A method and system for optimizing regional power grid carrying capacity under new energy access

Abstract

The present invention discloses a method and system for optimizing the carrying capacity of a regional power grid under the access of new energy sources. The method includes clustering the historical data of wind and solar power output and load to obtain a typical scenario set of load and wind and solar power; constructing a regional power grid carrying capacity model with the goal of maximizing the installed capacity of new energy sources in the regional power grid and maximizing the effective reactive power reserve of the transmission network; and decomposing the model into a transmission network carrying capacity optimization sub-model and a distribution network carrying capacity optimization sub-model; using a transmission and distribution step-by-step iterative algorithm to collaboratively solve the transmission network carrying capacity optimization sub-model and the distribution network carrying capacity optimization sub-model for the typical scenario set to obtain a solution set of new energy installed capacity; constructing an evaluation system of operation indicators, economic indicators and environmental protection indicators; and calculating the time-sharing score based on the evaluation system and the solution set of new energy installed capacity to obtain the optimal carrying capacity. The present invention considers the reactive power output of wind and solar units to participate in active voltage regulation, thereby improving the carrying capacity of the power grid; and considers the reactive support of the active distribution network, thereby reducing the risk of voltage fluctuations.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system carrying capacity, and specifically relates to a method and system for optimizing the carrying capacity of a regional power grid under the access of new energy. Background Art

[0002] As distributed renewable energy sources are continuously connected to the distribution network, the distribution network transitions from passive to active, changing the operation mechanism of the power grid. The access of renewable energy sources to the transmission network reduces the static voltage stability of the system. Due to the coupling characteristics of the active and reactive output of renewable energy sources, the active distribution network can provide reactive support and improve the static safety of the transmission network by actively adjusting the reactive output of renewable energy units. Therefore, in order to promote the development of renewable energy, it is of practical significance to study the optimization of the carrying capacity of regional power grids under the access of renewable energy sources.

[0003] The existing calculation method of regional power grid's renewable energy carrying capacity has some defects in terms of constraints and evaluation methods. First, it does not take into account that the reactive power reserve of the distribution network can improve the grid's ability to cope with risks. New energy units should reasonably allocate active and reactive power to avoid excessive use of dynamic reactive resources. Second, the participation of new energy units in voltage regulation will affect their active power output, resulting in reduced economic benefits. The social benefits generated by new energy units should be added to the evaluation system. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and system for optimizing the carrying capacity of a regional power grid under the access of new energy. The present invention constructs a regional power grid carrying capacity model, taking into account the reactive support of the active distribution network to the transmission network; adopts the HDA (Heterogeneous Decomposition Algorithm, HDA) algorithm to solve the two sub-problems after the global problem of the transmission and distribution network is split; performs a time-sharing quantitative evaluation of the installation scheme of new energy under different stability margins, and adopts a combined weighting method to calculate the score and select the optimal solution.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for optimizing the carrying capacity of a regional power grid under the access of new energy, wherein the regional power grid includes a transmission network and a distribution network, wherein the distribution network is connected to the transmission network through a transformer, wherein a node of the distribution network directly connected to the transformer is a root node of the distribution network, and a node of the transmission network connected to the distribution network through a transformer is a boundary node of the transmission network, wherein the optimization method comprises the following steps:

[0007] S1. Cluster the historical data of wind and solar power output and load to obtain a typical scene set of load and wind and solar power;

[0008] S2. Construct a regional power grid carrying capacity model with the goal of maximizing the installed capacity of new energy in the regional power grid and maximizing the effective reactive power reserve of the transmission network;

[0009] S3, decomposing the regional power grid carrying capacity model into a transmission network carrying capacity optimization sub-model and a distribution network carrying capacity optimization sub-model;

[0010] S4. Use the transmission and distribution step-by-step iterative algorithm for the typical scenario set to collaboratively solve the transmission network carrying capacity optimization sub-model and the distribution network carrying capacity optimization sub-model to obtain the new energy installation capacity solution set;

[0011] S5. Construct an evaluation system that takes into account operational indicators, economic indicators, and environmental indicators;

[0012] S6. Based on the evaluation system and the solution set of new energy installed capacity, the combined weighting method is used to calculate the time-sharing score to obtain the optimal carrying capacity.

[0013] To optimize the above technical solutions, the specific measures taken also include:

[0014] Furthermore, in S1, the method used for clustering the historical wind and solar power output data and the historical load data is TSK-Medoids clustering, which specifically includes:

[0015] S1.1, obtain historical data of wind and solar power output and load and the number of clusters N;

[0016] S1.2, randomly select N objects as center points in the historical data of wind and solar power output and historical data of load;

[0017] S1.3, calculate the time series distortion distance between the load historical data and the wind and solar output historical data and N center points;

[0018] S1.4, classify the load historical data and wind and solar output historical data according to the time series distortion distance;

[0019] S1.5, calculate the sum of the time series distortion distances between each center point and all data points in the class as the initial judgment value;

[0020] S1.6. In each class, select a data point outside the center point as the new center point;

[0021] S1.7. Calculate the time series distortion distances between the load history data and the wind and solar output history data and N new center points; reclassify the load history data and the wind and solar output history data according to the time series distortion distances; calculate the sum of the time series distortion distances between each new center point and all data points in the class as the judgment value of the current round;

[0022] S1.8. If the judgment value of the current round is less than the judgment value of the previous round, update the judgment value and return to step S1.6; otherwise, clustering ends.

[0023] Furthermore, in S2, the regional power grid carrying capacity model is specifically:

[0024]

[0025] Where maxF represents the objective function with the maximum installed capacity of new energy in the regional power grid and the maximum effective reactive power reserve of the transmission network as the goal, F1 is the reactive power reserve objective function, and F2 is the installed capacity objective function of new energy in the transmission and distribution network; a and b are weight coefficients determined by the analytic hierarchy process; k is the number of distribution networks connected to the transmission network, S is the number of distribution networks, m is the number of nodes in the transmission network, and n is the number of nodes in the distribution network; Q GR,T It is the effective reactive power reserve of the transmission network when the output of renewable energy accounts for the largest proportion; P i is the installed capacity of renewable energy at transmission grid node i; P j is the installed capacity of renewable energy on distribution network node j;

[0026] The expression of effective reactive power reserve of transmission network is:

[0027]

[0028] Among them, g i G is the sensitivity of the generator or new energy unit on node i to the key node j; j is the submatrix of column j in the sensitivity matrix; Max(G j ) is G j The maximum value in is the rated maximum reactive power of the reactive source at node i, is the reactive power currently emitted by node i.

