Hybrid power distribution network stochastic load flow calculation method and system adaptive to source load uncertainty
By conducting uncertainty analysis of the wind and light load and building a combined wind and light scene generation model, and combining the alternating iteration method to establish a deterministic flow calculation model of AC and DC hybrid distribution network, it solves the uncertainty and timing of source load in the existing technology. The characteristic problem realizes accurate random current calculation of medium and low voltage AC and DC distribution networks.
Patent Information
- Application Number
- CN202411445784.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-16
- Publication Date
- 2025-02-18
AI Technical Summary
The prior art is difficult to effectively consider the impact of the timing changes of multiple distributed resource power and source load uncertainty on the random current of medium and low voltage AC and DC distribution networks, resulting in the calculation results ignoring the timing characteristics.
By conducting uncertainty analysis of the wind and light load, the probability distribution and cumulative distribution characteristics of the prediction error were obtained using the kernel density estimation method, and a combined wind and light scene generation model was constructed in combination with the Copula function. The European distance algorithm and fast previous generation elimination technology were used to reduce the scene. The alternating iteration method establishes a deterministic current calculation model for AC-DC hybrid distribution network, and performs random current calculations based on the generated scenery and light joint scenarios.
The random current calculation of medium and low voltage AC and DC distribution network is realized, and the timing change characteristics of the uncertainty of wind and light output and its impact on the current are fully considered, providing a more accurate random current calculation result of medium and low voltage AC and DC distribution network. .
Smart Images
Figure CN119482470A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of power systems, and in particular to a random power flow calculation method for a medium- and low-voltage AC / DC hybrid distribution network adapting to source and load uncertainty. Background Art
[0002] Compared with traditional power systems, new power systems have undergone profound changes in technical basis, working principles and functional structure.
[0003] With the continuous deepening of the construction of new power systems, the large-scale access of highly uncertain distributed resources represented by electric vehicles, distributed photovoltaics, and small energy storage to the distribution network has significantly changed the form and structure of the distribution network. Among them, the output of renewable energy represented by wind power and photovoltaics has extremely strong uncertainty. At the same time, there is a certain amount of fluctuating power load in the AC power grid, which poses a huge challenge to the stable operation of the AC / DC hybrid distribution system. Therefore, as the uncertainty of system operation increases, treating uncertain factors such as wind power, photovoltaics, and load as random variables is an effective method to study uncertain systems.
[0004] At present, there have been some studies on the calculation methods of random power flows, but most of them are based on specific single operating scenarios, and do not consider the scenarios of power timing changes of multiple distributed resources. They do not take into account the impact of source-load uncertainty in random power flows, and cannot perform random power flow calculations on medium and low voltage AC / DC distribution networks from the perspective of timing changes in operating characteristics, ignoring the timing characteristics. Summary of the invention
[0005] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a method and system for calculating random power flow in a hybrid distribution network that adapts to source-load uncertainty. By taking into account the influence of source-load uncertainty in random power flow, random power flow calculation is performed on medium and low voltage AC / DC distribution networks from the perspective of timing changes in operating characteristics.
[0006] According to one aspect of the present invention, there is provided a method for calculating random power flow in a hybrid distribution network adapting to source-load uncertainty, comprising: Uncertainty analysis of wind and solar load is conducted, and the probability distribution and cumulative distribution characteristics of wind and solar load prediction errors are obtained by using the kernel density estimation method. Based on the above distribution characteristics, combined with the Copula function, a wind-solar joint scene generation model is constructed, and the Euclidean distance algorithm and fast previous generation elimination technology are used to reduce the scenes to several typical ones; The alternating iteration method is used to obtain the deterministic power flow calculation model of the AC / DC hybrid distribution network, and combined with the generated wind-solar joint scenario, the random power flow of the AC / DC hybrid distribution network is calculated.
