A power distribution network source and storage system carrying capacity evaluation method and device

Abstract

The application provides a power distribution network source storage system carrying capacity evaluation method. In the execution of the method, a calculation model of the total branch capacity of a center node is established first, which is used to calculate the branch capacity on multiple branches connected with the center node, sum the branch capacities to obtain the total branch capacity of the center node, and then process the total branch capacity by using a pre-set constraint condition to obtain the maximum and minimum values of the total branch capacity. In this way, the carrying capacity of the power distribution network under different environmental influences can be obtained by the power distribution network source storage system carrying capacity evaluation method.

Description

Technical Field

[0001] This application relates to the field of distribution network planning, and in particular to a method and apparatus for assessing the carrying capacity of a distribution network source-storage system. Background Technology

[0002] The integration of numerous distributed generation sources into the grid has altered the traditional single-source radial structure of distribution networks, causing numerous problems related to power quality, network loss variations, and relay protection. Furthermore, the output of distributed generation sources is significantly affected by the environment, exhibiting intermittency and strong randomness, which increases the uncertainty faced by distribution network planning and operation. The configuration of energy storage can mitigate the impact of the intermittent fluctuations in renewable energy generation through the transfer of energy over time. Therefore, determining the maximum capacity of distributed renewable energy that a distribution network can support has become a pressing research issue. Summary of the Invention

[0003] In view of this, this application provides a method and apparatus for evaluating the carrying capacity of a power distribution network source-storage system, which aims to evaluate the carrying capacity of the power distribution network source-storage system.

[0004] In a first aspect, embodiments of this application provide a method for assessing the carrying capacity of a power distribution network source-storage system, the method comprising: A calculation model for the total branch capacity of a central node is established. The calculation model is used to: calculate the branch capacity on multiple branches connected to the central node, and sum the branch capacities to obtain the total branch capacity of the central node. The total branch capacity is processed using pre-set constraints to obtain the maximum and minimum values ​​of the total branch capacity.

[0005] Optionally, summing the branch capacities to obtain the total branch capacity of the central node includes: Establish a constraint model between the branch capacity and the uncertainty coefficient of the access capacity of the flexible interconnected distribution network, and obtain the optimal branch capacity of the flexible interconnected distribution network when the uncertainty coefficient is 1 based on the constraint model; The total branch capacity of the central node is obtained by summing the optimal capacities of the branch paths.

[0006] Optionally, the constraints include: Power flow constraints in flexible interconnected distribution networks: , in, , For the first The flow through the branch road during each period active power, For the first The flow through the branch road during each period reactive power, For the first The flow through the branch road during each period active power, For the first The flow through the branch road during each period reactive power; For Let be the set of the first nodes of the tail node. For The set of the first and last nodes. , For nodes Injecting active and reactive power, N T A set of hour numbers for each day of the year. For the set of all nodes; The voltage constraint of the central node:

[0007] in, , For the resistance and reactance of the branch, For the first Time period nodes voltage, For the first Time period nodes The voltage; SOP active and reactive power constraints: ; SOP capacity constraints:

[0008] Where i and j are the node numbers of the flexible interconnected distribution network to which the SOP is connected; SOP active power The reactive power at SOP; , , , These represent the active power and reactive power injected into the converter by the flexible multi-state switch during the t-th time period; and The capacity of the SOP connected between nodes i and j; The state of charge constraints of the energy storage in the flexible interconnected distribution network are as follows: ,in, This represents the energy storage capacity during the t-th time period. and These represent the upper and lower limits of the energy storage state of charge, respectively; Constraints on power variation in energy storage devices: , , in, Current battery level. Indicates the battery level at the next moment. It is the charging power of the flexible interconnected distribution network in time period t. It is the discharge power of the flexible interconnected distribution network in time period t. It is the energy storage charging and discharging power of the flexible interconnected distribution network in time period t; Energy variation constraints of energy storage devices within a day: , It is the energy storage charging and discharging power of the flexible interconnected distribution network in time period t. This is the minimum scheduling period for energy storage; Upper limit constraints on the charging and discharging power of energy storage components:

[0009]

[0010] in, It is the charging power of the flexible interconnected distribution network. It is the discharge power of the flexible interconnected distribution network. It is the upper limit of the energy storage charging and discharging power of the flexible interconnected distribution network.

