Power flow calculation method and system for power distribution network, electronic equipment and storage medium

Abstract

The invention relates to the technical field of power system analysis, and particularly discloses a power distribution network load flow calculation method and system, electronic equipment and a storage medium, and the method comprises the steps: carrying out the load flow calculation through employing a Monte Carlo method, and obtaining a first probability density curve and a first cumulative distribution curve of a random variable of a power distribution network; by utilizing a semi-invariant and Gram series expansion method, selecting preset semi-invariants with different orders to carry out load flow calculation to obtain a second probability density curve and a second cumulative distribution curve of the random variable of the power distribution network under the preset semi-invariants of each order; and determining the order of the semi-invariant with the minimum curve difference value as the target order of the semi-invariant, and determining the second probability density curve and the second cumulative distribution curve of the random variable of the power distribution network under the semi-invariant of the target order as the load flow calculation result of the power distribution network. The calculation amount of power flow calculation of the power distribution network can be reduced, and valuable reference is provided for operation planning and decision making of the power distribution network.

Description

Background Art

[0002] The global demand for clean energy continues to grow, and governments around the world have introduced policies to encourage the development of renewable energy, which has promoted the large-scale construction and application of wind and solar power generation. Distributed power sources such as wind and solar are often distributed near distribution networks to reduce transmission losses and enhance power supply reliability, which has gradually increased the penetration rate of wind and solar energy in distribution networks and has had an important impact on the planning, operation and control of distribution networks.

[0003] At present, the Monte Carlo algorithm is mainly used for power flow calculation of distribution networks with uncertain factors. This is of course applicable when the scale of distributed power sources is small, but the large-scale access of distributed power sources requires the Monte Carlo algorithm to use a larger random sampling to solve the problem. Therefore, the power flow algorithm based on the Monte Carlo method is too slow when a large number of uncertain factors are involved, and the time will increase exponentially. This problem is more obvious in complex distribution networks.

[0004] In recent years, domestic and foreign scholars have actively explored the probability power flow algorithm for distribution network access to distributed power sources and other uncertain factors in view of the shortcomings of the Monte Carlo method in terms of calculation speed. At present, there are three main methods for the study of probability power flow algorithms: simulation method that changes the sampling method to reduce the calculation time and try not to affect the calculation accuracy, approximate method that adopts the idea of ​​approximation to approximate the probability density of variables, and analytical method that uses mathematical methods to simplify the convolution calculation of the complex relationship between each random variable. Table 1 lists the advantages and disadvantages of the above three methods in detail.

[0005] Table 1:

[0006]

[0007] Considering the limitations of the above algorithms and the fact that the current penetration rate of wind and solar power in the distribution network is constantly increasing, it is urgent to provide a technical solution to solve the above problems. Summary of the invention

[0008] In order to solve the above technical problems, the present invention provides a distribution network power flow calculation method, system, electronic device and storage medium.

[0009] In a first aspect, the present invention provides a method for calculating power flow in a distribution network, and the technical solution of the method is as follows:

[0010] Using the Monte Carlo method, a power flow calculation is performed on the target distribution network to obtain a first probability density curve and a first cumulative distribution curve of a random variable of the target distribution network;

[0011] By using the semi-invariant method and the Gram series expansion method, preset semi-invariants of different orders are selected to perform power flow calculation on the target distribution network, and a second probability density curve and a second cumulative distribution curve corresponding to the random variables of the target distribution network under each preset semi-invariant order are obtained respectively;

[0012] Based on the first probability density curve, the first cumulative distribution curve, the corresponding second probability density curve and the second cumulative distribution curve under each preset order semi-invariant, the semi-invariant order with the smallest curve difference value is determined as the target order of the semi-invariant, and the second probability density curve and the second cumulative distribution curve corresponding to the random variable of the target distribution network under the semi-invariant of the target order are determined as the target power flow calculation result of the target distribution network.

[0013] The beneficial effects of a distribution network power flow calculation method of the present invention are as follows:

[0014] The method of the present invention uses relatively simple expressions to approximate complex probability distributions, thereby reducing the amount of calculations for distribution network power flow calculations, improving the efficiency of distribution network power flow calculations, and providing valuable references for distribution network operation planning and decision-making.

[0015] On the basis of the above scheme, a distribution network power flow calculation method of the present invention can also be improved as follows.

[0016] In an optional manner, based on the first probability density curve, the first cumulative distribution curve, the corresponding second probability density curve and the second cumulative distribution curve under each order of the preset semi-invariant, the step of determining the semi-invariant order with the smallest curve difference value as the target order of the semi-invariant includes:

[0017] A qualitative analysis is performed on the first probability density curve, the first cumulative distribution curve, the corresponding second probability density curve and the second cumulative distribution curve under each preset order of semi-invariant to obtain the curve difference value corresponding to each order of semi-invariant, and the semi-invariant order with the smallest curve difference value is determined as the target order of the semi-invariant.

