A method and device for evaluating the flexible adjustment margin of a transmission and distribution connection node that aggregates dispersed resources

Abstract

A method and device for evaluating the flexible adjustment margin of a transmission and distribution connection node that aggregates distributed resources, comprising step S1, obtaining parameter information of each distributed resource; step S2, constructing a standard model of power-type and energy storage-type distributed resources; step S3, based on the standard model of power-type and energy storage-type distributed resources, considering the uncertainty of distributed resources, and using linearized Minkowski sum calculation and distributed blue-robust opportunity constraints to construct a distributed resource aggregation model that takes into account the uncertainty of distributed resources; step S4, obtaining target distribution network and load parameter information; step S5, taking the maximum and minimum values ​​of the active adjustable quantity within a time window of the transmission and distribution connection node as the solution target, constructing a distribution network aggregation flexible adjustment margin evaluation model for the transmission and distribution connection node; step S6, solving the distribution network aggregation flexible adjustment margin evaluation model, obtaining the upper and lower boundary evaluation values ​​of the aggregated flexible adjustment margin, and outputting a flexible adjustment margin curve.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network, and in particular to a method and device for evaluating the flexible adjustment margin of a transmission and distribution connection node that aggregates dispersed resources. Background Art

[0002] With the continuous improvement of the penetration rate of distributed resources in the power grid, a large number of small-capacity and scattered resources have emerged in the distribution network, such as distributed new energy, distributed energy storage and electric vehicles. These scattered resources have small single-machine capacity, but a large number, and the total amount after aggregation is considerable, which contains huge potential for flexible regulation. The flexibility of these resources after aggregation can participate in the peak regulation, frequency regulation and other auxiliary services of the power grid, relieve the regulation pressure of the power grid, and improve the stability of the power grid. However, these resources generally have the characteristics of small adjustable power, low adjustable capacity, wide distribution and large number, which makes their evaluation and regulation difficult, and the flexible regulation capacity has not been fully utilized. Therefore, it is particularly important to efficiently aggregate and evaluate the massive and scattered distributed resources at the transmission and distribution contact point. At present, there is an evaluation method for the regulation margin of distribution network resources in the industry, but the evaluation only takes into account the distribution network level, mainly for wind and solar resources and energy storage resources of 10kV lines and above, and the equivalent load nodes of the distribution station area. Although the uncertainty of new energy is also considered, it is only for the uncertainty within the distribution network, and does not cover the scattered resources under the distribution station area, such as energy storage, wind and solar resources, etc. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and device for evaluating the flexible adjustment margin of a transmission and distribution connection node that aggregates dispersed resources. The method takes into account the dispersed resources under the distribution station area and aggregates them to improve the utilization efficiency of network flexibility resources.

[0004] In order to solve the above technical problems, the present invention adopts the following technical method: a method for evaluating the flexible adjustment margin of a transmission and distribution connection node that aggregates dispersed resources, comprising:

[0005] Step S1, obtaining parameter information of each distributed resource, including power boundary, ramp rate, energy boundary, and capacity boundary of the distributed resource;

[0006] Step S2, classifying the distributed resources into power-type distributed resources and energy storage-type distributed resources according to the parameter information of the distributed resources, and constructing standard models of the power-type distributed resources and the energy storage-type distributed resources respectively;

[0007] Step S3, based on the standard model of power-type decentralized resources and energy storage-type decentralized resources obtained in step S2, considering the uncertainty of decentralized resources, using linearized Minkowski summation calculation and distributed robust chance constraints, constructing a decentralized resource aggregation model that takes into account the uncertainty of decentralized resources;

[0008] Step S4, obtaining parameter information of the target distribution network and load, including the target distribution network topology, line parameters, load size, and predicted output of new energy;

[0009] Step S5, based on the parameter information obtained in step S1 and step S4 and the aggregate equivalent parameters obtained by solving the decentralized resource aggregation model constructed in step S3, taking the maximum and minimum values ​​of the active adjustable quantity within a time window of the transmission and distribution connection node as the solution target, and taking power constraints, voltage constraints, aggregate decentralized resource constraints and system backup constraints as constraints, constructing a distribution network aggregate flexible adjustment margin evaluation model for the transmission and distribution connection node;

[0010] Step S6, solving the distribution network aggregate flexible adjustment margin assessment model constructed in step S5, obtaining the upper and lower boundary assessment values ​​of the aggregate flexible adjustment margin of decentralized resources at the transmission and distribution interconnection nodes under different decentralized resource uncertainty confidence levels, outputting the flexible adjustment margin curve on the transmission and distribution interconnection nodes, and realizing the flexible adjustment margin assessment of the transmission and distribution interconnection nodes of the distribution network containing massive decentralized resources.

