Photovoltaic bearing capacity assessment method, device and equipment containing energy storage power distribution network and medium

Abstract

The invention relates to the technical field of power system optimization operation, in particular to a photovoltaic bearing capacity evaluation method and device containing an energy storage power distribution network, equipment and a medium. In the application, a first safe operation model when the power distribution system operates in a safe state and a second safe operation model when the power distribution system operates in a fault state are determined, and meanwhile, a safe operation constraint and a constraint under a fault working condition are set; and when the first safe operation model satisfies a first constraint condition and the second safe operation model satisfies a second constraint condition, determining an optimal solution of the photovoltaic power, determining a value of an objective function according to the optimal solution of the photovoltaic power, and determining the value of the objective function as the photovoltaic bearing capacity of the power distribution network. The accuracy of photovoltaic bearing capacity evaluation of the power distribution network is improved, and the benefits of photovoltaic users are effectively protected while it is ensured that the power distribution network can operate safely in two scenes.

Description

Technical Field

[0001] This application relates to the field of power system optimization operation technology, and in particular to a method, device, equipment and medium for assessing the photovoltaic carrying capacity of a distribution network including energy storage. Background Technology

[0002] Distributed photovoltaic (PV) systems, with their core advantages of flexible deployment, low carbon emissions, high efficiency, and sustainable utilization, have become a key engine for global energy structure transformation. However, compared with conventional power sources, distributed PV output exhibits significant time-series characteristics with load differences (load time-series mismatch) and uncertainty. These characteristics can lead to multiple operational risks in distribution systems (EDS) with high PV penetration, such as overvoltage and overcurrent violations. In fault scenarios, high PV penetration exacerbates these potential safety risks. To guide users in the orderly configuration of PV and avoid these problems, it is necessary to accurately assess the maximum PV capacity, i.e., PV carrying capacity (PVHC). Most existing PV carrying capacity assessment methods for distribution networks focus on the capacity limits under voltage and power constraints during normal operation, neglecting the situation after a fault, resulting in low accuracy. Therefore, improving the accuracy of PV carrying capacity assessment in distribution networks is an urgent problem to be solved. Summary of the Invention

[0003] In view of this, embodiments of this application provide a method, apparatus, equipment and medium for assessing the photovoltaic carrying capacity of a distribution network containing energy storage, in order to solve the problem of low assessment accuracy in the process of assessing the photovoltaic carrying capacity of a distribution network.

[0004] In a first aspect, embodiments of this application provide a method for assessing the photovoltaic carrying capacity of a distribution network including energy storage, the photovoltaic carrying capacity assessment method comprising:

[0005] Determine the objective function, which characterizes the correspondence between the total photovoltaic power in the distributed photovoltaic system and the photovoltaic power at each photovoltaic access point;

[0006] A first safe operation model is determined for the power distribution system when it is operating under safe conditions. The first safe operation model is used to characterize the balance between the first input power and the first output power of the power distribution network under safe conditions. The first input power includes the first photovoltaic power at the photovoltaic access point under safe conditions.

[0007] A second safe operation model is determined for the power distribution system under fault conditions. The second safe operation model is used to characterize the balance between the second input power and the second output power of the power distribution network under fault conditions. The second input power includes the second photovoltaic power at the photovoltaic access location under fault conditions.

[0008] The model balances the two, where the second input power includes the photovoltaic power at the photovoltaic access point corresponding to the fault state;

[0009] Obtain the first constraint and the second constraint. Under the condition that the first safe operation model satisfies the first constraint and the second safe operation model satisfies the second constraint, determine the optimal solution of the photovoltaic power. Based on the optimal solution of the photovoltaic power, determine the value of the objective function and determine the value of the objective function as the photovoltaic carrying capacity of the distribution network.

[0010] Secondly, embodiments of this application provide a photovoltaic carrying capacity assessment device including an energy storage distribution network, the photovoltaic carrying capacity assessment device comprising:

[0011] The first determining module is used to determine the objective function, which is used to characterize the correspondence between the total photovoltaic power in the distributed photovoltaic system and the photovoltaic power at each photovoltaic access location;

[0012] The second determining module determines a first safe operation model of the power distribution system when it is running in a safe state. The first safe operation model is used to characterize the balance between the first input power and the first output power of the power distribution network in a safe state. The first input power includes the first photovoltaic power corresponding to the photovoltaic access location in a safe state.

[0013] The third determining module is used to determine the second safe operation model of the power distribution system when it is running under fault conditions. The second safe operation model is used to characterize the balance between the second input power and the second output power of the power distribution network under fault conditions. The second input power includes the second photovoltaic power at the photovoltaic access location under fault conditions.

[0014] The fourth determining module is used to obtain the first constraint condition and the second constraint condition, determine the optimal solution of the photovoltaic power under the condition that the first safe operation model satisfies the first constraint condition and the second safe operation model satisfies the second constraint condition, determine the value of the objective function based on the optimal solution of the photovoltaic power, and determine the value of the objective function as the photovoltaic carrying capacity of the distribution network.

[0015] Thirdly, embodiments of this application provide a computer device, the computer device including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the photovoltaic load-bearing capacity assessment method as described above.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the photovoltaic load-bearing capacity assessment method as described above.

[0017] The advantages of this application compared to the prior art are:

[0018] In this application, a first safe operation model for the power distribution system under safe operating conditions and a second safe operation model for the power distribution system under fault conditions are determined. Simultaneously, safe operation constraints and constraints under fault conditions are set. Under the condition that the first safe operation model satisfies the first constraint and the second safe operation model satisfies the second constraint, the optimal solution for photovoltaic power is determined. Based on the optimal solution for photovoltaic power, the value of the objective function is determined, and the value of the objective function is used as the photovoltaic carrying capacity of the power distribution network. This improves the accuracy of the photovoltaic carrying capacity assessment of the power distribution network, ensuring safe operation of the power distribution network in both scenarios and effectively protecting the interests of photovoltaic users. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application 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.

