Dynamic evaluation method, device and equipment for new energy bearing capacity of power distribution network and storage medium

Abstract

The invention provides a power distribution network new energy bearing capacity dynamic evaluation method and device, equipment and a storage medium, and relates to the technical field of power grid operation. The method comprises the following steps: extracting power distribution network operation data and calculating a plurality of indexes; defining each index as a game main body, and setting a strategy space containing conservative, neutral and aggressive strategies for each main body; constructing a multi-subject game model by endowing each strategy with reference earnings and calculating a symbiotic coefficient matrix representing nonlinear interaction among indexes; establishing a replicon kinetic equation, and dynamically obtaining an optimal strategy combination of the system under an internal index game by solving evolution stability equilibrium of the equation; and calculating a comprehensive bearing capacity score based on the stable strategy combination and grading. The method overcomes the limitation of a conventional static weight method, can accurately and dynamically reflect the complex symbiotic competition relationship between indexes, and provides a more scientific decision basis for power distribution network planning and new energy consumption.

Description

Technical Field

[0001] This application relates to the field of power grid operation technology, and in particular to a method, device, equipment and storage medium for dynamic evaluation of the carrying capacity of new energy sources in distribution networks. Background Technology

[0002] The location and capacity of high-proportion distributed generation (DG) connections significantly impact the operation of distribution networks. Assessing the DG connection carrying capacity based on various distribution network indicators is crucial for optimizing the reliability and economy of power system operation. This invention addresses the potential negative impacts of DG connection on distribution networks by proposing a new energy carrying capacity assessment method. Based on distribution network data containing DG, this method calculates various distribution network indicators. By calculating the game payoffs and co-existence coefficients of each indicator, a replicator dynamic equation is established to simulate the dynamic evolution of the system state. This establishes a set of new energy carrying capacity assessment methods for distribution networks, effectively determining the evaluation level of new energy carrying capacity, thus providing a scientific basis for formulating strategies to improve new energy carrying capacity. Existing assessment methods mostly rely on static weights for linear weighted summation of various indicators. However, static weights cannot reflect the dynamic changes in the importance of each indicator under different system operating states and ignore the complex nonlinear interactions and co-existence / competition relationships between indicators, leading to deviations between the assessment results and the actual system operating state, and resulting in weak physical interpretability. Summary of the Invention

[0003] This application provides a method, apparatus, equipment, and storage medium for dynamic assessment of the renewable energy carrying capacity of a distribution network. It aims to provide a renewable energy carrying capacity assessment scheme applicable to different distribution network topology structures, comprising three parts: data extraction, index calculation, and system scoring. The data extraction part obtains the required data based on the basic grid structure. The index calculation part calculates selected key data from the obtained data. The system scoring part obtains the final carrying capacity score.

[0004] Firstly, this application provides a method for dynamic assessment of the renewable energy carrying capacity of a distribution network, including: Obtain data from the power system to be evaluated; Based on the data of the power system to be evaluated, calculate the various indicators of the distribution network; The various indicators of the distribution network are used to form a set of game subjects. A strategy space is assigned to each indicator in the set of game subjects to reflect the decision-making tendency of the indicator under different operating conditions, and the first indicator in the set of game subjects is determined. i Individual indicators I i The policy distribution is represented as ;in, x ij As an indicator Ii Select strategy among all users s ij proportion, j =1, 2, or 3; For each indicator I i Define a benchmark return vector ;in, Indicates when the indicator I i When choosing a certain strategy, the benefit obtained from the perspective of the system as a whole, the benefit value reflects the contribution of the corresponding strategy to the overall carrying capacity of the system; Based on the physical coupling relationship and statistical correlation among the indicators, the co-occurrence coefficient matrix among the indicators is calculated. Based on the co-occurrence coefficient matrix and the benchmark payoff vector, the fitness of each indicator when choosing different strategies is calculated; based on the strategy distribution of each indicator and its fitness when choosing different strategies, a multi-agent replicon dynamic equation is established. By solving the evolutionary stable equilibrium of the multi-agent replicon dynamics equations, a stable strategy combination for the power system under dynamic game is obtained. Based on the aforementioned combination of evolutionary stabilization strategies, the comprehensive carrying capacity score of the power system to be evaluated is calculated and the carrying capacity level is classified.

