Photovoltaic bearing capacity evaluation method and system of power distribution network, and storage medium

Abstract

The invention provides a photovoltaic bearing capacity assessment method and system of a power distribution network and a storage medium, and relates to the technical field of power distribution network assessment, and the method comprises the steps: judging whether the access position of distributed photovoltaic in the power distribution network needs to be determined or not according to the assessment demand of the power distribution network when the access mode of the distributed photovoltaic in the power distribution network is multi-point access; performing photovoltaic bearing capacity evaluation according to a judgment result to obtain a photovoltaic bearing capacity evaluation result of the power distribution network; according to the method, under the condition of multi-point access, whether the access position is needed or not is judged according to the evaluation requirements, different evaluation methods corresponding to different evaluation requirements are combined, appropriate evaluation strategies are adopted in a targeted mode, the characteristics of different evaluation requirements are fully considered, and the evaluation precision and efficiency of the photovoltaic bearing capacity under the actual condition are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network assessment, and in particular, to a method, a system, and a storage medium for evaluating the photovoltaic carrying capacity of a distribution network. Background Art

[0002] At present, as a flexible, environmentally friendly, and efficient energy supply method, distributed photovoltaic power is increasingly widely used in the power system. Among them, the access location of distributed photovoltaic power plays a key role in the operating state of the distribution network. The access location of distributed photovoltaic power divides the access method into single-point access and multi-point access. Different access methods will directly affect key operating parameters such as power flow distribution, voltage level, and line loss in the distribution network.

[0003] In the related art, the traditional method for evaluating the photovoltaic carrying capacity of a distribution network mainly focuses on the scenario of single-point access with deterministic output. However, single-point access often cannot meet the actual access requirements, and the photovoltaic output characteristics have strong uncertainty due to the large influence of the environment. At the same time, the load characteristics also have certain uncertainty due to the influence of users' random behaviors, which further affects the evaluation of the photovoltaic carrying capacity of the distribution network in actual situations and results in the accuracy of the photovoltaic carrying capacity evaluation. Summary of the Invention

[0004] The problem solved by the present invention is how to improve the evaluation accuracy and efficiency of the photovoltaic carrying capacity of a distribution network in actual situations.

[0005] To solve the above problems, the present invention provides a method, a system, and a storage medium for evaluating the photovoltaic carrying capacity of a distribution network.

[0006] In a first aspect, a method for evaluating the photovoltaic carrying capacity of a distribution network according to the present invention includes: When the access method of distributed photovoltaic power in the distribution network is multi-point access, according to the evaluation requirements of the distribution network, it is judged whether it is necessary to determine the access location of distributed photovoltaic power in the distribution network, and the photovoltaic carrying capacity evaluation is performed according to the judgment result to obtain the photovoltaic carrying capacity evaluation result of the distribution network; Wherein, when the evaluation requirement is to determine the access capacity and access plan of the distribution network for photovoltaic power, it is determined that it is not necessary to determine the access location of the distributed photovoltaic power in the distribution network, and a multi-objective optimization model of the distribution network is constructed by a multi-objective optimization method; then the multi-objective optimization model is solved by the ε-constraint method to obtain the photovoltaic carrying capacity evaluation result of the distribution network; When the evaluation requirement is to determine the overall access capacity range of the distribution network, it is determined that the access location of the distributed photovoltaic in the distribution network needs to be determined, and the access location of the distributed photovoltaic in the distribution network is obtained. Through the photovoltaic center-of-gravity theory, according to the active power distribution of the load in the distribution network and the active power output distribution of the photovoltaic combined with the access location, the load center of gravity and the photovoltaic center of gravity are determined; according to the photovoltaic center of gravity and the load center of gravity, the global constraint conditions of the distributed photovoltaic bearing capacity are determined; according to the global constraint conditions, the photovoltaic bearing capacity evaluation result of the distribution network is obtained.

[0007] Optionally, constructing the multi-objective optimization model of the distribution network by the multi-objective optimization method includes: When the evaluation requirement of the photovoltaic bearing capacity is to determine the overall access capacity and access scheme of the distribution network, according to the topological structure of the distribution network, the positions used as the access locations in the distribution network are taken as the nodes of the distribution network; According to the maximum value of the access capacity corresponding to each node, the objective function is obtained; According to the power data of the branches corresponding to each node, the power flow constraint and the network security constraint are set, and the power flow constraint and the network security constraint are used as the constraint conditions; According to the constraint conditions and the objective function, the multi-objective optimization model of the distribution network is constructed.

[0008] Optionally, solving the multi-objective optimization model by the ε-constraint method to obtain the photovoltaic bearing capacity evaluation result of the distribution network includes: According to the access quantity of the multi-point access, the nodes in the distribution network are matched according to the access quantity to obtain a plurality of access schemes, and each access scheme includes two different nodes; Randomly select an access scheme as the main objective, and set the initial value and step size of ε; Iteratively optimize the main objective according to the initial value and the step size. When the iterative optimization is completed, a Pareto solution set including all the access schemes is obtained; According to the Pareto solution set, the photovoltaic bearing capacity evaluation result of the distribution network under the access quantity is obtained.

[0009] Optionally, obtaining the access location of the distributed photovoltaic in the distribution network includes: Obtain the access quantity of the distributed photovoltaic in the distribution network; According to the access quantity, determine the access location of the distributed photovoltaic in the distribution network.

[0010] Optionally, according to the photovoltaic center-of-gravity theory, determining the load center of gravity and the photovoltaic center of gravity based on the active power distribution of the load in the distribution network and the active power output distribution of the photovoltaic power generation in combination with the access location includes: When the evaluation requirement is to determine the overall access capacity range of the distribution network, according to the topological structure of the distribution network, the locations used as the access locations in the distribution network are taken as the nodes of the distribution network; According to the active power distribution of the load in the distribution network, determine the active power of each node of the distribution network, and obtain the line resistance of each node to the root node of the distribution network; Perform a multiplication operation on the active power of the load and the line resistance to obtain the load moment of the node; Obtain the load center of gravity based on the sum of the load moments of all the nodes and the sum of the line resistances.

[0011] Optionally, according to the photovoltaic center-of-gravity theory, determining the load center of gravity and the photovoltaic center of gravity based on the active power distribution of the load in the distribution network and the active power output distribution of the photovoltaic power generation in combination with the access location further includes: According to the active power output distribution of the photovoltaic power generation in the distribution network, determine the active power output of each node of the distribution network; Perform a multiplication operation on the active power output of the photovoltaic power generation and the line resistance to obtain the photovoltaic moment of the node; Obtain the center of gravity of the photovoltaic moment based on the sum of the photovoltaic moments of all the nodes and the sum of the line resistances.

[0012] Optionally, determining the global constraint condition of the distributed photovoltaic bearing capacity according to the photovoltaic center of gravity and the load center of gravity includes: Screen all the nodes of the distribution network through the positional relationship between the photovoltaic center and the load center of gravity of each node; Among them, if the photovoltaic center of the node is ahead of the load center of gravity, the node is taken as an access node, and the photovoltaic bearing capacity constraint corresponding to the access node is obtained, and the photovoltaic bearing capacity constraint is used as the global constraint condition.

[0013] Optionally, obtaining the photovoltaic bearing capacity evaluation result of the distribution network according to the global constraint condition includes: Obtain the active power distribution of the access node, and determine the accounting section of the access node according to the active power distribution. The accounting section is the section where active power reverse transmission continuously appears in the active power distribution; Calculate the accounting section according to the photovoltaic bearing capacity constraint of the access node to obtain the bearing capacity value corresponding to the accounting section; Based on the bearing capacity value, obtain the photovoltaic bearing capacity evaluation result of the distribution network.

