Distributed resource access assessment method, system and device under transmission and distribution network constraint and medium

Abstract

The invention discloses a distributed resource access evaluation method, system, equipment and medium under transmission and distribution network constraints, and relates to the technical field of power system operation and control, comprising the steps of constructing a joint analysis model of a transmission network and a distribution network, determining an interface relationship between the two, and analyzing an influence mechanism of distributed resource access on the transmission network; a transmission mechanism from transmission network constraints to power distribution network interface points is established, an optimization model considering transmission and distribution network joint constraints is constructed, distributed resource access capacity maximization, layered solution strategy design and transmission and distribution network coordinated optimization are performed, and a dynamic constraint updating mechanism is established to adapt to the power grid operation state and new energy output change. According to the method, the global optimal configuration of the distributed resource access capacity is realized by establishing the transmission and distribution power grid joint analysis model, optimizing the constraint conduction mechanism and designing the efficient hierarchical solution strategy and the dynamic constraint updating mechanism, and the new energy consumption capability and the operation safety of the power grid are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of power system operation and control technology, specifically to a method, system, equipment, and medium for evaluating distributed resource access under transmission and distribution network constraints. Background Technology

[0002] The penetration rate of distributed resources in distribution networks has increased significantly, and large-scale access poses new challenges to the coordinated operation of transmission and distribution networks. Currently, research on distributed resource access assessment mainly focuses on the distribution network level, with local power flow, voltage control, and equipment capacity constraints as the analysis focus, and traditional power flow calculation or optimization methods are used to assess access capacity. However, existing methods often ignore the dynamic coupling relationship between transmission and distribution networks and fail to fully consider the transmission effect of transmission network constraints on distribution network interface points, resulting in overly optimistic assessment results. In actual operation, problems such as line overload, voltage instability, and insufficient system inertia are likely to occur. In addition, existing research often uses simplified models or fixed boundary conditions when dealing with the joint optimization of transmission and distribution networks, which is difficult to adapt to the fluctuation of new energy output and changes in grid operating status. It lacks a comprehensive consideration of the dynamic response of the system, thus limiting the safe absorption capacity of distributed resources and the economic efficiency of grid operation.

[0003] The shortcomings of existing technologies are mainly reflected in three aspects: First, there is a lack of systematic research on the joint modeling and constraint transmission mechanism of transmission and distribution networks, which makes it impossible to accurately quantify and transmit the impact of distributed resource access on power flow distribution, voltage stability and system inertia of the transmission network to the distribution network side; Second, in the process of optimization model construction, the coupling between transmission network constraints and distribution network constraints is insufficient, making it difficult to achieve the globally optimal distributed resource access capacity configuration; Third, in terms of solution strategy, traditional methods mostly adopt centralized optimization or static constraint processing, which has low computational efficiency and is difficult to adapt to the dynamic changes in the power grid operation status. Summary of the Invention

[0004] In view of the above-mentioned existing problems, the present invention provides a method, system, device and medium for evaluating distributed resource access under transmission and distribution network constraints, in order to solve the problems of lack of systematic research on joint modeling and constraint transmission mechanism of transmission and distribution networks, insufficient coupling between transmission network constraints and distribution network constraints, difficulty in achieving globally optimal distributed resource access capacity configuration, low computational efficiency and difficulty in adapting to dynamic changes in grid operation status in the prior art.

[0005] To address the aforementioned technical issues, a distributed resource access evaluation method under transmission and distribution network constraints is proposed, including:

[0006] A joint analysis model of the transmission network and distribution network is constructed to determine the interface relationship between the two and analyze the impact mechanism of distributed resource access on the transmission network. A transmission network constraint transmission mechanism is established to the distribution network interface point, and an optimization model considering the joint constraints of the transmission and distribution networks is constructed to maximize the capacity of distributed resource access. A hierarchical solution strategy is designed to optimize the coordinated operation of the transmission and distribution networks and a dynamic constraint update mechanism is established to adapt to the changes in the power grid operation status and the output of new energy sources.

[0007] As a preferred embodiment of the distributed resource access evaluation method under transmission and distribution network constraints described in this invention, the mechanism for analyzing the impact of distributed resource access on the transmission network includes analyzing the impact of distributed resource access on the power flow distribution of the transmission network based on a joint analysis model.

[0008] Analyze the impact of distributed resource access on the voltage stability of the power transmission network;

[0009] Analyze the impact of distributed resource access on the inertia of the power transmission network system;

[0010] This also affects the relationship between the quantized power injection at the interface point.

[0011] As a preferred embodiment of the distributed resource access evaluation method under the constraints of the power transmission and distribution network described in this invention, the transmission mechanism includes receiving a quantitative relationship obtained from the influence mechanism analysis;

[0012] Transform the line operation constraints of the transmission network into power injection constraints at the distribution network interface points;

[0013] Transform the voltage stability constraints of the transmission network into power injection constraints at the distribution network interface points;

[0014] The system inertia constraint of the transmission network is transformed into the power injection constraint at the interface point of the distribution network.

[0015] As a preferred embodiment of the distributed resource access evaluation method under the constraints of the power transmission and distribution network described in this invention, the optimization model includes a function that maximizes the distributed resource access capacity of the distribution network.

[0016] Integrate the node power balance constraints, node voltage constraints, line capacity constraints, and distributed resource output constraints within the distribution network;

[0017] Integrate power grid conduction constraints generated by the conduction mechanism;

[0018] The power transfer and voltage coupling constraints of the integrated transmission and distribution network interface form a complete set of constraints.

[0019] As a preferred embodiment of the distributed resource access evaluation method under transmission and distribution network constraints described in this invention, the design hierarchical solution strategy includes performing basic power flow calculations of the transmission network and distribution network to determine the initial operating state;

[0020] Based on the AC power flow model of the transmission network, with the goal of minimizing the line power flow deviation and voltage deviation, the power injection constraints at the transmission and distribution network interface points are calculated, and the power transfer distribution factor, voltage sensitivity factor and inertia substitution factor are calculated.

[0021] Based on the improved forward and backward power flow models of the distribution network, the interface constraints, internal constraints of the distribution network, and distributed resource output constraints of the upper-level optimization are integrated, and the second-order cone programming method is used to solve the maximum distributed resource access capacity.

