Method and system for optimizing distributed photovoltaic bearing of power distribution network

Abstract

The invention discloses a distributed photovoltaic load optimization method and system for a power distribution network, and the method comprises the steps: obtaining the distributed photovoltaic scale of each node in a node set of the power distribution network and the power demand of a user, carrying out the source load prediction of each node, and obtaining a power output curve and a load curve; calculating a source-to-load ratio and a source-to-load matching degree of each node based on a distributed photovoltaic scale, a user power demand, a power supply output curve and a load curve, identifying key nodes from a node set based on the source-to-load ratio and the source-to-load matching degree according to a preset key node identification rule, and generating an alternative scheme set for each key node in a key node library, and calculating the unit investment increase photovoltaic consumption scale of each alternative scheme in the alternative scheme set, selecting an optimal scheme for each key node from the alternative scheme set by taking the maximum unit investment increase photovoltaic consumption scale as a target, and performing distributed photovoltaic bearing optimization on each key node by using the optimal scheme. Therefore, the distributed photovoltaic bearing capacity of the regional power distribution network is effectively and reliably improved.

Description

Technical Field

[0001] This invention relates to the field of distribution network optimization technology, and in particular to a method and system for optimizing the distributed photovoltaic load in a distribution network. Background Technology

[0002] The rapid development of new power systems, particularly the large-scale development of intermittent power sources such as distributed photovoltaic (PV) power, is posing challenges to the power flow distribution and stability of distribution networks. In some areas where PV resources have been developed rapidly, severe backflow is occurring, leading to insufficient capacity in the distribution network and hindering the further development of distributed PV. To improve the capacity of distribution networks to support distributed renewable energy, current distribution network capacity analysis uses the reverse load rate as a key indicator for different grid levels' capacity to support renewable energy, further dividing the network into red, yellow, and green zones, with restrictions placed on PV access in the red zones.

[0003] To enhance the load-bearing capacity of distribution networks for distributed photovoltaic (PV) power, current research and practice focus on different approaches for heavily loaded lines and transformers in different regions. These approaches include medium-voltage converters, energy storage configurations, group dispatching and control, and equipment expansion. However, this approach focuses on addressing localized issues rather than comprehensively considering optimal strategies for PV integration from a system-wide perspective. Furthermore, the measures fail to adequately consider the spatiotemporal matching of power sources and loads, neglecting key nodes or sections for PV integration. Consequently, these enhancement measures do not align well with regional development, only addressing immediate problems without addressing long-term sustainability. They are also relatively uneconomical and cannot effectively and reliably improve the load-bearing capacity of regional distribution networks for distributed PV. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for optimizing the load-bearing capacity of distributed photovoltaic power in distribution networks, which can effectively and reliably improve the load-bearing capacity of regional distribution networks for distributed photovoltaic power.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for optimizing the distributed photovoltaic (PV) carrying capacity of a distribution network includes the following steps: The distributed photovoltaic scale and user electricity demand of each node in the distribution network node set are obtained, and the source load is predicted for each node to obtain the power output curve and load curve. Based on the distributed photovoltaic scale, the user electricity demand, the power output curve, and the load curve, calculate the source-load ratio and source-load matching degree of each node; Based on the source-load ratio and the source-load matching degree, key nodes are identified from the node set according to the preset key node identification rules to obtain a key node library; Generate a set of alternative solutions for each key node in the key node library, and calculate the increase in photovoltaic grid integration capacity per unit investment for each alternative solution in the set of alternative solutions; With the objective of maximizing the increase in photovoltaic power consumption per unit investment, the optimal solution is selected from the set of alternative solutions for each key node. The optimal solution is used to optimize the distributed photovoltaic load of each key node.

[0006] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows: A distributed photovoltaic load optimization system for a distribution network includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps in the aforementioned distributed photovoltaic load optimization method for a distribution network.

[0007] The beneficial effects of this invention are as follows: It obtains the distributed photovoltaic (PV) scale and user electricity demand of each node in the distribution network node set, and performs source-load prediction for each node to obtain power output curves and load curves. Based on the distributed PV scale, user electricity demand, power output curves, and load curves, it calculates the source-load ratio and source-load matching degree of each node. According to preset key node identification rules, it identifies key nodes from the node set based on the source-load ratio and source-load matching degree, generates a set of alternative solutions for each key node in the key node library, and calculates the increase in PV consumption per unit investment for each alternative solution in the alternative solution set. With the maximum increase as the objective, it selects the optimal solution from the alternative solution set. The optimal solution is selected for each key node, and the distributed photovoltaic (PV) carrying capacity of each key node is optimized using the optimal solution. This is achieved by calculating the source-load ratio and source-load matching degree of each node, and subsequently identifying key nodes based on the source-load ratio and source-load matching degree, and optimizing the key nodes. The spatiotemporal matching relationship of source and load is fully considered, realizing the transformation from local governance to system optimization, and systematically optimizing the PV absorption capacity. At the same time, the optimal solution is selected with the goal of maximizing the increase in PV absorption capacity per unit investment of each alternative solution, which significantly reduces governance costs and improves investment efficiency, thereby effectively and reliably improving the regional distribution network's carrying capacity for distributed PV. Attached Figure Description

