Methods and apparatus for assessing the carrying capacity of power distribution networks

Abstract

This invention provides a method and apparatus for assessing the carrying capacity of a distribution network. The method includes: acquiring renewable energy generation data and load data of the distribution network in the area where the distribution network is located; constructing an optimization model based on the renewable energy generation data and load data, with the objective of maximizing the weighted sum of renewable energy access capacity and quality power; calculating multiple planning schemes by changing the weight ratio of access capacity and quality power based on the optimization model; calculating the evaluation indicators corresponding to each planning scheme; the evaluation indicators include: voltage limit exceedance risk level, comprehensive node vulnerability, renewable energy generation utilization rate, renewable energy generation absorption rate, average line load rate, and comprehensive distribution network loss rate; and assessing the renewable energy carrying capacity under each planning scheme based on the evaluation indicators corresponding to each planning scheme. This invention can improve the rationality of determining renewable energy access capacity and the rationality of the assessment results of renewable energy carrying capacity in the distribution network.

Description

Technical Field

[0001] This invention relates to the field of power transmission and transformation technology, and in particular to a method and apparatus for assessing the carrying capacity of a power distribution network. Background Technology

[0002] Driven by my country's "dual-carbon" goals, the power grid will construct a new power system with new energy sources as its mainstay. With the rapid development of distributed photovoltaic (PV) power, its high-proportion large-scale integration has become a future trend for distribution networks. However, due to the random fluctuations in PV power generation, problems such as voltage exceeding limits and backflow during distribution network operation are becoming more severe. To ensure the safe and coordinated development of the distribution network's power generation, grid, and load, assessing the maximum carrying capacity of distributed PV based on the distribution network's stable operating boundaries and actual operating conditions is crucial for guiding the large-scale integration of distributed PV.

[0003] Currently, the installed capacity of new energy sources is mostly connected as a percentage of the grid energy. Due to fluctuations in wind speed, solar radiation, and load, there is considerable uncertainty, leading to unreasonable determination of the new energy access capacity and unreasonable assessment results of the new energy carrying capacity in the distribution network. Summary of the Invention

[0004] This invention provides a method and apparatus for evaluating the carrying capacity of a distribution network, which can improve the rationality of determining the capacity of new energy access and the rationality of the evaluation results of the carrying capacity of new energy in the distribution network.

[0005] In a first aspect, the present invention provides a method for assessing the carrying capacity of a distribution network, comprising: acquiring renewable energy generation data and load data of the distribution network in the area where the distribution network is located; constructing an optimization model based on the renewable energy generation data and load data, with the objective of maximizing the weighted sum of renewable energy access capacity and quality power; calculating multiple planning schemes by changing the weight ratio of access capacity and quality power based on the optimization model; the planning schemes include the renewable energy access capacity of each node in the distribution network; each planning scheme corresponds to a weight ratio; calculating the evaluation index corresponding to each planning scheme; the evaluation indexes include: voltage limit exceedance risk level, comprehensive node vulnerability, renewable energy generation utilization rate, renewable energy generation absorption rate, line average load rate, and distribution network comprehensive network loss rate; and assessing the renewable energy carrying capacity under each planning scheme based on the evaluation indexes corresponding to each planning scheme.

[0006] This invention provides a method for assessing the carrying capacity of a distribution network. It constructs an optimization model with the goal of maximizing the weighted sum of renewable energy access capacity and quality power, calculating the renewable energy access capacity at each node in the distribution network. By changing the weight ratio of access capacity and quality power, multiple planning schemes are obtained. Since a larger renewable energy access capacity leads to a larger proportion of clean energy in the distribution network, and a higher renewable energy quality power leads to a higher utilization rate of renewable energy generation, maximizing the weighted sum of renewable energy access capacity and quality power can simultaneously improve the renewable energy access capacity and utilization rate of the distribution network, thus enhancing the rationality of determining the renewable energy access capacity. Furthermore, this invention calculates assessment indicators for renewable energy carrying capacity under multiple planning schemes, i.e., under multiple weights of access capacity and quality power, and comprehensively assesses the renewable energy carrying capacity under multiple planning schemes based on these assessment indicators, thereby reasonably assessing the renewable energy carrying capacity and improving the rationality of the assessment results for renewable energy carrying capacity in the distribution network.

[0007] In one possible implementation, an optimization model is constructed based on renewable energy generation data and load data, with the objective of maximizing the weighted sum of renewable energy access capacity and quality power. This includes: performing data fitting based on renewable energy output data and load data to determine multiple discrete scenarios and the probability of each scenario occurring; each discrete scenario includes renewable energy generation data and load data at a specific time point; based on multiple discrete scenarios and the probability of each scenario occurring, an objective function is constructed with the renewable energy access capacity at each node as the variable, aiming to maximize the weighted sum of renewable energy access capacity and quality power; the safe operation conditions of the distribution network are used as constraints; the safe operation conditions of the distribution network include voltage constraints at each node of the distribution network, power constraints at each branch, and single-point access capacity constraints for renewable energy; and the optimization model is determined based on the objective function and constraints.

[0008] In one possible implementation, data fitting is performed based on renewable energy output data and load data to determine multiple discrete scenarios and the probability of each discrete scenario occurring. This includes: calculating the mean and variance of renewable energy generation data; calculating model parameters for a renewable energy generation distribution probability model based on the mean and variance of the renewable energy generation data; determining the renewable energy generation distribution probability model based on the model parameters; the renewable energy generation distribution probability model is used to define the probability distribution of renewable energy generation power; calculating the mean and variance of load data; determining a normal distribution probability model of distribution network load based on the mean and variance of the load data; the normal distribution probability model of distribution network load is used to define the probability distribution of distribution network load power; and performing discrete analysis and data fitting based on the renewable energy generation distribution probability model and the normal distribution probability model of distribution network load to determine source-load joint scenarios, which include multiple discrete scenarios; each discrete scenario includes renewable energy generation data and load data at a specific time point.

[0009] In one possible implementation, based on an optimization model, the weight ratio of access capacity and quality power is changed to calculate multiple planning schemes, including: determining the weight ratio of access capacity and quality power as a first weight ratio; when the weight ratio of access capacity and quality power is the first weight ratio, solving for the optimal solution of the objective function based on the particle swarm optimization algorithm; the optimal solution is the access capacity of new energy sources at each node in the distribution network; changing the weight ratio of access capacity and quality power, and repeatedly solving for the optimal solution of the objective function to obtain multiple planning schemes.

[0010] In one possible implementation, the evaluation indicators corresponding to each planning scheme are calculated, including: for any planning scheme, determining the voltage exceedance risk level based on the voltage of each node in each discrete scenario and the probability of each discrete scenario occurring; determining the comprehensive node vulnerability based on the impedance of each node and the renewable energy access capacity of each node; determining the renewable energy utilization rate based on the actual renewable energy generation power and installed capacity of each node in each discrete scenario and the probability of each discrete scenario occurring; determining the renewable energy absorption rate based on the actual renewable energy generation power of each node in each discrete scenario and the power fed back from the distribution network to the upper-level power grid, and the probability of each discrete scenario occurring; determining the average line load rate based on the actual transmission power of each line in each discrete scenario and the probability of each discrete scenario occurring; and determining the comprehensive distribution network loss rate based on the actual renewable energy generation power and actual load demand power of each node in each discrete scenario and the probability of each discrete scenario occurring.

