Method, system, device and medium for calculating distributed photovoltaic grid-connected limit capacity

Abstract

The application relates to the technical field of photovoltaic grid-connected capacity calculation, and discloses a distributed photovoltaic grid-connected limit capacity calculation method, system, equipment and medium, which comprises the following steps: establishing a limit capacity optimization model according to the operation parameters and the topological structure of a power distribution network; solving the limit capacity optimization model to obtain the initial limit photovoltaic access capacity of the power distribution network and the operation state of the power distribution network; identifying key constraint nodes of the access nodes which are distributed with the initial limit photovoltaic access capacity according to the operation state of the power distribution network, obtaining the key constraint nodes, and setting adaptive capacity constraints of the key constraint nodes; iteratively solving the limit capacity optimization model according to the adaptive capacity constraints until an iteration stopping condition is reached, and obtaining the final limit photovoltaic access capacity and the node capacity distribution result. Through the node sensitivity adaptive adjustment mechanism, the overall photovoltaic receiving capacity of the power distribution network is significantly improved under the premise of ensuring the operation safety and controllable uncertainty risk.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic grid-connected capacity calculation technology, and in particular to a method, system, equipment and medium for calculating the ultimate capacity of distributed photovoltaic grid connection. Background Technology

[0002] With the continuous growth of distributed photovoltaic (PV) installed capacity, distribution networks are gradually evolving from traditional unidirectional power supply structures to bidirectional source-load interaction structures, placing higher demands on grid planning, operation, and safety assessment. Current technologies for assessing the grid connection capacity of large-scale distributed PV in distribution networks typically employ deterministic power flow calculation methods or combine empirical configuration rules to perform static analysis of PV grid-connected capacity. These methods often use constraints such as node voltage, branch current, and transformer capacity as criteria, gradually increasing the PV installed capacity to determine whether the system has reached its operational boundaries, thereby determining the maximum connectable capacity of distributed PV. However, with the high penetration rate of PV grid connection, these existing technologies have gradually revealed a series of significant defects and shortcomings.

[0003] First, under high-penetration operating conditions, the sensitivity of different access nodes to changes in photovoltaic output varies significantly. Influenced by factors such as distribution network topology, line impedance distribution, and load spatial distribution, the impact of photovoltaic access on node voltage rise, branch current changes, and transformer power flow direction is not uniform across different nodes. Existing assessment methods typically employ uniform or static photovoltaic access ceiling settings, failing to effectively characterize the differences in sensitivity of each node to system operating constraints. This can easily lead to non-critical nodes prematurely triggering voltage or current constraints, thereby limiting the overall photovoltaic capacity of the system and wasting potentially available capacity.

[0004] Secondly, during periods of high photovoltaic output but low load, large-scale distributed photovoltaic (PV) grid integration can easily trigger reverse power flow problems. Reverse power flow not only significantly increases the voltage at the feeder's end nodes, frequently triggering voltage over-limit risks, but can also lead to undesirable power backflow to transformers and the upstream grid, affecting equipment safety. Reverse power flow in distribution lines introduces additional losses, causing the total system loss to increase rather than decrease under high PV penetration conditions, contradicting the initial goal of distributed PV to reduce system losses. Existing technologies largely rely on empirical configuration strategies or post-event verification methods to avoid these problems, lacking proactive adjustment mechanisms based on system operating characteristics.

[0005] Furthermore, from the perspective of model construction and computational methods, traditional distributed photovoltaic (PV) capacity assessment methods have significant shortcomings in handling the nonlinear operating characteristics of distribution networks. On the one hand, some methods reduce model complexity by using linearization approximations or simplifying constraints, making it difficult to accurately reflect the nonlinear coupling relationships between voltage, current, and power flow under high penetration conditions, which can easily lead to conservative or distorted assessment results. On the other hand, with the increase in the number of PV nodes and the analysis scenarios, the model size expands rapidly, and the computational complexity increases significantly. Existing methods struggle to balance accuracy with solution efficiency, limiting their application in large-scale distribution network engineering practices.

[0006] Finally, most existing limit capacity calculations focus on the "calculation result of the limit capacity itself," lacking a deep characterization of the mechanism by which the limit capacity affects the system. In particular, as the limit capacity gradually approaches the system's operating boundary, it fails to dynamically identify the key nodes that play a dominant role in triggering the constraint and adjust the photovoltaic access strategies of each node accordingly. This results in a lack of specificity and adaptability in the capacity assessment process. This static, one-off assessment model is difficult to meet the actual needs of new distribution networks for refined capacity management and dynamic operation control. Summary of the Invention

[0007] To address the problem that existing technologies struggle to simultaneously balance system security, capacity utilization, and computational efficiency under high-penetration distributed photovoltaic (PV) grid connection conditions, this invention provides a method, system, device, and medium for calculating the maximum grid-connected capacity of distributed PV.

[0008] In a first aspect, the present invention provides a method for calculating the maximum grid-connected capacity of distributed photovoltaic power, the method comprising:

[0009] Based on the operating parameters and topology of the distribution network, a limit capacity optimization model is established with the objective function of maximizing the access capacity of distributed photovoltaic power to the distribution network, and with system power flow constraints, power balance constraints, and photovoltaic output and installed capacity upper limit constraints as constraints.

[0010] Solving the aforementioned limit capacity optimization model yields the initial limit photovoltaic access capacity of the distribution network and the corresponding distribution network operating state.

[0011] Based on the operating status of the distribution network, key constraint nodes are identified for the access nodes allocated initial limit photovoltaic access capacity, and adaptive capacity constraints are set for the key constraint nodes.

[0012] The extreme capacity optimization model is iteratively solved according to the adaptive capacity constraint until the preset iteration stopping condition is reached, so as to obtain the final extreme photovoltaic access capacity and node capacity allocation results.

[0013] Furthermore, the step of solving the limit capacity optimization model to obtain the initial limit photovoltaic access capacity of the distribution network and the corresponding distribution network operating state includes:

[0014] The limit capacity optimization model is transformed into a mixed integer second-order cone programming model by employing second-order cone relaxation treatment, and chance constraints are applied to node voltage and branch current to construct node voltage chance constraints and branch current chance constraints.

