Method and system for evaluating maximum bearing capacity of distributed photovoltaic access power distribution network

Abstract

The invention relates to the technical field of distributed photovoltaic grid connection, and particularly discloses a method and a system for evaluating the maximum bearing capacity of a distributed photovoltaic access power distribution network, and the method comprises the steps: generating a plurality of random scenes; for each random scene, respectively obtaining a probability density function and a cumulative distribution function of node voltage; determining a node voltage confidence interval based on the probability density function and the cumulative distribution function of the node voltage, and calculating a node voltage maximum out-of-limit probability and a power distribution network voltage average out-of-limit probability; evaluating the voltage quality of the current power distribution network; if the voltage quality of the power distribution network meets the requirement, the photovoltaic capacity is further increased, and load flow calculation and voltage quality evaluation are carried out again; and if not, recording the photovoltaic capacity at the moment as the maximum bearing capacity of the distributed photovoltaic access power distribution network in the random scene. According to the method, the uncertainty in operation of the power distribution network is considered, and a probability distribution result closer to reality than that of traditional deterministic load flow calculation is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed photovoltaic grid-connected technology, and in particular to a method and system for evaluating the maximum carrying capacity of a distributed photovoltaic access distribution network. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Distributed photovoltaic power generation has developed rapidly as a clean and renewable energy form. Distributed photovoltaic systems are usually installed on the user side, such as roofs and building facades, which can absorb solar energy nearby, reduce transmission losses, and improve energy efficiency. However, the large-scale access of distributed photovoltaics has also brought new challenges to the distribution network.

[0004] Distributed photovoltaic output is volatile and unpredictable. When photovoltaic power generation exceeds local electricity demand, excess electricity will flow back into the grid, which may cause the voltage level in the grid to increase, resulting in voltage exceeding the limit. At the same time, if the distributed photovoltaic system fails to operate at an ideal unity power factor, it will inject additional reactive power into the grid, which will also change the voltage level of the grid, thereby affecting the power supply quality and stability of the grid. Therefore, it is crucial to reasonably plan and manage the access of distributed photovoltaics and accurately evaluate the maximum carrying capacity of the distribution network for distributed photovoltaics in order to maintain the voltage stability of the grid.

[0005] The maximum carrying capacity of distributed photovoltaics refers to the maximum total capacity of distributed photovoltaics that can be connected under the conditions of safe operation of the distribution network. For the maximum carrying capacity evaluation method of distributed photovoltaics, the prior art discloses a method of determining the carrying capacity of distributed photovoltaics through random scene simulation. However, the uncertainty of distributed photovoltaics is not considered in the calculation process, resulting in a certain deviation in the calculation results. In addition, the existing power flow calculation method has a complex calculation process and low calculation efficiency. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a maximum carrying capacity assessment method and system for distributed photovoltaic access to a distribution network. Through probabilistic power flow analysis, the risks of distributed photovoltaic access to the distribution network are identified, and the maximum carrying capacity of the distributed photovoltaic access to the distribution network is determined according to the voltage quality standard.

[0007] In some embodiments, the following technical solutions are adopted:

[0008] A method for evaluating the maximum carrying capacity of a distributed photovoltaic access distribution network, comprising:

[0009] Assuming that the nodes and phases of distributed photovoltaics connected to the distribution network have the same probability, the number of distributed photovoltaic nodes, the phase placement position and the capacity of each distributed photovoltaic are determined by random allocation, thereby generating multiple random scenarios;

[0010] For each random scenario, the uncertainty of distributed photovoltaic output is taken into account, and the probability density function and cumulative distribution function of the node voltage are calculated by using the semi-invariant-based probabilistic power flow method.

[0011] Based on the probability density function and cumulative distribution function of the node voltage, the node voltage confidence interval is determined, and the maximum node voltage over-limit probability and the average distribution network voltage over-limit probability are calculated;

[0012] The obtained node voltage confidence interval, distribution network voltage average over-limit probability and node voltage maximum over-limit probability are used as evaluation indicators to evaluate the current distribution network voltage quality.

[0013] If the voltage quality of the distribution network meets the requirements, the distributed photovoltaic capacity is increased, and the power flow calculation and voltage quality assessment are re-performed; if it does not meet the requirements, the photovoltaic capacity at this time is recorded as the maximum carrying capacity of the distributed photovoltaic access distribution network in this random scenario;

[0014] The maximum carrying capacity of distributed photovoltaics in all random scenarios is calculated, and the probability that the maximum carrying capacity of each distributed photovoltaic system appears in different capacity intervals is calculated, so as to determine the maximum carrying capacity of distributed photovoltaics connected to the distribution network.

[0015] As a further solution, the probability power flow method based on semi-invariants is used to calculate the probability density function and cumulative distribution function of the node voltage, which are as follows:

[0016] Assuming that F(x) is the cumulative distribution function of the node voltage X, construct the characteristic function of F(x);

[0017] The characteristic function of F(x) is logarithmized and expanded into a Maclaurin series at t=0, and the semi-invariants of the node voltage X are solved by Monte Carlo method.

