Load gradeability evaluation control method, system, equipment and medium

Abstract

The invention belongs to the technical field of power systems, and discloses a load gradeability evaluation control method, system and device and a medium, and the method comprises the steps: constructing a control object set comprising a plurality of heat source units; the climbing ability index of the control object is obtained by testing the output power of the control object and combining the climbing index generation rule of the control object; on the basis of an artificial intelligence algorithm, the energy supply parameters and the temperature parameters, the number of operation control objects is calculated; determining a startup control object combination by using the running number of the control objects and taking a principle of preferentially starting a plurality of control objects which are separated from each other as a principle; and according to the control object combination of startup and the gradeability indexes of the control objects, calculating the upward gradeability and the downward gradeability of the cold storage and heat storage load. According to the method, the starting-up combination is reasonably determined, and online calculation of the climbing capacity of the cold storage and heat storage load is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a load climbing capability evaluation control method, system, equipment and medium. Background Art

[0002] As the proportion of new energy sources gradually increases, the power system has a more urgent need for flexible resources. New products such as ramping are gradually incorporated into the power market system, fully reflecting the regulatory value of the system's flexible resources. Cold and heat storage loads represented by air source heat pumps have good cold and heat storage characteristics, and can play a continuous regulatory role to meet the system's ramping needs. Cold and heat storage loads have the ability to store energy indirectly, which can reduce peak loads without affecting user comfort or sacrificing minimal comfort, improve the reliability of power grid operation, and enhance the emergency response capabilities of the power grid. Compared with traditional supply-side dispatching, cold and heat storage load regulation has the advantages of fast response speed, low cost, and high resource utilization.

[0003] However, the current technical form of how the main body of cold and heat storage loads can support the system's ramping needs is still unclear, and the evaluation method for ramping capacity is not complete, resulting in few practices that truly participate in grid regulation. Therefore, it is urgent to establish load ramping capacity evaluation methods, systems, equipment and media. Summary of the invention

[0004] The embodiments of the present invention provide a load climbing capability evaluation control method, system, device and medium to solve the problem that the technical form of climbing requirements in the prior art is still unclear, the climbing capability evaluation method is not complete, and there are few practices that actually participate in power grid regulation.

[0005] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0006] According to a first aspect of an embodiment of the present invention, a load climbing capability evaluation control method is provided.

[0007] In one embodiment, the load climbing capability evaluation control method includes:

[0008] Construct a set of control objects including several heat source units; obtain the climbing ability index of the control object by testing the output power of the control object and combining the climbing index generation rule of the control object;

[0009] Calculate the number of operating control objects based on artificial intelligence algorithms, energy supply parameters and temperature parameters; determine the combination of control objects to be started based on the number of operating control objects and the principle of giving priority to starting several control objects that are separated from each other;

[0010] According to the combination of controlled objects to be powered on and the climbing capacity index of the controlled objects, the upward climbing capacity and downward climbing capacity of the cold and heat storage load are calculated.

[0011] In one embodiment, by testing the output power of the control object and combining the climbing index generation rule of the control object, obtaining the climbing ability index of the control object includes:

[0012] Starting the control object, and when the control object runs stably for a preset period of time, obtaining the average output power of the control object during the stable operation period;

[0013] Obtaining the climbing range of the controlled object according to the average output power during the stable operation of the controlled object;

[0014] Obtain the ramp-up time of the controlled object according to the time from shutdown of the controlled object to stable operation of the controlled object;

[0015] The ramp rate of the controlled object is obtained by dividing the average output power of the controlled object during stable operation by the ramp time of the controlled object.

[0016] In one embodiment, based on the artificial intelligence algorithm, the energy supply parameters and the temperature parameters, calculating the number of operating units of the controlled object includes:

[0017] Obtain historical operation data, including the input parameters and corresponding output parameters of the artificial intelligence algorithm; set up an artificial intelligence algorithm training module, and use the historical operation data to train a system transfer model from input parameters to output parameters;

[0018] Based on the input parameters of online monitoring and through the system transfer model, the output parameters are calculated;

[0019] Among them, the input parameters include energy supply area, energy supply, outdoor average temperature, indoor average temperature, indoor set temperature, pipeline water supply temperature and pipeline return water temperature; the output parameters include the number of operating units of the control object.