[0022] The relaxation iteration method is used to perform a weighted average of the interface power obtained from the current lower-level optimization and the result of the previous iteration to update the power grid operation status;

[0023] Compare the power changes at the interface points in two consecutive iterations. If the power change is less than a preset threshold, output the result; otherwise, return to the previous optimization step.

[0024] As a preferred embodiment of the distributed resource access evaluation method under the constraints of the transmission and distribution network described in this invention, the calculation of the sensitivity factor in the upper-level optimization includes, based on the admittance matrix of the transmission network node, calculating the sensitivity of the node injected power to the line power flow, i.e., the power transfer distribution factor.

[0025] The sensitivity of nodal injected power to system voltage stability margin is calculated using the continuous power flow method or eigenvalue analysis.

[0026] Based on the generator inertia constant and distribution, calculate the influence factor of the nodal injected power change on the system's equivalent inertia, i.e., the inertia substitution factor.

[0027] The formula for calculating the sensitivity factor is expressed as follows:

[0028]

[0029] Among them, PTDF i'j',k F is the power transfer distribution factor. i'j' For the active power flow of the transmission network from node i to node j, P k The active power injected into node k of the transmission network. Let be the sensitivity factor of the active power at node k of the transmission network to the system voltage stability margin. Let Q be the sensitivity factor of reactive power at node k in the transmission network to the system voltage stability margin, where VSM is the voltage stability margin, i.e., the distance from the current operating point to the voltage collapse point. kThe reactive power injected into node k of the transmission network, ISF k H is the inertia substitution factor. sys This is the total equivalent inertia of the system;

[0030] The second-order cone programming method for solving the maximum distributed resource access capacity includes relaxing the non-convex constraints in the power flow equation of the distribution network, transforming the power balance equation into a second-order cone constraint form, transforming the line current amplitude constraint into a second-order cone inequality, transforming the node voltage amplitude constraint into a linear inequality, and calling a mathematical optimization solver to solve the transformed convex optimization model.

[0031] The transformed constraints are represented as follows:

[0032]

[0033] |S ij | 2 ≤v i ·l ij ,(i,j)∈L D

[0034] in, This is the lower limit of the square of the voltage at node i in the distribution network. This represents the upper limit of the square of the voltage at node i in the distribution network. is the maximum current amplitude of line ij.

[0035] As a preferred embodiment of the distributed resource access evaluation method under the constraints of the power transmission and distribution network described in this invention, the relaxation iteration method includes setting a relaxation factor, updating the active power and reactive power of the interface point, until the convergence criterion is reached, as expressed by the formula:

[0036]

[0037] in, This represents the active power injection value at the transmission and distribution network interface point r in the nth iteration. Let β be the reactive power injection value at the transmission and distribution network interface point r in the nth iteration, and β be the relaxation factor. This represents the active power injection value of the interface point r obtained after optimizing the lower-level distribution network in the current iteration. This represents the reactive power injection value of the interface point r obtained after optimizing the lower-level distribution network in the current iteration. The updated active power value for interface point r. The updated reactive power value for interface point r. This represents the active power injection value at the transmission and distribution network interface point r in the (n+1)th iteration. This represents the reactive power injection value at the transmission and distribution network interface point r in the (n+1)th iteration.

[0038] The dynamic constraint update mechanism includes generating various typical scenarios based on historical operating data and predicted output of new energy sources, including combinations of different peak load rates and new energy penetration rates. For each scenario, power flow calculation of the transmission network is performed to obtain the line power flow constraints, voltage stability constraints, and system inertia constraints under the current scenario. Based on the Lagrange multipliers in the optimization model solution process, the contribution index of each constraint condition is calculated, and constraints with a contribution greater than a preset threshold are selected as key constraints. According to the predicted changes in the power grid state, the set of key constraints is periodically updated and fed back to the optimization model.

[0039] The contribution index of each constraint is calculated by extracting the Lagrange multipliers of line power flow constraints, voltage stability constraints and system inertia constraints in the optimization model, and calculating the average value and variance of each constraint multiplier under different scenarios. The importance of the constraint is comprehensively evaluated based on the size and fluctuation of the multiplier. The larger and more stable the multiplier value, the higher the importance of the constraint.

[0040] The beneficial effects of this preferred technical solution are that by calculating the constraint contribution index based on Lagrange multipliers and screening key constraints, it achieves intelligent identification of key constraints from a large number of constraints, dynamically simplifies the scale of the optimization model, and significantly improves the speed of subsequent optimization calculations.

[0041] As a preferred embodiment of the distributed resource access evaluation system under the constraints of the power transmission and distribution network described in this invention, it is characterized by including a joint analysis and modeling module, an impact analysis and quantification module, a constraint transmission and transformation module, a joint optimization solution module, and a coordination and dynamic update module.

[0042] The joint analysis and modeling module is used to establish accurate transmission network and distribution network models, and to clearly define the interface coupling relationship between the two. It describes the power exchange and voltage coupling between the transmission network and the distribution network at the interface point through equations.

[0043] The impact analysis and quantification module is used to quantify the impact on line power flow by calculating the power transmission distribution factor, the impact on system voltage stability by calculating the voltage stability sensitivity factor, and the impact on system inertia level by calculating the inertia substitution factor, thereby establishing a quantitative correlation between the local behavior of distributed resource access and the overall safe and stable operation of the power transmission network.

[0044] The constraint transmission and transformation module is used to use the quantified relationship as a bridge to transform the global operation security constraints of the transmission network layer into linearized constraint conditions for power injection at the distribution network interface point, embedding the security requirements of the transmission network into the optimization model of the distribution network, and realizing the effective integration and transmission of constraints of different levels of the power grid.

[0045] The joint optimization solution module is used to maximize the distributed resource access capacity of the distribution network as the objective function, integrate the internal operation constraints of the distribution network, the coupling constraints of the transmission and distribution network interface, and the transmission constraints of the transmission network, and use a convex relaxation method based on second-order cone programming to transform the originally complex nonlinear nonconvex problem into a convex optimization problem that can be solved efficiently.

[0046] The coordination and dynamic update module is used to iterate between transmission network optimization and distribution network optimization using a relaxation iteration-based coordination algorithm, smooth the interaction of data, and establish a dynamic update mechanism by generating typical scenarios, evaluating the importance of constraints, screening key constraints, and continuously updating the constraint set in the optimization model.