[0007] As a further technical solution, uncertainty analysis of wind and solar loads is conducted, including: Construct a wind and solar load prediction error model to process the uncertain variables in the system as the sum of the certain prediction value and the uncertain prediction error; The per-unit value of the wind and solar load forecast error for several historical days is calculated, and the probability distribution and cumulative distribution of the wind and solar load forecast error are obtained based on the kernel density estimation method; Based on the nonlinear least squares fitting function, the probability distribution of wind and solar load prediction errors is fitted to obtain the basic characteristics of the probability distribution of the three.
[0008] As a further technical solution, the probability distribution of the wind-solar load prediction error conforms to the generalized error distribution:
[0009] In the formula, v and is the shape parameter, Γ(·) represents the gamma function, and the independent variable x Take the per-unit value of the prediction error.
[0010] As a further technical solution, the steps of constructing the wind-solar joint scene generation model include: According to the probability distribution characteristics of the prediction errors of wind and solar, the t-Copula function is used to obtain the joint probability distribution of wind and solar output. Based on the probability distribution of wind and solar prediction errors conforming to the generalized error distribution, the sampling values are obtained by generating corresponding random numbers and performing an inverse transformation on the joint probability distribution; Based on the large number of sample values generated above, the fast predecessor elimination technology is used to calculate the Euclidean distance, delete the scenes with Euclidean distance less than the preset value, merge them into new scenes, and reduce the number of scenes to several typical ones.
[0011] As a further technical solution, the calculation steps of the deterministic power flow calculation model of the AC / DC hybrid distribution network include: Import the original node and branch data of AC and DC distribution networks; Perform AC distribution network power flow calculation until the power flow results converge; Assigning the AC connection node voltage calculated by the AC distribution network to the DC distribution network connection node; Carry out power flow calculation of DC distribution network until the power flow results converge; Assigning the active power of the DC connection node calculated by the DC distribution network to the AC distribution network connection node; The AC distribution network performs new power flow calculations according to the values assigned by the DC distribution network until convergence; If the absolute value of the difference between the two power flow calculations of the AC distribution network connection node is less than the convergence tolerance, the process ends; otherwise, the AC distribution network power flow calculation is performed again until the power flow result converges.
[0012] As a further technical solution, combined with the generated wind-solar joint scenario, the formula for calculating the random power flow of the AC / DC hybrid distribution network is:
[0013] in, β n For the n The probability of a typical scenario, n =1, 2, 3, 4, 5, U ni For the n The expected value of node i voltage obtained by power flow calculation in a typical scenario.
[0014] According to one aspect of the present invention, there is provided a hybrid distribution network stochastic power flow calculation system adapting to source-load uncertainty, comprising: The first calculation module is used to perform uncertainty analysis on wind and solar loads and obtain the probability distribution and cumulative distribution characteristics of wind and solar load prediction errors by using the kernel density estimation method; The second calculation module is used to build a wind-solar joint scene generation model based on the above distribution characteristics and combined with the Copula function, and use the Euclidean distance algorithm and fast previous generation elimination technology to reduce the scenes to several typical ones; The third calculation module is used to use the alternating iteration method to obtain the deterministic power flow calculation model of the AC / DC hybrid distribution network, and calculate the random power flow of the AC / DC hybrid distribution network in combination with the generated wind-solar joint scenario.
[0015] According to one aspect of the present invention specification, there is provided an electronic device, comprising: at least one processor, at least one memory and a communication interface; wherein the processor, the memory and the communication interface communicate with each other; the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the random power flow calculation method for a hybrid distribution network that adapts to source-load uncertainty.
[0016] According to one aspect of the present specification, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions enable the computer to execute the steps of the method for calculating random power flow in a hybrid distribution network that adapts to source-load uncertainty.