[0020] Figure 1 This is a schematic diagram illustrating the application environment of a photovoltaic carrying capacity assessment method for a distribution network with energy storage, provided in an embodiment of this application.

[0021] Figure 2 This is a flowchart illustrating a photovoltaic carrying capacity assessment method for a distribution network including energy storage, provided in one embodiment of this application.

[0022] Figure 3 This application provides a wiring diagram for an IEEE 33-node power system according to one embodiment.

[0023] Figure 4 This is a schematic diagram of the charging state under conventional methods and the method of this application;

[0024] Figure 5 This is a schematic diagram of the PVHC variation curve with the amount of light discarded;

[0025] Figure 6 This is a schematic diagram of the PVHC variation curve with the amount of reduction;

[0026] Figure 7 This is a schematic diagram of the structure of a photovoltaic load-bearing capacity assessment device with energy storage distribution network provided in one embodiment of this application;

[0027] Figure 8 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0030] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0031] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0032] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0033] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0034] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0035] It should be understood that the sequence number of each step in the following embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0036] To illustrate the technical solution of this application, specific embodiments are described below.

[0037] This application provides an embodiment of a method for assessing the photovoltaic carrying capacity of a distribution network, which can be applied to applications such as... Figure 1 In this application environment, the client communicates with the server. The client includes, but is not limited to, handheld computers, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, cloud computing devices, and personal digital assistants (PDAs). The server can be a standalone server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0038] To illustrate the technical solution of this application, specific embodiments are described below.

[0039] See Figure 2 This is a flowchart illustrating a photovoltaic carrying capacity assessment method for a distribution network according to an embodiment of this application. Figure 2 As shown, the photovoltaic carrying capacity assessment method for this distribution network may include the following steps.

[0040] S201: Determine the objective function, which characterizes the correspondence between the total photovoltaic power in the distributed photovoltaic system and the photovoltaic power at each photovoltaic access location.

[0041] In step S201, the corresponding objective function is determined. The objective function is used to characterize the correspondence between the total photovoltaic power in the distributed photovoltaic system and the photovoltaic power at each photovoltaic access location. The photovoltaic power at each photovoltaic access location is related to the state of the photovoltaic system at the corresponding location.

[0042] In this embodiment, the objective function is determined, and the formula for the objective function is as follows:

[0043]

[0044] in, The value of the objective function is the sum of photovoltaic power at each photovoltaic access point. Let be the photovoltaic power at the i-th photovoltaic access point, and n be the number of photovoltaic access points.

[0045] In this embodiment, a corresponding objective function is determined so that the abstract decision objective can be transformed into a computable index through mathematical expressions.

[0046] S202: Determine the first safe operation model of the power distribution system when it is running in a safe state. The first safe operation model is used to characterize the model of the balance between the first input power and the first output power of the power distribution network in a safe state. The first input power includes the first photovoltaic power at the photovoltaic access location in a safe state.

[0047] In step S202, the safe state is the state under fault-free conditions. The distribution network is a power grid including multiple branches. The first input power of the distribution network is the total amount of electrical energy obtained by the distribution network from the upper-level power grid or power generation unit. The first output power of the distribution network is the total amount of electrical energy actually provided by the distribution network to the end user or load. Under the safe state, the first input power and the first output power of the distribution network are balanced, that is, energy is conserved under the safe state. The first input power includes the first photovoltaic power corresponding to the photovoltaic access location under the safe state.

[0048] In this embodiment, a first safe operation model is determined for the power distribution system to operate under safe conditions. The formula for the first safe operation model is as follows:

[0049]

[0050]

[0051]

[0052]

[0053] in, The active power injected into node i at time t before the fault. Let t represent the reactive power injected into node i at time t before the fault. The active load of node i at time t before the fault. The reactive load at node i at time t before the fault. Let represent the charging and discharging power of the energy stored at node i at time t before the fault. A value greater than zero indicates charging, and a value less than zero indicates discharging. Let be the active power generated by generator i at time t before the fault. Let t be the reactive power output of generator i at node i before the fault. Let be the photovoltaic power of node i at time t before the fault, i.e., the first photovoltaic power at the photovoltaic connection location of node i. Let i be a node in the distribution network. Let i be the set of all nodes connected to node i and located downstream of it. It is the set of all nodes that are connected to node i and are located upstream of it. For branch road collection, For a set of nodes, It is a time set. This represents the active power flowing through the branch connecting nodes i and j at time t before the fault. This represents the reactive power flowing through the branch connecting nodes i and j at time t before the fault. Let be the active power flowing through the branch connecting nodes i and k at time t before the fault. This represents the reactive power flowing through the branch connecting nodes i and k at time t before the fault.

[0054] Wherein, the first input power is the active power generated by the generator at node i at time t before the fault. Photovoltaic power at node i at time t before the fault The first output power is the active power injected into node i at time t before the fault. Active load at node i at time t before the fault The charging and discharging power of the energy stored at node i at time t before the fault. When it is less than zero, it is the first input power, which is the charging and discharging power of the energy stored at node i at time t before the fault. When the value is greater than zero, it represents the first output power.

[0055] In this example, a first safe operation model is determined for the power distribution system to operate under safe conditions, so that the first input power and the first output power are balanced, thereby keeping the power distribution network in a safe state in balance.

[0056] S203: Determine the second safe operation model of the power distribution system under fault conditions. The second safe operation model is used to characterize the balance between the second input power and the second output power of the power distribution network under fault conditions. The second input power includes the second photovoltaic power at the corresponding photovoltaic access location under fault conditions.