[0029] Further, in S3, the method used to decompose the regional power grid carrying capacity model into the transmission network carrying capacity optimization sub-model and the distribution network carrying capacity optimization sub-model is the generalized master-slave splitting method, and the generalized master-slave splitting method is expressed as:

[0030] f MB (u M ,x M ,x B )=S BS

[0031] S BS =f BS (u S ,x S ,x B )

[0032] Among them, S BS is the intermediate variable of the algorithm, representing the power transmitted between the transmission network and the distribution network; f MB 、f BSis the boundary power flow constraint of the decomposed transmission network and distribution network, u M is the transmission grid control variable, x M is the state variable of the transmission network, x B is the boundary system state variable, u S is the distribution network control variable, x S is the distribution network state variable;

[0033] After decomposition, the transmission network carrying capacity optimization sub-model includes the objective function, constraint conditions and wind and solar output model of the transmission network carrying capacity optimization sub-model;

[0034] The transmission network carrying capacity optimization sub-model includes the objective function F M for:

[0035]

[0036] In the formula, Q GR,T is the effective reactive power reserve of the transmission network when the renewable energy output accounts for the largest proportion, m is the number of nodes in the transmission network, P i is the installed capacity of renewable energy on the transmission grid node i, ζ BS The objective function F is the maximum installed capacity of new energy in the distribution network. S1 The sensitivity matrix of the constraints to the state variables of the boundary system, the superscript T is the matrix transpose, x B is the state variable of the boundary system;

[0037] The constraints of the transmission network capacity optimization sub-model include power balance constraints, generator output constraints, branch power constraints, new energy output constraints, static voltage stability constraints and operational safety and stability limit constraints;

[0038] The conditions for the static voltage stability constraint are:

[0039]

[0040] Among them, k p is the active power margin, P is the initial active power; k q is the reactive power margin; Q is the initial reactive power; Q max is the maximum reactive power of the load;

[0041] The operation safety and stability limit constraint expression is as follows:

[0042]

[0043] Where η is the photovoltaic penetration rate; ζ is the wind power penetration rate; V E is the voltage amplitude at the power grid connection point; X is the equivalent impedance; P load is the load active power; Pmax is the maximum power load, is the load power factor, Q load is the load reactive power; Q X It is the reactive power consumed by the equivalent impedance at the critical point of voltage static voltage stability;

[0044] The wind and solar output model of the transmission network carrying capacity optimization sub-model includes the double-fed asynchronous wind turbine output model and the photovoltaic unit output model;

[0045] Among them, the expression of the double-fed asynchronous wind turbine output model is:

[0046]

[0047] Among them, P DFIG is the active power of the doubly-fed asynchronous wind turbine; Q DFIG is the reactive power of the doubly-fed asynchronous wind turbine; S DFIG The installed capacity of double-fed asynchronous wind turbines; is the power factor;

[0048] The expression of the photovoltaic unit output model is:

[0049]

[0050] Q solar,i ≤0.48S solar

[0051] Among them, P solar,i is the active power output of the photovoltaic unit on node i; Q solar,i is the reactive power output of the photovoltaic unit on node i; S solar The capacity of the grid-connected inverter for the photovoltaic array;

[0052] After decomposition, the distribution network carrying capacity optimization sub-model includes the objective function, constraint conditions and wind and solar output model of the distribution network carrying capacity optimization sub-model;

[0053] The objective function of the distribution network carrying capacity optimization sub-model is:

[0054]

[0055] maxF S2 =Q up

[0056] Among them, λ MB，k is the Lagrange multiplier vector of the boundary power flow constraint in the k-th distribution network, and the superscript T is the matrix transpose; Q up F is the reactive power output from the distribution network to the upper grid; S1 represents the objective function of the maximum installed capacity of new energy in the distribution network, F S2It represents the objective function of maximizing the reactive power provided by the distribution network to the transmission network; xB,k represents the state variable of the kth distribution network boundary system; S is the number of distribution networks, n is the number of distribution network nodes, P j is the installed capacity of renewable energy on distribution network node j;

[0057] The constraints of the distribution network carrying capacity optimization sub-model include system power flow constraints, node voltage constraints, wind and solar power unit operation constraints, continuous reactive power compensation constraints, discrete reactive power compensation constraints and line transmission power constraints;

[0058] The wind and solar output model of the distribution network carrying capacity optimization sub-model includes the double-fed asynchronous wind turbine output model and the photovoltaic unit output model;

[0059] Among them, the expression of the double-fed asynchronous wind turbine output model is:

[0060]

[0061] Where, s is the slip rate; U s is the stator side voltage; I s is the stator winding current; X s is the stator leakage reactance; X m is the magnetizing reactance; I r is the rotor side converter current; P DFIG is the active power of the doubly-fed asynchronous wind turbine; Q DFIG is the reactive power of the doubly-fed asynchronous wind turbine;

[0062] The expression of the photovoltaic unit output model is:

[0063]

[0064] -P solar,i tanθ i ≤Q solar,i ≤P solar,i tanθ i .

[0065] 0≤P solar,i ≤P solar

[0066] Among them, P solar is the available active power of the photovoltaic unit; S solar is the grid-connected inverter capacity of the photovoltaic array; P solar,i is the active power output of the photovoltaic unit on node i; Q solar,i is the reactive power output of the photovoltaic unit at node i; θ i is the power factor of the PV system at node i.

[0067] Further, in S4, the transmission and distribution step-by-step iterative algorithm is specifically HDA, and the specific steps of collaboratively solving the transmission network carrying capacity optimization sub-model and the distribution network carrying capacity optimization sub-model include:

[0068] S4.1, initialize the access capacity of new energy sources at all levels of power grids, set the number of iterations z = 1, set the voltage of each distribution network root node to 1, the phase angle to 0, and the objective function F of the maximum installed capacity of new energy sources in the distribution network S1 The sensitivity matrix ζ of the constraints to the state variables of the boundary system BS Set to 0, the Lagrange multiplier vector λ of the boundary power flow constraint in the distribution network MB,k Set to 0;

[0069] S4.2, solve the distribution network carrying capacity optimization sub-model and obtain the apparent power and boundary sensitivity ζ of the root node BS , and transmitted to the transmission network carrying capacity optimization sub-model;

[0070] S4.3, solve the transmission network capacity optimization sub-model, and obtain the voltage vector of the boundary node and the Lagrange multiplier vector λ of the boundary flow constraint in the kth distribution network MB,k , and transmitted to the distribution network carrying capacity optimization sub-model;

[0071] S4.4, calculate the voltage vector and the Lagrange multiplier vector λ of the boundary power flow constraint in the kth distribution network MB,k The difference before and after the update is used to determine whether the convergence accuracy is met. If converged, the new energy installed capacity of the transmission network and distribution network is output. If not converged, the number of iterations is increased by one and returns to step S4.2.

[0072] Furthermore, the method used to solve the distribution network carrying capacity optimization sub-model is to improve the NSGA-Ⅱ multi-objective optimization algorithm, and the steps are as follows:

[0073] S4.2.1. Set distribution network parameters and improve NSGA-Ⅱ multi-objective optimization algorithm parameters;

[0074] S4.2.2, abstract the installed capacity of new energy into a population, generate an initial population, and set the number of iterations to 0;

[0075] S4.2.3. Calculate the power flow of individuals in the initial population and obtain the objective function F of the maximum installed capacity of new energy in the distribution network S1 The value of the distribution network and the objective function F that provides the maximum reactive power to the transmission network S2 The value of

[0076] S4.2.4, judging the convergence stage, in the early stage of convergence, selection, normal distribution crossover and uniform mutation are used to generate the offspring population, in the middle stage of convergence, selection, normal distribution crossover and Levy distribution mutation are used to generate the offspring population, and in the late stage of convergence, selection, normal distribution crossover and Gaussian distribution mutation operators are used to generate the offspring population;

[0077] The convergence stage judgment formula is:

[0078]

[0079] Among them, step represents the convergence stage, N S is the current iteration number, N S,max is the maximum number of iterations, when step<N S,max / 2, this is the early stage of convergence. S,max / 2≤step<2N S,max When step ≥ 2N S,max When , it belongs to the late convergence stage;

[0080] S4.2.5, the parent and offspring populations are merged, and non-dominated sorting and crowding calculation are performed;

[0081] S4.2.6. Individuals with crowding greater than the set value are regarded as superior individuals and form a new generation of population; the number of iterations increases by one;

[0082] S4.2.7. If the number of iterations is less than the set value, return to step S4.2.4; otherwise, the iteration ends and the solution set of the new energy installation capacity of the distribution network is output.

