Method and device for optimizing configuration of distributed power source participation in new energy fluctuation suppression capacity

Abstract

The present disclosure provides a method and device for optimizing the capacity allocation of distributed power sources to participate in the suppression of new energy fluctuations, belonging to the field of flexible resource planning of power systems. Among them, the method includes: selecting a set of transmission lines for investigation, constructing a generator output power transfer distribution factor matrix of new energy, traditional power sources, and distributed power sources with respect to the transmission lines in the set, and calculating the fluctuation amount of the transmission lines; establishing a probability distribution model of new energy fluctuations, determining the total amount of new energy fluctuations and the sharing amounts of traditional power sources and distributed power sources participating in the suppression of new energy fluctuations; establishing a probability distribution model of transmission line fluctuations, and calculating the fluctuation variance and fluctuation coefficient of the transmission lines; using the fluctuation coefficient, establishing and solving a distributed power source planning optimization model to obtain an optimized capacity allocation scheme for distributed power sources. The present disclosure can optimize the allocation of load-side regulation resources, reduce the amplitude of fluctuations of specific lines during new energy plant fluctuations, and improve the stability of the main power grid of the power system.

Description

Technical Field

[0001] The present disclosure belongs to the field of power system flexibility resource planning, and particularly relates to a method and device for optimizing the capacity allocation of distributed power sources to participate in suppressing new energy fluctuations. Background Art

[0002] With the high proportion of renewable energy connected to the power system, the random fluctuations on the power grid power supply side have become an important constraint affecting the safe and stable operation of the power system. The quantity and proportion of traditional power sources that can provide rapid regulation capabilities in the power system are continuously decreasing. In order to improve the system's ability to resist new energy fluctuations, the regulatory role of distributed power sources on the load side has become prominent. New communication networks such as 5G enable distributed resources such as electric vehicles, industrial loads, and backup energy storage batteries to easily access the power system dispatching communication network and participate in suppressing new energy fluctuations. The capacity allocation of distributed power sources at each node of the power grid is the main problem to solve new energy fluctuations. The connection location of distributed power sources to the power grid will affect the fluctuations of the main transmission lines of the power system, further affecting the maximum effective transmission power of the transmission lines, the main network relay protection of the power system, and even the generator power angle problem and the power grid voltage stability. Reasonably arranging distributed resources at each node of the power system helps to reduce the fluctuations of the power system frequency and the power flow of the main network transmission lines and promote the consumption of renewable energy.

[0003] Traditional methods for optimizing the allocation of distributed power sources are often limited to the distribution network, and the optimal allocation of distributed power sources is obtained through optimized calculations using the topology and operating characteristics of the distribution network. Or when considering the main network, only the generator-load model is considered, ignoring the grid framework factors. These two types of research have a large gap from the actual power system dispatching operation and planning and design reality and cannot be applied to the optimal allocation of distributed power sources in the actual power system. Summary of the Invention

[0004] The purpose of the present disclosure is to overcome the deficiencies of the existing technologies and propose a method and device for optimizing the capacity allocation of distributed power sources to participate in suppressing new energy fluctuations. The present disclosure establishes a probabilistic relationship between new energy, traditional power sources, distributed power sources, and the fluctuations of the main network lines of the power system through the generator output power transfer distribution factor matrix, optimizes the allocation of regulatory resources on the load side, reduces the amplitude of fluctuations of specific lines during new energy power station fluctuations, and improves the stability of the main grid framework of the power system.

[0005] The first aspect embodiment of the present disclosure proposes a method for optimizing the capacity allocation of distributed power sources to participate in suppressing new energy fluctuations, including:

[0006] Select a set of transmission lines for investigation, construct a generator output power transfer distribution factor matrix of new energy, traditional power sources, and distributed power sources with respect to the transmission lines in the set, and calculate the fluctuation amount of the transmission lines according to the transfer distribution factor matrix;

[0007] Establish a new energy fluctuation probability distribution model, determine the total amount of new energy fluctuations according to the new energy fluctuation probability distribution model, and determine the sharing amounts of the traditional power sources and the distributed power sources participating in the suppression of new energy fluctuations;

[0008] According to the transfer distribution factor matrix, the total amount of new energy fluctuations, and the sharing amounts of the traditional power sources and the distributed power sources, establish a transmission line fluctuation probability distribution model, and calculate the fluctuation variance of the transmission line according to the transmission line fluctuation probability distribution model;

[0009] Use the fluctuation variance of the transmission line to calculate the fluctuation coefficient of the transmission line;

[0010] Use the fluctuation coefficient of the transmission line to establish and solve a distributed power source planning optimization model to obtain the optimized configuration scheme of the distributed power source capacity.

[0011] In a specific embodiment of the present disclosure, the new energy includes wind power and photovoltaic power.

