Air conditioner cluster frequency modulation control method based on temperature set point

Abstract

The invention provides an air conditioner cluster frequency modulation control method based on a temperature set point, and the method comprises the steps: carrying out the modeling of a single air conditioner participating in a cluster through employing a second-order equivalent mechanical parameter model, carrying out the depiction of a building model where the air conditioner is located through employing a thermal parameter, and embedding a thermodynamic model of the air conditioner into the building model, obtaining a power change curve of the air conditioner; fitting the power change curve by adopting a piecewise linear function, and making a linearization hypothesis for the power near the working point of the air conditioner after fitting; within unit optimization time, modeling a power distribution optimization problem related to frequency modulation participated by the air conditioner cluster as a mixed integer optimization problem; and solving the mixed integer optimization problem to obtain an optimization result of power distribution. Based on the scheme provided by the invention, an actual solution is provided for air conditioners with uncontrollable power or unopened protocols to be controlled to form a virtual power plant to participate in a power market to provide frequency modulation service, so that wide application of demand response is promoted.

Description

Technical Field

[0001] The present application relates to the technical field of virtual power plants, and in particular to a frequency modulation control method for an air conditioning cluster based on a temperature set point. Background Art

[0002] At present, promoting the development of renewable energy has become a consensus for future development in the world. On the one hand, a high proportion of renewable energy access to the power grid can promote green power generation, energy conservation and emission reduction, but on the other hand, it also increases the uncertainty of the frequency balance of the power grid. In order to maintain the real-time supply and demand balance, the power grid needs additional power resources to provide various auxiliary services. Compared with traditional power resources, demand-side resources have the characteristics of large quantity, low frequency regulation cost, and convenient user control method, so they have become the focus of current research.

[0003] Among these demand-side resources, air conditioning clusters are considered to be one of the ideal resources for providing ancillary services. In recent years, the air conditioning load in China's load structure has increased significantly. Statistics show that during the summer peak load period, domestic air conditioning loads account for 30% to 40% of China's peak load. In addition to the characteristics of large regulation capacity, the buildings where the air conditioning loads are located have the ability to store heat and can convert electrical energy into thermal energy for storage within a specific period of time. Therefore, many studies have explored the use of air conditioning as a demand response (DSR) participant, and a series of useful results have been obtained by appropriately modeling the DSR as a generator or battery.

[0004] In recent years, the participation of air conditioning loads in power system frequency regulation services has attracted the attention of scholars, and many studies have proposed control strategies to adjust the power of air conditioners in response to the frequency regulation signals provided by the power grid. One of the common strategies for adjusting the power of air conditioners is on / off control, which adjusts the total power by changing the switch state of the air conditioning load. For example, through direct load control (DLC), the centralized controller uniformly controls the switch state of the air conditioning load cluster. The air conditioner can be effectively modeled as a state sequence model, revealing the state transition and corresponding energy storage efficiency change process in different temperature zones and switch modes. Based on this modeling, a research team proposed a control strategy that uses a central controller to adjust the switch state of each air conditioning load unit to achieve efficient load control during peak demand periods and participate in load peak regulation and frequency regulation services. Another way is to delegate the control of overload power to users or automatically respond to air conditioners through indirect load control (ILC). For example, a research team from Beijing Jiaotong University proposed an adaptive algorithm that uses a local controller to automatically adjust the switch state of the compressor according to the trigger frequency of the air conditioning unit to help the air conditioning load cluster better participate in the main frequency regulation. We can also let the demand-side resources automatically adjust their switch state according to the time-of-use electricity price to change the power participating in demand response.

[0005] Although the aforementioned studies have explored the participation of air conditioning loads in power system frequency regulation and put forward some useful conclusions, most of the studies focus on controlling air conditioning through compressor switch control or inverter air conditioning frequency adjustment. However, most of the existing air conditioning interfaces can only provide total power switching and temperature set point control, and there are many limitations of communication protocols. Based on the above-mentioned shortcomings of existing studies based on on / off control or inverter air conditioning frequency, it is quite challenging to adjust the air conditioning power without changing the temperature set point according to these studies. Therefore, the academic community needs to propose more convenient strategies to adapt to current communication protocols.

