A block partitioning method for distribution network considering wind and solar power uncertainty and correlation

Abstract

The power distribution network block division method considering wind, light and load power uncertainty and correlation belongs to the technical field of new energy power grid block division, and solves the problem that the generated scene lacks effectiveness when the existing technical method establishes a typical scene of the system; the technical scheme of the present application is based on wind, light and load probability statistical information, calculates the joint probability density and joint probability distribution of wind, light and load, constructs a wind, light and load correlation model, and generates a typical wind, light and load joint scene with correlation based thereon; considering the factors of economy, consumption capacity and control dimension, a power distribution network block division target is established, and a power distribution network block division constraint condition is established, and a power distribution network block division model is constructed; a multi-objective genetic algorithm is used to solve the division model, and the optimal power distribution network block division result is obtained through a fuzzy membership function; efficient integration and utilization of resources are realized, the system control difficulty is reduced, management and control are facilitated, the anti-interference performance of the block is improved, and the safe and stable operation of the block is ensured.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of new energy power grid block division, and relates to a power distribution network block division method considering wind-solar load power uncertainty and correlation. BACKGROUND

[0002] At present, distributed new energy mainly takes wind energy and light energy as the main source, but the output of wind turbines and photovoltaic power has intermittency and randomness, which increases the uncertainty factors of the power grid, reduces the anti-interference ability of the power grid, and affects the stability and safety of the power grid. The large number of access of distributed power supply makes the number of power supply points huge and dispersed, which increases the control dimension of the power grid; and with the increase of the penetration rate of distributed new energy, the system power flow is more and more serious, and the voltage fluctuation problem is more and more serious, which makes the control of the power grid in a difficult situation. Therefore, it is necessary to integrate resources in the power distribution network under the condition of considering system uncertainty, and divide it into small block power plants, so as to reduce the difficulty of system control, and make the system run, regulate and manage more stably.

[0003] The scene generation method can effectively represent the uncertain factors of the system and reduce the complexity of problem solving by describing the uncertainty problem in the system in a typical scene way. The literature "Virtual power plant and power distribution company coordinated dispatching model containing wind, light and water" (Dong Wenliu, Power System Automation) proposes a scene generation method based on autoregressive moving average model to deal with wind and light uncertainty problem, and the literature "Distribution network reactive power optimization considering correlation of multiple wind turbine output" (Wang Lingling, Power Grid Technology) uses Latin hypercube sampling method to stratified sample the forecast error probability distribution to generate a wind power output prediction scene set.

[0004] Experts and scholars in the field of power system have carried out extensive research on power grid division. The literature "Scale distributed photovoltaic power cluster division based on FastUnfolding clustering algorithm" (Wang Lei, Solar Energy) proposes a method of using node similarity improved modularity function as the power grid division index, and using Fast Unfolding clustering algorithm to divide the scale distributed photovoltaic power grid. The literature "Renewable energy generation cluster division method for improving consumption and consumption capacity" (Bi Rui, Proceedings of the Chinese Society of Electrical Engineering) proposes a power grid division method for planning to improve the local consumption capacity of renewable energy.

[0005] The above documents have the following disadvantages: 1) in the typical scenario of establishing a system, most of them only consider the uncertainty factors of wind and light, and fail to consider the influence of the uncertainty factors of load and the correlation between wind, light and load in the same region under the spatial limitation, so that the generated scenario lacks a certain effectiveness; 2) the power grid division method is mostly divided by a single index, without considering the influence of economic cost, energy consumption and control dimension on power grid division; 3) the division method is only divided at a certain moment of the steady state of the power grid, without considering the uncertainty factors of the power grid, so that the divided distribution network blocks cannot have good anti-interference performance and realize stable operation of each block. SUMMARY

[0006] The technical problem to be solved by the present application is how to design a distribution network block division method considering the uncertainty and correlation of wind, light and load power, so as to solve the problem that the existing technology does not consider the influence of the uncertainty factors of load and the correlation between wind, light and load in the same region under the spatial limitation when establishing a typical scenario of a system, resulting in the lack of effectiveness of the generated scenario.

[0007] The present application solves the above technical problems by the following technical solutions:

[0008] The distribution network block division method considering the uncertainty and correlation of wind, light and load power comprises the following steps:

[0009] S1, the edge probability distribution of wind, light and load power is calculated, and the correlation between wind, light and load power is calculated to determine the Pair-Copula structure of wind, light and load; a wind, light and load joint correlation model including wind, light and load joint probability density and joint probability distribution is constructed; a corresponding random sampling method is selected to generate a joint scenario, and scene reduction is performed to obtain a joint scenario representing the uncertainty and correlation of wind, light and load power;

[0010] S2, based on the joint scenario of the uncertainty and correlation of wind, light and load power, the economic efficiency, consumption capacity and control dimension factors of the distribution network block are considered to construct a distribution network block division objective function, and the distribution network block division constraint conditions are set, so as to establish a distribution network block division model;

[0011] S3, a multi-objective genetic algorithm is used to solve the distribution network block division model to obtain a block division result set, and a fuzzy membership function is used to solve and obtain an optimal block division result.