[0008] Figure 1 This is a flowchart of a method for optimizing the distributed photovoltaic load in a power distribution network according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a distributed photovoltaic load optimization system for a power distribution network according to an embodiment of the present invention; Figure 3 This is a topology diagram of the distribution network in a method for optimizing the distributed photovoltaic load in a distribution network according to an embodiment of the present invention. Figure 4This is a four-quadrant absorption capacity distribution diagram in a distributed photovoltaic load optimization method for power distribution networks according to an embodiment of the present invention. Figure 5 This is a schematic diagram of distributed photovoltaic load optimization in a distribution network distributed photovoltaic load optimization method according to an embodiment of the present invention. Detailed Implementation

[0009] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0010] Before detailing the embodiments of this application, some related concepts will first be explained: Source-load ratio: refers to the ratio of tiered power generation capacity to tiered electricity load. It is used to measure the overall proportional relationship between distributed photovoltaic power generation capacity and local load demand, reflecting whether the connected photovoltaic capacity is adapted to local load demand, and representing the spatial distribution of source and load. Source-load matching degree: refers to the correlation coefficient between the power output curve of the hierarchical power supply and the load curve of the hierarchical power supply. It is an important indicator for evaluating the synergy between distributed photovoltaic power generation and local load in terms of time distribution. Key nodes: These refer to the nodes in a region where distributed photovoltaic power generation is difficult to absorb, and are often the crux of the problem restricting the development of distributed photovoltaic power generation in the region.

[0011] Existing technologies primarily target heavily loaded lines and transformers in different regions, employing differentiated approaches such as medium-voltage converters, energy storage configurations, group dispatching and control, and equipment expansion to improve the regional distribution network's capacity to support photovoltaic (PV) power. However, this approach focuses on addressing localized issues rather than comprehensively considering optimization strategies for PV integration from a system-wide perspective. Furthermore, the measures fail to adequately consider the spatiotemporal matching of source and load, neglecting key nodes or sections for PV integration. Consequently, these enhancement measures do not align well with regional development, only addressing immediate problems without addressing long-term sustainability. They are also relatively uneconomical and cannot effectively and reliably improve the regional distribution network's capacity to support distributed PV power.

[0012] To at least solve the above problems, please refer to Figure 1 This invention provides a method for optimizing the distributed photovoltaic (PV) carrying capacity of a distribution network, comprising the following steps: The distributed photovoltaic scale and user electricity demand of each node in the distribution network node set are obtained, and the source load is predicted for each node to obtain the power output curve and load curve. Based on the distributed photovoltaic scale, the user electricity demand, the power output curve, and the load curve, calculate the source-load ratio and source-load matching degree of each node; Based on the source-load ratio and the source-load matching degree, key nodes are identified from the node set according to the preset key node identification rules to obtain a key node library; Generate a set of alternative solutions for each key node in the key node library, and calculate the increase in photovoltaic grid integration capacity per unit investment for each alternative solution in the set of alternative solutions; With the objective of maximizing the increase in photovoltaic power consumption per unit investment, the optimal solution is selected from the set of alternative solutions for each key node. The optimal solution is used to optimize the distributed photovoltaic load of each key node.

[0013] As can be seen from the above description, the beneficial effects of the present invention are as follows: It obtains the distributed photovoltaic scale and user electricity demand of each node in the distribution network node set, and performs source-load prediction for each node to obtain power output curves and load curves. Based on the distributed photovoltaic scale, user electricity demand, power output curves, and load curves, it calculates the source-load ratio and source-load matching degree of each node. According to preset key node identification rules, it identifies key nodes from the node set based on the source-load ratio and source-load matching degree, generates a set of alternative solutions for each key node in the key node library, and calculates the increase in photovoltaic consumption scale per unit investment for each alternative solution in the alternative solution set. With the maximum of these increases as the objective, it selects the appropriate alternative solution from the available options. The case focuses on selecting the optimal solution for each key node and using the optimal solution to optimize the distributed photovoltaic (PV) carrying capacity of each key node. This is achieved by calculating the source-load ratio and source-load matching degree of each node, and subsequently identifying key nodes based on the source-load ratio and source-load matching degree, and optimizing the key nodes. It fully considers the spatiotemporal matching relationship of source and load, realizing the transformation from local governance to system optimization, and systematically optimizing the PV absorption capacity. At the same time, the optimal solution is selected with the goal of maximizing the increase in PV absorption capacity per unit investment of each alternative solution, which significantly reduces governance costs and improves investment efficiency, thereby effectively and reliably improving the regional distribution network's carrying capacity for distributed PV.

