of wind power resources to be absorbed, the load aggregator issues instructions to the air conditioning load and other loads, and the air conditioning load and other loads will work together to absorb the wind power resources;

[0068] S42. The objective function of air conditioning load regulation is:

[0069] (16)

[0070] In formula (16), is the total power consumption of the air-conditioning unit at time t; is the output power of other loads at time t; is the output power value of the wind power equipment at time t; T is the time period;

[0071] S43. Air conditioning load users are divided into K categories, each category contains J air conditioning units, then the total power consumption of the air conditioning units at time t is:

[0072] (17)

[0073] In formula (17), j is the air conditioner user number; k is the air conditioner user group number; is the on / off status of the jth air conditioner in the kth type of air conditioner user at time t; is the power value of the jth air conditioner among the kth type of air conditioner users at time t;

[0074] The air conditioning load must meet the following constraints during the scheduling process:

[0075] (1) Power balance constraints:

[0076] (18)

[0077] In formula (18), is the power value on the tie line at time t;

[0078] (2) Air conditioning load switch constraints:

[0079] (19)

[0080] In formula (19), is an infinitesimally small time window; and are the PMV value and PPD value of the jth air conditioner in the kth air conditioner group at the end of the t-1 period; is the PDD threshold; and are the temperature setting value and indoor temperature value of the jth air conditioner in the kth air conditioner group, respectively.

[0081] An electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above-mentioned air conditioning load aggregation method considering the difference in user thermal comfort, and the processor is configured to execute the program stored in the memory.

[0082] A storage medium stores a computer program, which, when executed by a processor, executes the steps of the above-mentioned air conditioning load aggregation method considering differences in user thermal comfort.

[0083] The advantages of the present invention are:

[0084] (1) In view of the fact that the high-order ETP model has too much calculation amount and too long calculation time when simulating the indoor temperature change of the air conditioner, the present invention adopts a second-order ETP model that takes into account the indoor air temperature and solid temperature changes to model the indoor temperature change of the air conditioner. The model can achieve a very impressive calculation speed while maintaining a very high calculation accuracy;

[0085] (2) In view of the fact that existing studies on thermal comfort interval models do not take the differences in thermal tolerance and thermal comfort temperature intervals of different air-conditioning users as reference phases, this paper proposes a thermal comfort interval model based on environmental parameters and human body parameters, and comprehensively quantifies the user's comfort perception by calculating the predicted average voting value and the predicted percentage of dissatisfaction;

[0086] (3) The present invention uses the ISODATA clustering method to adaptively cluster the air conditioner group to form a group of air conditioner users that can characterize different thermal tolerance and different room temperature tolerance range characteristics; ISODATA has obvious advantages in dynamically adjusting the number of clusters, considering the shape and variance of the clusters, and overall adaptability;

[0087] (4) An air conditioning load control strategy based on user thermal comfort differences is proposed, with the thermal comfort threshold and room temperature tolerance range of each user group as the start and stop constraints of the air conditioning load. Compared with the control strategy that uses the preset temperature range as the start and stop constraints of the air conditioning load, the potential for each type of air conditioning load user to participate in scheduling is improved;

[0088] (5) Based on the thermal tolerance characteristics of a large number of air-conditioning load users, the present invention establishes an air-conditioning load aggregation model that comprehensively considers the differences in thermal comfort of users. The air-conditioning load after aggregation can be optimized and controlled to achieve the effect of smoothing the fluctuations of renewable energy and shaving peaks and filling valleys. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 It is a flow chart of the air conditioning load aggregation method considering the difference in thermal comfort of users of the present invention. DETAILED DESCRIPTION

[0090] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0091] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments:

[0092] Embodiment 1

[0093] like Figure 1 As shown, an air conditioning load aggregation method considering the difference in user thermal comfort includes the following steps:

[0094] Step 1: Establish a second-order ETP (Equivalent Thermal Parameters) model of air conditioning load that takes into account the changes in indoor air temperature and solid temperature, as follows:

[0095] Step 1.1, according to the equivalent circuit composed of resistors, capacitors and other circuit elements, the heat transfer and energy balance process between the air-conditioned room and the outside world is simulated, and the second-order ETP model used to describe the changes in indoor air temperature and solid temperature can be simplified as follows:

[0096] (1)

[0097] In formula (1), , , are the indoor air temperature, indoor solid temperature and outdoor air temperature at time t respectively; and are the equivalent heat capacity of indoor air and the equivalent heat capacity of indoor solid respectively; and They are the equivalent thermal resistance of indoor air and the equivalent thermal resistance of indoor solid respectively; The working power of the air conditioner.