[0014] In a second aspect, a photovoltaic bearing capacity evaluation system for a distribution network according to the present invention includes: A judgment unit, configured to, when the access mode of distributed photovoltaics in the distribution network is multi-point access, judge whether it is necessary to determine the access position of the distributed photovoltaics in the distribution network according to the evaluation requirements of the distribution network; An evaluation unit, configured to perform photovoltaic bearing capacity evaluation according to the judgment result to obtain the photovoltaic bearing capacity evaluation result of the distribution network; Among them, the judgment unit is specifically configured to, when the evaluation requirement is to determine the access capacity and access plan of the distribution network for photovoltaics, determine that it is not necessary to determine the access position of the distributed photovoltaics in the distribution network; The evaluation unit is specifically configured to construct a multi-objective optimization model of the distribution network through a multi-objective optimization method; and then solve the multi-objective optimization model by the ε-constraint method to obtain the photovoltaic bearing capacity evaluation result of the distribution network; The judgment unit is specifically further configured to, when the evaluation requirement is to determine the overall access capacity range of the distribution network, determine that it is necessary to determine the access position of the distributed photovoltaics in the distribution network; The evaluation unit is specifically further configured to obtain the access position of the distributed photovoltaics in the distribution network, and through the photovoltaic center of gravity theory, determine the load center of gravity and the photovoltaic center of gravity according to the load active power distribution and the photovoltaic active power output distribution of the distribution network in combination with the access position; determine the global constraint conditions of the distributed photovoltaic bearing capacity according to the photovoltaic center of gravity and the load center of gravity; and obtain the photovoltaic bearing capacity evaluation result of the distribution network according to the global constraint conditions.

[0015] In a third aspect, a computer-readable storage medium according to the present invention stores a computer program, and when the computer program is executed by a processor, it implements the photovoltaic bearing capacity evaluation method for a distribution network as described above.

[0016] The photovoltaic carrying capacity evaluation method, system and storage medium of the distribution network of the present invention. First, when the distributed photovoltaics in the distribution network are multi-point access, the present invention determines whether it is necessary to determine the access positions according to different evaluation requirements, and flexibly selects the evaluation means according to the presence or absence of the access positions. If it is necessary to determine the access capacity of the distribution network to photovoltaics and the specific access scheme, at this time, the specific access positions can be not considered, and a multi-objective optimization model can be constructed by the multi-objective optimization method and solved by the ε-constraint method, fully considering the mutual influence between multiple access points and various constraint conditions, avoiding the inaccurate evaluation problem caused by ignoring the mutual influence in the multi-point access scenario by the traditional method, thus improving the accuracy of the evaluation, reducing the judgment of the access positions and then improving the evaluation efficiency. When the evaluation requirement is to determine the overall access capacity range, it is necessary to consider the specific access positions of each distributed photovoltaic under multi-point access, combine the access positions, use the photovoltaic centroid theory, combine the active power distribution of the load in the distribution network and the active power output distribution of the photovoltaics to determine the load centroid and the photovoltaic centroid, and determine the global constraint conditions of the distributed photovoltaic carrying capacity according to the position relationship between the two for evaluation, transforming the complex photovoltaic carrying capacity problem into a matching problem with the load distribution, and transforming the matching problem between the complex photovoltaic distribution and the load distribution into the relative position relationship of the geometric centroids by the photovoltaic centroid theory, avoiding the complex multi-objective optimization solution process, not only reducing the calculation complexity and improving the calculation efficiency, but also effectively solving the problem that it is difficult to accurately evaluate by the traditional method in the face of the uncertainty of photovoltaics and loads, and further improving the accuracy of the evaluation of the photovoltaic carrying capacity in the actual situation. In the case of multi-point access, the present invention judges whether the access positions are needed according to the evaluation requirements, combines different evaluation methods corresponding to different evaluation requirements, and pertinently adopts appropriate evaluation strategies, fully considering the characteristics of different evaluation requirements, improving the evaluation accuracy and efficiency of the photovoltaic carrying capacity in the actual situation, making the evaluation of the photovoltaic carrying capacity more scientific and accurate, and better meeting the actual application requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flowchart of the photovoltaic carrying capacity evaluation method of the distribution network in an embodiment of the present invention; Figure 2 It is a schematic diagram of the main line topology structure of the medium-voltage distribution network line in another embodiment of the present invention; Figure 3 It is a schematic diagram of the carrying capacity result of two-point access in another embodiment of the present invention; Figure 4 It is a diagram of the carrying capacity restriction relationship between two nodes in another embodiment of the present invention; Figure 5 It is a schematic diagram of the carrying capacity result of three-node access in another embodiment of the present invention; Figure 6It is a diagram showing the bearing capacity relationship among three nodes in another embodiment of the present invention; Figure 7 It is a schematic diagram showing the changing trend of the photovoltaic bearing capacity under the traditional Monte Carlo method in another embodiment of the present invention; Figure 8 It is one of the schematic diagrams showing the changing trend of the photovoltaic bearing capacity under the photovoltaic center of gravity theory in another embodiment of the present invention; Figure 9 It is another schematic diagram showing the changing trend of the photovoltaic bearing capacity under the photovoltaic center of gravity theory in another embodiment of the present invention; Figure 10 It is a comparison diagram of the calculation efficiency between the traditional Monte Carlo method and the photovoltaic center of gravity theory in another embodiment of the present invention; Figure 11 It is a schematic structural diagram of the photovoltaic bearing capacity evaluation system of the distribution network in another embodiment of the present invention. Detailed implementation manner

[0018] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments described herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not used to limit the protection scope of the present invention.

[0019] It should be understood that the various steps recorded in the method embodiments of the present invention can be executed in different orders and / or executed in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this regard.

[0020] The term "including" and its variations used herein are open-ended, that is, "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts such as "first" and "second" mentioned in the present invention are only used to distinguish different devices, modules, or units, and are not used to limit the order of functions performed by these devices, modules, or units or their interdependent relationships.

[0021] It should be noted that the modifiers "one" and "more than one" mentioned in the present invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise clearly specified in the context, it should be understood as "one or more".

[0022] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0023] In view of the problems existing in the above-mentioned related technologies, this embodiment provides a photovoltaic carrying capacity evaluation method, system and storage medium.

[0024] Combined with Figure 1 As shown, a photovoltaic carrying capacity evaluation method for a distribution network provided by an embodiment of the present invention includes: When the access mode of distributed photovoltaic in the distribution network is multi-point access, according to the evaluation requirements of the distribution network, it is judged whether it is necessary to determine the access position of the distributed photovoltaic in the distribution network, and the photovoltaic carrying capacity evaluation is carried out according to the judgment result to obtain the photovoltaic carrying capacity evaluation result of the distribution network.

[0025] Specifically, since single-point access often cannot meet the actual access requirements, in actual applications, multi-point access is more common. In the case of multi-point access, it is first necessary to judge whether it is necessary to determine the specific access position of the photovoltaic according to the evaluation requirements. This step is the key starting point of the entire evaluation method. The evaluation requirements directly determine the subsequent work direction, that is, whether it is necessary to conduct a detailed analysis of the photovoltaic access position and what evaluation strategy to adopt to meet these requirements, so as to ensure the practicality of the evaluation result.

[0026] Among them, when the evaluation requirement is to determine the access capacity and access plan of the distribution network for photovoltaic, it is determined that it is not necessary to determine the access position of the distributed photovoltaic in the distribution network, and a multi-objective optimization model of the distribution network is constructed by a multi-objective optimization method; then the multi-objective optimization model is solved by the ε-constraint method to obtain the photovoltaic carrying capacity evaluation result of the distribution network.

[0027] Specifically, when the evaluation requirement focuses on determining the access capacity of the distribution network to PV and the specific access scheme, it is determined that there is no need to determine the access location of the distributed PV in the distribution network, and in this case, a multi-objective optimization method is adopted. When the evaluation requirement is to determine the access capacity of the distribution network to PV and the specific access scheme, the key lies in finding a scheme that can maximize the PV access capacity without pre-defining the specific access location. First, according to the topological structure of the distribution network and the characteristics of each access location, the locations in the distribution network that can be used as access locations are regarded as nodes, and the maximum value of the access capacity corresponding to each node is used as the objective function. At the same time, according to the power data of the branches corresponding to each node, power flow constraints and network security constraints are set as constraints, thereby constructing a multi-objective optimization model of the distribution network. Then, the ε-constraint method is used to solve the constructed multi-objective optimization model. Through the iterative optimization process, a Pareto solution set containing all access schemes is obtained, and finally, the PV carrying capacity evaluation result of the distribution network is determined according to the Pareto solution set, including the overall access capacity and the specific access scheme.