[0083] Furthermore, the solving of the transmission network carrying capacity optimization sub-model specifically includes:

[0084] S4.3.1, global calculations are performed using different stability margins under static voltage stability constraints;

[0085] S4.3.2. Calculate the effective reactive power reserve corresponding to different static voltage stability margins of the transmission network;

[0086] S4.3.3. Calculate the transmission network carrying capacity and the reactive power output of the distribution network to the transmission network based on different effective reactive power reserves;

[0087] S4.3.4. Determine whether the convergence requirements are met within 24 hours when new energy is connected. If not, proceed to step S4.2.1. If the convergence requirements are met, the installed capacity of new energy in the transmission network and the corresponding reactive power reserve are obtained.

[0088] Further, in S5, the operation index includes voltage deviation, voltage qualification rate, and net load balance rate; the economic index includes average line loss rate and voltage regulation cost; the environmental protection index includes new energy output ratio; the voltage regulation cost includes photovoltaic unit voltage regulation cost and wind turbine unit voltage regulation cost;

[0089] PV unit voltage regulation costa 5 The expression is:

[0090] a 5 =a Q Q solar,i +b P ΔP solar,i,loss

[0091] Among them, a Q is the cost coefficient of reactive power output, b P is the opportunity cost coefficient of active reduction; Q solar,i is the reactive power output of the photovoltaic unit at node i; ΔP solar,i,loss To increase reactive power output, active power output is reduced;

[0092] Wind turbine voltage regulation costa 6 The expression is:

[0093] a 6 =ρ dfig ·FIT·ΔP dfig

[0094] Among them, FIT is the on-grid electricity price of wind power; ρ dfig is the wind power conversion coefficient; ΔP dfig The active output is reduced when the reactive output is increased;

[0095] New energy output ratio a 7 , the expression is:

[0096]

[0097] Where, node represents the number of nodes in the power system; P DG,i represents the active power output of the new energy unit at node i, P load is the load active power.

[0098] Furthermore, S6 specifically includes:

[0099] S6.1. Calculate the power flow of the regional power grid for 24 hours and use the improved entropy weight method to calculate the objective weight of each indicator in the evaluation system.

[0100] S6.2, use the analytic hierarchy process to obtain the subjective weight of each indicator in the evaluation system.

[0101] S6.3, the combined weights are obtained by algebraic mean method,

[0102] S6.4. Calculate the hourly score of each solution in the renewable energy installation capacity solution set according to the combined weight, take the average score, and the solution with the highest average score is the regional power grid carrying capacity;

[0103] The improved entropy weight method calculates the objective weight β m The calculation formula is:

[0104]

[0105] Among them, E m is the information entropy of each indicator, is the average value of all information entropies except 1, c is the precision coefficient, and M is the total number of indicators to be evaluated.

[0106] The present invention also proposes a system for optimizing the carrying capacity of a regional power grid under the access of new energy sources that implements the above method, comprising:

[0107] The scenario set construction module is used to cluster the historical data of wind and solar power output and load to obtain the typical scenario sets of load and wind and solar power;

[0108] A module for constructing a regional power grid carrying capacity model, which is used to construct a regional power grid carrying capacity model with the goal of maximizing the installed capacity of new energy in the regional power grid and maximizing the effective reactive power reserve of the transmission network;

[0109] A model decomposition module is used to decompose the regional power grid carrying capacity model into a transmission network carrying capacity optimization sub-model and a distribution network carrying capacity optimization sub-model;

[0110] The model solving module is used to adopt the transmission and distribution step-by-step iterative algorithm for typical scenario sets, collaboratively solve the transmission network carrying capacity optimization sub-model and the distribution network carrying capacity optimization sub-model, and obtain the solution set of new energy installation capacity;

[0111] The indicator construction module is used to construct an evaluation system that takes into account operational indicators, economic indicators, and environmental indicators;

[0112] The optimal bearing capacity calculation module is used to calculate the time-sharing score based on the evaluation system and the solution set of new energy installation capacity, and obtain the optimal bearing capacity using the combined weighting method.

[0113] The beneficial effects of the present invention are:

[0114] The present invention proposes a regional power grid carrying capacity model, which incorporates the reactive support of the active distribution network to the transmission network into the static voltage stability calculation of the transmission network, reducing the risk of voltage fluctuation and voltage collapse; considering the reactive power output of wind and solar units to participate in active voltage regulation, improving the power grid carrying capacity; using the HDA algorithm to convert the transmission and distribution power grid carrying capacity problems into sub-problems of their respective regions, reducing the calculation scale and improving the convergence performance; considering the reactive support of the active distribution network, reducing the risk of voltage fluctuation and voltage collapse. An evaluation system that considers safety and economy is constructed, and a combined weighting method is used to calculate the score and select the optimal solution, realizing the time-sharing quantitative evaluation of carrying capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0115] Figure 1 A flow chart of a method for comprehensive optimization of the carrying capacity of a regional power grid provided by an embodiment of the present invention;

[0116] Figure 2 A collaborative optimization solution flow chart provided by an embodiment of the present invention;

[0117] Figure 3 An improved NSGA-II flow chart provided for an embodiment of the present invention;

[0118] Figure 4 A schematic diagram of an improved IEEE-30 node provided in an embodiment of the present invention;

[0119] Figure 5a The clustered wind power output scenario provided by the embodiment of the present invention;

[0120] Figure 5b The photovoltaic output scenario after clustering provided by the embodiment of the present invention;

[0121] Figure 6 This is a diagram of the voltage distribution result on the transmission side provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0122] The present invention will now be described in further detail with reference to the accompanying drawings.

[0123] Embodiment 1

[0124] like Figure 1 As shown, the present invention provides a method for optimizing the carrying capacity of a regional power grid under the access of new energy, wherein the regional power grid includes a transmission network and a distribution network, wherein the distribution network is connected to the transmission network through a transformer, wherein the node of the distribution network directly connected to the transformer is the root node of the distribution network, and the node of the transmission network connected to the distribution network through the transformer is the boundary node of the transmission network, and the optimization method comprises the following steps:

[0125] S1. Cluster the historical data of wind and solar power output and load to obtain a typical scene set of load and wind and solar power. The method used to cluster the historical data of wind and solar power output and load is TSK-Medoids clustering, which specifically includes:

[0126] S1.1, obtain historical data of wind and solar power output and load and the number of clusters N;

[0127] S1.2, randomly select N objects as center points in the historical data of wind and solar power output and historical data of load;

[0128] S1.3, calculate the time series distortion distance between the load historical data and the wind and solar output historical data and N center points; the time series distortion distance D is expressed as:

[0129]

[0130] Among them, X A (t), X B (t) are two time series data in the historical data of wind, solar and load, with a period of 24 hours and a sampling interval of 1 hour; w l (t) is the path vector of each data point in the two time series; L is the length of the path W.

[0131] S1.4, classify the load historical data and wind and solar output historical data according to the time series distortion distance;

[0132] S1.5, calculate the sum of the time series distortion distances between each center point and all data points in the class as the initial judgment value;

[0133] S1.6. In each class, select a data point outside the center point as the new center point;

[0134] S1.7. Calculate the time series distortion distances between the load history data and the wind and solar output history data and N new center points; reclassify the load history data and the wind and solar output history data according to the time series distortion distances; calculate the sum of the time series distortion distances between each new center point and all data points in the class as the judgment value of the current round;

[0135] S1.8. If the judgment value of the current round is less than the judgment value of the previous round, update the judgment value and return to step S1.6; otherwise, clustering ends.