[0011] In one possible implementation, the carrying capacity of new energy under each planning scheme is evaluated based on the evaluation indicators corresponding to each planning scheme. This includes: calculating the comprehensive performance index and balance index of each planning scheme based on the evaluation indicators corresponding to each planning scheme and combining the state-space evaluation method. The comprehensive performance index is used to characterize the strength of the new energy carrying capacity of the distribution network, and the balance index is used to characterize the degree of balance of each evaluation index; and evaluating the carrying capacity of new energy in each planning scheme based on the comprehensive performance index and balance index of each planning scheme.

[0012] In one possible implementation, the carrying capacity of new energy sources in each planning scheme is evaluated based on the comprehensive performance index and balance index of each planning scheme. This includes: if the difference between the comprehensive performance index of the first scheme and the comprehensive performance index of the second scheme is greater than a set value, then the carrying capacity of new energy sources corresponding to the first scheme is determined to be better than that of new energy sources corresponding to the second scheme; if the difference between the comprehensive performance index of the first scheme and the comprehensive performance index of the second scheme is less than a set value, and the balance index of the first scheme is less than that of the balance index of the second scheme, then the carrying capacity of new energy sources corresponding to the first scheme is determined to be better than that of new energy sources corresponding to the second scheme; wherein the first scheme and the second scheme are any one of the planning schemes.

[0013] Secondly, embodiments of the present invention provide an assessment device for the carrying capacity of a distribution network, comprising: a communication module for acquiring renewable energy generation data and load data of the distribution network in the area where the distribution network is located; a processing module for constructing an optimization model based on the renewable energy generation data and load data, with the objective of maximizing the weighted sum of renewable energy access capacity and quality power; based on the optimization model, changing the weight ratio of access capacity and quality power to calculate multiple planning schemes; the planning schemes include the renewable energy access capacity of each node in the distribution network; each planning scheme corresponds to a weight ratio; calculating the assessment indicators corresponding to each planning scheme; the assessment indicators include: voltage limit exceedance risk level, comprehensive node vulnerability, renewable energy generation utilization rate, renewable energy generation absorption rate, line average load rate, and distribution network comprehensive network loss rate; and assessing the renewable energy carrying capacity under each planning scheme based on the assessment indicators corresponding to each planning scheme.

[0014] In one possible implementation, the processing module is specifically used to perform data fitting based on renewable energy output data and load data to determine multiple discrete scenarios and the probability of each discrete scenario occurring. Each discrete scenario includes renewable energy power generation data and load data at a specific point in time. Based on the multiple discrete scenarios and the probability of each discrete scenario occurring, an objective function is constructed with the renewable energy access capacity at each node as the variable, aiming to maximize the weighted sum of renewable energy access capacity and quality power. The safe operation conditions of the distribution network are used as constraints. The safe operation conditions of the distribution network include voltage constraints at each node of the distribution network, power constraints at each branch, and renewable energy single-point access capacity constraints. Based on the objective function and constraints, an optimization model is determined.

[0015] In one possible implementation, the processing module is specifically used to calculate the mean and variance of the new energy power generation data; and based on the mean and variance of the new energy power generation data, calculate the model parameters of the new energy power generation distribution probability model; based on the model parameters, determine the new energy power generation distribution probability model; the new energy power generation distribution probability model is used to define the probability distribution of new energy power generation; calculate the mean and variance of the load data; and based on the mean and variance of the load data, determine the distribution network load normal distribution probability model; the distribution network load normal distribution probability model is used to define the probability distribution of distribution network load power; based on the new energy power generation distribution probability model and the distribution network load normal distribution probability model, perform discrete analysis and data fitting to determine the source-load joint scenario, which includes multiple discrete scenarios; each discrete scenario includes new energy power generation data and load data at a certain time point.

[0016] In one possible implementation, the processing module is specifically used to determine the weight ratio of access capacity and quality power as a first weight ratio; when the weight ratio of access capacity and quality power is the first weight ratio, the optimal solution of the objective function is solved based on the particle swarm optimization algorithm; the optimal solution is the access capacity of new energy sources at each node in the distribution network; the weight ratio of access capacity and quality power is changed, and the optimal solution of the objective function is solved repeatedly to obtain multiple planning schemes.

[0017] In one possible implementation, the processing module is specifically used to, for any planning scheme, determine the voltage exceedance risk level based on the voltage of each node in each discrete scenario and the probability of each discrete scenario occurring; determine the comprehensive node vulnerability based on the impedance of each node and the renewable energy access capacity of each node; determine the renewable energy utilization rate based on the actual renewable energy generation power and installed capacity of each node in each discrete scenario and the probability of each discrete scenario occurring; determine the renewable energy absorption rate based on the actual renewable energy generation power of each node in each discrete scenario and the power fed back from the distribution network to the upper-level power grid, and the probability of each discrete scenario occurring; determine the average line load rate based on the actual transmission power of each line in each discrete scenario and the probability of each discrete scenario occurring; and determine the comprehensive distribution network loss rate based on the actual renewable energy generation power and actual load demand power of each node in each discrete scenario and the probability of each discrete scenario occurring.

[0018] In one possible implementation, the processing module is specifically used to calculate the comprehensive performance index and balance index of each planning scheme based on the evaluation index corresponding to each planning scheme and in combination with the state space evaluation method. The comprehensive performance index is used to characterize the strength of the new energy carrying capacity of the distribution network, and the balance index is used to characterize the balance of each evaluation index. Based on the comprehensive performance index and balance index of each planning scheme, the carrying capacity of new energy in each planning scheme is evaluated.

[0019] In one possible implementation, the processing module is specifically configured to determine that the carrying capacity of new energy corresponding to the first scheme is better than that of the second scheme if the difference between the comprehensive performance index of the first scheme and the comprehensive performance index of the second scheme is greater than a set value; and to determine that the carrying capacity of new energy corresponding to the first scheme is better than that of the second scheme if the difference between the comprehensive performance index of the first scheme and the comprehensive performance index of the second scheme is less than a set value, and the balance index of the first scheme is less than that of the second scheme; wherein the first scheme and the second scheme are any one of the planning schemes.

[0020] Thirdly, embodiments of the present invention provide an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, the processor being configured to call and run the computer program stored in the memory to perform the steps of the method as described in the first aspect and any possible implementation thereof.

[0021] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of the method as described in the first aspect and any possible implementation thereof.

[0022] The technical effects of any of the implementation methods in the second to fourth aspects mentioned above can be found in the technical effects of the corresponding implementation method in the first aspect, and will not be repeated here. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, 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.

[0024] Figure 1 This is a flowchart illustrating a method for evaluating the carrying capacity of a power distribution network according to an embodiment of the present invention.