[0028] When the evaluation requirement is to determine the overall access capacity range of the distribution network, it is determined that it is necessary to determine the access location of the distributed PV in the distribution network, and the access location of the distributed PV in the distribution network is obtained. Through the PV centroid theory, the load centroid and the PV centroid are determined according to the active power distribution of the load and the active power output distribution of the PV in the distribution network in combination with the access location; according to the PV centroid and the load centroid, the global constraint conditions of the distributed PV carrying capacity are determined; according to the global constraint conditions, the PV carrying capacity evaluation result of the distribution network is obtained.

[0029] Specifically, when the evaluation requirement is to determine the overall access capacity range of the distribution network, at this time, it is necessary to obtain the access locations of distributed photovoltaics and, in combination with the active power distribution of the load in the distribution network and the active power output distribution of photovoltaics, determine the load center of gravity and the photovoltaic center of gravity. When the evaluation requirement is to have a general understanding of the overall access capacity range of the distribution network, it is necessary to clarify the photovoltaic access locations. Because the photovoltaic access locations directly affect the distribution of photovoltaic output in the distribution network, and thus affect the voltage distribution and power flow direction. Different access locations will result in different voltage boosts and power flow changes, thereby affecting the total photovoltaic capacity that the distribution network can accommodate. By clarifying the access locations, the relationship between photovoltaic output and load distribution can be analyzed more accurately, global constraint conditions can be determined, and then a reasonable estimate of the access capacity range can be obtained. First, it is necessary to obtain the access locations of distributed photovoltaics in the distribution network and, based on the topological structure of the distribution network, determine the nodes in the distribution network that can serve as access locations. Then, according to the active power distribution of the load in the distribution network, calculate the load moment of each node, and obtain the load center of gravity through the sum of the load moments of all nodes and the sum of the line resistances; similarly, according to the active power output distribution of photovoltaics in the distribution network, calculate the photovoltaic moment of each node, and obtain the photovoltaic center of gravity through the sum of the photovoltaic moments of all nodes and the sum of the line resistances. Then, according to the relative position relationship between the photovoltaic center of gravity and the load center of gravity, determine the global constraint conditions for the distributed photovoltaic carrying capacity. Finally, based on the global constraint conditions and in combination with the specific parameters of the distribution network, obtain the evaluation result of the photovoltaic carrying capacity of the distribution network, that is, the overall access capacity range.

[0030] The method for evaluating the photovoltaic carrying capacity of a distribution network according to the present invention is as follows. First, when distributed photovoltaics in the distribution network are multi-point connected, the present invention determines whether a specific connection location needs to be determined according to different evaluation requirements, and flexibly selects an evaluation method based on the presence or absence of the connection location. If it is necessary to determine the connection capacity of the distribution network to photovoltaics and the specific connection scheme, at this time, the specific connection location may not be considered, and a multi-objective optimization model can be constructed by a multi-objective optimization method and solved by the ε-constraint method, fully considering the mutual influence between multiple connection points and various constraint conditions, avoiding the inaccurate evaluation problem caused by ignoring the mutual influence in the multi-point connection scenario in the traditional method, thereby improving the accuracy of the evaluation, reducing the judgment of the connection location, and further improving the evaluation efficiency. When the evaluation requirement is to determine the overall connection capacity range, the specific connection location of each distributed photovoltaic under multi-point connection needs to be considered. Combining the connection location, using the photovoltaic centroid theory, and combining the active power distribution of the load and the active power output distribution of the photovoltaics in the distribution network to determine the load centroid and the photovoltaic centroid, and determining the global constraint conditions of the distributed photovoltaic carrying capacity based on the position relationship between the two for evaluation, transforming the complex photovoltaic carrying capacity problem into a matching problem with the load distribution, and transforming the matching problem between the complex photovoltaic distribution and the load distribution into the relative position relationship of the geometric centroid through the photovoltaic centroid theory, avoiding the complex multi-objective optimization solution process, not only reducing the computational complexity and improving the computational efficiency, but also effectively solving the problem that it is difficult to accurately evaluate in the face of the uncertainty of photovoltaics and loads in the traditional method, and further improving the accuracy of the evaluation of the photovoltaic carrying capacity in the actual situation. In the case of multi-point connection, the present invention determines whether a connection location is needed according to the evaluation requirement, combines different evaluation methods corresponding to different evaluation requirements, and pertinently adopts appropriate evaluation strategies, fully considering the characteristics of different evaluation requirements, improving the evaluation accuracy and efficiency of the photovoltaic carrying capacity in the actual situation, making the evaluation of the photovoltaic carrying capacity more scientific and accurate, and better meeting the actual application requirements.

[0031] Optionally, constructing the multi-objective optimization model of the distribution network by the multi-objective optimization method includes: When the evaluation requirement for the photovoltaic carrying capacity is to determine the overall connection capacity and the connection scheme of the distribution network, according to the topological structure of the distribution network, the positions used as the connection locations in the distribution network are used as the nodes of the distribution network; According to the maximum value of the connection capacity corresponding to each node, the objective function is obtained; According to the power data of the branches corresponding to each node, power flow constraints and network security constraints are set, and the power flow constraints and the network security constraints are used as the constraint conditions; According to the constraint conditions and the objective function, the multi-objective optimization model of the distribution network is constructed.

[0032] Specifically, in the assessment of distributed photovoltaic carrying capacity, when the assessment requirement is to determine the overall access capacity and specific access plan of the distribution network, a multi-objective optimization method is used to construct a multi-objective optimization model. First, according to the topological structure of the distribution network, since the topological structure of the distribution network determines the possible positions of photovoltaic access points, the positions in the distribution network that can be used as access positions are regarded as nodes. Second, in this method, the objective function is set based on the maximum access capacity corresponding to each node. This is because the purpose of photovoltaic carrying capacity assessment is to maximize the access capacity of distributed photovoltaics while ensuring the safe and stable operation of the distribution network. Therefore, the design of the objective function needs to maximize the access capacity of photovoltaics as much as possible under the premise of meeting various constraints. Finally, the constraints include power flow constraints and network security constraints. The power flow constraints mainly consider the power flow situation in the distribution network to ensure that after photovoltaic access, the power distribution in the distribution network still conforms to physical laws and operation requirements. The network security constraints mainly consider safety indicators such as line carrying capacity and node voltage to ensure that photovoltaic access does not pose a threat to the safe operation of the distribution network. By combining these constraints with the objective function, a complete multi-objective optimization model can be constructed, providing a basis for subsequent solution. In the process of constructing the multi-objective optimization model, the actual operation situation of the distribution network and the characteristics of photovoltaic access need to be fully considered. For example, factors such as the uncertainty of photovoltaic output, the randomness of load characteristics, and the topological structure of the distribution network will all affect the construction and solution of the model. Therefore, in practical applications, appropriate adjustments and optimizations need to be made according to specific situations to ensure the accuracy and practicality of the model.

[0033] In the preferred embodiment of the present invention, the objective function, constraint conditions, and solution method of the carrying capacity assessment method based on multi-objective optimization are as follows: The objective function is: ; Wherein, is the distributed photovoltaic access capacity of node i, i = 1, 2,..., N.

[0034] The power flow constraint is: ; ; ; Wherein, is the active power flowing through branch ij of the distribution line at time t, is the reactive power flowing through branch ij of the distribution line at time t, is the voltage of node j of the distribution line at time t, is the distribution line at time Active power flowing through; For the branch of the distribution line at time t Reactive power flowing through; For the distribution line At time Flowing out from node And flowing to node Of the active power; For the distribution line At time Flowing out from node And flowing to node Of the reactive power; For the distribution line At time Node Of the load active power; For the distribution line At time Node Of the distributed power generation power; For branch Of the resistance; For branch

[0035] The network security constraints are divided into line current-carrying capacity constraints and node voltage constraints. Among them, the line current-carrying capacity constraint is: ; Among them, For the Branch line capacity of the distribution line.

[0036] The node voltage constraint is: ; Among them, For the distribution line node Lower voltage limit for safe operation; For the distribution line node Upper voltage limit for safe operation; For the distribution line node At Time of the voltage.

[0037] In the embodiment of the present invention, by determining nodes according to the topological structure of the distribution network, it is ensured that the construction of the model conforms to the actual situation of the distribution network, and the practicability of the model is improved. At the same time, taking the maximum value of the access capacity corresponding to each node as the objective function can effectively guide the optimization process to maximize the photovoltaic access capacity. In addition, by setting power flow constraints and network security constraints, the safe and stable operation of the distribution network after photovoltaic access is ensured, and problems such as voltage over-limit and line overload caused by photovoltaic access are avoided.