[0018] Normalize the node voltage X to get the standardized node voltage ;

[0019] The standardized node voltage is calculated using the Gram-Charlier series expansion The probability density function and cumulative distribution function of are calculated; wherein the coefficients of the Gram-Charlier series expansion are calculated based on the semi-invariants of the node voltage X of each order;

[0020] The normalized node voltage The probability density function and cumulative distribution function of the node voltage X are obtained by performing standardized restoration.

[0021] As a further solution, the probability density function f of the node voltage X X (x) Specifically:

[0022]

[0023]

[0024] in, σ X is the standard deviation of the node voltage X, μ X is the expected value of the node voltage X, x is the value of the node voltage X; is the probability density function of the standardized node voltage X, φ(·) represents the probability density function of the node voltage that obeys the standard normal distribution; φ (n) (·) represents the result of the n-th order derivative of φ(·); A1, A2, …A n They represent the coefficients of the Gram-Charlier series expansion.

[0025] As a further solution, the cumulative distribution function F of the node voltage X x (x) Specifically:

[0026]

[0027]

[0028] in, is the cumulative distribution function of the normalized node voltage X.

[0029] As a further solution, the average voltage over-limit probability of the distribution network is specifically:

[0030]

[0031] Assuming that there are n nodes in the distribution network and the qualified interval of node voltage is set, the voltage over-limit probability of node i is expressed as P V_off , i=1,2,…,n; is the average voltage over-limit probability of the distribution network.

[0032] As a further solution, the node voltage confidence interval is determined as follows:

[0033] Given a confidence level of 1-CL, find the boundary value of the corresponding confidence interval on the obtained cumulative distribution function curve of each node voltage and Then we get the node voltage confidence interval

[0034] As a further solution, if it meets the requirements, increase the photovoltaic capacity and recalculate and evaluate the power flow, specifically:

[0035] If the average over-limit probability of distribution network voltage, the node voltage confidence interval and the maximum over-limit probability of node voltage all meet the set requirements, each DG node will increase the connected photovoltaic capacity according to the set proportion, and recalculate and evaluate the power flow.

[0036] In other embodiments, the following technical solutions are adopted:

[0037] A maximum carrying capacity assessment system for distributed photovoltaic access to a distribution network, comprising:

[0038] The random scenario generation module is used to assume that the nodes and phases of the distributed photovoltaic access to the distribution network have the same probability, and to determine the number of distributed photovoltaic nodes, the phase placement position and the capacity of each distributed photovoltaic by random allocation, thereby generating a variety of random scenarios;

[0039] The power flow calculation module is used to calculate the probability density function and cumulative distribution function of the node voltage for each random scenario, taking into account the uncertainty of distributed photovoltaic output, and using the semi-invariant-based probabilistic power flow method;

[0040] An indicator calculation module is used to determine the node voltage confidence interval based on the probability density function and cumulative distribution function of the node voltage, and calculate the maximum over-limit probability of the node voltage and the average over-limit probability of the distribution network voltage;

[0041] The voltage quality assessment module is used to assess the current distribution network voltage quality using the obtained node voltage confidence interval, the average over-limit probability of the distribution network voltage and the maximum over-limit probability of the node voltage as assessment indicators; if the distribution network voltage quality meets the requirements, the distributed photovoltaic capacity is increased, and the power flow calculation and voltage quality assessment are re-performed; if it does not meet the requirements, the photovoltaic capacity at this time is recorded as the maximum carrying capacity of the distributed photovoltaic access distribution network under this random scenario;

[0042] The maximum carrying capacity calculation module is used to calculate the maximum carrying capacity of distributed photovoltaics in all random scenarios, and calculate the probability that the maximum carrying capacity of each distributed photovoltaic system appears in different capacity intervals, so as to determine the maximum carrying capacity of distributed photovoltaics connected to the distribution network.

[0043] As a further solution, the probability power flow method based on semi-invariants is used to calculate the probability density function and cumulative distribution function of the node voltage, which are as follows:

[0044] Assuming that F(x) is the cumulative distribution function of the node voltage X, construct the characteristic function of F(x);

[0045] The characteristic function of F(x) is logarithmized and expanded into a Maclaurin series at t=0, and the semi-invariants of the node voltage X are solved by Monte Carlo method.

[0046] Normalize the node voltage X to get the standardized node voltage

[0047] The standardized node voltage is calculated using the Gram-Charlier series expansion The probability density function and cumulative distribution function of are calculated; wherein the coefficients of the Gram-Charlier series expansion are calculated based on the semi-invariants of the node voltage X of each order;

[0048] The normalized node voltage The probability density function and cumulative distribution function of the node voltage X are obtained by performing standardized restoration.