[0005] In one possible design, the methods for obtaining data on the power system to be evaluated include: Digitally characterize the power system to be evaluated and establish a corresponding simulation model; By changing the renewable energy access capacity and load parameters in the simulation model, different operating conditions are simulated, and the corresponding power grid operation data is output through simulation calculation as data for the power system to be evaluated.

[0006] In one possible design, the power distribution network indicators include voltage qualification rate. Reverse load rate Line load rate Distributed power generation output ratio Voltage harmonic distortion rate and average line loss rate .

[0007] In one possible design, the voltage qualification rate The calculation formula is: In the formula: n This indicates the number of voltage-qualified nodes in the power grid to be evaluated; N This represents the total number of nodes in the power system to be evaluated; The reverse load rate The calculation formula is: In the formula: To contribute to distributed power sources For equivalent load, These are the actual operating limits for transformers or lines; The line load rate The calculation formula is: In the formula: This represents the total load of the power system to be evaluated; This represents the maximum load of the power system to be evaluated; The output ratio of distributed power sources The calculation formula is: In the formula: This represents the output of all distributed energy resources in the power system to be evaluated. This represents the total load of the power system to be evaluated; The voltage harmonic distortion rate The calculation formula is: In the formula: Represented as the effective value of the fundamental voltage. Indicated as the second, third, ... k The effective value of the subharmonic voltage; The average line loss rate The calculation formula is: In the formula: Indicates the first in the power system to be evaluated m Line loss; This represents the total number of branch roads.

[0008] In one possible design, based on the physical coupling relationship and statistical correlation between the indicators, the co-occurrence coefficient matrix between the indicators is calculated using the following formula: In the formula, θ is the co-occurrence coefficient matrix, θ ik Indicates the first k Individual indicators I k For the first i Individual indicators I i The intensity of the impact, m This refers to the number of indicators.

[0009] In one possible design, based on the co-occurrence coefficient matrix and the benchmark return vector, the fitness of each metric when selecting different strategies is calculated using the following formula: In the formula, f ij Indicates the first i Individual indicators I i In choosing a strategy s ij Adaptability, θ ik Indicates the first k Individual indicators I k For the first i Individual indicators I i The intensity of the impact, u ij It is the first i Individual indicators I i Choose strategy s ij The benchmark return, It is the first k Individual indicators I k average benchmark return u kj It is the first k Individual indicators I k Choose strategy s kj The benchmark return, x kj As an indicator I k Select strategy among all users s kj proportion, m This refers to the number of indicators.

[0010] In one possible design, the multi-agent replicon dynamics equations are expressed as: In the formula, As an indicator I i The average fitness.

[0011] Secondly, this application provides a dynamic assessment device for the carrying capacity of new energy sources in a power distribution network, the device comprising: The data acquisition module is configured to acquire data from the power system to be evaluated. The indicator calculation module is configured to calculate various indicators of the distribution network based on the data of the power system to be evaluated; The strategy distribution determination module is configured to construct a set of game subjects from various indicators of the distribution network, assign a strategy space to each indicator in the set of game subjects to reflect the decision-making tendency of the indicators under different operating states, and determine the first strategy in the set of game subjects. i Individual indicators I i The policy distribution is represented as ;in, x ij As an indicator I i Select strategy among all users s ij proportion, j =1, 2, or 3; The benchmark return definition module is configured to define each metric. I i Define a benchmark return vector ;in, Indicates when the indicator I i When choosing a certain strategy, the benefit obtained from the perspective of the system as a whole, the benefit value reflects the contribution of the corresponding strategy to the overall carrying capacity of the system; The co-occurrence coefficient calculation module is configured to calculate the co-occurrence coefficient matrix between each indicator based on the physical coupling relationship and statistical correlation between each indicator. The dynamic equation construction module is configured to calculate the fitness of each indicator when choosing different strategies based on the co-occurrence coefficient matrix and the benchmark payoff vector; and to establish the multi-agent replier dynamic equation based on the strategy distribution of each indicator and its fitness when choosing different strategies. The equation solving module is configured to obtain the stable strategy combination of the power system under dynamic game by solving the evolutionary stable equilibrium of the multi-agent replicator dynamic equation; The carrying capacity assessment module is configured to calculate the comprehensive carrying capacity score of the power system to be evaluated and classify the carrying capacity level based on the combination of evolutionary stability strategies.

[0012] Thirdly, embodiments of this application provide an electronic device, including: at least one processor and a memory; the memory stores computer-executable instructions; the at least one processor executes the computer-executable instructions stored in the memory, causing the at least one processor to execute the dynamic assessment method for the carrying capacity of new energy in the power distribution network as described in the first aspect and various possible designs of the first aspect.