[0012] In a specific embodiment of the present disclosure, for the selected transmission line inspection set, constructing a generator output power transfer distribution factor matrix of new energy, traditional power sources, and distributed power sources for the transmission lines in the set, and calculating the fluctuation amount of the transmission line according to the transfer distribution factor matrix includes:

[0013] 1) Obtain the set B of traditional power source units participating in fluctuation suppression in a future set time period d , the set S of wind turbine units pv , the set S of photovoltaic units pv , and the set B of nodes where distributed resources can be accessed r ;

[0014] 2) Determine the transmission line inspection set, including: the 500 kV line set L 500 and the 220 kV line set L 220 ;

[0015] 3) According to the sets obtained in step 1), respectively establish generator output power transfer distribution factor matrices of wind power, photovoltaic power, traditional power sources, and distributed power source nodes for each transmission line in the transmission line inspection set;

[0016] Among them, the generator output power transfer distribution factor matrix G l of line l represents the change amount of the power flow of line l caused by the change in the active output power of the generator; the length of G l is the number of grid nodes, then the k-th element G l in G corresponding to the k-th node is G l (k) and the expression is as follows:

[0017]

[0018] where k is the grid node number where the power source is located, l is the line number; m and n are the start and end node numbers of line l respectively; X = B -1 is the inverse matrix of the susceptance matrix of the power grid in DC form, X mk represents the element in the m-th row and k-th column of matrix X, X nk represents the element in the n-th row and k-th column of matrix X; x l is the branch impedance of line l;

[0019] 4) Calculate the fluctuation quantity of the transmission lines in the set according to the transfer distribution factor matrix:

[0020]

[0021] In the formula, represents the fluctuation quantity of line l, l ∈ L, L represents the set of transmission lines under investigation, and L includes L 500 and L 220 ; ΔP wd , ΔP pv are the fluctuation quantities of the wind farm and the PV power station respectively, and ΔP td , ΔP DER are the fluctuation sharing quantities of the traditional power source and the distributed power source respectively; are the generator output power transfer distribution factor matrices of the wind power, PV power, traditional power source, and distributed power source nodes to line l respectively.

[0022] In a specific embodiment of the present disclosure, the establishment of the new energy fluctuation probability distribution model, the determination of the total new energy fluctuation quantity according to the new energy fluctuation probability distribution model, and the determination of the sharing quantities of the traditional power source and the distributed power source participating in the new energy fluctuation suppression include:

[0023] 1) Establish a new energy fluctuation probability distribution model;

[0024] Let the new energy random fluctuation follow a normal distribution, that is, ΔP wd ~N(0, ∑ wd ), ΔP Pv ~N(0, ∑ pv ), then the fluctuation correlation coefficient ∑ wd of the wind farm and the fluctuation correlation coefficient ∑ wd of the PV power station are calculated as follows:

[0025]

[0026]

[0027] In the formula, It represents the fluctuation correlation coefficient of wind power stations within the jurisdiction of each city's power grid. It represents the fluctuation correlation coefficient of photovoltaic power stations within the jurisdiction of each city's power grid; the standard deviation of wind power fluctuation σ wd = η wd ·P wd , the standard deviation of photovoltaic power fluctuation σ pv = η pv ·P pv ; η wd , η pv are the wind power volatility and the photovoltaic power volatility respectively, and P wd , P pv represent the output power of wind power and photovoltaic power respectively; M wd represents the correlation matrix between the wind power station and the jurisdiction of the wind power city, and M pv represents the correlation matrix between the photovoltaic power station and the jurisdiction of the photovoltaic city; M wd The columns of represent the wind power station numbers, and the rows represent the wind power city jurisdiction numbers; M pv The columns of represent the photovoltaic power station numbers, and the rows represent the photovoltaic city jurisdiction numbers; M wd and M pv For each column, the element corresponding to the area where the power station is located is 1, and the rest of the elements are 0; diag represents the diagonal matrix;

[0028] 2) Determine the total new energy fluctuation according to the new energy fluctuation probability distribution model. The expression is as follows:

[0029]

[0030] In the formula, ΔP S represents that the total new energy fluctuation is, and S represents the set of new energy fluctuation sources including wind power and photovoltaic power; represents the fluctuation amount of wind power station i, and the subscript i represents the number of the wind power station. ΔP j pv represents the fluctuation amount of photovoltaic power station j, and the subscript j represents the number of the photovoltaic power station;

[0031] ΔP S follows a normal distribution represents S the variance of ΔP, and the expression is as follows:

[0032]

[0033] In the formula, 1 represents a column vector of all 1s;

[0034] 3) Determine the sharing ratio of traditional power source and distributed power source for fluctuation suppression;

[0035] According to

[0036]

[0037] In the formula, represents the fluctuation amount of the traditional power unit d, and the subscript d is the traditional power unit number; represents the fluctuation amount of the distributed power node r, and r is the distributed power node number;

[0038] Then, let the traditional power source and the distributed power source share the fluctuation according to the ratios α and 1 - α, that is, it satisfies:

[0039]

[0040]

[0041] where α is the fluctuation sharing coefficient of the traditional power source;

[0042] Then, the fluctuation sharing amounts of the traditional power unit and the distributed power sources at each node are respectively:

[0043]

[0044]

[0045] In the formula, R d represents the regulation speed R of the traditional power unit d d , R r represents the adjustable capacity of the distributed power node r, d ∈ B d , r ∈ B r ;

[0046] Configure the distributed power sources according to the 2σ interval of the total new - energy fluctuation ΔP S , as shown in the following formula:

[0047]

[0048] In the formula, represents the maximum fluctuation amount;

[0049] Then

[0050]

[0051] The sharing amount of the distributed power source participating in fluctuation suppression is:

[0052]

[0053] In the formula, R n is the total adjustable capacity of the distributed power source.