[0136] S2. With the goal of maximizing the installed capacity of new energy in the regional power grid and maximizing the effective reactive power reserve of the transmission network, a regional power grid carrying capacity model is constructed; the specific regional power grid carrying capacity model is as follows:

[0137]

[0138] Where maxF represents the objective function with the maximum installed capacity of new energy in the regional power grid and the maximum effective reactive power reserve of the transmission network as the goal, F1 is the reactive power reserve objective function, and F2 is the installed capacity objective function of new energy in the transmission and distribution network; a and b are weight coefficients determined by the analytic hierarchy process; k is the number of distribution networks connected to the transmission network, S is the number of distribution networks, m is the number of nodes in the transmission network, and n is the number of nodes in the distribution network; Q GR,T It is the effective reactive power reserve of the transmission network when the output of renewable energy accounts for the largest proportion; P i is the installed capacity of renewable energy at transmission grid node i; P j is the installed capacity of renewable energy on distribution network node j;

[0139] The expression of effective reactive power reserve of transmission network is:

[0140]

[0141] Among them, g i G is the sensitivity of the generator or new energy unit on node i to the key node j; j is the submatrix of column j in the sensitivity matrix; Max(G j ) is G j The maximum value in is the rated maximum reactive power of the reactive source at node i, is the reactive power currently emitted by node i.

[0142] S3. Decompose the regional power grid carrying capacity model into a transmission network carrying capacity optimization sub-model and a distribution network carrying capacity optimization sub-model. In this embodiment, the method used to decompose the regional power grid carrying capacity model into a transmission network carrying capacity optimization sub-model and a distribution network carrying capacity optimization sub-model is the generalized master-slave splitting method, and the same effect can be achieved by using other methods.

[0143] The generalized master-slave splitting method is expressed as:

[0144] f MB (u M ,x M ,x B )=S BS

[0145] S BS =f BS (u S ,x S ,x B )

[0146] Among them, S BS is the intermediate variable of the algorithm, representing the power transmitted between the transmission network and the distribution network; f MB 、f BSis the boundary power flow constraint of the decomposed transmission network and distribution network, u M is the transmission grid control variable, x M is the state variable of the transmission network, x B is the boundary system state variable, u S is the distribution network control variable, x S is the distribution network state variable;

[0147] After decomposition, the transmission network carrying capacity optimization sub-model includes the objective function, constraint conditions and wind and solar output model of the transmission network carrying capacity optimization sub-model;

[0148] The transmission network carrying capacity optimization sub-model includes the objective function F M for:

[0149]

[0150] In the formula, Q GR,T is the effective reactive power reserve of the transmission network when the renewable energy output accounts for the largest proportion, m is the number of nodes in the transmission network, P i is the installed capacity of renewable energy on the transmission grid node i, ζ BS The objective function F is the maximum installed capacity of new energy in the distribution network. S1 The sensitivity matrix of the constraints to the state variables of the boundary system, the superscript T is the matrix transpose, x B is the state variable of the boundary system;

[0151] The constraints of the transmission network capacity optimization sub-model include power balance constraints, generator output constraints, branch power constraints, new energy output constraints, static voltage stability constraints and operational safety and stability limit constraints;

[0152] The conditions for power balance constraints are:

[0153]

[0154] Where P out,i , Q out,i is the active and reactive output of the new energy and reactive compensation equipment on node i.

[0155] The conditions for the static voltage stability constraint are:

[0156]

[0157] Among them, k p is the active power margin, P is the initial active power; k q is the reactive power margin; Q is the initial reactive power; Q max is the maximum reactive power of the load;

[0158] The operation safety and stability limit constraint expression is as follows:

[0159]

[0160] Where η is the photovoltaic penetration rate; ζ is the wind power penetration rate; V E is the voltage amplitude at the power grid connection point; X is the equivalent impedance; P load is the load active power; P max is the maximum power load, is the load power factor, Q load is the load reactive power; Q X It is the reactive power consumed by the equivalent impedance at the critical point of voltage static voltage stability;

[0161] The wind and solar output model of the transmission network carrying capacity optimization sub-model includes the double-fed asynchronous wind turbine output model and the photovoltaic unit output model;

[0162] Among them, the expression of the double-fed asynchronous wind turbine output model is:

[0163]

[0164] Among them, P DFIG is the active power of the doubly-fed asynchronous wind turbine; Q DFIG is the reactive power of the doubly-fed asynchronous wind turbine; S DFIG The installed capacity of double-fed asynchronous wind turbines; is the power factor;

[0165] The expression of the photovoltaic unit output model is:

[0166]

[0167] Q solar,i ≤0.48S solar

[0168] Among them, P solar,i is the active power output of the photovoltaic unit on node i; Q solar,i is the reactive power output of the photovoltaic unit on node i; S solar The capacity of the grid-connected inverter for the photovoltaic array;

[0169] After decomposition, the distribution network carrying capacity optimization sub-model includes the objective function, constraint conditions and wind and solar output model of the distribution network carrying capacity optimization sub-model;

[0170] The objective function of the distribution network carrying capacity optimization sub-model is:

[0171]

[0172] maxF S2 =Q up

[0173] Among them, λ MB，k is the Lagrange multiplier vector of the boundary power flow constraint in the k-th distribution network, and the superscript T is the matrix transpose; Q up F is the reactive power output from the distribution network to the upper grid; S1 represents the objective function of the maximum installed capacity of new energy in the distribution network, F S2 It represents the objective function of maximizing the reactive power provided by the distribution network to the transmission network; xB,k represents the state variable of the kth distribution network boundary system; S is the number of distribution networks, n is the number of distribution network nodes, P j is the installed capacity of renewable energy on distribution network node j;

[0174] The constraints of the distribution network carrying capacity optimization sub-model include system power flow constraints, node voltage constraints, wind and solar power unit operation constraints, continuous reactive power compensation constraints, discrete reactive power compensation constraints and line transmission power constraints;

[0175] Among them, the conditions of system power flow constraints are:

[0176]

[0177] Among them, P Li The active power injected into node i; Q Li is the reactive power injected into node i; P DGi The active power injected into node i by wind and solar power generation; Q DGi The reactive power injected into node i by wind and solar power generation; δ ij is the phase angle difference between node i and node j; G ij is the real part of the admittance on branch k between nodes i and j; B ij is the imaginary part of the admittance on branch k between nodes i and j.

[0178] The node voltage constraint conditions are:

[0179] U imin ≤U i ≤U imax ,i=1,2,…,node.

[0180] Among them, U imin is the minimum value of the voltage amplitude at node i; U imax is the maximum voltage amplitude at node i.

[0181] The operating constraints of wind and solar units are as follows:

[0182] Q DGimin ≤Q DGi ≤Q DGimax .

[0183] Among them, Q DGimin is the minimum value of the reactive power injected into node i by wind and solar power generation; Q DGimax is the maximum value of the reactive power injected into node i by wind and solar power generation.

[0184] The conditions for continuous reactive power compensation constraints are:

[0185]

[0186] in, and is the minimum and maximum value of the reactive power output of the static VAR compensator installed at node i; is the actual reactive power compensation amount of the static VAR compensator at node i at time t.