[0020] In one embodiment, the calculation of the energy supply includes:

[0021] Collect the energy supply area and determine the energy supply calculation coefficient according to the type of energy supply object; multiply the energy supply calculation coefficient by the energy supply area to obtain the energy supply.

[0022] In one embodiment, the combination of control objects to be started is determined by using the number of running control objects and taking a principle of giving priority to starting a number of control objects that are separated from each other, including:

[0023] According to the number of operating control objects, a subset is selected from the control object set, and non-adjacent control objects are given priority in the subset.

[0024] In one embodiment, the upward climbing capability includes: upward climbing capability, upward climbing time and upward climbing rate;

[0025] The downward climbing capability includes downward climbing capability, downward climbing time and downward climbing rate.

[0026] In one embodiment, according to the combination of the controlled objects that are powered on and the climbing capacity index of the controlled objects, calculating the upward climbing capacity and downward climbing capacity of the cold and heat storage load includes:

[0027] Subtract the sum of the average output power of all controlled objects from the sum of the average output power of the controlled objects in operation, and multiply it by the upward climbing willingness coefficient to obtain the upward climbing capacity of the cold and heat storage load;

[0028] The sum of the average output power of the combination of controlled objects that are turned on is multiplied by the downward climbing willingness coefficient to obtain the downward climbing capacity of the cold and heat storage load;

[0029] The ramp-up time of a single control object is multiplied by the number of control objects to be turned on during the ramp-up process to obtain the ramp-up time of the cold and heat storage load.

[0030] The ramp-up time of the cold and heat storage load is obtained by multiplying the ramp-up time of a single control object by the number of control objects that need to be shut down during the ramp-down process;

[0031] The climbing rate of a single control object is used as the upward climbing rate and downward climbing rate of the cold and heat storage load.

[0032] According to a second aspect of an embodiment of the present invention, a load climbing capability evaluation control system is provided.

[0033] In one embodiment, the load climbing capability evaluation control system comprises:

[0034] The climbing ability index acquisition module is used to construct a control object set including several heat source units; the climbing ability index of the control object is obtained by testing the output power of the control object and combining the climbing index generation rule of the control object;

[0035] The control object determination module is used to calculate the number of operating control objects based on artificial intelligence algorithms, energy supply parameters and temperature parameters; determine the combination of control objects to be started based on the number of operating control objects and the principle of giving priority to starting a number of control objects that are separated from each other;

[0036] The climbing capacity calculation module is used to calculate the upward climbing capacity and downward climbing capacity of the cold and heat storage load according to the combination of the control objects that are turned on and the climbing capacity indicators of the control objects.

[0037] In one embodiment, by testing the output power of the control object and combining the climbing index generation rule of the control object, obtaining the climbing ability index of the control object includes:

[0038] Starting the control object, and when the control object runs stably for a preset period of time, obtaining the average output power of the control object during the stable operation period;

[0039] Obtaining the climbing range of the controlled object according to the average output power during the stable operation of the controlled object;

[0040] Obtain the ramp-up time of the controlled object according to the time from shutdown of the controlled object to stable operation of the controlled object;

[0041] The ramp rate of the controlled object is obtained by dividing the average output power of the controlled object during stable operation by the ramp time of the controlled object.

[0042] In one embodiment, based on the artificial intelligence algorithm, the energy supply parameters and the temperature parameters, calculating the number of operating units of the controlled object includes:

[0043] Obtain historical operation data, including the input parameters and corresponding output parameters of the artificial intelligence algorithm; set up an artificial intelligence algorithm training module, and use the historical operation data to train a system transfer model from input parameters to output parameters;

[0044] Based on the input parameters of online monitoring and through the system transfer model, the output parameters are calculated;

[0045] Among them, the input parameters include energy supply area, energy supply, outdoor average temperature, indoor average temperature, indoor set temperature, pipeline water supply temperature and pipeline return water temperature; the output parameters include the number of operating units of the control object.

[0046] In one embodiment, the calculation of the energy supply includes:

[0047] Collect the energy supply area and determine the energy supply calculation coefficient according to the type of energy supply object; multiply the energy supply calculation coefficient by the energy supply area to obtain the energy supply.

[0048] In one embodiment, the combination of control objects to be started is determined by using the number of running control objects and taking a principle of giving priority to starting a number of control objects that are separated from each other, including:

[0049] According to the number of operating control objects, a subset is selected from the control object set, and non-adjacent control objects are given priority in the subset.

