Cluster air conditioner load regulation and control method, device and equipment based on station line transformer requirements

Abstract

The invention discloses a cluster air conditioner load regulation and control method, device and equipment based on station-line transformer requirements, relates to the technical field of air conditioner regulation and control, and can meet the air conditioner regulation and control requirements of station-line transformers in different areas compared with an existing air conditioner regulation and control method, determine a dispatching area and dispatching capacity according to different station-line transformer load rates, and improve the dispatching efficiency. The actual air conditioner regulation and control requirements of all the areas are accurately controlled, and the comfort level of a user and the flexibility of air conditioner load regulation and control are improved. On the basis of the multi-target demand-based cluster air conditioner day-ahead regulation and control strategy, the strategy difference under different types of regulation and control targets is comprehensively considered from the three aspects of power grid economic operation, participation user control and temperature comfort consideration, optimal regulation of the cluster air conditioner is realized, and the load of the cluster air conditioner is promoted to participate in power grid optimal operation. And on the basis of the intra-day short-time control scheme of the cluster air conditioner, the situation that the user intends to participate in regulation and control and the external temperature factor are fully considered, regulation and control deviation is corrected in time, and the regulation and control precision 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 increase in the proportion of air-conditioning loads in the hot summer season has become one of the important reasons for the difficulty in load scheduling and the chaos in distribution network planning. In addition, under long-term high temperature weather, power equipment in densely populated areas faces the risk of heavy load or overload, the pressure on power grid operation increases, and local areas will face a short-term power supply shortage. The power grid operation situation will become more severe, so it is extremely necessary to implement demand response load regulation for such loads.

[0003] In the related art, the existing cluster air conditioning load control method is mainly limited to the air conditioning control method that only considers physical factors such as temperature. For example, by predicting the outdoor temperature of the base station in the future target time, the optimal operation time period of the air conditioner is determined to minimize the total power consumption of the air conditioner without exceeding the first predetermined standard of the indoor temperature. However, the applicant realizes that this scheme mainly relies on outdoor temperature prediction to determine the air conditioning operation time period, and only considers a single factor affecting energy consumption, which makes the control strategy appear single, especially in the case 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 operation of the air conditioner, resulting in inaccurate control or failure to maximize energy consumption reduction, and lack of 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 scheme mainly relies on outdoor temperature prediction to determine the air-conditioning operation period, and only considers a single factor affecting energy consumption, which makes the control strategy appear to be 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, a target dispatching area and an air conditioning load control target of the target dispatching area are determined according to the station line variable overload signal;

[0007] Acquire 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 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, control requirements of participating users, and consideration of temperature comfort requirements;

[0008] The second actual control data after the target scheduling area is controlled according to the cluster air-conditioning day-ahead control strategy is obtained, a plurality of control targets are determined in the target scheduling area, and the control deviations of the plurality of control targets are corrected using the cluster air-conditioning intraday short-time control model, wherein the control target is an air-conditioner 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 dispatching area indicated by the station line change overload signal, and obtain the station line change load rate of the target dispatching 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] Among them, 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 is the adjustable upper limit of load factor, which is 90%. rate is the rated power.

[0014] Optionally, 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 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 normalized 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 participating users, 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 the 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] Among them, 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 The target scheduling capacity, n time is the number of controls under a single control task, n max It is the maximum number of controls for 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 cluster air conditioners, The user comfort level before the air conditioner is adjusted. The user comfort after the air conditioner 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 cluster air conditioners, 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 in 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 by comparison, the air-conditioning temperature at the current moment is obtained from the second actual control data, and the correction factor is calculated using the air-conditioning temperature at the current moment, wherein:

[0037]

[0038] Where 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, and T(t) is the air conditioning temperature at the current moment;

[0039] Obtaining a correction factor corresponding to each of the control targets to obtain a plurality of correction factors;

[0040] 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:

[0041]

[0042] Among them, 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;

[0043] 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:

[0044] T set ≤T ex

[0045]

[0046] n time ≤n max

[0047] 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 The target scheduling capacity, n time is the number of controls under a single control task, n max It is the maximum number of controls for a single control task.