[0025] Figure 2 This is a flowchart illustrating a particle swarm optimization algorithm provided in an embodiment of the present invention;

[0026] Figure 3 This is a schematic diagram of a state space provided in an embodiment of the present invention;

[0027] Figure 4 This is a schematic diagram of an IEEE-33 distribution network model provided in an embodiment of the present invention;

[0028] Figure 5 This is a schematic diagram of new energy output data provided in an embodiment of the present invention;

[0029] Figure 6 This is a schematic diagram of power distribution network load data provided in an embodiment of the present invention;

[0030] Figure 7 This is a schematic diagram of the evaluation results of a state-space evaluation method provided in an embodiment of the present invention;

[0031] Figure 8 This is a schematic diagram of the structure of a power distribution network carrying capacity assessment device provided in an embodiment of the present invention;

[0032] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0033] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0034] In the description of this invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" and "more than one" refer to two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0035] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0036] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not limited to the steps or modules listed, but may optionally include other steps or modules not listed, or may optionally include other steps or modules inherent to such process, method, product, or device.

[0037] To make the objectives, technical solutions, and advantages of the present invention clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0038] As described in the background section, due to fluctuations in wind speed, sunlight, and load, there are significant uncertainties, leading to unreasonable determination of new energy access capacity and unreasonable assessment results of new energy carrying capacity in the distribution network.

[0039] To solve the above technical problems, such as Figure 1 As shown, this embodiment of the invention provides a method for assessing the carrying capacity of a distribution network. The executing entity is a distribution network carrying capacity assessment device. The assessment method includes steps S101-S105.

[0040] S101. Obtain new energy power generation data and distribution network load data in the area where the distribution network is located.

[0041] In some embodiments, renewable energy generation data may include solar irradiance data and / or wind power data.

[0042] As one possible implementation, the assessment device can acquire renewable energy generation data and load data over a historical period. For example, the assessment device can acquire solar irradiance data and / or wind power data for one year.

[0043] S102. Based on new energy power generation data and load data, construct an optimization model with the goal of maximizing the weighted sum of the access capacity and quality power of new energy sources.

[0044] As one possible implementation, the evaluation device can construct an optimization model based on steps S1021-S1024.

[0045] S1021. Based on the power output data and load data of new energy sources, perform data fitting to determine multiple discrete scenarios and the probability of each discrete scenario occurring.

[0046] In this embodiment of the application, each discrete scenario includes new energy power generation data and load data at a point in time.

[0047] For example, the evaluation device can determine multiple discrete scenarios and the probability of each discrete scenario occurring based on steps A1-A6.

[0048] A1. Calculate the mean and variance of new energy power generation data.

[0049] In some embodiments, the evaluation device can calculate the mean and variance based on new energy power generation data at multiple points in time over a historical period.

[0050] A2. Based on the mean and variance of new energy power generation data, calculate the model parameters of the new energy power generation distribution probability model.

[0051] In some embodiments, the evaluation device may determine the model parameters of the new energy power generation distribution probability model using the following formula.

[0052]

[0053] Where μ1 is the average value of new energy power generation data over the historical period. Let α be the variance of the new energy power generation data over the historical period, α be the first model parameter of the new energy power generation distribution probability model, and β be the second model parameter of the new energy power generation distribution probability model.

[0054] A3. Based on the model parameters, determine the probability distribution model of new energy power generation.

[0055] In some embodiments, the new energy power generation distribution probability model is used to define the probability distribution of new energy power generation.

[0056] As one possible implementation method, taking photovoltaic as an example of new energy, the probability distribution model of new energy power generation can be expressed as the following formula.

[0057]

[0058] Among them, P N Let P be the rated photovoltaic output power, α be the actual photovoltaic output power, β be the shape parameters of the photovoltaic distribution probability model, and Γ() be the gamma function.

[0059] It should be noted that the rated photovoltaic output power is proportional to the amount of renewable energy connected to the grid. For example, the rated photovoltaic output power can be 80% of the renewable energy connected to the grid.

[0060] A4. Calculate the mean and variance of the load data.

[0061] In some embodiments, the evaluation device can calculate the mean and variance based on load data from multiple time points within a historical period.

[0062] A5. Based on the mean and variance of load data, determine the probability model of normal distribution of load in the distribution network.

[0063] In this embodiment, the normal distribution probability model of the distribution network load is used to define the probability distribution of the distribution network load power.

[0064] As one possible implementation method, taking photovoltaic as a new energy source as an example, the normal distribution probability model of distribution network load can be expressed as the following formula.

[0065]

[0066] Among them, P load The mean and variance of the load data, σ2 is the mean of the load data in the historical period, μ2 is the variance of the load data in the historical period, and exp[] is an exponential function with base e.

[0067] A6. Based on the probability distribution model of new energy power generation and the probability distribution model of normal distribution network load, discrete analysis and data fitting are performed to determine the source-load joint scenario.

[0068] In this embodiment of the application, the source-load joint scenario includes multiple discrete scenarios; each discrete scenario includes new energy power generation data and load data at a point in time.

[0069] As one possible implementation, the evaluation device can perform data fitting on new energy processing data and load data at multiple time points, calculate the optimal discrete point as a discrete scenario, and calculate the probability of each discrete scenario.

[0070] For example, the evaluation device can calculate the optimal discrete point based on the following formula.

[0071]

[0072] Where f(x) is the probability distribution model of new energy power generation, g(x) is the probability distribution model of normal distribution of distribution network load, r is the order of the function, S is the total number of discrete scenarios, s is the s-th discrete scenario, and z s Let be the value of the s-th optimal discrete point.

[0073] It should be noted that the evaluation device can first calculate the probability of the discrete scenario of new energy output and the probability of the discrete scenario of load in the discrete scenario, and then calculate the probability of the discrete scenario in the source-load combined scenario.

[0074] For example, the evaluation device can calculate the probability of the occurrence of a discrete scenario of new energy output or a discrete scenario of load in the s-th discrete scenario based on the following formula.

[0075]

[0076] Among them, P s Let h(x) be the probability of the discrete scenario of renewable energy output occurring in the s-th discrete scenario, or the probability of the discrete scenario of load occurring, and let f(x) or g(x) be the probability of the discrete scenario of load. s Let z be the value of the s-th optimal discrete point. s-1 Let z be the value of the (s-1)th optimal discrete point. s+1 This represents the value of the (s+1)th optimal discrete point.

[0077] For example, the evaluation device can calculate the probability of the occurrence of the s-th discrete scenario based on the following formula.

[0078]

[0079] Among them, S T S represents the number of discrete scenes; PV The number of discrete scenarios contributing to new energy; S L p represents the number of discrete scenarios of the load. s P represents the probability of the s-th discrete scene. s.PV The probability of the discrete scenario for the output of the s-th new energy source; P s.L Let be the probability of the discrete scenario for the s-th load.

[0080] S1022. Based on multiple discrete scenarios and the probability of each discrete scenario occurring, an objective function is constructed with the access capacity of new energy sources at each node as the variable, aiming to maximize the weighted sum of the access capacity and quality power of new energy sources.