[0018] In an optional manner, the method further includes:

[0019] Using the Monte Carlo method, a first verified probability density curve and a first verified cumulative distribution curve of the random variables of the target distribution network in a preset high penetration distribution network scenario are calculated;

[0020] Calculate a second verification probability density curve and a second verification cumulative distribution curve of the random variables of the target distribution network in the preset high penetration distribution network scenario by using the semi-invariant method of the target order and the Gram series expansion method;

[0021] Performing quantitative analysis based on the first verification probability density curve, the first verification cumulative distribution curve, the second verification probability density curve, and the second verification cumulative distribution curve to obtain a verification result;

[0022] When the verification result is less than a preset value, a step is performed to determine the second probability density curve and the second cumulative distribution curve corresponding to the random variable of the target distribution network under the semi-invariant of the target order as the target power flow calculation result of the target distribution network.

[0023] In an optional manner, the target order semi-invariant is an eighth-order semi-invariant.

[0024] In a second aspect, the present invention provides a distribution network power flow calculation system, the technical solution of the system is as follows:

[0025] It includes: a first calculation module, a second calculation module and a third calculation module;

[0026] The first calculation module is used to: perform power flow calculation on the target distribution network using the Monte Carlo method to obtain a first probability density curve and a first cumulative distribution curve of the random variables of the target distribution network;

[0027] The second calculation module is used to: use the semi-invariant method and the Gram series expansion method to select preset semi-invariants of different orders to perform power flow calculation on the target distribution network, and obtain the second probability density curve and the second cumulative distribution curve corresponding to the random variable of the target distribution network under each preset semi-invariant of each order;

[0028] The third calculation module is used to: determine the semi-invariant order with the smallest curve difference value as the target order of the semi-invariant based on the first probability density curve, the first cumulative distribution curve, the preset second probability density curve and the second cumulative distribution curve corresponding to each order of the semi-invariant, and determine the second probability density curve and the second cumulative distribution curve corresponding to the random variable of the target distribution network under the semi-invariant of the target order as the target power flow calculation result of the target distribution network.

[0029] The beneficial effects of a power distribution network flow calculation system of the present invention are as follows:

[0030] The system of the present invention uses relatively simple expressions to approximate complex probability distributions, thereby reducing the amount of calculations for distribution network power flow calculations, improving the efficiency of distribution network power flow calculations, and providing valuable references for distribution network operation planning and decision-making.

[0031] Based on the above solution, a distribution network power flow calculation system of the present invention can also be improved as follows.

[0032] In an optional manner, the third calculation module is specifically used to:

[0033] A qualitative analysis is performed on the first probability density curve, the first cumulative distribution curve, the corresponding second probability density curve and the second cumulative distribution curve under each preset order of semi-invariant to obtain the curve difference value corresponding to each order of semi-invariant, and the semi-invariant order with the smallest curve difference value is determined as the target order of the semi-invariant.

[0034] In an optional manner, the method further includes: a verification module; the verification module is used to:

[0035] Using the Monte Carlo method, a first verified probability density curve and a first verified cumulative distribution curve of the random variables of the target distribution network in a preset high penetration distribution network scenario are calculated;

[0036] Calculate a second verification probability density curve and a second verification cumulative distribution curve of the random variables of the target distribution network in the preset high penetration distribution network scenario by using the semi-invariant method of the target order and the Gram series expansion method;

[0037] Performing quantitative analysis based on the first verification probability density curve, the first verification cumulative distribution curve, the second verification probability density curve, and the second verification cumulative distribution curve to obtain a verification result;

[0038] When the verification result is less than a preset value, a step is performed to determine the second probability density curve and the second cumulative distribution curve corresponding to the random variable of the target distribution network under the semi-invariant of the target order as the target power flow calculation result of the target distribution network.

[0039] In an optional manner, the target order semi-invariant is an eighth-order semi-invariant.

[0040] In a third aspect, a technical solution of an electronic device of the present invention is as follows:

[0041] The invention comprises a memory, a processor and a program stored in the memory and running on the processor. When the processor executes the program, the steps of the distribution network flow calculation method of the present invention are realized.

[0042] In a fourth aspect, the present invention provides a computer-readable storage medium having the following technical solution:

[0043] Instructions are stored in the computer-readable storage medium. When the computer-readable storage medium reads the instructions, the computer-readable storage medium executes the steps of the power distribution network flow calculation method of the present invention.