[0011] Further, in step S2, distributed wind power, distributed photovoltaic, and micro fuel generator sets are classified as power-type distributed resources according to the parameter information of the distributed resources, and distributed energy storage, electric vehicles, and base station energy storage are classified as energy storage-type distributed resources;

[0012] A standard model of power-type decentralized resources is constructed, and the power feasible domain of the power-type decentralized resources is as follows:

[0013] (1)

[0014] In the formula, Representation Node Power-based distributed resources The power variable, , , , Respectively Time Node Power-based distributed resources The active and reactive output power, Indicates the time scale; and is the parameter matrix, as follows:

[0015] (2)

[0016] (3)

[0017] (4)

[0018] (5)

[0019] (6)

[0020] (7)

[0021] In the formula, express dimensional identity matrix; express Dimensional matrix; , and Indicates Group power constraint linearization constraint coefficient, ; , Respectively represent nodes Power-based distributed resources The upper and lower limits of active output; Representation Node Power-based distributed resources The upper limit of the climbing rate; Representation Node Power-based distributed resources The apparent power of

[0022] A standard model of energy storage type distributed resources is constructed, and the power feasible domain of the energy storage type distributed resources is as follows:

[0023] (8)

[0024] In the formula, Representation Node Energy storage distributed resources Power variable; , ; , Respectively Time Node Distributed energy storage resources Active and reactive output power; and is the parameter matrix, as follows:

[0025] (9)

[0026] (10)

[0027] (11)

[0028] (12)

[0029] (13)

[0030] (14)

[0031] (15)

[0032] In the formula, express dimensional square matrix; For Node Distributed energy storage resources The loss rate; Indicates a time interval; express Time Node Distributed energy storage resources energy; , Respectively Time Node Distributed energy storage resources The upper and lower limits of active output; , Respectively represent nodes Distributed energy storage resources The upper and lower limits of energy storage capacity; Representation Node Distributed energy storage resources The upper limit of the climbing rate; Representation Node Distributed energy storage resources The apparent power.

[0033] Furthermore, in step S3, the process of constructing a decentralized resource aggregation model taking into account the uncertainty of decentralized resources is as follows:

[0034] S301, based on the standard model of power-type distributed resources and energy storage-type distributed resources obtained in step S2, determine the power feasible region of distributed resources as follows:

[0035] (16)

[0036] In the formula, To disperse resources The power feasible region of and To disperse resources The parameter matrix of To disperse resources Power variable; express dimensional real number;

[0037] S302, linearly simplify the aggregation of the feasible domains of multiple distributed resources using the polyhedron-based Minkowski summation approximation method, and convert the Minkowski summation calculation into an algebraic summation calculation. Specifically, in each aggregation area, the average parameter of the distributed resources in the area is calculated as the aggregation polyhedron benchmark, and the distributed resources in the area are scaled and translated according to the benchmark to achieve the maximum inner approximation, as shown in the following formula:

[0038] (17)

[0039] In the formula, represents the Minkowski summation calculation; , Distributed resources 、 The power feasible region of , Represents distributed resources 、 The scaling factor of , Represents distributed resources 、 The translation coefficient of Represents the aggregation area The calculation formula of the reference polyhedron is as follows:

[0040] (18)

[0041] (19)

[0042] In the formula, The power variable matrix representing the reference polyhedron; and It is an aggregation area The parameter matrix of the reference polyhedron; Represents the aggregation area Aggregation of decentralized resources; Represents the aggregation area The number of distributed resources;

[0043] S303, using the distributed robust opportunity constraint to consider the uncertainty of decentralized resources, establish the following optimization problem:

[0044] (20)

[0045] In the formula, , , is an auxiliary matrix variable, represents the true probability distribution, represents a fuzzy set, Indicates that uncertainty variables are considered The probability distribution of Indicates the uncertainty confidence;

[0046] S304, combining formulas (17) and (20) to obtain a decentralized resource aggregation model that takes into account the uncertainty of decentralized resources in each distribution station area.

[0047] Furthermore, in step S303, an uncertain set is found in the uncertainty region. , the uncertain set By point set The convex hull formed transforms the last constraint in formula (20) into the following robust problem;

[0048] (twenty one)

[0049] In the formula, In the uncertain set The first convex hull that satisfies the confidence Vertices, Indicates that it contains The parameter matrix ; For uncertain set The number of vertices of the convex hull, Represents the dimension of the uncertainty variable in the uncertainty set.

[0050] Furthermore, in step S5, the distribution network aggregated flexible regulation margin assessment model constructed for the transmission and distribution connection node includes an aggregated flexible regulation margin upper boundary assessment model and a lower boundary assessment model;

[0051] The upper boundary assessment model of the aggregated flexible regulation margin takes the maximum value of the adjustable active power within a time window of the transmission and distribution connection node as the solution target, as shown in the following formula:

[0052] (twenty two)

[0053] In the formula, express Active power output by the transmission and distribution connection node at all times; for The weight coefficient of the moment;

[0054] The lower boundary assessment model of the aggregated flexible regulation margin takes the minimum value of the adjustable active power within a time window of the transmission and distribution connection node as the solution target, as shown in the following formula:

[0055] (twenty three).