[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a random power flow calculation method for medium and low voltage AC / DC hybrid distribution network that rolls over time, which fully considers the time series variation characteristics of wind and solar power output uncertainty and its impact on power flow, including: performing uncertainty analysis on wind and solar power load, establishing a mathematical model of wind and solar power load prediction error, and using the kernel density estimation method to obtain the probability distribution and cumulative distribution characteristics of the wind and solar power load prediction error; combining the Copula function to build a wind and solar power joint scenario generation model, using the Euclidean distance algorithm and the fast previous generation elimination technology to reduce the scenarios to several typical ones; using the alternating iteration method to obtain a deterministic power flow calculation model for the AC / DC hybrid distribution network, and combining the generated wind and solar power joint scenarios to form a random power flow calculation method for medium and low voltage AC / DC distribution networks, the calculation results are as follows: Figure 4 , Figure 5 shown. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction is given below to the drawings used in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0019] Figure 1 A schematic flow chart of a random power flow calculation method for a hybrid distribution network adapting to source-load uncertainty provided by an embodiment of the present invention.
[0020] Figure 2 A schematic diagram of the deterministic power flow calculation steps for an AC / DC hybrid distribution network provided in an embodiment of the present invention.
[0021] Figure 3 A schematic diagram of the AC / DC hybrid distribution network topology provided in an embodiment of the present invention.
[0022] Figure 4 A schematic diagram of AC random power flow calculation results provided by an embodiment of the present invention.
[0023] Figure 5 A schematic diagram of DC random power flow calculation results provided by an embodiment of the present invention.
[0024] Figure 6 A schematic diagram of the structure of a hybrid distribution network stochastic power flow calculation system that adapts to source-load uncertainty provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0025] The terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions, for example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to the steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.
[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In addition, the technical features in the various embodiments or single embodiments provided by the present invention are arbitrarily combined with each other to form a new technical solution. This combination is not restricted by the sequence of steps and / or the structural composition mode, but must be based on the ability of ordinary technicians in this field to achieve. When the combination of technical solutions is contradictory or cannot be achieved, it should be considered that this combination of technical solutions does not exist and is not within the scope of protection required by the present invention.
[0027] Figure 1 This is a flow chart of a random power flow calculation method for a medium and low voltage AC / DC hybrid distribution network that is adaptable to source and load uncertainty according to an embodiment of the present invention. Figure 3 The diagram is a topology diagram of an AC / DC hybrid distribution network according to an embodiment of the present invention. Among them, based on a 69-node AC system, three DC sub-networks are added as the embodiment topology, the reference capacity of the AC network is 10MVA, and the voltage reference value is 12.66kV; the reference capacity of the DC network is 10MVA, and the voltage reference value is 10kV. The convergence accuracy of the AC and DC networks is 0.0001. At the AC distribution network node 2, the reference node 73 of the DC sub-network 1 is connected via VSC; at the AC distribution network node 15, the reference node 75 of the DC sub-network 2 is connected via VSC, wherein the DC sub-network nodes 76 and 77 are respectively connected to the relevant wind turbine and photovoltaic output; at the AC distribution network node 42, the reference node 84 of the DC sub-network 3 is connected via VSC. The method can be applied to Figure 3 The topology shown.
[0028] like Figure 1 As shown, the method includes: Step 1: Conduct uncertainty analysis on wind and solar load, establish a mathematical model of wind and solar load prediction error, and use the kernel density estimation method to obtain the probability distribution and cumulative distribution characteristics of wind and solar load prediction error.
[0029] Specifically, a prediction error model for wind, solar and load was established, and the uncertain variables in the system were treated as the sum of the certain prediction value and the uncertain prediction error. The Gaussian kernel function was used to estimate the probability density of the three as follows: (4) Let the random variable X 1 , X 2 , …, X n It is an independent sample drawn from the population, and its probability density function expression is as follows: (5) in, n is the sample size, h is the bandwidth, k ( x ) represents the kernel function, ( x - X i ) is the estimated point x To the event X i distance.
[0030] By integrating the probability density function, we can get the cumulative distribution function as follows: (6) Specifically, the probability distribution characteristics of the prediction errors of wind, solar and load are as follows: The wind and solar power output prediction error conforms to the generalized error distribution: (7) (8) in, v and is the shape parameter, when v =2, the generalized error distribution is a normal distribution. v <2, the generalized error distribution shows a characteristic distribution of "peaked and thick tail"; Γ(·) represents the gamma function. When this model is used to predict the error distribution, the independent variable x Take the per-unit value of the prediction error x =( P real – P forecast ) / P base , P base It is the total installed capacity connected to the system.