[0044] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present invention. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings. In the accompanying drawings:

[0046] Figure 1 A schematic diagram of a flow chart of an embodiment of a method for calculating power flow in a distribution network according to the present invention;

[0047] Figure 2 Schematic diagram of the calculation process of the Monte Carlo method;

[0048] Figure 3 It is a schematic diagram of the calculation process of the semi-invariant method and the Gram series expansion method;

[0049] Figure 4 The overall flow chart for distribution network power flow calculation and verification;

[0050] Figure 5 This is the relationship diagram between wind turbine active power and wind speed;

[0051] Figure 6 A schematic diagram of the structure of an embodiment of a power distribution network flow calculation system of the present invention;

[0052] Figure 7 The figure is a schematic structural diagram of an embodiment of an electronic device of the present invention. DETAILED DESCRIPTION

[0053] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0054] Figure 1A flow chart of an embodiment of a method for calculating power flow in a distribution network provided by the present invention is shown, and the method for calculating power flow in a distribution network can be executed by an electronic device such as a terminal device or a server. The terminal device can be any fixed or mobile terminal such as a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be a single server or a server cluster composed of multiple servers. Any electronic device can implement the method for calculating power flow in a distribution network by calling computer-readable instructions stored in a memory through a processor. Figure 1 As shown, the following steps are included:

[0055] S1. Using the Monte Carlo method, a power flow calculation is performed on a target distribution network to obtain a first probability density curve and a first cumulative distribution curve of a random variable of the target distribution network.

[0056] Among them, the target distribution network is a wind-solar high-penetration distribution network that needs to perform power flow calculation in this embodiment. Random variables include the voltage amplitude and phase angle of each bus node of the distribution network, as well as the power distribution of each branch, the power loss of the network, etc. The first probability density curve is the probability density curve of the random variables of the target distribution network, and the first cumulative distribution curve is the cumulative distribution curve of the random variables of the target distribution network. The Monte Carlo method is the most accurate calculation method in the probability power flow calculation. The main method of the Monte Carlo method is to perform a large number of repeated calculations to count the random events under which the number of repetitions is the largest, thereby counting the probability of various situations of random events. When the Monte Carlo method has a higher accuracy, it is necessary to increase the number of repeated calculations, but this will also cause the calculation time of the Monte Carlo method to increase significantly, and the calculation time will also increase with the increase in the scale of the distribution network. Therefore, in practical applications, in order to verify the accuracy of other probabilistic power flow algorithms, they are usually compared with the Monte Carlo method. In the Monte Carlo-based probabilistic power flow calculation, the calculation process of the Monte Carlo method is as follows Figure 2 shown.

[0057] S2. Using the semi-invariant method and the Gram series expansion method, select preset semi-invariants of different orders to perform power flow calculation on the target distribution network, and obtain the second probability density curve and the second cumulative distribution curve corresponding to the random variables of the target distribution network under each preset order semi-invariant.

[0058] Among them, the semi-invariant method can replace complex convolution calculations with simple addition and subtraction calculations, thereby greatly reducing the difficulty of operations and reducing calculation time. In theory, the larger the order of the semi-invariant method, the smaller the difference, but it is cumbersome to handle when the order is large, so the upper limit of the order can be set (such as: the preset different orders are eight orders). In the probability flow calculation based on the semi-invariant method and the Gram series expansion method in this embodiment, the random variables considered include loads that obey the normal distribution, conventional generators that obey the binomial distribution, photovoltaic power generation that obeys the Beta distribution, and wind power generation that obeys the Wei-bull distribution. The correlation between random variables and changes in the system topology are not considered. The specific calculation process is as follows: Figure 3 As shown. The second probability density curve is the probability density curve corresponding to the random variable of the target distribution network under the semi-invariant, and the second cumulative distribution curve is the cumulative distribution curve corresponding to the random variable of the target distribution network under the semi-invariant. The semi-invariant is a multi-order semi-invariant, and the random variable of the target distribution network corresponds to a different second probability density curve and second cumulative distribution curve under each preset order of semi-invariant.

[0059] S3. Based on the first probability density curve, the first cumulative distribution curve, the corresponding second probability density curve and the second cumulative distribution curve under each preset order of semi-invariant, the semi-invariant order with the smallest curve difference value is determined as the target order of the semi-invariant, and the second probability density curve and the second cumulative distribution curve corresponding to the random variable of the target distribution network under the semi-invariant of the target order are determined as the target power flow calculation result of the target distribution network.

[0060] Among them, the target order semi-invariant is an eighth-order semi-invariant.

[0061] In S3, specifically:

[0062] The step of determining the semi-invariant order with the smallest curve difference value as the target order of the semi-invariant based on the first probability density curve, the first cumulative distribution curve, the preset second probability density curve and the second cumulative distribution curve corresponding to each order of the semi-invariant includes:

[0063] A qualitative analysis is performed on the first probability density curve, the first cumulative distribution curve, the corresponding second probability density curve and the second cumulative distribution curve under each preset order semi-invariant to obtain the curve difference value corresponding to each preset order semi-invariant, and the semi-invariant order with the smallest curve difference value is determined as the target order of the semi-invariant.