[0047] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of a method for evaluating distributed resource access under power transmission and distribution network constraints.

[0048] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for evaluating distributed resource access under power transmission and distribution network constraints.

[0049] The beneficial effects of this invention are as follows: By constructing a joint analysis model of the transmission and distribution networks, this invention achieves a systematic modeling of the coupling relationship between the transmission and distribution networks, clarifies the impact mechanism of distributed resource access on the dynamic behavior of the transmission network, and lays a scientific foundation for subsequent constraint transmission and optimization models. By establishing a transmission mechanism of transmission network constraints to distribution network interface points and constructing an optimization model considering the joint constraints of the transmission and distribution networks, the invention achieves the globally optimal configuration of distributed resource access capacity, breaking the limitations of traditional independent evaluation and improving the accuracy and practicality of the evaluation results. By designing a hierarchical solution strategy, including basic power flow calculation of the transmission and distribution networks, sensitivity factor calculation, second-order cone programming solution, and relaxation iteration method, the invention effectively reduces computational complexity and significantly improves solution efficiency, providing an efficient solution for large-scale power grid optimization problems. By establishing a dynamic constraint update mechanism, generating typical scenarios based on historical data and new energy predictions and periodically updating the key constraint set, the invention achieves adaptive adjustment of the optimization model and enhances the model's adaptability to changes in power grid state. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1This is a flowchart illustrating the overall process of a distributed resource access evaluation method under power transmission and distribution network constraints, as provided in one embodiment of the present invention.

[0052] Figure 2 The flowchart of a system scheme for a distributed resource access evaluation system under power transmission and distribution network constraints provided in an embodiment of the present invention is shown. Detailed Implementation

[0053] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0054] Example 1, referring to Figure 1 As an embodiment of the present invention, a distributed resource access evaluation method under power transmission and distribution network constraints is provided, comprising:

[0055] S100: Construct a joint analysis model of the transmission network and the distribution network, determine the interface relationship between the two, and analyze the impact mechanism of distributed resource access on the transmission network.

[0056] S200: Establish a transmission mechanism for transmission network constraints to distribution network interface points, and construct an optimization model that considers the joint constraints of transmission and distribution networks to maximize the capacity of distributed resource access.

[0057] S300: Design a hierarchical solution strategy to perform coordinated optimization of the transmission and distribution network, and establish a dynamic constraint update mechanism to adapt to changes in the power grid operation status and new energy output.

[0058] It should be noted that by constructing a joint analysis model of the transmission and distribution networks and determining the interface relationships, the transmission and distribution networks are treated as an organic whole, both physically and mathematically, rather than two independent systems. This provides a unified and accurate mathematical model foundation for all subsequent analyses. Furthermore, by establishing a dynamic constraint update mechanism, the evaluation model can self-adjust with fluctuations in grid load levels and renewable energy output. This transforms the evaluation from a static snapshot to a dynamic process, ensuring that the evaluation result is no longer a fixed, conservative extreme value, but a more instructive dynamic value that can adapt to changes in the actual operating state of the system. This improves the practicality and accuracy of the evaluation results.

[0059] Example 2, refer to Figure 1 This is a second embodiment of the present invention, which provides a distributed resource access evaluation method under power transmission and distribution network constraints, including:

[0060] In this embodiment of the application, step S100, the construction of the joint analysis model of the transmission network and the distribution network includes steps S101 to S103:

[0061] S101: The transmission network model adopts an AC power flow model, defining the relationship between active and reactive power injection at nodes and voltage amplitude, phase angle difference, and the real and imaginary parts of the admittance matrix. Among them, the operating constraints include that the node voltage must be maintained between the upper and lower limits, the apparent power of the line must not exceed the maximum capacity, the active and reactive power output of the generator must be within their respective minimum and maximum output ranges, and the system rotational inertia must meet the minimum inertia requirement determined by the generator inertia constant, rated capacity, and system reference capacity.

[0062] The formula for the AC power flow model of the power transmission network is expressed as follows:

[0063]

[0064] Among them, P i' For the active power injection at node i' of the transmission network, Q i' For reactive power injection at node i' in the transmission network, V i' V represents the voltage amplitude at node i' in the transmission network. j' Let θ be the voltage amplitude at node j' in the transmission network. i'j' G represents the voltage phase angle difference between transmission network node i' and transmission network node j'. i'j' B represents the real part of the elements in the admittance matrix of the power grid nodes. i'j' N represents the imaginary part of the elements in the nodal admittance matrix. T It is the set of power transmission network nodes.

[0065] The power transmission network operation constraints are expressed as follows:

[0066]

[0067]

[0068] in, This represents the lower voltage limit of node i' in the transmission network. S represents the upper voltage limit of node i' in the transmission network. i'j' The apparent power flowing through transmission line i'j' is... L represents the maximum apparent power allowed to pass through transmission line i'j'. T Let P be the set of all lines in the power transmission network. Ga and Q Ga Let A represent the active and reactive power outputs of generator a. and These are the minimum and maximum active power output technical limits for generator a. and G represents the minimum and maximum reactive power technical limits for generator a. T H is the set of all generators in the power transmission network. a Let S be the inertia constant of generator a. Ga The rated capacity of generator a is... S is the minimum inertia level required by the system. base N is the baseline capacity selected for the system. T It is the set of power transmission network nodes.

[0069] S102: The distribution network model adopts an improved forward and backward power flow calculation method, considering the power flow characteristics after the access of distributed resources. The node power balance equation involves node injected power, distributed resource output, load power and related line losses. Among them, the operating constraints include that the node voltage must be within the safe range, the line current amplitude must not exceed the maximum allowable current, and the active and reactive power output of distributed resources must be within their respective maximum output limits.