[0031] The load power prediction error conforms to the normal distribution: (9) in, μ is the mathematical expectation, which directly affects the position of the probability density function. σ is the variance, which directly affects the amplitude of the probability density function distribution.
[0032] Step 2: Combine the Copula function to build a wind-solar joint scene generation model, and use the Euclidean distance algorithm and fast predecessor elimination technology to reduce the scenes to several typical ones.
[0033] Specifically, the steps for generating and reducing the wind-solar combined output scenario include: Step 1.1, based on the basic characteristics of the probability distribution of wind and solar prediction errors, the t-Copula function is used to obtain the joint probability distribution of wind and solar output; Step 1.2, based on the fact that the probability distribution of wind and solar prediction errors conforms to the generalized error distribution, the sampling value is obtained by generating corresponding random numbers and performing an inverse transformation on the joint probability distribution; Step 1.3, use the fast previous generation elimination technology to calculate the Euclidean distance, delete the scenes with small Euclidean distance, merge them into new scenes, and reduce the number of scenes to the required number.
[0034] Step 3: Using the alternating iteration method, the deterministic power flow calculation model of the AC / DC hybrid distribution network is obtained, and combined with the generated wind-solar joint scenario, a random power flow calculation method for medium and low voltage AC / DC distribution networks is formed. Specifically, Figure 2 As shown, the specific steps of the alternating iteration method include: Step 3.1, import the original node and branch data of the AC and DC distribution network; Step 3.2, perform AC distribution network power flow calculation until the power flow results converge; Step 3.3, assigning the AC connection node voltage calculated by the AC distribution network to the DC distribution network connection node; Step 3.4, performing power flow calculation of the DC distribution network until the power flow results converge; Step 3.5, assigning the active power of the DC connection node calculated by the DC distribution network to the AC distribution network connection node; Step 3.6, the AC distribution network performs a new power flow calculation according to the assignment of the DC distribution network until convergence; Step 3.7: If the absolute value of the difference between the two power flow calculations of the AC distribution network connection node is less than the convergence tolerance, the process ends; otherwise, the process returns to step 3.2.
[0035] Specifically, combined with the generated wind and solar power combined output scenario, the formula for random power flow calculation is as follows: (10) In the formula, taking the node voltage amplitude of power flow calculation as an example, β n are the probabilities of five typical scenarios, U ni For the n Nodes obtained by power flow calculation in a typical scenario i Expected voltage value ( n =1, 2, 3, 4, 5).
[0036] The method provided by the present invention can adapt to the uncertainty characteristics of source and load, consider the temporal influence of wind and solar power output, and obtain the random power flow calculation results of medium and low voltage AC and DC hybrid distribution network rolling over time. Figure 4 , Figure 5 shown.
[0037] The implementation basis of each embodiment of the present invention is to implement programmed processing through a device with a processor function. Therefore, in engineering practice, the technical solutions and functions of each embodiment of the present invention are encapsulated into various modules. Based on this reality, on the basis of the above embodiments, an embodiment of the present invention provides a hybrid distribution network stochastic power flow calculation system that adapts to source-load uncertainty, and the system is used to execute the hybrid distribution network stochastic power flow calculation method that adapts to source-load uncertainty in the above method embodiment.
[0038] See also Figure 6 The system includes: a first calculation module, which is used to perform uncertainty analysis on wind and solar loads, and use the kernel density estimation method to obtain the probability distribution and cumulative distribution characteristics of wind and solar load prediction errors; a second calculation module, which is used to build a wind-solar joint scenario generation model based on the above distribution characteristics and combined with the Copula function, and use the Euclidean distance algorithm and fast predecessor elimination technology to reduce the scenarios to several typical ones; a third calculation module, which is used to use the alternating iteration method to obtain a deterministic power flow calculation model of the AC / DC hybrid distribution network, and calculate the random power flow of the AC / DC hybrid distribution network in combination with the generated wind-solar joint scenario.