[0011] Optionally, the constraint model includes: , in, The time series coefficient, ranging from 0 to 1, characterizes the temporal variation of light intensity; uncertainty coefficient. Let be a random variable that follows a normal distribution, with a mean of 1 and a variance of . ; This represents the maximum branch capacity of the flexible interconnected distribution network without considering uncertainties. The active power injected by the distributed power source at node j under the influence of uncertainty coefficients.

[0012] Optionally, the method further includes: Establish information about the uncertainty coefficient The fuzzy set is as follows:

[0013]

[0014] In the formula, and These are random parameters (photovoltaic output). Fuzzy set and mean of photovoltaic output and variance An uncertain set; For random variables The distribution set; To find the probability function, representing a random variable The distribution belongs to The probability is 1. To find the expected function; and These are the upper and lower limits of the mean; and These are the upper and lower limits of the standard deviation, respectively. and These are the predicted values ​​for the mean and variance, respectively. The upper and lower limits of the mean and standard deviation should meet the following requirements. , This can be obtained from historical data; and These are the allowable deviation coefficients, used to control robustness; The number of photovoltaic units to be configured.

[0015] Optionally, the method further includes: By using the linearization method of circular constraints to simplify line capacity constraints and SOP capacity constraints, the power flow constraints of the distribution network are rewritten as follows: , Using the properties of the normal distribution, the quantiles of the random variables corresponding to the confidence level are found, and these quantiles are used to transform them into deterministic constraints. The final constraint conditions of the rewritten distribution network power flow constraints are as follows:

[0016] The final capacity constraint of the rewritten SOP is:

[0017] Optionally, processing the total branch capacity using pre-set constraints to obtain the maximum and minimum values ​​of the total branch capacity includes: Based on the calculation model of the total branch capacity of the central node, a min-max power grid carrying capacity assessment model is constructed, and the objective function of the model is: Where P and Q include all , When the uncertainty coefficient is 1, the maximum value of the branch capacity is obtained using the model; when the uncertainty coefficient is not 1, the minimum value of the branch capacity is obtained using the model.

[0018] Secondly, embodiments of this application provide a power distribution network source-storage system carrying capacity assessment device, characterized in that it is applied to a flexible interconnected power distribution network with moment information distribution bars, the device comprising: A module is established to build a calculation model for the total branch capacity of the central node. The calculation model is used to: calculate the branch capacity on multiple branches connected to the central node, and sum the branch capacities to obtain the total branch capacity of the central node. The calculation module is used to process the total branch capacity using pre-set constraints to obtain the maximum and minimum values ​​of the total branch capacity.

[0019] Optionally, the establishment module is further configured to establish a constraint model between the branch capacity and the uncertainty coefficient of the access capacity of the flexible interconnected distribution network, and obtain the optimal branch capacity of the flexible interconnected distribution network when the uncertainty coefficient is 1 based on the constraint model. The total branch capacity of the central node is obtained by summing the optimal capacities of the branch paths.

[0020] Optionally, the constraints include: Power flow constraints in flexible interconnected distribution networks: , in, , For the first The flow through the branch road during each period active power, For the first The flow through the branch road during each period reactive power, For the first The flow through the branch road during each period active power, For the first The flow through the branch road during each period reactive power; For Let be the set of the first nodes of the tail node. For The set of the first and last nodes. , For nodes Injecting active and reactive power, N T A set of hour numbers for each day of the year. For the set of all nodes; The voltage constraint of the central node:

[0021] in, , For the resistance and reactance of the branch, For the first Time period nodes voltage, For the first Time period nodes The voltage; SOP active and reactive power constraints: ; SOP capacity constraints:

[0022] Where i and j are the node numbers of the flexible interconnected distribution network to which the SOP is connected; SOP active power The reactive power at SOP; , , , These represent the active power and reactive power injected into the converter by the flexible multi-state switch during the t-th time period; and The capacity of the SOP connected between nodes i and j; The state of charge constraints of the energy storage in the flexible interconnected distribution network are as follows: ,in, This represents the energy storage capacity during the t-th time period. and These represent the upper and lower limits of the energy storage state of charge, respectively; Constraints on power variation in energy storage devices: , , in, Current battery level. Indicates the battery level at the next moment. It is the charging power of the flexible interconnected distribution network in time period t. It is the discharge power of the flexible interconnected distribution network in time period t. It is the energy storage charging and discharging power of the flexible interconnected distribution network in time period t; Energy variation constraints of energy storage devices within a day: , It is the energy storage charging and discharging power of the flexible interconnected distribution network in time period t. This is the minimum scheduling period for energy storage; Upper limit constraints on the charging and discharging power of energy storage components:

[0023]

[0024] in, It is the charging power of the flexible interconnected distribution network. It is the discharge power of the flexible interconnected distribution network. It is the upper limit of the energy storage charging and discharging power of the flexible interconnected distribution network.