[0187] The discrete reactive power compensation constraints are:

[0188]

[0189] in, is the number of capacitor banks put into operation at node i at time t; is the reactive power that can be compensated by a single capacitor bank; the condition of line transmission power constraint is:

[0190] S l ≤S l,max

[0191] Among them, S l is the apparent power passing through line l; S l,max This is the maximum value allowed for line transmission.

[0192] The wind and solar output model of the distribution network carrying capacity optimization sub-model includes the double-fed asynchronous wind turbine output model and the photovoltaic unit output model;

[0193] Among them, the expression of the double-fed asynchronous wind turbine output model is:

[0194]

[0195] Where, s is the slip rate; U s is the stator side voltage; I s is the stator winding current; X s is the stator leakage reactance; X m is the magnetizing reactance; I r is the rotor side converter current; P DFIG is the active power of the doubly-fed asynchronous wind turbine; Q DFIG is the reactive power of the doubly-fed asynchronous wind turbine;

[0196] The expression of the photovoltaic unit output model is:

[0197]

[0198] -P solar,i tanθ i ≤Q solar,i ≤P solar,i tanθ i .

[0199] 0≤P solar,i ≤P solar

[0200] Among them, P solar is the available active power of the photovoltaic unit; S solar is the grid-connected inverter capacity of the photovoltaic array; P solar,i is the active power output of the photovoltaic unit on node i; Q solar,i is the reactive power output of the photovoltaic unit at node i; θ i is the power factor of the PV system at node i.

[0201] S4. A transmission and distribution step-by-step iterative algorithm is used for the typical scenario set. The transmission and distribution step-by-step iterative algorithm is specifically HDA (Heterogeneous Decomposition Algorithm), which collaboratively solves the transmission network carrying capacity optimization sub-model and the distribution network carrying capacity optimization sub-model to obtain a new energy installation capacity solution set. The specific steps include:

[0202] S4.1, initialize the access capacity of new energy sources at all levels of power grids, set the number of iterations z = 1, set the voltage of each distribution network root node to 1, the phase angle to 0, and the objective function F of the maximum installed capacity of new energy sources in the distribution network S1 The sensitivity matrix ζ of the constraints to the state variables of the boundary system BS Set to 0, the Lagrange multiplier vector λ of the boundary power flow constraint in the distribution network MB,k Set to 0;

[0203] S4.2, solve the distribution network carrying capacity optimization sub-model and obtain the apparent power and boundary sensitivity ζ of the root node BS , and transmitted to the transmission network carrying capacity optimization sub-model;

[0204] S4.3, solve the transmission network capacity optimization sub-model, and obtain the voltage vector of the boundary node and the Lagrange multiplier vector λ of the boundary flow constraint in the kth distribution network MB,k , and transmitted to the distribution network carrying capacity optimization sub-model;

[0205] S4.4, calculate the voltage vector and the Lagrange multiplier vector λ of the boundary power flow constraint in the kth distribution network MB,kThe difference before and after the update is used to determine whether the convergence accuracy is met. If converged, the new energy installed capacity of the transmission network and distribution network is output. If not converged, the number of iterations is increased by one and returns to step S4.2.

[0206] The method used to solve the distribution network carrying capacity optimization sub-model is to improve the NSGA-Ⅱ multi-objective optimization algorithm. The steps are as follows:

[0207] S4.2.1. Set distribution network parameters and improve NSGA-Ⅱ multi-objective optimization algorithm parameters;

[0208] S4.2.2, abstract the installed capacity of new energy into a population, generate an initial population, and set the number of iterations to 0;

[0209] S4.2.3. Calculate the power flow of individuals in the initial population and obtain the objective function F of the maximum installed capacity of new energy in the distribution network S1 The value of the distribution network and the objective function F that provides the maximum reactive power to the transmission network S2 The value of

[0210] S4.2.4, judging the convergence stage, in the early stage of convergence, selection, normal distribution crossover and uniform mutation are used to generate the offspring population, in the middle stage of convergence, selection, normal distribution crossover and Levy distribution mutation are used to generate the offspring population, and in the late stage of convergence, selection, normal distribution crossover and Gaussian distribution mutation operators are used to generate the offspring population;

[0211] The convergence stage judgment formula is:

[0212]

[0213] Among them, step represents the convergence stage, N S is the current iteration number, N S,max is the maximum number of iterations, when step<N S,max / 2, this is the early stage of convergence. S,max / 2≤step<2N S,max When step ≥ 2N S,max When , it belongs to the late convergence stage;

[0214] S4.2.5, the parent and offspring populations are merged, and non-dominated sorting and crowding calculation are performed;

[0215] S4.2.6. Individuals with crowding greater than the set value are regarded as superior individuals and form a new generation of population; the number of iterations increases by one;

[0216] S4.2.7. If the number of iterations is less than the set value, return to step S4.2.4; otherwise, the iteration ends and the solution set of the new energy installation capacity of the distribution network is output.

[0217] The normal distribution crossover expression is:

[0218]

[0219] in, is the kth gene of the offspring after the crossover operation, p 1,k 、p 2,k is the kth gene of the parent; β k is a random variable, and its expression is as follows:

[0220]

[0221] in, u is a random number in the interval [0,1]; ψ is the crossover parameter.

[0222] The uniform variation expression is:

[0223]

[0224] in, is the jth gene of the i-th individual before mutation; is the jth gene of the ith individual after mutation; ε uniform Variation domain The random numbers in is the upper limit of the j-th gene, is the lower limit of the jth gene, and γ is a constant between 0 and 1;

[0225] The variation expression of Levy distribution is:

[0226]

[0227] Among them, ε Levy Variation domain The random numbers in conformity with Levy distribution;

[0228] The Gaussian distribution mutation operator expression is:

[0229]

[0230] Among them, ε Gauss Variation domain A random number that conforms to a Gaussian distribution.

[0231] Solving the transmission network carrying capacity optimization sub-model specifically includes:

[0232] S4.3.1, global calculations are performed using different stability margins under static voltage stability constraints;

[0233] S4.3.2. Calculate the effective reactive power reserve corresponding to different static voltage stability margins of the transmission network;

[0234] S4.3.3. Calculate the transmission network carrying capacity and the reactive power output of the distribution network to the transmission network based on different effective reactive power reserves;

[0235] S4.3.4. Determine whether the convergence requirements are met within 24 hours when new energy is connected. If not, proceed to step S4.2.1. If the convergence requirements are met, the installed capacity of new energy in the transmission network and the corresponding reactive power reserve are obtained.

[0236] S5. Construct an evaluation system that takes into account operation indicators, economic indicators and environmental protection indicators; the operation indicators include voltage deviation, voltage qualification rate and net load balance rate; the economic indicators include average line loss rate and voltage regulation cost; the environmental protection indicators include new energy output ratio; the voltage regulation cost includes photovoltaic unit voltage regulation cost and wind turbine unit voltage regulation cost;

[0237] Voltage deviation 1 The expression is:

[0238]

[0239] Among them, U i,t is the actual voltage value of node i at time t; U' i is the rated value of the node voltage;

[0240] Voltage qualification rate 2 The expression is:

[0241]

[0242] Among them, N V is the number of qualified voltage nodes in the system

[0243] Net load balance ratea 3 The expression is:

[0244]

[0245] Economic indicators include average line loss rate a 4 , the expression is:

[0246]

[0247] Among them, P loss is the active power loss of the power system; P L Amount of power supplied to the power system;

[0248] PV unit voltage regulation costa 5 The expression is:

[0249] a5 =a Q Q solar,i +b P ΔP solar,i,loss

[0250] Among them, a Q is the cost coefficient of reactive power output, b P is the opportunity cost coefficient of active reduction; Q solar,i is the reactive power output of the photovoltaic unit at node i; ΔP solar,i,loss To increase reactive power output, active power output is reduced;

[0251] Wind turbine voltage regulation costa 6 The expression is:

[0252] a 6 =ρ dfig ·FIT·ΔP dfig

[0253] Among them, FIT is the on-grid electricity price of wind power; ρ dfig is the wind power conversion coefficient; ΔP dfig The active output is reduced when the reactive output is increased;

[0254] New energy output ratio a 7 , the expression is:

[0255]

[0256] Where, node represents the number of nodes in the power system; P DG,i represents the active power output of the new energy unit at node i, P load is the load active power.