[0013] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the dynamic assessment method for the carrying capacity of new energy in the power distribution network as described in the first aspect and various possible designs of the first aspect.

[0014] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the dynamic assessment method for the carrying capacity of new energy in the distribution network as described in the first aspect and various possible designs of the first aspect.

[0015] The method, apparatus, equipment, and storage medium for dynamic assessment of the renewable energy carrying capacity of distribution networks provided in this application have at least the following beneficial effects: This application is universally applicable to various distribution networks, and can still ensure the accuracy of new energy carrying capacity assessment even under different operating conditions such as changes in distribution network topology and new energy access capacity. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] Figure 1 A flowchart of a dynamic assessment method for the carrying capacity of new energy sources in a power distribution network, provided as an embodiment of this application; Figure 2 A diagram of an IEEE 33-node system provided for embodiments of this application; Figure 3 This is a structural diagram of a dynamic evaluation device for the carrying capacity of new energy in a power distribution network, provided in an embodiment of this application.

[0018] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0019] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0020] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0021] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0022] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0023] This application provides a method for dynamic evaluation of the renewable energy carrying capacity of a distribution network, such as... Figure 1 As shown, the dynamic assessment method for the carrying capacity of new energy sources in the power distribution network includes the following steps S10-S40.

[0024] S10: Obtain data on the power system to be evaluated.

[0025] In some embodiments, data of the power system to be evaluated can be obtained through the following steps S101-S102: S101: Digitally characterize the power system to be evaluated and establish a corresponding simulation model; S102: Change the renewable energy access capacity and load parameters in the simulation model to simulate different operating conditions, and output the corresponding power grid operation data through simulation calculation as data for the power system to be evaluated.

[0026] S20: Calculate various indicators of the distribution network based on the data of the power system to be evaluated.

[0027] In some embodiments, the distribution network indicators include voltage qualification rate. Reverse load rate Line load rate Distributed power generation output ratio Voltage harmonic distortion rate and average line loss rate .

[0028] Voltage qualification rate The calculation formula is: In the formula: n This indicates the number of voltage-qualified nodes in the power grid to be evaluated;N This indicates the total number of nodes in the power system to be evaluated.

[0029] Reverse load rate The calculation formula is: In the formula: To contribute to distributed power sources For equivalent load, These are the actual operating limits for transformers or lines.

[0030] Line load rate The calculation formula is: In the formula: This represents the total load of the power system to be evaluated; This represents the maximum load of the power system to be evaluated.

[0031] Distributed power generation output ratio The calculation formula is: In the formula: This represents the output of all distributed energy resources in the power system to be evaluated. This represents the total load of the power system to be evaluated.

[0032] Voltage harmonic distortion rate The calculation formula is: In the formula: Represented as the effective value of the fundamental voltage. Indicated as the second, third, ... k The effective value of the subharmonic voltage; Average line loss rate The calculation formula is: In the formula: Indicates the first in the power system to be evaluated m Line loss; This represents the total number of branch roads.

[0033] S30: Construct a set of game subjects from various indicators of the distribution network, assign a strategy space to each indicator in the set of game subjects to reflect the decision-making tendency of the indicators under different operating states, and determine the first... i Individual indicators I i The policy distribution is represented as ;in, x ij As an indicatorI i Select strategy among all users s ij proportion, j =1, 2, or 3.

[0034] In this embodiment, m key evaluation indicators of the distribution network's renewable energy carrying capacity constitute a set of game subjects. Each indicator I i Considered a rational player in a game, their decisions affect the overall state of the system. Each indicator... I i It possesses a discrete policy space containing three typical operating strategies. This reflects the decision-making tendency of the indicator under different operating conditions. Specifically: a conservative strategy indicates that the indicator is in a very safe operating state with a large margin; a neutral strategy indicates that the indicator is in a normal and economically reasonable operating state; and an aggressive strategy indicates that the indicator is close to its safe operating threshold with a very small margin. Let the indicator... I i Select strategy among all users s ij The proportion is x ij And satisfy .vector Called indicators I i The strategy distribution.

[0035] S40: For each indicator I i Define a benchmark return vector.

[0036] In this embodiment, based on the operating characteristics of the power system and engineering experience, each indicator... I i Define a 3-dimensional benchmark return vector. Its elements Indicates when the indicator I i When choosing a strategy, the benefit obtained from the perspective of the whole system is reflected in the value of the benefit, which shows the contribution of the strategy to the overall carrying capacity of the system.