[0054] In a specific embodiment of the present disclosure, establishing a transmission line fluctuation probability distribution model based on the transfer distribution factor matrix, the total amount of new energy fluctuations, and the sharing amounts of the traditional power source and the distributed power source, and calculating the fluctuation variance of the transmission line according to the transmission line fluctuation probability distribution model includes:

[0055] 1) Writing the fluctuation amount of the transmission line in the matrix expression form as shown below to establish a transmission line fluctuation probability distribution model:

[0056]

[0057] 2) According to the transmission line fluctuation probability distribution model, calculate the corresponding covariance matrix The expression is as follows:

[0058]

[0059] Wherein,

[0060]

[0061] 3) Simplify the diagonal elements of the covariance matrix After simplification, the diagonal element The expression is as follows:

[0062]

[0063] In the formula, the diagonal element of represents the fluctuation variance of the transmission line l.

[0064] In a specific embodiment of the present disclosure, calculating the fluctuation coefficient of the transmission line by using the fluctuation variance of the transmission line includes:

[0065] 1) Calculate the DC power flow of the transmission line:

[0066]

[0067] Wherein, represents the DC power flow of the transmission line l, P load respectively represent the generator output power transfer distribution factor matrix of the load for the line l and the power injected by the load into the power grid; P td respectively represent the output power of the traditional power source;

[0068] 2) Calculate the fluctuation coefficient of the transmission line, and the expression is as follows:

[0069]

[0070] In the formula, F l represents the fluctuation coefficient of the transmission line l, represents the thermal stability limit power of the transmission line l with response.

[0071] In a specific embodiment of the present disclosure, the method of using the fluctuation coefficient of the transmission line to establish and solve a distributed power source planning optimization model to obtain the optimized configuration scheme of the distributed power source capacity includes:

[0072] 1) Establish a distributed power source planning optimization model as follows:

[0073] min

[0074] s.t.

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] In the formula, β is used to adjust the line fluctuation ratio of the 500 kV transmission network and the 220 kV secondary transmission network, is the maximum distributed resource capacity that can be invested at node r;

[0083] 2) Solve the distributed power source planning optimization model to obtain R r , The solution of is the optimal capacity of the distributed resources configured at each node during the set period.

[0084] The second aspect of the present disclosure proposes a device for optimizing the capacity of distributed power sources to participate in suppressing new energy fluctuations, including:

[0085] A transfer distribution factor matrix construction module, configured to select a set of transmission lines for investigation, construct a generator output power transfer distribution factor matrix of new energy, traditional power sources, and distributed power sources with respect to the transmission lines in the set, and calculate the fluctuation amount of the transmission lines according to the transfer distribution factor matrix;

[0086] A new - energy fluctuation probability distribution model construction module, which is used to establish a new - energy fluctuation probability distribution model, determine the total amount of new - energy fluctuation according to the new - energy fluctuation probability distribution model, and determine the sharing amounts of the conventional power sources and the distributed power sources participating in the suppression of the new - energy fluctuation;

[0087] A fluctuation variance calculation module, which is used to establish a transmission - line fluctuation probability distribution model according to the transfer distribution factor matrix, the total amount of new - energy fluctuation, and the sharing amounts of the conventional power sources and the distributed power sources, and calculate the fluctuation variance of the transmission line according to the transmission - line fluctuation probability distribution model;

[0088] A fluctuation coefficient calculation module, which is used to calculate the fluctuation coefficient of the transmission line by using the fluctuation variance of the transmission line;

[0089] A distributed - power - source optimal configuration module, which is used to establish and solve a distributed - power - source planning optimization model by using the fluctuation coefficient of the transmission line to obtain an optimal configuration scheme for the distributed - power - source capacity.

[0090] The third - aspect embodiment of the present disclosure provides an electronic device, including:

[0091] At least one processor; and a memory communicatively connected to the at least one processor;

[0092] Wherein, the memory stores instructions executable by the at least one processor, and the instructions are configured to execute the above - mentioned method for optimizing the capacity of distributed power sources participating in the suppression of new - energy fluctuations.

[0093] The fourth - aspect embodiment of the present disclosure provides a computer - readable storage medium, and the computer - readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the above - mentioned method for optimizing the capacity of distributed power sources participating in the suppression of new - energy fluctuations.

[0094] The features and beneficial effects of the present disclosure:

[0095] The present disclosure first screens the transmission lines in the power system that need to consider fluctuations, and constructs the relationship between wind and light fluctuations, conventional power sources, distributed power sources and transmission - line fluctuations based on the generator output power transfer distribution factor matrix. The present disclosure further proposes a method for sharing the fluctuation amounts of conventional power sources and distributed power sources in the case of wind and light fluctuations, and calculates the line - fluctuation equation. On this basis, by setting the main - grid line fluctuation coefficient, the present disclosure constructs a quadratic programming problem with constraints. By solving this programming problem, the optimal configuration scheme of the distributed power sources in the power grid during the set period can be obtained and applied to the daily flexible - resource planning arrangement of the power system.