[0006] The method based on temperature set point control provides a frequency regulation method that can meet the communication protocol. It realizes frequency regulation by modeling the impact of temperature change set point on building temperature and air conditioning power. Some studies have explored the temperature set point control strategy. However, the existing research mainly focuses on providing hourly services. A research team proposed a strategy to change the set point to affect the power of air conditioning load. Users can change the set point within their acceptable temperature range. In addition, there is a control strategy based on temperature set point adjustment and state transition priority, which enables air conditioning load to participate in primary and secondary frequency regulation. In addition, a research team used a data-driven approach to model different types of air conditioners and buildings and formed a database through simulation. Then, a linear regression model was used to predict the sum of power changes over a period of time after the temperature set point was adjusted, thereby proposing a strategy to participate in demand response. However, these studies mainly focused on hourly behavioral needs, and there is still a lack of research on frequency regulation at the minute scale.

[0007] Although hourly services based on temperature set points have application scenarios, when providing regulation services, the accuracy standard of the power grid for resources requires responding to signals on a second-by-second basis, which requires accurate modeling of resources at the minute or even second level. In summary, existing temperature set point research focuses on the integral change of air conditioning power changes within a given time range, but fails to accurately model the change process of the power curve, resulting in the inability to effectively plan strategies to respond to minute-by-minute frequency regulation signals. However, providing minute-by-minute frequency regulation services requires an in-depth understanding of parameters, controllable variables, and signal allocation strategies for response resources, which is undoubtedly very difficult. Summary of the invention

[0008] The present application aims to at least to some extent solve the problem in the related art that large-scale air-conditioning clusters as demand-side resources cannot be effectively integrated and controlled and participate in the power auxiliary service market, thus causing waste of resources.

[0009] To this end, the first purpose of the present application is to propose an air conditioning cluster frequency modulation control method, device, electronic device and storage medium based on temperature set points. By physically modeling the air conditioners and buildings, the power allocation optimization problem is converted into a mixed integer optimization problem after linear approximation, so that the power of the air conditioning load cluster can be continuously adjusted, follow the frequency modulation signal, participate in secondary frequency modulation and achieve good results.

[0010] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a method for controlling frequency modulation of an air conditioning cluster based on a temperature set point, comprising:

[0011] A second-order equivalent mechanical parameter model is used to model a single air conditioner participating in the cluster, and the building model where the air conditioner is located is characterized by thermal parameters. The thermodynamic model of the air conditioner is embedded in the building model to obtain the power change curve of the air conditioner.

[0012] A piecewise linear function is used to fit the power variation curve, and a linear assumption is made on the power near the working point of the air conditioner after fitting;

[0013] In the unit optimization time, the power allocation optimization problem involved in frequency modulation of the air-conditioning cluster is modeled as a mixed integer optimization problem;

[0014] The mixed integer optimization problem is solved to obtain an optimization result of power allocation.

[0015] Optionally, before modeling a single air conditioner participating in the cluster using a second-order equivalent mechanical parameter model, the following is further included:

[0016] Define the thermal and electrical parameters of the air conditioner and the building in which it is located;

[0017] Define the parameters of the sorting linear function used to fit the air conditioner power variation curve;

[0018] Define the optimized period t for responding to the frequency modulation signal sent down 0 ;

[0019] Define the start time of each type of air conditioner participating in frequency modulation response as

[0020] Optionally, the single air conditioner participating in the cluster is modeled using a second-order equivalent mechanical parameter model, the building model where the air conditioner is located is characterized using thermal parameters, and the thermodynamic model of the air conditioner is embedded in the building model to obtain a power change curve of the air conditioner, including:

[0021] A single air conditioner participating in the cluster is modeled using a second-order equivalent mechanical parameter model to obtain a thermodynamic model of the air conditioner, wherein the parameters in the second-order equivalent mechanical parameter model include the indoor gas temperature T in, indoor solid temperature T m , outdoor temperature T out , air conditioning cooling / heating capacity Q, gas specific heat capacity C a , solid specific heat capacity C m , gas energy transfer thermal resistance R a , solid energy transfer thermal resistance R m ;