[0037] S50: Calculate the co-occurrence coefficient matrix among the indicators based on the physical coupling relationship and statistical correlation among the indicators.

[0038] In this embodiment, the co-occurrence coefficient matrix among indicators is calculated based on the physical coupling relationship and statistical correlation between indicators. , where θ ik Indicators Ik For indicators I i The strength of the influence. A positive symbiotic coefficient indicates a mutually beneficial symbiotic relationship, while a negative symbiotic coefficient indicates a competitive relationship; the symbiotic coefficient quantifies the strength of the nonlinear interaction between the indicators.

[0039] S60: Based on the co-occurrence coefficient matrix and the benchmark payoff vector, calculate the fitness of each indicator when selecting different strategies; based on the strategy distribution of each indicator and its fitness when selecting different strategies, establish the multi-agent replicon dynamic equation.

[0040] In this embodiment, the indicator I i Choose strategy s ij fitness f ij It depends not only on its benchmark return u ij It is also affected by the distribution of all other indicator strategies, and its calculation formula is: In the formula: u ij yes I i choose s ij Benchmark returns; θ ik yes I k right I i The co-existence coefficient. It is an indicator I k The average benchmark return reflects I k The overall state. u kj ′ It is an adjustment item, which can be regarded as I k The strategy against I i Weighting of indirect impact on returns.

[0041] A multi-agent replicon dynamics equation considering symbiotic effects is established, which describes the evolution of policy proportions over time: policies with fitness above average will have an increased proportion, while those with lower fitness will have a decreased proportion.

[0042] In the formula: It is an indicator I k The average fitness.

[0043] S70: By solving the evolutionary stable equilibrium of the multi-agent replicon dynamic equations, a stable strategy combination for the power system under dynamic game is obtained.

[0044] In this embodiment, the evolutionary stable equilibrium of the replicator dynamics equations is solved using the Runge-Kutta numerical iteration method until all strategies are proportional. x ij When the change approaches zero, the system reaches a steady state. Let the stable solution obtained at this time be denoted as x ij ∗ The matrix { x ij ∗ This is the evolutionary stable equilibrium representation of the strategy combination that the system reaches under dynamic game.

[0045] S80: Based on the combination of evolutionary stable strategies, calculate the comprehensive carrying capacity score of the power system to be evaluated and classify the carrying capacity level.

[0046] In this embodiment, the comprehensive carrying capacity score of the system is calculated based on the combination of evolutionary stable strategies; In the formula: x ij ∗ As an indicator I i Selecting a strategy in a stable equilibrium s ij The proportion; u ij As an indicator I i Choose strategy s ij Benchmark returns; Bearing capacity levels are determined based on bearing capacity scores: Excellent: S≥90; Good: 80≤S<90; Generally: 70≤S<80; Poor: S < 70; Output the equilibrium strategy distribution of each indicator and the overall carrying capacity score of the system.

[0047] To further illustrate the feasibility and progressiveness of this application, a simulation analysis will be conducted below based on the dynamic assessment method for the carrying capacity of new energy sources in distribution networks provided in the above embodiments. This embodiment verifies the effectiveness of the proposed method in an IEEE 33-bus system. The IEEE 33-bus system diagram is shown below. Figure 2 As shown. Figure 2In the attached figures, reference numerals 1 to 33 refer to the grid node numbers. This embodiment uses different data for simulation verification. The bearing capacity assessment results are shown in Table 1.

[0048] Table 1 Bearing capacity evaluation results

[0049] As shown in Table 1, the dynamic assessment method for the carrying capacity of new energy in distribution networks proposed in this application has good carrying capacity assessment capability in IEEE 33-node systems with new energy access and has strong applicability.