[0070] The power balance equations of the distribution network nodes are expressed as follows:

[0071]

[0072] in, Let i be the active power of the distribution network node. Let i be the reactive power of the distribution network node. The active power of distributed resources accessed by distribution network node i. The reactive power of distributed resources accessed by distribution network node i. Let i be the active power of the load at node i in the distribution network. Let P be the reactive power of the load at node i in the distribution network. loss,i Q represents the active power loss of the lines associated with distribution network node i. loss,i The reactive power loss of the lines associated with distribution network node i;

[0073] The operating constraints of the distribution network are expressed as follows:

[0074]

[0075]

[0076] Among them, V i Let i be the voltage amplitude at node i in the distribution network. This represents the lower limit of the voltage at node i in the distribution network. N represents the upper limit of the voltage at node i in the distribution network. D Let I be the set of all nodes in the distribution network. ij Let ij be the amplitude of the current flowing through the distribution network line. L represents the maximum allowable current through which a distribution network line ij can pass.D It is the collection of all lines in the distribution network. The active power emitted by the distributed resources of access node b. This represents the maximum active power that the distributed resources of access node b are allowed to emit. The reactive power emitted by the distributed resources of access node b. Let N be the maximum allowed reactive power output of node b's distributed resources. DG It is the set of all nodes in the distribution network that are connected to distributed resources.

[0077] S103: The transmission and distribution network interface model describes the interaction between the transmission network and the distribution network. The active and reactive power injection at the transmission network interface point is related to the injection power of the connected nodes in the distribution network and the total loss of the distribution network. The voltage at the transmission network interface point is consistent with the voltage at the balance node of the distribution network. The coupling of the transmission and distribution networks is achieved through these relationships.

[0078] The interaction between the transmission network and the distribution network is expressed by the following formula:

[0079]

[0080] in, To measure the active power injected at interface point c from the perspective of the transmission network side, To determine the reactive power injected at interface point c from the perspective of the transmission network side, D c This refers to the set of nodes in the distribution network area connected to the transmission network interface point c. For distribution network D c Net active power injection to all nodes in the process. For distribution network D c Net reactive power injection at all nodes in the middle, For distribution network D c Total active power loss, For distribution network D c Total reactive power loss.

[0081] The analysis mechanism of the impact of distributed resource access on the power transmission network includes steps S111 to S114:

[0082] S111: Based on the established AC power flow model of the power transmission network, calculate the sensitivity of node power injection changes to line active power flow, i.e., the power transmission distribution factor, and quantify the impact of distributed power source access on line load.

[0083] The formula for the impact on power flow distribution in the transmission network is expressed as:

[0084]

[0085] Among them, PTDF i'j',kLet ΔF be the power transfer distribution factor. i'j' For the power flow change of line i'j', Let N be the change in active power for node k's distributed resource access. K This is the set of boundary nodes.

[0086] S112: Analyze the impact on voltage stability margin by calculating the sensitivity of nodal power injection to the minimum eigenvalue of the system Jacobian matrix;

[0087] The impact of distributed resource access on voltage stability is assessed using the load margin index, expressed by the formula:

[0088]

[0089] Where ΔLM is the change in load margin. The sensitivity of load margin to active power injection at node k. The sensitivity of the load margin to reactive power injection at node k is given. Let be the change in active power for node k's access to distributed resources. Let be the change in reactive power for node k's distributed resource access.

[0090] S113: By analyzing the proportion of distributed power sources replacing traditional synchronous generators, the impact on the total equivalent inertia of the system is calculated, and the inertia substitution factor is used for quantification.

[0091] The formula for the influence of system inertia is expressed as:

[0092]

[0093] Where, ΔH sys The change in system inertia, ISF k Let k be the factor influencing the distributed resources of node k on the system inertia. Let be the change in active power for node k's access to distributed resources.

[0094] S114: Quantify the above effects into the relationship of changes in power injection at the interface point.

[0095] In an optional implementation, in step S100, the mechanism for analyzing the impact of distributed resource access on the power grid further includes generating typical operating scenarios with different distributed power output and load levels using historical data or stochastic planning, and performing accurate power flow calculations for the entire network for each scenario, directly obtaining power flow, node voltage, and system frequency change rate data of key lines, and using statistical analysis tools to process large batches of simulation results to obtain a quantitative relationship between the capacity of distributed power access and the risk of system over-limit.

[0096] In another optional implementation, in step S100, the mechanism for analyzing the impact of distributed resource access on the power grid may further include collecting historical power grid operation data, including SCADA and PMU measurement data under different distributed power source access levels, using the data to train a machine learning model, with the power injected by the distributed power source as input and the power flow and voltage deviation of the critical line as output.

[0097] It should be noted that the sensitivity analysis method used in this invention achieves the best balance between computational efficiency and physical clarity, without the need for massive repetitive simulations, and its calculation speed is much faster than that of the probability evaluation method.

[0098] In this embodiment of the application, in step S200, the conduction mechanism includes steps S201 to S204:

[0099] S201: Receive the quantitative relationship obtained from the influence mechanism analysis;

[0100] S202: Transform the line operation constraints of the transmission network into power injection constraints at the distribution network interface points, expressed by the formula:

[0101]

[0102] Among them, PTDF i'j',k Power transfer distribution factor, Inject active power to interface point k. Let (i',j') be the maximum permissible active power flow of the line. The active power flow of line (i',j') under the basic scheme. It is a collection of critical lines in the power transmission network.

[0103] S203: Transform the voltage stability constraints of the transmission network into power injection constraints at the distribution network interface points, expressed by the formula:

[0104]

[0105] in, Let be the sensitivity factor of the active power at node k of the transmission network to the system voltage stability margin. Let be the sensitivity factor of reactive power at node k of the transmission network to the system voltage stability margin. Reactive power injection at interface point k, For active power injection at interface point k, VSM min VSM is the minimum voltage stability margin required by the system. 0 Voltage stability margin under the basic scheme.

[0106] S204: Transform the system inertia constraint of the transmission network into the power injection constraint at the distribution network interface point, expressed by the formula:

[0107]

[0108] Among them, ISF k As the inertia substitution factor, The minimum inertia required by the system, System inertia N under the basic scheme T It is the set of power transmission network nodes.

[0109] In an optional implementation, in step S200, the transmission mechanism further includes obtaining the safe operating domain of the transmission network under the set of anticipated accidents through offline calculation or online evaluation. The safe domain is usually enclosed by multiple hyperplanes, and the interface point is regarded as a "virtual generator". The power injection range is projected onto the safe domain of the transmission network, and the maximum movable range of the power at the interface point within the projection range is obtained. This range is used as a constraint for transmission to the distribution network.

[0110] In another optional implementation, in step S200, the transmission mechanism may further include, based on power grid dispatching and operation experience, formulating a series of concise empirical rules, stipulating that when the power flow at a certain critical section of the transmission network exceeds a certain threshold, the various distribution network interface points connected to it must collectively reduce power injection according to a preset ratio, and when the system frequency drops, according to the preset dispatching instructions, the interface points must call upon the distributed resources within the distribution network to provide a rapid frequency response.