[0025] Optionally, the constraint model includes: , in, The time series coefficient, ranging from 0 to 1, characterizes the temporal variation of light intensity; uncertainty coefficient. Let be a random variable that follows a normal distribution, with a mean of 1 and a variance of . ; This represents the maximum branch capacity of the flexible interconnected distribution network without considering uncertainties. The active power injected by the distributed power source at node j under the influence of uncertainty coefficients.

[0026] Optionally, the establishment module is further configured to establish information about the uncertainty coefficient. The fuzzy set is as follows:

[0027]

[0028] In the formula, and These are random parameters (photovoltaic output). Fuzzy set and mean of photovoltaic output and variance An uncertain set; For random variables The distribution set; To find the probability function, representing a random variable The distribution belongs to The probability is 1. To find the expected function; and These are the upper and lower limits of the mean; and These are the upper and lower limits of the standard deviation, respectively. and These are the predicted values ​​for the mean and variance, respectively. The upper and lower limits of the mean and standard deviation should meet the following requirements. , This can be obtained from historical data; and These are the allowable deviation coefficients, used to control robustness; The number of photovoltaic units to be configured.

[0029] Optionally, the device further includes a rewriting module for simplifying line capacity constraints and SOP capacity constraints using a linearization method of circular constraints. The rewritten distribution network power flow constraints are as follows: , Using the properties of the normal distribution, the quantiles of the random variables corresponding to the confidence level are found, and these quantiles are used to transform them into deterministic constraints. The final constraint conditions of the rewritten distribution network power flow constraints are as follows:

[0030] The final capacity constraint of the rewritten SOP is:

[0031] Optionally, the calculation module is further configured to construct a min-max power grid carrying capacity assessment model based on the calculation model of the total branch capacity of the central node, wherein the objective function of the model is: Where P and Q include all , When the uncertainty coefficient is 1, the model is used to obtain the maximum value of the branch capacity; when the uncertainty coefficient is not 1, the model is used to obtain the minimum value of the branch capacity.

[0032] Thirdly, embodiments of this application provide an apparatus including a memory and a processor, the memory being used to store instructions or code, and the processor being used to execute the instructions or code to cause the apparatus to perform the method described in any of the preceding first aspects.

[0033] Fourthly, embodiments of this application provide a computer storage medium storing code, wherein when the code is executed, a device running the code implements the method described in any of the first aspects above.

[0034] This application provides a method for assessing the carrying capacity of a power distribution network's energy source and storage system. When executing the method, a calculation model for the total branch capacity of a central node is first established. This model is used to: calculate the branch capacity on multiple branches connected to the central node; sum the branch capacities to obtain the total branch capacity of the central node; and then process the total branch capacity using pre-set constraints to obtain its maximum and minimum values. Thus, this method for assessing the carrying capacity of a power distribution network's energy source and storage system can be used to obtain the carrying capacity of the power distribution network under different environmental influences. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in this embodiment or the prior art, the drawings used in the description of the embodiment or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 A flowchart of one method of the power distribution network source-storage system carrying capacity assessment method provided in the embodiments of this application; Figure 2 A schematic diagram illustrating a method for rewriting circular opportunity constraints provided in an embodiment of this application; Figure 3 This is a schematic diagram of the closed-loop flexible interconnection structure between feeders connected to the optical energy storage system in the embodiments of this application; Figure 4 This is a schematic diagram of the flexible interconnection structure accessing the optical storage system in an embodiment of this application; Figure 5 This is a schematic diagram of a data acquisition device provided in an embodiment of this application. Detailed Implementation

[0037] To make the structure and advantages of this application clearer, the structure of this application will be further described below with reference to the accompanying drawings.

[0038] See Figure 1 , Figure 1 A flowchart of a method for assessing the carrying capacity of a power distribution network source-storage system provided in this application embodiment, the method comprising: S101: Establish a calculation model for the total branch capacity of the central node. The calculation model is used to: calculate the branch capacity on multiple branches connected to the central node, and sum the branch capacities to obtain the total branch capacity of the central node.