[0038] Optionally, solving the multi-objective optimization model by the ε-constraint method to obtain the photovoltaic carrying capacity evaluation result of the distribution network includes: According to the access quantity of the multi-point access, matching the nodes in the distribution network according to the access quantity to obtain a plurality of access schemes, and each access scheme includes two different nodes; Randomly select an access scheme as the main objective, and set the initial value and step size of ε; Iteratively optimize the main objective according to the initial value and the step size. After the iterative optimization is completed, obtain a Pareto solution set including all the access schemes; According to the Pareto solution set, obtain the photovoltaic carrying capacity evaluation result of the distribution network under the access quantity.

[0039] Specifically, first, according to the access quantity of multi-point access, the nodes in the distribution network are matched according to the access quantity to obtain multiple access schemes. Each access scheme includes two different nodes. This process is to generate all possible photovoltaic access combinations to ensure that the impacts of various access schemes on the distribution network can be comprehensively considered in the subsequent optimization process. For example, if there is multi-point access and multiple potential nodes to choose from, through this matching method, all access schemes of pairwise node combinations can be generated, providing comprehensive inputs for the subsequent optimization. Then, randomly select an access scheme as the main target, and set the initial value and step size of ε. The core of the ε-constraint method lies in gradually optimizing the objective function by adjusting the ε value while satisfying other objectives as constraint conditions. The setting of the initial value and step size is the starting point of the optimization process and the key parameters of the adjustment range. The initial value determines the starting point of the optimization, while the step size affects the refinement degree and convergence speed of the optimization. For example, the initial value can be set to a relatively large value to quickly approach the feasible solution region, and the step size can be appropriately adjusted according to the problem scale and complexity to balance the optimization speed and accuracy. Then, iterate and optimize the main target according to the initial value and step size. In each iteration, the ε value is adjusted according to the set step size, gradually narrowing the optimization range of the objective function while ensuring that other objectives meet the constraint conditions. This process continues until the termination condition is met, such as reaching the maximum number of iterations or the convergence accuracy of the solution meets the requirements, etc. Each iteration generates a solution that satisfies the current ε value constraint, and these solutions constitute the Pareto solution set. The solutions in the Pareto solution set represent the optimal trade-off between the objective function and other objectives under the current ε value. Finally, according to the Pareto solution set, the photovoltaic carrying capacity evaluation of the distribution network under the given access quantity is obtained. The solutions in the Pareto solution set provide the trade-off relationships between different optimization objectives. By analyzing these solutions, the optimal photovoltaic access scheme and its corresponding carrying capacity evaluation results can be determined under various constraint conditions. For example, an optimal scheme that comprehensively considers the photovoltaic access capacity, node voltage constraint, and line current-carrying capacity can be selected from the Pareto solution set as the final photovoltaic carrying capacity evaluation result.

[0040] In the preferred embodiment of the present invention, the principle of the ε-constraint method is to first optimize the most important one among multiple objectives, and consider other objectives as constraint conditions, as specifically shown below:

[0041]

[0042] ; Among them, is the objective function of the main optimization objective, is the objective function of other objectives, as the upper bound, which takes different values during the optimization process to discover multiple Pareto optimal solutions. In this way, the multi-objective optimization problem can be transformed into a single-objective optimization problem, and then ordinary methods can be used to solve it. This method is relatively simple and easy to implement. However, it should be noted that the accuracy of this method depends on the reasonable value of

[0043] In the embodiment of the present invention, through the ε-constraint method, it is possible to systematically explore the optimization solutions under different access schemes, comprehensively consider the trade-off relationship between multiple objectives, and ensure that the obtained evaluation results not only meet the requirements of the safe and stable operation of the distribution network but also maximize the PV access capacity. This method not only improves the solution efficiency but also enhances the reliability and practicality of the evaluation results, providing a scientific basis for the reasonable planning and access of distributed PV.

[0044] Optionally, obtaining the access location of the distributed PV in the distribution network includes: obtaining the access quantity of the distributed PV in the distribution network; determining the access location of the distributed PV in the distribution network according to the access quantity.

[0045] Specifically, the judgment of the access location is mainly based on the quantity of the access location. Specifically, when the quantity of the access location is greater than 1, it is determined as multi-point access. Multi-point access involves multiple different nodes, and the PV capacity is dispersed for access. The mutual influence between different access points and the interaction with the overall distribution network need to be considered. In the case of multi-point access, due to the uncertainty of the PV output characteristics and the influence of the load characteristics by the random behavior of users, the PV centroid theory needs to be adopted to consider the mutual influence between multiple access points and various constraint conditions. Therefore, it is necessary to determine the access location of the distributed PV. This method of determining the access method according to the quantity of the access location provides a clear direction and basis for the subsequent bearing capacity evaluation, making the evaluation process more scientific and accurate in reflecting the actual situation. Specifically, it is necessary to obtain the access quantity to determine the specific access location of the PV in the distribution network. In the preferred embodiment of the present invention, multiple factors such as the topological structure of the distribution network, the load conditions of each node, the line parameters, and the distribution of PV resources need to be comprehensively considered. By analyzing the topological structure of the distribution network, based on the access quantity, multiple combinations of access locations can be formed according to permutations and combinations.

[0046] In the embodiment of the present invention, by obtaining the access quantity and then determining the specific access location according to the quantity, the rationality and feasibility of the PV access scheme are ensured. It not only improves the efficiency of determining the access location but also enhances the acceptance capacity of the distribution network for distributed PV, helps to maximize the utilization of PV resources, and at the same time ensures the safe and stable operation of the distribution network.

[0047] Optionally, according to the photovoltaic center-of-gravity theory, the load center of gravity and the photovoltaic center of gravity are determined based on the active power distribution of the load in the distribution network and the active power output distribution of the photovoltaic combined with the access location, including: When the evaluation requirement is to determine the overall access capacity range of the distribution network, according to the topological structure of the distribution network, the positions used as the access locations in the distribution network are taken as the nodes of the distribution network; According to the active power distribution of the load in the distribution network, the active power of each node in the distribution network is determined, and the line resistance from each node to the root node of the distribution network is obtained; The load moment of the node is obtained by multiplying the active power of the load by the line resistance; The load center of gravity is obtained based on the sum of the load moments of all the nodes and the sum of the line resistances.

[0048] Specifically, first of all, according to the topological structure of the distribution network, the positions in the distribution network that can be used as access locations are taken as nodes. Since the topological structure of the distribution network determines the possible positions of PV access points, and the selection of these positions is crucial for subsequent bearing capacity assessment. Therefore, the selection of each node needs to comprehensively consider factors such as the line layout, load distribution, and potential PV access capacity of the distribution network. Secondly, according to the active power distribution of the load in the distribution network, the active power of the load at each node of the distribution network is determined. The active power distribution of the load reflects the load demand situation of each node in the distribution network. At the same time, the line resistance from each node to the root node of the distribution network is obtained. Among them, in the distribution network, the root node is usually the power supply point or starting point of the distribution system, such as the busbar or main transformer of a substation. It is responsible for distributing electric power from the transmission system to each branch line and providing electric energy for the entire distribution network. The line resistance is an important parameter for measuring the electrical distance between a node and the root node. The line resistance directly affects the impact of PV output on the node voltage. For example, the greater the line resistance, the more obvious the boosting effect of PV output on the node voltage. Then, by multiplying the active power of the load by the line resistance, the load moment of each node is obtained. The load moment is the product of the active power of the load and the line resistance, and the load moment characterizes the electrical position of the load in the distribution network. By calculating the load moment of each node, the impact of the load distribution on the voltage distribution of the distribution network can be quantified. For example, for a node with a larger load moment, the load has a more significant impact on the voltage of the distribution network. Finally, based on the sum of the load moments of all nodes and the sum of the line resistances, the load center of gravity is obtained. The load center of gravity is the weighted average position of all load moments and reflects the central position of the load distribution in the distribution network. The position of the load center of gravity determines the matching degree between PV output and load for PV bearing capacity assessment. In the distribution network, when the PV center of gravity is between the load center of gravity and the root node, it is called that the PV center of gravity is ahead of the load center of gravity, indicating that the PV output is mainly concentrated at the front end of the load center of gravity, close to the root node. At this time, the PV output may flow back in the direction of the root node, resulting in the voltage rise of the nodes along the line. Especially when the PV output is large, the node voltage may exceed the allowable upper limit. On the contrary, when the PV center of gravity is on the side of the load center of gravity far from the root node, that is, the PV center of gravity lags behind the load center of gravity, it means that the PV output is mainly concentrated at the back end of the load center of gravity, far from the root node. At this time, the PV output needs to pass through a longer line to reach the load center. During the transmission process, the voltage of the nodes along the way will gradually increase, increasing the probability of exceeding the upper limit. For example, when the PV center of gravity is ahead of the load center of gravity, the boosting effect of PV output on the node voltage is more obvious, which may lead to the node voltage exceeding the upper limit; while when the PV center of gravity lags behind the load center of gravity, the PV output needs to pass through a longer line to reach the load center, and the voltage along the way gradually increases, and the probability of exceeding the upper limit increases significantly.In practical applications, by determining the photovoltaic moment and the photovoltaic center of gravity, the matching problem between the complex photovoltaic distribution and the load distribution can be transformed into the relative position relationship of the geometric center of gravity, thereby simplifying the evaluation process.