[0056] Furthermore, the constraints of the aggregate flexible adjustment margin upper boundary evaluation model include:

[0057] 1) Power Constraints

[0058] (twenty four)

[0059] (25)

[0060] (26)

[0061] (27)

[0062] (28)

[0063] (29)

[0064] In the above constraints, (24) and (26) are the active power and reactive power of the line, respectively, which are calculated by the power of the sub-node line and the injected power of the current node; (25) and (27) are the active power and reactive power injected by the node, respectively, which are calculated by the node load and the two types of distributed resources on the node; (28) and (29) represent the boundary constraints of active power and reactive power, respectively; where, and Respectively Time Node Active power and reactive power of the line; Representation Node The collection of child nodes; and Respectively Time Node The injected active and reactive power; and Respectively Time Node Active load and reactive load; and They represent the active power and reactive power of power-type decentralized resources respectively; and They represent the active power and reactive power of energy storage type decentralized resources respectively; and Represent the power upper bounds of active and reactive transfer capacity respectively;

[0065] 2) Voltage Constraint

[0066] (30)

[0067] (31)

[0068] Among the above constraints, (30) represents the distribution network flow constraint, and (31) represents the voltage boundary constraint; express Time Node The voltage, express The parent node of the node, and Respectively indicate lines The resistance and reactance, represents the set of all nodes, and Respectively represent the upper and lower boundaries of the voltage allowable range;

[0069] 3) Aggregate decentralized resource constraints

[0070] (32)

[0071] (33)

[0072] The above constraints are the boundary constraints for aggregating distributed resources; for equation (32), , and Node The scaling coefficient and translation coefficient matrix of power-type distributed resources; for equation (33), , and Node Scaling and translation coefficient matrices of energy storage distributed resources;

[0073] 4) System backup constraints

[0074] (34)

[0075] This constraint is the upward reserve constraint of the network; where, represents the reserve factor;

[0076] The constraints of the lower boundary assessment model of the aggregated flexible adjustment margin include the constraints shown in formulas (24)-(33), and also include the following system reserve constraints:

[0077] (35)

[0078] This constraint is the downward spare constraint of the network.

[0079] Preferably, the power constraint linearization constraint coefficient , and The values ​​are as follows:

[0080] (36).

[0081] As another aspect of the present invention, a device for evaluating the flexible adjustment margin of a transmission and distribution connection node that aggregates dispersed resources includes a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement a method for evaluating the flexible adjustment margin of a transmission and distribution connection node that aggregates dispersed resources.

[0082] As another aspect of the present invention, a computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the method for evaluating the flexible adjustment margin of the transmission and distribution connection nodes of the aggregated decentralized resources.

[0083] As another aspect of the present invention, a computer program product includes a computer program, which, when executed by a processor, implements the method for evaluating the flexible adjustment margin of the transmission and distribution connection node of the aggregated decentralized resources.

[0084] The method and device for evaluating the flexible adjustment margin of the transmission and distribution connection nodes that aggregate scattered resources provided by the present invention take into account the scattered resources under the distribution station area and aggregate them, fully demonstrating the flexible adjustment capabilities of the scattered resources at the transmission and distribution connection nodes, thereby effectively improving the utilization efficiency of the network flexibility resources. The present invention also classifies the scattered resources before aggregating them, which can greatly improve the degree of aggregation of the scattered resources under the distribution station area and further improve the resource utilization rate. Finally, the present invention also uses the method of distributed robust chance constraints to consider the uncertainty of the scattered resources under the distribution station area, thereby effectively improving the accuracy of the evaluation of the aggregated flexible adjustment margin of the scattered resources at the transmission and distribution connection nodes. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 The present invention is a flow chart of a method for evaluating the flexible adjustment margin of a transmission and distribution connection node for aggregating dispersed resources. DETAILED DESCRIPTION

[0086] In order to facilitate the understanding of those skilled in the art, the present invention is further described below in conjunction with embodiments and drawings. The contents mentioned in the implementation modes are not intended to limit the present invention.

[0087] like Figure 1 As shown, the method for evaluating the flexible adjustment margin of the transmission and distribution connection node for aggregating dispersed resources provided by the present invention mainly includes the following steps.

[0088] Step S1, obtaining parameter information of each distributed resource, specifically including parameter information such as power boundary, ramp rate, energy boundary, capacity boundary, etc. of the distributed resource.

[0089] Step S2: According to the parameter information of each distributed resource obtained in step S1, each distributed resource is divided into power-type distributed resources and energy storage-type distributed resources, and standard models of power-type distributed resources and energy storage-type distributed resources are constructed respectively. Specifically:

[0090] (1) Constructing a standard model for power-based distributed resources

[0091] In the present invention, the main components of power-type decentralized resources include distributed wind power, distributed photovoltaics, micro fuel generator sets, etc., and their operational feasible domain is expressed by the following formula:

[0092] (37)

[0093] (38)

[0094] (39)

[0095] In the formula, , Respectively Time Node Power-based distributed resources Active and reactive output power; Representation Node Power-based distributed resources The apparent power of , Respectively represent nodes Power-based distributed resources The upper and lower limits of active output; Representation Node Power-based distributed resources The upper limit of the climbing rate; , and Indicates Group power constraint linearization constraint coefficient, .

