Capacity adequacy calculation method, system and equipment considering load peak clipping

Abstract

The invention discloses a capacity adequacy calculation method, system and equipment considering load peak clipping, and particularly relates to the technical field of power system operation, and the technical key points are as follows: constructing a load estimation model used for estimating load data within a preset time by combining historical load data; constructing an energy capacity estimation model for estimating energy capacity data within a preset time by combining the historical energy capacity data, and generating load estimation data and energy capacity estimation data by using the load estimation model and the energy capacity estimation model; combining the load estimation data and the energy capacity estimation data to construct a power market clearing model and a capacity market clearing model; determining a first target function by taking the minimum net load of the power system as a target, establishing a first constraint condition, and constructing a capacity credit coefficient calculation model; and based on the capacity credit coefficient calculation model, a first capacity credit value coefficient of the power generation side and a second capacity credit value coefficient of the peak clipping capability of the load side are calculated by using the load duration curve.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation, and in particular to a capacity margin calculation method, system and device taking load peak shaving into consideration. Background Art

[0002] With the continuous acceleration of urbanization, the electricity demand in megacities has increased rapidly. Ensuring the capacity adequacy of the power system has become a key issue in ensuring the reliability and stability of power supply. The capacity adequacy assessment aims to determine whether the power system has sufficient power generation capacity to cope with peak loads and emergencies. As an important demand response method, load peak shaving significantly improves capacity adequacy by adjusting the electricity demand during peak hours and reducing the pressure on the power system. However, traditional evaluation methods are difficult to accurately quantify the actual effect of load peak shaving, especially in megacities with complex and changeable load characteristics.

[0003] The capacity credit value coefficient of load peak shaving is an important indicator to measure the contribution of peak shaving measures to capacity margin, and reflects the impact of peak shaving measures on the reliability of the power system under different conditions. Therefore, the present invention aims to propose a capacity margin calculation method taking into account the capacity credit value coefficient of load peak shaving, which can more accurately evaluate the benefits of peak shaving measures and provide a scientific basis for the planning and operation of super-large city power systems. Summary of the invention

[0004] The technical problem to be solved by the present invention is that traditional evaluation methods are difficult to accurately quantify the actual effect of load peak shaving. The purpose is to provide a capacity margin calculation method, system and equipment that take load peak shaving into consideration. By establishing a capacity credit coefficient calculation model that takes into consideration source-load balance constraints, energy storage power constraints and demand power constraints, a second capacity credit value coefficient that can calculate the load side peak shaving capability is realized. By inputting the second capacity credit value coefficient of the load side peak shaving capability into the power market clearing model, the capacity margin calculation that takes into consideration the actual peak shaving capability of the load side is realized, so that various load characteristics and peak shaving potential can be scientifically analyzed, and the capacity margin in the super-large city scenario can be accurately measured.

[0005] The present invention is achieved through the following technical solutions:

[0006] A capacity margin calculation method considering load peak shaving, the method comprising:

[0007] Building a load estimation model for estimating load data within a preset time in combination with historical load data, building an energy capacity estimation model for estimating energy capacity data within a preset time in combination with historical energy capacity data, and generating load estimation data and energy capacity estimation data using the load estimation model and the energy capacity estimation model;

[0008] Combining load estimation data and energy capacity estimation data, constructing power market clearing models and capacity market clearing models;

[0009] Taking the minimum net load of the power system as the goal, the first objective function is determined, the first constraint condition is established, and a capacity credit factor calculation model is constructed, wherein the first constraint condition includes a source-load balance constraint, an energy storage power constraint, and a demand power constraint;

[0010] Based on the capacity credit coefficient calculation model, the load continuity curve is used to calculate the first capacity credit value coefficient of the power generation side and the second capacity credit value coefficient of the load side peak shaving capacity;

[0011] The energy capacity estimation data and the first capacity credit value coefficient are used to calculate the actual available capacity of the power generation side, and the actual maximum output of the power generation side is generated by using the actual available capacity of the power generation side; the load estimation data and the second capacity credit value coefficient are used to calculate the maximum load that can be reduced on the load side, and the actual maximum peak reduction on the load side is generated by using the maximum load that can be reduced on the load side;

[0012] The actual available capacity on the power generation side, the actual maximum output on the power generation side, the maximum load that can be reduced on the load side, and the actual maximum peak reduction on the load side are input into the power market clearing model to obtain the capacity margin of the power system.