[0057] In step S203, the fault state refers to the operating state of the distributed photovoltaic system after a fault occurs in the distribution network. The second safe operation model is used to characterize the balance between the second input power and the second output power of the distribution network under the fault state. The second input power includes the second photovoltaic power at the corresponding photovoltaic access point under the fault state. The second input power of the distribution network is the total electrical energy obtained by the distribution network from the upstream grid or generation unit under the fault state, and the second output power is the total electrical energy actually provided by the distribution network to end users or loads under the fault state. The second photovoltaic power is the power generated by the photovoltaic system at the corresponding photovoltaic access point under the fault state.

[0058] In this embodiment, the formula for the second safe operation model is as follows:

[0059]

[0060]

[0061] Among them, superscript Let x be the starting node of the branch in the power distribution network where a fault occurs, and y be the ending node of the branch. Let t be the active power injected into node i at time t after the fault. The active and reactive power injected into node i at time t after the fault. For fault recovery, if node i is on the branch If the load is unaffected after a fault, or if it is affected but can be restored through network reconfiguration (i.e., the load is reconnected to the network), then... Set to 1. Let be the active load of node i at time t after the fault. Let i be the reactive load at time t after the fault. For branch roads The charging and discharging power of the energy stored at node i at time t after the fault is greater than zero when it is charging and less than zero when it is discharging. Let be the active power generated by generator i at time t after the fault. Let be the reactive power generated by generator i at time t after the fault. Let be the photovoltaic power emitted by node i at time t after the fault, which is the second photovoltaic power at the photovoltaic access location of node i. This is the curtailment factor, which ranges from 0 to 1. Post-fault curtailment refers to the situation where, under a distribution network fault scenario, the network's absorption capacity decreases due to the disconnection of some loads, and the system needs to actively reduce photovoltaic output to maintain safe operation.

[0062] The second input power is the active power generated by generator i at time t after the fault. The photovoltaic power emitted by the distributed photovoltaic system at node i at time t after the fault The second output power is the active power injected into node i at time t after the fault. The active load of node i at time t after the fault The charging and discharging power of the energy stored at node i at time t after the fault. When it is less than zero, it is the second input power, which is the charging and discharging power of the energy stored at node i at time t after the fault. When it is greater than zero, it is the second output power.

[0063] In this embodiment, a second safe operation model is determined for the power distribution system under fault conditions, so that the second input power and the second output power can be kept in balance according to the second safe operation model, thereby keeping the power distribution network in balance under fault conditions.

[0064] S204: Obtain the first constraint and the second constraint. Under the condition that the first safe operation model satisfies the first constraint and the second safe operation model satisfies the second constraint, determine the optimal solution of photovoltaic power. Based on the optimal solution of photovoltaic power, determine the value of the objective function and determine the value of the objective function as the photovoltaic carrying capacity of the distribution network.

[0065] In step S204, the first constraint condition is used to constrain the safe operation model to keep it in balance, and the second constraint condition is used to constrain the second safe operation model to keep it in balance. Under the condition that the first safe operation model satisfies the first constraint condition and the second safe operation model satisfies the second constraint condition, the optimal solution of photovoltaic power is determined. Based on the optimal solution of photovoltaic power, the value of the objective function is determined, and the value of the objective function is determined as the photovoltaic carrying capacity of the distribution network.

[0066] In this embodiment, the first constraint condition is used to constrain the voltage, branch capacity, and energy storage charging and discharging of the distribution network under safe conditions, so that the voltage, capacity, and energy storage charging and discharging in the distribution network remain in balance under safe conditions. The second constraint condition is used to constrain the voltage, branch capacity, energy storage charging and discharging, and post-fault power curtailment of the distribution network under fault conditions, so that the voltage, capacity, and energy storage charging and discharging in the distribution network remain in balance under fault conditions.

[0067] Under the conditions that the safe operation model satisfies the first constraint and the second constraint, the optimal solution for the photovoltaic power corresponding to each photovoltaic access location is determined. Based on the optimal solution for the photovoltaic power, the value of the objective function is determined, and this value is used as the photovoltaic carrying capacity of the distribution network. The optimal solution for the photovoltaic power at each photovoltaic access location is then substituted into... The value of the objective function is obtained, where, Let n be the optimal solution for the photovoltaic power at the i-th photovoltaic access point, where n is the number of photovoltaic access points.

[0068] In this embodiment, the first and second constraints respectively constrain the first and second safe operation models to determine the optimal solution for photovoltaic power. Based on the optimal solution for photovoltaic power, the value of the objective function is determined so that the photovoltaic power at the corresponding photovoltaic access location can generate the maximum power when the constraints are met, preventing excessive curtailment and energy waste. Furthermore, the optimal solution for photovoltaic power is calculated under equilibrium conditions, ensuring that the distribution network can operate normally when the photovoltaic power is at its maximum.

[0069] Optionally, the first constraint condition includes a first voltage constraint condition and a first energy storage constraint condition in the distribution network. The first voltage constraint condition is used to constrain the voltage of the distribution network under safe conditions, and the first energy storage constraint condition is used to constrain the energy storage of the distribution network under safe conditions.

[0070] In this embodiment, the first voltage constraint condition is used to constrain the voltage balance in the distribution network under safe conditions. The formula for the first voltage constraint condition is as follows:

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] in, , , t , , For time sets, For the number of nodes, This indicates the switch state before the fault in branch ij. =1 indicates a closed loop. =0 indicates that it is on. This represents the voltage at node i at time t before the fault. Indicates voltage The maximum value, Indicates voltage The minimum value, Let i represent the voltage at node j at time t before the fault, where i and j are the start and end nodes of branch ij in the distribution network branch set. This represents the active power flowing through the branch connecting nodes i and j at time t before the fault. This represents the maximum active power flowing through the branch connecting nodes i and j at time t before the fault. This represents the minimum active power flowing through the branch connecting nodes i and j at time t before the fault. This represents the reactive power flowing through the branch connecting nodes i and j at time t before the fault. This represents the maximum reactive power flowing through the branch connecting nodes i and j at time t before the fault. This represents the minimum reactive power flowing through the branch connecting nodes i and j at time t before the fault. The resistance of the branch connecting node i and node j. Let be the reactance of the branch connecting node i and node j. The parameters can be determined at the distribution network.