[0096] The present disclosure proposes an optimized configuration scheme for distributed power sources when considering line fluctuations and grid network structure in a power system.

[0097] The present disclosure establishes a probability distribution model for transmission line fluctuations and an optimized planning model for distributed power sources, which is applicable to power systems of various scales and grid network structures.

[0098] The present disclosure helps power system dispatchers and system planners understand the optimized configuration methods and results of distributed power sources in the main grid, and promotes the optimized dispatching operation of the power grid, the suppression of renewable energy fluctuations, and the investment planning of distributed power sources. Description of the Drawings

[0099] Figure 1 It is the overall flowchart of a method for optimizing the capacity configuration of distributed power sources participating in new energy fluctuation suppression in an embodiment of the present disclosure. Detailed Embodiment

[0100] An embodiment of the present disclosure proposes a method and device for optimizing the capacity configuration of distributed power sources participating in new energy fluctuation suppression. The following further elaborates in detail with reference to the drawings and specific embodiments.

[0101] An embodiment of the first aspect of the present disclosure proposes a method for optimizing the capacity configuration of distributed power sources participating in new energy fluctuation suppression. The overall process is as Figure 1 shown, including the following steps:

[0102] 1) Obtain the unit combination situation of the power system of any hour of the next day in the power grid; select the inspection set of transmission lines, construct the generator output power transfer distribution factor matrix of new energy, traditional power sources, and distributed power sources with respect to the transmission lines in this set, and calculate the fluctuation amount of the transmission lines according to the transfer distribution factor matrix. The specific steps are as follows:

[0103] 1-1) According to the operation mode information, obtain the set B of traditional power source units participating in fluctuation suppression in any hour of the selected next day in the day-ahead unit combination of the power grid d , the set S of new energy (including wind power and photovoltaic in the embodiments of the present disclosure) units wd , S pv , and the set B of nodes where distributed resources can be accessed r ; among them, S wd is the set of wind turbine units, and S pv is the set of photovoltaic units.

[0104] 1-2) Select the inspection set of transmission lines.

[0105] In the embodiments of the present disclosure, for a provincial power grid, the main grid is mainly at the voltage levels of 500 kV and 220 kV. Then the inspection set includes the 500 kV line set L 500 and the 220 kV line set L220 。

[0106] It should be noted that the selected lines for the inspection set are all lines that meet the voltage level. However, when considering the inspection set, the outgoing lines of power plants, the internal lines of substations, and some newly built lines with low loads should be excluded, etc.

[0107] 1 - 3) According to the set obtained in step 1 - 1), respectively establish the transfer distribution factor matrices of wind power, photovoltaic, traditional power sources, and distributed power source nodes for each transmission line in the transmission line inspection set;

[0108] In the embodiments of the present disclosure, the generator output power transfer distribution factor matrix (generation shift distribution factor, GSDF) G is defined l as the change amount of the power flow of line l caused by the change in the active output power of the generator. G l has a length equal to the number of grid nodes. Then, for the k - th node, the k - th element G l in G l (k) is expressed as shown in Equation (1):

[0109]

[0110] where k is the grid node number where the power source is located, l is the line number; m and n are the head and tail node numbers of line l respectively. X = B -1 is the inverse matrix of the susceptance matrix of the grid in DC form, and X mk represents the element in the m - th row and k - th column of matrix X, and X nk represents the element in the n - th row and k - th column of matrix X; x l is the branch impedance of line l.

[0111] According to each unit set obtained in step 1 - 1), calculate the GSDF matrices of traditional power sources, wind power, photovoltaic, and distributed power sources respectively, which are the GSDF matrices of wind power, photovoltaic, traditional power sources, and distributed power source nodes for line l respectively.

[0112] 1 - 4) According to the transfer distribution factor matrix, calculate the fluctuation amount of each transmission line in the transmission line inspection set.

[0113] In the embodiments of the present disclosure, the line fluctuation can be calculated using the generator output power transfer distribution factor matrix, mainly considering the influence of fluctuation sources such as wind power (wd), photovoltaic (pv), etc. on the power flow of traditional power sources (td) and distributed power sources (DER).

[0114] Let represent the fluctuation amount of line l, l ∈ L, where L represents the transmission line inspection set, The expression is as shown in Equation (2):

[0115]

[0116] Wherein, ΔP wd , ΔP pv are respectively the fluctuation amounts of the wind power and photovoltaic power stations, and ΔP td , ΔP DER are respectively the fluctuation sharing amounts of the conventional power sources and distributed power sources (power stations). L includes L 500 and L 220 . Therefore, the lines in the two investigation sets L 500 and L 220 in the embodiments of the present disclosure all satisfy the relationship shown in Equation (2).