[0013] Furthermore, the method also includes: the load estimation data includes the load value and the theoretical maximum peak shaving capacity of the load side, and the energy capacity estimation data includes the nameplate capacity of the new energy unit.

[0014] Furthermore, the actual available capacity of the power generation side is calculated using the energy capacity estimation data and the first capacity credit value coefficient, and the actual maximum output of the power generation side is generated using the actual available capacity of the power generation side; the maximum reducible load on the load side is calculated using the load estimation data and the second capacity value coefficient, and the actual maximum peak reduction on the load side is generated using the maximum reducible load on the load side, specifically:

[0015] The actual available capacity of the power generation side is calculated using the nameplate capacity of the new energy unit and the first capacity credit value coefficient, and the actual maximum output of the power generation side is generated using the actual available capacity of the power generation side;

[0016] The maximum load that can be reduced on the load side is calculated using the theoretical maximum peak shaving capacity on the load side and the second capacity value coefficient, and the actual maximum peak shaving on the load side is generated using the maximum load that can be reduced on the load side.

[0017] Furthermore, the method also includes: inputting the actual available capacity on the power generation side, the actual maximum output on the power generation side, the maximum curtailable load on the load side, and the actual maximum peak shaving on the load side into the capacity market clearing model to obtain the power clearing amount and clearing price.

[0018] Furthermore, the first objective function is specifically:

[0019]

[0020] In the formula, is the net load at H+1 hour, π h Indicates NL h and The maximum value of the difference between and 0.

[0021] Furthermore, the source-load balance constraint is specifically:

[0022] NL h =L h -Bc h +Bi h -Bo h

[0023] Where, L h is the actual load of the system at time h, Bc h Bi is the load reduction on the load side compared to the power consumption baseline value; h To charge the energy storage, Bo h Discharge electrical energy for energy storage.

[0024] 7. A capacity margin calculation method considering load peak shaving according to claim 1, characterized in that the energy storage power constraint is specifically:

[0025] Bl h ≤Bl Max

[0026] 0≤Bo h ≤Bp Max

[0027] 0≤Bi h ≤Bp Max

[0028] Where, Bl h is the energy storage charge state, Bl Max is the maximum energy storage capacity, Bp Max It is the maximum charging and discharging speed of energy storage.

[0029] The present invention further provides a capacity margin calculation system considering load peak shaving, the system is used in a capacity margin calculation method considering load peak shaving as described in any one of claims 1 to 7, the system comprising:

[0030] The first module is used to construct a load estimation model for estimating load data within a preset time in combination with historical load data, to construct an energy capacity estimation model for estimating energy capacity data within a preset time in combination with historical energy capacity data, and to generate load estimation data and energy capacity estimation data using the load estimation model and the energy capacity estimation model;

[0031] The second module is used to build a power market clearing model and a capacity market clearing model by combining load estimation data and energy capacity estimation data;

[0032] The third module is used to determine the first objective function with the goal of minimizing the net load of the power system, establish the first constraint condition, and construct a capacity credit factor calculation model, wherein the first constraint condition includes a source-load balance constraint, an energy storage power constraint, and a demand power constraint;

[0033] The fourth module is used to calculate the first capacity credit value coefficient of the power generation side and the second capacity credit value coefficient of the load side peak shaving capacity based on the capacity credit coefficient calculation model and the load continuity curve;

[0034] The fifth module is used to calculate the actual available capacity of the power generation side using the energy capacity estimation data and the first capacity credit value coefficient, and generate the actual maximum output of the power generation side using the actual available capacity of the power generation side; calculate the maximum reducible load on the load side using the load estimation data and the second capacity credit value coefficient, and generate the actual maximum peak reduction on the load side using the maximum reducible load on the load side;

[0035] The sixth module is used to input the actual available capacity on the power generation side, the actual maximum output on the power generation side, the maximum load that can be reduced on the load side, and the actual maximum peak reduction on the load side into the power market clearing model to obtain the capacity margin of the power system.

