Cluster air conditioning load regulation method, device and equipment based on variable demand of station line

Abstract

The application discloses a cluster air conditioner load regulation method and device based on station line variable demand and equipment, and relates to the technical field of air conditioner regulation. Compared with the existing air conditioner regulation method, the air conditioner regulation demand of different regional station line variables can be met, the dispatching area and the dispatching capacity are determined according to the different station line variable load rates, the actual demand of air conditioner regulation of each area is accurately controlled, the comfort of users and the flexibility of air conditioner load regulation are improved. Based on the multi-target demand, the cluster air conditioner day-ahead regulation strategy can start from the three aspects of power grid economic operation, control of participating users and consideration of temperature comfort, comprehensively consider the strategy difference under different types of regulation targets, realize the optimal adjustment of the cluster air conditioner, and promote the cluster air conditioner load to participate in the optimized operation of the power grid. Based on the cluster air conditioner short-time control scheme within a day, the user willingness participating in the regulation condition and the external temperature factor can be fully considered, the regulation deviation can be corrected in time, and the regulation accuracy is more accurate.

Description

Technical Field

[0001] The present application relates to the technical field of air conditioning control, and in particular to a cluster air conditioning load control method, device and equipment based on station-line variable demand. Background Art

[0002] With the rapid development of the national economy and the improvement of people's living standards, the growing proportion of air conditioning loads during the hot summer season has become a major factor in the difficulty of load scheduling and the chaos in distribution network planning. In addition, during prolonged high temperatures, power equipment in densely populated areas faces the risk of overloading or overloading, increasing the pressure on grid operations. Local areas will face short-term power supply shortages, further exacerbating the grid operation situation. Therefore, it is extremely necessary to implement demand response load regulation for such loads.

[0003] In the related art, existing cluster air conditioning load control methods are primarily limited to considering only physical factors, such as temperature, in the air conditioning control method. For example, by predicting the outdoor temperature of the base station within a future target time, the optimal operating time period of the air conditioner is determined to minimize the total power consumption of the air conditioner without exceeding a first predetermined indoor temperature standard. However, the applicants recognized that this solution relies primarily on outdoor temperature prediction to determine the air conditioner operating time period, considering only a single factor affecting energy consumption, resulting in a simplistic control strategy. This is particularly true in high-density population areas and when air conditioning loads are connected to the power grid in summer. This makes it impossible to fully optimize air conditioning operation, resulting in inaccurate control or failure to maximize energy consumption reduction, and lacks reliability. Summary of the Invention

[0004] In view of this, the present application provides a cluster air-conditioning load control method, device and equipment based on station-line variable demand. The main purpose is to solve the following problems: the existing solutions mainly rely on outdoor temperature prediction to determine the air-conditioning operation period, and only consider a single factor affecting energy consumption, which makes the control strategy appear single, especially in the face of high-density population areas and the connection of air-conditioning loads to the power grid in summer. It is impossible to fully optimize the air-conditioning operation, resulting in inaccurate control or failure to maximize energy consumption reduction, and lack of reliability.

[0005] According to the first aspect of the present application, a cluster air conditioning load control method based on station-line variable demand is provided, the method comprising:

[0006] When a station line variable overload signal is detected, determining a target dispatching area and an air conditioning load control target of the target dispatching area according to the station line variable overload signal;

[0007] Obtaining first actual control data, inputting the first actual control data and the air conditioning load control target of the target scheduling area into a day-ahead control model, and obtaining a day-ahead control strategy for cluster air conditioners in the target scheduling area, wherein the day-ahead control strategy for cluster air conditioners is generated based on economic operation requirements of the power grid, requirements of control participating users, and consideration of temperature comfort requirements;

[0008] Obtain second actual control data after the target scheduling area is controlled according to the cluster air-conditioning day-ahead control strategy, determine multiple control targets in the target scheduling area, and use the cluster air-conditioning intraday short-time control model to correct the control deviations of the multiple control targets, where the control targets are air conditioners with high potential for intraday control and low comfort requirements.

[0009] Optionally, determining a target scheduling area and an air conditioning load control target of the target scheduling area according to the station line variable overload signal includes:

[0010] Determine the target scheduling area indicated by the station-line change overload signal, and obtain the station-line change load rate of the target scheduling area carried by the station-line change overload signal;

[0011] The air conditioning load control target is determined based on the station-line variable load rate of the target scheduling area, wherein:

[0012]

[0013] Where ΔP tar is the air conditioning load control target, η is the station line variable load rate of the target scheduling area, Δη is the control demand load rate, which is 5%, η lim The upper limit of load rate control is 90%, P rate is the rated power.

[0014] Optionally, inputting the first actual control data and the air conditioning load control target of the target scheduling area into a day-ahead control model to obtain a day-ahead control strategy for cluster air conditioners in the target scheduling area includes:

[0015] The economic operation optimization module, the user participation optimization module, and the temperature comfort optimization module of the day-ahead control model are used to calculate the first actual control data respectively to obtain the minimum total grid operation cost, the minimum number of participating users, and the minimum comfort impact;

[0016] The minimum total grid operation cost, the minimum number of participating users, and the minimum comfort impact are used for normalization processing to obtain the cluster air conditioning day-ahead control strategy for the target scheduling area, wherein:

[0017]

[0018] Where F is the minimum total cost of grid operation, s G is the minimum number of participating users, H is the minimum comfort impact, F max is the maximum cost of system operation, S G max is the maximum number of user participation, H max is the maximum comfort impact, λ1 is the first weight coefficient, λ2 is the second weight coefficient, λ3 is the third weight coefficient, 0≤λ1≤1, 0≤λ2≤1, 0≤λ3≤1, and 0≤0≤λ1+λ2+λ3≤1;

[0019] The air conditioning load control target of the target scheduling area is used to generate a first constraint condition of the cluster air conditioning day-ahead control strategy of the target scheduling area, wherein the first constraint condition includes a first control target balance constraint and a first control number constraint, wherein:

[0020]

[0021] n time ≤n max

[0022] Where ΔP i is the control amount of air conditioner i based on the cluster air conditioner day-ahead control strategy, S is the total number of cluster air conditioners, ΔP tar is the target scheduling capacity, n time is the number of controls under a single control task, n max The maximum number of controls in a single control task.

[0023] Optionally, the economic operation optimization module, the user participation optimization module, and the temperature comfort optimization module using the day-ahead control model respectively calculate the actual control data to obtain the minimum total grid operation cost, the minimum number of participating users, and the minimum comfort impact, including:

[0024] The control amount of each air conditioner is obtained from the first actual control data, and the control amount of each air conditioner is calculated using the economic operation optimization module to obtain the minimum total grid operation cost, wherein:

[0025] F=μ1F1+μ2F2

[0026]

[0027] Wherein, F is the minimum total cost of grid operation, F1 is the grid regulation compensation cost, F2 is the grid comfort compensation cost, μ1 is the weight factor of the grid regulation compensation cost, μ2 is the weight factor of the grid comfort compensation cost, and μ1+μ2=1, ρ1 is the grid regulation compensation unit price, ρ2, The unit price for grid comfort compensation, is the compensation unit price of the maximum load control strategy I1, is the compensation unit price of the optimal comfort control strategy I2, ΔP i is the control quantity of air conditioner i, i∈[0,S], S is the total number of air conditioners in the cluster, The user comfort level before air conditioning adjustment. User comfort after air conditioning i is adjusted;

[0028] The user participation optimization module is used to calculate the first actual control data to obtain the minimum number of participating users, wherein:

[0029]

[0030] Among them, s G is the minimum number of participating users, ΔP i is the control quantity of air conditioner i, i∈[0,S], S is the total number of air conditioners in the cluster, ΔP i max is the maximum control amount of air conditioner i;

[0031] The temperature comfort optimization module is used to calculate the first actual control data to obtain the minimum comfort impact, wherein:

[0032]

[0033] Where H is the minimum comfort impact, ΔP i min The minimum control amount of air conditioner i, For maximum comfort with air conditioning i is the user comfort before air conditioner i is adjusted, i∈[0,S], S is the total number of air conditioners in the cluster.