[0050] This application also provides a dynamic assessment device for the carrying capacity of new energy sources in a distribution network, such as... Figure 3 As shown, the dynamic assessment device for the carrying capacity of new energy sources in the power distribution network includes: Data acquisition module 301 is configured to acquire data from the power system to be evaluated; The indicator calculation module 302 is configured to calculate various indicators of the distribution network based on the data of the power system to be evaluated. The strategy distribution determination module 303 is configured to construct a set of game subjects from various indicators of the distribution network, assign a strategy space to each indicator in the set of game subjects to reflect the decision-making tendency of the indicators under different operating states, and determine the first strategy in the set of game subjects. i Individual indicators I i The policy distribution is represented as ;in, x ij As an indicator I i Select strategy among all users s ij proportion, j =1, 2, or 3; Benchmark return definition module 304 is configured to define each metric I i Define a benchmark return vector ;in, Indicates when the indicator I i When choosing a certain strategy, the benefit obtained from the perspective of the system as a whole, the benefit value reflects the contribution of the corresponding strategy to the overall carrying capacity of the system; The co-occurrence coefficient calculation module 305 is configured to calculate the co-occurrence coefficient matrix between each indicator based on the physical coupling relationship and statistical correlation between each indicator. The dynamic equation construction module 306 is configured to calculate the fitness of each index when choosing different strategies based on the co-occurrence coefficient matrix and the benchmark payoff vector; and to establish the multi-agent replier dynamic equation based on the strategy distribution of each index and its fitness when choosing different strategies. The equation solving module 307 is configured to obtain the stable strategy combination of the power system under dynamic game by solving the evolutionary stable equilibrium of the multi-agent replicator dynamic equation; The carrying capacity assessment module 308 is configured to calculate the comprehensive carrying capacity score of the power system to be evaluated and classify the carrying capacity level based on the combination of evolutionary stabilization strategies.

[0051] This application provides an electronic device. The electronic device may include a processor and a memory, wherein the processor and the memory can communicate; exemplarily, the processor and the memory communicate via a communication bus.

[0052] The processor executes computer execution instructions stored in memory, causing the processor to perform the scheme in the above embodiments. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0053] The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0054] The electronic device provided in this application embodiment can be the terminal device described in the above embodiments.

[0055] This application also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer performs the technical solution of the dynamic assessment method for the carrying capacity of new energy in the distribution network described above.

[0056] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the dynamic assessment method for the carrying capacity of new energy in the distribution network described in the above embodiments.

[0057] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0058] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0059] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0060] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0061] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0062] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0063] Buses can be Industry Standard Architecture (ISA) buses, Peripheral Component Interconnect (PCI) buses, or Extended Industry Standard Architecture (EISA) buses, etc. Buses can be categorized into address buses, data buses, control buses, etc.

[0064] The aforementioned storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0065] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0066] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for dynamic evaluation of the carrying capacity of new energy sources in a distribution network, characterized in that, The method includes: Obtain data from the power system to be evaluated; Based on the data of the power system to be evaluated, calculate the various indicators of the distribution network; The various indicators of the distribution network are used to form a set of game subjects. A strategy space is assigned to each indicator in the set of game subjects to reflect the decision-making tendency of the indicator under different operating conditions, and the first indicator in the set of game subjects is determined. i Individual indicators I i The policy distribution is represented as ;in, x ij As an indicator I i Select strategy among all users s ij proportion, j =1, 2, or 3; For each indicator I i Define a benchmark return vector ;in, Indicates when the indicator I i When choosing a certain strategy, the benefit obtained from the perspective of the system as a whole, the benefit value reflects the contribution of the corresponding strategy to the overall carrying capacity of the system; Based on the physical coupling relationship and statistical correlation among the indicators, the co-occurrence coefficient matrix among the indicators is calculated. Based on the co-occurrence coefficient matrix and the benchmark payoff vector, the fitness of each indicator when choosing different strategies is calculated; based on the strategy distribution of each indicator and its fitness when choosing different strategies, a multi-agent replicon dynamic equation is established. By solving the evolutionary stable equilibrium of the multi-agent replicon dynamics equations, a stable strategy combination for the power system under dynamic game is obtained. Based on the aforementioned combination of evolutionary stabilization strategies, the comprehensive carrying capacity score of the power system to be evaluated is calculated and the carrying capacity level is classified.

2. The method for dynamic evaluation of the carrying capacity of new energy sources in a distribution network according to claim 1, characterized in that, The methods for obtaining data on the power system to be evaluated include: Digitally characterize the power system to be evaluated and establish a corresponding simulation model; By changing the renewable energy access capacity and load parameters in the simulation model, different operating conditions are simulated, and the corresponding power grid operation data is output through simulation calculation as data for the power system to be evaluated.

3. The method for dynamic evaluation of the carrying capacity of new energy sources in a distribution network according to claim 1, characterized in that, The power distribution network indicators include voltage qualification rate. Reverse load rate Line load rate Distributed power generation output ratio Voltage harmonic distortion rate and average line loss rate .