[0039] The hybrid distribution network stochastic power flow calculation system adapted to source-load uncertainty provided by the embodiment of the present invention adopts Figure 6 Several modules in the random power flow are used to calculate the random power flow of medium and low voltage AC and DC distribution networks from the perspective of the timing changes of operating characteristics by taking into account the influence of source and load uncertainty in the random power flow.
[0040] It should be noted that the system embodiment provided by the present invention is used to implement the methods in the above method embodiment as well as the methods in other method embodiments provided by the present invention. The only difference is that the corresponding functional modules are set. The principle is basically the same as the principle of the above system embodiment provided by the present invention. As long as the technical personnel in this field refer to the specific technical solutions in other method embodiments on the basis of the above system embodiment, obtain the corresponding technical means and the technical solutions composed of these technical means by combining the technical features, and on the premise of ensuring the practicality of the technical solutions, improve the modules in the above system embodiment to obtain the corresponding system class embodiments, which are used to implement the methods in other method class embodiments. For example: Based on the content of the above system embodiment, as a preferred embodiment, in the hybrid distribution network stochastic power flow calculation system adapted to source-load uncertainty provided in the embodiment of the present invention, the first calculation module is also used to execute the following instructions: Construct a wind and solar load prediction error model to process the uncertain variables in the system as the sum of the certain prediction value and the uncertain prediction error; The per-unit value of the wind and solar load forecast error for several historical days is calculated, and the probability distribution and cumulative distribution of the wind and solar load forecast error are obtained based on the kernel density estimation method; Based on the nonlinear least squares fitting function, the probability distribution of wind and solar load prediction errors is fitted to obtain the basic characteristics of the probability distribution of the three.
[0041] Based on the content of the above system embodiment, as a preferred embodiment, in the hybrid distribution network stochastic power flow calculation system adapted to source-load uncertainty provided in the embodiment of the present invention, the first calculation module is also used to execute the following instructions:
[0042] In the formula, v and is the shape parameter, Γ(·) represents the gamma function, and the independent variable x Take the per-unit value of the prediction error.
[0043] Based on the content of the above system embodiment, as a preferred embodiment, in the hybrid distribution network stochastic power flow calculation system adapted to source-load uncertainty provided in the embodiment of the present invention, the second calculation module is further used to execute the following instructions: According to the probability distribution characteristics of the prediction errors of wind and solar, the t-Copula function is used to obtain the joint probability distribution of wind and solar output. The probability distribution of wind and solar prediction errors conforms to the generalized error distribution. By generating corresponding random numbers and performing an inverse transformation on the joint probability distribution, we get the sampling value, which is the scenario of all wind and photovoltaic power combined output. Based on the large number of sample values generated above, in order to speed up the subsequent power flow calculation, the fast predecessor elimination technology is used to calculate the Euclidean distance, delete the scenes with Euclidean distance less than the preset value, merge them into new scenes, and reduce the number of scenes to several typical ones.
[0044] Based on the content of the above system embodiment, as a preferred embodiment, in the hybrid distribution network stochastic power flow calculation system adapted to source-load uncertainty provided in the embodiment of the present invention, the third calculation module is further used to execute the following instructions: Import the original node and branch data of AC and DC distribution networks; Perform AC distribution network power flow calculation until the power flow results converge; Assigning the AC connection node voltage calculated by the AC distribution network to the DC distribution network connection node; Carry out power flow calculation of DC distribution network until the power flow results converge; Assigning the active power of the DC connection node calculated by the DC distribution network to the AC distribution network connection node; The AC distribution network performs new power flow calculations according to the values assigned by the DC distribution network until convergence; If the absolute value of the difference between the two power flow calculations of the AC distribution network connection node is less than the convergence tolerance, the process ends; otherwise, the AC distribution network power flow calculation is performed again until the power flow result converges.