[0012] The application utilizes Pair-Copula theory to construct a wind-solar-load joint correlation model, and constructs a distribution network block division model, generates typical joint scenarios of wind-solar-load with correlation, describes the uncertainty factors of the system, proposes a distribution network block division model from three aspects of economy, energy consumption and control dimension, realizes the block division of the distribution network, improves the comprehensive performance and robustness of the distribution network block, and makes the distribution network block have good applicability and stability.

[0013] Further, the method for calculating the marginal probability distribution of wind-solar-load power in step S1 is as follows:

[0014] Based on the historical data of wind-solar-load, the non-parametric kernel density estimation method is used to calculate the marginal probability density corresponding to each unit at each time, and the marginal probability density function is as follows:

[0015]

[0016] Wherein, is the marginal probability density of random variable B at time t, h is the bandwidth, n is the number of historical data, b t is the random value of random variable B at time t, is the historical data of random variable B at time t on the qth day, and K(·) is a scaling kernel function.

[0017] The marginal probability distribution of each unit is calculated by integrating the marginal probability density of wind-solar-load, and the marginal probability distribution is as follows:

[0018]

[0019] Wherein, F B (b t ) is the marginal probability distribution of random variable B.

[0020] Further, the method for calculating the correlation between wind-solar-load powers in step S1 is as follows:

[0021] Let the wind-solar-load powers be random variables X, Y and Z, then the expression of Kendall-t coefficient of random variables X and Y at (X1, Y1) and (X2, Y2) and the expression of Kendall-t coefficient of random variables X and Z at (X1, Z1) and (X2, Z2) are as follows:

[0022]

[0023] Wherein, t1 is the Kendall-t coefficient of random variables X and Y, t2 is the Kendall-t coefficient of random variables X and Z, and P(·) is a probability function.

[0024] Further, the method for establishing the Pair-Copula structure of wind and light loads in step S1 is as follows:

[0025] I) The first layer of the Pair-Copula structure of wind and light loads is the marginal probability distribution of each random variable marginal random variable;

[0026] II) The second layer of the Pair-Copula structure of wind and light loads is established in the following process:

[0027] Based on the correlation coefficient between wind and light loads, a variable closely related to other two variables is selected as a dominant variable, and in this paper, the variable X is selected as the dominant variable. The joint probability distribution function of the binary random variable is obtained by fitting the dominant variable and another variable two by two, and the expression is as follows:

[0028]

[0029] Wherein, F1(·) is the joint probability distribution function of random variables X and Y, F2(·) is the joint probability distribution of random variables X and Z, and the function C(·) is a Copula function;

[0030] And the Euclidean distance between the empirical Copula function is calculated to test the goodness of fit, and the Copula function with the smallest Euclidean distance is used to establish the best joint probability distribution function between the two;

[0031] The Euclidean distance between the empirical distribution Copula function is as follows:

[0032]

[0033] Wherein, d1 is the Euclidean distance between random variables X and Y, d2 is the Euclidean distance between random variables X and Z, The output of random variables X, Y and Z at the t time of the q day is C n (·) is an empirical Copula function;

[0034] The conditional distribution is calculated and taken as new random variables U and V, and the new variables U and V belong to the [0,1] uniform distribution; The conditional distribution is as follows:

[0035]

[0036] Wherein, F Y|X , F Z|X are the conditional distribution functions of variables Y and Z under the condition of variable X; The conditional distribution of random variables Y and Z under the condition of random variable X is taken as the second layer of the Pair-Copula structure of wind and light loads;

[0037] III) The third layer of the wind-solar load Pair-Copula structure is established as follows:

[0038] The best joint probability distribution of random variables U and V is calculated to obtain the conditional distribution of random variable Z under the condition of random variables X and Y, and the expression is as follows:

[0039]

[0040] where F Z,Y|X is the conditional distribution of variable Z under the condition of variable Y and variable X.

[0041] Further, the joint probability density and joint probability distribution in the wind-solar load joint correlation model of the joint probability density and joint probability distribution in step S1 are as follows:

[0042]

[0043] where F(X,Y,Z) is the joint probability distribution function of three-dimensional random variables (X,Y,Z), f(X,Y,Z) is the joint probability density function of three-dimensional random variables (X,Y,Z), c(·) is the probability density function of the Copula function, f X and f Y , f Z are the marginal probability density functions of random variable X and random variable Y, and random variable Z, respectively.