[0049] As a further solution, the probability density function f of the node voltage X X (x) Specifically:

[0050]

[0051]

[0052] in, σ X is the standard deviation of the node voltage X, μ X is the expected value of the node voltage X, x is the value of the node voltage X; is the probability density function of the standardized node voltage X, φ(·) represents the probability density function of the node voltage that obeys the standard normal distribution; φ (n) (·) represents the result of the n-th order derivative of φ(·); A1, A2, …A n They represent the coefficients of the Gram-Charlier series expansion.

[0053] As a further solution, the cumulative distribution function F of the node voltage X x (x) Specifically:

[0054]

[0055]

[0056] in, is the cumulative distribution function of the normalized node voltage X.

[0057] As a further solution, the average voltage over-limit probability of the distribution network is specifically:

[0058]

[0059] Assuming that there are n nodes in the distribution network and the qualified interval of node voltage is set, the voltage over-limit probability of node i is expressed as P V_off , i=1,2,…,n; is the average voltage over-limit probability of the distribution network.

[0060] As a further solution, the node voltage confidence interval is determined as follows:

[0061] Given a confidence level of 1-CL, find the boundary value of the corresponding confidence interval on the obtained cumulative distribution function curve of each node voltage and Then we get the node voltage confidence interval

[0062] As a further solution, if it meets the requirements, increase the photovoltaic capacity and recalculate and evaluate the power flow, specifically:

[0063] If the average over-limit probability of distribution network voltage, the node voltage confidence interval and the maximum over-limit probability of node voltage all meet the set requirements, each DG node will increase the connected photovoltaic capacity according to the set proportion, and recalculate and evaluate the power flow.

[0064] In other embodiments, the following technical solutions are adopted:

[0065] A terminal device includes a processor and a memory, wherein the processor is used to implement instructions; the memory is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executing the above-mentioned maximum carrying capacity assessment method for distributed photovoltaic access to a distribution network.

[0066] In other embodiments, the following technical solutions are adopted:

[0067] A computer-readable storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor of a terminal device and executing the above-mentioned maximum carrying capacity assessment method for connecting distributed photovoltaic power generation to a power distribution network.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] (1) The semi-invariant-based probabilistic power flow method can more accurately simulate and calculate the random fluctuations of voltage and power in the distribution network containing distributed photovoltaics by combining the Gram-Charlier series expansion. This method takes into account the uncertainty in the operation of the distribution network and provides a probability distribution result that is closer to reality than the traditional deterministic power flow calculation.

[0070] (2) The voltage quality of the distribution network can be quantitatively analyzed and evaluated using evaluation indicators such as the average over-limit probability of the distribution network voltage, the node voltage confidence interval, and the maximum over-limit probability of the node voltage. These indicators provide a quantitative measure of voltage stability and reliability.

[0071] Other features and advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 Flow chart of the maximum carrying capacity evaluation method of distributed photovoltaic access to the distribution network in an embodiment of the present invention. DETAILED DESCRIPTION

[0073] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0074] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0075] Embodiment 1

[0076] In one or more embodiments, a method for evaluating the maximum carrying capacity of a distributed photovoltaic access distribution network is disclosed, combining Figure 1 , specifically including the following process:

[0077] S101: Assuming that the access nodes and phases of distributed photovoltaic (hereinafter referred to as DG) have the same probability, the number of DG nodes, the phase placement position and the capacity of each DG are determined by random allocation, thereby generating multiple random scenarios.

[0078] It can be assumed that the access nodes and phases of DG have the same probability, thus modeling the allocation of DG as random allocation. In order to define the single-phase DG placement scenario on the N-node three-phase feeder, the node and phase placement are performed by randomly selecting integers in the range of {1, N} and {1, 3}. For the deployment of a three-phase balanced DG system, only nodes need to be randomly selected, and DG is connected to all three phases of each node.

[0079] After selecting the DG location, the installed capacity is determined based on historical data. Based on the historical data of the distribution of each DG capacity, the interval range of DG capacity is determined, and a capacity is randomly selected from it to be allocated to the allocated location; then, the capacity allocation is repeated until the total allocated DG capacity matches the penetration level under evaluation.

[0080] Repeat the random placement and addition of DGs until all DGs for the allocation are placed.

[0081] To evaluate the carrying capacity of the distribution network under different allocation schemes, a dual iterative approach is implemented: the DG allocation scheme is repeated at a constant penetration level, and the entire experiment is repeated at different penetration levels.

[0082] Finally, multiple random scenarios are generated, each of which corresponds to a DG allocation plan, including the locations where the DG is connected and the capacity of the connected DG.

[0083] S102: For each random scenario, the uncertainty of photovoltaic output is taken into account, and a probability power flow method based on semi-invariants is used to obtain the probability density function and cumulative distribution function of the node voltage respectively.