[0015] Solving the mixed-integer second-order cone programming model yields the initial limit of photovoltaic grid connection capacity and the corresponding grid operation status.

[0016] Furthermore, the step of identifying key constraint nodes for access nodes allocated initial limit photovoltaic access capacity based on the operating status of the distribution network, and obtaining the key constraint nodes, includes:

[0017] Based on the operating status of the distribution network, the index of the access node allocated with the initial limit photovoltaic access capacity is calculated to obtain the node sensitivity of the access node, and the activation degree of the operating constraints of the distribution network is calculated to obtain the constraint activation degree of the distribution network node.

[0018] Based on the node sensitivity and the constraint activation degree, the key constraint score of the access node is calculated, and based on the key constraint score, several key constraint nodes are selected from the access nodes.

[0019] Further, the steps of calculating the index of the access node allocated with the initial limit photovoltaic access capacity based on the operating status of the distribution network to obtain the node sensitivity of the access node, and calculating the activation degree of the operating constraints of the distribution network to obtain the constraint activation degree of the distribution network node include:

[0020] Calculate the node voltage sensitivity of the access node based on the installed capacity of the access node and the node voltage of other distribution network nodes;

[0021] The node current sensitivity of the access node is calculated based on the installed capacity of the access node and the branch current of other distribution network nodes.

[0022] Calculate the voltage constraint activation degree of each distribution network node based on the node voltage and the upper limit of the node voltage.

[0023] Calculate the current constraint activation degree of each distribution network node based on the branch current and the upper limit of the branch current of each distribution network node.

[0024] Further, the step of calculating the key constraint score of the access node based on the node sensitivity and the constraint activation degree, and selecting several key constraint nodes from the access nodes based on the key constraint score, includes:

[0025] The product of the node voltage sensitivity of the access node and the voltage constraint activation degree of the corresponding distribution network node is used as the voltage constraint score of the access node.

[0026] The product of the node current sensitivity of the access node and the current constraint activation degree of the corresponding distribution network node is used as the current constraint score of the access node.

[0027] The sum of the maximum values ​​of the voltage constraint score and the current constraint score is taken as the key constraint score of the access node. The nodes are then sorted in descending order of the key constraint scores, and a preset number of nodes are selected from the access nodes as key constraint nodes.

[0028] Furthermore, the step of setting the adaptive capacity constraint of the key constraint node includes:

[0029] The allowed access capacity limit is updated based on the allowed access capacity limit of the key constraint node and the key constraint score;

[0030] Based on the updated upper limit of allowed access capacity, construct adaptive capacity constraints for key constraint nodes.

[0031] Further, the step of iteratively solving the limit capacity optimization model according to the adaptive capacity constraint until a preset iteration stopping condition is reached to obtain the final limit photovoltaic access capacity and node capacity allocation results includes:

[0032] Based on the difference between the voltage constraint activation degree and the preset first activation degree threshold, the node voltage margin in the node voltage opportunity constraint is updated to obtain the updated node voltage opportunity constraint.

[0033] Based on the difference between the current constraint activation degree and the preset second activation degree threshold, the branch current margin in the branch current opportunity constraint is updated to obtain the updated branch current opportunity constraint.

[0034] Based on the updated node voltage opportunity constraint, the updated branch current opportunity constraint, and the adaptive capacity constraint, the mixed integer second-order cone programming model is iteratively solved until the preset iteration stopping condition is reached, and the final limit photovoltaic access capacity and node capacity allocation results are obtained.

[0035] Secondly, the present invention provides a distributed photovoltaic grid-connected ultimate capacity calculation system, the system comprising:

[0036] The initial capacity calculation module is used to establish a limit capacity optimization model based on the operating parameters and topology of the distribution network, with the objective function of maximizing the access capacity of distributed photovoltaic power grid, and with system power flow constraints, power balance constraints, and photovoltaic output and installed capacity upper limit constraints as constraints.

[0037] Solving the aforementioned limit capacity optimization model yields the initial limit photovoltaic access capacity of the distribution network and the corresponding distribution network operating state.

[0038] The critical constraint identification module is used to identify critical constraint nodes for access nodes allocated with initial limit photovoltaic access capacity according to the operating status of the distribution network, obtain the critical constraint nodes, and set the adaptive capacity constraints of the critical constraint nodes.

[0039] The final capacity calculation module is used to iteratively solve the limit capacity optimization model according to the adaptive capacity constraints until the preset iteration stopping condition is reached, so as to obtain the final limit photovoltaic access capacity and node capacity allocation results.

[0040] Thirdly, embodiments of the present invention also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0041] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0042] This invention provides a method, system, device, and medium for calculating the grid-connected limit capacity of distributed photovoltaic (PV) systems. By introducing a sensitivity analysis mechanism for nodes to voltage and power flow constraints, it can characterize the differences in the impact of different PV access nodes on the system's operating boundary. During the limit capacity calculation process, the upper limit of PV access for each node is dynamically adjusted based on the sensitivity results, which can improve the accuracy of the limit capacity assessment results. Under the premise of ensuring operational safety and controllable uncertainty risks, it can accurately identify key nodes in the system and rationally allocate node-level access capacity, thereby effectively improving the limit acceptance capacity of distributed PV in the distribution network. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating the method for calculating the grid-connected limit capacity of distributed photovoltaic power in an embodiment of the present invention.

[0044] Figure 2 This is a distribution diagram of the maximum grid-connected photovoltaic capacity of each node in the effect verification experiment of this invention embodiment;

[0045] Figure 3 This is a schematic diagram of the iterative convergence curve of the total capacity in the effect verification experiment of this invention embodiment;

[0046] Figure 4 This is a schematic diagram of the change curve of the tightness index in the effect verification experiment in the embodiment of the present invention;

[0047] Figure 5 This is a schematic diagram illustrating the evolution of the key node set in the effect verification experiment of this invention.

[0048] Figure 6 This is a voltage margin distribution diagram from the effect verification experiment in this embodiment of the invention;

[0049] Figure 7 This is a voltage-constrained activation distribution diagram from the effect verification experiment in this embodiment of the invention;

[0050] Figure 8 This is a distribution diagram of the activation degree under current constraint in the effect verification experiment of this invention embodiment;

[0051] Figure 9 This is a distribution diagram of the key constraint scores for each node in the effect verification experiment of this invention embodiment;

[0052] Figure 10 This is a sensitivity distribution diagram of each node in the effect verification experiment in the embodiment of the present invention;

[0053] Figure 11 This is a schematic diagram of the distributed photovoltaic grid-connected limit capacity calculation system in an embodiment of the present invention;

[0054] Figure 12 This is an internal structural diagram of the computer device in an embodiment of the present invention.