[0257] S6. Based on the evaluation system and the solution set of new energy installation capacity, the combined weighting method is used to calculate the time-sharing score to obtain the optimal carrying capacity. Specifically, it includes:

[0258] S6.1. Calculate the power flow of the regional power grid for 24 hours and use the improved entropy weight method to calculate the objective weight of each indicator in the evaluation system.

[0259] S6.2. The subjective weight of each indicator in the evaluation system is obtained by using the hierarchical analysis method. The calculation of subjective weight by the hierarchical analysis method mainly includes the following steps:

[0260] Establish a hierarchical evaluation system for solution sets.

[0261] A judgment matrix is ​​established based on the scaling system table to indicate the relative importance of the optimization objectives.

[0262] Find the maximum eigenvalue of the judgment matrix and the corresponding eigenvector.

[0263] When the result passes the consistency check, the feature vector is normalized to obtain the subjective weight vector a m .

[0264] S6.3, the combined weight is obtained by algebraic mean method, the combined weight W m The expression is:

[0265]

[0266] Among them, a m and β m They are subjective weight and objective weight respectively.

[0267] S6.4. Calculate the hourly score of each solution in the renewable energy installation capacity solution set according to the combined weight, take the average score, and the solution with the highest average score is the regional power grid carrying capacity.

[0268] The improved entropy weight method calculates the objective weight β m The calculation formula is:

[0269]

[0270] Among them, E m is the information entropy of each indicator, is the average value of all information entropies except 1, c is the precision coefficient, and M is the total number of indicators to be evaluated.

[0271] The calculation formula of the accuracy coefficient c is as follows:

[0272]

[0273] Embodiment 2

[0274] The present invention proposes a system for optimizing the carrying capacity of a regional power grid under the access of new energy sources corresponding to the method of the first embodiment, comprising:

[0275] The scenario set construction module is used to cluster the historical data of wind and solar power output and load to obtain the typical scenario sets of load and wind and solar power;

[0276] A module for constructing a regional power grid carrying capacity model, which is used to construct a regional power grid carrying capacity model with the goal of maximizing the installed capacity of new energy in the regional power grid and maximizing the effective reactive power reserve of the transmission network;

[0277] A model decomposition module is used to decompose the regional power grid carrying capacity model into a transmission network carrying capacity optimization sub-model and a distribution network carrying capacity optimization sub-model;

[0278] The model solving module is used to adopt the transmission and distribution step-by-step iterative algorithm for typical scenario sets, collaboratively solve the transmission network carrying capacity optimization sub-model and the distribution network carrying capacity optimization sub-model, and obtain the solution set of new energy installation capacity;

[0279] The indicator construction module is used to construct an evaluation system that takes into account operational indicators, economic indicators, and environmental indicators;

[0280] The optimal bearing capacity calculation module is used to calculate the time-sharing score based on the evaluation system and the solution set of new energy installation capacity, and obtain the optimal bearing capacity using the combined weighting method.

[0281] The implementation methods of each module and the functions of the modules in the system are completely consistent with the steps of the method in Example 1, so they will not be repeated here.

[0282] Embodiment 3

[0283] The embodiment of the present invention adopts improved IEEE 30-node power system simulation analysis, such as Figure 4 As shown in the figure, nodes 4, 7, 10, 12, and 24 are connected to the IEEE-33 distribution system through transformers, nodes 2 and 7 of the distribution network are connected to photovoltaic power, nodes 12 and 17 are connected to wind power, node 25 is connected to capacitors, and node 33 is connected to a continuous reactive power compensation device. The apparent power of the photovoltaic inverter and wind turbine is set to 1.1 times the active power. The reactive output range of the SVC is between -100 kvar and 300 kvar. The parameters of the NSGA-Ⅱ algorithm are set as follows: population size is 50, number of iterations is 100, crossover probability is 0.9, and mutation probability is 0.1. The cost coefficient a of the reactive output of the photovoltaic unit Q At different power factors, they correspond to 0.3, 0.1, 0.2, b p Take 1.58; double-fed induction wind turbine production cost ρ dfig Take 0.92, and the on-grid electricity price FIT is 0.8 yuan / kW·h.

[0284] The scenario set generated by using the historical data of a typical power distribution network in a city for many years is shown in Figure 5. In the IEEE-30 and IEEE-33 node systems, the designated nodes are connected to renewable energy sources. When the effective reactive power reserve is greater than 258.8MW, the static voltage can be kept stable, and the carrying capacity of the regional power grid is calculated.

[0285] According to the two schemes obtained by the method of this application, the main network carrying capacity of Scheme 1 is 122.21MW, and the distribution network carrying capacity is 46.55MW. The transmission network carrying capacity of Scheme 2 is 155.58MW, and the transmission network carrying capacity is 43.15MW. The evaluation system of the method of this application is used for scoring, and the comparison is shown in Table 1.

[0286] Table 1 Comparison of weights and combined scores of the two schemes

[0287]

[0288] Option 2 scores higher, so the regional grid capacity in this example is 198.7MW. Figure 6 The 24h voltage distribution of the scheme meets the requirements of safe and stable operation.

[0289] In summary, the present invention proposes that a representative set of load and wind and solar power output scenarios can be generated based on historical power grid data by using the TSK-Medoids model clustering method; a distributed renewable energy carrying capacity calculation model considering the reactive power reserve of the power grid is proposed, which improves the power grid carrying capacity and reduces the risk of voltage fluctuation and voltage collapse; the regional power grid carrying capacity model is decomposed into a transmission network carrying capacity optimization sub-model and a distribution network carrying capacity optimization sub-model, and the HDA algorithm is used to collaboratively solve the transmission network carrying capacity optimization sub-model and the distribution network carrying capacity optimization sub-model to obtain a new energy installation capacity solution set, which reduces the calculation scale and improves the convergence performance; an evaluation system considering operating indicators, economic indicators and environmental protection indicators is constructed to reasonably evaluate the carrying capacity of renewable energy access to the power grid; a combined weighting method is used to obtain the optimal solution, and the scores of each solution are obtained comprehensively and objectively, which is easy to compare.