[0064] Among them, taking the first-order semi-invariant as an example, the first probability density curve, the first cumulative distribution curve, the second probability density curve corresponding to the first-order semi-invariant and the second cumulative distribution curve are qualitatively analyzed. When the difference between the first probability density curve and the second probability density curve corresponding to the first-order semi-invariant, and the difference between the first cumulative distribution curve and the second cumulative distribution curve corresponding to the first-order semi-invariant is small and the complexity is acceptable, it is determined that the first-order semi-invariant meets the requirements, and the first-order semi-invariant is determined as the semi-invariant of the target order; if the curve difference is large, it is determined that the first-order semi-invariant does not meet the requirements, then return to increase the order of the semi-invariant and continue the calculation until the requirements are met.

[0065] In an optional manner, the method further includes:

[0066] The Monte Carlo method is used to calculate a first verified probability density curve and a first verified cumulative distribution curve of the random variables of the target distribution network in a preset high penetration distribution network scenario.

[0067] Among them, the preset high-penetration distribution network scenarios include: increasing the number of components, accessing the same-source electric field, and accessing non-homologous electric fields. The proportion of wind and solar power is increased by increasing the number of components, accessing the same-source electric field, and accessing non-homologous electric fields, thereby verifying the applicability of the power flow algorithm under high wind-solar penetration rates.

[0068] The semi-invariant method of the target order and the Gram series expansion method are used to calculate a second verification probability density curve and a second verification cumulative distribution curve of the random variables of the target distribution network in the preset high penetration distribution network scenario.

[0069] A verification result is obtained by performing a quantitative analysis based on the first verification probability density curve, the first verification cumulative distribution curve, the second verification probability density curve and the second verification cumulative distribution curve.

[0070] The verification result includes the first verification result η1 and the second verification result H(F1, F2). By using the peak error calculation method, the abscissas corresponding to the peaks of the first verification probability density curve and the second verification probability density curve are compared and calculated. The specific calculation formula is: X1 is the abscissa of the peak of the first verification probability density curve, and X2 is the abscissa of the peak of the second verification probability density curve. The Hellinger distance is also an indicator to measure the difference between two probability distributions. For the first verification cumulative distribution curve F1(x) and the second verification cumulative distribution curve F2(x), the Hellinger distance is defined as: The value of H(F1,F2) is between 0 and 1. The smaller the value, the closer the two distributions are.

[0071] When the verification result is less than a preset value, a step is performed to determine the second probability density curve and the second cumulative distribution curve corresponding to the random variable of the target distribution network under the semi-invariant of the target order as the target power flow calculation result of the target distribution network.

[0072] Among them, the first verification result η1 and the second verification result H(F1, F2) correspond to a preset value respectively. When the first verification result η1 and the second verification result H(F1, F2) are both less than the corresponding preset values, the step of determining the second probability density curve and the second cumulative distribution curve corresponding to the random variables of the target distribution network under the semi-invariant of the target order as the target power flow calculation result of the target distribution network is executed.

[0073] The overall process of the technical solution of this embodiment is as follows Figure 4 It should be noted that, in this embodiment:

[0074] 1) Construct the probability model and semi-invariant of the random variables (uncertain input variables) of the target distribution network. The probability model is used for power flow calculation using the Monte Carlo method, and the semi-invariant is used for power flow calculation using the semi-invariant method and the Gram series expansion method.

[0075] ① Load probability model and its semi-invariants.

[0076] The load is modeled using normal distribution, and the probability density of load fluctuation active and reactive power is modeled as shown in formula (1):

[0077]

[0078] Among them, u P is the expected value of active power, u Q is the expected value of reactive power, σ P is the standard deviation of active power, σ Q is the standard deviation of reactive power. For loads that obey normal distribution, the first-order semi-invariant must be the same as the mathematical expectation value, the second-order semi-invariant must be the same as the variance, and the third-order and higher semi-invariants can be ignored. The specific results are shown in formula (2):

[0079]

[0080] ②Probabilistic model of photovoltaic power generation and its semi-invariants.

[0081] Light intensity is random, but it also has certain rules, which can be obtained through a large amount of statistical data. Therefore, Beta distribution can be used to model light intensity, and its probability density is shown in formula (3):

[0082]

[0083] Where r is the light intensity (W / m 2 ), r max is the maximum light intensity (W / m 2 ), a and b are the shape parameters of the Beta distribution.

[0084] Since the output of the photovoltaic motor is proportional to the light intensity, the output of the photovoltaic motor can be calculated by the probability density function of the light intensity. The probability model of the photovoltaic motor output is shown in formula (4):

[0085] P i = rA i η (4)

[0086] Among them, P i is the active output of each photovoltaic power generation group, A i is the effective area of ​​each power generation component, and η is the energy conversion efficiency of the photovoltaic power generation group.

[0087] The characteristic of photovoltaic power generation is that it is highly uncertain and is affected by factors such as geographical environment and weather. Since photovoltaic output is proportional to light intensity, and light intensity obeys Beta distribution, the shape parameter calculation method in Beta distribution is shown in formula (5):

[0088]

[0089] Among them, μ is the average value of light intensity within a certain number of days, δ 2 is the variance of light intensity.