[0084] Specifically, the basic idea of ​​probabilistic power flow calculation based on semi-invariants is: perform power flow calculation according to the initial conditions of the system, and use the calculation result as the benchmark operating point; then superimpose the random interference of various factors of the system on the benchmark operating point, and describe it with various orders of semi-invariants; finally, calculate the various orders of semi-invariants of each state variable of the system, and calculate their probability distribution in combination with the series expansion.

[0085] This embodiment integrates the probability distribution model of photovoltaic output into the probabilistic power flow calculation, and considers the uncertainty of photovoltaic output in the power flow calculation. The operating variables of the system (such as node voltage) are regarded as random variables, and their probability distribution needs to be determined by considering all random factors in the system (including photovoltaic output). The probability model of photovoltaic output is integrated into the probabilistic power flow calculation, and the uncertainty of photovoltaic output is considered in the power flow calculation. The probability distribution of photovoltaic output is converted into a random disturbance of node injection power and incorporated into the power flow equation.

[0086] This embodiment generates random samples to simulate output fluctuations based on the probability distribution model and semi-invariant of distributed photovoltaic output. Each sample represents a possible output fluctuation situation. The simulated distributed photovoltaic output fluctuation is used as a random interference and superimposed on the reference operating point. For each random sample, the power flow calculation is re-performed to obtain the system state under the output fluctuation. For each simulated output fluctuation situation, the corresponding node voltage value is recorded. Repeat this process multiple times to obtain a large amount of node voltage sample data. Use the collected node voltage sample data to calculate its statistical characteristics, such as mean, variance, etc. Use the statistical characteristics of the node voltage to construct its probability distribution model.

[0087] Assume that F(x) is the cumulative distribution function of the node voltage X, and let t be an arbitrary real number, then the function g(x) = e jtx =cos(tx)+jsin(tx)The integral of F(x) over (-∞,+∞) is called the characteristic function ψ(t) of F(x), which can be expressed as:

[0088]

[0089] In the formula, E(·) represents the expectation, j is the imaginary unit, and j satisfies 2 =-1, t is a real number parameter in the characteristic function, usually used as an exponential coefficient in the characteristic function, x is the node voltage, and its cumulative distribution function is expressed as F(x).

[0090] Taking the logarithm of the characteristic function of F(x) and expanding it into a Maclaurin series at t=0, we can rearrange the following equation:

[0091]

[0092] In the formula, γ k is a semi-invariant, also known as a cumulant. The subscript k represents the order of the semi-invariant. O(t n ) is an infinitesimal variable.

[0093] In this embodiment, the semi-invariant is solved by Monte Carlo method, which first calculates the moment characteristic (expectation or variance) of the node voltage and then calculates its semi-invariant, as shown in formula (3) and formula (4).

[0094]

[0095]

[0096] In the formula, α k represents the k-order origin moment of the node voltage; x si represents the ith data point in the data sample of the node voltage; n represents the data sample capacity; It means taking a combination of i elements from (k-1) different elements, where i≤k-1.

[0097] The Gram-Charlier series expansion is a series expansion method based on the standard normal distribution function. It can approximately solve the probability density function and cumulative distribution function of the node voltage according to the semi-invariants of each order of the node voltage.

[0098] Assume that the expected value of node voltage X is μ X , standard deviation is σ X ,Depend on The normalized node voltage is calculated.

[0099] The standardized node voltage can be calculated based on the A-type Gram-Charlier series expansion The probability density function and cumulative distribution function of are shown in equation (5) and equation (6) respectively.

[0100]

[0101]

[0102] Where φ(·) represents the probability density function of the node voltage that obeys the standard normal distribution; φ (n) (·) represents the result of the n-th order derivative of φ(·); A1, A2, …A n The coefficients of the Gram-Charlier series expansion can be calculated according to formula (7):

[0103]

[0104] In the formula, represents the normalized node voltage In practical applications, the k-order semi-invariant of the node voltage X to be determined is first calculated. According to its linear properties, we can calculate Finally, according to formula (5) and formula (6), we can calculate the standardized random variable X and

[0105] To calculate the probability density function f of the node voltage X to be determined X (x) and the cumulative distribution function F X (x) needs to be normalized and restored, as shown in equations (8) and (9):

[0106]

[0107]

[0108] Based on the above process, the probability density function f of the node voltage X can be obtained: X (x) and the cumulative distribution function F X (x).

[0109] S103: determining the node voltage confidence interval based on the probability density function and cumulative distribution function of the node voltage, and calculating the maximum node voltage over-limit probability and the average distribution network voltage over-limit probability;

[0110] In this embodiment, a confidence level is selected, represented by 1-CL (for example, CL=0.05 represents 95% confidence). The boundary value of the corresponding confidence interval is found on the probability distribution curve of the node voltage, that is, and This means that the actual value of the node voltage will fall within this interval. For example, if the confidence level is 95%, there is a 95% probability that the node voltage value is within the calculated confidence interval, thereby obtaining quantitative information about the voltage fluctuation.