[0034] Optionally, the adopting of the cluster air conditioning intraday short-term control model to perform control deviation correction on the multiple control targets includes:

[0035] For each of the control targets, obtaining an actual control power of the control target from the second actual control data, and comparing the actual control power with the expected control power;

[0036] If the actual control power is determined to be less than the expected control power, the current air-conditioning temperature is obtained from the second actual control data, and the correction factor is calculated using the current air-conditioning temperature, where:

[0037]

[0038] wherein θ is the correction factor, T set is the set temperature for regulation, T ex is the set temperature for maximum air conditioning load considering user comfort, T(t) is the air conditioning temperature at the current time;

[0039] obtaining a correction factor corresponding to each of the regulation targets, to obtain a plurality of correction factors;

[0040] obtaining the current regulation amount of the plurality of regulation targets in the second actual regulation data, calculating the current regulation amount of the plurality of regulation targets based on the cluster air conditioner short-time control model in a day using the plurality of correction factors to obtain a regulation deviation amount, and performing regulation deviation correction on the plurality of regulation targets using the regulation deviation amount, wherein

[0041]

[0042] wherein ΔP er is the regulation deviation amount, m is the number of regulation targets, ΔP i·short is the current regulation amount of air conditioner i, ΔP i·plan represents the target regulation power, θ i is the correction factor of air conditioner i;

[0043] generating a second constraint condition using the air conditioning load regulation target of the target scheduling area, the second constraint condition including a temperature set value constraint, a second regulation target balance constraint, and a second regulation frequency constraint, wherein

[0044] T set ≤ T ex

[0045]

[0046] n time ≤ n max

[0047] wherein T set is the set temperature for regulation, T ex is the set temperature for maximum air conditioning load considering user comfort, ΔP i is the regulation amount of air conditioner i in response to the cluster air conditioner day-ahead regulation strategy, S is the total number of cluster air conditioners, ΔP tar is the target scheduling capacity, n time is the regulation frequency under a single regulation task, n max is the maximum regulation frequency under a single regulation task.

[0048] Optionally, after the actual regulation power is compared with the expected regulation power, the method further includes:

[0049] If the comparison determines that the actual control power is greater than or equal to the expected control power, the value of the correction factor is 1.

[0050] Optionally, after correcting the control deviations of the multiple control targets using the control deviations, the method includes:

[0051] Obtaining the actual controlled power of each of the controlled targets after the control deviation correction operation, and comparing the actual controlled power of each of the controlled targets after the control with the expected controlled power;

[0052] If there is a control target among the multiple control targets whose actual control power after control is less than the expected control power, the correction factor of the control target is recalculated, and the control deviation of the control target is corrected based on the cluster air conditioning intraday short-time control model.

[0053] According to a second aspect of the present application, a cluster air conditioning load control device based on station-line transformer demand is provided, the device comprising:

[0054] a determination module, configured to, when a station line variable overload signal is detected, determine a target scheduling area and an air conditioning load control target of the target scheduling area according to the station line variable overload signal;

[0055] a day-ahead control module, configured to obtain first actual control data, input the first actual control data and the air conditioning load control target of the target scheduling area into a day-ahead control model, and obtain a day-ahead control strategy for the cluster air conditioning in the target scheduling area, wherein the day-ahead control strategy for the cluster air conditioning is generated based on the economic operation requirements of the power grid, the requirements of the control participating users, and the consideration of the temperature comfort requirements;

[0056] The intraday control module is used to obtain the second actual control data after the target scheduling area is controlled according to the cluster air-conditioning day-ahead control strategy, determine multiple control targets in the target scheduling area, and use the cluster air-conditioning intraday short-term control model to correct the control deviation of the multiple control targets. The control target is an air-conditioning with high potential for participating in intraday control and low comfort requirements.

[0057] Optionally, the determination module is configured to determine the target scheduling area indicated by the station-line variable overload signal, obtain the station-line variable load rate of the target scheduling area carried by the station-line variable overload signal; and determine the air-conditioning load control target based on the station-line variable load rate of the target scheduling area, wherein:

[0058]

[0059] Where ΔP taris the air conditioning load control target, η is the station line variable load rate of the target scheduling area, Δη is the control demand load rate, which is 5%, η lim The upper limit of load rate control is 90%, P rate is the rated power.

[0060] Optionally, the day-ahead control module is configured to respectively calculate the first actual control data using the economic operation optimization module, the user participation optimization module, and the temperature comfort optimization module of the day-ahead control model to obtain the minimum total grid operation cost, the minimum number of participating users, and the minimum comfort impact; and perform per-unit normalization processing using the minimum total grid operation cost, the minimum number of participating users, and the minimum comfort impact to obtain the day-ahead control strategy for the cluster air conditioner in the target scheduling area, wherein:

[0061]

[0062] Where F is the minimum total cost of grid operation, s G is the minimum number of participating users, H is the minimum comfort impact, F max is the maximum cost of system operation, is the maximum number of user participation, H max is the maximum comfort impact, λ1 is the first weight coefficient, λ2 is the second weight coefficient, λ3 is the third weight coefficient, 0≤λ1≤1, 0≤λ2≤1, 0≤λ3≤1, and 0≤0≤λ1+λ2+λ2≤1; the air-conditioning load control target of the target scheduling area is used to generate the first constraint condition of the cluster air-conditioning day-ahead control strategy of the target scheduling area, the first constraint condition includes the first control target balance constraint and the first control number constraint, wherein,

[0063]

[0064] n time ≤n max

[0065] Where ΔP i is the control amount of air conditioner i based on the cluster air conditioner day-ahead control strategy, S is the total number of cluster air conditioners, ΔP tar is the target scheduling capacity, n time is the number of controls under a single control task, n max The maximum number of controls in a single control task.

[0066] Optionally, the day-ahead control module is configured to obtain the control amount of each air conditioner from the first actual control data, and calculate the control amount of each air conditioner using the economic operation optimization module to obtain the minimum total grid operation cost, wherein:

[0067] F=μ1F1+μ2F2

[0068]

[0069] Wherein, F is the minimum total cost of grid operation, F1 is the grid regulation compensation cost, F2 is the grid comfort compensation cost, μ1 is the weight factor of the grid regulation compensation cost, μ2 is the weight factor of the grid comfort compensation cost, and μ1+μ2=1, ρ1 is the grid regulation compensation unit price, ρ 2, The unit price for grid comfort compensation, is the compensation unit price of the maximum load control strategy I1, is the compensation unit price of the optimal comfort control strategy I2, ΔP i is the control quantity of air conditioner i, i∈[0,S], S is the total number of air conditioners in the cluster, The user comfort level before air conditioning adjustment. is the user comfort after air conditioner i is regulated; the user participation optimization module is used to calculate the first actual regulation data to obtain the minimum number of participating users, where

[0070]

[0071] Among them, s G is the minimum number of participating users, ΔP i is the control quantity of air conditioner i, i∈[0,S], S is the total number of air conditioners in the cluster, ΔP i max is the maximum control amount of air conditioner i; the temperature comfort optimization module is used to calculate the first actual control data to obtain the minimum comfort impact, wherein,

[0072]

[0073] Where H is the minimum comfort impact, ΔP i min The minimum control amount of air conditioner i, For maximum comfort with air conditioning i is the user comfort before air conditioner i is adjusted, i∈[0,S], S is the total number of air conditioners in the cluster.