[0117] 2) Establish a new energy fluctuation probability distribution model, determine the total new energy fluctuation amount according to the new energy fluctuation probability distribution model, and determine the sharing ratio of the conventional power source and the distributed power source participating in the new energy fluctuation suppression; the specific steps are as follows:

[0118] 2-1) Establish a new energy fluctuation probability distribution model;

[0119] In the embodiments of the present disclosure, a fluctuation probability distribution model of each wind farm and photovoltaic power station is established.

[0120] Specifically, in the embodiments of the present disclosure, it is assumed that the random fluctuation of the new energy follows a normal distribution, that is, ΔP wd ~N(0, ∑ wd ), ΔP pv ~N(0, ∑ pv ). It is assumed that the fluctuation correlation coefficients of the wind farm and photovoltaic power station in the jurisdiction of each city power grid are respectively and Then the fluctuation correlation coefficient ∑ wd of the wind farm and the fluctuation correlation coefficient ∑ pv of the photovoltaic power station can be calculated by Equations (3a) and (3b). Among them, the wind power fluctuation standard deviation σ wd = η wd ·P wd , and the photovoltaic fluctuation standard deviation σ pv = η pv ·P pv ; η wd , η pv are respectively the wind power fluctuation rate and the photovoltaic power fluctuation rate, generally taking 1% - 2%; P wd , P pv are respectively the output powers of the wind power and photovoltaic power. M wd represents the association matrix between the wind farm and the wind power city jurisdiction, and M pv represents the association matrix between the photovoltaic power station and the photovoltaic city jurisdiction. Mwd The columns represent the wind farm station numbers, and the rows represent the wind power city jurisdiction numbers; M pv The columns represent the PV power station numbers, and the rows represent the PV power city jurisdiction numbers. M wd and M pv For each column, the element corresponding to the area where the station is located is 1, and the remaining elements are 0. diag represents the diagonal matrix.

[0121]

[0122]

[0123] 2-2) Determine the total amount of new energy distribution fluctuations according to the new energy fluctuation probability distribution model;

[0124] Denote the total amount of new energy fluctuations as ΔP S , where S represents the set of new energy fluctuation sources including wind power and PV power, as shown in Equation (4a); ΔP S obeys the normal distribution Its specific variance is as shown in Equation (4b).

[0125]

[0126]

[0127] In the formula, 1 represents a column vector of all 1s;

[0128] In Equation (4a), represents the fluctuation amount of wind farm station i, the subscript i represents the number of the wind farm station, and ΔP j pv represents the fluctuation amount of PV power station j, and the subscript j represents the number of the PV power station.

[0129] 2-3) Determine the sharing ratio of traditional power source and distributed power source for fluctuation suppression;

[0130] In this embodiment, it is assumed that the fluctuations of wind power and PV power are slow, and load fluctuations are not considered. That is, the power grid maintains a quasi-steady state during the fluctuation suppression process, and power generation and power consumption are balanced at all times:

[0131]

[0132] In the formula, represents the fluctuation amount of traditional power unit d, and the subscript d is the number of the traditional power unit; represents the fluctuation amount of distributed power node r, and r is the number of the distributed power node.

[0133] Due to the difference in the fluctuation suppression speed between traditional and distributed power sources, the two will bear the new energy fluctuation amount in a certain proportion. Specifically, the traditional power source and the distributed power source bear the fluctuation proportions of α and 1 - α respectively, that is, it satisfies:

[0134]

[0135]

[0136] Among them, α is the fluctuation sharing coefficient of the traditional power source;

[0137] The fluctuation amount borne by the traditional power source is positively correlated with its regulation speed R d The fluctuation amount borne by the distributed power source is positively correlated with its adjustable capacity R r d ∈ B d , is the set of units participating in fluctuation suppression at the current moment; r ∈ B r , is the set of nodes where distributed resources can be accessed. Among them, R r ≥0 is the variable to be optimized. The fluctuation sharing amounts of the traditional power source units and the distributed power sources at each node are respectively:

[0138]

[0139]

[0140] In the embodiment of the present disclosure, the distributed power source is configured according to the 2σ interval of the total new energy fluctuation ΔP S (as shown in Equation (9)), and at this time, 95.4% of the fluctuation situations can be satisfied. represents the maximum fluctuation amount. The new energy fluctuation is preferentially suppressed by the traditional power source, and the insufficient part is supplemented by the distributed power source. Then α can be calculated by Equation (10).

[0141]

[0142]

[0143] The total adjustable capacity R of the distributed power source n is the remaining part of the fluctuation amount borne by the traditional power source, that is:

[0144]

[0145] 3) Establish a probability distribution model of the transmission line fluctuation, and calculate the fluctuation variance of the transmission line according to the probability distribution model of the transmission line fluctuation; the specific steps are as follows:

[0146] 3 - 1) Establish a probability distribution model of the transmission line fluctuation;

[0147] The fluctuation quantity of the transmission line can be calculated by Equation (2) and written in the matrix form shown in Equation (12). Since and ΔP S both follow normal distributions, it can be inferred that also follows a normal distribution and has a mean of zero.

[0148]

[0149] Equation (12) is the fluctuation probability distribution model of the transmission line;

[0150] 3-2) According to the fluctuation probability distribution model of the transmission line, calculate the corresponding covariance matrix The expression is as follows:

[0151]

[0152] Among them, let W be:

[0153]

[0154] 3-3) Diagonal simplify the covariance matrix obtained in step 3-2).