[0044] Further, the method for selecting a corresponding random sampling method to generate a joint scene and performing scene reduction to obtain a joint scene representing wind-solar load power uncertainty and correlation is as follows:

[0045] Based on the wind-solar load correlation model, three random variable vectors w1,w2,w3 satisfying the uniform independent distribution of [0,1] are randomly generated, N is the length of each vector, and let:

[0046]

[0047] where F Z,Y|X is the conditional distribution of variable Z under the condition of variable Y and variable X.

[0048] According to formulas (6) and (9), F X , F Y , and F Z are calculated to discretize the joint probability distribution, and the discrete marginal probability is converted to the corresponding power value according to the inverse of the marginal probability distribution of each unit, to obtain N joint scenes of wind-solar loads with correlation. The final joint scene representing wind-solar load power uncertainty and correlation is obtained by k-means reduction.

[0049] Further, the power distribution network block division objective function in step S2 comprises: a block economic cost objective function, a block net power complement objective function, and a block control dimension objective function.

[0050] The block economic cost objective function obj1 is:

[0051]

[0052] Wherein, c l is the tie line cost, unit: yuan / km; L i is the i-th tie line; c a is the tie station construction cost, unit: yuan / unit; N l is the number of tie lines between blocks; N a is the number of power distribution network blocks; c p is the electricity purchase price at t time, unit: yuan / kwh; P buy.k (t) is the purchased power at t time, unit: kwh; p k is the scenario probability of scenario k; T is the optimization period;

[0053] The block net power complement objective function obj2 is:

[0054]

[0055] Wherein, P a.k (t) is the net load power of block a at t time in scenario k;

[0056] The block control dimension objective function obj3 is:

[0057] obj3 = min{max(n i )}

[0058] Wherein, n i is the number of nodes of the i-th block.

[0059] Further, the power distribution network block division constraint condition in step S2 comprises: a block tie line constraint condition, a block dimension constraint condition, a block net power constraint condition, a power flow constraint condition, and a system security and stability constraint condition.

[0060] The block tie line constraint condition is:

[0061] 0 ≤ |P cl (t)| ≤ P cl.max

[0062] Wherein, P cl (t) is the power of the tie line at t time, P cl.maxMaximum power allowed for tie line;

[0063] The block dimension constraint is:

[0064] 1 < n i ≤ n all

[0065] Wherein, n all is the total number of nodes of the system;

[0066] The block net power constraint is:

[0067] P a.k (t) > 0

[0068] The power flow constraint is:

[0069]

[0070] Wherein, P ij (t) and Q ij (t) are the active power flow and the reactive power flow of branch ij at time t respectively; U i (t) is the voltage of node i; r ij , x ij are the resistance and the reactance of branch ij respectively; P j (t) and Q j (t) are the active power injection and the reactive power injection of node j at time t respectively; up(j) and dn(j) are the set of nodes at the head of the branch whose end is node j and the set of nodes at the end of the branch whose head is node j respectively;

[0071] The system security and stability constraint is:

[0072]

[0073] Wherein, U i is the voltage of node i, U i.min is the minimum value of the voltage of node i, U i.max is the maximum value of the voltage of node i, I ij is the current value of line ij, I ij.max is the maximum current allowed for line ij.

[0074] Further, the method for obtaining the block division result set by solving the block division model of the distribution network by using the multi-objective genetic algorithm in step S3 is as follows:

[0075] 1) Input the numerical values of K wind-solar load power uncertainty and correlation joint scenarios and the basic parameters of the structure of the distribution network;

[0076] 2) initializing the genetic population based on the power distribution network adjacency matrix;

[0077] 3) non-dominant sorting of the population individuals and calculation of their crowding degree;

[0078] 4) generating a new parent population by using the elite reservation strategy;

[0079] 5) generating a new offspring population by selecting, crossing and mutating the new parent population;

[0080] 6) repeating steps 3) to 5) until the maximum number of iterations is met, to obtain a power distribution network block division result set;

[0081] 7) solving and obtaining the optimal power distribution network block division result by using a fuzzy membership function.

[0082] Further, the method for solving and obtaining the optimal block division result by using a fuzzy membership function in step S3 is as follows:

[0083] a) calculating the membership degree of each objective function corresponding to each solution in the block division result set, and the calculation formula is as follows:

[0084]

[0085] wherein λ a.b is the membership degree of the a-th objective function corresponding to the b-th solution, obj a.max is the maximum value of the a-th objective function, obj a.min is the minimum value of the a-th objective function, and obj a.b is the value of the a-th objective function at the b-th solution;

[0086] b) calculating the weight of each objective function, and the weight calculation formula is as follows:

[0087]

[0088] wherein n a is the number of objective functions, n b is the number of solutions in the block division result set, and k a is the weight value of the a-th objective function;

[0089] c) calculating the comprehensive membership degree of each solution in the block division result set, and the calculation formula is as follows:

[0090]

[0091] wherein ω b is the comprehensive membership degree of the b-th block solution;

[0092] d) selecting the solution with the maximum comprehensive membership degree as the optimal block division result.