[0074] Optionally, the intraday control module is configured to obtain, for each of the control targets, the actual control power of the control target from the second actual control data, and compare the actual control power with the expected control power; if the comparison determines that the actual control power is less than the expected control power, obtain the air-conditioning temperature at the current moment from the second actual control data, and calculate the correction factor using the air-conditioning temperature at the current moment, wherein:

[0075]

[0076] Wherein, θ is the correction factor, T set To control the set temperature, T ex The maximum air-conditioning load setting temperature is considered under user comfort, T(t) is the air-conditioning temperature at the current moment; a correction factor corresponding to each of the control targets is obtained to obtain multiple correction factors; current control amounts of multiple control targets are obtained from the second actual control data, and based on the cluster air-conditioning intraday short-term control model, the current control amounts of the multiple control targets are calculated using the multiple correction factors to obtain control deviations, and the control deviations are used to correct the control deviations of the multiple control targets, wherein,

[0077]

[0078] Where ΔP er is the control deviation, m is the number of control targets, ΔP i·short is the current control value of air conditioner i, ΔP i·olan represents the target control power, θ i is the correction factor of air conditioner i; the air conditioning load control target of the target scheduling area is used to generate a second constraint condition, the second constraint condition includes a temperature setting value constraint, a second control target balance constraint, and a second control number constraint, wherein,

[0079] T set ≤T ex

[0080]

[0081] n time ≤n max

[0082] Among them, T set To control the set temperature, T ex To set the temperature for the maximum air conditioning load considering user comfort, ΔP i is the control amount of air conditioner i based on the cluster air conditioner day-ahead control strategy, S is the total number of cluster air conditioners, ΔP tar is the target scheduling capacity, n time is the number of controls under a single control task, n max The maximum number of controls in a single control task.

[0083] Optionally, the intraday control module is configured to set the value of the correction factor to 1 if comparison determines that the actual control power is greater than or equal to the expected control power.

[0084] Optionally, the intraday control module is used to obtain the actual control power after control of each of the control targets after the control deviation correction operation, and compare the actual control power after control of each of the control targets with the expected control power; if there is a control target among the multiple control targets whose actual control power after control is less than the expected control power, the correction factor of the control target is recalculated, and the control deviation of the control target is corrected based on the intraday short-time control model of the cluster air conditioner.

[0085] According to a third aspect of the present application, a device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0086] By means of the above technical solution, the present application provides a cluster air-conditioning load control method, device and equipment based on station-line variable demand. When a station-line variable overload signal is detected, the present application determines the target scheduling area and the air-conditioning load control target of the target scheduling area according to the station-line variable overload signal, obtains the first actual control data, inputs the first actual control data and the air-conditioning load control target of the target scheduling area into the day-ahead control model, obtains the cluster air-conditioning day-ahead control strategy of the target scheduling area, obtains the second actual control data after the target scheduling area is controlled according to the cluster air-conditioning day-ahead control strategy, determines multiple control targets in the target scheduling area, and uses the cluster air-conditioning intraday short-term control model to correct the control deviation of the multiple control targets. The cluster air-conditioning day-ahead control strategy is generated based on the economic operation demand of the power grid, the control demand of participating users and the consideration of temperature comfort demand. The control target is the air-conditioning with high potential for participating in intraday control and low comfort requirements. Compared to existing air conditioning control methods, the proposed method for cluster air conditioning load day-ahead and intraday control based on station-line variable demand can meet the air conditioning control needs of station-line variable loads in different regions. It determines the dispatching area and dispatching capacity based on the different station-line variable load rates, and then controls and dispatches the power grid. The actual air conditioning control needs of each region are precisely controlled, greatly improving user comfort and the flexibility of air conditioning load control. The multi-objective day-ahead control strategy for cluster air conditioning, which takes into account the economic operation of the power grid, the control of participating users, and the consideration of temperature comfort, comprehensively considers the differences in strategies under different types of control objectives, achieves optimal regulation of cluster air conditioning, and promotes the participation of cluster air conditioning loads in optimized power grid operation. Furthermore, the cluster air conditioning intraday short-term control scheme fully considers user willingness to participate in control and external temperature factors, promptly corrects control deviations, and achieves iterative solution of the day-ahead control scheme and the intraday control scheme, achieving faster control time and more accurate control accuracy.

[0087] The above description is only a summary of the technical solutions of the present application. In order to enable one skilled in the art to better understand the technical means of the present application, the present application can be implemented according to the content of the description, and in order to enable the above and other purposes, characteristics and advantages of the present application to be more apparent and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0088] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a description of the preferred embodiments, and are not meant to limit the present application. Moreover, the same reference numerals are used throughout the various drawings to designate identical parts. In the drawings:

[0089] Figure 1 A flowchart of a method for cluster air conditioner load regulation based on variable demand of station line provided by an embodiment of the present application is shown;

[0090] Figure 2A A flowchart of another method for cluster air conditioner load regulation based on variable demand of station line provided by an embodiment of the present application is shown;

[0091] Figure 2B A flowchart of short-time control of cluster air conditioner within a day provided by an embodiment of the present application is shown;

[0092] Figure 2C A flowchart of cluster air conditioner load regulation within a day based on variable demand of station line provided by an embodiment of the present application is shown;

[0093] Figure 3 A structural diagram of cluster air conditioner load regulation based on variable demand of station line provided by an embodiment of the present application is shown;

[0094] Figure 4 A structural diagram of a device provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0095] Exemplary embodiments of the present application will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. Rather, these embodiments are provided so that the present application can be more thoroughly understood and the scope of the present application can be accurately conveyed to those skilled in the art.

[0096] An embodiment of the present application provides a method for cluster air conditioner load regulation based on variable demand of station line, as shown in Figure 1 The method comprises:

[0097] 101. When a station line overload signal is detected, the target dispatching area and the air conditioning load control target of the target dispatching area are determined according to the station line overload signal.

[0098] Existing cluster air conditioning load control methods primarily consider only physical factors, such as temperature. However, these approaches fail to fully address the complexities of air conditioning control strategies when multiple factors interact. In densely populated areas, the impact of summer air conditioning loads on the power system is significant, resulting in a single, unreliable control strategy.

[0099] To address this issue, this application proposes a cluster air conditioning load control method based on station-line variable demand. When a station-line variable load rate overload occurs in a certain area of ​​the power grid, the dispatching area and dispatching capacity are determined based on the station-line variable overload signal, and the power grid dispatching demand is issued. Based on the cluster air conditioning control demand of the station-line variable, a cluster air conditioning day-ahead control strategy that considers multiple objective demands is formed. Finally, for user groups with high control potential and low comfort requirements, a cluster air conditioning intraday short-term control scheme is adopted, and a correction factor is introduced to correct the actual responsiveness of the control load, making the control accuracy more accurate. The execution subject of this application can be an air conditioning control system, which relies on the computing power of the server to provide services to users. The server can be a standalone server or a server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and basic cloud computing such as big data and artificial intelligence platforms, so as to achieve precise day-ahead and intraday control of cluster air conditioning loads.

[0100] In an embodiment of the present application, when a station-line transformer overload signal is detected, it indicates that the station-line transformer load rate in a certain area of ​​the power grid is overloaded. Therefore, the air-conditioning control system determines the target scheduling area and the air-conditioning load control target of the target scheduling area according to the station-line transformer overload signal, and issues power grid control and scheduling requirements, so that the actual air-conditioning control requirements of each area are accurately controlled, greatly improving the user's comfort and the flexibility of air-conditioning load control.