[0050] In one embodiment, the upward climbing capability includes: upward climbing capability, upward climbing time and upward climbing rate;

[0051] The downward climbing capability includes downward climbing capability, downward climbing time and downward climbing rate.

[0052] In one embodiment, according to the combination of the controlled objects that are powered on and the climbing capacity index of the controlled objects, calculating the upward climbing capacity and downward climbing capacity of the cold and heat storage load includes:

[0053] Subtract the sum of the average output power of all controlled objects from the sum of the average output power of the controlled objects in operation, and multiply it by the upward climbing willingness coefficient to obtain the upward climbing capacity of the cold and heat storage load;

[0054] The sum of the average output power of the combination of controlled objects that are turned on is multiplied by the downward climbing willingness coefficient to obtain the downward climbing capacity of the cold and heat storage load;

[0055] The ramp-up time of a single control object is multiplied by the number of control objects to be turned on during the ramp-up process to obtain the ramp-up time of the cold and heat storage load.

[0056] The ramp-up time of the cold and heat storage load is obtained by multiplying the ramp-up time of a single control object by the number of control objects that need to be shut down during the ramp-down process;

[0057] The climbing rate of a single control object is used as the upward climbing rate and downward climbing rate of the cold and heat storage load.

[0058] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0059] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0060] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0061] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0062] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0063] The present invention scientifically calculates the number of units currently required to be operated and reasonably determines the startup combination based on the online collected data and the system transfer model obtained through training. Based on the operating unit combination, the present invention realizes the online calculation of the climbing capacity of the cold and heat storage load, supports the main body of such load to respond to the climbing demand of the power system, scientifically evaluates the climbing capacity, and promotes the application demonstration of large-scale cold and heat storage loads in power grid regulation.

[0064] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0066] Figure 1 is a flow chart of a load climbing capability evaluation control method according to an exemplary embodiment;

[0067] Figure 2 is a principle block diagram of a load climbing capability evaluation control system according to an exemplary embodiment;

[0068] Figure 3 is a structural schematic diagram of a computer device according to an exemplary embodiment;

[0069] Figure 4 is a flow chart of a method for calculating the number of operating units of a heat source unit according to an exemplary embodiment;

[0070] Figure 5 It is a flow chart of a technical solution according to an exemplary embodiment. DETAILED DESCRIPTION

[0071] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0072] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0073] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0074] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0075] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0076] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0077] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0078] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0079] Figure 1 An embodiment of a load climbing capability evaluation control method of the present invention is shown.

[0080] In this optional embodiment, the load climbing capability evaluation control method includes:

[0081] S101, constructing a control object set including several heat source units; obtaining a climbing ability index of the control object by testing the output power of the control object and combining a climbing index generation rule of the control object;

[0082] S103, calculating the number of operating control objects based on an artificial intelligence algorithm, energy supply parameters, and temperature parameters; determining a combination of control objects to be started based on the number of operating control objects and the principle of giving priority to starting a number of control objects that are separated from each other;

[0083] S105. Calculate the upward climbing capacity and downward climbing capacity of the cold and heat storage load according to the combination of the controlled objects that are turned on and the climbing capacity index of the controlled objects.

[0084] Figure 2 An embodiment of a load climbing ability evaluation control system of the present invention is shown.

[0085] In this optional embodiment, the load climbing capability evaluation control system includes:

[0086] The climbing ability index acquisition module 201 is used to construct a control object set including several heat source units; by testing the output power of the control object and combining the climbing index generation rule of the control object, the climbing ability index of the control object is acquired;

[0087] The control object determination module 203 is used to calculate the number of operating control objects based on the artificial intelligence algorithm, energy supply parameters and temperature parameters; determine the combination of control objects to be started based on the number of operating control objects and the principle of giving priority to starting a number of control objects that are separated from each other;

[0088] The climbing capacity calculation module 205 is used to calculate the upward climbing capacity and downward climbing capacity of the cold and heat storage load according to the combination of the controlled objects that are turned on and the climbing capacity index of the controlled objects.

[0089] In order to facilitate understanding of the above technical solution of the present invention, the above technical solution of the present invention is further explained from the perspective of architecture and principle. Figure 5 shown.

[0090] First, determine the control object set N of the cold and heat storage load, where N is composed of several independent heat source units. k}, k represents the total number of units.