[0039] The central node is a structure in the distribution network. Several branches are connected to both sides of the central node. One branch is used to input the electricity generated by photovoltaics into the node, and the other branch is used to transmit current to the power consumption end. By giving the location of distributed power source access, the access capacity of the photovoltaic-storage system is maximized. At the same time, considering the worst case of random output of distributed power source, a grid carrying capacity assessment model for the maximum and minimum values ​​is constructed, that is, the calculation model of the total branch capacity of the central node.

[0040] S102: The total branch capacity is processed using pre-set constraints to obtain the maximum and minimum values ​​of the total branch capacity.

[0041] It should be noted that the output of distributed power sources is greatly affected by the environment, exhibiting a certain degree of intermittency and strong randomness. Their grid connection also increases the uncertainty faced by distribution network planning and operation.

[0042] The calculation of the maximum capacity of the distribution network requires constraints from the power flow model and the flexible multi-state switch (SOP) model to obtain the maximum photovoltaic access capacity of the distribution network without considering the influence of the external environment, as well as the maximum capacity of the distribution network under the influence of uncertainty coefficients, and then analyze the impact of the uncertainty of distributed generation on the evaluation results.

[0043] In one implementation of this embodiment, the step of summing the branch capacities to obtain the total branch capacity of the central node includes: establishing a constraint model between the branch capacity and the uncertainty coefficient of the access capacity of the flexible interconnected distribution network; obtaining the optimal branch capacity of the flexible interconnected distribution network when the uncertainty coefficient is 1 based on the constraint model; and summing the optimal branch capacities to obtain the total branch capacity of the central node.

[0044] Due to uncertainties in the distribution network, the planned access capacity (under ideal conditions unaffected by external factors) of each branch is not the same as the actual access capacity. These influencing factors are represented by uncertainty coefficients, and a constraint model is established.

[0045] In one implementation of this embodiment, the constraints include: Power flow constraints in flexible interconnected distribution networks: , in, , For the first The flow through the branch road during each period active power, For the first The flow through the branch road during each period reactive power, For the first The flow through the branch road during each period active power, For the first The flow through the branch road during each period reactive power; For Let be the set of the first nodes of the tail node. For The set of the first and last nodes. , For nodes Injecting active and reactive power, N T A set of hour numbers for each day of the year. For the set of all nodes;

[0046] The power balance equation states that the injected power at a node is equal to the impact of all components on that line, including energy storage, photovoltaics, SOPs, and loads, on the injected power at that node. and They are nodes The active and reactive power injected into the load; The active power injected by the distributed power source at node j. This refers to the power injected into the nodes by the energy storage system.

[0047] The active and reactive power injected by the central node can be calculated through the power flow constraints of the flexible interconnected distribution network. This constraint can be rewritten as a chance constraint as follows: In the formula For line capacity, To satisfy the confidence level.

[0048] The voltage constraint of the central node:

[0049] in, , For the resistance and reactance of the branch, For the first Time period nodes voltage, For the first Time period nodes The voltage; SOP active and reactive power constraints: ; SOP capacity constraints:

[0050] Where i and j are the node numbers of the flexible interconnected distribution network to which the SOP is connected; SOP active power The reactive power at SOP; , , , These represent the active power and reactive power injected into the converter by the flexible multi-state switch during the t-th time period; and The capacity of the SOP connected between nodes i and j; State of charge constraints for energy storage in flexible interconnected distribution networks: ,in, This represents the energy storage capacity during the t-th time period. and These represent the upper and lower limits of the energy storage state of charge, respectively; Constraints on power variation in energy storage devices: , , in, Current battery level. Indicates the battery level at the next moment. It is the charging power of the flexible interconnected distribution network in time period t. It is the discharge power of the flexible interconnected distribution network in time period t. It is the energy storage charging and discharging power of the flexible interconnected distribution network in time period t; Energy variation constraints of energy storage devices within a day: , It is the energy storage charging and discharging power of the flexible interconnected distribution network in time period t. This is the minimum scheduling period for energy storage; Upper limit constraints on the charging and discharging power of energy storage components:

[0051]

[0052] in, It is the charging power of the flexible interconnected distribution network. It is the discharge power of the flexible interconnected distribution network. It is the upper limit of the energy storage charging and discharging power of the flexible interconnected distribution network.

[0053] In one implementation of this embodiment, the constraint model includes: , in, The time series coefficient, ranging from 0 to 1, characterizes the temporal variation of light intensity; uncertainty coefficient. Let be a random variable that follows a normal distribution, with a mean of 1 and a variance of . ; This represents the maximum branch capacity of the flexible interconnected distribution network without considering uncertainties. The active power injected by the distributed power source at node j under the influence of uncertainty coefficients.