[0055] Figure label:

[0056] 10. Initial capacity calculation module; 20. Key constraint identification module; 30. Final capacity calculation module. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Please see Figure 1The first embodiment of the present invention proposes a method for calculating the ultimate grid-connected capacity of distributed photovoltaic power, including steps S10 to S40:

[0059] Step S10: Based on the operating parameters and topology of the distribution network, with the objective function of maximizing the access capacity of distributed photovoltaic power to the distribution network, and with system power flow constraints, power balance constraints, and photovoltaic output and installed capacity upper limit constraints as constraints, establish a limit capacity optimization model.

[0060] Step S20: Solve the limit capacity optimization model to obtain the initial limit photovoltaic access capacity of the distribution network and the corresponding distribution network operation status;

[0061] Step S30: Based on the operating status of the distribution network, identify key constraint nodes for the access nodes allocated initial limit photovoltaic access capacity, obtain key constraint nodes, and set adaptive capacity constraints for the key constraint nodes.

[0062] Step S40: Iteratively solve the limit capacity optimization model according to the adaptive capacity constraint until the preset iteration stop condition is reached, and obtain the final limit photovoltaic access capacity and node capacity allocation results.

[0063] In this embodiment, operating parameters such as the distribution network topology, line impedance parameters, node load parameters, transformer capacity parameters, and information on candidate nodes for distributed photovoltaic (PV) power access are first obtained. A distribution network operation model including node voltage, branch current, and transformer power flow is then established. Specifically, the input data includes distribution network topology data, line impedance parameters, active and reactive power parameters of node loads, transformer capacity and operating limit parameters, the set of candidate nodes for distributed PV power access, and the upper and lower limits of node voltage. The set of candidate nodes for distributed PV power access refers to the set of all nodes in the distribution network that are candidate nodes for PV power access. A radial distribution network model is then constructed, defining the node set N and the branch set E, and using square voltage and square current as state variables to form a distribution network power flow description model based on DistFlow, as shown below:

[0064]

[0065]

[0066]

[0067]

[0068] The above model formulas are expressed in sequence as follows: the definition of the square of the node voltage and the definition of the square of the branch current, the branch voltage drop equation, the non-convex equation of branch power-current coupling, and the definition of the net injected power model at the node. Wherein, Ui,t I represents the node voltage of node i at time t. ij,t Represents the branch ij between node i and node j (i.e. The branch current at time t, Let represent the square of the node voltage at node i at time t. Let represent the square of the branch current ij between node i and node j at time t; , Let r be the active power and reactive power of branch ij at time t, respectively. ij x represents the resistance of branch ij. ij Indicates the reactance of branch ij; and Let represent the active power output and reactive power output of photovoltaic (PV) at node i, respectively. and Let be the net active power injection and net reactive power injection at node i at time t, respectively. and Let be the active load and reactive load of node i at time t, respectively.

[0069] Based on the aforementioned distribution network operation model, a limit capacity optimization model for distributed photovoltaic (PV) grid integration is established. This optimization model takes maximizing the distributed PV grid integration capacity as the objective function and uses the relevant constraints of distribution network operation and PV output as constraints. Specifically, this implementation defines decision variables for the new installed capacity of candidate nodes to achieve large-scale distributed PV grid integration limit capacity assessment. With the goal of maximizing the photovoltaic capacity that the distribution network can accommodate, the objective function is as follows:

[0070]

[0071] In the formula, This represents the maximum photovoltaic capacity that the distribution network can accommodate. Represents a node The distributed photovoltaic installed capacity configured at the location, This represents the set of candidate nodes for distributed photovoltaic (PV) access.

[0072] Since the limit capacity optimization model is built on the distribution network operation model, it needs to meet the above distribution network operation model. On this basis, it also needs to meet the relevant constraints such as power flow constraints and power balance constraints of the distribution network system.

[0073] In a preferred embodiment, the constraints of the limit capacity optimization model include node power balance constraints, node voltage constraints, branch current constraints, transformer power flow constraints, inverter capacity constraints, and photovoltaic output and installed capacity constraints.

[0074] Specifically, node voltage constraints and branch current constraints can be expressed as:

[0075]

[0076]

[0077] In the formula, , The first The upper and lower limits of the voltage at each node at time t. , Branch roads The upper and lower limits of the branch current at time t.

[0078] Transformer power flow constraints can be expressed as:

[0079]

[0080]

[0081] In the formula, , These are the transformer's maximum active power and maximum reactive power, respectively. , These represent the active and reactive power of the transformer at time t, respectively. This constraint is used to limit power flow reversal.

[0082] Photovoltaic output constraints can be expressed as:

[0083]

[0084] In the formula, Let be the maximum photovoltaic output of node i at time t.

[0085] To link "installed capacity" with "output over time", an output coefficient for time t is introduced. Then the photovoltaic installed capacity constraint can be expressed as:

[0086]

[0087] In the formula, Represents a node The installed capacity of distributed photovoltaic power is configured at the location.

[0088] Inverter capacity constraints can be expressed as:

[0089]

[0090]

[0091] In the formula, This represents the rated apparent power capacity of the photovoltaic inverter at node i. , Let be the minimum and maximum allowable power factor angles for the photovoltaic inverter at node i, respectively. This is the reactive / active ratio coefficient corresponding to the power factor angle.

[0092] The node power balance constraint can be expressed as:

[0093]

[0094]

[0095] In the formula, Let time t be from the upstream node Flow to Node active power, , These are the resistance and reactance of branch ki, respectively. Let be the square of the branch current ki at time t, used to characterize the line loss. , Let be the net active power injection and net reactive power injection at node i at time t, respectively. , From node at time t Flowing to downstream nodes Active power and reactive power, , They are nodes The upstream node set and the downstream node set.