[0091] Second, determine the gradeability of each heat source unit through test methods. The test steps are as follows:

[0092] (1) At time T0, the power consumption is 0 and the heat source unit is started.

[0093] (2) At time T1, the output power of the heat source unit reaches a stable value.

[0094] (3) Stable operation for 1 hour, the average output power during this hour is taken as P N .

[0095] The climbing capability of the heat source unit includes the following three indicators: climbing range, climbing time and climbing rate.

[0096] The climbing range of the heat source unit is [0, P N Its physical meaning is that when the heat source unit is shut down, the output power is 0; when the heat source unit is running stably, the output power is P N .

[0097] Heat source unit ramp-up time T P =T1-T0. Its physical meaning is that the ramp time is from output power 0 to output power P N time.

[0098] The ramp rate S of the heat source unitP =P N / T P Its physical meaning is that the ramp rate is the time from the ramp time to the output power P N rate.

[0099] If the test cannot be carried out on site, P N It is replaced by the rated power of the heat source unit.

[0100] Third, use artificial intelligence algorithms and online data collection to calculate the number of heat source units required. Figure 4 The specific steps are as follows:

[0101] (1) Collect the required cooling / heating area Q S .

[0102] (2) Calculate the required cooling / heating supply Q = Q T *Q S , Q T The coefficient is expressed as follows: it can be determined by looking up a table or by experience, depending on the type of cooling / heating object (such as residence, school, office building, hospital, hotel, etc.).

[0103] (3) Collect the following operating parameters online, including cooling / heating area, cooling / heating supply, outdoor average temperature, indoor average temperature, indoor set temperature, pipeline water supply temperature, pipeline return water temperature, and the number of operating heat source units.

[0104] (4) An artificial intelligence algorithm training module is set up, with the input parameters being the cooling / heating area, cooling / heating supply, outdoor average temperature, indoor average temperature, indoor set temperature, pipeline water supply temperature, and pipeline return water temperature; and the output parameter being the number of operating heat source units.

[0105] (5) Select 3-6 months of historical operation data and train a system transfer model from input parameters to output parameters.

[0106] (6) Based on the input parameters of online monitoring, the system model obtained through the above artificial intelligence algorithm training module is used to calculate the output parameters online, that is, the number of operating heat source units.

[0107] Fourth, according to the number of running heat source units obtained in the previous step, determine the startup combination M, where M is a subset of N. To ensure the heat dissipation efficiency of the heat source units, try not to start adjacent heat source units. The algorithm for selecting heat source units is: M = {N1, N 1+x , N 1+2x , ..., N k}, x is the number of heat source units in operation. For example, N has 20 heat source units and the number of operating units is 5, then M = {N1, N6, N 11 , N16 , N 20}.

[0108] Fifth, calculate the climbing capacity of the cold and heat storage load. There are k heat source units in total, and x represents the number of operating heat source units. The climbing capacity of the cold and heat storage load is divided into upward climbing capacity and downward climbing capacity.

[0109] (1) The upward climbing ability includes the following three indicators: upward climbing ability, upward climbing time, and upward climbing rate.

[0110] Upward climbing ability is Z UP *(kx)*P N Its physical meaning is that when the number of operating heat source units is x, the output power is x*P N The output power of the heat source unit is k*P N , so the maximum value of the upward climb is (kx)*P N . Z UP The willingness coefficient for upward climbing represents the intensity of the load aggregator's willingness to participate in upward climbing, and its value is between [0, 1].

[0111] Upward climbing time T P-UP =(T1-T0)*Z UP *(kx). Its physical meaning is that the ramp-up time is equal to the ramp-up time of a single heat source unit multiplied by the number of units to be turned on during the ramp-up process.

[0112] Upward climbing rate S P-UP =P N / T P Its physical meaning is that the upward climbing rate is equal to the climbing rate of a single unit.

[0113] (2) Downward climbing ability includes the following three indicators: downward climbing ability, downward climbing time, and downward climbing rate.

[0114] Downward climbing ability is Z DOWN *x*P N Its physical meaning is that when the number of operating heat source units is x, the output power is x*P N , the output power of the heat source unit is 0 when it is fully turned off, so the maximum value of the downward slope is x*P N . Z DOWN is the willingness coefficient for downward climbing, representing the intensity of the load aggregator's willingness to participate in downward climbing, and its value is between [0, 1].