[0101] 102. Obtain first actual control data, input the first actual control data and the air conditioning load control target of the target scheduling area into a day-ahead control model, and obtain a day-ahead control strategy for the cluster air conditioning in the target scheduling area.

[0102] In an embodiment of the present application, the air-conditioning control system obtains first actual control data, inputs the first actual control data and the air-conditioning load control target of the target scheduling area into the day-ahead control model, and obtains the day-ahead control strategy of the cluster air-conditioning in the target scheduling area, wherein the day-ahead control strategy of the cluster air-conditioning is generated based on the economic operation needs of the power grid, the control requirements of participating users, and the consideration of temperature comfort requirements. By comprehensively considering the strategy differences under different types of control targets, it is possible to achieve optimal adjustment of the cluster air-conditioning and promote the cluster air-conditioning load to participate in the optimized operation of the power grid.

[0103] 103. Obtain the second actual control data after the target scheduling area is controlled according to the cluster air conditioning day-ahead control strategy, determine multiple control targets in the target scheduling area, and use the cluster air conditioning intraday short-term control model to correct the control deviations of the multiple control targets.

[0104] In an embodiment of the present application, the air-conditioning control system obtains the second actual control data after the target scheduling area is controlled according to the cluster air-conditioning day-ahead control strategy, determines multiple control targets in the target scheduling area, and uses the cluster air-conditioning intraday short-time control model to correct the control deviations of the multiple control targets. By combining the day-ahead control and intraday control methods, the air-conditioning load control can achieve the goal of more reasonable optimization and more precise control, wherein the control target is an air-conditioning with high potential for intraday control and low comfort requirements.

[0105] The method provided in an embodiment of the present application, when a station-line transformer overload signal is detected, determines a target dispatching area and an air conditioning load control target for the target dispatching area based on the station-line transformer overload signal, obtains first actual control data, inputs the first actual control data and the air conditioning load control target for the target dispatching area into a day-ahead control model, obtains a cluster air conditioning day-ahead control strategy for the target dispatching area, obtains second actual control data after controlling the target dispatching area according to the cluster air conditioning day-ahead control strategy, determines multiple control targets for the target dispatching area, and uses a cluster air conditioning intraday short-term control model to correct control deviations for the multiple control targets. The cluster air conditioning day-ahead control strategy is generated based on the economic operation requirements of the power grid, the requirements of participating users, and thermal comfort requirements. The control target is air conditioners with high intraday control potential and low comfort requirements. Compared with existing air conditioning control methods, the cluster air conditioning load day-ahead and intraday control method based on station-line transformer demand proposed in this application can meet the air conditioning control requirements of station-line transformers in different regions. The dispatching area and dispatching capacity are determined based on the different station-line transformer load rates, and the power grid is controlled and dispatched. The actual air conditioning control requirements of each region are precisely controlled, greatly improving user comfort and the flexibility of air conditioning load control. The multi-objective day-ahead control strategy for cluster air conditioners takes into account the needs of grid economic operation, control of participating users, and thermal comfort. It comprehensively considers the differences in strategies under different control objectives to achieve optimal regulation of cluster air conditioners and promote the participation of cluster air conditioner loads in grid optimization. Furthermore, the cluster air conditioner's intraday short-term control scheme fully considers user willingness to participate in control and external temperature factors, promptly correcting control deviations and achieving iterative solution of day-ahead and intraday control schemes, resulting in faster control time and more accurate control precision.

[0106] Furthermore, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, the embodiment of the present application provides another cluster air conditioning load control method based on station line variable demand, such as Figure 2A As shown, the method includes:

[0107] 201. When a station line overload signal is detected, the target dispatching area and the air conditioning load control target of the target dispatching area are determined according to the station line overload signal.

[0108] In an embodiment of the present application, when a station-line transformer overload signal is detected, it indicates that the air conditioning control system determines that the station-line transformer load rate in a certain area of ​​the power grid is overloaded, and it is necessary to determine the dispatching area and dispatching capacity based on the station-line transformer overload signal, and issue the power grid control and dispatching requirements. Specifically, the air conditioning control system determines the target dispatching area indicated by the station-line transformer overload signal, and obtains the station-line transformer load rate of the target dispatching area carried by the station-line transformer overload signal. Then, the air conditioning control system determines the air conditioning load control target based on the station-line transformer load rate of the target dispatching area, and the calculation formula is the following formula 1:

[0109]

[0110] Where ΔP tar is the air conditioning load control target, η is the station line variable load rate in the target scheduling area, Δη is the control demand load rate, which is 5%, η lim The upper limit of load rate control is 90%, P rate is the rated power. It should be noted that the air conditioning control system can simultaneously receive multiple station line variable overload signals and control multiple areas at the same time. When the corresponding regional station line variable load rate η < 90%, no cluster air conditioning control is performed; when the corresponding regional station line variable load rate η∈(90%,100%), the cluster air conditioning load reduction demand is controlled by 5%; when the corresponding regional station line variable load rate η> 100%, the cluster air conditioning load reduction demand is controlled to the maximum limit percentage. Based on the cluster air conditioning control demand of the station line variable, it is possible to achieve the transition from the regional station line variable load rate requirement to the load reduction amount, so that the actual air conditioning control demand of each area can be accurately controlled, greatly improving user comfort and the flexibility of air conditioning load control.

[0111] 202. The economic operation optimization module, user participation optimization module, and temperature comfort optimization module of the day-ahead control model are used to calculate the first actual control data respectively to obtain the minimum total grid operation cost, the minimum number of participating users, and the minimum comfort impact.

[0112] In the embodiments of the present application, cluster air conditioning control modes vary according to their needs, and control strategies are also different. This application determines the control targets of the air conditioners participating in the control in a specific area based on the load rate requirements of the area corresponding to the air conditioner. By comprehensively considering the differences in strategies under different types of control targets, an optimization target and constraints are established that take into account economy, number of users, and comfort. Specifically, the air conditioning control system obtains the control quantity of each air conditioner from the first actual control data, and uses the economic operation optimization module to calculate the control quantity of each air conditioner to obtain the minimum total grid operation cost. The calculation formula is as follows:

[0113] Formula 2: F = μ1F1 + μ2F2

[0114]

[0115] Among them, F is the minimum total cost of grid operation, F1 is the grid regulation compensation cost, F2 is the grid comfort compensation cost, μ1 is the weight factor of grid regulation compensation cost, μ2 is the weight factor of grid comfort compensation cost, and μ1+μ2=1, ρ1 is the grid regulation compensation unit price, ρ 2, The unit price for grid comfort compensation, is the compensation unit price of the maximum load control strategy I1, is the compensation unit price of the optimal comfort control strategy I2, ΔP i is the control quantity of air conditioner i, i∈[0,S], S is the total number of air conditioners in the cluster, The user comfort level before air conditioning adjustment. The user comfort level after air conditioner i is adjusted is calculated by using the predicted mean voting value (PMV) based on data such as the actual human metabolic rate, indoor temperature, relative humidity, and air velocity. If PMV = 0, the user is comfortable. A PMV greater than 0 indicates a warmer environment, while a PMV less than 0 indicates a cooler environment.

[0116] Next, the air conditioning control system uses the user participation optimization module to calculate the first actual control data to obtain the minimum number of participating users. The calculation formula is as follows:

[0117] Formula 3:

[0118] Among them, s G is the minimum number of participating users, ΔP i is the control quantity of air conditioner i, i∈[0,S], S is the total number of air conditioners in the cluster, ΔP i max is the maximum control amount of air conditioner i. Under the user participation target, the minimum number of users participating in the control needs to be met. Therefore, according to regional differences, the control is carried out under this target demand. When the operation optimization target is user participation, the maximum load limit strategy is adopted. Therefore, ΔP i max It means that users need to meet the minimum number of participating users under the maximum load reduction control strategy. If the number of participating users exceeds the minimum, no control will be carried out.