[0096] The objective function and constraints described above constitute the limit capacity optimization model for distributed photovoltaic (PV) grid connection. By solving this model using conventional algorithms, the installed PV capacity of distributed PV connected to each candidate node of the distribution network can be obtained. It should be noted that the constraints of the above model can be flexibly set according to specific circumstances in practical applications. Here, only the preferred method is given, not a specific limitation.

[0097] In a preferred embodiment, the present invention employs a solution algorithm based on second-order cone programming to solve the limit capacity optimization model, the specific steps of which include:

[0098] The limit capacity optimization model is transformed into a mixed integer second-order cone programming model by employing second-order cone relaxation treatment, and chance constraints are applied to node voltage and branch current to construct node voltage chance constraints and branch current chance constraints.

[0099] Solving the mixed-integer second-order cone programming model yields the initial limit of photovoltaic grid connection capacity and the corresponding grid operation status.

[0100] In this embodiment, a second-order cone programming algorithm is employed. By performing second-order cone relaxation on the nonlinear power flow coupling constraints in the limit capacity optimization model, the model is transformed into a mixed-integer second-order cone programming model. Specifically, to facilitate solving the problem by converting the nonlinear constraints into a second-order cone form and forming a solvable MISOCP problem, the non-convex equation of the branch power-current coupling mentioned above is relaxed to the following form:

[0101]

[0102] Further, the standard second-order cone expression is introduced:

[0103]

[0104] To avoid the risk of relaxation being solvable but deviating from the true trend, the compactness deviation of each branch at each time period is defined:

[0105]

[0106] Based on the above compactness deviation, the compactness index of the distribution network is defined as follows:

[0107]

[0108] In the formula, is the relaxation-compactness deviation index of branch ij at time t, which is equivalent to local relaxation deviation; The weight of branch ij at time t (can be 1 or set according to branch capacity / importance). As a global second-order cone relaxation compactness index, it is required during the iteration process. , The compactness convergence threshold is T, where T is the set of evaluation periods.

[0109] Considering the errors in photovoltaic power output prediction and load prediction, a net injection uncertainty model is constructed based on the nodal net injection power model:

[0110]

[0111] In the formula, Represents a node At any moment The actual active power injected, which includes the effects of prediction errors. Represents a node At any moment The predicted photovoltaic power output, Represents a node At any moment The predicted active power of the load, Represents a node At any moment The error in photovoltaic power output prediction Represents a node At any moment The load forecasting error.

[0112] Furthermore, a chance constraint is applied to the upper voltage limit:

[0113]

[0114] In the formula, This represents a probability operator used to measure the probability of an event occurring. This represents the maximum permissible probability threshold for default, i.e., the acceptable risk level when the voltage exceeds the upper limit. This is the confidence level.

[0115] In the implementation of engineering projects, the above opportunity constraints can be equivalently approximated as a "deterministic margin tightening" model, resulting in nodal voltage opportunity constraints and branch current opportunity constraints:

[0116]

[0117]

[0118] In the formula, , These are the node voltage margin of node i at time t and the branch current margin of branch ij at time t, respectively, obtained by mapping error statistics to confidence level, and can be adaptively updated with iteration.

[0119] Through the above transformation, the limit capacity optimization model is converted into a mixed-integer second-order cone programming model. Based on both multi-node and single-node access scenarios, optimization solvers (such as CPLEX, Gurobi, and MOSEK) are invoked to solve the model, obtaining the initial limit capacity solution under the current constraints, along with the corresponding node voltages, branch currents, and transformer operating states. The output shows the initial limit photovoltaic access capacity of the distribution network under the current constraints, the photovoltaic capacity allocation results for each node, and the activation status of each operating constraint, corresponding to the distribution network operating state.

[0120] Under high-penetration operating conditions, the sensitivity of different access nodes to changes in photovoltaic output varies significantly. Influenced by factors such as distribution network topology, line impedance distribution, and load spatial distribution, the impact of photovoltaic access on node voltage rise, branch current changes, and transformer power flow direction is not uniform across different nodes. In a preferred embodiment, this invention calculates the key constraint scores of each access node to accurately characterize the differences in sensitivity of each access node to system operating constraints, thereby achieving the identification of key constraint nodes. Specific steps include:

[0121] Based on the operating status of the distribution network, the index of the access node allocated with the initial limit photovoltaic access capacity is calculated to obtain the node sensitivity of the access node, and the activation degree of the operating constraints of the distribution network is calculated to obtain the constraint activation degree of the distribution network node.

[0122] Based on the node sensitivity and the constraint activation degree, the key constraint score of the access node is calculated, and based on the key constraint score, several key constraint nodes are selected from the access nodes.

[0123] In this embodiment, firstly, based on the initial limit photovoltaic access capacity output by the limit capacity optimization model, the capacity allocation results, and the distribution network operating status, the node sensitivity of the access node allocated the initial limit photovoltaic access capacity is calculated, and the activation degree of the distribution network voltage and current operating constraints is calculated. The specific calculation steps include:

[0124] Calculate the node voltage sensitivity of the access node based on the installed capacity of the access node and the node voltage of other distribution network nodes;

[0125] The node current sensitivity of the access node is calculated based on the installed capacity of the access node and the branch current of other distribution network nodes.

[0126] Calculate the voltage constraint activation degree of each distribution network node based on the node voltage and the upper limit of the node voltage.

[0127] Calculate the current constraint activation degree of each distribution network node based on the branch current and the upper limit of the branch current.

[0128] In this embodiment, the activation degree of the voltage and current operating constraints of the distribution network is represented by the square ratio of the node voltage and branch current of the distribution network node to their corresponding extreme values. Specifically, the activation degree of the voltage constraint of the distribution network node is represented by the square ratio of the node voltage and the upper limit value of the node voltage.

[0129]

[0130] The activation degree of current constraints at a distribution network node is represented by the ratio of the branch current to the square of the upper limit of the branch current:

[0131]

[0132] In the formula, Represents a node Voltage-constrained activation degree at time t; Indicates that branch ij is at time 1 / 2. The current-constrained activation degree. This indicates that the voltage is close to the upper limit. This indicates that the current is close to the upper limit of the branch current.

[0133] By calculating the activation degree of voltage and current constraints at each node in the distribution network, the activation degree of voltage and current operating constraints in the distribution network after distributed photovoltaic access can be determined.