[0093] The present application has the advantages of:

[0094] (1) The technical scheme of the present application considers the influence of power uncertainty of the power distribution network and the correlation between units on the power distribution network block division, establishes a typical wind-solar-load combined scenario with correlation, so that the division result is more applicable, and the robustness of the power distribution network block division result is improved;

[0095] (2) The power distribution network block division model under the combined scenario is constructed from the economic, energy consumption and control dimension levels, the power distribution network block division result is optimized, and the performance of the power distribution network block division result is improved;

[0096] (3) The multi-objective genetic algorithm and fuzzy membership function are used to solve the division model, which avoids the subjectivity caused by normalizing and weighting the multi-objective, so that the power distribution network block division result is more objective. BRIEF DESCRIPTION OF DRAWINGS

[0097] Figure 1 is the power distribution network block division method flowchart considering wind-solar-load power uncertainty and correlation of the present application embodiment one;

[0098] Figure 2 is the wind-solar-load Pair-Copula structure model diagram of the present application embodiment one;

[0099] Figure 3 is the modified IEEE33 node structure diagram of the present application embodiment one;

[0100] Figure 4 is the power distribution network block division result diagram considering wind-solar-load power uncertainty and correlation of the present application embodiment one;

[0101] Figure 5 is the block tie line average power and node voltage comparison diagram under the present application scheme and without considering uncertainty of the present application embodiment one. DETAILED DESCRIPTION

[0102] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be described clearly and completely below in combination with the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0103] The technical scheme of the present application will be further described below in combination with the drawings in the specification and specific embodiments:

[0104] Embodiment one

[0105] As Figure 1 and Figure 2 shown, considering the wind and light load power uncertainty and correlation of power distribution network block division method, including the following steps:

[0106] Step 1, with the historical data of wind and light load power as statistical samples, the marginal probability density function of each unit is calculated, and the correlation coefficient between wind, light and load (the correlation coefficient is a parameter describing the correlation degree between wind, light and load) is calculated; Select the dominant variable, use Pair-Copula theory to determine the wind, light and load Pair-Copula structure, the wind, light and load Pair-Copula structure is a structure model that can well describe the correlation between wind, light and load, and the structure model is a three-layer structure. Calculate the joint probability density function and joint probability distribution of wind, light and load, and construct the wind, light and load correlation model; A large number of correlation scenarios are generated by using random sampling method, and typical scenarios describing wind, light and load power uncertainty and joint correlation are formed by cutting;

[0107] Step 1.1, the non-parametric kernel density estimation method is used to calculate the marginal probability density of each unit at each time, and the marginal probability density function is as follows:

[0108]

[0109] In formula (1), is the marginal probability density of random variable B at time t, h is the bandwidth, n is the number of historical data, b t is the random value of random variable B at time t, is the historical data of random variable B at time t on the qth day, and K(·) is the scaling kernel function.

[0110] Step 1.2, the marginal probability distribution of each unit is calculated by integrating the marginal probability density of each unit, and the marginal probability distribution is as follows:

[0111]

[0112] In formula (2), F B (b t ) is the marginal probability distribution of random variable B.

[0113] Step 1.3, let wind, light and load be random variables X, Y and Z, use Kendall-t coefficient to describe the correlation between two variables, and the expression of Kendall-t coefficient of random variables X and Y at (X1, Y1) and (X2, Y2) and Kendall-t coefficient of random variables X and Z at (X1, Z1) and (X2, Z2) is as follows:

[0114]

[0115] In formula (3), t1 is the Kendall-t coefficient of random variables X and Y, t2 is the Kendall-t coefficient of random variables X and Z, and P(·) is a probability function.

[0116] Step 1.4, taking the marginal probability distribution of each random variable as the first layer, and based on the correlation coefficient between wind and light loads, selecting a variable closely related to other two variables as the dominant variable, in this paper, variable X is selected as the dominant variable, and the joint probability distribution function of the binary random variable is obtained by fitting the dominant variable with another variable, as shown in the following expression:

[0117]

[0118] In formula (4), F1(·) is the joint probability distribution function of random variables X and Y, F2(·) is the joint probability distribution of random variables X and Z, and C(·) is a Copula function.

[0119] Step 1.5, the goodness-of-fit test is performed by calculating the Euclidean distance between the empirical Copula function, and the best joint probability distribution function between the two is established by the Copula function with the smallest Euclidean distance. The Euclidean distance between the empirical distribution Copula function is as follows:

[0120]

[0121] In formula (5), d1 is the Euclidean distance of random variables X and Y, d2 is the Euclidean distance of random variables X and Z, are the outputs of random variables X, Y and Z at t time on the qth day, and C n (·) is an empirical Copula function.

[0122] Step 1.6, the conditional distribution is calculated and taken as new random variables U and V, and the new variables U and V belong to [0,1] uniform distribution. The conditional distribution is as follows:

[0123]

[0124] In formula (6), F Y|X , F Z|X are the conditional distribution functions of variables Y and Z under the condition of variable X.