[0155] The diagonal elements of represent the fluctuation variance of transmission line l, that is The first item of is a constant term. The results after diagonal element simplification are as follows:

[0156]

[0157] 4) Use the fluctuation variance of the transmission line to calculate the transmission line fluctuation coefficient; the specific steps are as follows:

[0158] 4-1) Calculate the DC power flow of the transmission line as shown in Equation (16):

[0159]

[0160] Among them, represents the DC power flow of transmission line l, P load respectively represent the GSDF matrix of the load for line l and the load injection power into the power grid, which can be obtained from the power system operation mode information. P wd , P pv , P td respectively represent the output powers of wind power, photovoltaic power, and traditional power sources, which can be obtained from the power system operation mode information.

[0161] 4-2) Each transmission line has a corresponding thermal stability limit power When the power grid is operating normally, it should satisfy Considering the factors of line overstepping and power flow reversal, the fluctuation coefficient F of transmission line l can be obtained l The expression is as follows:

[0162]

[0163] 5) Using the transmission line fluctuation coefficient, establish a distributed generation planning optimization model and solve it to obtain the optimized distributed generation configuration plan;

[0164] The established optimization model is shown in Equation (18):

[0165]

[0166] Among them, the objective function in this model is to minimize the transmission line fluctuation coefficient; β is used to adjust the line fluctuation proportion between the 500 kV power transmission network and the 220 kV secondary power transmission network. When the weights of the two are the same, β = 0.5. is the maximum distributed resource capacity that can be invested at grid node r.

[0167] Since this optimization model is a quadratic programming problem with constraints, there must be an optimal solution. By solving the optimization model, R is obtained r , which is the optimal capacity of distributed resources configured at each node corresponding to the next day's corresponding hour determined in step 1).

[0168] Furthermore, according to the above method, grid dispatching personnel can calculate the optimal distributed resource configuration situation per hour and arrange the optimal distributed resource configuration in advance according to the operation mode of the next day.

[0169] To implement the above embodiments, a distributed generation capacity optimization configuration device for participating in new energy fluctuation suppression according to the second aspect of the present disclosure includes:

[0170] A transfer distribution factor matrix construction module, configured to select a set of transmission lines for investigation, construct a generator output power transfer distribution factor matrix of new energy, traditional power sources, and distributed generation with respect to the transmission lines in the set, and calculate the fluctuation amount of the transmission lines according to the transfer distribution factor matrix;

[0171] A new energy fluctuation probability distribution model construction module, configured to establish a new energy fluctuation probability distribution model, determine the total new energy fluctuation amount according to the new energy fluctuation probability distribution model, and determine the sharing amounts of the traditional power sources and the distributed generation participating in the new energy fluctuation suppression;

[0172] A fluctuation variance calculation module, configured to establish a fluctuation probability distribution model of a transmission line according to the transfer distribution factor matrix, the total new energy fluctuation amount, and the sharing amounts of the conventional power source and the distributed power source, and calculate the fluctuation variance of the transmission line according to the fluctuation probability distribution model of the transmission line;

[0173] A fluctuation coefficient calculation module, configured to calculate the fluctuation coefficient of the transmission line by using the fluctuation variance of the transmission line;

[0174] A distributed power source optimal configuration module, configured to establish and solve a distributed power source planning optimization model by using the fluctuation coefficient of the transmission line to obtain an optimal configuration scheme for the distributed power source capacity.

[0175] To implement the above embodiments, a third aspect embodiment of the present disclosure proposes an electronic device, including:

[0176] At least one processor; and a memory communicatively connected to the at least one processor;

[0177] Wherein, the memory stores instructions executable by the at least one processor, and the instructions are configured to execute the above method for optimizing the capacity of a distributed power source to participate in suppressing new energy fluctuations.

[0178] To implement the above embodiments, a fourth aspect embodiment of the present disclosure proposes a computer-readable storage medium storing computer instructions for causing the computer to execute the above method for optimizing the capacity of a distributed power source to participate in suppressing new energy fluctuations.

[0179] It should be noted that the above-mentioned computer-readable medium in the present disclosure can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, and this computer-readable signal medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0180] The above-mentioned computer-readable medium can be included in the above-mentioned electronic device; or it can exist separately without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device executes a method for optimizing the configuration of the capacity of distributed power sources to participate in suppressing new energy fluctuations in the above-mentioned embodiment.

[0181] Computer program code for performing the operations of this disclosure may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partially on the user's computer, execute as a stand-alone software package, execute partially on the user's computer and partially on a remote computer, or execute entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0182] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0183] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be construed as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically and clearly defined.

[0184] Any process or method description in a flowchart or described otherwise herein may be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a specific logical function or process. The scope of the preferred embodiments of this application includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in the reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art of the embodiments of this application.