[0290] The above are only preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A method for optimizing the carrying capacity of a regional power grid under the access of new energy, wherein the regional power grid includes a transmission network and a distribution network, wherein the distribution network is connected to the transmission network through a transformer, wherein the node of the distribution network directly connected to the transformer is the root node of the distribution network, and the node of the transmission network connected to the distribution network through the transformer is the boundary node of the transmission network, wherein: The optimization method comprises the following steps: S1. Cluster the historical data of wind and solar power output and load to obtain a typical scene set of load and wind and solar power; S2. With the goal of maximizing the installed capacity of new energy in the regional power grid and maximizing the effective reactive power reserve of the transmission network, a regional power grid carrying capacity model is constructed; in S2, the regional power grid carrying capacity model is specifically: Where maxF represents the objective function with the maximum installed capacity of new energy in the regional power grid and the maximum effective reactive power reserve in the transmission network as the goal, F1 is the reactive power reserve objective function, and F2 is the installed capacity objective function of new energy in the transmission and distribution network; α and β are weight coefficients determined by the analytic hierarchy process; k is the number of distribution networks connected to the transmission network, S is the number of distribution networks, m is the number of nodes in the transmission network, and n is the number of nodes in the distribution network; Q GR,T It is the effective reactive power reserve of the transmission network when the output of renewable energy accounts for the largest proportion; P i is the installed capacity of renewable energy at transmission grid node i; P j is the installed capacity of renewable energy on distribution network node j; The expression of effective reactive power reserve of transmission network is: Among them, g i G is the sensitivity of the generator or new energy unit on node i to the key node j; j is the submatrix of column j in the sensitivity matrix; Max(G j ) is G j The maximum value in is the rated maximum reactive power of the reactive source at node i, is the reactive power currently emitted by node i; S3, decomposing the regional power grid carrying capacity model into a transmission network carrying capacity optimization sub-model and a distribution network carrying capacity optimization sub-model; in S3, the method used to decompose the regional power grid carrying capacity model into a transmission network carrying capacity optimization sub-model and a distribution network carrying capacity optimization sub-model is a generalized master-slave splitting method, and the generalized master-slave splitting method is expressed as: f MB (u M ,x M ,x B )=S BS S BS =f BS (u S ,x S ,x B ) Among them, S BS is the intermediate variable of the algorithm, representing the power transmitted between the transmission network and the distribution network; f MB 、f BS is the boundary power flow constraint of the decomposed transmission network and distribution network, u M is the transmission grid control variable, x M is the state variable of the transmission network, x B is the boundary system state variable, u S is the distribution network control variable, x S is the distribution network state variable; After decomposition, the transmission network carrying capacity optimization sub-model includes the objective function, constraint conditions and wind and solar output model of the transmission network carrying capacity optimization sub-model; The transmission network carrying capacity optimization sub-model includes the objective function F M for: In the formula, Q GR,T is the effective reactive power reserve of the transmission network when the renewable energy output accounts for the largest proportion, m is the number of nodes in the transmission network, P i is the installed capacity of renewable energy on the transmission grid node i, ζ BS The objective function F is the maximum installed capacity of new energy in the distribution network. S1 The sensitivity matrix of the constraints to the state variables of the boundary system, the superscript T is the matrix transpose, x B is the state variable of the boundary system; The constraints of the transmission network capacity optimization sub-model include power balance constraints, generator output constraints, branch power constraints, new energy output constraints, static voltage stability constraints and operational safety and stability limit constraints; The conditions for the static voltage stability constraint are: Among them, k p is the active power margin, P is the initial active power; k q is the reactive power margin; Q is the initial reactive power; Q max is the maximum reactive power of the load; P max is the maximum power load; The operation safety and stability limit constraint expression is as follows: Where η is the photovoltaic penetration rate; ζ is the wind power penetration rate; V E is the voltage amplitude at the power grid connection point; X is the equivalent impedance; P load is the load active power; P max is the maximum power load, is the load power factor, Q load is the load reactive power; Q X It is the reactive power consumed by the equivalent impedance at the critical point of voltage static voltage stability; The wind and solar output model of the transmission network carrying capacity optimization sub-model includes the double-fed asynchronous wind turbine output model and the photovoltaic unit output model; Among them, the expression of the double-fed asynchronous wind turbine output model is: Among them, P DFIG is the active power of the doubly-fed asynchronous wind turbine; Q DFIG is the reactive power of the doubly-fed asynchronous wind turbine; S DFIG The installed capacity of double-fed asynchronous wind turbines; is the power factor; The expression of the photovoltaic unit output model is: Q solar,i ≤0.48S solar Among them, P solar,i is the active power output of the photovoltaic unit on node i; Q solar,i is the reactive power output of the photovoltaic unit on node i; S solar The capacity of the grid-connected inverter for the photovoltaic array; After decomposition, the distribution network carrying capacity optimization sub-model includes the objective function, constraint conditions and wind and solar output model of the distribution network carrying capacity optimization sub-model; The objective function of the distribution network carrying capacity optimization sub-model is: maxF S2 =Q up Among them, λ MB,k is the Lagrange multiplier vector of the boundary power flow constraint in the k-th distribution network, and the superscript T is the matrix transpose; Q up F is the reactive power output from the distribution network to the upper grid; S1 represents the objective function of the maximum installed capacity of new energy in the distribution network, F S2 It represents the objective function of the distribution network providing the maximum reactive power to the transmission network; x B,k represents the state variable of the kth distribution network boundary system; S is the number of distribution networks, n is the number of distribution network nodes, P j is the installed capacity of renewable energy on distribution network node j; The constraints of the distribution network carrying capacity optimization sub-model include system power flow constraints, node voltage constraints, wind and solar power unit operation constraints, continuous reactive power compensation constraints, discrete reactive power compensation constraints and line transmission power constraints; The wind and solar output model of the distribution network carrying capacity optimization sub-model includes the double-fed asynchronous wind turbine output model and the photovoltaic unit output model; Among them, the expression of the double-fed asynchronous wind turbine output model is: Where, s is the slip rate; U s is the stator side voltage; I s is the stator winding current; X s is the stator leakage reactance; X m is the magnetizing reactance; I r is the rotor side converter current; P DFIG is the active power of the doubly-fed asynchronous wind turbine; Q DFIG is the reactive power of the doubly-fed asynchronous wind turbine; The expression of the photovoltaic unit output model is: -P solar,i tanθ i ≤Q solar,i ≤P solar,i tanθ i ; 0≤P solar,i ≤P solar Among them, P solar is the available active power of the photovoltaic unit; S solar is the grid-connected inverter capacity of the photovoltaic array; P solar,i is the active power output of the photovoltaic unit on node i; Q solar,i is the reactive power output of the photovoltaic unit at node i; θ i is the power factor of the photovoltaic unit on node i; S4. A transmission and distribution step-by-step iterative algorithm is used for the typical scenario set to collaboratively solve the transmission network carrying capacity optimization sub-model and the distribution network carrying capacity optimization sub-model to obtain a solution set of new energy installation capacity. In S4, the transmission and distribution step-by-step iterative algorithm is specifically a Heterogeneous Decomposition Algorithm, and the specific steps of collaboratively solving the transmission network carrying capacity optimization sub-model and the distribution network carrying capacity optimization sub-model include: S4.1, initialize the access capacity of new energy sources at all levels of power grids, set the number of iterations z = 1, set the voltage of each distribution network root node to 1, the phase angle to 0, and the objective function F of the maximum installed capacity of new energy sources in the distribution network S1 The sensitivity matrix ζ of the constraints to the state variables of the boundary system BS Set to 0, the Lagrange multiplier vector λ of the boundary power flow constraint in the distribution network MB,k Set to 0; S4.2, solve the distribution network carrying capacity optimization sub-model and obtain the apparent power and boundary sensitivity ζ of the root node BS , and transmitted to the transmission network carrying capacity optimization sub-model; S4.3, solve the transmission network capacity optimization sub-model, and obtain the voltage vector of the boundary node and the Lagrange multiplier vector λ of the boundary flow constraint in the kth distribution network MB,k , and transmitted to the distribution network carrying capacity optimization sub-model; S4.4, calculate the voltage vector and the Lagrange multiplier vector λ of the boundary power flow constraint in the kth distribution network MB,k The difference before and after the update is used to determine whether the convergence accuracy is met. If converged, the installed capacity of new energy sources for the transmission network and the distribution network is output. If not, the number of iterations is increased by one, and the process returns to step S4.