[0119] Subsequently, the air conditioning control system uses the temperature comfort optimization module to calculate the first actual control data to obtain the minimum comfort impact, which is calculated using the following formula 4:

[0120] Formula 4:

[0121]

[0122] Wherein, H is the minimum comfort influence, ΔP i min The minimum regulation amount of air conditioner i, The maximum comfort degree regulated by air conditioner i, The user comfort degree before the regulation of air conditioner i, i∈[0, S], S is the total number of cluster air conditioners. Under the temperature comfort target, the regulation amount of the power grid to each cluster air conditioner is required to be minimum, and the comfort influence is required to be minimum, so according to the regional difference, the regulation is controlled under this target demand, and the operation optimization target is to adopt the best comfort control strategy when the temperature comfort is used.

[0123] 203, the minimum total cost of power grid operation, the minimum number of participating users, and the minimum comfort influence are standardized to obtain the cluster air conditioner day-ahead regulation strategy of the target scheduling area.

[0124] In the embodiment of the present application, the three targets F, s G , and G units are different, and cannot be directly added to obtain a comprehensive target, so they are maximally standardized to scale the data to [0, 1]. Specifically, the air conditioner regulation system adopts the minimum total cost of power grid operation, the minimum number of participating users, and the minimum comfort influence to standardize and obtain the cluster air conditioner day-ahead regulation strategy of the target scheduling area, and the calculation formula is formula 5 as follows:

[0125] Formula 5:

[0126] Wherein, F is the minimum total cost of power grid operation, s G is the minimum number of participating users, H is the minimum comfort influence, F max is the maximum system operation cost, is the maximum number of users participating, H max is the maximum comfort influence, λ1 is the first weight coefficient, λ2 is the second weight coefficient, λ3 is the third weight coefficient, 0≤λ1≤1, 0≤λ2≤1, 0≤λ3≤1, and 0≤0≤λ1+λ2+λ3≤1. The cluster air conditioner day-ahead regulation strategy based on the multi-target demand is from the demand of power grid economic operation, control of participating users, and consideration of temperature comfort, comprehensively considers the strategy difference under different types of regulation targets, and can realize the optimal adjustment of the cluster air conditioner, and promote the cluster air conditioner load to participate in the optimized operation of the power grid.

[0127] 204, the air conditioner load regulation target of the target scheduling area is used to generate the first constraint condition of the cluster air conditioner day-ahead regulation strategy of the target scheduling area.

[0128] In an embodiment of the present application, the air conditioning control system uses the air conditioning load control target of the target scheduling area to generate the first constraint of the cluster air conditioning day-ahead control strategy of the target scheduling area. The first constraint includes a first control target balance constraint and a first control number constraint. The first control target constraint means that the air conditioning control response must meet the control target of the power grid. The first control number constraint means that the control number of the power grid when executing a single control task cannot exceed the maximum control number. The calculation formula is as shown in the following formula 6:

[0129] Formula 6:

[0130] n time ≤n max

[0131] Where ΔP i is the control quantity of air conditioner i under the day-ahead control strategy of cluster air conditioners, S is the total number of cluster air conditioners, ΔP tar is the target scheduling capacity, n time is the number of controls under a single control task, n max The maximum number of controls in a single control task.

[0132] 205. Obtain second actual control data after the target scheduling area is controlled according to the cluster air conditioning day-ahead control strategy, and determine multiple control targets in the target scheduling area.

[0133] In an embodiment of the present application, the air conditioning control system obtains the second actual control data after the target scheduling area is controlled according to the cluster air conditioning's day-ahead control strategy, and determines multiple control targets in the target scheduling area, wherein the control target is an air conditioner with high potential for intraday control and low comfort requirements. This method has fast control time and high control accuracy, which can further improve the accuracy of cluster air conditioning control.

[0134] 206. For each control target, obtain the actual control power of the control target in the second actual control data, and compare the actual control power with the expected control power; if the comparison determines that the actual control power is less than the expected control power, execute the following step 207; if the comparison determines that the actual control power is greater than or equal to the expected control power, execute the following step 208.

[0135] In an embodiment of the present application, for each control target, the air-conditioning control system obtains the actual control power of the control target in the second actual control data, and compares the actual control power with the expected control power; if the comparison determines that the actual control power is less than the expected control power, it means that deviation correction is required based on factors such as the air-conditioning temperature, and then the following step 207 is executed; if the comparison determines that the actual control power is greater than or equal to the expected control power, it means that deviation correction is not required, and then the following step 208 is executed.

[0136] 207. If the comparison determines that the actual control power is less than the expected control power, obtain the current air-conditioning temperature from the second actual control data, and calculate the correction factor using the current air-conditioning temperature.

[0137] In the embodiment of the present application, considering the user's willingness to participate in the control and the external temperature factors, control deviation is prone to occur. Therefore, a correction factor is introduced to correct the actual responsiveness of the control load for the control deviation. Specifically, if the comparison determines that the actual control power is less than the expected control power, the air conditioning control system obtains the current air conditioning temperature from the second actual control data and uses the current air conditioning temperature to calculate the correction factor. The calculation formula is as follows:

[0138] Formula 7:

[0139] Where θ is the correction factor, T set To control the set temperature, T ex The temperature is set to the maximum air-conditioning load considering user comfort, and T(t) is the air-conditioning temperature at the current moment.

[0140] 208. If the comparison determines that the actual control power is greater than or equal to the expected control power, the value of the correction factor θ is 1.

[0141] 209. Obtain current control quantities of multiple control targets from the second actual control data, calculate the current control quantities of the multiple control targets based on the cluster air conditioning intraday short-term control model using multiple correction factors to obtain control deviations, and use the control deviations to correct the control deviations of the multiple control targets.

[0142] In the embodiment of the present application, the air conditioning control system obtains a correction factor corresponding to each control target, obtaining multiple correction factors. Next, the air conditioning control system obtains the current control amounts of the multiple control targets from the second actual control data, and based on the cluster air conditioning intraday short-term control model, uses the multiple correction factors to calculate the current control amounts of the multiple control targets to obtain control deviations. The control deviations are then used to correct the control deviations of the multiple control targets, using the following formula 8:

[0143] Formula 8:

[0144] Where ΔP er is the control deviation, m is the number of control targets, ΔP i·t is the current control value of air conditioner i, ΔP i·n Indicates the target control power, which is issued by the power grid, θ iis the correction factor of air conditioner i. By targeting users with high control potential and low comfort requirements, the control amount ΔP of air conditioner i at this time is adjusted. i`short Make corrections to achieve precise control.

[0145] The second constraint condition is generated by using the air conditioning load control target of the target scheduling area. The second constraint condition for the short-term control of the cluster air conditioning during the day includes the temperature setting value constraint, the second control target balance constraint, and the second control number constraint. Among them, the temperature setting value constraint means that the air conditioning temperature setting temperature at any time during the control period cannot be greater than T ex , to prevent too much impact on user comfort. The second control target balance constraint indicates that the air conditioning control response must meet the control target of the power grid. The second control number constraint indicates that the number of controls when the power grid performs a single control task cannot exceed the maximum number of controls. The calculation formula is as follows:

[0146] Formula 9: T set ≤T ex

[0147]

[0148] n time ≤n max

[0149] Among them, T set To control the set temperature, T ex To set the temperature for the maximum air conditioning load considering user comfort, ΔP i is the control quantity of air conditioner i under the day-ahead control strategy of cluster air conditioners, S is the total number of cluster air conditioners, ΔP tar is the target scheduling capacity, n time is the number of controls under a single control task, n max The maximum number of controls in a single control task.