[0081] In some embodiments, the objective function can be expressed as the following formula.

[0082] max f = max(w1r1 + w2r2);

[0083] Where maxf represents the objective function, w1 is the first weighting coefficient, r1 is the normalized value of the access capacity of new energy, w2 is the second weighting coefficient, and r2 is the normalized value of the quality power.

[0084] For example, the evaluation device can normalize the access capacity of new energy sources to obtain a normalized value. The access capacity of new energy sources before normalization can be expressed by the following formula.

[0085]

[0086] Where f1 represents the sum of the access capacities of new energy sources at each node of the distribution network; P G,i represents the photovoltaic grid connection capacity of the i-th node; N represents the number of distribution network nodes.

[0087] For example, the evaluation device can normalize the quality power to obtain a normalized value. The evaluation device can obtain the unnormalized quality power based on the following formula.

[0088]

[0089] Where f2 represents the quality power of distributed photovoltaic power in the distribution network; P1 is the annual quality power of renewable energy consumption; P2 is the annual power purchased by the distribution network; and P3 is the annual power sold by the distribution network.

[0090] p s Let be the probability of the s-th discrete scene. P represents the actual power output of the new energy source at the i-th node in the s-th scenario. I s The power purchased by the distribution network from the upper-level grid in the s-th scenario The power sold from the distribution network to the upper-level grid in the s-th scenario, where m is the number of discrete scenarios.

[0091] As one possible approach, the evaluation device can normalize the access capacity and quality power of new energy sources based on the following formula.

[0092]

[0093] Where, r i f is the normalized value of the access capacity or quality power of new energy sources. i This refers to the capacity or quality power of new energy sources.

[0094] It should be noted that, according to f = w1r1 + w2r2, the multi-objective programming problem concerning access volume and economic quality is transformed into a single programming problem. Different weights are assigned according to different planning focuses or application scenarios to form different objective functions and construct multiple planning schemes.

[0095] S1023. Determine the safe operation conditions of the distribution network as constraints.

[0096] In some embodiments, the safe operation conditions of the distribution network include voltage constraints at each node of the distribution network, power constraints at each branch, and capacity constraints for single-point access to new energy sources.

[0097] For example, the voltage constraints of each node in a distribution network can be expressed as the following formula.

[0098] V min ≤V i ≤V max ;

[0099] Among them, V i Let V be the voltage of the i-th node in the distribution network. min V represents the minimum voltage limit at each node in the distribution network. max This represents the maximum voltage limit at each node in the distribution network.

[0100] For example, the voltage constraints of each node in a distribution network can be expressed as the following formula.

[0101] S ijmin ≤S ij ≤S ijmax ;

[0102] Among them, S ij S represents the power of branch ij in the distribution network. ijmin S is the minimum power limit for branch ij in the distribution network. ijmax This represents the maximum power limit of branch ij in the distribution network.

[0103] For example, the voltage constraints of each node in a distribution network can be expressed as the following formula.

[0104] 0≤P G,i ≤P max ;

[0105] Among them, P G,i Let P be the renewable energy access capacity of the i-th node in the distribution network. max This represents the maximum limit for the new energy access capacity at each node in the distribution network.

[0106] S1024. Determine the optimization model based on the objective function and constraints.

[0107] S103. Based on the optimization model, the weight ratio of access capacity and quality power is changed to calculate a variety of planning schemes.

[0108] In this embodiment of the application, the planning scheme includes the access capacity of new energy sources at each node in the distribution network; one planning scheme corresponds to one weight ratio.

[0109] As one possible implementation, the evaluation device can determine multiple planning schemes based on steps S1031-S1033.

[0110] S1031. Determine the weight ratio of access capacity and quality power as the first weight ratio.

[0111] S1032. When the weight ratio of access capacity and quality power is the first weight ratio, the optimal solution of the objective function is solved based on the particle swarm optimization algorithm.

[0112] In some embodiments, the optimal solution is the access capacity of new energy sources at each node in the distribution network.

[0113] For example, such as Figure 2 As shown in the figure, this embodiment of the invention provides a process for solving an objective function based on the particle swarm optimization algorithm.

[0114] Step 1: Initialize algorithm parameters and new energy access capacity of each node.

[0115] Step 2: Initialize k = 1.

[0116] Step 3: Calculate the quality power of new energy sources throughout the entire time period, and find the optimal new energy access scheme and the target optimal value.

[0117] Step 4: Update the distributed photovoltaic access capacity of each node.

[0118] Step 5: Calculate the distributed photovoltaic quality power over the entire time period, and update the optimal distributed photovoltaic access scheme and the target optimal value.

[0119] Step Six: Determine if the current iteration count is greater than the maximum iteration count. If yes, output the result. If no, increment the iteration count by one, and repeat steps Four, Five, and Six until the iteration process ends.

[0120] For example, the maximum number of iterations can be 200.

[0121] It should be noted that when applying the particle swarm optimization algorithm, in each iteration process, the speed and position of the control particles (i.e., the new energy access capacity of each node) can be updated according to the following formula.

[0122]

[0123] Among them, vk This indicates the particle's velocity, v, when the number of iterations is k. k+1 This indicates the particle's velocity at iteration number k+1; x k x represents the spatial position of the control particle when the number of iterations is k. k+1 pbest represents the spatial position of the control particle when the iteration number is k+1. k gbest represents the optimal solution for the control particle at the k-th iteration. k Let represent the global optimal solution controlling the particle at the k-th iteration, c1 represent the first learning factor, c2 represent the second learning factor, r1 and r2 are both random numbers uniformly distributed between (0,1), and ω represent the inertia factor. max ω represents the maximum limit of the inertia factor. min This represents the minimum limit of the inertia factor, where k represents the number of iterations. max This indicates the maximum number of iterations.

[0124] When the number of iterations k is greater than the maximum number of iterations k max If the iteration process ends, the distributed photovoltaic access capacity of each node and the target optimal solution (gbest) are obtained. k The value obtained is the optimal solution of the objective function.

[0125] S1033. By changing the weight ratio of access capacity and quality power, the optimal solution of the objective function is repeatedly solved to obtain multiple planning schemes.

[0126] S104. Calculate the evaluation indicators corresponding to each planning scheme.

[0127] In this embodiment of the application, the evaluation indicators may include: voltage over-limit risk level, comprehensive node vulnerability, new energy power generation utilization rate, new energy power generation absorption rate, line average load rate, and distribution network comprehensive network loss rate.

[0128] In some embodiments, the evaluation indicators may include new energy grid connection characteristics indicators, new energy utilization characteristics indicators, and distribution network adaptability indicators.

[0129] For example, the grid connection characteristics indicators of new energy sources may include the degree of voltage over-limit risk and the overall node vulnerability.

[0130] For example, the characteristics indicators of new energy utilization may include new energy power generation utilization rate and new energy power generation absorption rate.

[0131] For example, distribution network adaptability indicators may include the average line load rate and the overall distribution network loss rate.