[0022] For the building model of a single building where the air conditioner is located, thermal parameters are used for characterization, wherein the thermal parameters include the wall area WA a , roof area R a , window area WI a ; Wall thickness WA l , roof thickness R l , window thickness WI l ; Wall quality WA m , roof quality R m , window quality WI m , air quality A m ; Wall specific heat WA c , roof specific heat R c , window specific heat WI c , air specific heat A c ; Wall thermal conductivity WA k , roof thermal conductivity P k , window thermal conductivity WI k ; The thermal conductivity coefficients of indoor air and outdoor environment with walls, roofs and windows are h A-WA ,h A-R ,h A-WI ,h WA-Atm ,h R-Atm ,h WI-Atm ;

[0023] The thermodynamic model of the air conditioner is embedded in the building model, and the power variation curve of the air conditioner is obtained through simulation.

[0024] Optionally, the step of fitting the power variation curve using a piecewise linear function and making a linear assumption about the power near the air conditioner operating point after fitting includes:

[0025] For each air conditioner participating in demand response, it is assumed that the air conditioner set temperature is the same as the room temperature before participating in demand response, which is the initial value. The time point when the air conditioner starts to participate in demand response is Each air conditioner in each t 0 The temperature setting value can be adjusted only once at most. The adjusted temperature setting value is At each t 0 In the example, the power of each air conditioner is PT,i (t);

[0026] The power allocation optimization problem involved in the frequency modulation of the air-conditioning cluster is transformed into the following objective function.

[0027]

[0028] By discretizing time, the objective function is transformed into:

[0029]

[0030] For the power function P T,i (t), when the building and air conditioning parameters are determined, it is Function of

[0031] Defining the standard power function For tsr t,i =0s, The power variation curve of an air conditioner at , where It can be obtained through simulation in the modeling of air conditioning and building models;

[0032] right As an approximation, it is assumed that the steady-state power p is subtracted 0 Then there are five piecewise constant functions Fitting, that is It is characterized by five amplitudes and four segmentation points, which are respectively denoted as A T,1 ~A T,5 ; Then the following relationship holds:

[0033]

[0034] Assume the objective function By introducing 01 variable I t,k (=1,2,3,4,5) Convert the optimization problem into a mixed integer optimization problem. T,1 ~A T,5 ; As decision variables, the transformed constraints are optimized as follows:

[0035]

[0036] Optionally, within the unit optimization time, the power allocation optimization problem involved in the air conditioning cluster participating in the frequency modulation is modeled as a mixed integer optimization problem, including:

[0037] Add the constraints of the working environment corresponding to the power allocation optimization problem, assuming that the temperature setting value of the air conditioner has a lower limit Upper limit The upper limit of the time when the air conditioner starts to participate in demand response is Regulation The upper limit is ΔT max , then the constraint condition can be expressed as:

[0038]

[0039] Optionally, solving the mixed integer optimization problem to obtain an optimization result of power allocation includes:

[0040] Based on the objective function and the constraint conditions, the mixed integer optimization problem is solved by a solver to obtain an optimization result of power allocation.

[0041] To achieve the above-mentioned purpose, the second embodiment of the present application proposes an air conditioning cluster frequency modulation control device based on temperature setting point, comprising:

[0042] The first modeling module is used to model a single air conditioner participating in the cluster using a second-order equivalent mechanical parameter model, characterize the building model where the air conditioner is located using thermal parameters, embed the thermodynamic model of the air conditioner into the building model, and obtain the power change curve of the air conditioner;

[0043] A fitting and assumption module, used for fitting the power variation curve using a piecewise linear function, and making a linear assumption about the power near the air conditioner operating point after fitting;

[0044] The second modeling module is used to model the power allocation optimization problem involved in the air-conditioning cluster participating in frequency modulation as a mixed integer optimization problem within a unit optimization time;

[0045] The solution module is used to solve the mixed integer optimization problem to obtain an optimization result of power allocation.