[0098] Step 1.2: When the air conditioner is running, its power is the rated power. Under the cooling model, the relationship between its operating power and temperature is:

[0099] (2)

[0100] In formula (2), is the air conditioning energy efficiency ratio; is the rated power of the air conditioner; For air conditioning Working power at all times; and They are the upper and lower limits of the operating temperature, respectively, and are related to the set temperature The relationship between them is:

[0101] (3)

[0102] In formula (3), Dead zone for temperature control of air conditioner.

[0103] Step 1.3: If the air-conditioning unit is composed of N air conditioners, the total power consumption of the air-conditioning unit at time t is:

[0104] (4)

[0105] In formula (4), is the power consumption value of the nth air-conditioning device at time t.

[0106] The second-order ETP model describes the temperature changes of indoor air and solids by establishing heat balance equations, and considers the heat exchange process between them. Compared with the first-order ETP model, the second-order ETP model can more comprehensively and accurately describe the temperature changes in the building environment by introducing more dynamic parameters and complex heat transfer mechanisms, thereby improving the accuracy of temperature control and the energy management efficiency of the system.

[0107] Step 2: Establish a thermal comfort interval model that comprehensively considers environmental parameters and human parameters to quantify the user's thermal comfort experience, as follows:

[0108] Step 2.1: Based on the indoor temperature, mean radiant temperature, air velocity, air humidity, human energy metabolism rate and clothing thermal resistance, the predicted mean vote (PMV) is calculated to measure the thermal comfort experience of air-conditioning load users.

[0109] The calculation formula for the predicted average number of votes is:

[0110] (5)

[0111] In formula (5), is the function value for predicting the average number of votes; M is the energy metabolism rate of the human body, which is related to the degree of human activity; W is the mechanical work generated by human activity; is the water vapor pressure of the environment; is the indoor temperature; is the clothing surface temperature; is the clothing coefficient, which is the thermal resistance of clothing Function of is the mean radiant temperature; is the convective heat transfer coefficient; and The functional relationship is:

[0112] (6)

[0113] Step 2.2: Considering the differences among individual users, their perception of hot and cold is not exactly the same. The predicted percent dissatisfaction (PPD) is used to measure the user's thermal comfort experience.

[0114] The calculation formula for the predicted dissatisfaction percentage is:

[0115] (7)

[0116] In formula (7), is the function value for predicting the percentage of dissatisfaction.

[0117] In the process of air conditioning scheduling, the user's thermal comfort experience is an important factor to be considered. Previous studies mostly preset the user's thermal comfort temperature range based on experience, but this cannot reflect the user's actual thermal comfort situation. This embodiment introduces a thermal comfort range model, which combines environmental parameters and human body parameters, and accurately quantifies the user's thermal comfort experience by calculating PMV, PPD and tolerable room temperature range.

[0118] Step 3: Based on the human body parameters in the thermal comfort interval model, ISODATA clustering is used to divide user groups with different thermal tolerance characteristics, as follows:

[0119] Step 3.1: Energy metabolism rate based on different users Thermal resistance of clothing , using equations (5) and (7) to calculate the function values ​​of different predicted average votes , the function value of predicting the percentage of dissatisfaction .

[0120] Step 3.2, order and Minimum, according to formula (5)-formula (7) calculate the different user parameter arrays The most suitable indoor temperature ; The method is as follows: Use formula (6) to calculate , then combine , the formula (5) Minimal back-testing , and The monotonicity is the same when At minimum, Also the smallest.

[0121] Step 3.3: Based on the different tolerances of different users for temperature changes, calculate the upper and lower limits of the tolerable indoor temperature changes as follows:

[0122] (8)

[0123] In formula (9), and are the upper and lower limits of the temperature that the i-th user can tolerate; and are the upper dead zone and lower dead zone of the tolerable temperature of the i-th user respectively.

[0124] Step 3.4: Calculate the room temperature range that the i-th user can tolerate according to formula (8):

[0125] (9)

[0126] Step 3.5: Use the ISODATA (Iterative Self-Organizing Data Analysis Technique) clustering method to cluster the function values ​​of the predicted dissatisfaction percentages and the tolerable room temperature ranges corresponding to different users, as follows:

[0127] Step 3.5.1: Assume that there are N air-conditioning users. The function value of the predicted dissatisfaction percentage corresponding to each user is and the tolerable room temperature range are clustered into , construct the matrix , , which is used to store two indicators: the function value of the predicted dissatisfaction percentage of N air-conditioning users and the tolerable room temperature range.