[0132] It should be noted that the calculated parameters for each scenario include the photovoltaic grid connection capacity of each node, the actual photovoltaic output of each node, the voltage value of each node, the transmission power of each line, the power supplied from the main grid to the distribution network, and the power returned from the distribution network to the main grid. These parameters are used to calculate the distribution network carrying capacity assessment indicators.

[0133] As one possible approach, the evaluation device can determine the evaluation indicators for each planning scheme based on the node data of each node.

[0134] For example, for any planning scheme, the evaluation device can determine the degree of voltage over-limit risk based on the voltage of each node in each discrete scenario and the probability of each discrete scenario occurring.

[0135] For example, the assessment device can determine the degree of voltage over-limit risk based on the following formula.

[0136]

[0137]

[0138]

[0139] Where U1 represents the risk level of voltage exceeding the limit, P{} indicates whether the voltage exceeds the limit (value 1 if exceeding the limit, value 0 if not), m represents the number of discrete scenarios, and N represents the number of nodes. For node voltage exceeding the upper limit rate, The rate at which the node voltage crosses the lower limit. The severity of node voltage exceeding the upper limit. The severity of node voltage falling below the lower limit. U is the voltage of the i-th node in the S-th scene. N U represents the rated voltage of each node in the distribution network. max U represents the upper limit of voltage at each node of the distribution network. min p represents the lower voltage limit at each node of the distribution network. s Let S be the probability of the S-th scenario occurring.

[0140] It should be noted that P{} indicates whether the voltage exceeds the limit; a value of 1 indicates exceeding the limit, and a value of 0 indicates not exceeding the limit. According to relevant standards, the permissible voltage deviation limit for a 10kV distribution network is 1.07U. N and 0.93U N U NThe voltage is the rated voltage of the distribution network. The node voltage upper limit exceedance rate represents the ratio of nodes exceeding the upper voltage limit to all nodes in a given scenario. The node voltage lower limit exceedance rate represents the ratio of nodes exceeding the lower voltage limit to all nodes in a given scenario. The node voltage upper limit exceedance severity represents the sum of the numerical values ​​of each node exceeding the upper voltage limit in a given scenario. The node voltage lower limit exceedance severity represents the sum of the numerical values ​​of each node exceeding the lower voltage limit in a given scenario.

[0141] For example, for any planning scheme, the evaluation device can determine the overall node vulnerability based on the impedance of each node and the new energy access capacity of each node.

[0142] For example, the assessment device can determine the overall node vulnerability based on the following formula.

[0143]

[0144]

[0145] Where A represents the overall node vulnerability, and P... G,i T represents the new energy access capacity of the i-th node; i Let L be the importance of the i-th node, and L be the number of nodes connected to the i-th node. i Let be the comprehensive electrical distance of the i-th node, representing the degree of electrical connection between the i-th node and other nodes in the distribution network, where N is the number of nodes, and d is the distance between the i-th node and other nodes in the distribution network. ij Let be the electrical coupling distance between the i-th node and the j-th node.

[0146] It should be noted that d ij =Z ii +Z jj -2Z ij ;

[0147] Z ii Z is the self-impedance of the i-th node. jj Z is the self-impedance of the j-th node. ij Let be the mutual impedance between the i-th node and the j-th node.

[0148] For example, for any planning scheme, the evaluation device can determine the utilization rate of new energy power generation based on the actual power generation and installed capacity of new energy at each node in each discrete scenario, as well as the probability of each discrete scenario occurring.

[0149] For example, the evaluation device can determine the utilization rate of new energy power generation based on the following formula.

[0150]

[0151] Where R1 is the utilization rate of new energy power generation, p sLet be the probability of the s-th scenario occurring. Let be the actual power generation of the new energy source at the i-th node in the s-th scenario. Let m be the installed capacity of new energy at the i-th node in the s-th scenario, m be the number of scenarios, and N be the number of nodes.

[0152] For example, for any planning scheme, the evaluation device can determine the renewable energy consumption rate based on the actual renewable energy power generation of each node in each discrete scenario, the power fed back from the distribution network to the upper-level power grid, and the probability of each discrete scenario occurring.

[0153] For example, the evaluation device can determine the renewable energy power generation absorption rate based on the following formula.

[0154]

[0155] Wherein, R2 is the renewable energy power generation absorption rate. Let be the actual power generation of the new energy source at the ith node in the s-th scenario. Let p be the power fed back from the distribution network to the upper-level power grid in the s-th scenario. s Let m be the probability of the s-th scenario occurring, m be the number of scenarios, and N be the number of nodes.

[0156] For example, for any planning scheme, the evaluation device can determine the average load rate of the lines based on the actual transmission power of each line in each discrete scenario and the probability of each discrete scenario occurring.

[0157] For example, the evaluation device can determine the average load rate of the line based on the following formula.

[0158]

[0159] Where K1 is the average load factor of the line, p s Let be the probability of the s-th scenario occurring. Let S be the actual transmission power of the j-th line in the s-th scenario. N J represents the rated transmission power of the line, J represents the number of lines, and M represents the number of scenarios.

[0160] For example, for any planning scheme, the evaluation device can determine the comprehensive network loss rate of the distribution network based on the actual power generation of new energy sources and the actual load demand power of each node in each discrete scenario, as well as the probability of each discrete scenario occurring.

[0161] For example, the assessment device can determine the overall network loss rate of the distribution network based on the following formula.

[0162]

[0163] Where K2 is the overall network loss rate of the distribution network, ps Let be the probability of the s-th scenario occurring. Let P be the actual power generation of the new energy source at the i-th node in the s-th scenario. I s Let s be the power purchased by the distribution network from the upper-level power grid in the s-th scenario. Let m be the actual load demand power of the i-th node in the s-th scenario, m be the number of scenarios, and N be the number of nodes.

[0164] S105. Based on the evaluation indicators corresponding to each planning scheme, evaluate the carrying capacity of new energy under each planning scheme.

[0165] As one possible implementation, the evaluation device can normalize the evaluation indicators corresponding to each planning scheme based on the following formula.

[0166]

[0167]

[0168] Among them, z i For the i-th indicator, m i For the normalized i-th index, min{z} i} represents the minimum value among all indicators, max{z} i} represents the maximum value among all indicators.

[0169] As one possible implementation, the evaluation device can, in steps S1051-S1052, evaluate the carrying capacity of new energy under each planning scheme.

[0170] S1051. Based on the evaluation indicators corresponding to each planning scheme, and combined with the state-space evaluation method, calculate the comprehensive performance index and balance index of each planning scheme.

[0171] In this embodiment of the application, the comprehensive performance index is used to characterize the strength of the new energy carrying capacity of the distribution network, and the balance index is used to characterize the balance of each evaluation index.

[0172] It should be noted that, as Figure 3 As shown in the figure, this embodiment of the invention provides a state space diagram. Each planning scheme of the power distribution network can correspond to a set of evaluation index values. Each evaluation index constitutes a dimension of the spatial coordinate system, and each planning scheme can be represented as a point in the spatial coordinate system, thereby constructing a state space representation of the planning scheme. Here, OM is a line passing through the origin and having equal angles with each coordinate axis.