[0046] To achieve the above-mentioned purpose, the third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0047] The memory stores computer-executable instructions;

[0048] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects above.

[0049] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present application proposes a computer-readable storage medium, in which computer execution instructions are stored. When the computer execution instructions are executed by a processor, they are used to implement the method as described in any one of the above-mentioned first aspects.

[0050] To achieve the above-mentioned purpose, the fifth aspect of the present application proposes a computer program product, including a computer program, which, when executed by a processor, implements the method as described in any one of the above-mentioned first aspects.

[0051] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0052] Based on the existing control strategy based on the change of air conditioning temperature set point, a framework based on changing the temperature set point of air conditioning load is proposed, and the air conditioning load is coordinated for frequency regulation using the method supported by the current communication protocol, which makes up for the defects of the existing strategies (such as switch control or adjusting the inverter air conditioning frequency) in current research that are difficult to apply due to protocol restrictions. The proposed framework also fills the current gap in using air conditioning load to change the temperature set value to provide minute-level frequency regulation. And through case study verification, the scheme proposed in this application provides a practical solution for air conditioning controlled by uncontrollable power or non-open protocols to form a virtual power plant to participate in the power market to provide frequency regulation services, thereby promoting the widespread application of demand response.

[0053] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0055] Figure 1 is a flow chart of a method for controlling frequency modulation of an air conditioning cluster based on a temperature set point according to an embodiment of the present application;

[0056] Figure 2 is a schematic diagram of a second-order equivalent mechanical parameter model of an air conditioner according to an embodiment of the present application;

[0057] Figure 3 is a block diagram of an air conditioning cluster frequency modulation control device based on temperature set points according to an embodiment of the present application;

[0058] Figure 4 It is a block diagram of an electronic device. DETAILED DESCRIPTION

[0059] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0060] The following describes an air conditioning cluster frequency modulation control method based on temperature set points according to an embodiment of the present application with reference to the accompanying drawings.

[0061] Figure 1 FIG. 1 is a flow chart of a method for controlling frequency modulation of an air conditioning cluster based on a temperature set point according to an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0062] Step 101, a single air conditioner participating in the cluster is modeled using a second-order equivalent mechanical parameter model, a building model where the air conditioner is located is characterized using thermal parameters, the thermodynamic model of the air conditioner is embedded in the building model, and a power change curve of the air conditioner is obtained.

[0063] In the embodiment of the present application, before modeling the air conditioner and the building model, it is first necessary to define the thermal and electrical parameters of the air conditioner, that is, the building in which it is located; define the parameters of the sorting linear function used to fit the air conditioner power change curve; define the optimized period t of the response to the frequency modulation signal sent down 0 ; Define the start time of each type of air conditioner participating in frequency modulation response as

[0064] Subsequently, for a single air conditioner participating in the cluster, a second-order equivalent mechanical parameter model is used to model it and obtain the thermodynamic model of the air conditioner.

[0065] Among them, the schematic diagram of the second-order equivalent mechanical parameter model is as follows Figure 2 As shown, the parameters involved include the indoor gas temperature T in , indoor solid temperature T m , outdoor temperature T out , air conditioning cooling / heating capacity Q, gas specific heat capacity C a , solid specific heat capacity C m , gas energy transfer thermal resistance R a , solid energy transfer thermal resistance R m .

[0066] Also, the building model of a single building where the air conditioner is located is characterized by using thermal parameters.

[0067] In some embodiments of the present application, 23 thermal parameters are used to characterize the building model of a single building where the air conditioner is located. These thermal parameters include the wall area WA a , roof area Ra , window area WI a ; Wall thickness WA l , roof thickness R l , window thickness WI l ; Wall quality WA m , roof quality R m , window quality WI m , air quality A m ; Wall specific heat WA c , roof specific heat R c , window specific heat WI c , air specific heat A c ; Wall thermal conductivity WA k , roof thermal conductivity R k , window thermal conductivity WI k ; The thermal conductivity coefficients of indoor air and outdoor environment with walls, roofs and windows are h A-WA ,h A-R ,h A-wI ,h WA-Atm ,h R-Atm ,h WI-Atm .