[0125] The conditional distribution of random variables Y and Z under the condition of random variable X is taken as the second layer of Pair-Copula structure.

[0126] Step 1.7, calculate the best joint probability distribution of random variables U and V, derive the conditional distribution of random variable Z under the condition of random variables X, Y, the expression is as follows, and take it as the third layer of the wind-solar load Pair-Copula structure, and complete the establishment of the wind-solar load Pair-Copula structure.

[0127]

[0128] In formula (7), F Z,Y|X is the conditional distribution of variable Z under the condition of variable Y and variable X.

[0129] Step 1.8, based on the wind-solar load Pair-Copula structure, calculate the joint probability density and joint probability distribution as follows, and derive the wind-solar load joint correlation model.

[0130]

[0131] In formula (8), F(X,Y,Z) is the joint probability distribution function of three-dimensional random variables (X,Y,Z), f(X,Y,Z) is the joint probability density function of three-dimensional random variables (X,Y,Z), c(·) is the probability density function of the Copula function, f X and f Y , f Z are the marginal probability density functions of random variable X and random variable Y, and random variable Z, respectively.

[0132] Step 1.9, based on the obtained wind-solar load correlation model, randomly generate three random variable vectors w1, w2, w3 satisfying [0,1] uniform distribution and independence, N is the length of each vector, and let

[0133]

[0134] In formula (9), F Z,Y|X is the conditional distribution of variable Z under the condition of variable Y and variable X.

[0135] According to formula (6) and (9), F X , F Y , F Z are solved, the discretization of the joint probability distribution is realized, and the discrete marginal probability is converted into the corresponding power value according to the inverse of the marginal probability distribution of each unit, N joint scenes with correlation of wind, light and load are obtained, and k-means reduction is performed to obtain the final K typical joint scenes.

[0136] Step 2, based on the wind and light power uncertainty and correlation joint scenario generated in step S1, considering the economic, consumption capacity and control dimension factors of the power distribution network block, the power distribution network block division objective function is constructed, and the power distribution network block division constraint condition is set, and the establishment of the power distribution network block division model is realized;

[0137] Step 2.1, considering that the interaction between blocks requires interconnection equipment, and the stable operation of each block cannot be separated from the support of the upper power grid, therefore the block economic cost objective expression obj1 is:

[0138]

[0139] In formula (10), c l is the cost of the interconnection line (yuan / km), which is set to 8000 in this paper; L i is the i-th interconnection line; c a is the construction cost of the interconnection station (yuan / unit), which is set to 92000 in this paper; N l is the number of interconnection lines between the power distribution network blocks, N a is the number of power distribution network blocks; c p is the electricity purchase price at time t (yuan / kWh); P buy.k (t) is the electricity purchase amount at time t (yuan / kWh -1 ), which is set to 0.891 in the peak period, 0.318 in the valley period, and 0.572 in other periods; p k is the scene probability of scene k, and T is the optimization period.

[0140] Step 2.2, in order to realize the internal power balance of the block and promote the consumption of distributed new energy, it is hoped that the net power of the internal nodes of the block has complementary characteristics, so the block net power complementary target obj2 is:

[0141]

[0142] In formula (11), P a.k (t) is the net load power of block a at time t in scene k.

[0143] Step 2.3, considering that the allocation of internal control nodes in the block will affect the control effect of each block, therefore, the block with the most internal control nodes is quantified as the difficulty and dimension of block control, then the block control dimension target obj3 is:

[0144] obj3=min{max(n i )}(12)

[0145] In formula (12), n i is the number of nodes of the i-th block.

[0146] Step 2.4, in order to enable the block to run safely and stably, avoid the case that the block contains independent nodes and the situation that the power flow reverses outside the block, the distribution network block division needs to meet the constraint conditions including block tie line constraint, block dimension constraint, block net power constraint, power flow constraint, system security and stability constraint:

[0147] The block tie line constraint is:

[0148] 0≤|P cl (t)|≤P cl.max (13)

[0149] In formula (13), P cl (t) is the power of the tie line at time t; P cl.max is the maximum power allowed by the tie line (kW), which is set to 300 in this paper.

[0150] The block dimension constraint is:

[0151] 1<n i ≤n all (14)

[0152] In formula (14), n all is the total number of nodes in the system.

[0153] The block net power constraint is:

[0154] P a.k (t)>0 (15)

[0155] The power flow constraint is:

[0156]

[0157] In formula (16), P ij (t) and Q ij (t) are the active power flow and the reactive power flow of branch ij at time t, respectively; U i (t) is the voltage of node i; r ij and x ij are the resistance and reactance of branch ij, respectively; P j (t) and Q j (t) are the active power injection and the reactive power injection of node j at time t, respectively; up(j) and dn(j) are the set of branch head nodes of the branch whose end node is node j and the set of branch end nodes of the branch whose head node is node j, respectively.