[0173] Based on the established state space, the projection M' of point M onto OM is used as the comprehensive evaluation index. The distance OM' from point M' to the origin characterizes the comprehensive performance of the evaluation index. The larger the distance, the better the comprehensive performance of the distribution network; this is the primary criterion. When the distances OM' and OM' are equal, the distance MM' from point M to OM characterizes the balance of the evaluation index. The smaller the distance, the more balanced the various indicators of the distribution network are, and the better the performance; this is the secondary criterion.

[0174] according to Calculate the comprehensive performance indicators of the distribution network; based on Calculate the distribution network balance index; where n is the number of indices, which can be derived using the law of cosines. m i Let be the i-th evaluation index.

[0175] S1052. Based on the comprehensive performance indicators and balance indicators of each planning scheme, evaluate the carrying capacity of new energy in each planning scheme.

[0176] For example, if the difference between the comprehensive performance index of the first scheme and the comprehensive performance index of the second scheme is greater than a set value, then it is determined that the carrying capacity of the new energy corresponding to the first scheme is better than that of the new energy corresponding to the second scheme.

[0177] As another example, if the difference between the comprehensive performance index of the first scheme and the comprehensive performance index of the second scheme is less than a set value, and the balance index of the first scheme is less than the balance index of the second scheme, then it is determined that the carrying capacity of the new energy corresponding to the first scheme is better than the carrying capacity of the new energy corresponding to the second scheme.

[0178] Among them, the first option and the second option are either of the various planning options.

[0179] It should be noted that, based on the comprehensive performance index of the distribution network, the comparison of various planning schemes is conducted. A higher comprehensive performance index value indicates a better distribution network carrying capacity. When the difference in comprehensive performance index values ​​between two planning schemes is less than 1%, they are considered to have the same comprehensive performance. Therefore, it is necessary to further compare the distribution network balance index. A lower balance index indicates a more balanced distribution network across all indicators, resulting in better carrying capacity performance. This two-level evaluation method, which includes both comprehensive performance and balance, greatly reduces the probability of inaccurate comparisons of carrying capacity. Normally, the comprehensive performance index alone is sufficient to assess the carrying capacity of a distribution network; only in a few cases is it necessary to compare the distribution network balance index. The probability of both indices being equal is extremely low. Therefore, the evaluation method proposed in this invention can effectively assess the carrying capacity of different distribution network planning schemes.

[0180] This invention provides a method for assessing the carrying capacity of a distribution network. It constructs an optimization model with the goal of maximizing the weighted sum of renewable energy access capacity and quality power, calculating the renewable energy access capacity at each node in the distribution network. By changing the weight ratio of access capacity and quality power, multiple planning schemes are obtained. Since a larger renewable energy access capacity leads to a larger proportion of clean energy in the distribution network, and a higher renewable energy quality power leads to a higher utilization rate of renewable energy generation, maximizing the weighted sum of renewable energy access capacity and quality power can simultaneously improve the renewable energy access capacity and utilization rate of the distribution network, thus enhancing the rationality of determining the renewable energy access capacity. Furthermore, this invention calculates assessment indicators for renewable energy carrying capacity under multiple planning schemes, i.e., under multiple weights of access capacity and quality power, and comprehensively assesses the renewable energy carrying capacity under multiple planning schemes based on these assessment indicators, thereby reasonably assessing the renewable energy carrying capacity and improving the rationality of the assessment results for renewable energy carrying capacity in the distribution network.

[0181] For example, such as Figure 4 As shown, taking the IEEE-33 distribution network model as an example, the main transformer has a rated capacity of 5MVA, a transformation ratio of 110kV / 10kV, and the maximum transmission capacity of each feeder is 600kVA. Figure 5 As shown, the annual trend of solar radiation intensity in a certain region is strong in summer and weak in winter; specifically, the daily trend is strong at noon and weak at night. Figure 6 As shown, the annual load power variation trend of a certain region is strong in summer and autumn, and weak in winter and spring. Overall, it is relatively balanced throughout the year.

[0182] pass Figure 5 and Figure 6 The trend charts of light intensity and load power variation are shown. Beta distribution parameters and normal distribution parameters are calculated. With the Wasserstein distance exponent r = 1 and scenario S = 5, the optimal scenario power distribution points and corresponding probability values ​​for photovoltaic output power and load power are obtained. Furthermore, the photovoltaic-load joint probability scenarios shown in Table 1 are established. Among these, the scenario probability for photovoltaic output is highest for scenarios with output less than 0.1 pu, lowest for scenarios near full capacity, and the probabilities of other scenarios are approximately equal. For load power, the scenario with output of 0.5 pu has the highest probability, while the probabilities of other scenarios are relatively low. This indicates that the scenario generation method based on Wasserstein distance can effectively simulate the probabilistic scenarios of photovoltaic and load output throughout the year.

[0183] It should be noted that P PV For photovoltaic power generation, P LThe per-unit value (pu) represents the load power. The per-unit value of photovoltaic power generation is the ratio of the actual photovoltaic power generation to the rated output power. The per-unit value of load power is the ratio between the actual power of the load and the rated power of the load.

[0184] Table 1

[0185]

[0186] Based on the different weights set in Table 2, the particle swarm optimization algorithm was used to solve the problem, resulting in three distributed photovoltaic planning schemes. For each planning scheme, the statistical values ​​of each evaluation index shown in Table 3 under different scenarios throughout the year were calculated. Furthermore, the six evaluation indicators were transformed into comprehensive performance and balance indicators shown in Table 4 using the state-space evaluation method. Figure 7 The radar chart shown intuitively presents the evaluation results of the three schemes.

[0187] Table 2

[0188]

[0189]

[0190] Table 3

[0191]

[0192] Table 4

[0193] plan Overall performance Balance Option 1 1.2387 0.0847 Option 2 1.2309 0.0669 Option 3 1.2949 0.1058

[0194] Combine Tables 3 and 4 and Figure 7 Scheme 3 has the best overall performance, with its A1 and R2 indices being the best among the three schemes. Its U1, R1, and K2 indices are moderate, with only K1 being relatively poor. Scheme 1 has moderate overall performance, with average R1 performance and the worst performance in U1 and A1. However, it performs best in K1, K2, and R1. Scheme 2 has the worst overall performance among the three schemes, but because the difference in overall performance from Scheme 1 is too small, their balance indices can be compared. In this case, Scheme 2 is significantly better than Scheme 1, but it still has room for improvement in U1, R1, K1, and K2 indices. In summary, this demonstrates the effectiveness of the novel distribution network comprehensive carrying capacity index system proposed in this invention, and its results can comprehensively and objectively reflect the distribution network's comprehensive carrying capacity for distributed photovoltaic power.

[0195] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0196] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0197] Figure 8 A schematic diagram of a distribution network carrying capacity assessment device 200 provided in an embodiment of the present invention is shown. The distribution network carrying capacity assessment device 200 includes a communication module 201 and a processing module 202.

[0198] The communication module 201 is used to acquire new energy power generation data and load data of the distribution network in the area where the distribution network is located.