[0054] It should be noted that the output of a photovoltaic system is affected by the external environment, such as the intensity of solar radiation and the surface temperature of photovoltaic modules. Therefore, it is necessary to introduce uncertainty coefficients and time series coefficients to represent the impact of the external environment on photovoltaic output at different time periods.

[0055] In one implementation of this embodiment, an information about the uncertainty coefficient is established. The fuzzy set is as follows:

[0056]

[0057] In the formula, and These are random parameters (photovoltaic output). Fuzzy set and mean of photovoltaic output and variance An uncertain set; For random variables The distribution set; To find the probability function, representing a random variable The distribution belongs to The probability is 1. To find the expected function; and These are the upper and lower limits of the mean; and These are the upper and lower limits of the standard deviation, respectively. and These are the predicted values ​​for the mean and variance, respectively. The upper and lower limits of the mean and standard deviation should meet the following requirements. , This can be obtained from historical data; and These are the allowable deviation coefficients, used to control robustness; The number of photovoltaic units to be configured.

[0058] A fuzzy set is the entire set of objects that possess the attributes described by a certain fuzzy concept, in order to reduce the uncertainty coefficient. The various situations are expressed.

[0059] In one implementation of this embodiment, the line capacity constraint and SOP capacity constraint are simplified using the linearization method of circular constraints, and the rewritten distribution network power flow constraints are as follows: , Using the properties of the normal distribution, the quantiles of the random variables corresponding to the confidence level are found, and these quantiles are used to transform them into deterministic constraints. The final constraint conditions of the rewritten distribution network power flow constraints are as follows:

[0060] The final capacity constraint of the rewritten SOP is:

[0061] Power flow refers to a stable operating state of a power system, characterized by the steady-state distribution of voltage at each node and active and reactive power in each branch. Power flow calculation, given the grid's wiring configuration, parameters, and operating conditions, calculates the steady-state voltage of each bus, current in each branch, power output, and network losses. For an existing power system, power flow calculation can determine if bus voltage, branch current, and power exceed limits; if so, measures should be taken to adjust the operating mode. For a planned power system, power flow calculation can provide a basis for selecting power supply schemes and electrical equipment. Power flow calculation can also provide raw data for relay protection and automatic device setting calculations, power system fault calculations, and stability calculations.

[0062] Simplifying line capacity constraints and SOP (Standard Operating Plan) capacity constraints using a linearization method for circular constraints can reduce the difficulty of solving them. Line capacity is a chance constraint within a circular constraint, which needs to be linearized to facilitate subsequent formula derivation. The circular linearization method allows several linear constraints to approximate the circular constraint, providing sufficient accuracy for engineering applications. See also... Figure 2 , Figure 2 A schematic diagram illustrating the method for rewriting circular opportunity constraints provided in an embodiment of this application.

[0063] In one implementation of this embodiment, processing the total branch capacity using pre-set constraints to obtain the maximum and minimum values ​​of the total branch capacity includes: Based on the calculation model of the total branch capacity of the central node, a min-max power grid carrying capacity assessment model is constructed, and the objective function of the model is: Where P and Q include all , When the uncertainty coefficient is 1, the model is used to obtain the maximum value of the branch capacity; when the uncertainty coefficient is not 1, the model is used to obtain the minimum value of the branch capacity.

[0064] After linearization and chance-constraint transformation, the power grid carrying capacity assessment model is transformed into a linear min-max problem. The inner max function needs to be Lagrangian-dualized, transforming it into a single-layer optimization problem. For the bilinear term in the single-layer problem, the variables... For an uncertain set belonging to a polyhedron, its extreme points are finite. All extreme points can be found, and then the worst-case scenario can be determined through enumeration. Based on this, other dual variables are optimized to obtain the optimal solution to the dual problem, namely the carrying capacity of the power distribution network source-storage system.

[0065] The effectiveness of the above model and method can be verified in the following ways. First, compare the maximum carrying capacity of the distributed power supply in the feeder-to-feeder closed-loop flexible interconnection structure under three conditions: no adjustment means, reconfiguration, and SOP. Then, analyze the carrying capacity of the photovoltaic-storage system with different photovoltaic / storage ratios in two typical grid structures.