[0158] The system security and stability constraint is:

[0159]

[0160] In formula (17), Ui Vi is the voltage of node i; U i.min Vi,min is the minimum value of the voltage of node i, herein set as 0.9; U i.max Vi,max is the maximum value of the voltage of node i, herein set as 1.1; I ij Iij is the current value of line ij, I ij.max Iij,max is the maximum value of the current allowed by line ij, herein set as 1.5.

[0161] Step 3, considering that each target property and dimension in the power distribution network block division model obtained in step S2 are not unified and interact with each other, a multi-objective genetic algorithm is used to solve the division model to obtain a block division result set, and a fuzzy membership function is used to obtain the best block division result.

[0162] The step of using a multi-objective genetic algorithm to solve the division model to obtain a block division result set is as follows:

[0163] Step 3.11, input the numerical values of K wind-solar load combined scenarios and the basic parameters of the structure of the power distribution network;

[0164] Step 3.12, initialize the population by using real number coding based on an adjacency matrix, search and randomly modify the elements that are 1 on the power grid adjacency matrix;

[0165] Step 3.13, calculate the dominance number of individuals in the population, perform pareto hierarchical sorting on the individuals in the population, and normalize the sub-objective function values corresponding to each individual, sort the individuals in ascending order according to the size of the normalized sub-objective values corresponding to the individuals, and calculate the crowding degree of the individuals;

[0166] Step 3.14, at the s-th iteration, combine the new population Q s and the parent population P s to form a hybrid population R s , perform non-dominated sorting on R s to generate non-dominated sorting sets Z1, Z2…Z m , and place them in the new parent population P s+1 in order, when the population size is greater than the set value 100, use the crowding degree comparison operator to remove individuals in Z m to make the population reach the set value;

[0167] Step 3.15, select individuals with high Pareto levels into the next generation population, select individuals with large crowding degrees when the levels are the same, and use the polynomial crossover and polynomial mutation methods to complete the selection, crossover and mutation of the population;

[0168] Step 3.16, repeat steps 3.13 to 3.15 until the maximum number of iterations 50 is met, and obtain a block division result set;

[0169] The step of solving and obtaining the optimal block division result by using the fuzzy membership function is as follows:

[0170] Step 3.21, the membership degree of each objective function corresponding to each solution in the block division result set is calculated, and the calculation formula is as follows:

[0171]

[0172] In formula (18), λ a.b is the membership degree of the bth solution corresponding to the ath objective function, obj a.max is the maximum value of the ath objective function, obj a.min is the minimum value of the ath objective function, and obj a.b is the value of the ath objective function at the bth solution.

[0173] Step 3.22, the weight of each objective function is calculated, and the weight calculation formula is as follows:

[0174]

[0175] In formula (19), n a is the number of objective functions, n b is the number of solutions in the block division result set, k a is the weight value of the ath objective function.

[0176] Step 3.23, the comprehensive membership degree of each solution in the block division result set is calculated, and the calculation formula is as follows:

[0177]

[0178] In formula (20), ω b is the comprehensive membership degree of the bth block solution.

[0179] Step 3.24, the solution with the maximum comprehensive membership degree is selected as the optimal block division result.

[0180] In order to verify the effectiveness of the scheme, the modified IEEE33 node as shown in Figure 3 is used for verification. There are 6 photovoltaic and 5 wind turbines in the system, and the installation positions Figure 3 have been given, and the installation capacity of each photovoltaic is 600kW, and the installation capacity of each wind turbine is 800kW.

[0181] The result of block division of the system by using the scheme is as shown in Figure 4 From Figure 4As can be seen, each block is connected to each other through a tie line, and the nodes in the block are also connected to each other, and there is no single node in the block and the node is not connected. Each block has photovoltaic, fan, load, and has the condition of improving the block accommodation, which is conducive to the power balance stability of the block. The control dimension of the block is about 10, which effectively reduces the control difficulty of the system. Compared with the power distribution network block division method without considering uncertainty, the comparison results are shown in Table 1.

[0182] Table 1 comparison results

[0183] Scenario Inter-block tie line Number of blocks [obj1 (meta)] [obj2] ​ Without considering uncertainty 4,8 3 365730 0.432 12 Invention scheme 3,6,12 4 472390 0.517 10

[0184] From Table 1, the control dimension of the power distribution network block obtained by the present application is higher than that without considering uncertainty, but the economic cost of the block is reduced by 22.467%, and the block net power complementary ability is improved by 16.44%. It shows that the present application scheme not only reduces the control dimension, but also takes into account the economic and energy consumption, and improves the comprehensive performance of the power distribution network block.