[0096] Formulas (37)-(39) are converted into the following compact form:

[0097] (1)

[0098] In the formula, Representation Node Power-based distributed resources The power variable, , , , Respectively Time Node Power-based distributed resources The active and reactive output power, represents the time scale, According to the actual situation, in this implementation mode, , indicating that a day is divided into 24 hours, and is the parameter matrix, as follows:

[0099] (2)

[0100] (3)

[0101] (4)

[0102] (5)

[0103] (6)

[0104] (7)

[0105] In the formula, express dimensional identity matrix; express Dimensional matrix; , and Indicates Group power constraint linearization constraint coefficient, ; , Respectively represent nodes Power-based distributed resources The upper and lower limits of active output; Representation Node Power-based distributed resources The upper limit of the climbing rate; Representation Node Power-based distributed resources The apparent power.

[0106] The above power constraint linearization constraint coefficient , and is generated after linearizing the power, where the subscript Indicates According to the accuracy, it can be divided into 4, 8, 12 groups, etc. The present invention adopts 12 groups, and the value of each group is calculated according to the angle relationship. Because this is a well-known technology in the industry, the aforementioned power constraint linearization constraint coefficients will not be repeated here. , and The specific values ​​of these coefficients are as follows:

[0107] (36).

[0108] (1) Constructing a standard model for energy storage distributed resources

[0109] In the present invention, the main components of energy storage type decentralized resources include distributed energy storage, electric vehicles, base station energy storage, etc., and their operation feasible domain is expressed by the following formula:

[0110] (40)

[0111] (41)

[0112] (42)

[0113] (43)

[0114] (44)

[0115] In the formula, , Respectively Time Node Energy storage distributed resources Active and reactive output power; express Time Node Energy storage distributed resources energy; Representation Node Energy storage distributed resources The apparent power of , Respectively Time Node Energy storage distributed resources The upper and lower limits of active output; , Respectively represent nodes Energy storage distributed resources The upper and lower limits of energy storage capacity; Representation Node Distributed energy storage resources The upper limit of the climbing rate; For Node Distributed energy storage resources The loss rate; Indicates a time interval.

[0116] Formulas (40)-(44) are converted into the following compact form:

[0117] (8)

[0118] In the formula, Representation Node Distributed energy storage resources Power variable; , ; , Respectively Time Node Distributed energy storage resources Active and reactive output power; and is the parameter matrix, as follows:

[0119] (9)

[0120] (10)

[0121] (11)

[0122] (12)

[0123] (13)

[0124] (14)

[0125] (15)

[0126] In the formula, express dimensional square matrix; For Node Distributed energy storage resources The loss rate; Indicates a time interval; express Time Node Distributed energy storage resources energy; , Respectively Time Node Distributed energy storage resources The upper and lower limits of active output; , Respectively represent nodes Distributed energy storage resources The upper and lower limits of energy storage capacity; Representation Node Distributed energy storage resources The upper limit of the climbing rate; Representation Node Distributed energy storage resources The apparent power.

[0127] Step S3, based on the standard model of power-type decentralized resources and energy storage-type decentralized resources obtained in step S2, considering the uncertainty of decentralized resources, using linearized Minkowski summation calculation and distributed robust opportunity constraints, construct a decentralized resource aggregation model that takes into account the uncertainty of decentralized resources. The details are as follows:

[0128] S301, the power feasible region of the distributed resources is defined as follows:

[0129] (16)

[0130] In the formula, To disperse resources The power feasible region of and To disperse resources The parameter matrix of To disperse resources Power variable; express dimensional real number.

[0131] It is worth noting that the feasible domain of power-type distributed resources can be expressed by formula (1), and the feasible domain of energy storage-type distributed resources can be expressed by formula (8).

[0132] S302, in order to make better use of the scattered resources, they need to be aggregated and coordinated as a whole. The aggregation of the feasible domains of multiple scattered resources is a Minkowski summation calculation process, but the Minkowski summation calculation process is a nonlinear calculation and is difficult to directly calculate and solve. In order to ensure the feasibility of the aggregated calculation, it is necessary to perform polyhedral equivalent approximation on the aggregated feasible domain. In order to further ensure the feasibility of the aggregation result, the inner approximation method is used to perform polyhedral approximation on the aggregated feasible domain. The present invention uses a polyhedron-based Minkowski summation approximation method to linearly simplify the Minkowski summation calculation process. In each aggregation area, the average parameter of the distributed resources in the calculation area is used as the aggregation polyhedron benchmark, and the distributed resources in the area are scaled and translated according to the benchmark to achieve maximum inner approximation. Therefore, the Minkowski summation calculation is converted into an algebraic summation calculation, and the specific process is as follows:

[0133] (17)

[0134] In the formula, represents the Minkowski summation calculation; , Distributed resources 、 The power feasible region of , Represents distributed resources 、 The scaling factor of , Represents distributed resources 、 The translation coefficient of Represents the aggregation area The calculation formula of the reference polyhedron is as follows:

[0135] (18)

[0136] (19)

[0137] In the formula, The power variable matrix representing the reference polyhedron; and It is an aggregation area The parameter matrix of the reference polyhedron; Represents the aggregation area Aggregation of decentralized resources; Represents the aggregation area The number of distributed resources.