[0128] Step 3.5.2, Initialization: Select the initial number of clusters K and the minimum number of samples in each cluster , variance parameter , the minimum allowed distance between two clusters , maximum number of iterations .

[0129] Step 3.5.3: Normalize each dimension component in the matrix Y. The normalization formula is:

[0130] (10)

[0131] In formula (10), and are the mean and standard deviation of the features, are the standardized sample points.

[0132] Step 3.5.4: Calculate each standardized sample point With each cluster center The Euclidean distance is:

[0133] (11)

[0134] In formula (11), is the standardized sample point Coordinates in the mth dimension; is the cluster center Coordinates in the mth dimension;

[0135] Each standardized sample point Assign to the nearest cluster center:

[0136] (12)

[0137] In formula (12), represents the jth cluster, Represents sample points The distance to the center of the kth cluster is the smallest.

[0138] Step 3.5.5: For each cluster , calculate the mean of all data points belonging to the cluster as the new cluster center:

[0139] (13)

[0140] In formula (13), is the number of samples in the jth cluster.

[0141] Step 3.5.6: If the change in the cluster center is less than the preset threshold Or if the maximum number of iterations is reached, stop the iteration, otherwise go to step 3.5.4.

[0142] Step 3.5.7: Split and merge clusters

[0143] If a cluster Variance Exceeding the preset threshold , and the number of samples in the cluster is greater than 2 times the minimum number of samples , the cluster is split into two clusters; the new cluster center is determined by:

[0144] (14)

[0145] In formula (14), is the distance moved in the direction of maximum variance; is the unit vector in the direction of maximum variance, and They are the two new cluster centers obtained after the j-th cluster splits;

[0146] If two clusters and Center distance Less than the preset minimum distance threshold , or the number of samples in a cluster is less than the minimum number of samples , then the new cluster center obtained by merging these two clusters is:

[0147] (15)

[0148] In formula (15), and are represented as the k-th cluster and the center of the k-th cluster respectively.

[0149] Step 3.5.8: Repeat steps 3.5.4 to 3.5.7 until the preset number of iterations is reached or the number and shape of clusters are stable.

[0150] For N air-conditioning users, users with strong heat tolerance are not sensitive to temperature changes. The value will be smaller; users with low thermal tolerance are sensitive to temperature changes. The value will be larger. and ,The ISODATA clustering method is used to cluster users with similar heat tolerance and tolerable room temperature range characteristics, thus realizing the division of air-conditioning user groups.

[0151] Step 4: Cluster control of air conditioning load based on thermal comfort interval differences, using the thermal comfort threshold and room temperature tolerance range of each user group as the start and stop constraints of the air conditioner, as follows:

[0152] Step 4.1: After obtaining the information of wind power resources that need to be absorbed, the load aggregator issues instructions to the air-conditioning load and other loads, and the air-conditioning load and other loads will work together to absorb the wind power resources.

[0153] Step 4.2: The objective function of air conditioning load regulation is:

[0154] (16)

[0155] In formula (16), is the total power consumption of the air-conditioning unit at time t; is the output power of other loads at time t; is the output power value of the wind power equipment at time t; T is the time period.

[0156] Step 4.3: The air conditioning load users are divided into K categories, each category contains J air conditioning units, and the total power consumption of the air conditioning units at time t is:

[0157] (17)

[0158] In formula (17), j is the air conditioner user number; k is the air conditioner user group number; is the on / off status of the jth air conditioner in the kth type of air conditioner user at time t; is the power value of the jth air conditioner among the kth type of air conditioner users at time t;

[0159] The air conditioning load must meet the following constraints during the scheduling process:

[0160] (1) Power balance constraints:

[0161] (18)

[0162] In formula (18), is the power value on the tie line at time t;

[0163] (2) Air conditioning load switch constraints:

[0164] (19)

[0165] In formula (19), is an infinitesimally small time window; and are the PMV value and PPD value of the jth air conditioner in the kth air conditioner group at the end of the t-1 period; is the PDD threshold; and are the temperature setting value and indoor temperature value of the jth air conditioner in the kth air conditioner group, respectively.