[0036] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0037] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any one of the methods described above are implemented.

[0038] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0039] In the present invention, a capacity credit coefficient calculation model that takes into account the source-load balance constraint, energy storage power constraint and demand power constraint is established to realize the second capacity credit value coefficient that can calculate the peak shaving capacity on the load side. The second capacity credit value coefficient of the peak shaving capacity on the load side is input into the power market clearing model to realize the capacity margin calculation that takes into account the actual peak shaving capacity on the load side. This makes it possible to scientifically analyze various load characteristics and peak shaving potentials, and accurately measure the capacity margin in the super-large city scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0041] Figure 1 This is a flow chart of a method for calculating capacity margin taking load peak shaving into consideration in this embodiment;

[0042] Figure 2 This is a structural schematic diagram of a capacity margin calculation system considering load peak shaving in this embodiment;

[0043] Figure 3 It is a structural schematic diagram of a computer device in this embodiment. DETAILED DESCRIPTION

[0044] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0045] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0046] The terms used in the description of various examples in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations.

[0047] Example 1

[0048] See also Figure 1 As shown, a capacity margin calculation method considering load peak shaving includes:

[0049] S1: constructing a load estimation model for estimating load data within a preset time in combination with historical load data, constructing an energy capacity estimation model for estimating energy capacity data within a preset time in combination with historical energy capacity data, and generating load estimation data and energy capacity estimation data using the load estimation model and the energy capacity estimation model;

[0050] It should be noted that, in this embodiment, the historical load data and historical energy capacity data refer to the load data and energy capacity data of the power system over the years, which are obtained from the database of the power system; the power system includes a load side, a power generation side and an energy storage side, and the load side includes industrial load, commercial load, agricultural load and residential load, each load fluctuates over time, and the adjustable load of commercial electricity is the air conditioning load, and the adjustable load of residential electricity is the electric vehicle charging load. The ideal maximum adjustable load P a,l =∑P i η i i∈(id,ar,co,re), i is the various types of power loads, where id, ar, co, and re represent industrial, agricultural, commercial, and residential power consumption, respectively. i and η i Respectively represent the adjustable load amount and adjustable ratio of each type of load;

[0051] At the same time, the load estimation model is achieved by setting up initial load values, which include industrial load initial values, commercial load initial values, agricultural load initial values ​​and residential load initial values, and then adding a time increase index to the initial load value, so that each initial load value changes dynamically over time, thereby achieving the load estimation data within a preset time; the time increase index is obtained by analyzing historical load data and is consistent with the load changes of the power system; the energy capacity estimation model is consistent with the load estimation model, and will not be elaborated here, and those skilled in the art will be able to understand the technical solution.

[0052] It should be noted that the load estimation data includes the load value on the load side, the theoretical maximum peak shaving capacity on the load side, the adjustment speed of agricultural electricity load, the adjustment speed of commercial electricity load, the adjustment speed of residential electricity load, the adjustment speed of industrial electricity load, the already shaving load of agricultural electricity, the already shaving load of commercial electricity, the already shaving load of residential electricity, the already shaving load of industrial electricity, the maximum peak shaving load of agricultural electricity, the maximum peak shaving load of commercial electricity, the maximum peak shaving load of residential electricity and the maximum peak shaving load of industrial electricity; the energy capacity estimation data includes the nameplate capacity of new energy units, wind power generation, solar energy capacity, energy storage system capacity and thermal capacity; it should be noted that the above only describes some types, and each type of data is collected according to the data collection time interval preset by the power system. In other embodiments, other power system data used to construct a clearing model may also be included.