[0068] Furthermore, after obtaining the thermodynamic model of the air conditioner and the depicted building model, the thermodynamic model of the air conditioner is embedded in the building model to obtain the power change curve of the air conditioner.

[0069] It should be noted that the heat flow output by the air conditioning model is denoted as H f , the building model will produce room temperature changes under the action of heat flow. The change of room temperature is a function of the mass, specific heat, thermal conductivity and thermal conductivity of each element in the building. The changed room temperature will give feedback to the power of the air conditioner, so that the air conditioner power change curve can be obtained through simulation.

[0070] The thermodynamic model of the air conditioner is embedded in the building model, and the power variation curve of the air conditioner is obtained through simulation.

[0071] Step 102, a piecewise linear function is used to fit the power variation curve, and a linear assumption is made for the power near the air conditioner operating point after fitting.

[0072] In the embodiment of the present application, a piecewise linear function is used to fit the air-conditioning power change curve obtained above, and nine parameters, four segmentation points and five amplitudes, are used to characterize a standard air-conditioning power change curve (power change when the temperature rises by one degree at 26°C), and a linear assumption is made for the power near the air-conditioning operating point to facilitate the subsequent optimization problem solving.

[0073] Specifically, for each air conditioner participating in demand response, it is assumed that the air conditioner set temperature is the same as the room temperature before participating in demand response, which is the initial value The time point when the air conditioner starts to participate in demand response is Each air conditioner in each t 0 The temperature setting value can be adjusted only once at most. The adjusted temperature setting value is At each t 0 In the example, the power of each air conditioner is P T,i (t).

[0074] Then, in order to achieve the best possible following of the frequency modulation signal, the power allocation optimization problem involved in the air conditioning cluster participating in frequency modulation can be transformed into the following objective function.

[0075]

[0076] For the above power allocation optimization problem, the objective function can be further transformed into:

[0077]

[0078] In addition, for the power function P T,i (), when the building and air conditioning parameters are determined, it is function.

[0079] Therefore, in order to solve the above power allocation optimization problem, the standard power function can be further defined as For tsr t,i =0s, The power variation curve of an air conditioner at , where can be obtained through simulation in the modeling of air conditioning and building models. As an approximation, assume that the steady-state power p is subtracted 0 Then there are five piecewise constant functions Fitting, that is It is characterized by five amplitudes and four segmentation points, and the five amplitudes and four segmentation points are respectively denoted as A T,1 ~A T,5 ; Then the following relationship holds:

[0080]

[0081] In order to make the above piecewise constant function fit the original curve best, let the objective function By introducing 01 variable I t,k(=1,2,3,4,5), the above optimization problem can be transformed into a mixed integer optimization problem. T,1 ~A T,5 ; As decision variables, the transformed constraints are optimized as follows:

[0082]

[0083] Step 103: within a unit optimization time, the power allocation optimization problem involved in the air-conditioning cluster participating in frequency modulation is modeled as a mixed integer optimization problem.

[0084] In the embodiment of the present application, the constraints of the working environment corresponding to the power allocation optimization problem are added, assuming that the temperature setting value of the air conditioner has a lower limit Upper limit And assuming that the time when the air conditioner starts to participate in demand response cannot be too late, the upper limit of the time when the air conditioner starts to participate in demand response is set to Regulation The upper limit is ΔT max , then the above constraints can be expressed as:

[0085]

[0086] Step 104, solving the mixed integer optimization problem to obtain an optimization result of power allocation.

[0087] Finally, the conclusion in step 102 is regarded as the objective function of the power allocation optimization problem, and the discussion in steps 102 and 103 is regarded as the constraint conditions of the power allocation optimization problem. By solving the mixed integer optimization problem through the solver, the optimization result of power allocation can be obtained, that is, the decision variable Thus, a complete algorithm for frequency modulation signal following by changing the temperature set point is established.