2. S5. Construct an evaluation system that considers operation indicators, economic indicators and environmental indicators; in S5, the operation indicators include voltage deviation, voltage qualification rate and net load balance rate; the economic indicators include average line loss rate and voltage regulation cost; the environmental indicators include new energy output ratio; the voltage regulation cost includes photovoltaic unit voltage regulation cost and wind turbine unit voltage regulation cost; The expression of the voltage regulation cost a5 of the photovoltaic unit is: a5=a Q Q solar,i +b P ΔP solar,i,loss Among them, a Q is the cost coefficient of reactive power output, b P is the opportunity cost coefficient of active reduction; Q solar,i is the reactive power output of the photovoltaic unit at node i; ΔP solar,i,loss To increase reactive power output, active power output is reduced; The expression of wind turbine voltage regulation cost a6 is: a6=ρ dfig ·FIT·ΔP dfig Among them, FIT is the on-grid electricity price of wind power; ρ dfig is the wind power conversion coefficient; ΔP dfig The active output is reduced when the reactive output is increased; The output of new energy is a7, and the expression is: Where, node represents the number of nodes in the power system; P DG,i represents the active power output of the new energy unit at node i, P load is the load active power; S6. Based on the evaluation system and the solution set of new energy installed capacity, the combined weighting method is used to calculate the time-sharing score to obtain the optimal carrying capacity.

2. The method for optimizing the carrying capacity of a regional power grid under the access of new energy as claimed in claim 1, characterized in that: In S1, the method used for clustering the historical data of wind and solar power output and historical load data is TSK-Medoids clustering, which specifically includes: S1.1, obtain historical data of wind and solar power output and load and the number of clusters N; S1.2, randomly select N objects as center points in the historical data of wind and solar power output and historical data of load; S1.3, calculate the time series distortion distance between the load historical data and the wind and solar output historical data and N center points; S1.4, classify the load historical data and wind and solar output historical data according to the time series distortion distance; S1.5, calculate the sum of the time series distortion distances between each center point and all data points in the class as the initial judgment value; S1.

6. In each class, select a data point outside the center point as the new center point; S1.

7. Calculate the time series distortion distances between the load history data and the wind and solar output history data and N new center points; reclassify the load history data and the wind and solar output history data according to the time series distortion distances; calculate the sum of the time series distortion distances between each new center point and all data points in the class as the judgment value of the current round; S1.

8. If the judgment value of the current round is less than the judgment value of the previous round, update the judgment value and return to step S1.6; otherwise, clustering ends.

3. The method for optimizing the carrying capacity of a regional power grid under the access of new energy as claimed in claim 1, characterized in that: The method used to solve the distribution network carrying capacity optimization sub-model is to improve the NSGA-Ⅱ multi-objective optimization algorithm, and the steps are as follows: S4.2.

1. Set distribution network parameters and improve NSGA-Ⅱ multi-objective optimization algorithm parameters; S4.2.2, abstract the installed capacity of new energy into a population, generate an initial population, and set the number of iterations to 0; S4.2.

3. Calculate the power flow of individuals in the initial population and obtain the objective function F of the maximum installed capacity of new energy in the distribution network S1 The value of the distribution network and the objective function F that provides the maximum reactive power to the transmission network S2 The value of S4.2.4, judging the convergence stage, in the early stage of convergence, selection, normal distribution crossover and uniform mutation are used to generate the offspring population, in the middle stage of convergence, selection, normal distribution crossover and Levy distribution mutation are used to generate the offspring population, and in the late stage of convergence, selection, normal distribution crossover and Gaussian distribution mutation operators are used to generate the offspring population; The convergence stage judgment formula is: Among them, step represents the convergence stage, N S is the current iteration number, N S,max is the maximum number of iterations, when step<N S,max / 2, this is the early stage of convergence. S,max / 2≤step<2N S,max When step ≥ 2N S,max When , it belongs to the late convergence stage; S4.2.5, the parent and offspring populations are merged, and non-dominated sorting and crowding calculation are performed; S4.2.

6. Individuals with crowding greater than the set value are regarded as superior individuals and form a new generation of population; the number of iterations increases by one; S4.2.

7. If the number of iterations is less than the set value, return to step S4.2.4; otherwise, the iteration ends and the solution set of the new energy installation capacity of the distribution network is output.

4. The method for optimizing the carrying capacity of a regional power grid under the access of new energy as claimed in claim 1, characterized in that: The sub-model for solving the transmission network carrying capacity optimization specifically includes: S4.3.1, global calculations are performed using different stability margins under static voltage stability constraints; S4.3.

2. Calculate the effective reactive power reserve corresponding to different static voltage stability margins of the transmission network; S4.3.

3. Calculate the transmission network carrying capacity and the reactive power output of the distribution network to the transmission network based on different effective reactive power reserves; S4.3.

4. Determine whether the convergence requirements are met within 24 hours when new energy is connected. If not, proceed to step S4.2.

1. If the convergence requirements are met, the installed capacity of new energy in the transmission network and the corresponding reactive power reserve are obtained.

5. The method for optimizing the carrying capacity of a regional power grid under the access of new energy as claimed in claim 1, characterized in that: S6 specifically includes: S6.

1. Calculate the power flow of the regional power grid for 24 hours and use the improved entropy weight method to calculate the objective weight of each indicator in the evaluation system. S6.2, use the analytic hierarchy process to obtain the subjective weight of each indicator in the evaluation system. S6.3, the combined weights are obtained by algebraic mean method, S6.

4. Calculate the hourly score of each solution in the renewable energy installation capacity solution set according to the combined weight, take the average score, and the solution with the highest average score is the regional power grid carrying capacity; The improved entropy weight method calculates the objective weight β m The calculation formula is: Among them, E m is the information entropy of each indicator, is the average value of all information entropies except 1, c is the precision coefficient, and M is the total number of indicators to be evaluated.

6. A system for optimizing the carrying capacity of a regional power grid under the access of new energy sources implementing the method as claimed in claim 1, characterized in that: include: The scenario set construction module is used to cluster the historical data of wind and solar power output and load to obtain the typical scenario sets of load and wind and solar power; A module for constructing a regional power grid carrying capacity model, which is used to construct a regional power grid carrying capacity model with the goal of maximizing the installed capacity of new energy in the regional power grid and maximizing the effective reactive power reserve of the transmission network; A model decomposition module is used to decompose the regional power grid carrying capacity model into a transmission network carrying capacity optimization sub-model and a distribution network carrying capacity optimization sub-model; The model solving module is used to adopt the transmission and distribution step-by-step iterative algorithm for typical scenario sets, collaboratively solve the transmission network carrying capacity optimization sub-model and the distribution network carrying capacity optimization sub-model, and obtain the solution set of new energy installation capacity; The indicator construction module is used to construct an evaluation system that takes into account operational indicators, economic indicators, and environmental indicators; The optimal bearing capacity calculation module is used to calculate the time-sharing score based on the evaluation system and the solution set of new energy installation capacity, and obtain the optimal bearing capacity using the combined weighting method.

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