[0053] S2: Combine load estimation data and energy capacity estimation data to build power market clearing model and capacity market clearing model;

[0054] Specifically, in this embodiment, the capacity market clearing model is constructed as follows:

[0055] The second objective function is determined with the goal of maximizing the sum of consumer surplus and producer surplus, the second constraint condition is established, and the capacity market clearing model is constructed. The second objective function is specifically:

[0056]

[0057] Where D is the minimum value of the load demand curve; Pr0 is the maximum price limit for electricity demand, x m Binary variable, 0 or 1; m is the load increment number; Pr m is the capacity price of the mth segment, and ΔD is the small change on the demand side. i It is also a binary variable, which is 1 when the capacity quotation is accepted and 0 when the capacity quotation is rejected; B i is the capacity quotation of unit i, P i U is the upper limit (starting point) of the generator capacity quotation;

[0058] It should be noted that, in this embodiment, consumer surplus refers to the difference between the price consumers are willing to pay and the actual price they pay, reflecting the net welfare obtained by consumers in market transactions; producer surplus refers to the difference between the actual income obtained by producers and the marginal cost of providing goods or services, reflecting the net benefit obtained by producers in market transactions.

[0059] The second constraint condition is specifically:

[0060]

[0061] Where D P For peak load. and R D are the random coefficients of the power generation side and peak load respectively. i I is the nameplate capacity of new energy unit i. T is the load side demand, and H D The current power is converted to annual generation / consumption hours, respectively, for annual generation / consumption.

[0062] S3: Determine the first objective function with the goal of minimizing the net load of the power system, establish the first constraint condition, and construct a capacity credit factor calculation model, wherein the first constraint condition includes a source-load balance constraint, an energy storage power constraint, and a demand power constraint;

[0063] Specifically, in this embodiment, the first objective function is:

[0064]

[0065] In the formula, is the net load at H+1 hour, π h Indicates NL h and The maximum value of the difference between and 0 indicates that this model will ignore the load data during the non-peak period and only study the reliable capacity calculation during the peak period. In addition, H in the formula represents the horizontal coordinate of the load duration curve, not the actual time.

[0066] At the same time, the first constraint condition includes source-load balance constraint, energy storage power constraint and demand power constraint, among which the source-load balance constraint is specifically:

[0067] NL h =L h -Bc h +Bi h -Bo h

[0068] Where, L h is the actual load of the system, Bc h Bi is the load reduction on the load side compared to the power consumption baseline value; h Charging electricity for energy storage; Bo h Discharging electrical energy for energy storage;

[0069] The specific energy storage power constraints are:

[0070] Bl h ≤Bl Max

[0071] 0≤Bo h ≤BpMax

[0072] 0≤Bi h ≤Bp Max

[0073] Where, Bl h is the energy storage charge state, Bl Max is the maximum energy storage capacity, Bp Max is the maximum charging and discharging speed of energy storage;

[0074] The required power constraint is:

[0075] 0≤Bar h ≤Bar Max

[0076] 0≤Bco h ≤Bco Max

[0077] 0≤Bre h ≤Bre Max

[0078] 0≤Bin h ≤Bin Max

[0079] 0≤Blar h ≤Blar Max

[0080] 0≤Blre h ≤Blre Max

[0081] 0≤Blco h ≤Blco Max

[0082] 0≤Blin h ≤Blin Max

[0083] Where Bar Max , Bco Max , Bre Max , Bin Max Represents the maximum load adjustment speed of agricultural electricity, commercial electricity, residential electricity and industrial electricity respectively. h , Blco h ,Blre h , Blin h Represents the peak load of agricultural electricity, commercial electricity, residential electricity and industrial electricity. Max , Blco Max ,Blre Max , BlinMax They represent the maximum peak load reduction of agricultural electricity, commercial electricity, residential electricity and industrial electricity respectively.