4. The method for dynamic evaluation of the carrying capacity of new energy sources in a distribution network according to claim 3, characterized in that, The voltage qualification rate The calculation formula is: In the formula: n This indicates the number of voltage-qualified nodes in the power grid to be evaluated; N This represents the total number of nodes in the power system to be evaluated; The reverse load rate The calculation formula is: In the formula: To contribute to distributed power sources For equivalent load, These are the actual operating limits for transformers or lines; The line load rate The calculation formula is: In the formula: This represents the total load of the power system to be evaluated; This represents the maximum load of the power system to be evaluated; The output ratio of distributed power sources The calculation formula is: In the formula: This represents the output of all distributed energy resources in the power system to be evaluated. This represents the total load of the power system to be evaluated; The voltage harmonic distortion rate The calculation formula is: In the formula: Represented as the effective value of the fundamental voltage. Indicated as the second, third, ... k The effective value of the subharmonic voltage; The average line loss rate The calculation formula is: In the formula: Indicates the first in the power system to be evaluated m Line loss; This represents the total number of branch roads.

5. The method for dynamic evaluation of the carrying capacity of new energy sources in a distribution network according to claim 1, characterized in that, Based on the physical coupling and statistical correlation among the indicators, the co-occurrence coefficient matrix among the indicators is calculated using the following formula: In the formula, θ is the co-occurrence coefficient matrix, θ ik Indicates the first k Individual indicators I k For the i Individual indicators I i The intensity of the impact, m This refers to the number of indicators.

6. The method for dynamic evaluation of the carrying capacity of new energy sources in a distribution network according to claim 1, characterized in that, Based on the co-occurrence coefficient matrix and the benchmark return vector, the fitness of each indicator when selecting different strategies is calculated using the following formula: In the formula, f ij Indicates the first i Individual indicators I i In choosing a strategy s ij Adaptability, θ ik Indicates the first k Individual indicators I k For the i Individual indicators I i The intensity of the impact, u ij It is the first i Individual indicators I i Choose strategy s ij The benchmark return, It is the first k Individual indicators I k average benchmark return u kj It is the first k Individual indicators I k Choose strategy s kj The benchmark return, x kj As an indicator I k Select strategy among all users s kj proportion, m This refers to the number of indicators.

7. The method for dynamic evaluation of the carrying capacity of new energy sources in a distribution network according to claim 6, characterized in that, The multi-account replicon dynamics equation is expressed as: In the formula, As an indicator I i The average fitness.

8. A dynamic assessment device for the carrying capacity of new energy sources in a power distribution network, characterized in that, The device includes: The data acquisition module is configured to acquire data from the power system to be evaluated. The indicator calculation module is configured to calculate various indicators of the distribution network based on the data of the power system to be evaluated; The strategy distribution determination module is configured to construct a set of game subjects from various indicators of the distribution network, assign a strategy space to each indicator in the set of game subjects to reflect the decision-making tendency of the indicators under different operating states, and determine the first strategy in the set of game subjects. i Individual indicators I i The policy distribution is represented as ;in, x ij As an indicator I i Select strategy among all users s ij proportion, j =1, 2, or 3; The benchmark return definition module is configured to define each metric. I i Define a benchmark return vector ;in, Indicates when the indicator I i When choosing a certain strategy, the benefit obtained from the perspective of the system as a whole, the benefit value reflects the contribution of the corresponding strategy to the overall carrying capacity of the system; The co-occurrence coefficient calculation module is configured to calculate the co-occurrence coefficient matrix between each indicator based on the physical coupling relationship and statistical correlation between each indicator. The dynamic equation construction module is configured to calculate the fitness of each indicator when choosing different strategies based on the co-occurrence coefficient matrix and the benchmark payoff vector; and to establish the multi-agent replier dynamic equation based on the strategy distribution of each indicator and its fitness when choosing different strategies. The equation solving module is configured to obtain the stable strategy combination of the power system under dynamic game by solving the evolutionary stable equilibrium of the multi-agent replicator dynamic equation; The carrying capacity assessment module is configured to calculate the comprehensive carrying capacity score of the power system to be evaluated and classify the carrying capacity level based on the combination of evolutionary stability strategies.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes the computer execution instructions stored in the memory to implement the dynamic assessment method for the carrying capacity of new energy in the distribution network as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the dynamic assessment method for the carrying capacity of new energy in the distribution network as described in any one of claims 1-7.

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