[0111] In order to evaluate the voltage over-limit situation of the distribution network containing distributed photovoltaics in steady-state operation, it is necessary to comprehensively consider the voltage over-limit situation of each node in the system. By calculating the average over-limit probability of the system voltage, the voltage over-limit situation of the entire system can be described.

[0112] Assuming that there are n nodes in the distribution network and the qualified interval of node voltage is set, the voltage limit probability of node i can be expressed as P V-off (i=1,2,…,n), the average voltage over-limit probability of the distribution network is:

[0113]

[0114] Due to the volatility of distributed photovoltaic power generation, the voltage fluctuation of the distribution network becomes more complicated. In order to identify areas where voltage safety may be problematic in the system, this embodiment introduces a node voltage maximum over-limit probability indicator:

[0115] P V_offmax =max(P V1_off ,P V2_off ,…,P Vn_off ) (11)

[0116] Among them, P V1_off ,P V2_off ,…,P Vn_off are the voltage exceeding limit probabilities of n nodes respectively.

[0117] When using the evaluation index system to evaluate the voltage quality of the distribution network, the qualified interval of the node voltage is specified to be [0.95, 1.05]. The probability of the voltage exceeding the limit at node i can be expressed as P(V i<0.95∪V i >1.05); the given confidence level (1-CL) was 0.95.

[0118] S104: Evaluate the current distribution network voltage quality using the average over-limit probability of the distribution network voltage, the node voltage confidence interval, and the maximum over-limit probability of the node voltage as evaluation indicators;

[0119] After obtaining the probability distribution of node voltage and branch power, the confidence level and voltage qualification standard or threshold are given. For example, for most power systems, the qualified voltage is usually within ±10% of the rated voltage. Through the probability density function and cumulative distribution function of the node voltage, the average voltage over-limit probability of the distribution network, the node voltage confidence interval and the maximum voltage over-limit probability of the node voltage can be calculated.

[0120] The voltage quality can be evaluated based on the calculated evaluation indicators. For example:

[0121] If the average probability of voltage exceeding the limit in the distribution network is very low, lower than a certain set threshold, it means that the voltage exceeding the limit in the entire system is less and the voltage quality is better.

[0122] The node voltage confidence interval can be used to evaluate the voltage stability. If the confidence interval is narrow and within the allowable voltage range, it means that the voltage fluctuation is small and the voltage quality is high.

[0123] When the average voltage over-limit probability of all nodes is lower than a certain threshold, it can be considered that the voltage quality meets the requirements.

[0124] When the confidence interval of the node voltage is completely within the specified voltage qualified interval and the confidence level meets the requirements, the voltage quality meets the requirements.

[0125] If the average voltage over-limit probability of the distribution network is too high and exceeds a certain threshold, it means that the voltage over-limit occurs frequently and the voltage quality does not meet the requirements.

[0126] If the node voltage confidence interval exceeds the specified voltage qualified interval, or the confidence interval is too wide, it indicates that the voltage fluctuation is too large and the voltage quality does not meet the requirements.

[0127] If the maximum node voltage over-limit probability exceeds a certain safety threshold, it indicates that there is a voltage safety risk and the voltage quality does not meet the requirements.

[0128] S105: If the voltage quality of the distribution network meets the requirements, it means that the system can accommodate more distributed photovoltaics at this time, and the photovoltaic capacity can be further increased. The power flow calculation and voltage quality assessment can be performed again according to the steps of S102-S104; when increasing the photovoltaic capacity, all connected photovoltaics can be increased uniformly according to the set step size.

[0129] If any of the above voltage quality assessment conditions are not met, it means that the distributed photovoltaic system has reached the saturation point or exceeded the saturation point. Further increasing the distributed photovoltaic capacity may lead to abnormal operation of the distribution network, resulting in serious safety accidents. Therefore, the total distributed photovoltaic capacity at this time can be used as the maximum distributed photovoltaic carrying capacity of the scheme.

[0130] S106: Finally, the maximum carrying capacity of distributed photovoltaics in all random scenarios is calculated, and by defining different capacity value intervals, the probability of each distributed photovoltaic maximum carrying capacity appearing in different capacity intervals is calculated, thereby determining the maximum carrying capacity of distributed photovoltaics connected to the distribution network in different capacity intervals.