[0111] Furthermore, in step S200, the optimization model includes steps S211 to S214:

[0112] S211: The objective function is to maximize the capacity of distributed resource access in the distribution network. The objective function is expressed as follows:

[0113]

[0114] Where, N D C is the set of distribution network nodes. i Let i be the distributed resource access weighting coefficient for node i in the distribution network. The active power of the distributed resources connected to node i in the distribution network.

[0115] S212: Integrates node power balance constraints, node voltage constraints, line capacity constraints, and distributed resource output constraints within the distribution network.

[0116] The node power balance constraint is expressed as:

[0117]

[0118] in, Let i be the active power of the distribution network node. Let i be the reactive power of the distribution network node. The active power of distributed resources accessed by distribution network node i. The reactive power of distributed resources accessed by distribution network node i. Let i be the active power of the load at node i in the distribution network. Let P be the reactive power of the load at node i in the distribution network. loss,i Q represents the active power loss of the lines associated with distribution network node i. loss,i The reactive power loss of the lines associated with distribution network node i;

[0119] The operating constraints of the distribution network are expressed as follows:

[0120]

[0121] Among them, V i Let i be the voltage amplitude at node i in the distribution network. This represents the lower limit of the voltage at node i in the distribution network. N represents the upper limit of the voltage at node i in the distribution network. D Let I be the set of all nodes in the distribution network. ij Let ij be the amplitude of the current flowing through the distribution network line. L represents the maximum allowable current through which a distribution network line ij can pass. D It is the collection of all lines in the distribution network. The active power emitted by the distributed resources of access node b. This represents the maximum active power that the distributed resources of access node b are allowed to emit. The reactive power emitted by the distributed resources of access node b. Let N be the maximum allowed reactive power output of node b's distributed resources. DG It is the set of all nodes in the distribution network that are connected to distributed resources.

[0122] S213: Integrate transmission network conduction constraints generated by the conduction mechanism; transmission and distribution network interface constraints are represented as follows:

[0123]

[0124] in, To measure the active power injected at interface point c from the perspective of the transmission network side, To determine the reactive power injected at interface point c from the perspective of the transmission network side, D c This refers to the set of nodes in the distribution network area connected to the transmission network interface point c. For distribution network D c Net active power injection to all nodes in the process. For distribution network D c Net reactive power injection at all nodes in the middle, For distribution network D c Total active power loss, For distribution network D c Total reactive power loss.

[0125] S214: Power transfer and voltage coupling constraints of the integrated transmission and distribution network interface form a complete constraint set.

[0126] It should be noted that, to improve the robustness of the model, a fuzzy constraint processing mechanism is introduced. The membership function of the line power flow constraint is defined, and the function is based on the line's safe power flow limit. The safe limit is usually determined by multiplying the line's maximum capacity by a safety factor between 0.8 and 0.9. A trade-off coefficient is introduced into the optimization objective, and the membership functions of voltage stability constraint and system inertia constraint are combined. The definition of the membership function is similar to that of the line power flow constraint. In this way, the improved optimization objective can comprehensively consider the fuzzy characteristics of different constraints, thereby improving the model's adaptability and robustness under complex operating conditions.

[0127] The formula for the fuzzy constraint processing mechanism is expressed as follows:

[0128]

[0129] Where, μi'j'(F i'j' F is the membership function of the power flow constraint for line (i',j'), where F is the membership function. i'j' Let (i',j') represent the active power flow along line (i',j'). Let (i',j') be the safe power flow limit for line (i',j'). This represents the maximum permissible active power flow of line (i',j');

[0130] The improved optimization objective is expressed as:

[0131]

[0132] Where α is the weighting factor. Let F be the original objective function, min(μi'j'(F) y ),μ VSM ,μ HSys ) represents the system safety margin index, μ VSM μ is the membership function of the voltage stability constraint. HSys is the membership function for the system inertia constraint.

[0133] In this embodiment of the application, in step S300, the design hierarchical solution strategy includes steps S301 to S305:

[0134] S301: Perform basic power flow calculations for the transmission and distribution networks to determine the initial operating state;

[0135] S302: Based on the AC power flow model of the transmission network, with the goal of minimizing the line power flow deviation and voltage deviation, the power injection constraints at the transmission and distribution network interface point are calculated, and the power transfer distribution factor, voltage sensitivity factor and inertia substitution factor are calculated.

[0136] The upper-level optimization formula is expressed as:

[0137]

[0138] Among them, L T w is the set of all lines in the power transmission network. i'j' F is the weighting coefficient for the power flow of line (i',j'). i'j' Let (i',j') represent the actual active power flow of the line. For the reference active power flow of line (i',j'), N T Let w be the set of all nodes in the power transmission network. k V is the weighting coefficient for the voltage at node k. k The actual voltage amplitude at node k. The reference voltage amplitude for node k.

[0139] The calculation of sensitivity factors in the upper-level optimization includes: calculating the sensitivity of node injected power to line power flow based on the transmission network node admittance matrix, i.e., the power transfer distribution factor; calculating the sensitivity of node injected power to system voltage stability margin using the continuous power flow method or eigenvalue analysis; and calculating the influence factor of node injected power changes on system equivalent inertia based on generator inertia constant and distribution, i.e., the inertia substitution factor.

[0140] The formula for calculating the sensitivity factor is expressed as follows:

[0141]

[0142] Among them, PTDF i'j',k F is the power transfer distribution factor. i'j' For the active power flow of the transmission network from node i to node j, P k The active power injected into node k of the transmission network. Let be the sensitivity factor of the active power at node k of the transmission network to the system voltage stability margin. Let Q be the sensitivity factor of reactive power at node k in the transmission network to the system voltage stability margin, where VSM is the voltage stability margin, i.e., the distance from the current operating point to the voltage collapse point. k The reactive power injected into node k of the transmission network, ISF k H is the inertia substitution factor.sys This is the total equivalent inertia of the system.

[0143] S303: Based on the improved forward and backward power flow model of the distribution network, the interface constraints, internal constraints of the distribution network and distributed resource output constraints of the upper-level optimization are integrated, and the second-order cone programming method is used to solve the maximum distributed resource access capacity.