[0045] Based on the content of the above system embodiment, as a preferred embodiment, in the hybrid distribution network stochastic power flow calculation system adapted to source-load uncertainty provided in the embodiment of the present invention, the third calculation module is further used to calculate the stochastic power flow of the AC / DC hybrid distribution network using the following formula: in, β n For the n The probability of a typical scenario, n =1, 2, 3, 4, 5, U ni For the n The expected value of node i voltage obtained by power flow calculation in a typical scenario.
[0046] Based on the same inventive concept as the above-mentioned embodiment, an embodiment of the present invention further provides an electronic device, comprising: at least one processor, at least one memory and a communication interface; wherein the processor, the memory and the communication interface communicate with each other; the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the hybrid distribution network stochastic power flow calculation method adapted to source-load uncertainty, including: Uncertainty analysis of wind and solar load is conducted, and the probability distribution and cumulative distribution characteristics of wind and solar load prediction errors are obtained by using the kernel density estimation method. Based on the above distribution characteristics, combined with the Copula function, a wind-solar joint scene generation model is constructed, and the Euclidean distance algorithm and fast previous generation elimination technology are used to reduce the scenes to several typical ones; The alternating iteration method is used to obtain the deterministic power flow calculation model of the AC / DC hybrid distribution network, and combined with the generated wind-solar joint scenario, the random power flow of the AC / DC hybrid distribution network is calculated.
[0047] The embodiment of the present invention further provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions enable the computer to execute the steps of the hybrid distribution network stochastic power flow calculation method adapted to source-load uncertainty, including: Uncertainty analysis of wind and solar load is conducted, and the probability distribution and cumulative distribution characteristics of wind and solar load prediction errors are obtained by using the kernel density estimation method. Based on the above distribution characteristics, combined with the Copula function, a wind-solar joint scene generation model is constructed, and the Euclidean distance algorithm and fast previous generation elimination technology are used to reduce the scenes to several typical ones; The alternating iteration method is used to obtain the deterministic power flow calculation model of the AC / DC hybrid distribution network, and combined with the generated wind-solar joint scenario, the random power flow of the AC / DC hybrid distribution network is calculated.
[0048] In summary, the present invention provides a random power flow calculation method for medium and low voltage AC / DC hybrid distribution networks that adapts to the uncertainty of source and load. First, uncertainty analysis of wind, solar and load is performed, with the aim of clarifying the probability distribution characteristics of wind, solar and load, so as to facilitate subsequent scenario generation. Secondly, the Copula function is used to generate the wind and solar joint output scenario, and at the same time, the fast previous generation elimination technology is used to reduce the scenario, with the aim of generating a wind and solar output scenario with typical characteristics and accelerating the subsequent random power flow calculation speed; finally, a deterministic power flow calculation model for AC / DC hybrid distribution networks is established, and a random power flow calculation method that rolls over time is obtained in combination with typical scenarios. The purpose is to consider the scenarios of various distributed resource power time series changes, and analyze the impact of operating characteristics time series changes on the power flow of medium and low voltage AC / DC distribution networks.
[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.
Claims
1. A random power flow calculation method for hybrid distribution network adapting to source-load uncertainty is characterized by: include: Uncertainty analysis of wind and solar load is conducted, and the probability distribution and cumulative distribution characteristics of wind and solar load prediction errors are obtained by using the kernel density estimation method. Based on the above distribution characteristics, combined with the Copula function, a wind-solar joint scene generation model is constructed, and the Euclidean distance algorithm and fast previous generation elimination technology are used to reduce the scenes to several typical ones; The alternating iteration method is used to obtain the deterministic power flow calculation model of the AC / DC hybrid distribution network, and combined with the generated wind-solar joint scenario, the random power flow of the AC / DC hybrid distribution network is calculated.