[0088] It is understandable that the appropriate optimization solver is selected according to the scale and complexity of the problem. For mixed integer optimization problems, commercial or open source software packages such as CPLEX, Gurobi, CBC, etc. can be used, and relevant parameters of the solver, such as accuracy requirements, time limits, etc., can be set during the solution process. This application does not make specific restrictions and explanations on this.

[0089] In summary, based on the existing control strategy based on the change of air conditioning temperature set point, this application proposes a framework based on changing the temperature set point of air conditioning load, and uses the method supported by the current communication protocol to coordinate the air conditioning load for frequency regulation, which makes up for the defects of the existing strategies in current research (such as switch control or adjusting the inverter air conditioning frequency) that are difficult to apply due to protocol restrictions, and the proposed framework also fills the current gap in using air conditioning load to change the temperature set value to provide minute-level frequency regulation. And through case study verification, the scheme proposed in this application provides a practical solution for air conditioning with uncontrollable power or non-open protocol to form a virtual power plant to participate in the power market to provide frequency regulation services, thereby promoting the widespread application of demand response.

[0090] In order to implement the above embodiment, the present application also proposes an air conditioning cluster frequency modulation control device based on temperature setting points.

[0091] Figure 3 1 is a block diagram of an air conditioning cluster frequency modulation control device 10 based on a temperature set point according to an embodiment of the present application, comprising:

[0092] The first modeling module 100 is used to model a single air conditioner participating in the cluster using a second-order equivalent mechanical parameter model, characterize the building model where the air conditioner is located using thermal parameters, embed the thermodynamic model of the air conditioner into the building model, and obtain a power change curve of the air conditioner;

[0093] The fitting and assumption module 200 is used to fit the power change curve using a piecewise linear function and make a linear assumption about the power near the air conditioner operating point after fitting;

[0094] The second modeling module 300 is used to model the power allocation optimization problem involved in the air conditioning cluster participating in frequency modulation as a mixed integer optimization problem within a unit optimization time;

[0095] The solving module 400 is used to solve the mixed integer optimization problem and obtain the optimization result of power allocation.

[0096] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0097] In order to implement the above embodiments, the present application also proposes an electronic device.

[0098] Figure 4A schematic block diagram of an example electronic device 700 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0099] like Figure 4 As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0100] A number of components in the device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0101] The computing unit 701 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above. For example, in some embodiments, the scheme proposed in the present application may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the method shown in the present application in any other appropriate manner (e.g., by means of firmware).

[0102] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0103] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0104] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0105] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0106] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0107] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0108] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution of this application can be achieved, and this document is not limited here.

[0109] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for frequency modulation control of air conditioning cluster based on temperature set point, characterized in that: include: A second-order equivalent mechanical parameter model is used to model a single air conditioner participating in the cluster, and the building model where the air conditioner is located is characterized by thermal parameters. The thermodynamic model of the air conditioner is embedded in the building model to obtain the power change curve of the air conditioner. A piecewise linear function is used to fit the power variation curve, and a linear assumption is made on the power near the working point of the air conditioner after fitting; In the unit optimization time, the power allocation optimization problem involved in frequency modulation of the air-conditioning cluster is modeled as a mixed integer optimization problem; The mixed integer optimization problem is solved to obtain an optimization result of power allocation.

