Distributed power generation bearing capacity analysis method based on generalized extreme value distribution

Abstract

The invention discloses a distributed power generation bearing capacity analysis method, device and equipment based on generalized extreme value distribution and a storage medium, and belongs to the technical field of power systems. The method comprises the steps of obtaining power distribution network data; adopting a maximum likelihood estimation method to output generalized extreme value distribution parameters; establishing a linearized power flow model, and determining linear mapping relations between node voltage and branch power and distributed generation power injection and between the node voltage and branch power and distributed generation power injection; deducing a tail distribution function of node voltage and branch power based on tail characteristics of generalized extreme value distribution; and according to a preset operation constraint violation probability threshold, calculating the distributed power generation bearing capacity meeting the voltage limitation and the branch power limitation. The device, the equipment and the storage medium can execute the power generation bearing capacity analysis method. According to the method, the dependence on high-resolution data is remarkably reduced, the calculation efficiency is improved, and the method is particularly suitable for first screening evaluation of a power distribution network with limited data and distributed power generation access.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method for analyzing the carrying capacity of distributed generation based on generalized extreme value distribution. Background Technology

[0002] With the global transition to carbon neutrality, the integration of renewable energy has accelerated significantly, with wind and solar power systems becoming the dominant contributors to modern power systems. Distributed generation (DG) has become the dominant mode of renewable energy integration, but its large-scale integration has brought significant operational challenges to distribution networks, particularly voltage violations and line overload issues in situations with high DG penetration. Therefore, accurate and computationally efficient assessment of Distributed Generation Hosting Capacity (DGHC) is crucial for ensuring safe grid planning for DG-dominated systems.

[0003] Existing DGHC analysis methods are mainly divided into two categories: stochastic optimization methods and sensitivity-based analysis methods. Stochastic optimization methods utilize uncertainty sets or extensive scenario simulations to represent the stochastic nature of DG, but their high computational intensity and dependence on high-resolution temporal data hinder practical implementation. Sensitivity-based analysis methods employ linearized power flow models to ensure computational efficiency, but still require complete probabilistic characteristics of distributed energy output, limiting their application in data-scarce environments.

[0004] The main limitations of the current method include:

[0005] 1. High data requirements: Traditional methods rely on detailed DG profiles to model stochastic processes, but for some distribution networks, such as rural areas, such high-resolution data is rarely available.

[0006] 2. Poor model scalability: The existing framework lacks sufficient adaptability to different low-voltage power grid architectures, and the parameter assumptions introduce errors in rural networks without weather sensors;

[0007] 3. High operational complexity: The exponential growth of DG connection requests makes it nearly impossible for utility engineers to perform detailed analysis on all requests. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the prior art and provide a distributed generation carrying capacity analysis based on generalized extreme value distribution. This method can accurately analyze the distributed generation carrying capacity under limited distributed energy data conditions, significantly reducing data requirements and computational complexity. It is particularly suitable for the initial screening and evaluation stage of distribution networks.

[0009] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, the present invention provides a method for analyzing the carrying capacity of distributed generation based on generalized extreme value distribution, comprising:

[0010] Obtain the maximum block size data, network topology information, and load demand data of distributed energy resources in the power distribution network;

[0011] The maximum likelihood estimation method is used to fit the generalized extreme value distribution parameters of the distributed energy output based on the block maximum value data of the distributed energy resources;

[0012] Based on the network topology information and load demand data, the linear mapping relationship between node voltage and branch power and distributed generation power injection is determined, and a linearized power flow model is obtained.

[0013] Based on the generalized extreme value distribution parameters and linearized power flow model of the distributed energy output, the tail distribution functions of node voltage and branch power are obtained according to the affine transformation property of the generalized extreme value distribution.

[0014] By using the tail distribution functions of node voltage and branch power, the distributed generation carrying capacity that meets voltage and branch power limits can be calculated.