2. The method for calculating random power flow in a hybrid distribution network adapted to source-load uncertainty according to claim 1 is characterized in that: Uncertainty analysis of wind and solar loads, including: Construct a wind and solar load prediction error model to process the uncertain variables in the system as the sum of the certain prediction value and the uncertain prediction error; The per-unit value of the wind and solar load forecast error for several historical days is calculated, and the probability distribution and cumulative distribution of the wind and solar load forecast error are obtained based on the kernel density estimation method; Based on the nonlinear least squares fitting function, the probability distribution of wind and solar load prediction errors is fitted to obtain the basic characteristics of the probability distribution of the three.
3. The method for calculating random power flow in a hybrid distribution network adapted to source-load uncertainty according to claim 2 is characterized in that: The probability distribution of the wind and solar load prediction error conforms to the generalized error distribution: ， In the formula, v and is the shape parameter, Γ(·) represents the gamma function, and the independent variable x Take the per-unit value of the prediction error.
4. The method for calculating random power flow in a hybrid distribution network adapted to source-load uncertainty according to claim 1 is characterized in that: The steps of constructing the wind-solar joint scene generation model include: According to the probability distribution characteristics of the prediction errors of wind and solar, the t-Copula function is used to obtain the joint probability distribution of wind and solar output. Based on the probability distribution of wind and solar prediction errors conforming to the generalized error distribution, the sampling values are obtained by generating corresponding random numbers and performing an inverse transformation on the joint probability distribution; Based on the large number of sample values generated above, the fast predecessor elimination technology is used to calculate the Euclidean distance, delete the scenes with Euclidean distance less than the preset value, merge them into new scenes, and reduce the number of scenes to several typical ones.
5. The method for calculating random power flow in a hybrid distribution network adapted to source-load uncertainty according to claim 1 is characterized in that: The calculation steps of the deterministic power flow calculation model of the AC / DC hybrid distribution network include: Import the original node and branch data of AC and DC distribution networks; Perform AC distribution network power flow calculation until the power flow results converge; Assigning the AC connection node voltage calculated by the AC distribution network to the DC distribution network connection node; Carry out power flow calculation of DC distribution network until the power flow results converge; Assigning the active power of the DC connection node calculated by the DC distribution network to the AC distribution network connection node; The AC distribution network performs new power flow calculations according to the values assigned by the DC distribution network until convergence; If the absolute value of the difference between the two power flow calculations of the AC distribution network connection node is less than the convergence tolerance, the process ends; otherwise, the AC distribution network power flow calculation is performed again until the power flow result converges.
6. The method for calculating random power flow in a hybrid distribution network adapted to source-load uncertainty according to claim 1 is characterized in that: Combined with the generated wind-solar joint scenario, the formula for calculating the random power flow of the AC / DC hybrid distribution network is: ， in, β n For the n The probability of a typical scenario, n =1, 2, 3, 4, 5, U ni For the n The expected value of node i voltage obtained by power flow calculation in a typical scenario.
7. A hybrid distribution network stochastic power flow calculation system adapting to source-load uncertainty is characterized by: include: The first calculation module is used to perform uncertainty analysis on wind and solar loads and obtain the probability distribution and cumulative distribution characteristics of wind and solar load prediction errors by using the kernel density estimation method; The second calculation module is used to build a wind-solar joint scene generation model based on the above distribution characteristics and combined with the Copula function, and use the Euclidean distance algorithm and fast previous generation elimination technology to reduce the scenes to several typical ones; The third calculation module is used to use the alternating iteration method to obtain the deterministic power flow calculation model of the AC / DC hybrid distribution network, and calculate the random power flow of the AC / DC hybrid distribution network in combination with the generated wind-solar joint scenario.
8. An electronic device, characterized in that: include: At least one processor, at least one memory and a communication interface; wherein the processor, memory and communication interface communicate with each other; The memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the random power flow calculation method for a hybrid distribution network adapting to source-load uncertainty as described in any one of claims 1 to 6.
9. A non-transitory computer readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the steps of the random power flow calculation method for a hybrid distribution network that adapts to source-load uncertainty as described in any one of claims 1 to 6.