[0048] Optionally, after comparing the actual regulated power with the expected regulated 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 the control deviation is corrected for the multiple control targets by using the control deviation, the method includes:

[0051] Obtaining the actual control power after control of each of the control targets after the control deviation correction operation, and comparing the actual control power after control of each of the control targets with the expected control 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 intra-day short-time control model.

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

[0054] A determination module, for 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 when a station line variable overload signal is detected;

[0055] a day-ahead control module, for 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 conditioning in the target scheduling area, wherein the day-ahead control strategy for cluster air conditioning is generated based on the economic operation demand of the power grid, the control participating user demand, and the consideration of the temperature comfort demand;

[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 deviations of the multiple control targets. The control target is an air-conditioner with high potential for intraday control and low comfort requirements.

[0057] Optionally, the determination module 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,

[0058]

[0059] Among them, 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 is the adjustable upper limit of load factor, which is 90%. rate is the rated power.

[0060] Optionally, the day-ahead control module is used 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; the minimum total grid operation cost, the minimum number of participating users, and the minimum comfort impact are normalized 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 participating users, 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 The target scheduling capacity, n time is the number of controls under a single control task, n max It is the maximum number of controls for a single control task.

[0066] Optionally, the day-ahead control module is used to obtain the control amount of each air conditioner in the first actual control data, and use the economic operation optimization module to calculate the control amount of each air conditioner 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 cluster air conditioners, The user comfort level before the air conditioner is adjusted. 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, wherein,

[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 cluster air conditioners, ΔP i max is the maximum control amount of air conditioner i; the first actual control data is calculated using the temperature comfort optimization module 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 used to obtain, for each of the control targets, 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, then obtain the air-conditioning temperature at the current moment from the second actual control data, and use the air-conditioning temperature at the current moment to calculate the correction factor, wherein,

[0075]

[0076] Where 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; the correction factor corresponding to each of the control targets is obtained to obtain multiple correction factors; the current control amounts of the multiple control targets are obtained in 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 the control deviation, and the control deviation is used to correct the control deviation 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 conditioner 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 The target scheduling capacity, n time is the number of controls under a single control task, n max It is the maximum number of controls for a single control task.

[0083] Optionally, the intraday control module is used 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.

[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, including 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 scheme, the present application provides a cluster air-conditioning load control method, device and equipment based on station-line transformer demand. When a station-line transformer 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 transformer 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 deviations 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, and the control target is the air-conditioning with high potential for participating in intraday control and low comfort requirement. The cluster air-conditioning load day-ahead-intraday control method based on the demand of station-line transformers proposed in this application can meet the air-conditioning control demand of station-line transformers in different regions compared with the existing air-conditioning control method. The dispatching area and dispatching capacity are determined according to the different load rates of station-line transformers, and the power grid is controlled and dispatched. The actual demand for air-conditioning control in each region is accurately controlled, which greatly improves the user's comfort and the flexibility of air-conditioning load control. The cluster air-conditioning day-ahead control strategy based on multi-objective demand can start from the three aspects of power grid economic operation, control of participating users, and consideration of temperature comfort, comprehensively consider the strategy differences under different types of control objectives, realize the optimal adjustment of cluster air-conditioning, and promote the cluster air-conditioning load to participate in the optimized operation of the power grid. It can also fully consider the user's willingness to participate in the control and the external temperature factors based on the cluster air-conditioning intraday short-term control plan, timely correct the control deviation, and realize the iterative solution of the day-ahead control plan-intraday control plan, with fast control time and more accurate control accuracy.