[0049] In the embodiments of the present invention, by transforming the matching problem between the complex photovoltaic distribution and the load distribution into the relative position relationship of the geometric center of gravity, not only the evaluation process is simplified, but also the calculation efficiency is improved, thereby effectively handling the uncertainty of the photovoltaic output and the load distribution, and ensuring the accuracy and reliability of the evaluation results. By eliminating infeasible access schemes through global constraint conditions, the practicality of the evaluation is further improved, providing strong support for the reasonable planning and access of distributed photovoltaics.

[0050] Optionally, the method of determining the load center of gravity and the photovoltaic center of gravity according to the active power distribution of the load and the active power output distribution of the photovoltaic in the distribution network by using the photovoltaic center of gravity theory further includes: Determine the active power output of each node in the distribution network according to the active power output distribution of the photovoltaic in the distribution network; Multiply the active power output of the photovoltaic by the line resistance to obtain the photovoltaic moment of the node; Obtain the center of gravity of the photovoltaic moment according to the sum of the photovoltaic moments of all the nodes and the sum of the line resistances.

[0051] Specifically, in the distributed photovoltaic carrying capacity assessment, the matching problem between photovoltaic distribution and load distribution is transformed into the relative position relationship of geometric centers of gravity, thereby simplifying the complex assessment process. First, it is necessary to determine the photovoltaic active power output of each node in the distribution network according to the photovoltaic active power output distribution of the distribution network, so as to reflect the photovoltaic access capabilities of different nodes. The photovoltaic active power output distribution is affected by various factors, including geographical location, weather conditions, installation angle of photovoltaic panels, etc. Therefore, accurately obtaining the photovoltaic active power output of each node is crucial for subsequent assessments. Next, multiply the photovoltaic active power output of each node by the line resistance from the node to the root node of the distribution network to obtain the photovoltaic moment of each node. The photovoltaic moment is the product of the photovoltaic active power output and the line resistance, which characterizes the electrical position of the photovoltaic output in the distribution network. The calculation of the photovoltaic moment is similar to that of the load moment, but focuses on the photovoltaic output rather than the load demand. The magnitude of the photovoltaic moment reflects the degree of influence of the photovoltaic output on the voltage distribution of the distribution network. The greater the line resistance, the more obvious the voltage boosting effect of the photovoltaic output on the node voltage. Finally, by calculating the sum of the photovoltaic moments of all nodes and the sum of the line resistances, the center of gravity of the photovoltaic moment is obtained. The center of gravity of the photovoltaic moment is the weighted average position of all photovoltaic moments, which reflects the central position of the photovoltaic output distribution in the distribution network. The position of the center of gravity of the photovoltaic moment determines the matching degree between the photovoltaic output and the load. The center of gravity of the load is the weighted average position of all load moments, which reflects the central position of the load distribution in the distribution network. The position of the center of gravity of the load determines the matching degree between the photovoltaic output and the load for the photovoltaic carrying capacity assessment. In the distribution network, when the center of gravity of the photovoltaic is between the center of gravity of the load and the root node, it is called that the center of gravity of the photovoltaic is ahead of the center of gravity of the load. On the contrary, when the center of gravity of the photovoltaic is on the side of the center of gravity of the load far from the root node, that is, the center of gravity of the photovoltaic lags behind the center of gravity of the load. For example, when the center of gravity of the photovoltaic is ahead of the center of gravity of the load, the photovoltaic output is mainly concentrated at the front end of the distribution network, which may cause the node voltage to exceed the upper limit; while when the center of gravity of the photovoltaic lags behind the center of gravity of the load, the photovoltaic output needs to pass through a longer line to reach the load center, and the voltage gradually increases along the way, and the probability of exceeding the upper limit increases significantly. By determining the photovoltaic moment and the center of gravity of the photovoltaic, the complex matching problem between the photovoltaic distribution and the load distribution is transformed into the relative position relationship of geometric centers of gravity, thereby simplifying the assessment process. It not only considers the uncertainty of the photovoltaic output and the load distribution, but also eliminates infeasible access schemes through global constraint conditions, improving the assessment efficiency and accuracy.

[0052] In an embodiment of the present invention, the load moment is the product of the load active power and the line resistance from the node where the load is located to the root node of the distribution network : ; Wherein, is the load moment, is the load active power, is the resistance of the line from the node where the load is located to the root node of the distribution network.

[0053] Load center of gravity is the sum of all load moments divided by the sum of all active powers of the loads, representing the electrical distance of the line between the equivalent total active power of the load and the root node of the distribution network: ; wherein, is the load center of gravity, is the active power of the load at node j, is the line resistance from node j to the root node of the distribution network.

[0054] Photovoltaic moment is the product of the active power output of the photovoltaic and the line resistance between the node where the photovoltaic is located and the root node of the distribution network : ; wherein, is the photovoltaic moment, is the active power output of the photovoltaic, is the resistance of the line between the node where the photovoltaic is located and the root node of the distribution network.

[0055] Photovoltaic center of gravity (PVBarycenter) : The sum of all photovoltaic moments divided by the sum of all active power outputs of the photovoltaics represents the electrical distance of the line between the equivalent total photovoltaic output and the root node of the distribution network.

[0056] ; wherein, the line resistance , is the resistance per unit length of the distribution line. For different line models, this value is different. For a uniform distribution network with the same line model for all lines, this value is a constant, is proportional to , and l is the line length. The line length can be directly used to replace the resistance, and the above definition of the center of gravity is exactly the geometric center of gravity of the load distribution and the photovoltaic distribution.

[0057] In the embodiments of the present invention, by converting the complex matching problem of the photovoltaic distribution and the load distribution into the relative position relationship of the geometric center of gravity, not only the evaluation process is simplified, but also the calculation efficiency is improved. This method can effectively handle the uncertainty of the photovoltaic output and the load distribution, ensuring the accuracy and reliability of the evaluation results. By eliminating infeasible access schemes through global constraint conditions, the practicality of the evaluation is further improved.

[0058] Optionally, the global constraint condition for determining the distributed photovoltaic carrying capacity according to the photovoltaic center of gravity and the load center of gravity includes: Screen all the nodes of the distribution network according to the positional relationship between the photovoltaic center and the load center of gravity of each node; Among them, if the photovoltaic center of the node is ahead of the load center of gravity, the node is taken as an access node, and the photovoltaic carrying capacity constraint corresponding to the access node is obtained, and the photovoltaic carrying capacity constraint is used as the global constraint condition.