[0115] Downward climbing time T P-DOWN =(T1-T0)*Z DOWN*x, its physical meaning is that the ramp-down time is equal to the ramp-down time of a single heat source unit multiplied by the number of units that need to be shut down during the ramp-down process.

[0116] Downward climbing rate S P-DOWN =P N / T P The physical meaning is that the downward ramp rate is equal to the ramp rate of a single unit.

[0117] According to the load climbing capacity evaluation control method, it is applied to the implementable load climbing capacity evaluation control system. Through the above load climbing capacity evaluation control method and system, according to the online collected data and the system transfer model obtained by training, the number of units currently required to operate is scientifically calculated, and the startup combination is reasonably determined. Based on the running unit combination, the online calculation of the climbing capacity of the cold and heat storage load is realized, supporting the main body of this type of load to respond to the climbing demand of the power system, scientifically evaluating the climbing capacity, and promoting the application demonstration of large-scale cold and heat storage loads in power grid regulation.

[0118] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0119] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0120] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0121] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0122] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0123] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A load climbing ability evaluation control method, characterized in that: The method includes: Construct a set of control objects including several heat source units; obtain the climbing ability index of the control object by testing the output power of the control object and combining the climbing index generation rule of the control object; Calculate the number of operating control objects based on artificial intelligence algorithms, energy supply parameters and temperature parameters; determine the combination of control objects to be started based on the number of operating control objects and the principle of giving priority to starting several control objects that are separated from each other; According to the combination of controlled objects to be powered on and the climbing capacity index of the controlled objects, the upward climbing capacity and downward climbing capacity of the cold and heat storage load are calculated.

2. A load climbing ability evaluation control method according to claim 1, characterized in that: The step of performing an output power test on the control object and combining the control object's climbing index generation rule to obtain the control object's climbing ability index includes: Starting the control object, and when the control object runs stably for a preset period of time, obtaining the average output power of the control object during the stable operation period; Obtaining the climbing range of the controlled object according to the average output power during the stable operation of the controlled object; Obtain the ramp-up time of the controlled object according to the time from shutdown of the controlled object to stable operation of the controlled object; The ramp rate of the controlled object is obtained by dividing the average output power of the controlled object during stable operation by the ramp time of the controlled object.

3. A load climbing ability evaluation and control method according to claim 1, characterized in that: The calculation of the number of operating units of the controlled object based on the artificial intelligence algorithm, energy supply parameters and temperature parameters includes: Obtain historical operation data, including the input parameters and corresponding output parameters of the artificial intelligence algorithm; set up an artificial intelligence algorithm training module, and use the historical operation data to train a system transfer model from input parameters to output parameters; Based on the input parameters of online monitoring and through the system transfer model, the output parameters are calculated; Among them, the input parameters include energy supply area, energy supply, outdoor average temperature, indoor average temperature, indoor set temperature, pipeline water supply temperature and pipeline return water temperature; the output parameters include the number of operating units of the control object.

4. A load climbing ability evaluation control method according to claim 3, characterized in that: The calculation of the energy supply includes: Collect the energy supply area and determine the energy supply calculation coefficient according to the type of energy supply object; multiply the energy supply calculation coefficient by the energy supply area to obtain the energy supply.

5. A load climbing ability evaluation and control method according to claim 1, characterized in that: The combination of control objects to be started by using the number of running control objects and taking the principle of giving priority to starting a number of control objects separated from each other includes: According to the number of operating control objects, a subset is selected from the control object set, and non-adjacent control objects are given priority in the subset.

6. A load climbing ability evaluation and control method according to claim 1, characterized in that: The upward climbing ability includes: upward climbing ability, upward climbing time and upward climbing rate; The downward climbing capability includes downward climbing capability, downward climbing time and downward climbing rate.

7. A load climbing capability evaluation and control method according to claim 1, characterized in that: The calculation of the upward climbing capacity and downward climbing capacity of the cold and heat storage load according to the combination of the controlled objects started up and the climbing capacity index of the controlled objects includes: Subtract the sum of the average output power of all controlled objects from the sum of the average output power of the controlled objects in operation, and multiply it by the upward climbing willingness coefficient to obtain the upward climbing capacity of the cold and heat storage load; The sum of the average output power of the combination of controlled objects that are turned on is multiplied by the downward climbing willingness coefficient to obtain the downward climbing capacity of the cold and heat storage load; The ramp-up time of a single control object is multiplied by the number of control objects to be turned on during the ramp-up process to obtain the ramp-up time of the cold and heat storage load. The ramp-up time of the cold and heat storage load is obtained by multiplying the ramp-up time of a single control object by the number of control objects that need to be shut down during the ramp-down process; The climbing rate of a single control object is used as the upward climbing rate and downward climbing rate of the cold and heat storage load.