[0134] The node voltage sensitivity and node current sensitivity refer to the degree of impact of changes in the installed capacity of a node (e.g., node k) allocated with initial limit photovoltaic access capacity on the square of the voltage and the square of the branch current of a distribution network node (e.g., node i). The change in installed capacity at time t relative to the node Sensitivity of voltage square and the branch road Sensitivity of the square of the current It can be represented as:

[0135]

[0136]

[0137] In the formula, This represents the distributed photovoltaic installed capacity configured at node k. Indicates partial derivative, This represents the node voltage sensitivity of access node k to distribution network node i at time t. This represents the node current sensitivity of access node k to distribution network node i at time t.

[0138] It's important to note that since the node voltages and branch currents in a distribution network are not directly and explicitly functionally related to the installed capacity of the connected nodes, they cannot be directly and explicitly differentiated. The partial derivatives mentioned above reflect the remote impact of the distributed photovoltaic (PV) connection location on the operational constraints of other nodes / branches in the system. This impact is realized through power flow transmission in the distribution network. Therefore, when solving for the partial derivatives, it is necessary to combine the chain rule and the power flow Jacobian matrix. Taking the partial derivative of the installed capacity of node k with respect to the square of the voltage of node i as an example, the chain rule decomposes the indirect impact into two steps: the partial derivative of the square of the voltage of node i with respect to the PV output of node k, and the partial derivative of the PV output of node k with respect to the installed capacity of node k. The product of partial derivatives can be expressed using the output coefficient for the second term, while the first term represents the cross-node sensitivity of the voltage at node i to the photovoltaic output at node k, which can be obtained by solving the Jacobian matrix of the power flow equations. In large-scale distribution networks, directly solving the inverse Jacobian matrix involves a large computational burden. Therefore, in practical engineering applications, a linear approximation combined with a small perturbation method can be used. For example, the power flow equations can be locally linearized first, then a small increment can be applied to the installed capacity at node k, and the power flow can be recalculated to obtain the change in the square of the voltage at node i. The ratio of the change in the square of the voltage to the increment of the installed capacity can then be used as an approximation of the node voltage sensitivity, thereby improving the practicality of the engineering. The above is only a preferred calculation method; other calculation methods can also be used, which will not be elaborated here.

[0139] After calculating the node sensitivity and constraint activation degree of each access node and the distribution network, the key constraint score of the access node is calculated based on the node sensitivity and constraint activation degree, thereby screening out key constraint nodes. The specific steps include:

[0140] The product of the node voltage sensitivity of the access node and the voltage constraint activation degree of the corresponding distribution network node is used as the voltage constraint score of the access node.

[0141] The product of the node current sensitivity of the access node and the current constraint activation degree of the corresponding distribution network node is used as the current constraint score of the access node.

[0142] The sum of the maximum values ​​of the voltage constraint score and the current constraint score is taken as the key constraint score of the access node. The nodes are then sorted in descending order of the key constraint scores, and a preset number of nodes are selected from the access nodes as key constraint nodes.

[0143] In this embodiment, for the access nodes with allocated installed capacity in the optimization model output, taking node k as an example, the voltage sensitivity of node k's installed capacity to other distribution network nodes (such as node i) (i.e., node voltage sensitivity of node k) is multiplied by the voltage constraint activation degree of the corresponding distribution network node (node ​​i) to obtain the voltage constraint score of node k; simultaneously, the node current sensitivity of node k's installed capacity to other node i is multiplied by the current constraint activation degree of node i to obtain the current constraint score of node k. Since there are multiple nodes i, these two constraint scores of node k are multidimensional scores. The largest current constraint score and the largest voltage constraint score are found from the multidimensional scores, and the sum of these two largest constraint scores is taken as the key constraint score of node k. .

[0144] For access node k, the maximum values ​​of its voltage constraint score and current constraint score during time period t are defined as follows:

[0145]

[0146]

[0147] In the formula, N is the set of distribution network nodes, and E is the set of distribution network branches; , These are the maximum values ​​of the voltage constraint score and the current constraint score for the access node k during time period t, respectively. The calculation of these maximum values ​​is performed independently in the node set and the branch set, and the extreme value positions corresponding to them are not required to be the same node or the same branch.

[0148] Based on this, the key constraint score for access node k is defined. for:

[0149]

[0150] In the formula, T represents the set of assessment periods. This key constraint score comprehensively reflects the maximum potential impact of changes in the photovoltaic installed capacity at node k on the distribution network voltage and current constraints across all assessment periods.

[0151] Based on the critical constraint scores of the access nodes, several nodes with the highest scores (e.g., the top K nodes) are selected as critical constraint nodes, forming a set of critical nodes. These critical constraint nodes are essentially those nodes whose photovoltaic access significantly impacts the voltage and branch current of the distribution network nodes. Then, adaptive capacity constraints are introduced for these critical constraint nodes. The specific steps include:

[0152] The allowed access capacity limit is updated based on the allowed access capacity limit of the key constraint node and the key constraint score;

[0153] Based on the updated upper limit of allowed access capacity, construct adaptive capacity constraints for key constraint nodes.

[0154] In this embodiment, since the installed capacity of the limit capacity optimization model is obtained by solving for the maximum access capacity of the distribution network, the installed capacity of each access node is equal to the ratio of the maximum photovoltaic output to the output coefficient of that node. Therefore, the limit photovoltaic access capacity output by the model can be understood as being equivalent to the upper limit of the allowable access capacity of that access node. Thus, this embodiment uses the initial allocated capacity of the key constraint node output by the model as the upper limit of the allowable access capacity in the first iteration, and updates this upper limit based on the key constraint score of the key constraint node. The adaptive capacity constraint of the installed capacity of the key constraint node can then be expressed as:

[0155]

[0156] In the formula, This represents the installed capacity of distributed photovoltaic power configured at the critical constraint node k in the r-th iteration round. This represents the upper limit of photovoltaic capacity allowed to be connected to the key constraint node k in the r-th iteration. Its value is the installed capacity amplitude of the distributed photovoltaic system configured at the key constraint node k obtained in the previous iteration, i.e. , The step size is adaptive and can be adjusted with iteration. This represents the critical constraint score of critical constraint node k in the r-th iteration round.