[0199] Processing module 202 is used to construct an optimization model based on new energy power generation data and load data, with the goal of maximizing the weighted sum of new energy access capacity and quality power. Based on the optimization model, the weight ratio of access capacity and quality power is changed to calculate multiple planning schemes. The planning schemes include the access capacity of new energy at each node in the distribution network. Each planning scheme corresponds to a weight ratio. The module calculates the evaluation indicators corresponding to each planning scheme. The evaluation indicators include: voltage limit exceedance risk, comprehensive node vulnerability, new energy power generation utilization rate, new energy power generation absorption rate, line average load rate, and distribution network comprehensive network loss rate. Based on the evaluation indicators corresponding to each planning scheme, the module evaluates the carrying capacity of new energy under each planning scheme.

[0200] In one possible implementation, the processing module 202 is specifically used to perform data fitting based on renewable energy output data and load data to determine multiple discrete scenarios and the probability of each discrete scenario occurring; each discrete scenario includes renewable energy power generation data and load data at a point in time; based on multiple discrete scenarios and the probability of each discrete scenario occurring, an objective function is constructed with the renewable energy access capacity of each node as a variable, aiming to maximize the weighted sum of renewable energy access capacity and quality power; the safe operation conditions of the distribution network are used as constraints; the safe operation conditions of the distribution network include voltage constraints of each node of the distribution network, power constraints of each branch, and renewable energy single-point access capacity constraints; and an optimization model is determined based on the objective function and constraints.

[0201] In one possible implementation, processing module 202 is specifically used to calculate the mean and variance of new energy power generation data; and based on the mean and variance of new energy power generation data, calculate the model parameters of the new energy power generation distribution probability model; based on the model parameters, determine the new energy power generation distribution probability model; the new energy power generation distribution probability model is used to limit the probability distribution of new energy power generation; calculate the mean and variance of load data; and based on the mean and variance of load data, determine the distribution network load normal distribution probability model; the distribution network load normal distribution probability model is used to limit the probability distribution of distribution network load power; based on the new energy power generation distribution probability model and the distribution network load normal distribution probability model, perform discrete analysis and data fitting to determine the source-load joint scenario, which includes multiple discrete scenarios; each discrete scenario includes new energy power generation data and load data at a certain time point.

[0202] In one possible implementation, the processing module 202 is specifically used to determine the weight ratio of access capacity and quality power as a first weight ratio; when the weight ratio of access capacity and quality power is the first weight ratio, the optimal solution of the objective function is solved based on the particle swarm optimization algorithm; the optimal solution is the access capacity of new energy sources at each node in the distribution network; the weight ratio of access capacity and quality power is changed, and the optimal solution of the objective function is solved repeatedly to obtain multiple planning schemes.

[0203] In one possible implementation, the processing module 202 is specifically used to, for any planning scheme, determine the voltage exceedance risk level based on the voltage of each node in each discrete scenario and the probability of each discrete scenario occurring; determine the comprehensive node vulnerability based on the impedance of each node and the renewable energy access capacity of each node; determine the renewable energy utilization rate based on the actual renewable energy generation power and installed capacity of each node in each discrete scenario and the probability of each discrete scenario occurring; determine the renewable energy absorption rate based on the actual renewable energy generation power of each node in each discrete scenario and the power fed back from the distribution network to the upper-level power grid, and the probability of each discrete scenario occurring; determine the average line load rate based on the actual transmission power of each line in each discrete scenario and the probability of each discrete scenario occurring; and determine the comprehensive network loss rate of the distribution network based on the actual renewable energy generation power and actual load demand power of each node in each discrete scenario and the probability of each discrete scenario occurring.

[0204] In one possible implementation, the processing module 202 is specifically used to calculate the comprehensive performance index and balance index of each planning scheme based on the evaluation index corresponding to each planning scheme and in combination with the state space evaluation method. The comprehensive performance index is used to characterize the strength of the new energy carrying capacity of the distribution network, and the balance index is used to characterize the balance of each evaluation index. Based on the comprehensive performance index and balance index of each planning scheme, the carrying capacity of new energy in each planning scheme is evaluated.

[0205] In one possible implementation, the processing module 202 is specifically configured to determine that the carrying capacity of the new energy corresponding to the first scheme is better than that of the second scheme if the difference between the comprehensive performance index of the first scheme and the comprehensive performance index of the second scheme is greater than a set value; and to determine that the carrying capacity of the new energy corresponding to the first scheme is better than that of the second scheme if the difference between the comprehensive performance index of the first scheme and the comprehensive performance index of the second scheme is less than a set value and the balance index of the first scheme is less than that of the second scheme; wherein the first scheme and the second scheme are any one of the planning schemes.

[0206] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. For example... Figure 9 As shown, the electronic device 300 of this embodiment includes: a processor 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program 303, it implements the steps in the above-described method embodiments, for example... Figure 1 Steps 101 to 105 are shown. Alternatively, when the processor 301 executes the computer program 303, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 8 The functions of the communication module 201 and the processing module 202 shown are illustrated.

[0207] For example, the computer program 303 can be divided into one or more modules / units, which are stored in the memory 302 and executed by the processor 301 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 303 in the electronic device 300. For example, the computer program 303 can be divided into... Figure 8 The communication module 201 and the processing module 202 are shown.

[0208] The processor 301 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0209] The memory 302 can be an internal storage unit of the electronic device 300, such as a hard disk or memory of the electronic device 300. The memory 302 can also be an external storage device of the electronic device 300, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD) card, flash card, etc., equipped on the electronic device 300. Furthermore, the memory 302 can include both internal and external storage units of the electronic device 300. The memory 302 is used to store the computer program and other programs and data required by the terminal. The memory 302 can also be used to temporarily store data that has been output or will be output.

[0210] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0211] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0212] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0213] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components 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 through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0214] The units described as separate components may or may not be physically separate. The components shown as units 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0215] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0216] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0217] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for evaluating the carrying capacity of a power distribution network, characterized in that, include: Acquire new energy power generation data and distribution network load data in the area where the distribution network is located; Based on the aforementioned new energy power generation data and load data, an optimization model is constructed with the goal of maximizing the weighted sum of the access capacity and quality power of new energy sources. Based on the optimization model, by changing the weight ratio of access capacity and quality power, a variety of planning schemes are calculated; the planning schemes include the access capacity of new energy sources at each node in the distribution network. Each planning scheme corresponds to a specific weighting ratio; Calculate the evaluation indicators for each planning scheme; The evaluation indicators include: voltage over-limit risk level, overall node vulnerability, renewable energy generation utilization rate, renewable energy generation absorption rate, average line load rate, and overall distribution network loss rate. Based on the evaluation indicators corresponding to each planning scheme, the carrying capacity of new energy under each planning scheme is evaluated, including: based on the evaluation indicators corresponding to each planning scheme, combined with the state-space evaluation method, calculating the comprehensive performance index and balance index of each planning scheme, wherein the comprehensive performance index is used to characterize the strength of the new energy carrying capacity of the distribution network, and the balance index is used to characterize the degree of balance of each evaluation index; based on the comprehensive performance index and balance index of each planning scheme, the carrying capacity of new energy in each planning scheme is evaluated. The quality power is obtained by the following formula; ； in, Indicates the quality power of distributed photovoltaic power in the distribution network; The annual quality power of new energy consumption; Annual power purchase capacity for the power distribution network; This refers to the annual power sales of the power distribution network. Let be the probability of the s-th discrete scene. For the actual output of the new energy source at the i-th node in the s-th scenario, The power purchased by the distribution network from the upper-level grid in the s-th scenario In the s-th scenario, the power sold by the distribution network to the upper-level grid is m, where m is the number of discrete scenarios and N is the number of distribution network nodes.