[0185] As Figure 5 shown, the tie line power between the blocks obtained by the present application is lower than that without considering uncertainty, and is within the limit value, and the line overload between the blocks will not occur. Node 8 belongs to different blocks in two cases, and node 8 in different cases is compared, as Figure 5 shown, the voltage fluctuation of node 8 obtained by the present application is small, and the voltage is stable. It shows that the power distribution network block considering uncertainty effectively reduces the influence of line overload and frequent voltage fluctuation, improves the anti-interference ability of the block, and maintains the stable operation of the system.

[0186] The technical scheme of the present application considers the influence of system uncertainty, based on the wind-light-load joint correlation model and the multi-objective power distribution network block division model, a power distribution network block division method considering wind-light-load power uncertainty and correlation is proposed. The non-parametric kernel density estimation method is used to calculate the marginal probability density distribution of wind, light and load power, the Copula connection function is used to establish the joint probability distribution of wind, light and load, the wind-light-load correlation model is constructed, and the most typical joint scene is obtained by random sampling scene generation and scene reduction, which represents the uncertainty of system power; under the typical joint scene, the power distribution network block division target and constraint condition are established, and the power distribution network block division model is formed; the multi-objective genetic algorithm and fuzzy membership function are used to solve the division model, and the optimal block division result is obtained, so that the power distribution network block has good robustness and stability, the control dimension of the system is reduced, and the dimension reduction of the power distribution network is realized.

[0187] The above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalent features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A power distribution network partitioning method considering wind and load power uncertainty and correlation, characterized in that, The method comprises the following steps: S1, calculating the edge probability distribution of wind and light load power, and calculating the correlation coefficient between wind and light load power to determine the wind and light load Pair-Copula structure; constructing a wind and light load joint correlation model including wind and light load joint probability density and joint probability distribution; selecting a corresponding random sampling method to generate a joint scene, and performing scene reduction to obtain a joint scene representing the uncertainty and correlation of wind and light load power; S2, based on the wind and light load power uncertainty and correlation joint scene, considering the economic, consumption capacity and control dimension factors of the distribution network block, constructing a distribution network block division objective function, and setting a distribution network block division constraint condition, thereby establishing a distribution network block division model; S3, solving the distribution network block division model by using a multi-objective genetic algorithm to obtain a block division result set, and solving and obtaining the optimal block division result by using a fuzzy membership function.

2. The method of claim 1, wherein the method further comprises: The method for calculating the edge probability distribution of wind and light load power in step S1 is as follows: Based on the historical data of wind and light load, the non-parametric kernel density estimation method is used to calculate the edge probability density of each unit at each time, and the edge probability density function is as follows: where f h t (b t ) is the marginal probability density of the random variable B at time t, h is the bandwidth, n is the number of historical data, b t is the random value of the random variable B at time t, B q t is the historical data of the random variable B at time t on the qth day, K(·) is a scaling kernel function; The edge probability distribution of each unit is calculated by integrating the edge probability density of wind and light load, and the edge probability distribution is as follows: where F B (b t ) is the marginal probability distribution of the random variable B.

3. The method of claim 2, wherein the method further comprises: The method for calculating the correlation coefficient between wind and light load power in step S1 is as follows: Let the wind and light load power be random variables X, Y and Z, then the Kendall-t coefficient of random variables X and Y at (X1, Y1) and (X2, Y2) and the Kendall-t coefficient of random variables X and Z at (X1, Z1) and (X2, Z2) are expressed as follows: Wherein, t1 is the Kendall-t coefficient of random variables X and Y, t2 is the Kendall-t coefficient of random variables X and Z, and P(·) is a probability function.

4. The method of claim 3, wherein the method further comprises: The establishment method of the wind and light load Pair-Copula structure in step S1 is as follows: Ⅰ) The first layer of the wind and light load Pair-Copula structure is the edge probability distribution of each random variable edge random variable; Ⅱ) The second layer establishment process of the wind and light load Pair-Copula structure is: Based on the correlation coefficient between wind and light load, select the variable closely related to the other two variables as the dominant variable, and select variable X as the dominant variable in this paper, and fit the dominant variable with another variable to obtain the joint probability distribution function of the binary random variable, and the expression is as follows: Wherein, F1(·) is the joint probability distribution function of random variables X and Y, F2(·) is the joint probability distribution of random variables X and Z, and function C(·) is a Copula function; And calculate the Euclidean distance between the empirical Copula function to test the goodness of fit, and the Copula function with the minimum Euclidean distance is used to establish the best joint probability distribution function between the two; The Euclidean distance between the empirical distribution Copula function is as follows: where d1 is the Euclidean distance between random variables X and Y, d2 is the Euclidean distance between random variables X and Z, X q T , Y q T , Z q T are the outputs of random variables X, Y, Z at time t on day q, respectively, C n (·) is an empirical Copula function; The conditional distribution is calculated, and the new random variables U and V are obtained, and the new variables U and V belong to [0,1] uniform distribution; the conditional distribution is as follows: where F Y|X , F Z|X are the conditional distribution functions of the variables Y and Z, respectively, given the variable X; the conditional distributions of the random variables Y, Z, respectively, given the random variable X are taken as the second layer of the wind and light load Pair-Copula structure; III) The third layer of the wind and light load Pair-Copula structure is established as follows: The best joint probability distribution of the random variables U and V is calculated, and the conditional distribution of the random variable Z under the condition of the random variables X and Y is obtained, and the expression is as follows: where F Z,Y|X is the conditional distribution of the variable Z given the variables Y and X.