[0185] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0186] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0187] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above embodiments can be completed by a program instructing relevant hardware. The program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0188] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, may exist physically alone for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0189] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for optimizing the capacity allocation of distributed power sources to participate in the suppression of new energy fluctuations, characterized in that, Including: Select a transmission line inspection set, construct a generator output power transfer distribution factor matrix of new energy, traditional power sources, and distributed power sources with respect to the transmission lines in the set, and calculate the fluctuation amount of the transmission lines according to the transfer distribution factor matrix; Establish a new energy fluctuation probability distribution model, determine the total new energy fluctuation amount according to the new energy fluctuation probability distribution model, and determine the sharing amounts of the traditional power sources and the distributed power sources participating in the suppression of the new energy fluctuation; According to the transfer distribution factor matrix, the total new energy fluctuation amount, and the sharing amounts of the traditional power sources and the distributed power sources, establish a transmission line fluctuation probability distribution model, and calculate the fluctuation variance of the transmission lines according to the transmission line fluctuation probability distribution model; Use the fluctuation variance of the transmission lines to calculate the fluctuation coefficient of the transmission lines; Use the fluctuation coefficient of the transmission lines to establish and solve a distributed power source planning optimization model to obtain the optimized configuration scheme of the distributed power source capacity.

2. The method according to claim 1, characterized in that, The new energy includes wind power and photovoltaic power.

3. The method according to claim 2, wherein The step of selecting a transmission line inspection set, constructing a generator output power transfer distribution factor matrix of new energy, traditional power sources, and distributed power sources with respect to the transmission lines in the set, and calculating the fluctuation amount of the transmission lines includes: 1) Obtain the set B of traditional power generation units participating in fluctuation suppression in the future set time period d , the set S of wind turbines pv , the set S of photovoltaic units pv , and the set B of nodes where distributed resources can be accessed r ; 2) Determine the inspection set of transmission lines, including: 500 kV line set L 500 and 220 kV line set L 220 ; 3) According to the set obtained in step 1), respectively establish a generator output power transfer distribution factor matrix of wind power, photovoltaic power, traditional power sources, and distributed power source nodes with respect to each transmission line in the transmission line inspection set; Among them, the generator output power transfer distribution factor matrix \(G\) of line \(l\) l represents the change in the power flow of line \(l\) caused by the change in the active output power of the generator; \(G\) l has a length equal to the number of grid nodes. Then, the \(k\)-th element \(G\) l in the \(k\)-th node corresponding \(G\) l (k) has the following expression: where k is the grid node number where the power source is located, l is the line number; m and n are the start and end node numbers of line l respectively; X = B -1 is the inverse matrix of the susceptance matrix in the DC form of the power grid, and X mk represents the element in the m-th row and k-th column of matrix X, and X nk represents the element in the n-th row and k-th column of matrix X; x l is the branch impedance of line l; 4) Calculate the fluctuation amount of the transmission lines in the set according to the transfer distribution factor matrix; In the formula, represents the fluctuation quantity of line l, where l ∈ L, and L represents the set of transmission lines under investigation, and L includes L 500 and L 220 ; ΔP wd , ΔP pv are respectively the fluctuation quantities of the wind farm and the photovoltaic power station, and ΔP td , ΔP DER are respectively the fluctuation sharing quantities of the traditional power source and the distributed power source; are respectively the generator output power transfer distribution factor matrices of the wind power, photovoltaic power, traditional power source, and distributed power source nodes to line l.

4. The method according to claim 3, characterized in that The step of establishing a new energy fluctuation probability distribution model, determining the total new energy fluctuation amount according to the new energy fluctuation probability distribution model, and determining the sharing amounts of the traditional power sources and the distributed power sources participating in the suppression of the new energy fluctuation includes: 1) Establish a new energy fluctuation probability distribution model; Let the random fluctuations of new energy follow a normal distribution, i.e., ΔP wd ~N(0,∑ wd ), ΔP pv ~N(0,∑ pv ). Then the calculation expressions for the fluctuation correlation coefficient ∑ wd of the wind farm and the fluctuation correlation coefficient ∑ pv of the PV farm are as follows: In the formula, represents the fluctuation correlation coefficient of wind power stations within the jurisdiction of each city's power grid, represents the fluctuation correlation coefficient of photovoltaic power stations within the jurisdiction of each city's power grid; the standard deviation of wind power fluctuation σ wd = η wd ·P wd , and the standard deviation of photovoltaic power fluctuation σ pv = η pv ·P pv ; η wd , η pv are the wind power volatility and photovoltaic power volatility respectively, and P wd , P pv represent the output powers of wind power and photovoltaic power respectively; M wd represents the correlation matrix between the wind power station and the jurisdiction of the wind power city, and M pv represents the correlation matrix between the photovoltaic power station and the jurisdiction of the photovoltaic city; the columns of M wd represent the wind power station numbers, and the rows represent the wind power city jurisdiction numbers; the columns of M pv represent the photovoltaic power station numbers, and the rows represent the photovoltaic city jurisdiction numbers; for M wd and M pv , the corresponding elements of the region where each column of the power station is located are 1, and the rest of the elements are 0; diag represents the diagonal matrix; 2) Determine the total new energy fluctuation amount according to the new energy fluctuation probability distribution model, and the expression is as follows: Where, ΔP S represents the total new energy fluctuation, S represents the set of new energy fluctuation sources including wind power and photovoltaic power; represents the fluctuation of wind farm i, and the subscript i represents the number of the wind farm; represents the fluctuation of photovoltaic power plant j, and the subscript j represents the number of the photovoltaic power station; ΔP S obeys a normal distribution represents ΔP S The variance of is expressed as follows: In the formula, 1 represents a column vector of all 1s; 3) Determine the sharing ratio of the traditional power sources and the distributed power sources for suppressing fluctuations; According to In the formula, represents the fluctuation of the traditional power unit d, where the subscript d is the traditional power unit number; represents the fluctuation of the distributed power source node r, where r is the distributed power source node number; Let the traditional power sources and the distributed power sources share the fluctuations according to the ratios α and 1 - α, that is, satisfy: where α is the fluctuation sharing coefficient of the traditional power sources; Then the fluctuation sharing amounts of the traditional power source units and each node distributed power source are respectively: Wherein, R d represents the regulation speed R of the traditional power unit d d , R r represents the adjustable capacity of the distributed power node r, d ∈ B d , r ∈ B r ; Configure distributed power sources according to the 2σ interval of the total new energy fluctuation ΔP S as shown in the following formula: Wherein, represents the maximum fluctuation amount; represents the standard deviation of the total new energy power generation; Then: The sharing amount of the distributed power sources participating in the suppression of fluctuations is: wherein, R n is the total adjustable capacity of the distributed power source.