[0166] Based on the aggregation of air-conditioning users, the thermal comfort threshold and tolerable room temperature range of each user group are used as the switch constraints for air-conditioning start and stop. By minimizing the objective function of air-conditioning load participation in scheduling, the wind power resources in the system can be timely absorbed while ensuring the thermal comfort of users participating in scheduling. The air-conditioning load needs to meet the power balance constraint and PPD switch constraint during the scheduling process.

[0167] The present invention adopts a second-order ETP model to simulate the heat transfer and energy balance process between the air-conditioned room and the outside world, and realizes accurate simulation of indoor air temperature and solid temperature changes. Based on environmental parameters and human body parameters, a user thermal comfort interval model is established, and the user's comfort feeling is comprehensively quantified by calculating the predicted average voting value and the predicted dissatisfaction percentage. The ISODATA clustering algorithm is further used to adaptively cluster the air-conditioning group to form an air-conditioning user group cluster that can characterize the characteristics of thermal tolerance and room temperature tolerance range. Finally, the comfort threshold and room temperature tolerance range of each user group are used as the start and stop constraints of the air conditioner to realize the air-conditioning load cluster control strategy based on the difference in thermal comfort intervals.

[0168] Embodiment 2

[0169] An electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the air conditioning load aggregation method considering user thermal comfort differences in embodiment one, and the processor is configured to execute the program stored in the memory.

[0170] Embodiment 3

[0171] A storage medium stores a computer program, which, when executed by a processor, executes the steps of the air conditioning load aggregation method considering differences in thermal comfort of users in embodiment 1.