[0144] The second-order cone programming method for solving the maximum distributed resource access capacity includes relaxing the non-convex constraints in the power flow equations of the distribution network, transforming the power balance equations into second-order cone constraint forms, transforming the line current magnitude constraints into second-order cone inequalities, transforming the node voltage magnitude constraints into linear inequalities, and then calling a mathematical optimization solver to solve the transformed convex optimization model. The formula is expressed as follows:

[0145]

[0146] l ij = |I ij | 2 ,(i,j)∈L D

[0147] Where, N D C is the set of distribution network nodes. i Let i be the distributed resource access weighting coefficient for node i in the distribution network. v represents the active power of the distributed resources connected to node i in the distribution network. i V is the squared variable of the voltage at node i in the distribution network. i Let S be the voltage amplitude at node i in the distribution network. ij V' represents the complex power of distribution network line ij. i Let i be the voltage phasor of node i in the distribution network. For the conjugate of the ij current phasor in the distribution network line, l ij Let I be the square variable of the current ij in the distribution network line. ij Let L be the current amplitude of the distribution network line ij. D A collection of distribution network lines;

[0148] The transformed constraints are represented as follows:

[0149]

[0150] |S ij | 2 ≤v i ·l ij ,(i,j)∈L D

[0151] in, This is the lower limit of the square of the voltage at node i in the distribution network. This represents the upper limit of the square of the voltage at node i in the distribution network. is the maximum current amplitude of line ij.

[0152] S304: Relaxation Iteration Method, which calculates a weighted average of the interface power obtained from the current lower-level optimization and the result of the previous iteration to update the power grid operation status;

[0153] The relaxation iteration method includes setting a relaxation factor, updating the active and reactive power at the interface point, until the convergence criterion is met, as expressed by the formula:

[0154]

[0155] in, This represents the active power injection value at the transmission and distribution network interface point r in the nth iteration. Let β be the reactive power injection value at the transmission and distribution network interface point r in the nth iteration, and β be the relaxation factor. This represents the active power injection value of the interface point r obtained after optimizing the lower-level distribution network in the current iteration. This represents the reactive power injection value of the interface point r obtained after optimizing the lower-level distribution network in the current iteration. The updated active power value for interface point r. The updated reactive power value for interface point r. This represents the active power injection value at the transmission and distribution network interface point r in the (n+1)th iteration. This represents the reactive power injection value at the transmission and distribution network interface point r in the (n+1)th iteration.

[0156] S305: Compare the interface point power changes between two consecutive iterations. If the power change is less than the preset threshold, output the result; otherwise, return to the previous optimization step.

[0157] The convergence criterion is expressed as:

[0158]

[0159] Where, N B Let ε be the set of all interface points. P ε is the convergence threshold for active power. Q This is the convergence threshold for reactive power.

[0160] In an optional implementation, in step S300, the hierarchical solution strategy further includes setting the transmission network operator as the leader, with the strategy being to formulate power limits or electricity price signals at the interface points, and setting the distribution network operator or aggregator as the follower, with the strategy being to optimize the scheduling of internal distributed resources to maximize its own benefits (or access capacity) under the rules formulated by the leader, and the two parties eventually reach an equilibrium solution through multiple strategy interactions.

[0161] In another optional implementation, in step S300, the design hierarchical solution strategy may further include constructing a complete integrated model of the transmission and distribution network, integrating the AC power flow equations and constraints of the transmission network with the power flow equations and constraints of the distribution network into the same mathematical optimization problem, and using a large-scale optimization solver to directly solve the large-scale, non-convex nonlinear programming problem to obtain the global optimal solution in one go.

[0162] Furthermore, in this embodiment of the application, in step S300, the dynamic constraint update mechanism includes steps S311 to S314:

[0163] S311: Based on historical operating data and predicted output of new energy sources, generate a variety of typical scenarios, including combinations of different peak load rates and new energy penetration rates;

[0164] S312: For each scenario, perform power flow calculation of the power grid to obtain the line power flow constraints, voltage stability constraints and system inertia constraints under the current scenario;

[0165] The formula for calculating power flow in a power transmission network is as follows:

[0166]

[0167]

[0168] Where Ω represents the set of typical scenarios, m represents the total number of scenarios, and s m As a typical scenario, Let be the power transmission distribution factor of the injected power at the transmission network interface point c on the line (i',j') at a specific operating point. For active power injection at transmission network interface point c, Let (i',j') be the maximum permissible active power flow of the line. For scene s m Without considering the addition of distributed resources, the initial active power flow of line (i',j') is as follows: It is a collection of critical lines in the power transmission network. For scene s mBelow, the sensitivity factor of active power injection at transmission network interface point c to the system voltage stability margin. For scene s m Below, the sensitivity factor of reactive power injection at transmission network interface point c to the system voltage stability margin. For reactive power injection at transmission network interface point c, VSM min This represents the minimum allowable voltage stability margin for the system. For scene s m Initial voltage stability margin For scene s m The inertia substitution factor at the downstream power grid interface point c. The minimum inertia required by the system, For scene s m The initial inertia of the system.

[0169] S313: Based on the Lagrange multipliers in the optimization model solution process, calculate the contribution index of each constraint condition, expressed by the formula:

[0170]

[0171] in, For scene s m The contribution index of the power flow constraints of the downstream line (i',j'). For scene s m Lagrange multipliers for power flow constraints on the lower path (i',j').

[0172] S314: Select constraints with a contribution greater than a preset threshold as key constraints, and periodically update the set of key constraints and feed them back to the optimization model based on the predicted changes in the power grid state.

[0173] The key constraints for screening are expressed as follows:

[0174]

[0175] Where τ is the contribution threshold;

[0176] The dynamic update of constraints adopts a rolling time-domain strategy, which is expressed by the formula:

[0177]

[0178] VSM min (t+1)=VSM min (t)-ΔVSM forecast (t+1)

[0179]

[0180] in, This represents the maximum permissible active power flow of line (i', j') at the current time t. 1) This refers to the predicted change in power flow margin of line (i', j') from the current time t to the next time t+1. VSM is used to optimize the new line capacity limits for the next time step t+1 after the update. min (t) represents the minimum voltage stability margin required by the system at the current time t, ΔVSM. forecast (t+1) represents the predicted change in voltage stability margin up to the next time step t+1, VSM min (t+1) is the updated lower bound of the voltage stability margin used for optimization at the next time step t+1. The minimum inertia required by the system at the current time t. To predict the change in the system's inertia up to the next time step t+1, The new minimum inertia requirement for the system after the update, to be optimized for the next time step t+1.