[0077] The formula for the first energy storage constraint is shown below:

[0078]

[0079]

[0080]

[0081]

[0082]

[0083] in, , t This represents the maximum charging and discharging power of the energy stored at node i at time t before the fault. This represents the minimum charging and discharging power of the energy stored at node i at time t before the fault. Let represent the charging and discharging power of the energy stored at node i at time t before the fault. A value greater than zero indicates charging, and a value less than zero indicates discharging. For charging power loss, For energy storage charging efficiency, For discharge loss power, For energy storage and discharge efficiency, To determine the actual power loss during charging / discharging, the larger of the charging loss and discharging loss is taken to ensure that the loss is positive. This represents the state of charge of the energy stored at node i at time t before the fault. This represents the energy stored at node i at time t-1 before the fault. The parameters can be determined at the distribution network.

[0084] In this embodiment, the corresponding voltage and energy storage are constrained under safe conditions to keep the power at both ends of the voltage in each branch balanced with the power of the load in the corresponding branch, and the value is between the maximum and minimum values ​​to prevent the generation of new faults.

[0085] Optionally, determining the first voltage constraint includes:

[0086] Based on the correlation of photovoltaic power between adjacent photovoltaic access locations, the normal distribution of photovoltaic power between adjacent photovoltaic access locations is determined;

[0087] Based on the normal distribution, determine the first voltage constraint condition.

[0088] It should be noted that the first voltage constraint condition is used to constrain the voltage of the distribution network under safe conditions, because the photovoltaic power at different locations at the same time... The relationship between uncertainties and the correlation between these uncertainties can be modeled using a normal distribution. The photovoltaic output correlation between node i and node j can be modeled as follows:

[0089]

[0090]

[0091] in, Let be the actual photovoltaic power at node i at time t. Let be the actual photovoltaic power at node j at time t. Let be the predicted photovoltaic power at node i at time t. This represents the power deviation of the photovoltaic power. () represents the normal distribution model. In the normal distribution model... Let be the mean power of node i. Let be the mean power of node j. Let Variance be the power of node i. Let be the variance of the power at node j. Let be the correlation coefficient between the power of nodes i and j.

[0092] Rewriting the above equation in matrix form, the photovoltaic output for each time period follows an N-dimensional multivariate Gaussian distribution, as shown in the following equation:

[0093]

[0094] in, , For time sets, Indicates the number of photovoltaic units connected. Let be the actual photovoltaic power at time t. Let be the power mean vector at time t. The covariance matrix is ​​a symmetric matrix representing the power of all photovoltaic nodes at time t.

[0095] Under this influence, the active power injection If it becomes a random variable, then the first safe operating model is updated as follows:

[0096]

[0097] in, , For time sets, Let be the actual active power injected into node i at time t. Let be the actual photovoltaic power at node i at time t. The above equation is determined as the final first safe operation model.

[0098] Considering uncertainties, the N-dimensional multivariate Gaussian distribution followed by the active power injection at time t can be expressed as follows:

[0099]

[0100] in, The actual active power injected at time t. Let be the mean of the active power injected at time t. Let be the variance of the active power injected at time t.

[0101] Based on the information of the relevant random variables in the previous equation, the expressions for each element in the above equation are as follows:

[0102]

[0103]

[0104] in, Let t be the active load. Let t be the charging and discharging power of the stored energy. Let be the active power generated by the generator at time t. Let be the predicted photovoltaic power of node i at time t.

[0105] Since the state variables have been transformed into random variables, it is necessary to rewrite the safety constraints as probabilistic constraints, which reflect the limitation on the probability of exceeding the limit. The probabilistic constraints are as follows:

[0106]

[0107]

[0108] in, Let be the probability that the inequality constraint holds. This indicates the maximum voltage value. This represents the minimum voltage value. Let be the actual voltage of node i at time t. Let be the maximum active power flowing through the branches at nodes i and j. Let be the minimum active power flowing through the branches at nodes i and j. Let be the actual active power flowing through the branches at nodes i and j at time t. For the risk tolerance of voltage exceeding the limit, such as This indicates that the probability of voltage exceeding the limit is no more than 5%. The risk tolerance for branch power exceeding the limit.

[0109] Under the influence of uncertainties in photovoltaics and They all follow a normal distribution. The normal distribution of voltage is as follows:

[0110]

[0111] Among them, t , For time sets, Let be the covariance matrix of the voltage. The average voltage. It is a vector that is standardized to a normal distribution.

[0112] The probability of both sides of the probabilistic constraint being violated simultaneously is extremely small. Therefore, the formula for the first voltage constraint can be transformed into:

[0113]

[0114]

[0115] in, Let be the actual voltage of node i at time t. This indicates the maximum voltage value. This represents the minimum voltage value. Tolerance for risks related to voltage exceeding limits.

[0116] Based on the normal distribution described above, the first voltage constraint condition is further transformed to obtain the following formula:

[0117]

[0118]

[0119] in, It is the inverse function of the standard normal distribution function. Let be the covariance matrix of voltage, when When the value is less than 0.5, the above formula can be converted into linear constraints and second-order cone constraints, as follows:

[0120]

[0121]

[0122]

[0123]

[0124] in, , As an auxiliary variable, its value is greater than or equal to the variance of the voltage at node i. , This represents a linear combination. Through the above transformation, the uncertain model has been transformed into a deterministic model, yielding the final first voltage constraint. The final photovoltaic carrying capacity assessment model, which includes all the above constraints, is a mixed-integer second-order cone programming model, which can be solved efficiently. The handling of branch power is similar and will not be elaborated here.