2. The method according to claim 1, characterized in that Before modeling the single air conditioner participating in the cluster using the second-order equivalent mechanical parameter model, it also includes: Define the thermal and electrical parameters of the air conditioner and the building in which it is located; Define the parameters of the sorting linear function used to fit the air conditioner power variation curve; Define the optimized period t0 for responding to the frequency modulation signal sent down; Define the start time of each type of air conditioner participating in frequency modulation response as 3. The method according to claim 2, characterized in that The single air conditioner participating in the cluster is modeled using a second-order equivalent mechanical parameter model, the building model where the air conditioner is located is characterized using thermal parameters, the thermodynamic model of the air conditioner is embedded in the building model, and the power change curve of the air conditioner is obtained, including: A single air conditioner participating in the cluster is modeled using a second-order equivalent mechanical parameter model to obtain a thermodynamic model of the air conditioner, wherein the parameters in the second-order equivalent mechanical parameter model include the indoor gas temperature T in , indoor solid temperature T m , outdoor temperature T out , air conditioning cooling / heating capacity Q, gas specific heat capacity C a , solid specific heat capacity C m , gas energy transfer thermal resistance R a , solid energy transfer thermal resistance R m ; For the building model of a single building where the air conditioner is located, thermal parameters are used for characterization, wherein the thermal parameters include the wall area WA a , roof area R a , window area WI a ; Wall thickness WA l , roof thickness R l , window thickness WI l ; Wall quality WA m , roof quality R m , window quality WI m , air quality A m ; Wall specific heat WA c , roof specific heat R c , window specific heat WI c , air specific heat A c ; Wall thermal conductivity WA k , roof thermal conductivity R k , window thermal conductivity WI k ; The thermal conductivity coefficients of indoor air and outdoor environment with walls, roofs and windows are h A-WA ,h A-R ,h A-WI ,h WA-Atm ,h R-Atm ,h WI-Atm ; The thermodynamic model of the air conditioner is embedded in the building model, and the power variation curve of the air conditioner is obtained through simulation.

4. The method according to claim 3, characterized in that The step of fitting the power variation curve using a piecewise linear function and making a linear assumption about the power near the air conditioner operating point after fitting includes: For each air conditioner participating in demand response, it is assumed that the air conditioner set temperature is the same as the room temperature before participating in demand response, which is the initial value. The time point when the air conditioner starts to participate in demand response is Each air conditioner can adjust the temperature setting value at most once in each t0, and the adjusted temperature setting value is In each t0, the power of each air conditioner is recorded as P T,i (t); The power allocation optimization problem involved in the frequency modulation of the air-conditioning cluster is transformed into the following objective function. By discretizing time, the objective function is transformed into: For the power function P T,i (), when the building and air conditioning parameters are determined, it is Function of Defining the standard power function For tsr t,i =0s, The power variation curve of an air conditioner at , where It can be obtained through simulation in the modeling of air conditioning and building models; right As an approximation, assume that after subtracting the steady-state power p0, it is composed of five piecewise constant functions Fitting, that is It is characterized by five amplitudes and four segmentation points, which are respectively denoted as A T,1 ~A T,5 ; Then the following relationship holds: Assume the objective function By introducing 01 variable I t,k (=1,2,3,4,5) Convert the optimization problem into a mixed integer optimization problem. T,1 ~A T,5 ; As decision variables, the transformed constraints are optimized as follows:

5. The method according to claim 3, characterized in that: The power allocation optimization problem involved in the frequency modulation of the air conditioning cluster is modeled as a mixed integer optimization problem within the unit optimization time, including: Add the constraints of the working environment corresponding to the power allocation optimization problem, assuming that the temperature setting value of the air conditioner has a lower limit Upper limit The upper limit of the time when the air conditioner starts to participate in demand response is Regulation The upper limit is ΔT max , then the constraint condition can be expressed as:

6. The method according to claim 5, characterized in that The step of solving the mixed integer optimization problem to obtain an optimization result of power allocation includes: Based on the objective function and the constraint conditions, the mixed integer optimization problem is solved by a solver to obtain an optimization result of power allocation.

7. An air conditioning cluster frequency modulation control device based on temperature set point, characterized in that: include: The first modeling module is used to model a single air conditioner participating in the cluster using a second-order equivalent mechanical parameter model, characterize the building model where the air conditioner is located using thermal parameters, embed the thermodynamic model of the air conditioner into the building model, and obtain the power change curve of the air conditioner; A fitting and assumption module, used for fitting the power variation curve using a piecewise linear function, and making a linear assumption about the power near the air conditioner operating point after fitting; The second modeling module is used to model the power allocation optimization problem involved in the air-conditioning cluster participating in frequency modulation as a mixed integer optimization problem within a unit optimization time; The solution module is used to solve the mixed integer optimization problem to obtain an optimization result of power allocation.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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