[0090] After obtaining the shape parameters of the Beta distribution, the origin moments of the photovoltaic output can be obtained through formula (6):

[0091]

[0092] After obtaining the origin moments of photovoltaic power output, the semi-invariants of photovoltaic power generation can be obtained through equation (7).

[0093]

[0094]

[0095] ③Probabilistic model of wind power generation and its semi-invariants.

[0096] The volatility of wind speed generally conforms to the Wei-bull distribution, and the Wei-bull distribution has excellent fitting effect on wind speed and is applicable to most areas. References and others also use the Wei-bull distribution to model wind speed. Based on the above reasons, the wind speed in this paper is also modeled using the Wei-bull distribution, where the probability density of wind speed is shown in formula (8):

[0097]

[0098] In formula (8), v is the wind speed in m / s; k and λ are its shape parameters and size parameters. The two parameters of Wei-bull distribution can be calculated using formulas (9) and (10):

[0099]

[0100]

[0101] Where μ is the mean wind speed, δ is the standard deviation of wind speed, and Γ is the Gamma function.

[0102] The core of wind turbine output power lies in the size of wind speed. After obtaining the distribution function of wind speed, when there is no sudden change in wind speed, the relationship between the active power output of the wind turbine and the size of wind speed is as follows: Figure 5 shown. Figure 5 Middle: V cut—in is the cut-in wind speed of the fan, V cut—out is the cut-out wind speed of the fan, that is, the fan can only use the wind speed within this range, V r is the rated wind speed, P r is the rated output of the fan. According to the figure, the relationship between the fan output and wind speed can be obtained as shown in formula (11):

[0103]

[0104] in, k2=-k1v cut-in Since the output of the wind turbine is related to the wind speed in equation (11), we only need to know the probability distribution of the wind speed in a certain area to obtain the probability distribution of the output power of the wind power according to equation (11). After obtaining the two parameters k and λ of the Wei-bull distribution according to equations (9) and (10), we can obtain the origin moments of each order of the wind turbine according to equation (12).

[0105]

[0106] Among them, similar to photovoltaic power generation, the semi-invariant of wind power generation can also be obtained according to formula (7).

[0107] ④Conventional generator probability model and semi-invariant.

[0108] Since this embodiment only considers the normal and fault conditions of conventional generators, the characteristics of binomial distribution are used for modeling, as shown in equations (13) and (14):

[0109]

[0110]

[0111] In the formula, m p is the availability of active power, m Q is the availability of reactive power. C P and C Q It is the rated capacity of active and reactive power.

[0112] When a conventional generator in the distribution network has C i Rated capacity, P i The active power of , then the γ-order central moment of its output power can be calculated as shown in formula (15):

[0113]

[0114] Formula (16) can be used to transform the central moments of various orders of conventional generators into semi-invariants of various orders of conventional generators.

[0115]

[0116]

[0117] ⑤Semi-invariant calculation of node voltage and branch power flow.

[0118] Since the probability density function of the sum of multiple random variables that do not affect each other is equal to the result of the convolution of the probability density function of a single random variable, but due to the excessive number of nodes and branches in the distribution network, the convolution operation will become complicated. The significance of using the semi-invariant method in this paper is to simplify the convolution calculation. Assume that the sum of the injected power disturbance of each node is W, and the calculation formula is:

[0119] ΔW=ΔW G *ΔW L *ΔW pv (17)

[0120] In equation (17), ΔW G , ΔW L , ΔW pv They are the random changes of four uncertain variables, and * represents convolution.

[0121] When the general power flow calculation method is used, the change of each node is related to the complex convolution calculation, which increases the calculation time. However, according to the characteristics of semi-invariants, the sum of the semi-invariants of each order of the random variables is equal to the semi-invariants of their sum. The convolution calculation of formula (17) can be transformed into the following calculation formula:

[0122]

[0123] In equation (18), ΔWG (γ) , ΔW L (γ) , ΔW pv (γ) They are the γ-order semi-invariants of the four uncertain variables. In this way, simple addition and subtraction operations between the semi-invariants replace the complex convolution calculations. Then, the semi-invariants of the node voltage and branch power flow can be obtained. The specific formula is shown in formula (19):

[0124]

[0125] In formula (19), S (γ) is a matrix composed of the γth power of each element of the sensitivity matrix, T0 (γ) It is a matrix composed of the γth power of each element of the conversion matrix, ΔX is the unbalanced amount of the node voltage, and Z is the disturbance component of the branch power flow.

[0126] 2) Experimental preparation.

[0127] The core of the Monte Carlo method is the selection of the number of repetitions. As the number of repetitions increases, the calculation accuracy will increase. However, too high a number of repetitions may have an adverse effect on the calculation. For engineering and technical applications, 5000 repetitions can usually meet the requirements. Based on this, this embodiment sets the number of repetitions of the Monte Carlo method to 6000 according to the actual research situation, and uses this as a benchmark to study the semi-invariant method.