[0087] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0089] Figure 1 A schematic diagram of a method flow for cluster air conditioning load control based on station-line variable demand provided in an embodiment of the present application is shown;

[0090] Figure 2A A schematic diagram of another method flow for cluster air conditioning load control based on station-line variable demand provided in an embodiment of the present application is shown;

[0091] Figure 2B A schematic diagram of a process flow of short-term control of a cluster air conditioner during the day provided by an embodiment of the present application is shown;

[0092] Figure 2C A schematic diagram of a day-ahead and day-intraday control process of cluster air conditioning load based on station-line variable demand provided in an embodiment of the present application is shown;

[0093] Figure 3 A schematic diagram of the structure of cluster air conditioning load control based on station line variable demand provided in an embodiment of the present application is shown;

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

[0095] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0096] The present application embodiment provides a cluster air conditioning load control method based on station line variable demand, such as Figure 1 As shown, the method includes:

[0097] 101. When a station line transformer 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 transformer overload signal.

[0098] The existing cluster air conditioning load control method is mainly limited to the air conditioning control method that only considers physical factors such as temperature, but this solution cannot fully cover the air conditioning control strategy when multiple influencing factors work together. Especially in densely populated areas, the summer air conditioning load brings a huge impact to the power system when it is connected to the power grid, resulting in a single control strategy and lack of reliability.

[0099] To solve this problem, this application proposes a cluster air conditioning load control method based on station-line transformer demand. When a certain area of ​​the power grid is overloaded due to the station-line transformer load rate, the dispatching area and dispatching capacity are determined according to the station-line transformer overload signal, and the power grid is controlled and dispatched. Based on the cluster air conditioning control demand of the station-line transformer, a cluster air conditioning day-ahead control strategy considering multi-objective demands is formed. Finally, for the user group 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, so that the control accuracy is more accurate. The execution subject of this application can be an air conditioning control system. The air conditioning control system relies on the computing power of the server to provide services to users. The server can be an independent server, or it can provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN) and big data and artificial intelligence platforms and other basic cloud computing servers, so as to achieve accurate control of cluster air conditioning loads day-ahead and intraday.

[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 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 cluster air conditioners in the target scheduling area.

[0102] In an embodiment of the present application, an 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 a day-ahead control model, and obtains 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 the economic operation needs of the power grid, the control needs of participating users, and the consideration of temperature comfort needs. By comprehensively considering the strategy differences under different types of control targets, it is possible to achieve optimal regulation of cluster air conditioners 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 controlling the target scheduling area 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 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 the air-conditioning with high potential for intraday control and low comfort requirements.

[0105] The method provided in the embodiment of the present application, when a station-line transformer 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 transformer overload signal, the first actual control data are obtained, the first actual control data and the air conditioning load control target of the target dispatching area are input into the day-ahead control model, the cluster air conditioning day-ahead control strategy of the target dispatching area is obtained, the second actual control data after the target dispatching area is controlled according to the cluster air conditioning day-ahead control strategy is obtained, multiple control targets are determined in the target dispatching area, and the cluster air conditioning intraday short-term control model is used to correct the control deviation of 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 participating user demand and the consideration of the temperature comfort demand, and the control target is the air conditioner with high potential for participating in the intraday control and low comfort requirement. Compared with the existing air conditioning control method, the cluster air conditioning load day-ahead-intraday control method based on the station-line transformer demand proposed in the present application can meet the air conditioning control demand of station-line transformers in different regions, determine the dispatching area and dispatching capacity according to the different load rates of station-line transformers, and perform power grid control and dispatch, so that the actual demand for air conditioning control in each region is accurately controlled, which greatly improves the user's comfort and the flexibility of air conditioning load control. The day-ahead control strategy of cluster air conditioners based on multi-objective requirements can start from the three requirements of power grid economic operation, control of participating users, and temperature comfort, comprehensively consider the differences in strategies under different types of control objectives, achieve optimal adjustment of cluster air conditioners, and promote cluster air conditioner loads to participate in the optimized operation of the power grid. It can also fully consider the user's willingness to participate in the control and external temperature factors based on the cluster air conditioner intraday short-term control plan, timely correct the control deviation, and realize the iterative solution of the day-ahead control plan-intraday control plan, with fast control time and more accurate control accuracy.