[0172] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An air conditioning load aggregation method considering differences in user thermal comfort, characterized in that: The following steps are involved: S1. Establish a second-order ETP model of air conditioning load that takes into account the changes in indoor air temperature and solid temperature. The specific method is as follows: S11. Based on the equivalent circuit composed of resistors, capacitors and other circuit elements, the heat transfer and energy balance process between the air-conditioned room and the outside world is simulated, and the second-order ETP model used to describe the changes in indoor air temperature and solid temperature is obtained as follows: （1） In formula (1), , , are the indoor air temperature, indoor solid temperature and outdoor air temperature at time t respectively; and are the equivalent heat capacity of indoor air and the equivalent heat capacity of indoor solid respectively; and They are the equivalent thermal resistance of indoor air and the equivalent thermal resistance of indoor solid respectively; The working power of the air conditioner; S12. When the air conditioner is running, its power is the rated power. Under the cooling model, the relationship between its operating power and temperature is: （2） In formula (2), is the air conditioning energy efficiency ratio; is the rated power of the air conditioner; For air conditioning Working power at all times; and They are the upper and lower limits of the operating temperature, respectively, and are related to the set temperature The relationship between them is: （3） In formula (3), Provide dead zone for temperature control of air conditioner; S13. If the air-conditioning unit is composed of N air conditioners, the total power consumption of the air-conditioning unit at time t is: （4） In formula (4), is the power consumption value of the nth air-conditioning device at time t; S2, establish a thermal comfort interval model that comprehensively considers environmental parameters and human parameters to quantify the user's thermal comfort experience. The specific method is as follows: S21. Calculate the predicted average vote number to measure the thermal comfort experience of air-conditioning load users based on indoor temperature, average radiant temperature, air flow velocity, air humidity, human energy metabolism rate and clothing thermal resistance; The calculation formula for the predicted average number of votes is: （5） In formula (5), is the function value for predicting the average number of votes; M is the energy metabolism rate of the human body, which is related to the degree of human activity; W is the mechanical work generated by human activity; is the water vapor pressure of the environment; is the indoor temperature; is the clothing surface temperature; is the clothing coefficient, which is the thermal resistance of clothing Function of is the mean radiant temperature; is the convective heat transfer coefficient; and The functional relationship is: （6） S22. Considering the differences among individual users, their perception of hot and cold is not exactly the same. The predicted dissatisfaction percentage is used to measure the user's thermal comfort experience. The calculation formula for the predicted dissatisfaction percentage is: （7） In formula (7), is the function value for predicting the percentage of dissatisfaction; S3. Based on the human body parameters in the thermal comfort interval model, ISODATA clustering is used to divide user groups with different thermal tolerance characteristics. The specific method is as follows: S31. Energy metabolism rate based on different users Thermal resistance of clothing , using equations (5) and (7) to calculate the function values ​​of different predicted average votes , the function value of predicting the percentage of dissatisfaction ; S32, Order and Minimum, according to formula (5)-formula (7) calculate the different user parameter arrays The most suitable indoor temperature ; S33. According to the different tolerances of different users for temperature changes, the upper and lower limits of the tolerable indoor temperature changes are calculated as follows: （8） In formula (9), and are the upper and lower limits of the temperature that the i-th user can tolerate; and are the upper dead zone and lower dead zone of the tolerable temperature of the i-th user respectively; S34. According to formula (8), the room temperature range that the i-th user can tolerate is calculated as: （9） S35. Using ISODATA clustering, cluster the function values ​​of the predicted dissatisfaction percentages and the tolerable room temperature ranges corresponding to different users. The specific method is as follows: S351. Assuming that there are N air-conditioning users, the function value of the predicted dissatisfaction percentage corresponding to each user is and the tolerable room temperature range are clustered into , construct the matrix , , used to store the function value of the predicted dissatisfaction percentage of N air-conditioning users and the tolerable room temperature range; S352, Initialization: Select the initial number of clusters K, the minimum number of samples in each cluster , variance parameter , the minimum allowed distance between two clusters , maximum number of iterations ; S353, normalize each dimension component in the matrix Y, and the normalization formula is: （10） In formula (10), and are the mean and standard deviation of the features, is the standardized sample point; S354, calculate each standardized sample point With each cluster center The Euclidean distance is: （11） In formula (11), is the standardized sample point Coordinates in the mth dimension; is the cluster center Coordinates in the mth dimension; Each standardized sample point Assign to the nearest cluster center: （12） In formula (12), represents the jth cluster, Represents sample points The distance to the center of the kth cluster is the smallest; S355. For each cluster , calculate the mean of all data points belonging to the cluster as the new cluster center: （13） In formula (13), is the number of samples in the jth cluster; S356: If the change in the cluster center is less than the preset threshold Or if the maximum number of iterations is reached, the iteration is stopped, otherwise go to S354; S357, Split cluster and merge cluster If a cluster Variance Exceeding the preset threshold , and the number of samples in the cluster is greater than 2 times the minimum number of samples , the cluster is split into two clusters; the new cluster center is determined by: （14） In formula (14), is the distance moved in the direction of maximum variance; is the unit vector in the direction of maximum variance, and They are the two new cluster centers obtained after the j-th cluster splits; If two clusters and Center distance Less than the preset minimum distance threshold , or the number of samples in a cluster is less than the minimum number of samples , then the new cluster center obtained by merging these two clusters is: （15） In formula (15), and They are represented as the k-th cluster and the center of the k-th cluster respectively; S358, repeat S354 to S357 until a preset number of iterations is reached or the number and shape of clusters are stable; S4. Cluster control of air conditioning load based on thermal comfort interval differences. The thermal comfort threshold and room temperature tolerance range of each user group are used as the start and stop constraints of the air conditioner. The specific method is as follows: S41. After obtaining the information of wind power resources to be absorbed, the load aggregator issues instructions to the air conditioning load and other loads, and the air conditioning load and other loads will work together to absorb the wind power resources; S42. The objective function of air conditioning load regulation is: （16） In formula (16), is the total power consumption of the air-conditioning unit at time t; is the output power of other loads at time t; is the output power value of the wind power equipment at time t; T is the time period; S43. Air conditioning load users are divided into K categories, each category contains J air conditioning units, then the total power consumption of the air conditioning units at time t is: （17） In formula (17), j is the air conditioner user number; k is the air conditioner user group number; is the on / off status of the jth air conditioner in the kth type of air conditioner user at time t; is the power value of the jth air conditioner among the kth type of air conditioner users at time t; The air conditioning load must meet the following constraints during the scheduling process: (1) Power balance constraints: （18） In formula (18), is the power value on the tie line at time t; (2) Air conditioning load switch constraints: （19） In formula (19), is an infinitesimally small time window; and are the PMV value and PPD value of the jth air conditioner in the kth air conditioner group at the end of the t-1 period; is the PDD threshold; and are the temperature setting value and indoor temperature value of the jth air conditioner in the kth air conditioner group, respectively.

2. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the air conditioning load aggregation method considering user thermal comfort differences as described in claim 1, and the processor is configured to execute the program stored in the memory.

3. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the air conditioning load aggregation method considering the difference in thermal comfort of users as claimed in claim 1 are executed.

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