[0128] The core of the semi-invariant + Gram series expansion method is the selection of the semi-invariant order. The larger the semi-invariant order, the more accurate the calculation result, but the calculation complexity will also increase geometrically. Although the calculation time is still short, it will cause a huge burden on other work. Therefore, it is very necessary to choose a suitable semi-invariant order when using the semi-invariant method. Taking the Monte Carlo method with N=6000 as the standard, the known wind farm data and the known photovoltaic power station data are substituted into the distribution system for semi-invariant + Gram series expansion flow calculation. Comparing the semi-invariant + Gram series expansion methods of various orders with the Monte Carlo method can obtain the optimal semi-invariant order. This embodiment selects the eighth-order semi-invariant.

[0129] 3) The wind-solar ratio is increased by increasing the number of components, accessing the same source electric field, and accessing the non-homologous source electric field in three ways (scenarios), thereby verifying the applicability of the power flow algorithm under high wind-solar penetration rates.

[0130] ① Use two algorithms to calculate the power flow for three scenarios respectively.

[0131] Scenario 1: When the nodes of wind farms and photovoltaic farms are connected to the distribution system and remain unchanged, the number of wind turbines in the wind farm is increased by 1, 2, 3 times, etc., and the number of photovoltaic modules in the photovoltaic farm is also increased by 1, 2, 3 times, etc., respectively. The simulations are performed using the eighth-order semi-invariant + Gram series expansion method, and then the Monte Carlo method is used for simulation again for comparison.

[0132] Scenario 2: While keeping all parameters such as the number of wind turbines, rated output, expected wind speed in the wind farm and the number of PV panels, PV panel area, expected light intensity in the PV farm unchanged, the number of nodes connected to the wind farm and PV farm is increased by 1, 2, 3, etc., and the simulations are performed using the eighth-order semi-invariant + Gram series expansion method, and then the Monte Carlo method is used for simulation again for comparison.

[0133] Scenario 3: When the number of wind turbines in the wind farm and the number of PV panels in the PV farm remain unchanged, first change all the parameters of the wind farm and the PV farm so that they fluctuate by 20% respectively. Then increase the number of nodes connected to the wind farm and the PV farm after the parameter changes by 1, 2, 3, etc., and use the eighth-order semi-invariant + Gram series expansion method to simulate them respectively, and then use the Monte Carlo method to simulate them again for comparison.

[0134] ② Use indicators to evaluate the probability density function and cumulative distribution curve.

[0135] In the three scenarios, a large number of simulation results can be obtained by using the eighth-order semi-invariant + Gram series expansion method. The Monte Carlo method under the same conditions can also obtain the same number of calculation results for comparison, including the probability density curve and cumulative distribution curve of the required variables. For the probability density curve, the peak of the curve represents the maximum probability of a random variable taking a value near this point. Therefore, the evaluation index of the probability density curve should focus on the peak value. Therefore, this paper proposes the peak error to evaluate the peak value of the probability density curve obtained by the eighth-order semi-invariant + Gram series expansion method; for the cumulative distribution curve, the overall similarity of the curve is more important, so the Hellinger distance can be used to measure the difference between two cumulative distribution curves under the same conditions.

[0136] This embodiment proposes a power flow calculation and verification method for a distribution network under high wind-solar penetration. This embodiment determines the number of repeated sampling times of the Monte Carlo method, and uses this as a benchmark to select the semi-invariant order for the semi-invariant + Gram series expansion method. Under the same conditions, two algorithms are used to obtain the probability density curve and cumulative distribution curve of the random variable, and then a qualitative analysis shows that the eighth-order semi-invariant + Gram series expansion method is the best. The wind-solar penetration rate is improved by increasing the number of components, connecting to the same source electric field, and connecting to a non-homologous electric field. The proposed method and the Monte Carlo method are used to perform power flow calculation in a variety of scenarios. The curves obtained by the proposed method and the curves obtained by the Monte Carlo method are quantitatively compared and analyzed using two indicators to verify the applicability of the proposed method in a high-penetration distribution network.

[0137] The technical solution of this embodiment uses relatively simple expressions to approximate complex probability distributions, thereby reducing the amount of calculations for distribution network power flow calculations, improving the efficiency of distribution network power flow calculations, and providing valuable reference for distribution network operation planning and decision-making.