8. A load climbing capability evaluation control system, characterized in that: The system includes: The climbing ability index acquisition module is used to construct a control object set including several heat source units; the climbing ability index of the control object is obtained by testing the output power of the control object and combining the climbing index generation rule of the control object; The control object determination module is used to calculate the number of operating control objects based on artificial intelligence algorithms, energy supply parameters and temperature parameters; determine the combination of control objects to be started based on the number of operating control objects and the principle of giving priority to starting a number of control objects that are separated from each other; The climbing capacity calculation module is used to calculate the upward climbing capacity and downward climbing capacity of the cold and heat storage load according to the combination of the control objects that are turned on and the climbing capacity indicators of the control objects.

9. A load climbing capability evaluation control system according to claim 8, characterized in that: The step of performing an output power test on the control object and combining the control object's climbing index generation rule to obtain the control object's climbing ability index includes: Starting the control object, and when the control object runs stably for a preset period of time, obtaining the average output power of the control object during the stable operation period; Obtaining the climbing range of the controlled object according to the average output power during the stable operation of the controlled object; Obtain the ramp-up time of the controlled object according to the time from shutdown of the controlled object to stable operation of the controlled object; The ramp rate of the controlled object is obtained by dividing the average output power of the controlled object during stable operation by the ramp time of the controlled object.

10. A load climbing capability evaluation control system according to claim 8, characterized in that: The calculation of the number of operating units of the controlled object based on the artificial intelligence algorithm, energy supply parameters and temperature parameters includes: Obtain historical operation data, including the input parameters and corresponding output parameters of the artificial intelligence algorithm; set up an artificial intelligence algorithm training module, and use the historical operation data to train a system transfer model from input parameters to output parameters; Based on the input parameters of online monitoring and through the system transfer model, the output parameters are calculated; Among them, the input parameters include energy supply area, energy supply, outdoor average temperature, indoor average temperature, indoor set temperature, pipeline water supply temperature and pipeline return water temperature; the output parameters include the number of operating units of the control object.

11. A load climbing capability evaluation control system according to claim 10, characterized in that: The calculation of the energy supply includes: Collect the energy supply area and determine the energy supply calculation coefficient according to the type of energy supply object; multiply the energy supply calculation coefficient by the energy supply area to obtain the energy supply.

12. A load climbing capability evaluation control system according to claim 8, characterized in that: The combination of control objects to be started by using the number of running control objects and taking the principle of giving priority to starting a number of control objects separated from each other includes: According to the number of operating control objects, a subset is selected from the control object set, and non-adjacent control objects are given priority in the subset.

13. A load climbing capability evaluation control system according to claim 8, characterized in that: The upward climbing ability includes: upward climbing ability, upward climbing time and upward climbing rate; The downward climbing capability includes downward climbing capability, downward climbing time and downward climbing rate.

14. A load climbing capability evaluation control system according to claim 8, characterized in that: The calculation of the upward climbing capacity and downward climbing capacity of the cold and heat storage load according to the combination of the controlled objects started up and the climbing capacity index of the controlled objects includes: Subtract the sum of the average output power of all controlled objects from the sum of the average output power of the controlled objects in operation, and multiply it by the upward climbing willingness coefficient to obtain the upward climbing capacity of the cold and heat storage load; The sum of the average output power of the combination of controlled objects that are turned on is multiplied by the downward climbing willingness coefficient to obtain the downward climbing capacity of the cold and heat storage load; The ramp-up time of a single control object is multiplied by the number of control objects to be turned on during the ramp-up process to obtain the ramp-up time of the cold and heat storage load. The ramp-up time of the cold and heat storage load is obtained by multiplying the ramp-up time of a single control object by the number of control objects that need to be shut down during the ramp-down process; The climbing rate of a single control object is used as the upward climbing rate and downward climbing rate of the cold and heat storage load.

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

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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