[0066] 1) Comparison of maximum carrying capacity of flexible interconnected distribution networks: By establishing, such as Figure 3 The example system of a closed-loop flexible interconnected distribution network between feeders is shown to verify the effectiveness of the above model and method. For example, the SOP installed capacity is 9MVA, and the energy storage in the source-storage system is configured as 10% for 2 hours. The maximum energy carrying capacity of distributed power sources with different regulation methods is shown in Table 1: Table 1 Maximum carrying capacity of distributed power sources with different regulation methods

[0067] As shown in Table 1, the distributed power generation capacity under different regulation methods is greater than that of other regulation methods, reaching 19.08MW.

[0068] During the solution process, it was verified that when the mean and variance of photovoltaic output both reached the maximum value in the interval, it represented the worst scenario in fuzzy sets D1 and D2.

[0069] 2) The carrying capacity of photovoltaic-storage systems with different photovoltaic / storage ratios: Flexible interconnection distribution networks between ring networks and photovoltaic-storage access schemes can be established, and tests can be conducted, such as... Figure 3 and Figure 4 The system carrying capacity of the two flexible interconnected distribution network structures under different light / storage ratios is shown in Tables 2 and 3.

[0070] Table 2 Closed-loop flexible interconnection structure between feeders

[0071] Table 3 Flexible Interconnection Structure between Ring Networks

[0072] Among them, the flexible interconnection structure between rings can simultaneously perform network reconfiguration, and the reconfiguration scheme under the maximum capacity is as follows: Figure 4 The switch status is shown in the diagram.

[0073] The above are some specific implementations of the power distribution network source-storage system carrying capacity assessment method provided in this application. Based on this, this application also provides a corresponding device. The device provided in this application will be described below from the perspective of functional modularization.

[0074] See Figure 5 The diagram shows a structural schematic of a power distribution network source-storage system carrying capacity assessment device, which includes a setup module 500 and a calculation module 510.

[0075] Module 500 is established to establish a calculation model for the total branch capacity of the central node. The calculation model is used to: calculate the branch capacity on multiple branches connected to the central node, and sum the branch capacities to obtain the total branch capacity of the central node. The calculation module 510 is used to process the total branch capacity using pre-set constraints to obtain the maximum and minimum values ​​of the total branch capacity.

[0076] In one implementation of this embodiment, the establishment module 500 is further configured to establish a constraint model between the branch capacity and the uncertainty coefficient of the access capacity of the flexible interconnected distribution network, and obtain the optimal branch capacity of the flexible interconnected distribution network when the uncertainty coefficient is 1 based on the constraint model. The total branch capacity of the central node is obtained by summing the optimal capacities of the branch paths.

[0077] In one implementation of this embodiment, the constraints include: Power flow constraints in flexible interconnected distribution networks: , in, , For the first The flow through the branch road during each period active power, For the first The flow through the branch road during each period reactive power, For the first The flow through the branch road during each period active power, For the first The flow through the branch road during each period reactive power; For Let be the set of the first nodes of the tail node. For The set of the first and last nodes. , For nodes Injecting active and reactive power, N T A set of hour numbers for each day of the year. For the set of all nodes; The voltage constraint of the central node:

[0078] in, , For the resistance and reactance of the branch, For the first Time period nodes voltage, For the first Time period nodes The voltage; SOP active and reactive power constraints: ; SOP capacity constraints:

[0079] Where i and j are the node numbers of the flexible interconnected distribution network to which the SOP is connected; SOP active power The reactive power at SOP; , , , These represent the active power and reactive power injected into the converter by the flexible multi-state switch during the t-th time period; and The capacity of the SOP connected between nodes i and j; The state of charge constraints of the energy storage in the flexible interconnected distribution network are as follows: ,in, This represents the energy storage capacity during the t-th time period. and These represent the upper and lower limits of the energy storage state of charge, respectively; Constraints on power variation in energy storage devices: , , in, Current battery level. Indicates the battery level at the next moment. It is the charging power of the flexible interconnected distribution network in time period t. It is the discharge power of the flexible interconnected distribution network in time period t. It is the energy storage charging and discharging power of the flexible interconnected distribution network in time period t; Energy variation constraints of energy storage devices within a day: , It is the energy storage charging and discharging power of the flexible interconnected distribution network in time period t. This is the minimum scheduling period for energy storage; Upper limit constraints on the charging and discharging power of energy storage components:

[0080]

[0081] in, It is the charging power of the flexible interconnected distribution network. It is the discharge power of the flexible interconnected distribution network. It is the upper limit of the energy storage charging and discharging power of the flexible interconnected distribution network.