[0015] Optionally, the maximum block value data of the distributed energy resource is the maximum block value data in the historical data of distributed energy output power;

[0016] The method employs maximum likelihood estimation to fit the generalized extreme value distribution parameters of the distributed energy output based on the block maximum value data of the distributed energy resources; including:

[0017] The maximum value of the block maximum value is taken from the historical data of the output power of distributed energy sources at a specified time interval.

[0018] Based on the block maximum data, the generalized extreme value distribution of the distributed energy power output factor is fitted using the maximum likelihood estimation method. To obtain the corresponding position parameters Scale parameters and shape parameters ;

[0019] Based on the relationship between the generation capacity, power output factor, and power output of distributed energy resources, and the generalized extreme value distribution parameters of the power output factor of distributed energy resources, the generalized extreme value distribution of distributed energy resource output is obtained: ,in, For distributed energy output power, This refers to the power generation capacity of distributed energy sources.

[0020] The above method utilizes maximum likelihood estimation to supplement missing data and reduce the reliance on high-resolution data.

[0021] Optionally, the step of determining the linear mapping relationship between node voltage and branch power and distributed generation power injection based on network topology information and load demand data to obtain a linearized power flow model includes:

[0022] Construct the admittance matrix based on network topology information;

[0023] Based on the admittance matrix of the distribution network, a decoupling linearization method is used to establish a linear relationship between node voltage magnitude and phase angle and power injection, thus obtaining a linearized power flow model, expressed as:

[0024] (1)

[0025] (2)

[0026] in, and These represent the bus voltage magnitude and phase angle vector, respectively. and These represent the active power and reactive power injection vectors, respectively. , M and N are constant matrices that can be obtained through matrix decomposition.

[0027] The above method utilizes decoupling linearization to establish a linearized power flow model with high accuracy and efficiency using network topology information, such as the admittance matrix.

[0028] Optionally, based on the generalized extreme value distribution parameters and the linearized power flow model, the tail distribution function of the node voltage is obtained according to the affine transformation property of the generalized extreme value distribution, including:

[0029] Based on the linear power flow model, assuming that the power injection of other nodes remains constant, the bus... upper node The voltage magnitude at that point is expressed as a linear function of the active power injection:

[0030] (3)

[0031] in, and The node voltage linearization coefficients are calculated from the constant matrix in the linearized power flow model. For active power injection, Let J be the voltage at node j;

[0032] Based on the affine transformation properties of the generalized extreme value distribution, and the output of distributed energy... The generalized extremum distribution determines the tail distribution function of the node voltage as follows:

[0033] (4).

[0034] The relationship between the node voltage and the coefficient of distributed generation power injection obtained from the power flow model is used to establish the tail distribution function of the generalized extreme value distribution. The tail distribution function is used to show the relationship between the various parameters.

[0035] Optionally, based on the generalized extreme value distribution parameters and the linearized power flow model, the tail distribution function of the branch power is obtained according to the affine transformation property of the generalized extreme value distribution, including:

[0036] Based on the linear relationship between node phase angle and power injection in the linearized power flow model, the line... Branch power at the location Represented as a linear function of active power injection:

[0037] (5)

[0038] in, and The branch power linearization coefficient is calculated from the constant matrix of the linearized power flow model;

[0039] Based on the affine transformation properties of the generalized extreme value distribution, and the output of distributed energy... The generalized extreme value distribution determines the tail distribution function of the branch power as follows:

[0040] (6).

[0041] The tail distribution function of the generalized extreme value distribution is established by the coefficient relationship between the branch power and the distributed generation power injection obtained by the power flow model. The tail distribution function shows the relationship between the various parameters.