[0138] S303, in order to obtain the maximum inner approximate feasible region of distributed resources, it is necessary to find the maximum scaling factor of each distributed resource. And the uncertainty of distributed resources is considered by using the distributed robust chance constraint, thus establishing the following optimization problem:

[0139] (20)

[0140] In the formula, , , is an auxiliary matrix variable, represents the true probability distribution, represents a fuzzy set, Indicates that uncertainty variables are considered The probability distribution of Represents the uncertainty confidence.

[0141] The last constraint in the optimization problem of formula (20) is a distributed robust chance constraint, which is a non-convex constraint and cannot be solved directly. To solve this constraint, find an uncertainty set in the uncertainty region: , transforming the constraint into the following robust problem:

[0142] (45)

[0143] In the formula, Indicates that it contains uncertain variables The parameter matrix .

[0144] Obviously, as long as the probability that the sample data point is not in the region is less than the confidence level, the distribution robustness chance constraint in equation (20) can be transformed into equation (45).

[0145] For a given sample data with unknown probability distribution , the average value can be calculated and the covariance matrix . The sample data is put through a standardization process , we can obtain a sample set with a mean value of 0 and a covariance matrix of the identity matrix . You can use the support set Find a collection in , under the premise of satisfying the constraints, ensure the length of the uncertain set edge The value of is minimized to reduce conservatism. This leads to the following question:

[0146] (46)

[0147] By further transforming the problem in equation (46), we can obtain a solvable problem:

[0148] (47)

[0149] In the formula, , is a computational auxiliary variable. This problem can be solved using a binary search algorithm.

[0150] In order to obtain the optimal uncertain set edge length After that, the uncertain set can be represented as the convex hull of its vertices. dimensional random variable, .

[0151] (48)

[0152] Through formula (48), the uncertain set It can be expressed as , that is, the uncertain set By point set Constructed convex hull.

[0153] Finally, the optimization problem of formula (20) can be transformed into the optimization problem of the following formula (49). The optimization problem shown in formula (49) can be directly solved by optimization calculation software.

[0154] (49)

[0155] In the formula, In the uncertain set The first convex hull that satisfies the confidence Vertices, Indicates that it contains The parameter matrix ; For uncertain set The number of vertices of the convex hull, represents the dimension of the uncertainty variable in the uncertainty set. If is 2, which means that the number of variables in the uncertainty set is 4.

[0156] S304, by combining the above formulas (17) and (20), the decentralized resource aggregation model considering the uncertainty of decentralized resources in each distribution area can be obtained. And by solving formulas (17), (49) and , , you can pass The feasible region of aggregated power of decentralized resources on a distribution node is obtained.

[0157] Step S4, obtaining relevant parameter information such as the target distribution network and load, specifically including parameter information such as the target distribution network topology, line parameters, load size, and predicted output of new energy.

[0158] Step S5, based on the parameter information obtained in steps S1 and S4 and the aggregation equivalent parameters obtained by solving the decentralized resource aggregation model constructed in step S3, construct a distribution network aggregation flexible adjustment margin assessment model for the transmission and distribution connection node, and the distribution network aggregation flexible adjustment margin assessment model includes an aggregation flexible adjustment margin upper boundary assessment model and an aggregation flexible adjustment margin lower boundary assessment model. At the transmission and distribution connection node, when the power generation in the distribution network is insufficient, it is necessary to transmit power to the distribution network through the transmission and distribution connection node. At this time, the active power output by the transmission and distribution connection node is positive; when the power generation in the distribution network is excessive, the distribution network needs to transmit power to the transmission network through the transmission and distribution connection node. At this time, the active power output by the transmission and distribution connection node is negative. Therefore, it is only necessary to accurately assess the range of active power variation output by the transmission and distribution connection node to achieve an accurate assessment of the adjustable flexibility resources in the distribution network, and provide strong support for the safe and reliable regulation of the power grid control agency. Specifically including:

[0159] (1) Aggregate flexible adjustment margin upper boundary assessment model

[0160] Objective function: The upper boundary evaluation model of the aggregated flexible regulation margin takes the maximum value of the adjustable active power within a time window of the transmission and distribution connection node as the solution target, as shown in the following formula:

[0161] (twenty two)

[0162] In the formula, express Active power output by the transmission and distribution connection node at all times; for The weight coefficient of the moment, in the present invention, gradually decreases with the passage of time.