[0106] Further, 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 transformer overload signal is detected, a target dispatching area and an air conditioning load control target of the target dispatching area are determined according to the station line transformer 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 according to the station-line transformer overload signal, and place the power grid control dispatching demand. 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] Among them, ΔP tar is the air conditioning load control target, η is the station line variable load rate in the target dispatching area, Δη is the control demand load rate, which is 5%, η lim is the adjustable upper limit of load factor, which is 90%. rate is the rated power. It should be noted that the air conditioning control system can receive multiple station line variable overload signals at the same time 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 regulated by 5%; when the corresponding regional station line variable load rate η>100%, the cluster air conditioning load reduction demand is regulated to the maximum limit proportion. Based on the cluster air conditioning control demand of the station line variable, the transformation from the regional station line variable load rate requirement to the load reduction amount can be realized, so that the actual demand for air conditioning control in each area can be accurately controlled, greatly improving the user's comfort and the flexibility of air conditioning load control.

[0111] 202. 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.

[0112] In the embodiment of the present application, the cluster air conditioning control mode has different control strategies according to its needs. The present application determines the control target of the air conditioners participating in the control in a specific area according to the load rate requirements of the area corresponding to the air conditioner, comprehensively considers the strategy differences under different types of control targets, and establishes optimization targets and constraints that consider economy, number of users, and comfort. Specifically, the air conditioning control system obtains the control amount of each air conditioner in the first actual control data, and uses the economic operation optimization module to calculate the control amount 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 unit price of grid regulation compensation, 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 cluster air conditioners, The user comfort level before the air conditioner is adjusted. is the user comfort after air conditioner i is regulated. The user comfort before and after air conditioner i is calculated by predicting the mean voting index (PMV) based on the actual human metabolic rate, indoor temperature, relative humidity, air velocity and other data. If PMV = 0, it is comfortable. The greater the PMV is, the hotter it is. The smaller the PMV is, the colder it is.

[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, and 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 cluster air conditioners, 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, and the maximum load limit strategy is adopted when the operation optimization target is user participation. 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 performed.

[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, and the calculation formula is the following formula 4:

[0120] Formula 4:

[0121]

[0122] 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 regulated, i∈[0,S], S is the total number of cluster air conditioners. Under the temperature comfort target, the power grid is required to minimize the regulation of each cluster air conditioner and minimize the impact on comfort. Therefore, according to regional differences, regulation is carried out under this target demand. When the operation optimization target is temperature comfort, the best comfort control strategy is adopted.

[0123] 203. 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 dispatching area.

[0124] In the embodiment of the present application, the F and s obtained by the three targets are G , G have different units and cannot be directly added to obtain the comprehensive target, so they are subjected to maximum standardization processing and the data are scaled to [0,1]. Specifically, the air conditioning control system uses the minimum total grid operation cost, the minimum number of participating users, and the minimum comfort impact for standardization processing to obtain the cluster air conditioning day-ahead control strategy in the target scheduling area. The calculation formula is the following formula 5:

[0125] Formula 5:

[0126] 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 participating users, 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 day-ahead control strategy of cluster air conditioners based on multi-objective requirements starts from the three requirements of power grid economic operation, control of participating users, and consideration of temperature comfort, comprehensively considers the strategy differences under different types of control objectives, can achieve optimal regulation of cluster air conditioners, and promote cluster air conditioner loads to participate in the optimization operation of the power grid.

[0127] 204. 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.