[0042] Optionally, the calculation of distributed generation carrying capacity with voltage and branch power limitations using the tail distribution functions of node voltage and branch power includes:

[0043] Based on voltage limit constraints and a preset voltage violation probability, the maximum carrying capacity satisfying the voltage constraints is calculated according to the tail distribution function of the node voltage. The formula is:

[0044] (7)

[0045] in, This is the upper limit of voltage. This is a preset threshold for the probability of violation;

[0046] Based on the branch power constraint, the maximum carrying capacity that satisfies the power constraint is calculated according to the tail distribution function of the branch power. The formula is:

[0047] (8)

[0048] in, This is the upper limit of branch power;

[0049] Based on the maximum carrying capacity satisfying voltage constraints and the maximum carrying capacity satisfying power constraints, the smaller of the two values ​​is determined as the actual carrying capacity at the distributed energy access node n. , represented as:

[0050] (9)

[0051] in, This represents the actual carrying capacity at node n.

[0052] The above calculations, by introducing voltage limits, power limits, and violation probability threshold limits, determine the carrying capacity under voltage limits and violation probability threshold limits, and the carrying capacity under power limits and violation probability threshold limits. The minimum of the two carrying capacities is the actual carrying capacity of the node, and this actual carrying capacity can meet multiple limiting requirements.

[0053] Optionally, the violation probability threshold The value ranges from 0.01% to 5%.

[0054] Secondly, this application also provides a distributed generation carrying capacity analysis device based on generalized extreme value distribution, comprising:

[0055] The data acquisition module is configured to acquire block maximum value data, network topology information, and load demand data of distributed energy resources in the power distribution network.

[0056] The first generalized extreme value distribution fitting module is configured to fit the generalized extreme value distribution parameters of the distributed energy output based on the block maximum value data of the distributed energy resources using the maximum likelihood estimation method.

[0057] The linear power flow model construction module is configured to determine the linear mapping relationship between node voltage and branch power and distributed generation power injection based on the network topology information and load demand data, thereby obtaining a linearized power flow model.

[0058] The second generalized extreme value distribution fitting module is configured to obtain the tail distribution functions of node voltage and branch power based on the generalized extreme value distribution parameters and the linearized power flow model of the distributed energy output, according to the affine transformation properties of the generalized extreme value distribution.

[0059] And a generation carrying capacity determination module is configured to calculate the distributed generation carrying capacity that meets the voltage limit and branch power limit by using the tail distribution function of node voltage and branch power.

[0060] Thirdly, this application also provides a computer device, the device including a processor and a memory, the memory storing at least one instruction and at least one program, the at least one instruction and the at least one program being loaded and executed by the processor to implement the distributed generation carrying capacity analysis method based on generalized extreme value distribution as described in the first aspect.

[0061] Fourthly, this application also provides a computer storage medium storing at least one instruction and at least one program, wherein the at least one instruction and the at least one program are loaded and executed by a processor to implement the distributed generation carrying capacity analysis method based on generalized extreme value distribution as described in the first aspect.

[0062] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0063] In this invention, the location, scale, and shape parameters of the generalized extreme value distribution are obtained by fitting the maximum value data of distributed energy sources using the maximum likelihood estimation method. This method requires only easily obtainable block maximum value data instead of high-resolution time series data, significantly reducing data requirements and making it suitable for most application scenarios. By combining the linearized power flow model, the mapping relationship between node voltage and branch power and distributed generation power injection is obtained, achieving efficient analysis and calculation and simplifying the calculation process when new DG connection requests are added. The load capacity assessment framework based on load demand data can quickly and accurately determine the distributed generation capacity of each node while ensuring the safe operation of the system.

[0064] Meanwhile, the method of the present invention is particularly suitable for the initial screening and evaluation of data-limited distribution networks and distributed generation access, which can significantly improve the efficiency of utility companies in processing DG connection applications and provide strong support for the large-scale integration of distributed energy. Attached Figure Description

[0065] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1This is a flowchart illustrating the distributed generation carrying capacity analysis method provided in an embodiment of the present invention;

[0067] Figure 2 The diagram shown is a schematic representation of the technical concept of a distributed generation carrying capacity analysis method based on generalized extreme value distribution provided by an embodiment of the present invention.