[0125] Optionally, the second constraint includes a second voltage constraint, a second energy storage constraint, and a post-fault curtailment constraint. The second voltage constraint is used to constrain the voltage of the distribution network under fault conditions, the second energy storage constraint is used to constrain the energy storage of the distribution network under fault conditions, and the post-fault curtailment constraint is used to constrain the amount of curtailed solar power that is actively cut off due to insufficient carrying capacity of the distribution network under fault conditions.

[0126] In this embodiment, the second voltage constraint condition is used to constrain the voltage balance in the distribution network under fault conditions. The formula for the second voltage constraint condition is as follows:

[0127]

[0128]

[0129]

[0130]

[0131] in, , , The set of nodes in a distribution network, superscript Let x be the starting node of the branch in the distribution network where a fault occurs, and y be the ending node of the branch. This represents the connection status of branch ij at time t. A value of 1 indicates a closed branch, while a value of 0 indicates an open branch. Let be the active power flowing through the branch connecting nodes i and j at time t after the fault. This represents the maximum active power flowing through the branch connecting nodes i and j at time t after the fault. This represents the minimum active power flowing through the branch connecting nodes i and j at time t after the fault. Let be the reactive power flowing through the branch connecting nodes i and j at time t after the fault. This represents the maximum reactive power flowing through the branch connecting nodes i and j at time t after the fault. Let be the minimum reactive power flowing through the branch connecting nodes i and j at time t after the fault. The resistance of the branch connecting node i and node j. For the reactance of the branch connecting node i and node j, Let be the voltage at node i at time t after the fault. The voltage at node j at time t after the fault. This represents the maximum voltage at node j at time t after the fault. This represents the minimum voltage at node j at time t after the fault. Each parameter can be determined by looking up information in the distribution network.

[0132] The formula for the second energy storage constraint is shown below:

[0133]

[0134]

[0135]

[0136] Among them, i , For the set of nodes equipped with energy storage, superscript Let x be the starting node of the branch in the distribution network where a fault occurs, and y be the ending node of the branch. The charging and discharging power of the energy stored at node i at time t after the fault. This represents the maximum charging and discharging power of the energy stored at node i at time t after the fault. This represents the minimum charging and discharging power of the energy stored at node i at time t after the fault. This represents the state of charge of the energy stored at node i at time t after the fault. This represents the maximum charge of the energy stored at node i at time t after the fault. It represents the minimum charge of the energy stored at node i at time t after the fault. The duration of maintenance and switching interruptions related to line faults connecting nodes x and y. This represents the interruption duration of switching operations only when a fault occurs in the line connecting nodes x and y. The parameters can be determined at the distribution network level. This formula ensures that the Energy Storage (ES) system maintains sufficient capacity to support charging and discharging operations during the line reconfiguration and recovery phase following a fault in line x and y.

[0137] Optionally, the second constraint also includes a post-fault curtailment constraint and a reduction constraint. The post-fault curtailment constraint is used to constrain the amount of curtailment that is actively cut off due to insufficient carrying capacity of the distribution network under fault conditions. The reduction constraint is used to constrain the total amount of load reduction operations carried out for the safe and stable operation of the distribution network.

[0138] In this embodiment, photovoltaic curtailment constraints and load reduction constraints are set to protect the interests of photovoltaic users and load users. The post-fault curtailment constraint condition is used to constrain the amount of curtailed photovoltaic power that is actively cut off due to insufficient distribution network capacity during a fault state. The curtailment constraint formula is as follows:

[0139]

[0140]

[0141] in, This is the light waste factor, which ranges from 0 to 1. Let be the photovoltaic power emitted by node i at time t after the fault. This represents the amount of photovoltaic power reduction caused by various operational constraints and system requirements. The amount of light discarded is allowed.

[0142] The formula for the reduction constraint is as follows:

[0143]

[0144] in, This represents the failure rate of branch xy. It is a binary indicator; if node i is affected by a fault in branch xy at time t, then... It is 1 if it is true, otherwise it is 0. This is the upper limit of the load reduction. Let xy be the load recovery ratio of node i at time t under branch fault xy.

[0145] Optionally, the optimal solution for photovoltaic power is determined, and the optimal solution for photovoltaic power includes:

[0146] Based on the segmented McCormick technique, a convex McCormick model is constructed according to the first constraint condition, the second constraint condition, the first safe operation model, and the second safe operation model.

[0147] Based on the convex McCormick model, the optimal solution of the convex McCormick model is calculated, and the optimal solution of the convex McCormick model is determined as the optimal solution of photovoltaic power at the corresponding photovoltaic access location.

[0148] In this embodiment, a convex McCormick model is constructed based on the McCormick relaxation algorithm, according to the first constraint condition, the second constraint condition, the first safe operation model, and the second safe operation model. The formula for the convex McCormick model is as follows:

[0149]

[0150]

[0151]

[0152]

[0153]

[0154]

[0155]

[0156]

[0157]

[0158]

[0159]

[0160]

[0161] in, , where S is the set of s, and S is the number of partitions. This represents the minimum photovoltaic power generated by distributed photovoltaic systems. This represents the maximum photovoltaic power generated by distributed photovoltaic systems. In the above formula, when binary variables At that time, all variables specified in the s-th segment and and The possible values ​​of , where, Let be the photovoltaic power emitted by node i at time t after the fault. Conversely, variables in all other segments are forced to zero. If If the constraint is 0, then all constraints in the s-th segment will be enforced, while the constraints in other segments will be ignored.

[0162] In this embodiment, a result is calculated using the convex McCormick model, and this result is determined as the optimal solution for the photovoltaic power at the photovoltaic access point at node i, that is, the maximum photovoltaic power that the photovoltaic at the photovoltaic access point at node i can generate. The result of the convex McCormick model can be calculated using the cplex solver in MATLAB.