[0157] After adding adaptive capacity constraints, the limit capacity optimization model is iteratively solved again to obtain the limit photovoltaic access capacity of the distribution network and the installed capacity allocated to each distributed photovoltaic candidate access node. During the iterative solution, the iteration stops when the results of two adjacent iterations meet a preset condition. This can be understood as one iteration being a solution process for the limit capacity optimization model. In each iteration, the adaptive constraints are determined based on the calculation results of the previous iteration; that is, the adaptive constraints in this embodiment are dynamic constraints.

[0158] In a preferred embodiment, the stopping conditions for model iteration are set as capacity stability condition, critical constraint node stability condition, and relaxation compactness condition, which are expressed by the following formula:

[0159]

[0160] In the formula, and The first Wheel and the first In each iteration, the total grid-connected capacity of distributed photovoltaic power in the distribution network; This is the capacity convergence threshold, used to measure whether the capacity change between two adjacent rounds is small enough. This expression is used to determine the capacity stability of the access capacity. , The first Wheel and the first The set of key constraint nodes identified in the round of iterations; this equation is used to determine the stability of the key constraint nodes. It is a global second-order cone relaxation and tightness index. This expression is used to determine the relaxation compactness of a second-order cone programming model, serving as the compactness convergence threshold.

[0161] Only when the above three conditions of "capacity stability + key set stability + relaxation tightness satisfaction" are met simultaneously, or the maximum number of iterations is reached, is the limit capacity calculation process considered to have converged, and the final limit photovoltaic access capacity and node capacity allocation results are output. If the conditions are not met or the maximum number of iterations has not been reached, the next round of key constraint identification and model tightening will continue until the iteration stop condition is met.

[0162] In a preferred embodiment, when iteratively solving the limit capacity optimization model, in addition to adding adaptive capacity constraints to key constraint nodes, the constraint margins in the chance constraints of voltage and current when transformed into a second-order cone programming model can also be updated. The model solution steps at this time are as follows:

[0163] Based on the difference between the voltage constraint activation degree and the preset first activation degree threshold, the node voltage margin in the node voltage opportunity constraint is updated to obtain the updated node voltage opportunity constraint.

[0164] Based on the difference between the current constraint activation degree and the preset second activation degree threshold, the branch current margin in the branch current opportunity constraint is updated to obtain the updated branch current opportunity constraint.

[0165] Based on the updated node voltage opportunity constraint, the updated branch current opportunity constraint, and the adaptive capacity constraint, the mixed integer second-order cone programming model is iteratively solved until the preset iteration stopping condition is reached, and the final limit photovoltaic access capacity and node capacity allocation results are obtained.

[0166] In this embodiment, taking the model solution process of one iteration as an example, for the transformed second-order cone programming model, the update formulas for the node voltage margin and branch current margin in the node voltage chance constraint and branch current chance constraint are as follows:

[0167]

[0168]

[0169] Where r represents the number of iterations in the limit capacity solution process, , Let be the node voltage margin of node i at time t and the branch current margin of branch ij at time t, respectively, in the (r+1)th iteration. , These are the preset target safety activation levels for voltage and current, respectively. , These are all update coefficients used to control the rate of change of the margin. This indicates the maximum upper limit allowed by the voltage machine constraint margin. This represents the maximum upper limit allowed by the current machine constraint margin.

[0170] By combining the iterative update of the opportunity constraint margin in the above opportunity constraints and the adaptive capacity constraint of the key constraint nodes, the model is iteratively calculated to obtain the final limit photovoltaic access capacity and node capacity allocation results. In order to further verify the safety of the model output results, it is also necessary to perform safety verification on the node voltage, branch current and transformer operating status under the limit state.

[0171] Specifically, first obtain the output results, including the maximum capacity: Node-level capacity allocation: Voltage distribution: Voltage margin: Current: Load rate: Transformer power flow: Relaxation and firmness indicators: .

[0172] Then, a safety verification is performed, which includes deterministic verification and uncertainty verification. Deterministic verification checks all node voltages, branch currents, and transformer power flows against constraints to verify whether these parameters meet the relevant constraints in the limit capacity optimization model. Uncertainty verification uses a sampling verification method, generating photovoltaic output prediction errors and load prediction errors based on error samples, calculating whether the voltage exceeds limits, and obtaining the empirical default rate.

[0173]

[0174] In the formula, This represents the empirical default rate obtained under uncertain sampling conditions; This is the maximum permissible probability threshold for default. This is an indicator function that takes the value 1 when the condition inside the parentheses is true, and 0 otherwise. This represents the total number of error samples. Indicates the first Given a set of error samples, the node voltage of the i-th node at time t is calculated. If the above formula is not satisfied, the calculation is returned to iteratively to increase the chance margin. and Alternatively, the capacity cap for critical nodes may be tightened, and the maximum photovoltaic access capacity and node capacity allocation results may be recalculated. This embodiment ensures the security and stability of the model output results in practical applications through security verification.

[0175] To verify the effectiveness and stability of the method provided in this invention for assessing the limit access capacity of distribution networks under high penetration conditions of distributed photovoltaic (PV) grids, a typical IEEE 33-node distribution network was selected as a case study. Multiple nodes in the network were designated as candidate PV grid connection points, with the PV installed capacity of each candidate node serving as the optimization decision variable. The goal was to maximize the total PV installed capacity that the entire network could accept while meeting operational safety constraints. In the case study calculation, the initial limit access capacity solution for distributed PV was first solved under initial constraints, obtaining the corresponding node voltage and current distributions and constraint activation status. Based on this, a key node identification mechanism based on constraint activation degree and node sensitivity was introduced. By measuring the impact of changes in PV capacity at each node on voltage and current constraints, key constraint nodes that play a dominant role in the system's limit capacity were identified. Subsequently, the upper limit of PV installed capacity and related operational margins of the key nodes were adaptively adjusted, and the limit capacity model was resolved after each adjustment until the requirements of capacity change convergence, key node set stability, and second-order cone relaxation compactness were met, thus obtaining the final assessment result of the distributed PV limit access capacity.