[0150] 210. Obtain the actual control power after control of each control target after the control deviation correction operation, and compare the actual control power after control of each control target with the expected control power; if there is a control target among multiple control targets whose actual control power after control is less than the expected control power, recalculate the correction factor of the control target, and correct the control deviation of the control target based on the cluster air conditioning intraday short-term control model.

[0151] In the embodiment of the present application, the air conditioning control system obtains the actual control power of each control target after the control deviation correction operation, and compares the actual control power of each control target with the expected control power; if there is a control target whose actual control power after control is less than the expected control power among multiple control targets, the correction factor of the control target is recalculated, and the control deviation of the control target is corrected based on the cluster air conditioning intraday short-term control model. Figure 2B As shown in the figure, after cluster air conditioning control based on multi-objective demands for the day ahead, control is then applied intraday to target user groups with high control potential and low comfort requirements. If an air conditioner fails to meet the scheduling requirements (i.e., the daily air conditioner control quantity demand), a correction factor is used to calculate the control deviation and perform short-term intraday corrections until the scheduling requirements are met. Here, i represents the air conditioner i participating in the intraday short-term control.

[0152] In summary, this application proposes a cluster air conditioning load day-ahead and intraday control method based on station-line variable demand. The schematic diagram is as follows:

[0153] like Figure 2C As shown, when the load rate of station-line transformers is overloaded in a certain area of ​​the power grid, a cluster air-conditioning control demand based on station-line transformers is proposed, and then a cluster air-conditioning day-ahead control strategy based on multi-objective demands is formed. Finally, a cluster air-conditioning intraday short-term control model is constructed to correct the control deviation. Compared with the existing air-conditioning control method, the control method proposed in this application can meet the air-conditioning control demand of station-line transformers in different areas, determine the dispatching area and dispatching capacity according to the different load rates of station-line transformers, and perform power grid control and dispatch. The actual demand for air-conditioning control in each area is accurately controlled, which greatly improves the user's comfort and the flexibility of air-conditioning load control, and promotes the cluster air-conditioning load to participate in the optimized operation of the power grid.

[0154] The method provided in an embodiment of the present application, when a station-line transformer overload signal is detected, determines a target dispatching area and an air conditioning load control target for the target dispatching area based on the station-line transformer overload signal, obtains first actual control data, inputs the first actual control data and the air conditioning load control target for the target dispatching area into a day-ahead control model, obtains a cluster air conditioning day-ahead control strategy for the target dispatching area, obtains second actual control data after controlling the target dispatching area according to the cluster air conditioning day-ahead control strategy, determines multiple control targets for the target dispatching area, and uses a cluster air conditioning intraday short-term control model to correct control deviations for the multiple control targets. The cluster air conditioning day-ahead control strategy is generated based on the economic operation requirements of the power grid, the requirements of participating users, and thermal comfort requirements. The control target is air conditioners with high intraday control potential and low comfort requirements. Compared with existing air conditioning control methods, the cluster air conditioning load day-ahead and intraday control method based on station-line transformer demand proposed in this application can meet the air conditioning control requirements of station-line transformers in different regions. The dispatching area and dispatching capacity are determined based on the different station-line transformer load rates, and the power grid is controlled and dispatched. The actual air conditioning control requirements of each region are precisely controlled, greatly improving user comfort and the flexibility of air conditioning load control. The multi-objective day-ahead control strategy for cluster air conditioners takes into account the needs of grid economic operation, control of participating users, and thermal comfort. It comprehensively considers the differences in strategies under different control objectives to achieve optimal regulation of cluster air conditioners and promote the participation of cluster air conditioner loads in grid optimization. Furthermore, the cluster air conditioner's intraday short-term control scheme fully considers user willingness to participate in control and external temperature factors, promptly correcting control deviations and achieving iterative solution of day-ahead and intraday control schemes, resulting in faster control time and more accurate control precision.

[0155] Further, as Figure 1 The specific implementation of the method, the embodiment of the present application provides a cluster air conditioning load control device based on station line change demand, such as Figure 3 As shown, the device includes: a determination module 301, a day-ahead control module 302 and an intraday control module 303.

[0156] A determination module 301 is configured to, when a station line variable overload signal is detected, determine a target scheduling area and an air conditioning load control target of the target scheduling area according to the station line variable overload signal;

[0157] A day-ahead control module 302 is configured to obtain first actual control data, input the first actual control data and the air conditioning load control target of the target scheduling area into a day-ahead control model, and obtain a day-ahead control strategy for the cluster air conditioning in the target scheduling area. The day-ahead control strategy for the cluster air conditioning is generated based on the economic operation requirements of the power grid, the requirements of the control participating users, and the consideration of temperature comfort requirements.

[0158] The intraday control module 303 is used to obtain the second actual control data after the target scheduling area is controlled according to the cluster air conditioner's day-ahead control strategy, determine multiple control targets in the target scheduling area, and use the cluster air conditioner's intraday short-term control model to correct the control deviation of the multiple control targets. The control target is an air conditioner with high potential for participating in intraday control and low comfort requirements.

[0159] In a specific application scenario, the determination module 301 is used to determine the target scheduling area indicated by the station-line variable overload signal, obtain the station-line variable load rate of the target scheduling area carried by the station-line variable overload signal; determine the air conditioning load control target based on the station-line variable load rate of the target scheduling area, wherein,

[0160]

[0161] Where ΔP tar is the air conditioning load control target, η is the station line variable load rate of the target scheduling area, Δη is the control demand load rate, which is 5%, η lim The upper limit of load rate control is 90%, P rate is the rated power.

[0162] In a specific application scenario, the day-ahead control module 302 is configured to use the economic operation optimization module, the user participation optimization module, and the temperature comfort optimization module of the day-ahead control model to calculate the first actual control data respectively to obtain the minimum total grid operation cost, the minimum number of participating users, and the minimum comfort impact; and perform per-unit normalization processing using the minimum total grid operation cost, the minimum number of participating users, and the minimum comfort impact to obtain the day-ahead control strategy for the cluster air conditioner in the target scheduling area, wherein:

[0163]

[0164] Where F is the minimum total cost of grid operation, s G is the minimum number of participating users, H is the minimum comfort impact, F max is the maximum cost of system operation, is the maximum number of user participation, H max is the maximum comfort impact, λ1 is the first weight coefficient, λ2 is the second weight coefficient, λ3 is the third weight coefficient, 0≤λ1≤1, 0≤λ2≤1, 0≤λ3≤1, and 0≤0≤λ1+λ2+λ3≤1; the air-conditioning load control target of the target scheduling area is used to generate the first constraint condition of the cluster air-conditioning day-ahead control strategy of the target scheduling area, the first constraint condition includes the first control target balance constraint and the first control number constraint, wherein,

[0165]

[0166] n time ≤n max

[0167] Where ΔP i is the control amount of air conditioner i based on the cluster air conditioner day-ahead control strategy, S is the total number of cluster air conditioners, ΔP tar is the target scheduling capacity, n time is the number of controls under a single control task, n max The maximum number of controls in a single control task.