[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 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, the first control target constraint is that the air conditioning control response amount must meet the control target of the power grid, the first control number constraint is that the control number of the power grid when executing a single control task cannot exceed the maximum control number, and the calculation formula is as follows Formula 6:

[0129] Formula 6:

[0130] n time ≤n max

[0131] Where ΔP i is the control amount of air conditioner i based on 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 It is the maximum number of controls for 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 controlling the target scheduling area according to the cluster air conditioning's previous 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 a fast control time and high control accuracy, and 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 execute the following step 207; 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 execute the following step 208.

[0136] 207. If the actual control power is determined to be less than the expected control power through comparison, the air-conditioning temperature at the current moment is obtained from the second actual control data, and the correction factor is calculated using the air-conditioning temperature at the current moment.

[0137] In the embodiment of the present application, considering the user's willingness to participate in the control and the external temperature factors, it is easy to have a control deviation, so for the control deviation, a correction factor is introduced to correct the actual responsiveness of the control load. 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: Formula 7:

[0138] Formula 7:

[0139] Where θ is the correction factor, T set To control the set temperature, T ex In order to consider the maximum air conditioning load setting temperature under user comfort, 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 in the second actual control data, calculate the current control quantities of multiple control targets based on the intraday short-term control model of the cluster air conditioner, use 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 the correction factor corresponding to each control target and obtains multiple correction factors. Then, the air conditioning control system obtains the current control amount of multiple control targets in the second actual control data, and based on the cluster air conditioning intraday short-term control model, uses multiple correction factors to calculate the current control amount of multiple control targets to obtain the control deviation, and uses the control deviation to correct the control deviation of multiple control targets. The calculation formula is 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 represents 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 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 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 control number of the power grid when executing a single control task cannot exceed the maximum control number. 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 amount of air conditioner i based on 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 It is the maximum number of controls for 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 whose actual control power after control is less than the expected control power among multiple control targets, recalculate the correction factor of the control target, and correct the control deviation of the control target based on the intraday short-term control model of the cluster air conditioner.

[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 the cluster air conditioning control of multi-objective demand in the day, the user groups with high control potential and low comfort requirements are controlled during the day. If the air conditioner does not meet the scheduling demand, that is, the air conditioner control demand during the day, the correction factor is used to calculate the control deviation for short-term correction during the day until the scheduling demand is met. Among them, i represents the air conditioner i participating in the short-term control during the day.

[0152] In summary, this application proposes a cluster air conditioning load day-ahead and day-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 the station-line transformer in a certain area of ​​the power grid is overloaded, a cluster air-conditioning control demand based on the station-line transformer is proposed, and then a cluster air-conditioning day-ahead control strategy based on multi-objective demands is formed, and 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 needs of station-line transformers in different areas, determine the dispatching area and dispatching capacity according to the different load rates of the station-line transformers, and control and dispatch the power grid. 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 the embodiment of the present application, when a station-line transformer 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 transformer overload signal, the first actual control data are obtained, the first actual control data and the air conditioning load control target of the target dispatching area are input into the day-ahead control model, the cluster air conditioning day-ahead control strategy of the target dispatching area is obtained, the second actual control data after the target dispatching area is controlled according to the cluster air conditioning day-ahead control strategy is obtained, multiple control targets are determined in the target dispatching area, and the cluster air conditioning intraday short-term control model is used to correct the control deviation of 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 participating user demand and the consideration of the temperature comfort demand, and the control target is the air conditioner with high potential for participating in the intraday control and low comfort requirement. Compared with the existing air conditioning control method, the cluster air conditioning load day-ahead-intraday control method based on the station-line transformer demand proposed in the present application can meet the air conditioning control demand of station-line transformers in different regions, determine the dispatching area and dispatching capacity according to the different load rates of station-line transformers, and perform power grid control and dispatch, so that the actual demand for air conditioning control in each region is accurately controlled, which greatly improves the user's comfort and the flexibility of air conditioning load control. The day-ahead control strategy of cluster air conditioners based on multi-objective requirements can start from the three requirements of power grid economic operation, control of participating users, and temperature comfort, comprehensively consider the differences in strategies under different types of control objectives, achieve optimal adjustment of cluster air conditioners, and promote cluster air conditioner loads to participate in the optimized operation of the power grid. It can also fully consider the user's willingness to participate in the control and external temperature factors based on the cluster air conditioner intraday short-term control plan, timely correct the control deviation, and realize the iterative solution of the day-ahead control plan-intraday control plan, with fast control time and more accurate control accuracy.