[0163] Constraints: The constraints of the aggregate flexible adjustment margin upper boundary assessment model include:

[0164] 1) Power Constraints

[0165] (twenty four)

[0166] (25)

[0167] (26)

[0168] (27)

[0169] (28)

[0170] (29)

[0171] In the above constraints, (24) and (26) are the active power and reactive power of the line, respectively, which are calculated by the power of the sub-node line and the injected power of the current node; (25) and (27) are the active power and reactive power injected by the node, respectively, which are calculated by the node load and the two types of distributed resources on the node; (28) and (29) represent the boundary constraints of active power and reactive power, respectively; where, and Respectively Time Node Active power and reactive power of the line; Representation Node The collection of child nodes; and Respectively Time Node The injected active and reactive power; and Respectively Time Node Active load and reactive load; and They represent the active power and reactive power of power-type decentralized resources respectively; and They represent the active power and reactive power of energy storage type decentralized resources respectively; and They represent the power upper bounds of active and reactive transmission capacity respectively.

[0172] 2) Voltage Constraint

[0173] (30)

[0174] (31)

[0175] Among the above constraints, (30) represents the distribution network flow constraint, and (31) represents the voltage boundary constraint; express Time Node The voltage, express The parent node of the node, and Respectively indicate lines The resistance and reactance, represents the set of all nodes, and They represent the upper and lower boundaries of the allowable voltage range respectively.

[0176] 3) Aggregate decentralized resource constraints

[0177] (32)

[0178] (33)

[0179] The above constraints are the boundary constraints for aggregating distributed resources; for equation (32), , and Node The scaling coefficient and translation coefficient matrix of power-type distributed resources; for equation (33), , and Node Scaling and translation coefficient matrices for energy storage distributed resources.

[0180] 4) System backup constraints

[0181] (34)

[0182] This constraint is the upward reserve constraint of the network; where, Indicates the reserve factor.

[0183] (2) Aggregate Flexible Adjustment Margin Lower Boundary Assessment Model

[0184] Objective function: The lower boundary evaluation model of the aggregated flexible regulation margin takes the minimum value of the adjustable active power within a time window of the transmission and distribution connection node as the solution target, as shown in the following formula:

[0185] (twenty three)

[0186] Constraints: The lower boundary assessment model of the aggregated flexible adjustment margin also requires power constraints, voltage constraints, aggregated distributed resource constraints, and system backup constraints. The first three are consistent with the upper boundary assessment model of the aggregated flexible adjustment margin, and will not be repeated below. The system backup constraints of the lower boundary assessment model of the aggregated flexible adjustment margin consider downward backup constraints, which are different from the previous ones, as follows:

[0187] (35).

[0188] Step S6, solving the distribution network aggregated flexible adjustment margin assessment model constructed in step S5, and obtaining the upper and lower boundary assessment values ​​of the aggregated flexible adjustment margin of decentralized resources at the transmission and distribution interconnection nodes under different decentralized resource uncertainty confidence levels, that is, the maximum and minimum values ​​of the active power output by the transmission and distribution interconnection nodes. Based on the obtained upper and lower boundary assessment values, a flexible adjustment margin curve on the transmission and distribution interconnection nodes is drawn and output, thereby realizing the flexible adjustment margin assessment of the transmission and distribution interconnection nodes of the distribution network containing massive decentralized resources.

[0189] On the other hand, based on the same principle as the method for evaluating the flexible adjustment margin of the transmission and distribution connection node of aggregated decentralized resources described in the above embodiment, the present invention also provides a device for evaluating the flexible adjustment margin of the transmission and distribution connection node of aggregated decentralized resources, the device comprising a memory, a processor and a computer program stored in the memory, the processor executes the computer program to implement the method for evaluating the flexible adjustment margin of the transmission and distribution connection node of aggregated decentralized resources described in the above embodiment. Specifically, the device can be an electronic computer or a tablet computer, the processor can be a CPU, a GPU, etc., the memory can be a RAM, a ROM, an EEPROM, a CDROM, a disk storage medium, or any other medium that can be used to carry or store a computer program and can be read by a computer, which is not limited here.

[0190] On the other hand, the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the method for evaluating the flexible adjustment margin of the transmission and distribution connection node of the aggregated decentralized resources described in the above embodiment is implemented. Specifically, the computer-readable storage medium can be RAM, ROM, EEPROM, SSD, CDROM, DVD, USB flash drive, or any other medium that can be used to carry or store a computer program and can be read by a computer.

[0191] On the other hand, the present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the method for evaluating the flexible adjustment margin of the transmission and distribution connection node of aggregated decentralized resources described in the above embodiment.

[0192] The above embodiments are preferred implementation schemes of the present invention. In addition, the present invention may also be implemented in other ways. Any obvious replacement without departing from the concept of the present technical solution is within the protection scope of the present invention.