[0176] The calculation results of the example show that, Figure 2 The distributed photovoltaic (PV) limit capacity at different nodes exhibits a clear non-uniform distribution characteristic. The node-level limit capacity allocation results show that some nodes can support a large PV installation capacity, while the access capacity of others is subject to stricter limitations. This indicates that under high-penetration distributed PV access conditions, the distribution network's capacity is not determined by a single operational constraint, but rather by the combined effects of multiple factors such as node location, electrical distance, and power flow transmission paths. The method of this invention, through the differentiation and adjustment of node sensitivity, achieves precise exploration of the system's access potential, fully releasing the access capacity of non-critical nodes. It should be noted here that... Figure 2 , Figure 6 , Figure 7 , Figure 8 , Figure 9 and Figure 10 All are bar charts. For parameters with smaller values, they will be displayed as line segments and shorter bars on the horizontal axis.

[0177] Figure 3 The total capacity iterative convergence curve, Figure 4 The curve shows the change in SOC (Solar Oxygen Content) firmness index. Figure 5 For the evolution process of the key node set, Figure 3 , Figure 4 and Figure 5 Together, they demonstrate the iterative convergence process of the limit capacity. It can be seen that as the upper limit of photovoltaic capacity at key nodes is gradually adaptively adjusted, the limit capacity of distributed photovoltaic grid-wide gradually stabilizes within a finite number of iterations. Simultaneously, the second-order cone relaxation tightness index continuously decreases during the iteration process, indicating that the model relaxation maintains high accuracy as the capacity approaches the limit, and the obtained solution effectively reflects the actual operating state of the distribution network. Furthermore, the set of key constraint nodes may change in the early stages of iteration, but gradually stabilizes as the calculation progresses, indicating that the bottleneck nodes that play a dominant role in the limit capacity are accurately identified and stably locked, thus verifying the rationality and effectiveness of the key node identification mechanism.

[0178] From such Figure 6 The node voltage margin distribution diagram shows that although the overall system operation is close to the limit of grid connection conditions, not all nodes' voltages are simultaneously approaching the upper limit. Some nodes still retain a certain voltage margin, while the voltage margin of a few nodes is significantly smaller, becoming the main factor limiting the system's ability to further increase distributed photovoltaic grid connection capacity. This indicates that the method of this invention does not simply push all node operating points to the constraint boundary, but rather maximizes the overall grid connection capacity of the system while meeting operational safety constraints by differentially adjusting the capacity of key nodes.

[0179] like Figure 7 The voltage-constrained activation distribution diagram and Figure 8 The current constraint activation distribution diagram further reveals that voltage constraints play a dominant role in the formation of the maximum grid-connected capacity of distributed photovoltaic systems in the calculation example, while current constraints in individual branches can also become local bottlenecks. Nodes or branches with high activation typically correspond to locations where the operating state is close to the constraint boundary (i.e., the target value in the diagram), and these locations require close attention during capacity expansion. By introducing a constraint activation index, the method of this invention can effectively identify critical operating locations in the system that are close to triggering constraints.

[0180] like Figure 9 The node sensitivity distribution diagram shown and Figure 10The key constraint score distribution diagram further explains the reasons for the differences in the maximum access capacity of each node. Nodes with higher sensitivity scores have a more significant impact on the system voltage or current constraints when their photovoltaic installed capacity increases. Therefore, they are preferentially included in the key node set during the maximum capacity calculation and are subject to stricter capacity limits. Nodes with lower sensitivity scores have a relatively smaller impact on system operation constraints, and their accessible photovoltaic capacity is relatively larger. By combining constraint activation degree with node sensitivity, the method of this invention forms a clear and interpretable closed-loop capacity allocation logic in the example.

[0181] The distributed photovoltaic grid-connected limit capacity calculation method based on node sensitivity adaptive adjustment proposed in this invention can accurately identify key nodes in the system and reasonably allocate node-level access capacity under the premise of ensuring operational safety and controllable uncertainty risks, thereby effectively improving the limit acceptance capacity of distributed photovoltaics in the entire distribution network.

[0182] Please see Figure 11 Based on the same inventive concept, the second embodiment of this invention proposes a distributed photovoltaic grid-connected ultimate capacity calculation system, comprising:

[0183] The initial capacity calculation module 10 is used to establish a limit capacity optimization model based on the operating parameters and topology of the distribution network, with the objective function of maximizing the access capacity of distributed photovoltaics connected to the distribution network, and with system power flow constraints, power balance constraints, and photovoltaic output and installed capacity upper limit constraints as constraints.

[0184] Solving the aforementioned limit capacity optimization model yields the initial limit photovoltaic access capacity of the distribution network and the corresponding distribution network operating state.

[0185] The critical constraint identification module 20 is used to identify critical constraint nodes for access nodes allocated with initial limit photovoltaic access capacity according to the operation status of the distribution network, obtain the critical constraint nodes, and set the adaptive capacity constraints of the critical constraint nodes.

[0186] The final capacity calculation module 30 is used to iteratively solve the limit capacity optimization model according to the adaptive capacity constraint until the preset iteration stop condition is reached, so as to obtain the final limit photovoltaic access capacity and node capacity allocation results.

[0187] The technical features and effects of the distributed photovoltaic grid-connected maximum capacity calculation system proposed in this invention are the same as those of the method proposed in this invention, and will not be repeated here. Each module in the above-mentioned distributed photovoltaic grid-connected maximum capacity calculation system can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or it can be stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0188] Furthermore, embodiments of the present invention also propose a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0189] Please see Figure 12 The diagram illustrates the internal structure of a computer device in one embodiment. This computer device can specifically be a terminal or a server. The computer device includes a processor, memory, network interface, display, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for calculating the maximum capacity of distributed photovoltaic grid connection. The display screen of the computer device can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0190] Those skilled in the art will understand that Figure 12 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computing devices may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0191] Furthermore, embodiments of the present invention also propose a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0192] In summary, the present invention proposes a method, system, device, and medium for calculating the maximum grid-connected capacity of distributed photovoltaic (PV) power. The method establishes a maximum capacity optimization model based on the operating parameters and topology of the distribution network, with the objective function being maximizing the access capacity of distributed PV power to the distribution network, and with constraints including system power flow constraints, power balance constraints, and PV output and installed capacity limits. The maximum capacity optimization model is solved to obtain the initial maximum PV access capacity of the distribution network and the corresponding distribution network operating state. Based on the distribution network operating state, key constraint nodes are identified for the access nodes allocated the initial maximum PV access capacity, and adaptive capacity constraints are set for these key constraint nodes. The maximum capacity optimization model is iteratively solved based on the adaptive capacity constraints until a preset iteration stop condition is reached, yielding the final maximum PV access capacity and node capacity allocation results. This invention introduces a sensitivity analysis mechanism for nodes to voltage and power flow constraints, which can characterize the differences in the impact of different photovoltaic (PV) access nodes on the system operating boundary. During the calculation of the ultimate capacity, the upper limit of PV access for each node is dynamically adjusted based on the sensitivity results. This realizes the transformation of PV capacity allocation from experience-based judgment to model-driven, adaptive adjustment, enabling the system to prioritize the release of the access potential of low-sensitivity nodes, improving the accuracy of the ultimate capacity assessment results. Thus, under the premise of meeting safe operation constraints, it significantly improves the overall acceptance capacity of the distribution network for distributed PV, while reducing the impact of human experience intervention on the assessment results, and improving the versatility and engineering replicability of the method.