[0068] Figure 3 This is a flowchart illustrating the fitting process of the generalized extreme value distribution parameters provided in the embodiments of the present invention;

[0069] Figure 4 This is a flowchart illustrating the process of establishing a linearized power flow model according to an embodiment of the present invention;

[0070] Figure 5 This is a flowchart illustrating the derivation process of node voltage and branch power tail distribution provided in an embodiment of the present invention;

[0071] Figure 6 This is a flowchart illustrating the distributed generation carrying capacity calculation process provided in an embodiment of the present invention. Detailed Implementation

[0072] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0073] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in sequences other than those illustrated or described herein.

[0074] Example 1

[0075] This embodiment introduces a distributed generation carrying capacity analysis method based on generalized extreme value distribution, referencing... Figure 1 and Figure 2 It includes:

[0076] Obtain the maximum block size data, network topology information, and load demand data of distributed energy resources in the power distribution network;

[0077] The maximum likelihood estimation method is used to fit the generalized extreme value distribution parameters of the distributed energy output based on the block maximum value data of the distributed energy resources;

[0078] Based on the network topology information and load demand data, the linear mapping relationship between node voltage and branch power and distributed generation power injection is determined, and a linearized power flow model is obtained.

[0079] Based on the generalized extreme value distribution parameters and linearized power flow model of the distributed energy output, the tail distribution functions of node voltage and branch power are obtained according to the affine transformation property of the generalized extreme value distribution.

[0080] By using the tail distribution functions of node voltage and branch power, the distributed generation carrying capacity that meets voltage and branch power limits can be calculated.

[0081] Example 2

[0082] Combination Figures 1-6 Based on Example 1, this example specifically introduces the implementation of a distributed generation carrying capacity analysis method based on generalized extreme value distribution, which includes the following contents.

[0083] S101: Obtain network topology information, load demand data, and maximum block size data of distributed energy resources for the power distribution network.

[0084] In one specific embodiment, network topology information includes the number of nodes in the distribution network, branch connections, and line parameters (resistance, reactance, susceptance). Load demand data includes the active power demand and reactive power demand of each node. The maximum block value data for distributed energy resources can be daily, weekly, or monthly maximum values. These data are easier to obtain compared to high-resolution time-series data, reducing the difficulty and cost of data collection.

[0085] S102: Fit the generalized extreme value distribution parameters of the distributed energy output using the maximum likelihood estimation method;

[0086] The Generalized Extreme Value (GEV) distribution is a core distribution in extreme value statistics theory, effectively describing the extreme behavior of stochastic processes. The GEV distribution has three parameters: location parameter... (Indicates the center of the distribution), scale parameter (Determine the magnitude of the deviation around the position parameters) and shape parameters (Tail behavior of the control distribution).

[0087] Specifically, in this embodiment, the distributed energy output factor The GEV distribution is as follows:

[0088]

[0089] Maximum Likelihood Estimation (MLE) can be used to estimate the parameters of the generalized extreme value distribution based on observational data. The MLE method finds the optimal combination of parameters by maximizing the likelihood function, maximizing the probability of the observed data occurring under given parameters, thereby obtaining the generalized extreme value distribution of the distributed energy output power factor. and corresponding position parameters Scale parameters and shape parameters .

[0090] Figure 3 This is a flowchart illustrating the fitting process of the generalized extreme value distribution parameters provided in this embodiment. Fitting the generalized extreme value distribution parameters of distributed energy power output using the maximum likelihood estimation method may further include:

[0091] S201: Collect historical output data of distributed energy resources;

[0092] S202: Extract the maximum value data in the block, such as the daily maximum value, weekly maximum value, or monthly maximum value;

[0093] S203: Applying the maximum likelihood estimation method to fit the GEV distribution parameters of historical distributed energy outputs. , , ;

[0094] S204: Verify the fit quality, such as using the Kolmogorov-Smirnov test.