5. The method according to claim 4, characterized in that, The step of establishing a transmission line fluctuation probability distribution model according to the transfer distribution factor matrix, the total new energy fluctuation amount, and the sharing amounts of the traditional power sources and the distributed power sources, and calculating the fluctuation variance of the transmission lines according to the transmission line fluctuation probability distribution model includes: 1) Write the fluctuation amount of the transmission lines in the following matrix expression form to establish a transmission line fluctuation probability distribution model: 2) Calculate according to the transmission line fluctuation probability distribution model The corresponding covariance matrix The expression is as follows: where 3) Simplify the diagonal elements of the covariance matrix After simplification, the diagonal elements The expression is as follows: In the formula, The diagonal elements of represent the fluctuation variance of the transmission line l.

6. The method according to claim 5, characterized in that The step of using the fluctuation variance of the transmission lines to calculate the fluctuation coefficient of the transmission lines includes: 1) Calculate the DC power flow of the transmission lines; Among them, represents the DC power flow of the transmission line l, P load respectively represent the generator output power transfer distribution factor matrix of the load for line l and the power injected by the load into the power grid; P td respectively represent the output power of traditional power sources; P wd represents the wind power generation power vector; P pv represents the photovoltaic power generation power vector; 2) Calculate the fluctuation coefficient of the transmission lines, and the expression is as follows: Where, F l represents the fluctuation coefficient of the transmission line l, represents the thermal stability limit power of the transmission line l with response, represents the fluctuation variance of the power on the power transmission line.

7. The method according to claim 6, characterized in that, Using the fluctuation coefficient of the transmission line, establishing and solving a distributed power source planning optimization model to obtain the optimized configuration scheme of the distributed power source capacity, including: 1) Establishing a distributed power source planning optimization model as follows: where β is used to adjust the line fluctuation ratio of the 500 kV power transmission network and the 220 kV secondary power transmission network, is the maximum capacity of distributed resources that can be invested at node r; 2) Solve the distributed power planning optimization model to obtain R r , The solution is the optimal capacity of distributed resources configured at each node during the set period.

8. A device for optimizing the capacity allocation of distributed power sources to participate in suppressing the fluctuations of new energy, characterized in that, Including: A transfer distribution factor matrix construction module, configured to select a transmission line inspection set, construct a generator output power transfer distribution factor matrix of new energy, traditional power sources, and distributed power sources with respect to the transmission lines in the set, and calculate the fluctuation amount of the transmission line according to the transfer distribution factor matrix; A new energy fluctuation probability distribution model construction module, configured to establish a new energy fluctuation probability distribution model, determine the total new energy fluctuation amount according to the new energy fluctuation probability distribution model, and determine the sharing amounts of the traditional power sources and the distributed power sources participating in the suppression of the new energy fluctuation; A fluctuation variance calculation module, configured to establish a transmission line fluctuation probability distribution model according to the transfer distribution factor matrix, the total new energy fluctuation amount, and the sharing amounts of the traditional power sources and the distributed power sources, and calculate the fluctuation variance of the transmission line according to the transmission line fluctuation probability distribution model; A fluctuation coefficient calculation module, configured to calculate the fluctuation coefficient of the transmission line by using the fluctuation variance of the transmission line; A distributed power source optimized configuration module, configured to establish and solve a distributed power source planning optimization model by using the fluctuation coefficient of the transmission line to obtain the optimized configuration scheme of the distributed power source capacity.

9. An electronic device, characterized in that, Including: At least one processor; And a memory communicatively connected to the at least one processor; Wherein, the memory stores instructions executable by the at least one processor, and the instructions are configured to execute the method according to any one of the above claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

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