[0193] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system 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. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0194] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A method for calculating the ultimate grid-connected capacity of distributed photovoltaic power, characterized in that, include: Based on the operating parameters and topology of the distribution network, a limit capacity optimization model is established with the objective function of maximizing the access capacity of distributed photovoltaic power to the distribution network, and with system power flow constraints, power balance constraints, and photovoltaic output and installed capacity upper limit constraints as constraints. Solving the aforementioned limit capacity optimization model yields the initial limit photovoltaic access capacity of the distribution network and the corresponding distribution network operating state. Based on the operating status of the distribution network, the index of the access node allocated with the initial limit photovoltaic access capacity is calculated to obtain the node sensitivity of the access node, and the activation degree of the operating constraints of the distribution network is calculated to obtain the constraint activation degree of the distribution network node; wherein, the activation degree of the voltage and current operating constraints of the distribution network is represented by the square ratio of the node voltage and branch current of the distribution network node to the corresponding extreme value. The product of the node voltage sensitivity of the access node and the voltage constraint activation degree of the corresponding distribution network node is used as the voltage constraint score of the access node. The product of the node current sensitivity of the access node and the current constraint activation degree of the corresponding distribution network node is used as the current constraint score of the access node. The sum of the maximum values ​​of the voltage constraint score and the current constraint score is taken as the key constraint score of the access node, and the nodes are selected as a preset number of key constraint nodes by sorting them in descending order of the key constraint scores. The allowed access capacity limit is updated based on the allowed access capacity limit of the key constraint node and the key constraint score; Based on the updated upper limit of allowed access capacity, construct adaptive capacity constraints for key constraint nodes; The extreme capacity optimization model is iteratively solved according to the adaptive capacity constraint until the preset iteration stopping condition is reached, so as to obtain the final extreme photovoltaic access capacity and node capacity allocation results.

2. The method for calculating the ultimate capacity of distributed photovoltaic grid connection according to claim 1, characterized in that, The steps of solving the limit capacity optimization model to obtain the initial limit photovoltaic access capacity of the distribution network and the corresponding distribution network operating state include: The limit capacity optimization model is transformed into a mixed integer second-order cone programming model by employing second-order cone relaxation treatment, and chance constraints are applied to node voltage and branch current to construct node voltage chance constraints and branch current chance constraints. Solving the mixed-integer second-order cone programming model yields the initial limit of photovoltaic grid connection capacity and the corresponding grid operation status.

3. The method for calculating the ultimate capacity of distributed photovoltaic grid connection according to claim 2, characterized in that, The steps of calculating the index of the access node allocated with the initial limit photovoltaic access capacity based on the operating status of the distribution network to obtain the node sensitivity of the access node, and calculating the activation degree of the operating constraints of the distribution network to obtain the constraint activation degree of the distribution network node include: Calculate the node voltage sensitivity of the access node based on the installed capacity of the access node and the node voltage of other distribution network nodes; The node current sensitivity of the access node is calculated based on the installed capacity of the access node and the branch current of other distribution network nodes. Calculate the voltage constraint activation degree of each distribution network node based on the node voltage and the upper limit of the node voltage. Calculate the current constraint activation degree of each distribution network node based on the branch current and the upper limit of the branch current.

4. The method for calculating the ultimate capacity of distributed photovoltaic grid connection according to claim 3, characterized in that, The step of iteratively solving the limit capacity optimization model according to the adaptive capacity constraint until the preset iteration stopping condition is reached, and obtaining the final limit photovoltaic access capacity and node capacity allocation results, includes: Based on the difference between the voltage constraint activation degree and the preset first activation degree threshold, the node voltage margin in the node voltage opportunity constraint is updated to obtain the updated node voltage opportunity constraint. Based on the difference between the current constraint activation degree and the preset second activation degree threshold, the branch current margin in the branch current opportunity constraint is updated to obtain the updated branch current opportunity constraint. Based on the updated node voltage opportunity constraint, the updated branch current opportunity constraint, and the adaptive capacity constraint, the mixed integer second-order cone programming model is iteratively solved until the preset iteration stopping condition is reached, and the final limit photovoltaic access capacity and node capacity allocation results are obtained.

5. A distributed photovoltaic grid-connected ultimate capacity calculation system, characterized in that, The system is applied to the method as described in any one of claims 1 to 4, comprising: The initial capacity calculation module is used to establish a limit capacity optimization model based on the operating parameters and topology of the distribution network, with the objective function of maximizing the access capacity of distributed photovoltaic power grid, and with system power flow constraints, power balance constraints, and photovoltaic output and installed capacity upper limit constraints as constraints. Solving the aforementioned limit capacity optimization model yields the initial limit photovoltaic access capacity of the distribution network and the corresponding distribution network operating status. The critical constraint identification module is used to identify critical constraint nodes for access nodes allocated with initial limit photovoltaic access capacity according to the operating status of the distribution network, obtain the critical constraint nodes, and set the adaptive capacity constraints of the critical constraint nodes. The final capacity calculation module is used to iteratively solve the limit capacity optimization model according to the adaptive capacity constraints until the preset iteration stopping condition is reached, so as to obtain the final limit photovoltaic access capacity and node capacity allocation results.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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