[0095] S103: Establish a linearized power flow model to determine the linear mapping relationship between node voltage and branch power and distributed generation power injection.

[0096] Due to the nonlinearity and nonconvexity of AC power flow, the calculation results may suffer from poor accuracy and low efficiency. This embodiment employs a decoupled linearized power flow model, which has sufficient accuracy for assessing the carrying capacity of distributed generation.

[0097] The idea behind establishing the decoupled linearized power flow model is to establish a linear relationship between node voltage magnitude and phase angle and power injection based on the admittance matrix of the distribution network and using the decoupled linearization method.

[0098] Figure 4 This is a schematic diagram illustrating the process of establishing a linearized power flow model provided in the embodiment. The process of constructing a linearized power flow model may include:

[0099] S301: Constructing the admittance matrix based on network topology information;

[0100] S302: Perform matrix decomposition to obtain constant matrices H, L, M, N;

[0101] S303: Calculate the decoupled matrix and ;

[0102] S304: Establish a linear mapping relationship between voltage and power injection.

[0103] The construction method for the above-mentioned decoupled linear power flow model can refer to existing technologies. Specifically:

[0104] For a power distribution network containing m buses and m lines, the linearized power flow model can be expressed as:

[0105] (1)

[0106] (2)

[0107] in, and These are the voltage amplitude and phase angle vectors, respectively. and Active and reactive power injection vectors, constant matrix , M and N can be obtained through matrix decomposition based on the network admittance matrix, where , This is the constant matrix after decoupling.

[0108] S104: Based on the affine transformation properties of GEV distribution, the tail characteristics of the generalized extreme value distribution of distributed energy power output, and the linear relationship between node voltage, phase angle, and power output in the decoupled linear power flow model, the tail distribution functions of node voltage and branch power are derived.

[0109] Figure 5 This is a flowchart illustrating the derivation process of node voltage and branch power tail distribution provided in this embodiment. The derivation process may include:

[0110] S401: Determine the coefficients of voltage and power injection based on the linearized power flow model;

[0111] S402: Determine the coefficients of branch power and power injection based on the linearized power flow model;

[0112] S403: Derive the voltage tail distribution using the affine transformation properties of the GEV distribution;

[0113] S404: Derive the branch power tail distribution by applying the affine transformation properties of the GEV distribution.

[0114] Specifically, regarding node voltage, assuming constant power injection at other nodes, the bus... upper node The voltage magnitude at that point can be expressed as a linear function of the active power injection:

[0115] (3)

[0116] in, and The node voltage linearization coefficients are calculated from the constant matrix in the linearized power flow model. Let J be the voltage amplitude at node j. Injecting active power.

[0117] Based on the affine transformation property of the GEV distribution, the tail distribution of the node voltage is as follows:

[0118] (4)

[0119] Similarly, based on the linear relationship between node phase angle and power injection in the linearized power flow model, the line... Branch power at the location Represented as a linear function of active power injection:

[0120] (5)

[0121] in, and The branch power linearization coefficient is calculated from the constant matrix of the linearized power flow model.

[0122] The tail distribution of branch power is as follows:

[0123] (6)

[0124] S105: Calculate the distributed generation carrying capacity that meets voltage and branch power limits based on the preset violation probability threshold.

[0125] Distributed generation capacity is constrained by bus voltage limitations and branch power limitations. Considering that distribution network operators may reduce DG output in extreme circumstances, these constraints are considered as opportunity constraints with a predetermined probability of violation.

[0126] Figure 6 This is a flowchart illustrating the distributed generation carrying capacity calculation process provided in this embodiment, including:

[0127] S501: Set voltage limits and branch power limits;

[0128] S502: Set the threshold for the probability of violation of operational constraints;

[0129] S503: Calculate the load-carrying capacity that meets voltage constraints;

[0130] S504: Calculate the carrying capacity that satisfies the branch power constraints;

[0131] S505: Take the minimum of the two values ​​as the final load-bearing capacity.