[0082] Optionally, the constraint model includes: , in, The time series coefficient, ranging from 0 to 1, characterizes the temporal variation of light intensity; uncertainty coefficient. Let be a random variable that follows a normal distribution, with a mean of 1 and a variance of . ; This represents the maximum branch capacity of the flexible interconnected distribution network without considering uncertainties. The active power injected by the distributed power source at node j under the influence of uncertainty coefficients.

[0083] In one implementation of this embodiment, the establishing module is further configured to establish information about the uncertainty coefficient. The fuzzy set is as follows:

[0084]

[0085] In the formula, and These are random parameters (photovoltaic output). Fuzzy set and mean of photovoltaic output and variance An uncertain set; For random variables The distribution set; To find the probability function, representing a random variable The distribution belongs to The probability is 1. To find the expected function; and These are the upper and lower limits of the mean; and These are the upper and lower limits of the standard deviation, respectively. and These are the predicted values ​​for the mean and variance, respectively. The upper and lower limits of the mean and standard deviation should meet the following requirements. , This can be obtained from historical data; and These are the allowable deviation coefficients, used to control robustness; The number of photovoltaic units to be configured.

[0086] In one implementation of this embodiment, the device further includes a rewriting module, used to simplify the line capacity constraints and SOP capacity constraints using a linearization method for circular constraints. The rewritten distribution network power flow constraints are as follows: , Using the properties of the normal distribution, the quantiles of the random variables corresponding to the confidence level are found, and these quantiles are used to transform them into deterministic constraints. The final constraint conditions of the rewritten distribution network power flow constraints are as follows:

[0087] The final capacity constraint of the rewritten SOP is:

[0088] Optionally, the calculation module is further used to construct a min-max power grid carrying capacity assessment model based on the calculation model of the total branch capacity of the central node, wherein the objective function of the model is: Where P and Q include all , When the uncertainty coefficient is 1, the maximum value of the branch capacity is obtained using the model; when the uncertainty coefficient is not 1, the minimum value of the branch capacity is obtained using the model.

[0089] This application also provides corresponding devices and computer storage media for implementing the solutions provided in this application.

[0090] The device includes a memory and a processor. The memory stores instructions or code, and the processor executes the instructions or code to cause the device to perform the method described in any embodiment of this application.

[0091] The computer storage medium stores code, and when the code is run, the device running the code implements the method described in any embodiment of this application.

[0092] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that all or part of the steps in the methods of the above embodiments can be implemented by means of software plus a general-purpose hardware platform. Based on this understanding, the technical solution of this application can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as a read-only memory (ROM) / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network communication device such as a router) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0093] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0094] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0095] It should also be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for the device and apparatus embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiments. The device and apparatus embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components indicated as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the solution in this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0096] The above description is merely one specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for evaluating the carrying capacity of a power grid source storage system, characterized in that, The method, applied to a flexible interconnected distribution network with a matrix information distribution bar, includes: A calculation model for the total branch capacity of a central node is established. The calculation model is used to: calculate the branch capacity on multiple branches connected to the central node, and sum the branch capacities to obtain the total branch capacity of the central node. The total branch capacity is processed using pre-set constraints to obtain the maximum and minimum values ​​of the total branch capacity; The process of summing the branch capacities to obtain the total branch capacity of the central node includes: Establish a constraint model between the branch capacity and the uncertainty coefficient of the access capacity of the flexible interconnected distribution network, and obtain the optimal branch capacity of the flexible interconnected distribution network when the uncertainty coefficient is 1 based on the constraint model; The total branch capacity of the central node is obtained by summing the optimal capacities of the branch paths. The constraint model includes: ， in, The time series coefficient, ranging from 0 to 1, characterizes the temporal variation of light intensity; uncertainty coefficient. Let be a random variable that follows a normal distribution, with a mean of 1 and a variance of . ; This represents the maximum branch capacity of the flexible interconnected distribution network without considering uncertainties. Active power injected into the distributed power source at node j under the influence of uncertainty coefficient; The method further includes: A fuzzy set is established for the uncertainty coefficient as follows: ； ； wherein the uncertainty coefficient of the photovoltaic power output is a fuzzy set, the uncertainty coefficient of the photovoltaic power output is the mean and the variance of the uncertainty set; For random variables The distribution set; To find the probability function, representing a random variable The distribution belongs to The probability is 1. To find the expected function; The upper limit of the mean. It is the lower limit of the mean; The upper limit of the standard deviation, This is the lower limit of the standard deviation; The predicted value of the mean. This represents the predicted value of the variance; the upper and lower limits of the mean satisfy the upper and lower limits of the standard deviation satisfy are obtained from historical data; and are respectively allowed deviation coefficients to control robustness; is a photovoltaic configuration number; The process of processing the total branch capacity using pre-set constraints to obtain the maximum and minimum values ​​of the total branch capacity includes: Based on the calculation model of the total branch capacity of the central node, a min-max power grid carrying capacity assessment model is constructed, and the objective function of the model is: ， P and Q include all of them. When the uncertainty coefficient is 1, the model is used to obtain the maximum value of the total branch capacity; when the uncertainty coefficient is not 1, the model is used to obtain the minimum value of the total branch capacity.