[0084] S4: Based on the capacity credit coefficient calculation model, the load continuity curve is used to calculate the first capacity credit value coefficient of the power generation side and the second capacity credit value coefficient of the load side peak shaving capacity;

[0085] It should be noted that, in this embodiment, the load continuity curve is a curve constructed by estimating the load value of the load side within a period of time through the load estimation model, and then taking the collection time corresponding to the load value as the horizontal axis and the size of the load value as the vertical axis.

[0086] S5: Using the nameplate capacity of the new energy unit and the first capacity credit value coefficient, the actual available capacity of the power generation side is calculated, and the actual maximum output of the power generation side is generated using the actual available capacity of the power generation side; using the theoretical maximum peak shaving capacity of the load side and the second capacity value coefficient, the maximum load that can be reduced on the load side is calculated, and the actual maximum peak shaving on the load side is generated using the maximum load that can be reduced on the load side;

[0087] S6: Input the actual available capacity on the power generation side, the actual maximum output on the power generation side, the maximum load that can be reduced on the load side, and the actual maximum peak reduction on the load side into the preset power market clearing model to obtain the capacity margin of the power system; input into the capacity market clearing model to obtain the power clearing volume and clearing price.

[0088] Specifically, in this embodiment, a capacity credit coefficient calculation model that takes into account the source-load balance constraint, energy storage power constraint and demand power constraint is established to realize the second capacity credit value coefficient that can calculate the peak shaving capability of the load side. The second capacity credit value coefficient of the peak shaving capability of the load side is input into the power market clearing model to realize the capacity margin calculation that takes into account the actual peak shaving capability of the load side. This enables scientific analysis of various load characteristics and peak shaving potential, and accurate measurement of capacity margin in super-large city scenarios.

[0089] Example 2

[0090] See also Figure 2 As shown, the present invention further provides a capacity margin calculation system considering load peak shaving, which is used in any one of the above-mentioned capacity margin calculation methods considering load peak shaving, and the system includes:

[0091] The first module 100 is used to construct a load estimation model for estimating load data within a preset time in combination with historical load data, to construct an energy capacity estimation model for estimating energy capacity data within a preset time in combination with historical energy capacity data, and to generate load estimation data and energy capacity estimation data using the load estimation model and the energy capacity estimation model;

[0092] The second module 200 is used to construct a power market clearing model and a capacity market clearing model by combining the load estimation data and the energy capacity estimation data;

[0093] The third module 300 is used to determine the first objective function with the goal of minimizing the net load of the power system, establish a first constraint condition, and construct a capacity credit factor calculation model, wherein the first constraint condition includes a source-load balance constraint, an energy storage power constraint, and a demand power constraint;

[0094] The fourth module 400 is used to calculate the first capacity credit value coefficient of the power generation side and the second capacity credit value coefficient of the load side peak shaving capability based on the capacity credit coefficient calculation model and using the load continuity curve;

[0095] The fifth module 500 is used to calculate the actual available capacity of the power generation side using the energy capacity estimation data and the first capacity credit value coefficient, and generate the actual maximum output of the power generation side using the actual available capacity of the power generation side; calculate the maximum curtailable load on the load side using the load estimation data and the second capacity credit value coefficient, and generate the actual maximum peak reduction on the load side using the maximum curtailable load on the load side;

[0096] The sixth module 600 is used to input the actual available capacity on the power generation side, the actual maximum output on the power generation side, the maximum curtailable load on the load side, and the actual maximum peak reduction on the load side into the power market clearing model to obtain the capacity margin of the power system.

[0097] It should be noted that the modules in the system of Example 2 correspond to the steps in the method of Example 1. The steps in the method of Example 1 have been described in detail in Example 1. In this Example 2, the contents of the modules in the system will not be described in detail.

[0098] Example 3

[0099] See also Figure 3 As shown, this embodiment further provides a computer device, including a memory 1005 and a processor 1001, wherein the memory 1005 stores a computer program, and the processor 1001 implements the steps of any one of the above methods when executing the computer program.