[0163] Based on the optimal solution for photovoltaic power, the value of the objective function is determined, and this value is defined as the photovoltaic carrying capacity of the distribution network. In other words, the optimal solution for photovoltaic power is substituted into the objective model to determine the value of the objective function, i.e., PVHC (Photovoltaic Hosting Capacity).

[0164] In this embodiment, under McCormick relaxation, the reconstructed model becomes a convex optimization problem. The optimal solution for the photovoltaic power at node i is obtained through the convex McCormick model. Transforming a complex non-convex problem into a more easily solvable convex optimization problem improves computational efficiency.

[0165] In this application, by evaluating a first safe operation model before a fault and a second safe operation model under a fault condition, and by setting both safe operation constraints and constraints under fault conditions, the optimal solution for photovoltaic power is determined under the condition that the first safe operation model satisfies the first constraint and the second safe operation model satisfies the second constraint. Based on the optimal solution for photovoltaic power, the value of the objective function is determined, and the value of the objective function is used as the photovoltaic carrying capacity of the distribution network. This improves the accuracy of the photovoltaic carrying capacity assessment of the distribution network, ensures that the distribution network can operate safely in both scenarios, and effectively protects the interests of photovoltaic users.

[0166] like Figure 3 As shown, Figure 3 This application provides an embodiment of an IEEE 33-bus power system wiring diagram, analyzing a 10-bus power system. Based on the established objective function and dual-scenario model, various scenarios are established using MATLAB for research. In a specific scenario, two photovoltaic (PV) access points are set up, at nodes 4 and 6 of the 10-bus power system. ES (Electronic Power Grid) systems are set at nodes 3 and 7. In general operating scenarios, ES can play a role in peak shaving and valley filling; in capacity assessment, it can also effectively improve PVHC (PV Harnessing Capacity).

[0167] In analyzing the embodiments, we discovered a potential inherent contradiction between improving load supply reliability and reducing PV power cut-off in fault conditions. That is, attempts to reduce load cut-off (improving load supply reliability) often require more PV power cut-off. Conversely, efforts to reduce PV power cut-off may lead to increased load cut-off (reduced load supply reliability). We speculate that the ES system is a key factor causing this contradiction. This relationship may further affect the effectiveness of reliability constraints and PV power cut-off constraints, posing significant challenges to protecting the interests of PV owners and load consumers. The following case study will analyze this interaction in detail.

[0168] To compare the proposed method with existing research methods in depth, we set up three comparison scenarios. The PVHC assessment results under different scenarios are shown in Table 1. Scenario 1: Considering only safety constraints; Scenario 2: Considering PV curtailment constraints after a failure; Scenario 3: Considering both PV curtailment and load reduction constraints after a failure.

[0169] Table 1

[0170] Scene PVHC Wasted light after failure Load reduction after failure Scene 1 21.71 MW 11.50 MWh 4.35 MWh Scene 2 14.17 MW The upper limit has been adjusted to 0.15 MWh. 5.58 MWh Scene 3 13.92 MW The upper limit has been adjusted to 0.15 MWh. The upper limit has been adjusted to 4.35 MWh.

[0171] Scenario 1 represents the PVHC assessment results under the safety constraints typically considered in existing studies. Comparing Scenario 1 with Scenario 2, although the PVHC in Scenario 1 is significantly higher than in Scenario 2, the amount of curtailed photovoltaic (PV) power in Scenario 1 is extremely large—dozens of times higher than in Scenario 2. This will severely harm the interests of PV owners. Furthermore, the load shedding in Scenario 2 is increased compared to Scenario 1. This is because ES (Enhanced Power Supply) operations tend to reduce PV shedding after faults, thus weakening its effectiveness in load recovery. This reflects a trade-off between these two factors. Therefore, to simultaneously protect the interests of PV users and load consumers, PV curtailment and load shedding can be considered as constraints (Scenario 3), which is also the focus of this paper. Under these constraints, the final PVHC is 13.92 MW. Compared to Scenario 2, PVHC decreases by only 1.76%, while reliability is significantly improved (load shedding is reduced by 22.04%). These results demonstrate the feasibility and effectiveness of the proposed method.

[0172] To investigate the behavioral differences of an ES system under different methods, this study compares the SOC (State of Charge) curves of an ES system using a conventional method (considering only normal operating conditions) and a novel method proposed in this paper that incorporates pre-fault and post-fault user requirements. The comparison results are as follows: Figure 4 As shown, Figure 4This diagram illustrates the state of charge (SOC) under conventional and the methods described in this application. The comparison clearly demonstrates the crucial role of ES (Extended Energy Storage) in reducing photovoltaic power reduction and load shedding after faults. In conventional methods, photovoltaic and load losses after faults are not considered, resulting in significant fluctuations in battery SOC. ES completes a full charge-discharge cycle within a day to profit from the price difference between peak and off-peak electricity rates. Furthermore, the ES system charges at 12:00 to increase PVHC. Significant differences exist in the behavior of ES under different methods. These differences are evident in two time periods: 11:00-15:00 (primarily daytime) and 18:00-24:00, 00:00-10:00 (primarily nighttime). These time periods reflect the impact of photovoltaic shedding constraints and reliability limitations, respectively. The following discussion will elaborate on these differences.

[0173] During periods of high photovoltaic output (11:00-15:00), the proposed method maintains a lower State of Charge (SOC) level than conventional methods. This strategy aims to reserve sufficient capacity for charging after a fault, thereby reducing photovoltaic load shedding and demonstrating the effectiveness of photovoltaic load shedding constraints. During the remaining periods (18:00-24:00, 00:00-10:00), the proposed method maintains a higher SOC than conventional methods. This is to ensure that the ES (Electric Power Supply) can retain sufficient energy to support the load in the event of a fault, thereby reducing load shedding and protecting user interests.