[0059] Specifically, in the evaluation of the distributed photovoltaic carrying capacity, determining the global constraint condition is a key step to ensure the safe and stable operation of the distribution network. In this process, it is mainly realized based on the positional relationship between the photovoltaic center of gravity and the load center of gravity. Specifically, first, all the nodes of the distribution network need to be screened according to the positional relationship between the photovoltaic center of gravity and the load center of gravity of each node. This screening process is based on the conclusion of theoretical analysis: after the distributed photovoltaic is connected to the distribution network, the relative position between the photovoltaic center of gravity and the load center of gravity is crucial for the node voltage. When the photovoltaic center of gravity is ahead of the load center of gravity, the photovoltaic output may flow back in the direction of the root node of the distribution network, resulting in the voltage rise of the nodes along the line. This voltage rise effect is particularly significant when the photovoltaic output is large, and it may cause the node voltage to exceed the allowable upper limit, thereby restricting the access capacity of the photovoltaic. In the distribution network, when the photovoltaic center of gravity is between the load center of gravity and the root node, it is called that the photovoltaic center of gravity is ahead of the load center of gravity. On the contrary, when the photovoltaic center of gravity is on the side of the load center of gravity far from the root node, that is, the photovoltaic center of gravity lags behind the load center of gravity. Therefore, by comparing the positions of the photovoltaic center of gravity and the load center of gravity, the nodes whose photovoltaic center of gravity is ahead of the load center of gravity can be screened out, and these nodes are used as potential access nodes. For these access nodes, the corresponding photovoltaic carrying capacity constraints are further obtained, and these constraints are used as the global constraint conditions. The photovoltaic carrying capacity constraint is determined according to the parameters of the distribution network (such as the root node voltage, line resistance, etc.) and the distribution characteristics of the photovoltaic and the load. Its purpose is to ensure that the node voltage does not exceed the allowable upper limit after the photovoltaic is connected, so as to ensure the safe and stable operation of the distribution network. This process involves complex theoretical analysis and calculations, and factors such as the uncertainty of photovoltaic output, the randomness of load characteristics, and the topological structure of the distribution network need to be considered comprehensively. The global constraint conditions determined in this way can provide clear guidance for the access of distributed photovoltaic, and ensure that the access capacity of photovoltaic is maximized on the premise of meeting the safe operation of the distribution network.

[0060] In the embodiment of the present invention, when the photovoltaic center of gravity is ahead of the load center of gravity, that is: When the equivalent total PV power flows reversely from the PV center of gravity to the root node of the distribution network, the node voltage may exceed the upper limit. To ensure that the node voltage at the most severe PV center of gravity does not exceed the upper limit, the distributed PV carrying capacity should satisfy: ; ; Among them, is the equivalent total PV power; is the equivalent total load; is the PV center of gravity; is the upper limit value of the allowable node voltage; At this time, it is the voltage at the root node of the distribution network ; For a given distribution network, it is a constant.

[0061] When the PV center of gravity lags behind the load center of gravity, that is: When the equivalent total PV power flows reversely from the PV center of gravity to the load center of gravity and then to the root node of the distribution network, the active power output always flows reversely, and the node voltage gradually increases, greatly increasing the probability of exceeding the upper limit. To ensure that the node voltage at the most severe PV center of gravity does not exceed the upper limit, the distributed PV carrying capacity should satisfy: ; ; Among them, is the load center of gravity. For a given distribution network, the upper limit value of the allowable node voltage and the upper limit value of the allowable line current carrying capacity are determined. If the load distribution is certain at this time, as the electrical distance between the PV center of gravity and the root node of the distribution network increases, the distributed PV carrying capacity as a whole shows an approximately linear downward trend.

[0062] In the embodiment of the present invention, by screening nodes based on the positional relationship between the PV center of gravity and the load center of gravity and obtaining the corresponding PV carrying capacity constraints, the problem of node voltage over-limit caused by PV access can be effectively avoided, ensuring the safe and stable operation of the distribution network after accepting distributed PV.

[0063] Optionally, obtaining the PV carrying capacity evaluation result of the distribution network according to the global constraint condition includes: Obtain the active power distribution of the access node, and determine the accounting section of the access node according to the active power distribution, where the accounting section is the section where active power reverse transmission continuously appears in the active power distribution; Calculate the accounting section according to the PV carrying capacity constraint of the access node to obtain the carrying capacity value corresponding to the accounting section; Obtain the PV carrying capacity evaluation result of the distribution network according to the carrying capacity value.

[0064] Specifically, first, it is necessary to obtain the active power distribution of the access node. The active power distribution refers to the active power flow conditions of each node in the distribution network, including the generation of active power (such as photovoltaic output), consumption (such as load demand), and transmission (such as power flow in the line). By analyzing the active power distribution, the power changes of each node in the distribution network after photovoltaic access can be determined. Specifically, it is necessary to calculate the active power of each node, including the active output of the photovoltaic access point, the active demand of the load node, and the active transmission in the line. These data can be obtained through power flow calculation. Power flow calculation is a commonly used power system analysis method that can calculate the voltage, current, and power distribution of each node based on the topological structure of the distribution network, the power injection of each node, and the line parameters. Then, determine the accounting section of the access node according to the active power distribution. The accounting section refers to the section where active power reverse transmission continuously appears in the active power distribution. Active power reverse transmission refers to the phenomenon that the photovoltaic output exceeds the local load demand, resulting in the reverse flow of power back to the root node of the distribution network. This phenomenon is particularly common when the photovoltaic access point is close to the front end of the distribution network (i.e., the photovoltaic center of gravity is ahead of the load center of gravity). By identifying these sections with continuous active power reverse transmission, it can be determined which parts of the distribution network may be significantly affected by photovoltaic access, especially in terms of voltage rise and line current carrying. The determination of the accounting section is based on the analysis of the active power distribution. By checking the power flow direction and magnitude of each node, the continuous sections with reverse power flow are identified. These sections usually start from the photovoltaic access point and extend along the lines of the distribution network to a certain node where the power flow direction changes (i.e., from reverse to forward). Then, calculate the accounting section according to the photovoltaic carrying capacity constraint of the access node to obtain the corresponding carrying capacity value of the accounting section. The photovoltaic carrying capacity constraint is obtained based on the global constraint conditions, which consider the safe operation requirements of the distribution network, such as the upper limit of node voltage and the line current carrying capacity. When calculating the carrying capacity value, it is necessary to ensure that the photovoltaic access within the accounting section does not cause the node voltage to exceed the allowable upper limit, nor does it cause the line current carrying capacity to exceed its rated value. The specific calculation method includes establishing a mathematical model based on the photovoltaic output, load demand, and line parameters, and solving it through an optimization algorithm to obtain the maximum allowable photovoltaic access capacity. Finally, obtain the photovoltaic carrying capacity assessment of the distribution network according to the carrying capacity value corresponding to the accounting section. The photovoltaic carrying capacity assessment is a quantitative description of the maximum capacity of distributed photovoltaic that the distribution network can accommodate. By analyzing the carrying capacity values of all accounting sections, the overall photovoltaic carrying capacity of the distribution network under different access schemes can be determined. The assessment results are usually given in the form of the maximum allowable access capacity, which is the maximum distributed photovoltaic capacity that the distribution network can accommodate under the premise of meeting the safe and stable operation requirements of the distribution network. The assessment results can be further refined. For example, according to the carrying capacity values of different access points, the optimal photovoltaic access scheme can be given, including the selection of access points, the allocation of access capacity, etc., providing specific guidance for the planning and access of distributed photovoltaic.

[0065] In a preferred embodiment of the present invention, the calculation relationships with the photovoltaic carrying capacity are respectively derived from two dimensions of the node voltage and the line current-carrying capacity. Among them, for the calculation of the node voltage constraint, the following calculation steps are derived: Ignoring the influence of line losses in the distribution network, each randomly generated photovoltaic (PV) access scheme is inspected. The network segments with continuous reverse active power flow are identified as the accounting units where the node voltage exceeds the limit. These units have opposite active power transmission directions at the starting point and the ending point, while the reactive power flow always moves "downstream" from the root node of the distribution network. If the terminal node voltage of such a calculation unit does not exceed the limit, it is assumed that the voltages of other nodes within the unit will not exceed the limit either. The calculation formula is as follows: ; ; ; ; Among them, and are respectively the reverse active and reactive power outputs at node , and the same applies to the rest; , , are the active power / reactive power of the photovoltaic and the load at node , and the same applies to the rest. Substituting the proportions of each photovoltaic, we get . Let the voltage at the starting point of the accounting section be , and let , that is, let the reverse active power output torque of the accounting section with the most serious over-limit of the node voltage meet the constraint requirements. After sorting out, the distributed photovoltaic carrying capacity value of the kth accounting section of the distribution network is obtained. After completing the calculations for all K accounting sections, taking the minimum value among them, the expression for the distributed photovoltaic carrying capacity value that satisfies the over-limit constraint of the node voltage under the corresponding photovoltaic access scheme of the distribution network is:

[0066] ; ; Among them, is the distributed photovoltaic carrying capacity value of the kth accounting section of the distribution network, is the minimum value of the carrying capacity values of all sections, that is, the final distributed photovoltaic carrying capacity value.