2. The method for evaluating the carrying capacity of a distribution network according to claim 1, characterized in that, The optimization model, constructed based on the new energy power generation data and load data, aims to maximize the weighted sum of the new energy access capacity and quality power, including: Based on the new energy power output data and load data, data fitting is performed to determine multiple discrete scenarios and the probability of each discrete scenario occurring; each discrete scenario includes new energy power generation data and load data at a point in time. Based on the multiple discrete scenarios and the probability of each discrete scenario occurring, an objective function is constructed with the access capacity of new energy sources at each node as the variable, aiming to maximize the weighted sum of the access capacity and quality power of new energy sources. The safe operation conditions of the distribution network are defined as constraints; the safe operation conditions of the distribution network include voltage constraints of each node of the distribution network, power constraints of each branch, and capacity constraints of single-point access to new energy sources. The optimization model is determined based on the objective function and the constraints.

3. The method for evaluating the carrying capacity of a distribution network according to claim 2, characterized in that, The process of performing data fitting based on the new energy output data and load data to determine multiple discrete scenarios and the probability of each discrete scenario occurring includes: Calculate the mean and variance of the new energy power generation data; and based on the mean and variance of the new energy power generation data, calculate the model parameters of the new energy power generation distribution probability model; Based on the model parameters, the probability distribution model of new energy power generation is determined; the probability distribution model of new energy power generation is used to limit the probability distribution of new energy power generation. Calculate the mean and variance of the load data; and based on the mean and variance of the load data, determine the normal distribution probability model of the distribution network load; the normal distribution probability model of the distribution network load is used to define the probability distribution of the distribution network load power. Based on the probability distribution model of new energy power generation and the probability distribution model of normal distribution network load, discrete analysis and data fitting are performed to determine the source-load joint scenario. The source-load joint scenario includes multiple discrete scenarios; each discrete scenario includes new energy power generation data and load data at a point in time.

4. The method for evaluating the carrying capacity of a distribution network according to claim 2, characterized in that, Based on the optimization model, by changing the weight ratio of access capacity and quality power, various planning schemes are calculated, including: The weight ratio between access capacity and quality power is determined as the first weight ratio. When the weight ratio of access capacity and quality power is the first weight ratio, the optimal solution of the objective function is solved based on the particle swarm optimization algorithm; the optimal solution is the access capacity of new energy sources at each node in the distribution network. By changing the weight ratio of the access capacity and quality power, and repeatedly solving for the optimal solution of the objective function, multiple planning schemes can be obtained.

5. The method for evaluating the carrying capacity of a distribution network according to claim 2, characterized in that, The calculation of the evaluation indicators corresponding to each planning scheme includes: For any planning scheme Based on the voltage of each node in each discrete scenario and the probability of each discrete scenario occurring, the degree of voltage limit exceedance risk is determined. Based on the impedance of each node and the new energy access capacity of each node, the overall node vulnerability is determined. Based on the actual power generation and installed capacity of new energy at each node in each discrete scenario, and the probability of each discrete scenario occurring, the utilization rate of new energy power generation is determined. Based on the actual power generation of new energy at each node in each discrete scenario and the power fed back from the distribution network to the upper-level power grid, as well as the probability of each discrete scenario occurring, the new energy power generation absorption rate is determined. The average load rate of the line is determined based on the actual transmission power of each line in each discrete scenario and the probability of each discrete scenario occurring. Based on the actual power generation of new energy sources and the actual load demand of each node in each discrete scenario, as well as the probability of each discrete scenario occurring, the comprehensive network loss rate of the distribution network is determined.

6. The method for evaluating the carrying capacity of a distribution network according to claim 1, characterized in that, The assessment of the carrying capacity of new energy sources in each planning scheme, based on the comprehensive performance indicators and balance indicators of each planning scheme, includes: If the difference between the comprehensive performance index of the first scheme and the comprehensive performance index of the second scheme is greater than the set value, then it is determined that the carrying capacity of the new energy corresponding to the first scheme is better than the carrying capacity of the new energy corresponding to the second scheme. If the difference between the comprehensive performance index of the first scheme and the comprehensive performance index of the second scheme is less than the set value, and the balance index of the first scheme is less than the balance index of the second scheme, then it is determined that the carrying capacity of the new energy corresponding to the first scheme is better than the carrying capacity of the new energy corresponding to the second scheme. Among them, the first option and the second option are either of the various planning options.

7. A device for evaluating the carrying capacity of a power distribution network, characterized in that, include: The communication module is used to acquire new energy power generation data and distribution network load data in the area where the distribution network is located; The processing module is used to construct an optimization model based on the new energy power generation data and load data, with the goal of maximizing the weighted sum of the access capacity and quality power of the new energy. Based on the optimization model, by changing the weight ratio of access capacity and quality power, a variety of planning schemes are calculated; the planning schemes include the access capacity of new energy sources at each node in the distribution network. Each planning scheme corresponds to a specific weighting ratio; Calculate the evaluation indicators corresponding to each planning scheme; the evaluation indicators include: voltage over-limit risk level, comprehensive node vulnerability, new energy power generation utilization rate, new energy power generation absorption rate, line average load rate, and distribution network comprehensive network loss rate; based on the evaluation indicators corresponding to each planning scheme, evaluate the carrying capacity of new energy under each planning scheme; The processing module is specifically used to calculate the comprehensive performance index and balance index of each planning scheme based on the evaluation index corresponding to each planning scheme and in combination with the state space evaluation method. The comprehensive performance index is used to characterize the strength of the new energy carrying capacity of the distribution network, and the balance index is used to characterize the balance of each evaluation index. Based on the comprehensive performance index and balance index of each planning scheme, the carrying capacity of new energy in each planning scheme is evaluated. The quality power is obtained by the following formula; ； in, Indicates the quality power of distributed photovoltaic power in the distribution network; The annual quality power of new energy consumption; Annual power purchase capacity for the power distribution network; This refers to the annual power sales of the power distribution network. Let be the probability of the s-th discrete scene. For the actual output of the new energy source at the i-th node in the s-th scenario, The power purchased by the distribution network from the upper-level grid in the s-th scenario In the s-th scenario, the power sold by the distribution network to the upper-level grid is m, where m is the number of discrete scenarios and N is the number of distribution network nodes.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor being configured to invoke and run the computer program stored in the memory to perform the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6 above.

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