[0174] In the preceding text, we briefly introduced the trade-off between solar curtailment and load shedding. Here, we will analyze this relationship in detail through three case studies, and present the corresponding results in Table 2, "The Trade-off Between Solar Curtailment and Load Shedding." Case 1: Basic Parameters; Case 2: Tightening the Upper Limit Constraint on Solar Curtailment; Case 3: Adjusting the Expected Load Shedding to Restore the PVHC Level in Case 1.

[0175] Case Load reduction Solar curtailment PVHC Case 1 4.35 MWh 0.15 MWh 13.92 MW Case 2 4.35 MWh 0.1 MWh 13.39 MW Case 3 4.57 MWh 0.1 MWh 13.92 MW

[0176] By setting the upper limit of expected load shedding at 4.35 MWh, PVHC was evaluated under different levels of photovoltaic curtailment, and the results are as follows: Figure 5 As shown, Figure 5This is a schematic diagram showing the change in PVHC as a function of curtailment, where the horizontal axis represents curtailment. When the expected load reduction is set at 4.35 MWh, as the PV curtailment constraint increases from 0 to 0.25 MWh, the system's PVHC increases from 12.33 MW to 14.98 MW. This indicates that as the allowable PV curtailment increases, the system's PV carrying capacity correspondingly strengthens. The reason for this phenomenon is that when the installed PV capacity approaches the system's operating limit, power injection can cause the bus voltage to rise to the allowable upper limit. By actively reducing PV power generation that might trigger voltage exceedances, the system can accommodate more installed PV capacity.

[0177] The upper limit for photovoltaic curtailment was set at 0.1 MWh. PVHC was tested by changing the load shedding constraint, and the results are as follows: Figure 6 As shown, Figure 6 This is a schematic diagram of the PVHC change curve with the reduction amount, where the horizontal axis represents the reduction amount. When the upper limit of photovoltaic curtailment is maintained at 0.1MWh, as the load reduction constraint increases from 4.35MWh to 4.6MWh, the system PVHC increases from 13.39MW to 13.94MW.

[0178] In this application, a first safe operation model for the power distribution system under safe operating conditions and a second safe operation model for the power distribution system under fault conditions are determined. Simultaneously, safe operation constraints and constraints under fault conditions are set. Under the condition that the first safe operation model satisfies the first constraint and the second safe operation model satisfies the second constraint, the optimal solution for photovoltaic power is determined. Based on the optimal solution for photovoltaic power, the value of the objective function is determined, and the value of the objective function is used as the photovoltaic carrying capacity of the power distribution network. This improves the accuracy of the photovoltaic carrying capacity assessment of the power distribution network, ensuring safe operation of the power distribution network in both scenarios and effectively protecting the interests of photovoltaic users.

[0179] Please see Figure 7 , Figure 7 This is a schematic diagram of a photovoltaic carrying capacity assessment device with an energy storage distribution network provided in one embodiment of this application. This photovoltaic carrying capacity assessment device with an energy storage distribution network corresponds one-to-one with the photovoltaic carrying capacity assessment method with an energy storage distribution network described in the above embodiments. Please refer to [link / reference] for details. Figure 2 as well as Figure 2 The relevant descriptions in the corresponding embodiments are shown below. For ease of explanation, only the parts relevant to this embodiment are shown. See also... Figure 7 The photovoltaic load-bearing capacity assessment device 70 includes: a first determining module 71, a second determining module 72, a third determining module 73, and a fourth determining module 74.

[0180] The first determining module 71 is used to determine the objective function, which is used to characterize the correspondence between the total photovoltaic power in the distributed photovoltaic system and the photovoltaic power at each photovoltaic access location;

[0181] The second determining module 72 is used to determine the safe operation model of the power distribution system when it is running in a safe state. The safe operation model is used to characterize the balance between the first input power and the first output power of the power distribution system in a safe state. The first input power includes the photovoltaic power at the photovoltaic access location corresponding to the fault state.

[0182] The third determining module 73 is used to determine the second safe operation model of the power distribution system under fault conditions. The second safe operation model is used to characterize the balance between the second input power and the second output power of the power distribution network under fault conditions. The second input power includes the photovoltaic power at the corresponding photovoltaic access location under fault conditions.

[0183] The fourth determining module 74 is used to obtain the first constraint condition and the second constraint condition. Under the condition that the first safe operation model satisfies the first constraint condition and the second safe operation model satisfies the second constraint condition, the optimal solution of photovoltaic power is determined. Based on the optimal solution of photovoltaic power, the value of the objective function is determined, and the value of the objective function is determined as the photovoltaic carrying capacity of the distribution network.

[0184] Optionally, the fourth determining module 74 mentioned above includes:

[0185] A building unit is used to construct a convex McCormick model based on the segmented McCormick technique, according to the first constraint condition, the second constraint condition, the safe operation model, and the second safe operation model.

[0186] The calculation unit is used to calculate the optimal solution of the convex McCormick model based on the convex McCormick model, and determine the optimal solution of the convex McCormick model as the optimal solution of the photovoltaic power at the corresponding photovoltaic access location.

[0187] It should be noted that the information interaction and execution process between the above-mentioned units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0188] Figure 8 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. For example... Figure 8 As shown, the computer device of this embodiment includes: at least one processor ( Figure 8 Only one is shown in the diagram), a memory, and a computer program stored in the memory and capable of running on at least one processor. When the processor executes the computer program, it implements the steps in any of the above embodiments of the photovoltaic carrying capacity assessment method for distribution networks containing energy storage.

[0189] This computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 8 The examples of computer devices are merely examples and do not constitute a limitation on computer devices. Computer devices may include more or fewer components than shown in the illustration, or combinations of certain components, or different components, such as network interfaces, displays, and input devices.