[0168] In a specific application scenario, the day-ahead control module 302 is configured to obtain the control amount of each air conditioner from the first actual control data, calculate the control amount of each air conditioner using the economic operation optimization module, and obtain the minimum total grid operation cost, wherein:

[0169] F=μ1F1+μ2F2

[0170]

[0171] Wherein, F is the minimum total cost of grid operation, F1 is the grid regulation compensation cost, F2 is the grid comfort compensation cost, μ1 is the weight factor of the grid regulation compensation cost, μ2 is the weight factor of the grid comfort compensation cost, and μ1+μ2=1, ρ1 is the grid regulation compensation unit price, ρ 2, The unit price for grid comfort compensation, is the compensation unit price of the maximum load control strategy I1, is the compensation unit price of the optimal comfort control strategy I2, ΔP i is the control quantity of air conditioner i, i∈[0,S], S is the total number of air conditioners in the cluster, The user comfort level before air conditioning adjustment. is the user comfort after air conditioner i is regulated; the user participation optimization module is used to calculate the first actual regulation data to obtain the minimum number of participating users, where

[0172]

[0173] Among them, s G is the minimum number of participating users, ΔP i is the control quantity of air conditioner i, i∈[0,S], S is the total number of air conditioners in the cluster, ΔP i max is the maximum control amount of air conditioner i; the temperature comfort optimization module is used to calculate the first actual control data to obtain the minimum comfort impact, wherein,

[0174]

[0175] Where H is the minimum comfort impact, ΔP i min The minimum control amount of air conditioner i, For maximum comfort with air conditioning i control, is the user comfort before air conditioner i is adjusted, i∈[0,S], S is the total number of air conditioners in the cluster.

[0176] In a specific application scenario, the intraday control module 303 is used to obtain the actual control power of the control target in the second actual control data for each control target, and compare the actual control power with the expected control power; if the comparison determines that the actual control power is less than the expected control power, then obtain the current air-conditioning temperature in the second actual control data, and use the current air-conditioning temperature to calculate the correction factor, wherein,

[0177]

[0178] Wherein, θ is the correction factor, T set To control the set temperature, T ex The maximum air-conditioning load setting temperature is considered under user comfort, T(t) is the air-conditioning temperature at the current moment; a correction factor corresponding to each of the control targets is obtained to obtain multiple correction factors; current control amounts of multiple control targets are obtained from the second actual control data, and based on the cluster air-conditioning intraday short-term control model, the current control amounts of the multiple control targets are calculated using the multiple correction factors to obtain control deviations, and the control deviations are used to correct the control deviations of the multiple control targets, wherein,

[0179]

[0180] Where ΔP er is the control deviation, m is the number of control targets, ΔP i·short is the current control value of air conditioner i, ΔP i·plan represents the target control power, θ i is the correction factor of air conditioner i; the air conditioning load control target of the target scheduling area is used to generate a second constraint condition, the second constraint condition includes a temperature setting value constraint, a second control target balance constraint, and a second control number constraint, wherein,

[0181] T set ≤T ex

[0182]

[0183] n time ≤n max

[0184] Among them, T set To control the set temperature, T ex To set the temperature for the maximum air conditioning load considering user comfort, ΔP i is the control amount of air conditioner i based on the cluster air conditioner day-ahead control strategy, S is the total number of cluster air conditioners, ΔP tar is the target scheduling capacity, n time is the number of controls under a single control task, n max The maximum number of controls in a single control task.

[0185] In a specific application scenario, the intraday control module 303 is configured to set the value of the correction factor to 1 if it is determined by comparison that the actual control power is greater than or equal to the expected control power.

[0186] In a specific application scenario, the intraday control module 303 is used to obtain the actual control power after control of each of the control targets after the control deviation correction operation, and compare the actual control power after control of each of the control targets with the expected control power; if there is a control target among the multiple control targets whose actual control power after control is less than the expected control power, the correction factor of the control target is recalculated, and the control deviation of the control target is corrected based on the intraday short-time control model of the cluster air conditioner.

[0187] The device provided in an embodiment of the present application, when a station-line transformer overload signal is detected, determines a target dispatching area and an air conditioning load control target for the target dispatching area based on the station-line transformer overload signal, obtains first actual control data, inputs the first actual control data and the air conditioning load control target for the target dispatching area into a day-ahead control model, obtains a cluster air conditioning day-ahead control strategy for the target dispatching area, obtains second actual control data after controlling the target dispatching area according to the cluster air conditioning day-ahead control strategy, determines multiple control targets for the target dispatching area, and uses a cluster air conditioning intraday short-term control model to correct control deviations for the multiple control targets. The cluster air conditioning day-ahead control strategy is generated based on the economic operation requirements of the power grid, the requirements of participating users, and thermal comfort requirements. The control target is air conditioners with high intraday control potential and low comfort requirements. Compared with existing air conditioning control methods, the cluster air conditioning load day-ahead and intraday control method based on station-line transformer demand proposed in this application can meet the air conditioning control requirements of station-line transformers in different regions. The dispatching area and dispatching capacity are determined based on the different station-line transformer load rates, and the power grid is controlled and dispatched. The actual air conditioning control requirements of each region are precisely controlled, greatly improving user comfort and the flexibility of air conditioning load control. The multi-objective day-ahead control strategy for cluster air conditioners takes into account the needs of grid economic operation, control of participating users, and thermal comfort. It comprehensively considers the differences in strategies under different control objectives to achieve optimal regulation of cluster air conditioners and promote the participation of cluster air conditioner loads in grid optimization. Furthermore, the cluster air conditioner's intraday short-term control scheme fully considers user willingness to participate in control and external temperature factors, promptly correcting control deviations and achieving iterative solution of day-ahead and intraday control schemes, resulting in faster control time and more accurate control precision.

[0188] It should be noted that for other corresponding descriptions of the functional units involved in the cluster air conditioning load control device based on station line change demand provided in the embodiment of the present application, please refer to Figure 1 and Figures 2A to 2C The corresponding description in will not be repeated here.

[0189] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0190] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0191] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

[0192] In an exemplary embodiment, see Figure 4 A device is also provided, comprising a bus, a processor, a memory, and a communication interface. The device may also include an input / output interface and a display device, wherein the various functional units can communicate with each other via the bus. The memory stores a computer program, and the processor is configured to execute the program stored in the memory and implement the cluster air conditioning load control method based on station-line variable demand in the above-described embodiment.

[0193] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the cluster air-conditioning load control method based on station-line variable demand.

[0194] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented through hardware or by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0195] Those skilled in the art will understand that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required to implement the present application.

[0196] Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the implementation scenario description, or can be modified accordingly and located in one or more devices different from the implementation scenario. The modules in the above implementation scenario can be combined into one module or further split into multiple submodules.

[0197] The above application serial numbers are for description only and do not represent the advantages or disadvantages of the implementation scenarios.

[0198] The above disclosure only describes several specific implementation scenarios of the present application. However, the present application is not limited thereto, and any changes that can be conceived by those skilled in the art should fall within the scope of protection of the present application.

Claims

1. A cluster air conditioning load control method based on station-line variable demand, characterized in that: include: When a station-line variable overload signal is detected, a target scheduling area and an air-conditioning load control target of the target scheduling area are determined according to the station-line variable overload signal, including: determining the target scheduling area indicated by the station-line variable overload signal, obtaining the station-line variable load rate of the target scheduling area carried by the station-line variable overload signal, and determining the air-conditioning load control target based on the station-line variable load rate of the target scheduling area, wherein, Where ΔP tar is the air conditioning load control target, η is the station line variable load rate of the target scheduling area, Δη is the control demand load rate, which is 5%, η lim The upper limit of load rate control is 90%, P rate is the rated power; Obtaining first actual control data, inputting the first actual control data and the air conditioning load control target of the target scheduling area into a day-ahead control model, and obtaining a day-ahead control strategy for cluster air conditioners in the target scheduling area, wherein the day-ahead control strategy for cluster air conditioners is generated based on economic operation requirements of the power grid, requirements of control participating users, and consideration of temperature comfort requirements; Obtain second actual control data after the target scheduling area is controlled according to the cluster air-conditioning day-ahead control strategy, determine multiple control targets in the target scheduling area, and use the cluster air-conditioning intraday short-time control model to correct the control deviations of the multiple control targets, where the control targets are air conditioners with high potential for intraday control and low comfort requirements.