[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 variable 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 used to determine a target scheduling area and an air conditioning load control target of the target scheduling area according to the station line change overload signal when a station line change overload signal is detected;

[0157] The day-ahead control module 302 is used 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 the day-ahead control model, and obtain 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 demand of the power grid, the control participating user demand and the consideration of the temperature comfort demand;

[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-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. The control target is an air-conditioner with high potential for 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 is the adjustable upper limit of load factor, which is 90%. rate is the rated power.

[0162] In a specific application scenario, the day-ahead control module 302 is used 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; the minimum total grid operation cost, the minimum number of participating users, and the minimum comfort impact are normalized 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 participating users, 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] Among them, 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 The target scheduling capacity, n time is the number of controls under a single control task, n max It is the maximum number of controls for a single control task.

[0168] In a specific application scenario, the day-ahead control module 302 is used to obtain the control amount of each air conditioner in the first actual control data, and use the economic operation optimization module to calculate the control amount of each air conditioner to 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 cluster air conditioners, The user comfort level before the air conditioner is adjusted. 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, wherein,

[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 cluster air conditioners, ΔP i max is the maximum control amount of air conditioner i; the first actual control data is calculated using the temperature comfort optimization module 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 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, the air-conditioning temperature at the current moment is obtained from the second actual control data, and the correction factor is calculated using the air-conditioning temperature at the current moment, wherein,

[0177]

[0178] Where 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; the correction factor corresponding to each of the control targets is obtained to obtain multiple correction factors; the current control amounts of the multiple control targets are obtained in 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 the control deviation, and the control deviation is used to correct the control deviation of the multiple control targets, wherein,

[0179]

[0180] Among them, 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 conditioner 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 The target scheduling capacity, n time is the number of controls under a single control task, n max It is the maximum number of controls for a single control task.

[0185] In a specific application scenario, the intraday control module 303 is used 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 the embodiment of the present application, when a station-line transformer overload signal is detected, determines the target dispatching area and the air conditioning load control target of the target dispatching area according to the station-line transformer overload signal, obtains the first actual control data, inputs the first actual control data and the air conditioning load control target of the target dispatching area into the day-ahead control model, obtains the cluster air conditioning day-ahead control strategy of the target dispatching area, obtains the second actual control data after the target dispatching area is controlled according to the cluster air conditioning day-ahead control strategy, determines multiple control targets in the target dispatching area, adopts the cluster air conditioning intraday short-term control model to correct the control deviation of 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 participating user demand and the consideration of the temperature comfort demand, and the control target is the air conditioner with high potential for participating in the intraday control and low comfort requirement. Compared with the existing air conditioning control method, the cluster air conditioning load day-ahead-intraday control method based on the station-line transformer demand proposed in the present application can meet the air conditioning control demand of station-line transformers in different regions, determines the dispatching area and dispatching capacity according to the different load rates of station-line transformers, and performs power grid control and dispatching, and the actual demand for air conditioning control in each region is accurately controlled, which greatly improves the user's comfort and the flexibility of air conditioning load control. The day-ahead control strategy of cluster air conditioners based on multi-objective requirements can start from the three requirements of power grid economic operation, control of participating users, and temperature comfort, comprehensively consider the differences in strategies under different types of control objectives, achieve optimal adjustment of cluster air conditioners, and promote cluster air conditioner loads to participate in the optimized operation of the power grid. It can also fully consider the user's willingness to participate in the control and external temperature factors based on the cluster air conditioner intraday short-term control plan, timely correct the control deviation, and realize the iterative solution of the day-ahead control plan-intraday control plan, with fast control time and more accurate control accuracy.