[0131] Embodiment 2

[0132] In one or more embodiments, a maximum carrying capacity assessment system for distributed photovoltaic access to a distribution network is disclosed, comprising:

[0133] The random scenario generation module is used to assume that the nodes and phases of the distributed photovoltaic access to the distribution network have the same probability, and to determine the number of distributed photovoltaic nodes, the phase placement position and the capacity of each distributed photovoltaic by random allocation, thereby generating a variety of random scenarios;

[0134] The power flow calculation module is used to calculate the probability density function and cumulative distribution function of the node voltage for each random scenario, taking into account the uncertainty of distributed photovoltaic output, and using the semi-invariant-based probabilistic power flow method;

[0135] An indicator calculation module is used to determine the node voltage confidence interval based on the probability density function and cumulative distribution function of the node voltage, and calculate the maximum over-limit probability of the node voltage and the average over-limit probability of the distribution network voltage;

[0136] The voltage quality assessment module is used to assess the current distribution network voltage quality using the obtained node voltage confidence interval, the average over-limit probability of the distribution network voltage and the maximum over-limit probability of the node voltage as assessment indicators; if the distribution network voltage quality meets the requirements, the distributed photovoltaic capacity is increased, and the power flow calculation and voltage quality assessment are re-performed; if it does not meet the requirements, the photovoltaic capacity at this time is recorded as the maximum carrying capacity of the distributed photovoltaic access distribution network under this random scenario;

[0137] The maximum carrying capacity calculation module is used to calculate the maximum carrying capacity of distributed photovoltaics in all random scenarios, and calculate the probability that the maximum carrying capacity of each distributed photovoltaic system appears in different capacity intervals, so as to determine the maximum carrying capacity of distributed photovoltaics connected to the distribution network.

[0138] Among them, the probability power flow method based on semi-invariant is used to calculate the power flow of the current scenario, and the probability density function and cumulative distribution function of the node voltage are obtained respectively, which are specifically:

[0139] Assuming that F(x) is the cumulative distribution function of the node voltage X, construct the characteristic function of F(x);

[0140] The characteristic function of F(x) is logarithmized and expanded into a Maclaurin series at t=0, and the semi-invariants of node voltages of various orders are solved by Monte Carlo method;

[0141] The node voltage X is standardized, and the probability density function and cumulative distribution function of the standardized node voltage X are calculated according to the Gram-Charlier series expansion;

[0142] The probability density function and the cumulative distribution function of the standardized node voltage X are subjected to standardized restoration processing to obtain the probability density function and the cumulative distribution function of the node voltage X.

[0143] The probability density function and cumulative distribution function of the node voltage are calculated by using the semi-invariant-based probability power flow method, which are as follows:

[0144] Assuming that F(x) is the cumulative distribution function of the node voltage X, construct the characteristic function of F(x);

[0145] The characteristic function of F(x) is logarithmized and expanded into a Maclaurin series at t=0, and the semi-invariants of the node voltage X are solved by Monte Carlo method.

[0146] Normalize the node voltage X to get the standardized node voltage

[0147] The standardized node voltage is calculated using the Gram-Charlier series expansion The probability density function and cumulative distribution function of are calculated; wherein the coefficients of the Gram-Charlier series expansion are calculated based on the semi-invariants of the node voltage X of each order;

[0148] The normalized node voltage The probability density function and cumulative distribution function of the node voltage X are obtained by performing standardized restoration.

[0149] The probability density function f of the node voltage X X (x) Specifically:

[0150]

[0151]

[0152] in, σ X is the standard deviation of the node voltage X, μ X is the expected value of the node voltage X, x is the value of the node voltage X; is the probability density function of the standardized node voltage X, φ(·) represents the probability density function of the node voltage that obeys the standard normal distribution; φ (n) (·) represents the result of the n-th order derivative of φ(·); A1, A2, …A n They represent the coefficients of the Gram-Charlier series expansion.

[0153] Cumulative distribution function F of node voltage X x (x) Specifically:

[0154]

[0155]

[0156] in, is the cumulative distribution function of the normalized node voltage X.

[0157] The average over-limit probability of the distribution network voltage is specifically:

[0158]

[0159] Assuming that there are n nodes in the distribution network and the qualified interval of node voltage is set, the voltage over-limit probability of node i is expressed as P V_off , i=1,2,…,n; is the average voltage over-limit probability of the distribution network.

[0160] The node voltage confidence interval is determined as follows:

[0161] Given a confidence level of 1-CL, find the boundary value of the corresponding confidence interval on the obtained cumulative distribution function curve of each node voltage and Then we get the node voltage confidence interval

[0162] If the requirements are met, increase the photovoltaic capacity and recalculate and evaluate the power flow, specifically:

[0163] If the average over-limit probability of distribution network voltage, the node voltage confidence interval and the maximum over-limit probability of node voltage all meet the set requirements, each DG node will increase the connected photovoltaic capacity according to the set proportion, and recalculate and evaluate the power flow.

[0164] The specific implementation of the above modules is the same as that in Example 1 and will not be described in detail.

[0165] Embodiment 3

[0166] In one or more embodiments, a terminal device is disclosed, including a server, the server including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the maximum carrying capacity evaluation method of distributed photovoltaic access to a distribution network in Example 1 when executing the program. For the sake of brevity, the specific process is not repeated.

[0167] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0168] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0169] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software.