[0100] It should be noted that the processor 1001 is used to execute the steps in the above method embodiments according to the instructions in the program code. Alternatively, the processor 1001 implements the functions of each module / unit in the above system / device embodiments when executing the computer program.

[0101] Specifically, in this embodiment, the computer program may be divided into one or more modules / units, one or more modules / units are stored in the memory 1005, and are executed by the processor 1001 to complete the present application. One or more modules / units may be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0102] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor 1001 and a memory 1005. Those skilled in the art will appreciate that this does not constitute a limitation on the terminal device, and may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include an input / output device 1003, a network access device 1002, a bus 1006, etc.

[0103] The processor 1001 may be a central processing unit (CPU), or other general-purpose processors 1001, digital signal processors 1001 (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor 1001 may be a microprocessor or any conventional processor, etc.

[0104] The memory 1005 may be an internal storage unit of the terminal device, such as a hard disk or memory of the terminal device. The memory 1005 may also be an external storage device 1004 of the terminal device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal device. Further, the memory 1005 may also include both the internal storage unit of the terminal device and the external storage device 1004. The memory 1005 is used to store computer programs and other programs and data required by the terminal device. The memory 1005 may also be used to temporarily store data that has been output or is to be output.

[0105] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0106] Example 4

[0107] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the above methods are implemented.

[0108] Among them, the computer readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system or device, or any combination of the above. More specific examples of computer readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk. Random Access Memory (RAM), Read-Only Memory (ROM), Erasable Programmable Read Only Memory (EPROM), registers, hard disks, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or the above in a suitable combination, or any other form of computer readable storage medium known in the art.

[0109] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (ASIC). In an embodiment of the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system, or device.

[0110] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A capacity margin calculation method considering load peak shaving, characterized in that: Methods include: Building a load estimation model for estimating load data within a preset time in combination with historical load data, building an energy capacity estimation model for estimating energy capacity data within a preset time in combination with historical energy capacity data, and generating load estimation data and energy capacity estimation data using the load estimation model and the energy capacity estimation model; Combining load estimation data and energy capacity estimation data, constructing power market clearing models and capacity market clearing models; Taking the minimum net load of the power system as the goal, the first objective function is determined, the first constraint condition is established, and a capacity credit factor calculation model is constructed, wherein the first constraint condition includes a source-load balance constraint, an energy storage power constraint, and a demand power constraint; Based on the capacity credit coefficient calculation model, the load continuity curve is used to calculate the first capacity credit value coefficient of the power generation side and the second capacity credit value coefficient of the load side peak shaving capacity; The energy capacity estimation data and the first capacity credit value coefficient are used to calculate the actual available capacity of the power generation side, and the actual maximum output of the power generation side is generated by using the actual available capacity of the power generation side; the load estimation data and the second capacity credit value coefficient are used to calculate the maximum load that can be reduced on the load side, and the actual maximum peak reduction on the load side is generated by using the maximum load that can be reduced on the load side; The actual available capacity on the power generation side, the actual maximum output on the power generation side, the maximum load that can be reduced on the load side, and the actual maximum peak reduction on the load side are input into the power market clearing model to obtain the capacity margin of the power system.

2. A method for calculating capacity margin considering load peak shaving according to claim 1, characterized in that: The method also includes: the load estimation data includes the load value and the theoretical maximum peak shaving capacity on the load side, and the energy capacity estimation data includes the nameplate capacity of the new energy unit.