[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 variable demand provided in the embodiment of the present application, reference can be made to Figure 1 and FIG. 2A to FIG. 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 may 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 only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

[0192] In an exemplary embodiment, see Figure 4 , and also provides a device, which includes a bus, a processor, a memory and a communication interface, and may also include an input and output interface and a display device, wherein each functional unit can communicate with each other through the bus. The memory stores a computer program, and the processor is used to execute the program stored in the memory and execute the cluster air conditioning load control method based on station line variable demand in the above 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 by hardware, or by means of software plus a necessary general hardware platform. Based on such an 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 appreciate that the accompanying drawings are merely schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present application.

[0196] Those skilled in the art will appreciate that the modules in the device in the implementation scenario can be distributed in the device in the implementation scenario according to the implementation scenario description, or can be changed 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 can be further split into multiple sub-modules.

[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 discloses several specific implementation scenarios of the present application. However, the present application is not limited thereto, and any changes that can be conceived by technicians in this field 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 dispatching area and an air conditioning load control target of the target dispatching area are determined according to the station line variable overload signal; Acquire 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 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, control requirements of participating users, and consideration of temperature comfort requirements; The second actual control data after the target scheduling area is controlled according to the cluster air-conditioning day-ahead control strategy is obtained, a plurality of control targets are determined in the target scheduling area, and the control deviations of the plurality of control targets are corrected using the cluster air-conditioning intraday short-time control model, wherein the control target is an air-conditioner with high potential for intraday control and low comfort requirements.

2. The method according to claim 1, characterized in that The step of 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 comprises: Determine the target dispatching area indicated by the station line change overload signal, and obtain the station line change load rate of the target dispatching area carried by the station line change overload signal; The air conditioning load control target is determined based on the station-line variable load rate of the target scheduling area, wherein: Among them, 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 is the adjustable upper limit of load factor, which is 90%. rate is the rated power.

3. 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 the day-ahead control model to obtain the day-ahead control strategy of the 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 grid operation cost, the minimum number of participating users, and the minimum comfort impact are normalized to obtain the cluster air conditioning day-ahead control strategy for the target scheduling area, wherein: 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 participating users, 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, wherein the first constraint condition includes a first control target balance constraint and a first control number constraint, wherein: n time ≤n max Among them, 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 The target scheduling capacity, n time is the number of controls under a single control task, n max It is the maximum number of controls for a single control task.

4. The method according to claim 3, 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 2, ΔP i is the control quantity of air conditioner i, i∈[0,S], S is the total number of cluster air conditioners, The user comfort level before the air conditioner is adjusted. The user comfort after the air conditioner 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 cluster air conditioners, 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.

5. The method according to claim 1, characterized in that 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 in 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 by comparison, the air-conditioning temperature at the current moment is obtained from the second actual control data, and the correction factor is calculated using the air-conditioning temperature at the current moment, wherein: Where 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, 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: Among them, 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 The target scheduling capacity, n time is the number of controls under a single control task, n max It is the maximum number of controls for a single control task.

6. The method according to claim 5, 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.

7. The method according to claim 5, characterized in that After the control deviation is corrected for the multiple control targets using the control deviation, the method includes: Obtaining the actual control power after control of each of the control targets after the control deviation correction operation, and comparing 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 cluster air-conditioning intra-day short-time control model.

8. A cluster air conditioning load control device based on station line variable demand, characterized in that: include: A determination module, for 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 when a station line variable overload signal is detected; a day-ahead control module, for 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 conditioning in the target scheduling area, wherein the day-ahead control strategy for cluster air conditioning is generated based on the economic operation demand of the power grid, the control participating user demand, and the consideration of the temperature comfort demand; 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 deviations of the multiple control targets. The control target is an air-conditioner with high potential for intraday control and low comfort requirements.

9. A device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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