[0193] In order to make it easier for ordinary technicians in the field to understand the improvements of the present invention over the prior art, some drawings and descriptions of the present invention have been simplified, and for the sake of clarity, some other elements are omitted in this application document. Ordinary technicians in the field should realize that these omitted elements may also constitute the content of the present invention.

Claims

1. A method for evaluating the flexible adjustment margin of a transmission and distribution connection node that aggregates dispersed resources, characterized in that: include: Step S1, obtaining parameter information of each distributed resource, including power boundary, ramp rate, energy boundary, and capacity boundary of the distributed resource; Step S2, classifying the distributed resources into power-type distributed resources and energy storage-type distributed resources according to the parameter information of the distributed resources, and constructing standard models of the power-type distributed resources and the energy storage-type distributed resources respectively; Step S3, based on the standard model of power-type decentralized resources and energy storage-type decentralized resources obtained in step S2, considering the uncertainty of decentralized resources, using linearized Minkowski summation calculation and distributed robust chance constraints, constructing a decentralized resource aggregation model that takes into account the uncertainty of decentralized resources; Step S4, obtaining parameter information of the target distribution network and load, including the target distribution network topology, line parameters, load size, and predicted output of new energy; Step S5, based on the parameter information obtained in step S1 and step S4 and the aggregate equivalent parameters obtained by solving the decentralized resource aggregation model constructed in step S3, taking the maximum and minimum values ​​of the active adjustable quantity within a time window of the transmission and distribution connection node as the solution target, and taking power constraints, voltage constraints, aggregate decentralized resource constraints and system backup constraints as constraints, constructing a distribution network aggregate flexible adjustment margin evaluation model for the transmission and distribution connection node; Step S6, solving the distribution network aggregate flexible adjustment margin assessment model constructed in step S5, obtaining the upper and lower boundary assessment values ​​of the aggregate flexible adjustment margin of decentralized resources at the transmission and distribution interconnection nodes under different decentralized resource uncertainty confidence levels, outputting the flexible adjustment margin curve on the transmission and distribution interconnection nodes, and realizing the flexible adjustment margin assessment of the distribution network transmission and distribution interconnection nodes containing massive decentralized resources.

2. The method for evaluating the flexible adjustment margin of the transmission and distribution connection node for aggregating dispersed resources according to claim 1 is characterized by: In step S2, distributed wind power, distributed photovoltaic power, and micro fuel generator sets are classified as power-type distributed resources according to parameter information of distributed resources, and distributed energy storage, electric vehicles, and base station energy storage are classified as energy storage-type distributed resources; A standard model of power-type decentralized resources is constructed, and the power feasible domain of the power-type decentralized resources is as follows: （1） In the formula, Representation Node Power-based distributed resources The power variable, , , , Respectively Time Node Power-based distributed resources The active and reactive output power, Indicates the time scale; and is the parameter matrix, as follows: （2） （3） （4） （5） （6） （7） In the formula, express dimensional identity matrix; express Dimensional matrix; , and Indicates Group power constraint linearization constraint coefficient, ; , Respectively represent nodes Power-based distributed resources The upper and lower limits of active output; Representation Node Power-based distributed resources The upper limit of the climbing rate; Representation Node Power-based distributed resources The apparent power of A standard model of energy storage type distributed resources is constructed, and the power feasible domain of the energy storage type distributed resources is as follows: （8） In the formula, Representation Node Energy storage distributed resources Power variable; , ; , Respectively Time Node Energy storage distributed resources Active and reactive output power; and is the parameter matrix, as follows: （9） （10） （11） （12） （13） （14） （15） In the formula, express dimensional square matrix; For Node Energy storage distributed resources The loss rate; Indicates a time interval; express Time Node Energy storage distributed resources energy; , Respectively Time Node Energy storage distributed resources The upper and lower limits of active output; , Respectively represent nodes Energy storage distributed resources The upper and lower limits of energy storage capacity; Representation Node Energy storage distributed resources The upper limit of the climbing rate; Representation Node Energy storage distributed resources The apparent power.

3. The method for evaluating the flexible adjustment margin of the transmission and distribution connection node for aggregating dispersed resources according to claim 2 is characterized by: In step S3, the process of constructing a decentralized resource aggregation model taking into account the uncertainty of decentralized resources is as follows: S301, based on the standard model of power-type distributed resources and energy storage-type distributed resources obtained in step S2, determine the power feasible region of distributed resources as follows: （16） In the formula, To disperse resources The power feasible region of and To disperse resources The parameter matrix of To disperse resources Power variable; express dimensional real number; S302, linearly simplify the aggregation of the feasible domains of multiple distributed resources using the polyhedron-based Minkowski summation approximation method, and convert the Minkowski summation calculation into an algebraic summation calculation. Specifically, in each aggregation area, the average parameter of the distributed resources in the area is calculated as the aggregation polyhedron benchmark, and the distributed resources in the area are scaled and translated according to the benchmark to achieve the maximum inner approximation, as shown in the following formula: （17） In the formula, represents the Minkowski summation calculation; , Distributed resources 、 The power feasible region of , Represents distributed resources 、 The scaling factor of , Represents distributed resources 、 The translation coefficient of Represents the aggregation area The calculation formula of the reference polyhedron is as follows: （18） （19） In the formula, The power variable matrix representing the reference polyhedron; and It is an aggregation area The parameter matrix of the reference polyhedron; Represents the aggregation area Aggregation of decentralized resources; Represents the aggregation area The number of distributed resources; S303, using the distributed robust opportunity constraint to consider the uncertainty of decentralized resources, establish the following optimization problem: （20） In the formula, , , is an auxiliary matrix variable, represents the true probability distribution, represents a fuzzy set, Indicates that uncertainty variables are considered The probability distribution of Indicates the uncertainty confidence; S304, combining formulas (17) and (20) to obtain a decentralized resource aggregation model that takes into account the uncertainty of decentralized resources in each distribution station area.