[0181] In an optional implementation, in step S300, the dynamic constraint update mechanism further includes solving an optimization problem in a finite time domain based on the current state and the predicted information (load, new energy output) for a period of time in the future at each decision moment, implementing the control command at the current moment (such as the output plan of distributed resources), and then re-optimizing with new measured data and updated predicted data at the next moment, and so on repeatedly.

[0182] In another optional implementation, in step S300, the dynamic constraint update mechanism may further include: pre-establishing a rule base containing various typical operating modes and over-limit situations, each rule defining the logic of "IF (one or more measurement values ​​meet the condition), THEN (enable / adjust a constraint)", the system monitors the power grid operating status in real time, and when a status change triggers a rule, it dynamically enables or adjusts the corresponding constraint in the optimization model. The rule base can be expanded and adjusted through machine learning or expert experience.

[0183] Furthermore, the contribution index of each constraint is calculated by extracting the Lagrange multipliers of line power flow constraints, voltage stability constraints and system inertia constraints in the optimization model, and calculating the average value and variance of each constraint multiplier under different scenarios. The importance of the constraint is comprehensively evaluated based on the size and fluctuation of the multiplier. The larger and more stable the multiplier value, the higher the importance of the constraint.

[0184] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0185] Example 3, referring to Figure 2 This is the third embodiment of the present invention, which provides a distributed resource access evaluation system under the constraints of a power transmission and distribution network, including a joint analysis and modeling module, an impact analysis and quantification module, a constraint transmission and transformation module, a joint optimization solution module, and a coordination and dynamic update module.

[0186] The joint analysis and modeling module is used to establish accurate transmission network and distribution network models, and to clearly define the interface coupling relationship between the two. It describes the power exchange and voltage coupling between the transmission network and the distribution network at the interface point through equations.

[0187] The impact analysis and quantification module is used to quantify the impact on line power flow by calculating the power transmission distribution factor, the impact on system voltage stability by calculating the voltage stability sensitivity factor, and the impact on system inertia level by calculating the inertia substitution factor, thereby establishing a quantitative correlation between the local behavior of distributed resource access and the overall safe and stable operation of the power transmission network.

[0188] The constraint transmission and transformation module is used to use the quantified relationship as a bridge to transform the global operation security constraints of the transmission network layer into linearized constraint conditions for power injection at the distribution network interface point, embedding the security requirements of the transmission network into the optimization model of the distribution network, and realizing the effective integration and transmission of constraints of different levels of the power grid.

[0189] The joint optimization solution module is used to maximize the distributed resource access capacity of the distribution network as the objective function, integrate the internal operation constraints of the distribution network, the coupling constraints of the transmission and distribution network interface, and the transmission constraints of the transmission network, and use a convex relaxation method based on second-order cone programming to transform the originally complex nonlinear nonconvex problem into a convex optimization problem that can be solved efficiently.

[0190] The coordination and dynamic update module is used to iterate between transmission network optimization and distribution network optimization using a relaxation iteration-based coordination algorithm, smooth the interaction of data, and establish a dynamic update mechanism by generating typical scenarios, evaluating the importance of constraints, screening key constraints, and continuously updating the constraint set in the optimization model.

[0191] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0192] Example 4, the fourth embodiment of the present invention, differs from the previous three embodiments in that:

[0193] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0194] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0195] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0196] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

Claims

1. A method for distributed resource access evaluation under transmission and distribution network constraints, characterized in that: The application relates to a power grid analysis method and device. The application includes, A joint analysis model of a power transmission grid and a power distribution grid is constructed, an interface relationship between the two is determined, and an influence mechanism of distributed resource access on the power transmission grid is analyzed; A conduction mechanism of the power transmission grid to a power distribution grid interface point is established, an optimization model considering joint constraints of the power transmission grid and the power distribution grid is constructed, and distributed resource access capacity maximization is carried out; 2. The method of claim 1, wherein: A hierarchical solving strategy is designed, power transmission grid and power distribution grid coordinated optimization is carried out, and a dynamic constraint updating mechanism is established to adapt to power grid operation states and new energy output changes. The analysis of the influence mechanism of distributed resource access on the power transmission grid includes, based on the joint analysis model, analyzing the influence of distributed resource access on power flow distribution of the power transmission grid; The influence of distributed resource access on voltage stability of the power transmission grid is analyzed; The influence of distributed resource access on system inertia of the power transmission grid is analyzed; 3. The method of claim 2, wherein: And the influence is quantified as a change relationship of the power injection of the interface point. The conduction mechanism includes, Receiving the quantified relationship obtained by the influence mechanism analysis; Converting line operation constraints of the power transmission grid into power injection constraints of the power distribution grid interface point; 4. The method for distributed resource access evaluation under transmission and distribution network constraints according to claim 3, characterized in that: Converting voltage stability constraints of the power transmission grid into power injection constraints of the power distribution grid interface point; Converting system inertia constraints of the power transmission grid into power injection constraints of the power distribution grid interface point. The optimization model includes, Taking the maximum distributed resource access capacity of the power distribution grid as an objective function; 5. The method for distributed resource access evaluation under transmission and distribution network constraints according to claim 4, characterized in that: Integrating node power balance constraints, node voltage constraints, line capacity constraints and distributed resource output constraints in the power distribution grid; Integrating the conduction constraints of the power transmission grid generated by the conduction mechanism; Integrating power transmission and voltage coupling constraints of the power transmission grid and the power distribution grid interface to form a complete constraint set. The hierarchical solving strategy includes, Carrying out basic power flow calculation of the power transmission grid and the power distribution grid to determine an initial operation state; 6. The method for distributed resource access evaluation under transmission and distribution network constraints according to claim 5, characterized in that: Based on the power transmission grid alternating current power flow model, power injection constraints of the power transmission grid and the power distribution grid interface point are calculated by taking the minimum line power flow deviation and voltage deviation as the target, and power transmission distribution factors, voltage sensitivity factors and inertia replacement factors are calculated; Based on the improved forward and backward power flow model of the power distribution grid, the interface constraints transmitted by the upper optimization, the internal constraints of the power distribution grid and the distributed resource output constraints are integrated, and the maximum distributed resource access capacity is solved by using a second-order cone programming method; The current interface power obtained by the lower optimization is weighted and averaged with the last iteration result by using a relaxation iteration method, and the power transmission grid operation state is updated; The interface point power changes of two consecutive iterations are compared, and if the changes are less than a preset threshold, the result is output, otherwise the upper optimization step is returned. In the upper optimization, the sensitivity factor is calculated, Based on the node admittance matrix of the power transmission grid, the sensitivity of node injection power to line power flow, i.e. the power transmission distribution factor, is calculated; The sensitivity of node injection power to system voltage stability margin is calculated by using a continuous power flow method or eigenvalue analysis; According to the generator inertia constant and distribution, the influence factor of node injection power change on system equivalent inertia, i.e. the inertia replacement factor, is calculated; The calculation formula of the sensitivity factor is represented as: where PTDF i'j',k is the power transfer distribution factor, F i'j' is the line active power flow from node i to node j in the transmission network, P k is the active power injected by node k in the transmission network, is the sensitivity factor of active power of node k in the transmission network to the system voltage stability margin, is the sensitivity factor of reactive power of node k in the transmission network to the system voltage stability margin, VSMis the voltage stability margin, i.e., the distance of the current operating point to the voltage collapse point, Q k is the reactive power injected by node k in the transmission network, ISF k is the inertia substitution factor, H sys is the total equivalent inertia of the system; The second-order cone programming method includes relaxing non-convex constraints in power flow equations of the power distribution network, converting the power balance equation into a second-order cone constraint form, converting the line current amplitude constraint into a second-order cone inequality, converting the node voltage amplitude constraint into a linear inequality, and calling a mathematical optimization solver to solve the converted convex optimization model; The converted constraint is expressed as: |S ij | 2 ≤v i ·l ij ,(i,j)∈L D wherein, is the lower bound of the voltage square at the distribution grid node i, is the upper bound of the voltage square at the distribution grid node i, is the maximum current amplitude of the line ij.