[0132] Specifically, let the upper limit of voltage be... The acceptable voltage violation probability, i.e., the preset violation probability threshold, is... Based on the quantile function of GEV, the load-carrying capacity that meets voltage constraints can be obtained:

[0133] (7)

[0134] For branch power constraints, let the branch power constraint be Pmax ij, then the carrying capacity that meets the power constraint can be obtained:

[0135] (8)

[0136] The actual carrying capacity at bus node n where distributed energy is connected is the smaller of the maximum carrying capacity satisfying voltage constraints and the maximum carrying capacity satisfying power constraints, i.e.:

[0137]

[0138] In a practical application case, a 59-node rural power distribution system is used as an example for verification. The voltage amplitude of the substation bus is 10.5kV, and the voltage range of all nodes is [0.93, 1.07]. The total load demand of the test system is 8.47MW. The system includes both photovoltaic and wind power generation.

[0139] By fitting the daily maximum output of photovoltaic and wind power to the GEV distribution, the following parameters were obtained:

[0140] Table 1: GEV parameters of distributed energy resources

[0141] parameter Photovoltaic output Wind power output μ 0.6486 0.3029 σ 0.2256| 0.1248 ξ -0.6374 -0.1101

[0142] The fitting results were validated by the Kolmogorov-Smirnov test. At the 95% confidence level, the p-values ​​for all test scenarios were greater than 0.05, indicating that the data followed the GEV distribution.

[0143] The DGHC results calculated using the method of this invention are compared with those calculated using traditional optimization-based methods. The results show that the method of this invention can provide accurate and conservative DGHC estimates, while reducing the computation time from more than one hour for traditional methods to less than one second, significantly improving computational efficiency.

[0144] Compared with traditional optimization-based DGHC methods, the method proposed in this embodiment has the following advantages:

[0145] 1. Simplicity and efficiency of analysis: Due to its analytical framework, the method is more simplified and efficient, which is particularly beneficial for analyzing large-scale distribution networks with stochastic DG outputs.

[0146] 2. Bottleneck Identification: Its analytical and sensitivity features enhance the detection of DGHC bottlenecks in the network. Distribution network operators can promptly modify the network structure or implement improvements based on the calculation results.

[0147] 3. Reduced Data Requirements and Broader Applicability: This method utilizes readily available extreme value data, such as block maximum values, instead of high-resolution data. This significantly reduces the need for distributed energy data, extending the application of DGHC to networks with limited data and the initial screening stage requiring rapid filtering.

[0148] Example 3

[0149] Based on the same inventive concept as Embodiment 1, this embodiment introduces a distributed generation carrying capacity analysis device based on generalized extreme value distribution, comprising:

[0150] The data acquisition module is configured to acquire block maximum value data, network topology information, and load demand data of distributed energy resources in the power distribution network.

[0151] The first generalized extreme value distribution fitting module is configured to fit the generalized extreme value distribution parameters of the distributed energy output based on the block maximum value data of the distributed energy resources using the maximum likelihood estimation method.

[0152] The linear power flow model construction module is configured to determine the linear mapping relationship between node voltage and branch power and distributed generation power injection based on the network topology information and load demand data, thereby obtaining a linearized power flow model.

[0153] The second generalized extreme value distribution fitting module is configured to obtain the tail distribution functions of node voltage and branch power based on the generalized extreme value distribution parameters and the linearized power flow model of the distributed energy output, according to the affine transformation properties of the generalized extreme value distribution.

[0154] And a generation carrying capacity determination module is configured to calculate the distributed generation carrying capacity that meets the voltage limit and branch power limit by using the tail distribution function of node voltage and branch power.

[0155] Regarding the apparatus in this embodiment, the specific manner in which each module performs its operations has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0156] Example 4

[0157] Based on the same inventive concept as Embodiment 1, this embodiment introduces an electronic device, which includes a processor and a memory. The memory stores at least one instruction, at least one program, a code set, or an instruction set. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the distributed generation carrying capacity analysis method based on generalized extreme value distribution as described in either Method Embodiment 1 or 2.