4. The method for evaluating the flexible adjustment margin of the transmission and distribution connection node for aggregating dispersed resources according to claim 3 is characterized by: In step S303, an uncertain set is found in the uncertainty region , the uncertain set By point set The convex hull formed transforms the last constraint in formula (20) into the following robust problem; （21） In the formula, In the uncertain set The first convex hull that satisfies the confidence Vertices, Indicates that it contains The parameter matrix ; For uncertain set The number of vertices of the convex hull, Represents the dimension of the uncertainty variable in the uncertainty set.

5. The method for evaluating the flexible adjustment margin of the transmission and distribution connection node for aggregating dispersed resources according to claim 4 is characterized by: In step S5, the distribution network aggregated flexible regulation margin assessment model for the transmission and distribution connection node constructed includes an aggregated flexible regulation margin upper boundary assessment model and a lower boundary assessment model; The upper boundary assessment model of the aggregated flexible regulation margin takes the maximum value of the adjustable active power within a time window of the transmission and distribution connection node as the solution target, as shown in the following formula: （22） In the formula, express Active power output of the transmission and distribution connection node at all times; for The weight coefficient of the moment; The lower boundary assessment model of the aggregated flexible regulation margin takes the minimum value of the adjustable active power within a time window of the transmission and distribution connection node as the solution target, as shown in the following formula: （23）。 6. The method for evaluating the flexible adjustment margin of the transmission and distribution connection node for aggregating dispersed resources according to claim 5 is characterized by: The constraints of the aggregate flexible adjustment margin upper boundary assessment model include: 1) Power Constraints （24） （25） （26） （27） （28） （29） In the above constraints, (24) and (26) are the active power and reactive power of the line, respectively, which are calculated by the power of the sub-node line and the injected power of the current node; (25) and (27) are the active power and reactive power injected by the node, respectively, which are calculated by the node load and the two types of distributed resources on the node; (28) and (29) represent the boundary constraints of active power and reactive power, respectively; where, and Respectively Time Node Active power and reactive power of the line; Representation Node The collection of child nodes; and Respectively Time Node The injected active and reactive power; and Respectively Time Node Active load and reactive load; and They represent the active power and reactive power of power-type decentralized resources respectively; and They represent the active power and reactive power of energy storage type decentralized resources respectively; and Represent the power upper bounds of active and reactive transfer capacity respectively; 2) Voltage Constraint （30） （31） Among the above constraints, (30) represents the distribution network flow constraint, and (31) represents the voltage boundary constraint; express Time Node The voltage, express The parent node of the node, and Respectively indicate lines The resistance and reactance, represents the set of all nodes, and Respectively represent the upper and lower boundaries of the voltage allowable range; 3) Aggregate decentralized resource constraints （32） （33） The above constraints are the boundary constraints for aggregating distributed resources; for equation (32), , and Node The scaling coefficient and translation coefficient matrix of power-type distributed resources; for equation (33), , and Node Scaling and translation coefficient matrices of energy storage distributed resources; 4) System backup constraints （34） This constraint is the upward reserve constraint of the network; where, represents the reserve factor; The constraints of the lower boundary assessment model of the aggregated flexible adjustment margin include the constraints shown in formulas (24)-(33), and also include the following system reserve constraints: （35） This constraint is the downward spare constraint of the network.

7. The method for evaluating the flexible adjustment margin of the transmission and distribution connection node for aggregating dispersed resources according to claim 6 is characterized by: The power constraint linearization constraint coefficient , and The values ​​are as follows: （36）。 8. A device for evaluating the flexible adjustment margin of a transmission and distribution connection node that aggregates dispersed resources, comprising a memory and a processor, wherein a computer program is stored in the memory, characterized in that: The processor executes the computer program to implement the method for evaluating the flexible adjustment margin of the transmission and distribution connection node for aggregating decentralized resources as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the method for evaluating the flexible adjustment margin of the transmission and distribution connection node of aggregated decentralized resources as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that: When the computer program is executed by a processor, it implements the method for evaluating the flexible adjustment margin of the transmission and distribution connection node of aggregated decentralized resources as described in any one of claims 1-7.

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