[0190] The processor referred to can be a CPU, but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0191] Memory includes readable storage media, internal memory, etc., wherein internal memory can be the RAM of a computer device, providing an environment for the operation of the operating system and computer-readable instructions stored in the readable storage media. The readable storage media can be the hard drive of a computer device, or in other embodiments, it can be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, memory can include both internal storage units and external storage devices of the computer device. Memory is used to store the operating system, applications, bootloader, data, and other programs, such as program code for computer programs. Memory can also be used to temporarily store data that has been output or will be output.

[0192] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here. If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium can include at least: any entity or device capable of carrying computer program code, a recording medium, a computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0193] The implementation of all or part of the processes in the methods of the above embodiments can also be accomplished by a computer program product. When the computer program product is run on a computer device, it enables the computer device to execute the steps in the above method embodiments.

[0194] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0195] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0196] In the embodiments provided in this application, it should be understood that the disclosed apparatus / computer devices and methods can be implemented in other ways. For example, the apparatus / computer device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0197] The units described as separate components may or may not be physically separate. The components shown 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0198] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for assessing the photovoltaic carrying capacity of a distribution network including energy storage, characterized in that, The photovoltaic load-bearing capacity assessment method includes: Determine the objective function, which characterizes the correspondence between the total photovoltaic power in the distributed photovoltaic system and the photovoltaic power at each photovoltaic access point; A first safe operation model is determined for the power distribution system when it is operating under safe conditions. The first safe operation model is used to characterize the balance between the first input power and the first output power of the power distribution network under safe conditions. The first input power includes the first photovoltaic power at the photovoltaic access point under safe conditions. A second safe operation model is determined for the power distribution system under fault conditions. The second safe operation model is used to characterize the balance between the second input power and the second output power of the power distribution network under fault conditions. The second input power includes the second photovoltaic power at the photovoltaic access location under fault conditions. Obtain the first constraint and the second constraint. Under the condition that the first safe operation model satisfies the first constraint and the second safe operation model satisfies the second constraint, determine the optimal solution of the photovoltaic power. Based on the optimal solution of the photovoltaic power, determine the value of the objective function and determine the value of the objective function as the photovoltaic carrying capacity of the distribution network.

2. The photovoltaic carrying capacity assessment method as described in claim 1, characterized in that, The first constraint condition includes a first voltage constraint condition and a first energy storage constraint condition in the distribution network. The first voltage constraint condition is used to constrain the voltage of the distribution network under safe conditions, and the first energy storage constraint condition is used to constrain the energy storage of the distribution network under safe conditions.

3. The photovoltaic carrying capacity assessment method as described in claim 2, characterized in that, Determining the first voltage constraint includes: Based on the correlation of photovoltaic power between adjacent photovoltaic access locations, the normal distribution of photovoltaic power between adjacent photovoltaic access locations is determined; Based on the normal distribution, the first voltage constraint condition is determined.

4. The photovoltaic load-bearing capacity assessment method as described in claim 1, characterized in that, The second constraint includes a second voltage constraint and a second energy storage constraint. The second voltage constraint is used to constrain the voltage of the distribution network under fault conditions, and the second energy storage constraint is used to constrain the energy storage of the distribution network under fault conditions.

5. The photovoltaic load-bearing capacity assessment method as described in claim 4, characterized in that, The second constraint also includes a post-fault curtailment constraint and a reduction constraint. The post-fault curtailment constraint is used to constrain the amount of power curtailment that is actively cut off due to insufficient carrying capacity of the distribution network under fault conditions. The reduction constraint is used to constrain the total amount of load reduction operations performed for the safe and stable operation of the distribution network.

6. The photovoltaic carrying capacity assessment method as described in claim 1, characterized in that, The determination of the optimal solution for the photovoltaic power, based on the optimal solution for the photovoltaic power, includes: Based on the segmented McCormick technique, a convex McCormick model is constructed according to the first constraint, the second constraint, the first safe operation model, and the second safe operation model. Based on the convex McCormick model, the optimal solution of the convex McCormick model is calculated, and the optimal solution of the convex McCormick model is determined as the optimal solution of photovoltaic power at the corresponding photovoltaic access location.

7. A photovoltaic carrying capacity assessment device incorporating an energy storage distribution network, characterized in that, The photovoltaic load-bearing capacity assessment device includes: The first determining module is used to determine the objective function, which is used to characterize the correspondence between the total photovoltaic power in the distributed photovoltaic system and the photovoltaic power at each photovoltaic access location; The second determining module determines a first safe operation model of the power distribution system when it is running in a safe state. The first safe operation model is used to characterize the model of the balance between the first input power and the first output power of the power distribution network in a safe state. The first input power includes the first photovoltaic power corresponding to the photovoltaic access location in a safe state. The third determining module is used to determine the second safe operation model of the power distribution system when it is running under fault conditions. The second safe operation model is used to characterize the balance between the second input power and the second output power of the power distribution network under fault conditions. The second input power includes the second photovoltaic power at the photovoltaic access location under fault conditions. The fourth determining module is used to obtain the first constraint condition and the second constraint condition, determine the optimal solution of the photovoltaic power under the condition that the first safe operation model satisfies the first constraint condition and the second safe operation model satisfies the second constraint condition, determine the value of the objective function based on the optimal solution of the photovoltaic power, and determine the value of the objective function as the photovoltaic carrying capacity of the distribution network.

8. The photovoltaic load-bearing capacity assessment device as described in claim 7, characterized in that, The fourth determining module includes: A construction unit is used to construct a convex McCormick model based on the segmented McCormick technique, according to the first constraint, the second constraint, the safe operation model, and the second safe operation model. The calculation unit is used to calculate the optimal solution of the convex McCormick model based on the convex McCormick model, and determine the optimal solution of the convex McCormick model as the optimal solution of the photovoltaic power at the corresponding photovoltaic access location.

9. A computer device, characterized in that, The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the photovoltaic load-bearing capacity assessment method as described in any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the photovoltaic load-bearing capacity assessment method as described in any one of claims 1 to 6.

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