7. The method of distributed resource access evaluation under transmission and distribution network constraints as claimed in claim 6, wherein: The relaxation iteration method includes setting a relaxation factor, updating active power and reactive power of the interface point until a convergence criterion is reached, and is expressed by a formula: wherein, Pn r is the active power injection value of the transmission-distribution grid interface point r in the n-th iteration, Qn r is the reactive power injection value of the transmission-distribution grid interface point r in the n-th iteration, and β is a relaxation factor, Pn r is the active power injection value of the transmission-distribution grid interface point r in the n-th iteration, Qn r is the reactive power injection value of the transmission-distribution grid interface point r in the n-th iteration, and β is a relaxation factor, Pn r is the active power injection value of the transmission-distribution grid interface point r in the n-th iteration, Qn r is the reactive power injection value of the transmission-distribution grid interface point r in the n-th iteration, and β is a relaxation factor, Pn+1 r is the active power injection value of the transmission-distribution grid interface point r in the n+1-th iteration, Qn+1 r is the reactive power injection value of the transmission-distribution grid interface point r in the n+1-th iteration. The dynamic constraint updating mechanism includes generating a plurality of typical scenarios according to historical operation data and predicted output of new energy, including different combinations of peak load rate and new energy penetration rate, performing power transmission network power flow calculation for each scenario to obtain line power flow constraints, voltage stability constraints and system inertia constraints under the current scenario, calculating a contribution index of each constraint condition based on a Lagrange multiplier in the optimization model solving process, and selecting a constraint with a contribution index greater than a preset threshold as a key constraint, periodically updating the key constraint set according to the predicted change of the power grid state and feeding back to the optimization model; The contribution index of each constraint condition includes extracting the Lagrange multiplier of the line power flow constraint, the voltage stability constraint and the system inertia constraint in the optimization model, and calculating the average value and variance of each constraint multiplier under different scenarios, and comprehensively evaluating the importance of the constraint according to the multiplier size and fluctuation degree, the larger and more stable the multiplier value is, the higher the importance of the constraint is.

8. A system for distributed resource access evaluation under transmission and distribution network constraints, applying the method for distributed resource access evaluation under transmission and distribution network constraints according to any one of claims 1 to 7, characterized in that, The method comprises a joint analysis and modeling module, an influence analysis and quantification module, a constraint transmission and conversion module, a joint optimization solving module, and a coordination and dynamic updating module. The joint analysis and modeling module is configured to establish accurate power transmission network and power distribution network models, and to clearly define the interface coupling relationship between the two, and to describe the power exchange and voltage coupling of the power transmission network and the power distribution network at the interface point by equations. The influence analysis and quantification module is configured to quantify the influence on the line power flow by calculating a power transmission distribution factor, to quantify the influence on the system voltage stability by a voltage stability sensitivity factor, and to quantify the influence on the system inertia level by an inertia replacement factor, and to quantitatively correlate the local behavior of the distributed resource access with the overall safe and stable operation of the power transmission network. The constraint transmission and conversion module is configured to use the quantified relationship as a bridge to convert the global operation safety constraints of the power transmission network layer into linearized constraint conditions for power injection at the interface point of the power distribution network, and to embed the safety requirements of the power transmission network into the optimization model of the power distribution network, thereby achieving effective fusion and transmission of constraints of different levels of power grids. The joint optimization solving module is configured to take the maximum distributed resource access capacity of the power distribution network as an objective function, integrate internal operation constraints of the power distribution network, interface coupling constraints of the power transmission and distribution network, and transmission constraints of the power transmission network, and convert the originally complex nonlinear and non-convex problem into a convex optimization problem that can be efficiently solved by using a convex relaxation method based on second-order cone programming. The coordination and dynamic updating module is configured to adopt a relaxation iteration-based coordination algorithm to iteratively smooth the interaction data between the transmission network optimization and the distribution network optimization, and establish a dynamic updating mechanism by generating typical scenarios, evaluating constraint importance, screening key constraints, and rolling updating the constraint set in the optimization model. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-7. The processor implements the steps of the distributed resource access evaluation method under the constraints of the transmission and distribution network as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the distributed resource access evaluation method under the constraints of the transmission and distribution network as claimed in any one of claims 1 to 7.

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