3. A method for calculating capacity margin considering load peak shaving according to claim 2, characterized in that: Using the energy capacity estimation data and the first capacity credit value coefficient, the actual available capacity of the power generation side is calculated, and the actual maximum output of the power generation side is generated using the actual available capacity of the power generation side; Using the load estimation data and the second capacity value coefficient, the maximum load that can be reduced on the load side is calculated, and the actual maximum peak reduction on the load side is generated using the maximum load that can be reduced on the load side, which is specifically: The actual available capacity of the power generation side is calculated using the nameplate capacity of the new energy unit and the first capacity credit value coefficient, and the actual maximum output of the power generation side is generated using the actual available capacity of the power generation side; The maximum load that can be reduced on the load side is calculated using the theoretical maximum peak shaving capacity on the load side and the second capacity value coefficient, and the actual maximum peak shaving on the load side is generated using the maximum load that can be reduced on the load side.

4. The method for calculating capacity margin considering load peak shaving according to claim 1, characterized in that: The method also includes: inputting the actual available capacity on the power generation side, the actual maximum output on the power generation side, the maximum curtailable load on the load side, and the actual maximum peak reduction on the load side into the capacity market clearing model to obtain the power clearing amount and clearing price.

5. The method for calculating capacity margin considering load peak shaving according to claim 1, characterized in that: The first objective function is: In the formula, is the net load at time H+1, π h Indicates NL h and The maximum value of the difference between and 0.

6. A capacity margin calculation method considering load peak shaving according to claim 1, characterized in that: The source-load balance constraints are as follows: <h2 style=";text-align:left;direction:ltr">NL<h2 style=";text-align:left;direction:ltr"> h <h2 style=";text-align:left;direction:ltr"> =L<h2 style=";text-align:left;direction:ltr"> h <h2 style=";text-align:left;direction:ltr"> -Bc<h2 style=";text-align:left;direction:ltr"> h <h2 style=";text-align:left;direction:ltr"> +Bi<h2 style=";text-align:left;direction:ltr"> h <h2 style=";text-align:left;direction:ltr"> -Bo<h2 style=";text-align:left;direction:ltr"> h Where, L h is the actual load of the system at time h, Bc h Bi is the load reduction on the load side compared to the power consumption baseline value; h To charge the energy storage, Bo h Discharge electrical energy for energy storage.

7. A method for calculating capacity margin considering load peak shaving according to claim 1, characterized in that: The specific energy storage power constraints are: With h ≤Bl Max 0≤Bo h ≤Bp Max 0≤Bi h ≤Bp Max Where, Bl h is the energy storage charge state, Bl Max is the maximum energy storage capacity, Bp Max It is the maximum charging and discharging speed of energy storage.

8. A capacity margin calculation system considering load peak shaving, characterized in that: The system is used in a capacity margin calculation method considering load peak shaving as described in any one of claims 1 to 7, and the system comprises: The first module is used to construct a load estimation model for estimating load data within a preset time in combination with historical load data, to construct an energy capacity estimation model for estimating energy capacity data within a preset time in combination with historical energy capacity data, and to generate load estimation data and energy capacity estimation data using the load estimation model and the energy capacity estimation model; The second module is used to build a power market clearing model and a capacity market clearing model by combining load estimation data and energy capacity estimation data; The third module is used to determine the first objective function with the goal of minimizing the net load of the power system, establish the first constraint condition, and construct a capacity credit factor calculation model, wherein the first constraint condition includes a source-load balance constraint, an energy storage power constraint, and a demand power constraint; The fourth module is used to calculate the first capacity credit value coefficient of the power generation side and the second capacity credit value coefficient of the load side peak shaving capacity based on the capacity credit coefficient calculation model and the load continuity curve; The fifth module is used to calculate the actual available capacity of the power generation side using the energy capacity estimation data and the first capacity credit value coefficient, and generate the actual maximum output of the power generation side using the actual available capacity of the power generation side; calculate the maximum reducible load on the load side using the load estimation data and the second capacity credit value coefficient, and generate the actual maximum peak reduction on the load side using the maximum reducible load on the load side; The sixth module is used to input the actual available capacity on the power generation side, the actual maximum output on the power generation side, the maximum load that can be reduced on the load side, and the actual maximum peak reduction on the load side into the power market clearing model to obtain the capacity margin of the power system.

9. 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.

10. 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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