2. The method according to claim 1, characterized in that The step of inputting the first actual control data and the air conditioning load control target of the target scheduling area into a day-ahead control model to obtain a day-ahead control strategy for cluster air conditioning in the target scheduling area includes: The economic operation optimization module, the user participation optimization module, and the temperature comfort optimization module of the day-ahead control model are used to calculate the first actual control data respectively to obtain the minimum total grid operation cost, the minimum number of participating users, and the minimum comfort impact; The minimum total cost of grid operation, the minimum number of participating users, and the minimum comfort impact are used. The response is normalized to obtain the cluster air conditioning day-ahead control strategy for the target scheduling area, where: Where F is the minimum total cost of grid operation, s G is the minimum number of participating users, H is the minimum comfort impact, F max is the maximum cost of system operation, is the maximum number of user participation, H max is the maximum comfort influence, λ1 is the first weight coefficient, λ3 is the second weight coefficient, λ3 is the third weight coefficient, 0≤λ1≤1, 0≤λ2≤1, 0≤λ3≤1, and 0≤0≤λ1+λ2+λ3≤1; The air conditioning load control target of the target scheduling area is used to generate a first constraint condition of the cluster air conditioning day-ahead control strategy of the target scheduling area, wherein the first constraint condition includes a first control target balance constraint and a first control number constraint, wherein: n time ≤n max Where ΔP i is the control amount of air conditioner i based on the cluster air conditioner day-ahead control strategy, S is the total number of cluster air conditioners, ΔP tar is the target scheduling capacity, n time is the number of controls under a single control task, n max The maximum number of controls in a single control task.

3. The method according to claim 2, characterized in that The economic operation optimization module, user participation optimization module, and temperature comfort optimization module using the day-ahead control model respectively calculate the actual control data to obtain the minimum total grid operation cost, the minimum number of participating users, and the minimum comfort impact, including: The control amount of each air conditioner is obtained from the first actual control data, and the control amount of each air conditioner is calculated using the economic operation optimization module to obtain the minimum total grid operation cost, wherein: F=μ1F1+μ2F2 Wherein, F is the minimum total cost of grid operation, F1 is the grid regulation compensation cost, F2 is the grid comfort compensation cost, μ1 is the weight factor of the grid regulation compensation cost, μ2 is the weight factor of the grid comfort compensation cost, and μ1+μ2=1, ρ1 is the grid regulation compensation unit price, ρ 2,i The unit price for grid comfort compensation, is the compensation unit price of the maximum load control strategy I1, is the compensation unit price of the optimal comfort control strategy I2, ΔP i is the control quantity of air conditioner i, i∈[0,S], S is the total number of air conditioners in the cluster, The user comfort level before air conditioning adjustment. User comfort after air conditioning i is adjusted; The user participation optimization module is used to calculate the first actual control data to obtain the minimum number of participating users, wherein: Among them, s G is the minimum number of participating users, ΔP i is the control quantity of air conditioner i, i∈[0,S], S is the total number of air conditioners in the cluster, is the maximum control amount of air conditioner i; The temperature comfort optimization module is used to calculate the first actual control data to obtain the minimum comfort impact, wherein: Wherein, H is the minimum comfort impact, The minimum control amount of air conditioner i, For maximum comfort with air conditioning i is the user comfort before air conditioner i is adjusted, i∈[0,S], S is the total number of air conditioners in the cluster.

4. The method according to claim 1, wherein The method of using the cluster air conditioning intraday short-term control model to correct the control deviations of the multiple control targets includes: For each of the control targets, obtaining an actual control power of the control target from the second actual control data, and comparing the actual control power with the expected control power; If the actual control power is determined to be less than the expected control power, the current air-conditioning temperature is obtained from the second actual control data, and the correction factor is calculated using the current air-conditioning temperature, where: Wherein, θ is the correction factor, T set To control the set temperature, T ex The maximum air conditioning load setting temperature is set considering user comfort, and T(t) is the air conditioning temperature at the current moment; Obtaining a correction factor corresponding to each of the control targets to obtain a plurality of correction factors; The current control amounts of the multiple control targets are obtained from the second actual control data, and based on the cluster air conditioning intraday short-term control model, the current control amounts of the multiple control targets are calculated using the multiple correction factors to obtain control deviations, and the control deviations are corrected for the multiple control targets using the control deviations, wherein: Where ΔP er is the control deviation, m is the number of control targets, ΔP i·short is the current control value of air conditioner i, ΔP i·plan represents the target control power, θ i is the correction factor of air conditioner i; The air conditioning load control target of the target scheduling area is used to generate a second constraint condition, wherein the second constraint condition includes a temperature setting value constraint, a second control target balance constraint, and a second control number constraint, wherein: T set ≤T ex n time ≤n max Among them, T set To control the set temperature, T ex To set the temperature for the maximum air conditioning load considering user comfort, ΔP i is the control amount of air conditioner i based on the cluster air conditioner day-ahead control strategy, S is the total number of cluster air conditioners, ΔP tar is the target scheduling capacity, n time is the number of controls under a single control task, n max The maximum number of controls in a single control task.

5. The method according to claim 4, characterized in that After comparing the actual regulated power with the expected regulated power, the method further includes: If the comparison determines that the actual control power is greater than or equal to the expected control power, the value of the correction factor is 1.

6. The method according to claim 4, characterized in that After correcting the control deviations of the multiple control targets using the control deviations, the method includes: Obtaining the actual controlled power of each of the controlled targets after the control deviation correction operation, and comparing the actual controlled power of each of the controlled targets after the control with the expected controlled power; If there is a control target among the multiple control targets whose actual control power after control is less than the expected control power, the correction factor of the control target is recalculated, and the control deviation of the control target is corrected based on the cluster air conditioning intraday short-time control model.

7. A cluster air conditioning load control device based on station-line variable demand, applied to the method of claim 1, characterized in that: include: A determination module is configured to determine a target scheduling area and an air conditioning load control target for the target scheduling area according to the station line change overload signal when a station line change overload signal is detected, including: determining the target scheduling area indicated by the station line change overload signal, obtaining the station line change load rate of the target scheduling area carried by the station line change overload signal, and determining the air conditioning load control target based on the station line change load rate of the target scheduling area, wherein: Where ΔP tar is the air conditioning load control target, η is the station line variable load rate of the target scheduling area, Δη is the control demand load rate, which is 5%, η lim The upper limit of load rate control is 90%, P rate is the rated power; a day-ahead control module, configured to obtain first actual control data, input the first actual control data and the air conditioning load control target of the target scheduling area into a day-ahead control model, and obtain a day-ahead control strategy for the cluster air conditioning in the target scheduling area, wherein the day-ahead control strategy for the cluster air conditioning is generated based on the economic operation requirements of the power grid, the requirements of the control participating users, and the consideration of the temperature comfort requirements; The intraday control module is used to obtain the second actual control data after the target scheduling area is controlled according to the cluster air-conditioning day-ahead control strategy, determine multiple control targets in the target scheduling area, and use the cluster air-conditioning intraday short-term control model to correct the control deviation of the multiple control targets. The control target is an air-conditioning with high potential for participating in intraday control and low comfort requirements.

8. A device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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