[0158] Example 5

[0159] Based on the same inventive concept as Embodiment 1, this embodiment introduces a computer storage medium that can be located in a server to store at least one instruction, at least one program, code set, or instruction set for implementing the method embodiments. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the steps of the distributed generation carrying capacity analysis method based on generalized extreme value distribution as described in either Method Embodiment 1 or 2.

[0160] Optionally, in embodiments of the present invention, the storage medium may be located at at least one of a plurality of network servers in a computer network. Optionally, in embodiments of the present invention, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0161] As can be seen from the technical solutions provided in the embodiments of this specification above, the present invention models the tail characteristics of distributed energy output by using a generalized extreme value distribution, establishes a linear mapping relationship between node voltage and branch power and distributed generation output by combining a linearized power flow model, and calculates the distributed generation carrying capacity based on probabilistic constraints. This significantly reduces the dependence on high-resolution data, improves computational efficiency, and can accurately assess the distributed generation carrying capacity of each node. It is particularly suitable for distribution networks with limited data and the initial screening and evaluation stage of distributed generation access, providing strong support for the large-scale integration of distributed energy.

[0162] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0163] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more flowcharts and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0164] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more flowcharts and / or boxes Figure 1 The function specified in one or more boxes.

[0165] These computer program instructions may also be loaded onto a computer or other programmable device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more flowcharts and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0166] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0167] Finally, it should be noted that the embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for analyzing the carrying capacity of distributed generation based on generalized extreme value distribution, characterized in that, include: Obtain the maximum block size data, network topology information, and load demand data of distributed energy resources in the power distribution network; The maximum likelihood estimation method is used to fit the generalized extreme value distribution parameters of the distributed energy output based on the block maximum value data of the distributed energy resources; Based on the network topology information and load demand data, the linear mapping relationship between node voltage and branch power and distributed generation power injection is determined, and a linearized power flow model is obtained. Based on the generalized extreme value distribution parameters and linearized power flow model of the distributed energy output, the tail distribution functions of node voltage and branch power are obtained according to the affine transformation property of the generalized extreme value distribution. By using the tail distribution functions of node voltage and branch power, the distributed generation carrying capacity that meets voltage and branch power limits can be calculated.

2. The power generation carrying capacity analysis method according to claim 1, characterized in that, The maximum block value data of the distributed energy resources is the maximum block value data in the historical data of distributed energy output power; The method employs maximum likelihood estimation to fit the generalized extreme value distribution parameters of the distributed energy output based on the block maximum value data of the distributed energy resources; including: The maximum value of the block maximum value is taken from the historical data of the output power of distributed energy sources at a specified time interval. Based on the block maximum data, the generalized extreme value distribution of the distributed energy power output factor is fitted using the maximum likelihood estimation method. To obtain the corresponding position parameters Scale parameters and shape parameters ; Based on the relationship between the generation capacity, power output factor, and power output of distributed energy resources, and the generalized extreme value distribution parameters of the power output factor of distributed energy resources, the generalized extreme value distribution of distributed energy resource output is obtained: ,in, For distributed energy output power, This refers to the power generation capacity of distributed energy sources.

3. The power generation carrying capacity analysis method according to claim 1, characterized in that, The linear power flow model is obtained by determining the linear mapping relationship between node voltage and branch power and distributed generation power injection based on network topology information and load demand data, including: Construct the admittance matrix based on network topology information; Based on the admittance matrix of the distribution network, a decoupling linearization method is used to establish a linear relationship between node voltage magnitude and phase angle and power injection, thus obtaining a linearized power flow model, expressed as: （1）， （2）， in, and These represent the bus voltage magnitude and phase angle vector, respectively. and These represent the active power and reactive power injection vectors, respectively. , M and N are constant matrices that can be obtained through matrix decomposition.