[0138] Figure 6 FIG. 2 is a schematic diagram showing a structure of an embodiment of a power distribution network flow calculation system 200 provided by the present invention. Figure 6 As shown, the system 200 includes: a first calculation module 210, a second calculation module 220 and a third calculation module 230;

[0139] The first calculation module 210 is used to: perform power flow calculation on the target distribution network using the Monte Carlo method to obtain a first probability density curve and a first cumulative distribution curve of the random variables of the target distribution network;

[0140] The second calculation module 220 is used to: use the semi-invariant method and the Gram series expansion method to select preset semi-invariants of different orders to perform power flow calculation on the target distribution network, and obtain the second probability density curve and the second cumulative distribution curve corresponding to the random variable of the target distribution network under each preset semi-invariant of each order;

[0141] The third calculation module 230 is used to: determine the semi-invariant order with the smallest curve difference value as the target order of the semi-invariant based on the first probability density curve, the first cumulative distribution curve, the preset second probability density curve and the second cumulative distribution curve corresponding to each order of the semi-invariant, and determine the second probability density curve and the second cumulative distribution curve corresponding to the random variable of the target distribution network under the semi-invariant of the target order as the target power flow calculation result of the target distribution network.

[0142] In an optional manner, the third calculation module 230 is specifically used to:

[0143] A qualitative analysis is performed on the first probability density curve, the first cumulative distribution curve, the corresponding second probability density curve and the second cumulative distribution curve under each preset order semi-invariant to obtain the curve difference value corresponding to each preset order semi-invariant, and the semi-invariant order with the smallest curve difference value is determined as the target order of the semi-invariant.

[0144] In an optional manner, the method further includes: a verification module; the verification module is used to:

[0145] Using the Monte Carlo method, a first verified probability density curve and a first verified cumulative distribution curve of the random variables of the target distribution network in a preset high penetration distribution network scenario are calculated;

[0146] Calculate a second verification probability density curve and a second verification cumulative distribution curve of the random variables of the target distribution network in the preset high penetration distribution network scenario by using the semi-invariant method of the target order and the Gram series expansion method;

[0147] Performing quantitative analysis based on the first verification probability density curve, the first verification cumulative distribution curve, the second verification probability density curve, and the second verification cumulative distribution curve to obtain a verification result;

[0148] When the verification result is less than a preset value, a step is performed to determine the second probability density curve and the second cumulative distribution curve corresponding to the random variable of the target distribution network under the semi-invariant of the target order as the target power flow calculation result of the target distribution network.

[0149] In an optional manner, the target order semi-invariant is an eighth-order semi-invariant.

[0150] It should be noted that the beneficial effects of the distribution network flow calculation system provided in the above embodiment are the same as the beneficial effects of the distribution network flow calculation method described above, which will not be described in detail here. In addition, when the system provided in the above embodiment realizes its functions, it only takes the division of the above functional modules as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual conditions to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be described in detail here.

[0151] Among them, the distribution network flow calculation system of the present invention can be a computer program (including program code) running in a computer device. For example, the distribution network flow calculation system of the present invention is an application software that can be used to execute the corresponding steps in the distribution network flow calculation method of the present invention.

[0152] In some embodiments, the distribution network flow calculation system of the present invention can be implemented in a combination of software and hardware. As an example, the distribution network flow calculation system of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the distribution network flow calculation method of the present invention. For example, the processor in the form of a hardware decoding processor can adopt one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs) or other electronic components.

[0153] The modules involved in the embodiments of the present invention may be implemented in software or hardware. The name of a module does not limit the module itself in some cases.

[0154] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any of the above-mentioned distribution network flow calculation methods is implemented. That is to say, an electronic device according to an embodiment of the present invention may include but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the distribution network flow calculation method shown in any embodiment of the present invention by calling the computer program.

[0155] In an alternative embodiment, an electronic device is provided, such as Figure 7 As shown, Figure 7 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may also include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0156] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0157] The bus 4002 may include a path to transmit information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 In the figure, only one thick line is used to represent the bus 4002, but this does not mean that there is only one bus or one type of bus.

[0158] The memory 4003 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0159] The memory 4003 is used to store the application code (computer program) for executing the solution of the present invention, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the application code stored in the memory 4003 to implement the content shown in the above method embodiment.

[0160] Among them, the electronic device can also be a terminal device, and the terminal device can be any terminal device that can install applications and access web pages through applications, including at least one of a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a smart TV, and a smart car device.

[0161] It should be noted that Figure 7 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0162] A computer-readable storage medium according to an embodiment of the present invention stores a computer program, and when the computer program is executed by a processor, any of the above-mentioned distribution network power flow calculation methods is implemented.

[0163] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0164] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device performs the above-mentioned distribution network flow calculation method.

[0165] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0166] It should be understood that the flow charts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the module, the program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0167] The computer-readable storage medium provided by the embodiment of the present invention may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or component.

[0168] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0169] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the disclosure scope involved in the present invention is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) to form a technical solution.