[0067] For the calculation of the line current-carrying capacity constraint, the following calculation steps are derived: Using the calculation results of the expression of the distributed photovoltaic carrying capacity value that satisfies the over-limit constraint of the node voltage above Based on the photovoltaic access locations and various photovoltaic ratios obtained from Monte Carlo simulation, recalculate the active power distribution and reactive power distribution of the entire distribution network. Taking the node as an example, where N is the number of nodes in the distribution network, and its calculation formula is as follows: ; Among them, is the upper limit value of the allowable line current-carrying capacity of the given distribution network, is the apparent power of the section with the most serious line current-carrying capacity over-limit. Let , and after sorting, obtain the distributed photovoltaic bearing capacity value considering the line current-carrying capacity constraint: ; Among them, is the reactive power of the load at node h, is the distributed photovoltaic bearing capacity value after considering the line current-carrying capacity constraint, is the active power output of the photovoltaic at node h, is the active power of the load at node h, is the photovoltaic ratio coefficient at node h.

[0068] Finally, the obtained calculation result is the distributed photovoltaic bearing capacity of the entire distribution network with branch structure that satisfies both the upper limit constraint of node voltage and the line current-carrying capacity constraint.

[0069] Similarly, for a homogeneous distribution network with the same line type, the line resistance in all the above formulas can be directly represented by the line length , which does not affect the theoretical analysis results.

[0070] In the embodiment of the present invention, by obtaining the active power distribution of the access node and determining the accounting section, the key sections in the distribution network that may be affected by photovoltaic access can be accurately identified. Further calculating the accounting section according to the photovoltaic bearing capacity constraint ensures that the evaluation result not only meets the safe operation requirements of the distribution network but also maximizes the photovoltaic access capacity.

[0071] To sum up, in order to verify the improvement of the evaluation accuracy and efficiency of the present invention for photovoltaic bearing capacity. As shown in Figure 2 , in the preferred embodiment of the present invention, taking the main line topological structure of a typical medium-voltage distribution network line as an example, a 5-section structure is adopted. Assume the total length of the line is L, and the length of each section is L / 5.

[0072] In this embodiment, the bearing capacity evaluation method based on multi-objective optimization can select the 5-section line shown in Figure 2 to carry out a case analysis of multi-point access. L is taken as 10 km, P is taken as 3 MW, considering multiple scenarios such as two-point access and three-point access of photovoltaic. The bearing capacity results of two-point access are as shown in Figure 3As shown below, taking the PV access nodes 4 and 5 as examples, the bearing capacity constraint relationship between the two nodes is drawn as follows Figure 4 shown. Combining Figure 4 it can be seen that there is a constraint relationship between the bearing capacities of the two nodes. When the bearing capacity of one node increases, the bearing capacity of the other node will decrease, and it is basically a linear relationship. Therefore, the multi-objective bearing capacity result is similar to / the same as the higher bearing capacity of the two nodes. The bearing capacity results of three-point access are as Figure 5 shown. Taking the PV access nodes 3, 4 and 5 as examples, the bearing capacity relationship diagram between the three nodes is drawn as follows Figure 6 shown. Combining Figure 6 it can be seen that there is a constraint relationship between the bearing capacities of the three nodes. Any change in the bearing capacity of one node will cause changes in the bearing capacities of the other two nodes, and it is basically a linear relationship. Therefore, the multi-objective bearing capacity result is similar to / the same as the higher bearing capacity of the three nodes. Through the above analysis, it can be obtained that since the bearing capacities between multiple nodes approximately show a linear relationship, the sum of the multi-point bearing capacities is similar to the node with the largest bearing capacity among them. Therefore, the single-point access bearing capacity result can approximately reflect the multi-point bearing capacity result.

[0073] In this embodiment, the bearing capacity evaluation method based on the PV center-of-gravity theory can still select Figure 2 the 5-segment line shown below to carry out the case analysis of multi-point access. L is taken as 10 km and P is taken as 3 MW. When using the traditional Monte Carlo simulation method and the method based on the PV center-of-gravity theory, the variation trends of the PV bearing capacity with the PV center-of-gravity are respectively as Figure 7 , Figure 8 and Figure 9 shown, where Figure 8 is the variation trend of the PV bearing capacity based on the PV center-of-gravity theory and discarding the scheme with a center-of-gravity greater than the load center-of-gravity, Figure 9 is the variation trend of the PV bearing capacity based on the PV center-of-gravity theory without discarding the scheme with a center-of-gravity greater than the load center-of-gravity. The comparison of the calculation efficiencies of the traditional Monte Carlo simulation method and the method based on the PV center-of-gravity theory is as Figure 10 shown.

[0074] Combining Figure 10 it can be seen that compared with the traditional Monte Carlo algorithm considering reactive power distribution and line loss, the calculation result of the method based on the PV center-of-gravity theory is slightly lower and relatively more conservative. At the same time, since this method directly obtains the result through numerical calculation without going through multiple optimization iterations, the calculation efficiency is greatly improved, and the time is only 1 / 50 of the traditional Monte Carlo method.

[0075] Combining Figure 11 shown, a PV bearing capacity evaluation system for a distribution network provided by an embodiment of the present invention includes: A judgment unit, configured to, when the access mode of the distributed photovoltaic in the distribution network is multi-point access, determine whether it is necessary to determine the access position of the distributed photovoltaic in the distribution network according to the evaluation requirements of the distribution network; An evaluation unit, configured to perform a photovoltaic carrying capacity evaluation according to the judgment result to obtain the photovoltaic carrying capacity evaluation result of the distribution network; Wherein, the judgment unit is specifically configured to, when the evaluation requirement is to determine the access capacity and access scheme of the distribution network to the photovoltaic, determine that it is not necessary to determine the access position of the distributed photovoltaic in the distribution network; The evaluation unit is specifically configured to construct a multi-objective optimization model of the distribution network through a multi-objective optimization method; and then solve the multi-objective optimization model by the ε-constraint method to obtain the photovoltaic carrying capacity evaluation result of the distribution network; The judgment unit is specifically further configured to, when the evaluation requirement is to determine the overall access capacity range of the distribution network, determine that it is necessary to determine the access position of the distributed photovoltaic in the distribution network; The evaluation unit is specifically further configured to obtain the access position of the distributed photovoltaic in the distribution network, and determine the load center of gravity and the photovoltaic center of gravity according to the load active power distribution and the photovoltaic active power output distribution of the distribution network in combination with the access position through the photovoltaic center of gravity theory; determine the global constraint conditions of the distributed photovoltaic carrying capacity according to the photovoltaic center of gravity and the load center of gravity; and obtain the photovoltaic carrying capacity evaluation result of the distribution network according to the global constraint conditions.

[0076] The photovoltaic carrying capacity evaluation system of the distribution network of the present invention has the same advantages as those of the above-mentioned photovoltaic carrying capacity evaluation method of the distribution network compared with the prior art, and will not be elaborated here.

[0077] A computer-readable storage medium provided by an embodiment of the present invention, on which a computer program is stored, and when the computer program is executed by a processor, the photovoltaic carrying capacity evaluation method of the distribution network as described above is implemented.

[0078] Or, a non-volatile computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the processor is caused to perform the following operations: When the access mode of the distributed photovoltaic in the distribution network is multi-point access, determine whether it is necessary to determine the access position of the distributed photovoltaic in the distribution network according to the evaluation requirements of the distribution network, and perform a photovoltaic carrying capacity evaluation according to the judgment result to obtain the photovoltaic carrying capacity evaluation result of the distribution network; Among them, when the evaluation requirement is to determine the access capacity of the distribution network to photovoltaic power and the access scheme, it is determined that there is no need to determine the access location of the distributed photovoltaic power in the distribution network, and a multi-objective optimization model of the distribution network is constructed by means of a multi-objective optimization method; then the multi-objective optimization model is solved by the ε-constraint method to obtain the evaluation result of the photovoltaic carrying capacity of the distribution network; When the evaluation requirement is to determine the overall access capacity range of the distribution network, it is determined that it is necessary to determine the access location of the distributed photovoltaic power in the distribution network, and the access location of the distributed photovoltaic power in the distribution network is obtained. According to the photovoltaic center-of-gravity theory, the load center of gravity and the photovoltaic center of gravity are determined by combining the access location with the active power distribution of the load and the active power output distribution of the photovoltaic in the distribution network; according to the photovoltaic center of gravity and the load center of gravity, the global constraint conditions of the distributed photovoltaic carrying capacity are determined; according to the global constraint conditions, the evaluation result of the photovoltaic carrying capacity of the distribution network is obtained.