[0170] Embodiment 4

[0171] In one or more embodiments, a computer-readable storage medium is disclosed, in which a plurality of instructions are stored, wherein the instructions are suitable for being loaded by a processor of a terminal device and executing the maximum carrying capacity assessment method for the distributed photovoltaic access distribution network described in Example 1.

[0172] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for evaluating the maximum carrying capacity of a distributed photovoltaic access distribution network, characterized in that: include: Assuming that the nodes and phases of distributed photovoltaics connected to the distribution network have the same probability, the number of distributed photovoltaic nodes, the phase placement position and the capacity of each distributed photovoltaic are determined by random allocation, thereby generating multiple random scenarios; For each random scenario, the uncertainty of distributed photovoltaic output is taken into account, and the probability density function and cumulative distribution function of the node voltage are calculated by using the semi-invariant-based probabilistic power flow method. Based on the probability density function and cumulative distribution function of the node voltage, the node voltage confidence interval is determined, and the maximum node voltage over-limit probability and the average distribution network voltage over-limit probability are calculated; The obtained node voltage confidence interval, distribution network voltage average over-limit probability and node voltage maximum over-limit probability are used as evaluation indicators to evaluate the current distribution network voltage quality. If the voltage quality of the distribution network meets the requirements, the distributed photovoltaic capacity is increased, and the power flow calculation and voltage quality assessment are re-performed; if it does not meet the requirements, the photovoltaic capacity at this time is recorded as the maximum carrying capacity of the distributed photovoltaic access distribution network in this random scenario; The maximum carrying capacity of distributed photovoltaics in all random scenarios is calculated, and the probability that the maximum carrying capacity of each distributed photovoltaic system appears in different capacity intervals is calculated, so as to determine the maximum carrying capacity of distributed photovoltaics connected to the distribution network.

2. A method for evaluating the maximum carrying capacity of a distributed photovoltaic access distribution network as claimed in claim 1, characterized in that: The probability density function and cumulative distribution function of the node voltage are calculated by using the semi-invariant-based probability power flow method, which are as follows: Assuming that F(x) is the cumulative distribution function of the node voltage X, construct the characteristic function of F(x); The characteristic function of F(x) is logarithmized and expanded into a Maclaurin series at t=0, and the semi-invariants of the node voltage X are solved by Monte Carlo method. Normalizing the node voltage X to obtain a standardized node voltage X; The probability density function and the cumulative distribution function of the standardized node voltage X are calculated using the Gram-Charlier series expansion; wherein the coefficients of the Gram-Charlier series expansion are calculated based on the semi-invariants of each order of the node voltage X; The normalized node voltage The probability density function and cumulative distribution function of the node voltage X are obtained by performing standardized restoration.

3. A method for evaluating the maximum carrying capacity of a distributed photovoltaic access distribution network as claimed in claim 1, characterized in that: The probability density function f of the node voltage X X (x) Specifically: in, σ X is the standard deviation of the node voltage X, μ X is the expected value of the node voltage X, x is the value of the node voltage X; is the probability density function of the standardized node voltage X, φ(·) represents the probability density function of the node voltage that obeys the standard normal distribution; φ (n) (·) represents the result of the n-th order derivative of φ(·); A1, A2, …A n They represent the coefficients of the Gram-Charlier series expansion.

4. A method for evaluating the maximum carrying capacity of a distributed photovoltaic access distribution network as claimed in claim 3, characterized in that: Cumulative distribution function F of node voltage X x (x) Specifically: in, is the cumulative distribution function of the normalized node voltage X.

5. A method for evaluating the maximum carrying capacity of a distributed photovoltaic access distribution network as claimed in claim 1, characterized in that: The average over-limit probability of the distribution network voltage is specifically: Assuming that there are n nodes in the distribution network and the qualified interval of node voltage is set, the voltage over-limit probability of node i is expressed as P V_off , i=1,2,…,n; is the average voltage over-limit probability of the distribution network.

6. A method for evaluating the maximum carrying capacity of a distributed photovoltaic access distribution network as claimed in claim 1, characterized in that: The node voltage confidence interval is determined as follows: Given a confidence level of 1-CL, find the boundary value of the corresponding confidence interval on the obtained cumulative distribution function curve of each node voltage and Then we get the node voltage confidence interval 7. A method for evaluating the maximum carrying capacity of a distributed photovoltaic access distribution network as claimed in claim 1, characterized in that: If the requirements are met, increase the photovoltaic capacity and recalculate and evaluate the power flow, specifically: If the average over-limit probability of distribution network voltage, the node voltage confidence interval and the maximum over-limit probability of node voltage all meet the set requirements, each DG node will increase the connected photovoltaic capacity according to the set proportion, and recalculate and evaluate the power flow.