[0170] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects and represent the definition of a specific order or sequence. The order of use of similar objects can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0171] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0172] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for calculating power flow in a distribution network, characterized in that: include: Using the Monte Carlo method, a power flow calculation is performed on the target distribution network to obtain a first probability density curve and a first cumulative distribution curve of a random variable of the target distribution network; By using the semi-invariant method and the Gram series expansion method, preset semi-invariants of different orders are selected to perform power flow calculation on the target distribution network, and a second probability density curve and a second cumulative distribution curve corresponding to the random variables of the target distribution network under each preset semi-invariant order are obtained respectively; Based on the first probability density curve, the first cumulative distribution curve, the corresponding second probability density curve and the second cumulative distribution curve under each preset order semi-invariant, the semi-invariant order with the smallest curve difference value is determined as the target order of the semi-invariant, and the second probability density curve and the second cumulative distribution curve corresponding to the random variable of the target distribution network under the semi-invariant of the target order are determined as the target power flow calculation result of the target distribution network.

2. The method for calculating power flow in a distribution network according to claim 1, characterized in that: The step of determining the semi-invariant order with the smallest curve difference value as the target order of the semi-invariant based on the first probability density curve, the first cumulative distribution curve, the preset second probability density curve and the second cumulative distribution curve corresponding to each order of the semi-invariant includes: A qualitative analysis is performed on the first probability density curve, the first cumulative distribution curve, the corresponding second probability density curve and the second cumulative distribution curve under each preset order of semi-invariant to obtain the curve difference value corresponding to each order of semi-invariant, and the semi-invariant order with the smallest curve difference value is determined as the target order of the semi-invariant.

3. The method for calculating power flow in a distribution network according to claim 1, characterized in that: Also includes: Using the Monte Carlo method, a first verified probability density curve and a first verified cumulative distribution curve of the random variables of the target distribution network in a preset high penetration distribution network scenario are calculated; Calculate a second verification probability density curve and a second verification cumulative distribution curve of the random variables of the target distribution network in the preset high penetration distribution network scenario by using the semi-invariant method of the target order and the Gram series expansion method; Performing quantitative analysis based on the first verification probability density curve, the first verification cumulative distribution curve, the second verification probability density curve, and the second verification cumulative distribution curve to obtain a verification result; When the verification result is less than a preset value, a step is performed to determine the second probability density curve and the second cumulative distribution curve corresponding to the random variable of the target distribution network under the semi-invariant of the target order as the target power flow calculation result of the target distribution network.

4. The method for calculating power flow in a distribution network according to any one of claims 1 to 3, characterized in that: The target order semi-invariant is an eighth order semi-invariant.

5. A distribution network power flow calculation system, characterized in that: include: A first computing module, a second computing module, and a third computing module; The first calculation module is used to: perform power flow calculation on the target distribution network using the Monte Carlo method to obtain a first probability density curve and a first cumulative distribution curve of the random variables of the target distribution network; The second calculation module is used to: use the semi-invariant method and the Gram series expansion method to select preset semi-invariants of different orders to perform power flow calculation on the target distribution network, and obtain the second probability density curve and the second cumulative distribution curve corresponding to the random variable of the target distribution network under each preset semi-invariant of each order; The third calculation module is used to: determine the semi-invariant order with the smallest curve difference value as the target order of the semi-invariant based on the first probability density curve, the first cumulative distribution curve, the preset second probability density curve and the second cumulative distribution curve corresponding to each order of the semi-invariant, and determine the second probability density curve and the second cumulative distribution curve corresponding to the random variable of the target distribution network under the semi-invariant of the target order as the target power flow calculation result of the target distribution network.

6. The power distribution network flow calculation system according to claim 5, characterized in that: The third calculation module is specifically used for: A qualitative analysis is performed on the first probability density curve, the first cumulative distribution curve, the corresponding second probability density curve and the second cumulative distribution curve under each preset order of semi-invariant to obtain the curve difference value corresponding to each order of semi-invariant, and the semi-invariant order with the smallest curve difference value is determined as the target order of the semi-invariant.

7. The power distribution network flow calculation system according to claim 5, characterized in that: Also includes: Verification module; the verification module is used to: Using the Monte Carlo method, a first verified probability density curve and a first verified cumulative distribution curve of the random variables of the target distribution network in a preset high penetration distribution network scenario are calculated; Calculate a second verification probability density curve and a second verification cumulative distribution curve of the random variables of the target distribution network in the preset high penetration distribution network scenario by using the semi-invariant method of the target order and the Gram series expansion method; Performing quantitative analysis based on the first verification probability density curve, the first verification cumulative distribution curve, the second verification probability density curve, and the second verification cumulative distribution curve to obtain a verification result; When the verification result is less than a preset value, a step is performed to determine the second probability density curve and the second cumulative distribution curve corresponding to the random variable of the target distribution network under the semi-invariant of the target order as the target power flow calculation result of the target distribution network.

8. The distribution network power flow calculation system according to any one of claims 5 to 7, characterized in that: The target order semi-invariant is an eighth order semi-invariant.

9. An electronic device, characterized in that: The electronic device includes a processor, the processor is coupled to a memory, the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor so that the electronic device implements the distribution network flow calculation method as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: At least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by the processor, so that the computer-readable storage medium implements the distribution network power flow calculation method according to any one of claims 1 to 4.

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