[0079] The advantages of the computer-readable storage medium of the present invention are the same as those of the above-mentioned photovoltaic carrying capacity evaluation method for the distribution network compared with the prior art, and will not be elaborated here.

[0080] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will all fall within the protection scope of the present invention.

Claims

1. A method for evaluating the photovoltaic carrying capacity of a distribution network, characterized in that Including: When the access mode of distributed photovoltaics in the distribution network is multi-point access, according to the evaluation requirements of the distribution network, it is judged whether it is necessary to determine the access location of distributed photovoltaics in the distribution network, and according to the judgment result, the photovoltaic carrying capacity evaluation is carried out to obtain the photovoltaic carrying capacity evaluation result of the distribution network; Among them, when the evaluation requirement is to determine the access capacity and access scheme of the distribution network for photovoltaics, it is determined that there is no need to determine the access location of the distributed photovoltaics in the distribution network, and a multi-objective optimization model of the distribution network is constructed by a multi-objective optimization method; then the multi-objective optimization model is solved by the ε-constraint method to obtain the photovoltaic carrying capacity evaluation result of the distribution network; When the evaluation requirement is to determine the overall access capacity range of the distribution network, it is determined that it is necessary to determine the access location of the distributed photovoltaics in the distribution network, and the access location of the distributed photovoltaics in the distribution network is obtained. Through the photovoltaic center of gravity theory, according to the active power distribution of the load in the distribution network and the active power output distribution of the photovoltaics combined with the access location, the load center of gravity and the photovoltaic center of gravity are determined; according to the photovoltaic center of gravity and the load center of gravity, the global constraint conditions of the distributed photovoltaic carrying capacity are determined; according to the global constraint conditions, the photovoltaic carrying capacity evaluation result of the distribution network is obtained.

2. The photovoltaic carrying capacity evaluation method of the distribution network according to claim 1, wherein The construction of the multi-objective optimization model of the distribution network by the multi-objective optimization method includes: When the evaluation requirement for photovoltaic carrying capacity is to determine the overall access capacity and access scheme of the distribution network, according to the topological structure of the distribution network, the positions used as the access locations in the distribution network are taken as the nodes of the distribution network; According to the maximum value of the access capacity corresponding to each node, the objective function is obtained; According to the power data of the branches corresponding to each node, the power flow constraint and the network security constraint are set, and the power flow constraint and the network security constraint are used as the constraint conditions; According to the constraint conditions and the objective function, the multi-objective optimization model of the distribution network is constructed.

3. The photovoltaic carrying capacity evaluation method for the distribution network according to claim 2, wherein The solution of the multi-objective optimization model by the ε-constraint method to obtain the photovoltaic carrying capacity evaluation result of the distribution network includes: According to the access number of the multi-point access, the nodes in the distribution network are matched according to the access number to obtain a plurality of access schemes, and each access scheme includes two different nodes; Randomly select an access scheme as the main objective, and set the initial value and step size of ε; Iteratively optimize the main objective according to the initial value and the step size. When the iterative optimization is completed, a Pareto solution set including all the access schemes is obtained; According to the Pareto solution set, the photovoltaic carrying capacity evaluation result of the distribution network under the access number is obtained.

4. The method for evaluating the photovoltaic carrying capacity of a distribution network according to claim 1, wherein, The obtaining of the access location of the distributed photovoltaics in the distribution network includes: Obtain the access number of the distributed photovoltaics in the distribution network; According to the access number, determine the access location of the distributed photovoltaics in the distribution network.

5. The method for evaluating the photovoltaic carrying capacity of a distribution network according to claim 1, characterized in that, The determination of the load center of gravity and the PV center of gravity by means of the PV center of gravity theory, according to the active power distribution of the load in the distribution network and the active power output distribution of the PV in combination with the access location, includes: When the evaluation requirement is to determine the overall access capacity range of the distribution network, according to the topological structure of the distribution network, the positions used as the access locations in the distribution network are taken as the nodes of the distribution network; According to the active power distribution of the load in the distribution network, determine the active power of the load at each node of the distribution network, and obtain the line resistance from each node to the root node of the distribution network; Perform a multiplication operation on the active power of the load and the line resistance to obtain the load moment of the node; Obtain the load center of gravity according to the sum of the load moments of all the nodes and the sum of the line resistances; 6. The photovoltaic carrying capacity assessment method for a distribution network according to claim 5, characterized in that The determination of the load center of gravity and the PV center of gravity by means of the PV center of gravity theory, according to the active power distribution of the load in the distribution network and the active power output distribution of the PV in combination with the access location, further includes: According to the active power output distribution of the PV in the distribution network, determine the active power output of the PV at each node of the distribution network; Perform a multiplication operation on the active power output of the PV and the line resistance to obtain the PV moment of the node; Obtain the PV moment center of gravity according to the sum of the PV moments of all the nodes and the sum of the line resistances; 7. The method for evaluating the photovoltaic carrying capacity of a distribution network according to claim 5, characterized in that The determination of the global constraint conditions of the distributed PV bearing capacity according to the PV center of gravity and the load center of gravity includes: Screen all the nodes of the distribution network through the positional relationship between the PV center and the load center of gravity of each node; Among them, if the PV center of the node is ahead of the load center of gravity, the node is taken as an access node, and the PV bearing capacity constraint corresponding to the access node is obtained, and the PV bearing capacity constraint is used as the global constraint condition.

8. The photovoltaic carrying capacity assessment method for a distribution network according to claim 7, characterized in that The obtaining of the PV bearing capacity evaluation result of the distribution network according to the global constraint conditions includes: Obtain the active power distribution of the access node, and determine the accounting section of the access node according to the active power distribution, where the accounting section is the section where active power feedback continuously appears in the active power distribution; Calculate the accounting section according to the PV bearing capacity constraint of the access node to obtain the bearing capacity value corresponding to the accounting section; Obtain the PV bearing capacity evaluation result of the distribution network according to the bearing capacity value.

9. A photovoltaic carrying capacity evaluation system for a distribution network, characterized in that, It includes: A judgment unit, configured to, when the access mode of the distributed PV in the distribution network is multi-point access, judge whether it is necessary to determine the access location of the distributed PV in the distribution network according to the evaluation requirement of the distribution network; An evaluation unit, configured to perform PV bearing capacity evaluation according to the judgment result to obtain the PV bearing capacity evaluation result of the distribution network; Among them, the judgment unit is specifically configured to, when the evaluation requirement is to determine the access capacity and access scheme of the distribution network for the PV, determine that it is not necessary to determine the access location of the distributed PV in the distribution network; The evaluation unit is specifically configured to construct a multi-objective optimization model of the distribution network through a multi-objective optimization method; and then solve the multi-objective optimization model by the ε-constraint method to obtain the evaluation result of the photovoltaic carrying capacity of the distribution network. The judgment unit is specifically further configured to determine the access position of the distributed photovoltaic in the distribution network when the evaluation requirement is to determine the overall access capacity range of the distribution network. The evaluation unit is specifically further configured to obtain the access position of the distributed photovoltaic in the distribution network, and determine the load center of gravity and the photovoltaic center of gravity according to the active power distribution of the load and the active power output distribution of the photovoltaic in the distribution network in combination with the access position through the photovoltaic center of gravity theory; determine the global constraint conditions of the distributed photovoltaic carrying capacity according to the photovoltaic center of gravity and the load center of gravity; and obtain the evaluation result of the photovoltaic carrying capacity of the distribution network according to the global constraint conditions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method for evaluating the photovoltaic carrying capacity of a distribution network according to any one of claims 1-8.

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