2. The method of claim 1, wherein, The constraints include: Power flow constraints in flexible interconnected distribution networks: ， in, For the first The flow through the branch road during each period active power, For the first The flow through the branch road during each period reactive power, For the first The flow through the branch road during each period active power, For the first The flow through the branch road during each period reactive power; For Let be the set of the first nodes of the tail node. For The set of the first and last nodes. For nodes Injected active power, For nodes Injected reactive power, N T A set of hour numbers for each day of the year. For the set of all nodes; The voltage constraint of the central node: ， in, , For the resistance and reactance of the branch, For the first Voltage at node j in time period For the first Time period nodes The voltage; SOP active and reactive power constraints: ； SOP capacity constraints: ； Where i and j are the node numbers of the flexible interconnected distribution network to which the SOP is connected; The active power at SOP. The reactive power at SOP; , , , and are the active power and reactive power injected into the converter by the flexible multi-state switch in the t-th time period, respectively; and The capacity of the SOP connected between nodes i and j; The state-of-charge constraints of the energy storage in the flexible interconnected distribution network are as follows: ,in, This represents the energy storage capacity during the t-th time period. This indicates the upper limit of the energy storage state of charge. Indicates the lower limit of the energy storage's state of charge; Constraints on power variation in energy storage devices: , ， in, Indicates the current battery level. Indicates the battery level at the next moment. It is the charging power of the flexible interconnected distribution network in time period t. It is the discharge power of the flexible interconnected distribution network in time period t. It is the energy storage charging and discharging power of the flexible interconnected distribution network in time period t; Energy variation constraints of energy storage devices within a day: ； is the energy storage charge-discharge power of the flexible interconnected power distribution grid at the t-th time period, is the minimum scheduling time period of the energy storage; Upper limit constraints on the charging and discharging power of energy storage components: ； ； in, It is the charging power of the flexible interconnected distribution network. It is the discharge power of the flexible interconnected distribution network. It is the upper limit of the energy storage charging and discharging power of the flexible interconnected distribution network.

3. The method of claim 2, wherein, The method further includes: By using the linearization method of circular constraints to simplify line capacity constraints and SOP capacity constraints, the power flow constraints of the distribution network are rewritten as follows: ， Using the properties of the normal distribution, the quantiles of the random variables corresponding to the confidence level are found, and these quantiles are used to transform them into deterministic constraints. The final constraint conditions after rewriting the power flow constraints of the distribution network are as follows: ； The final capacity constraint of the rewritten SOP is: 。 4. A power distribution grid source storage system carrying capacity assessment device, characterized in that, For executing the power distribution network source-storage system carrying capacity assessment method of claim 1, applied to a flexible interconnected power distribution network with moment information distribution rods, the device comprises: A module is established to build a calculation model for the total branch capacity of the central node. The calculation model is used to: calculate the branch capacity on multiple branches connected to the central node, and sum the branch capacities to obtain the total branch capacity of the central node. The calculation module is used to process the total branch capacity using pre-set constraints to obtain the maximum and minimum values ​​of the total branch capacity; The establishment module is also used to establish a constraint model between the branch capacity and the uncertainty coefficient of the access capacity of the flexible interconnected distribution network, and to obtain the optimal branch capacity of the flexible interconnected distribution network when the uncertainty coefficient is 1 based on the constraint model. The total branch capacity of the central node is obtained by summing the optimal capacities of the branch paths.

5. A computing device, characterized in that, The device includes: a memory and a processor; The memory is used to store computer programs; The processor is configured to implement the steps of the method as described in any one of claims 1 to 3 when executing the computer program.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 3.

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