8. A maximum carrying capacity assessment system for distributed photovoltaic access to a distribution network, characterized in that: include: The random scenario generation module is used to assume that the nodes and phases of the distributed photovoltaic access to the distribution network have the same probability, and to determine the number of distributed photovoltaic nodes, the phase placement position and the capacity of each distributed photovoltaic by random allocation, thereby generating a variety of random scenarios; The power flow calculation module is used to calculate the probability density function and cumulative distribution function of the node voltage for each random scenario, taking into account the uncertainty of distributed photovoltaic output, and using the semi-invariant-based probabilistic power flow method; An indicator calculation module is used to determine the node voltage confidence interval based on the probability density function and cumulative distribution function of the node voltage, and calculate the maximum over-limit probability of the node voltage and the average over-limit probability of the distribution network voltage; The voltage quality assessment module is used to assess the current distribution network voltage quality using the obtained node voltage confidence interval, the average over-limit probability of the distribution network voltage and the maximum over-limit probability of the node voltage as assessment indicators; if the distribution network voltage quality meets the requirements, the distributed photovoltaic capacity is increased, and the power flow calculation and voltage quality assessment are re-performed; if it does not meet the requirements, the photovoltaic capacity at this time is recorded as the maximum carrying capacity of the distributed photovoltaic access distribution network under this random scenario; The maximum carrying capacity calculation module is used to calculate the maximum carrying capacity of distributed photovoltaics in all random scenarios, and calculate the probability that the maximum carrying capacity of each distributed photovoltaic system appears in different capacity intervals, so as to determine the maximum carrying capacity of distributed photovoltaics connected to the distribution network.

9. A maximum carrying capacity assessment system for distributed photovoltaic access to a distribution network as claimed in claim 8, characterized in that: The probability density function and cumulative distribution function of the node voltage are calculated by using the semi-invariant-based probability power flow method, which are as follows: Assuming that F(x) is the cumulative distribution function of the node voltage X, construct the characteristic function of F(x); The characteristic function of F(x) is logarithmized and expanded into a Maclaurin series at t=0, and the semi-invariants of the node voltage X are solved by Monte Carlo method. Normalize the node voltage X to get the standardized node voltage The standardized node voltage is calculated using the Gram-Charlier series expansion The probability density function and cumulative distribution function of are calculated; wherein the coefficients of the Gram-Charlier series expansion are calculated based on the semi-invariants of the node voltage X of each order; The normalized node voltage The probability density function and cumulative distribution function of the node voltage X are obtained by performing standardized restoration.

10. A maximum carrying capacity assessment system for distributed photovoltaic access to a distribution network as claimed in claim 8, characterized in that: The probability density function f of the node voltage X X (x) Specifically: in, σ X is the standard deviation of the node voltage X, μ X is the expected value of the node voltage X, x is the value of the node voltage X; is the probability density function of the standardized node voltage X, φ(·) represents the probability density function of the node voltage that obeys the standard normal distribution; φ (n) (·) represents the result of the n-th order derivative of φ(·); A1, A2, …A n They represent the coefficients of the Gram-Charlier series expansion.

11. A maximum carrying capacity assessment system for distributed photovoltaic access to a distribution network as claimed in claim 10, characterized in that: Cumulative distribution function F of node voltage X x (x) Specifically: in, is the cumulative distribution function of the normalized node voltage X.

12. A maximum carrying capacity assessment system for distributed photovoltaic access to a distribution network as claimed in claim 8, characterized in that: The average over-limit probability of the distribution network voltage is specifically: Assuming that there are n nodes in the distribution network and the qualified interval of node voltage is set, the voltage over-limit probability of node i is expressed as P V_off , i=1,2,…,n; is the average voltage over-limit probability of the distribution network.

13. A maximum carrying capacity assessment system for distributed photovoltaic access to a distribution network as claimed in claim 8, characterized in that: The node voltage confidence interval is determined as follows: Given a confidence level of 1-CL, find the boundary value of the corresponding confidence interval on the obtained cumulative distribution function curve of each node voltage and Then we get the node voltage confidence interval 14. A maximum carrying capacity assessment system for distributed photovoltaic access to a distribution network as claimed in claim 8, characterized in that: If the requirements are met, increase the photovoltaic capacity and recalculate and evaluate the power flow, specifically: If the average over-limit probability of distribution network voltage, the node voltage confidence interval and the maximum over-limit probability of node voltage all meet the set requirements, each DG node will increase the connected photovoltaic capacity according to the set proportion, and recalculate and evaluate the power flow.

15. A terminal device comprising a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions, characterized in that: The instructions are suitable for being loaded by a processor and executing the maximum carrying capacity assessment method for distributed photovoltaic access to a distribution network as described in any one of claims 1-7.

16. A computer-readable storage medium storing a plurality of instructions, characterized in that: The instructions are suitable for being loaded by a processor of a terminal device and executing the maximum carrying capacity assessment method for a distributed photovoltaic access distribution network as described in any one of claims 1-7.

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