4. The power generation carrying capacity analysis method according to claim 3, characterized in that, Based on the generalized extreme value distribution parameters and the linearized power flow model, the tail distribution function of the node voltage is obtained according to the affine transformation property of the generalized extreme value distribution, including: Based on the linear power flow model, assuming that the power injection of other nodes remains constant, the bus... upper node The voltage magnitude at that point is expressed as a linear function of the active power injection: （3）， in, and The node voltage linearization coefficients are calculated from the constant matrix in the linearized power flow model. For active power injection, Let J be the voltage at node j; Based on the affine transformation properties of the generalized extreme value distribution, and the output of distributed energy... The generalized extremum distribution determines the tail distribution function of the node voltage as follows: （4）。 5. The power generation carrying capacity analysis method according to claim 3, characterized in that, Based on the generalized extreme value distribution parameters and the linearized power flow model, the tail distribution function of the branch power is obtained according to the affine transformation property of the generalized extreme value distribution, including: Based on the linear relationship between node phase angle and power injection in the linearized power flow model, the line... Branch power at the location Represented as a linear function of active power injection: （5）， in, and The branch power linearization coefficient is calculated from the constant matrix of the linearized power flow model; Based on the affine transformation properties of the generalized extreme value distribution, and the output of distributed energy... The generalized extreme value distribution determines the tail distribution function of the branch power as follows: （6）。 6. The power generation carrying capacity analysis method according to claim 4, characterized in that, The calculation of distributed generation carrying capacity with voltage and branch power limitations using the tail distribution function of node voltage and branch power includes: Based on voltage limit constraints and a preset voltage violation probability, the maximum carrying capacity satisfying the voltage constraints is calculated according to the tail distribution function of the node voltage. The formula is: （7）， in, This is the upper limit of voltage. This is a preset threshold for the probability of violation; Based on the branch power constraint, the maximum carrying capacity that satisfies the power constraint is calculated according to the tail distribution function of the branch power. The formula is: （8）， in, This is the upper limit of branch power; Based on the maximum carrying capacity satisfying voltage constraints and the maximum carrying capacity satisfying power constraints, the smaller of the two values ​​is determined as the actual carrying capacity at the distributed energy access node n. , is represented as: （9）， in, This represents the actual carrying capacity at node n.

7. The power generation carrying capacity analysis method according to claim 6, characterized in that, The violation probability threshold The value ranges from 0.01% to 5%.

8. A distributed generation carrying capacity analysis device based on generalized extreme value distribution, characterized in that, include: The data acquisition module is configured to acquire block maximum value data, network topology information, and load demand data of distributed energy resources in the power distribution network. The first generalized extreme value distribution fitting module is configured to fit the generalized extreme value distribution parameters of the distributed energy output based on the block maximum value data of the distributed energy resources using the maximum likelihood estimation method. The linear power flow model construction module is configured to determine the linear mapping relationship between node voltage and branch power and distributed generation power injection based on the network topology information and load demand data, thereby obtaining a linearized power flow model. The second generalized extreme value distribution fitting module is configured to obtain the tail distribution functions of node voltage and branch power based on the generalized extreme value distribution parameters and the linearized power flow model of the distributed energy output, according to the affine transformation properties of the generalized extreme value distribution. And a generation carrying capacity determination module is configured to calculate the distributed generation carrying capacity that meets the voltage limit and branch power limit by using the tail distribution function of node voltage and branch power.

9. A computer device, characterized in that, The system includes a processor and a memory, wherein the memory stores at least one instruction and at least one program, the at least one instruction and the at least one program being loaded and executed by the processor to implement the distributed generation carrying capacity analysis method based on generalized extreme value distribution as described in any one of claims 1 to 8.

10. A computer storage medium, characterized in that, The computer storage medium stores at least one instruction and at least one program, which are loaded and executed by a processor to implement the steps of the distributed generation carrying capacity analysis method based on generalized extreme value distribution as described in any one of claims 1 to 8.

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