5. The method of claim 4, wherein, The joint probability density and joint probability distribution in the wind and light load joint correlation model of the joint probability density and joint probability distribution in step S1 are as follows: where F(X, Y, Z) is the joint probability distribution function of the three-dimensional random variables (X, Y, Z), f(X, Y, Z) is the joint probability density function of the three-dimensional random variables (X, Y, Z), c(·) is the probability density function of the Copula function, f X and f Y , f Z are the marginal probability density functions of the random variable X and the random variable Y, the random variable Z, respectively.

6. The method according to claim 5, wherein, In step S1, the corresponding random sampling method is selected to generate a joint scene, and scene reduction is performed to obtain a method for representing the wind and light load power uncertainty and correlation joint scene: Based on the wind and light load correlation model, three random variable vectors w1, w2, and w3 satisfying the uniform independent distribution of [0, 1] are randomly generated, N is the length of each vector, and let: where F Z,Y|X is the conditional distribution of the variable Z given the variables Y and X; According to the formula (6) and (9), F X , F Y , F Z The discretization of the joint probability distribution is realized, and the discrete marginal probability is converted into the corresponding power value according to the inverse of the marginal probability distribution of each unit, N joint scenes with correlation of wind and light load are obtained, and the final scenes representing the uncertainty and correlation of wind and light load power are obtained by k-means reduction.

7. The method of claim 6, wherein the method further comprises: The block economic cost objective function obj1 is: The block net power complementarity objective function obj2 is: wherein c l is the cost of the tie-line, with the unit of yuan / km; L i is the i-th tie-line; c a is the cost of the tie-station, with the unit of yuan / terminal; N l is the number of tie-lines between blocks; N a is the number of blocks in the distribution network; c p is the electricity purchase price at time t, with the unit of yuan / kwh; P buy.k (t) is the electricity purchase amount at time t, with the unit of kwh; p k is the scenario probability of scenario k; T is the optimization period; The block control dimension objective function obj3 is: where P a.k (t) is the net load power of block a at time t in scenario k. The block economic cost objective function obj1 is: obj3 = min{max(n i )} wherein n i is the number of nodes of the i-th block.

8. The method of claim 7, wherein the method further comprises: The block net power complementarity objective function obj2 is: The block control dimension objective function obj3 is: 0 < |P cl (t)|≤P cl.max P cl (t) is the power of the tie line at time t, P cl.max is the maximum power allowed for the tie line; The block economic cost objective function obj1 is: 1 < n i ≤ n all wherein n all is the total number of nodes of the system; The block net power complementarity objective function obj2 is: P a.k (t)>0 The block control dimension objective function obj3 is: where P ij (t) and Q ij (t) are the active and reactive power flow of branch ij at time t, respectively; U i (t) is the voltage of node i; r ij , x ij are the resistance and reactance of branch ij, respectively; P j (t) and Q j (t) are the active and reactive power injection of node j at time t, respectively; up(j) and dn(j) are the set of nodes whose branches end at node j and the set of nodes whose branches start at node j, respectively. The block economic cost objective function obj1 is: where U i is the voltage at node i, U i.min is the minimum voltage at node i, U i.max is the maximum voltage at node i, I ij is the current value of line ij, I ij.max is the maximum current allowed for line ij.

9. The method of claim 8, wherein the method further comprises: The block net power complementarity objective function obj2 is: The block control dimension objective function obj3 is: The block economic cost objective function obj1 is: The block net power complementarity objective function obj2 is: The block control dimension objective function obj3 is: The block economic cost objective function obj1 is: The block net power complementarity objective function obj2 is: The block control dimension objective function obj3 is:

10. The power distribution network partitioning method of claim 9, wherein, The block economic cost objective function obj1 is: The block net power complementarity objective function obj2 is: where λ a.b is the membership of the bth solution to the ath objective function, obj a.max is the maximum value of the ath objective function, obj a.min is the minimum value of the ath objective function, obj a.b is the value of the ath objective function at the bth solution; The block control dimension objective function obj3 is: wherein n a is the number of objective functions, n b is the number of solutions in the block partitioning result set, k a is the weight value of the a-th objective function; The block economic cost objective function obj1 is: where ω b is the integrated membership of the bth block solution; The block net power complementarity objective function obj2 is: The block control dimension objective function obj3 is: The block economic cost objective function obj1 is: The block net 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