[0014] Furthermore, calculating the source-load ratio and source-load matching degree of each node based on the distributed photovoltaic scale, the user electricity demand, the power output curve, and the load curve includes: The source-load ratio of each node is calculated based on the scale of the distributed photovoltaic system and the user's electricity demand. The source-load matching degree of each node is calculated using the Pearson correlation coefficient based on the power output curve and the load curve.

[0015] As described above, the source-load ratio of each node is calculated based on the scale of distributed photovoltaic power generation and user electricity demand. The source-load matching degree of each node is calculated using the Pearson correlation coefficient based on the power output curve and load curve. The source-load ratio reflects the spatial distribution of source and load, while the source-load matching degree reflects the temporal distribution of source and load. Therefore, by calculating the source-load ratio and source-load matching degree of the nodes, the spatiotemporal matching relationship of source and load is fully considered, which can more effectively improve the carrying capacity of the regional distribution network for distributed photovoltaic power generation.

[0016] Furthermore, based on the scale of the distributed photovoltaic system and the user's electricity demand, the source-to-load ratio of each node is calculated, specifically as follows: ; In the formula, Indicates the source-to-charge ratio of a node. P z Indicates the scale of photovoltaic installations. P f Indicates the maximum load in the area; The source-load matching degree of each node is calculated using the Pearson correlation coefficient based on the power output curve and the load curve, specifically as follows: ; In the formula, This represents the source-load matching degree of a node. N This represents the total number of time points in a day when data was collected. x i Indicating the power output curve of the first... i Make an effort at that moment. y i Indicating the first load curve i The load at any given moment.

[0017] As described above, the source-load ratio is the ratio of tiered power generation capacity to tiered electricity load. It measures the overall proportional relationship between distributed photovoltaic (PV) power capacity and local load demand, reflecting whether the connected PV capacity is compatible with local load demand. It represents the spatial distribution of source and load; a high source-load ratio indicates that the scale of power connection is relatively large compared to the load. The source-load matching degree is the correlation coefficient between the tiered power output curve and the tiered load curve. It is an important indicator for evaluating the temporal synergy between distributed PV power generation and local load. It focuses on measuring the degree of temporal matching between power supply and load demand, rather than just the balance in total volume. A high matching degree indicates that PV power generation can be more effectively absorbed by local load. Through the source-load ratio and source-load matching degree, key nodes that need to be optimized can be identified more accurately.

[0018] Furthermore, based on the source-load ratio and the source-load matching degree, key nodes are identified from the node set according to the preset key node identification rules, resulting in a key node library including: Based on the preset key node identification rules, a four-quadrant absorption capacity distribution map of the node set is generated according to the source-load ratio and the source-load matching degree. Select the nodes with the highest percentage of absorption difficulty from the four-quadrant absorption capacity distribution map and add them to the candidate pool; The nodes in the candidate library are sorted based on the source-load ratio and the source-load matching degree to obtain the sorted candidate library. Analyze the hierarchical relationships between nodes in the sorted candidate library. If two nodes with a hierarchical relationship are adjacent in sorting order, adjust the sorting order of the lower-level node to be before the sorting order of the upper-level node to obtain the key node library.

[0019] As described above, the four-quadrant absorption capacity distribution map of the node set is generated based on the source-load ratio and source-load matching degree. At the same time, considering the relationship between the upper and lower levels of the nodes, nodes with high source-load ratio and low matching degree can be selected first to build a key node library and accurately identify the core nodes that restrict the development of distributed photovoltaics.

[0020] Furthermore, generating a set of alternative solutions for each key node in the key node library includes: Based on the type of each key node in the key node library, and according to the source-load ratio and the source-load matching degree, typical technical means are selected from the preset photovoltaic absorption capacity improvement strategy tree to obtain a set of alternative solutions.

[0021] As described above, by selecting typical technical means from the preset photovoltaic absorption capacity improvement strategy tree based on the source-load ratio and source-load matching degree according to the type of each key node in the key node library, a set of alternative solutions can be obtained, which can ensure the technical feasibility of the selected alternative solutions.

[0022] Furthermore, calculating the increase in photovoltaic grid integration capacity per unit investment for each alternative in the set of alternative solutions includes: Obtain the equipment purchase cost, construction and installation cost, operation and maintenance management cost, other costs, and increased photovoltaic consumption capacity for each alternative scheme in the set of alternative schemes; Calculate the comprehensive investment of each alternative scheme based on the equipment purchase cost, the construction and installation cost, the operation and maintenance management cost, and the other costs; The increase in photovoltaic absorption capacity per unit investment for each alternative scheme is calculated based on the overall investment and the increased photovoltaic absorption capacity.

[0023] Furthermore, the comprehensive investment of each alternative scheme is calculated based on the equipment purchase cost, the construction and installation cost, the operation and maintenance management cost, and the other costs, specifically as follows: ; In the formula, Si Indicates the first i The total investment of the alternative options S i,设备 This indicates the cost of purchasing the equipment. S i,施工 This indicates the construction and installation costs. S i,运维 This indicates the operation and maintenance management costs. S i,其他 Indicates other expenses; The increase in photovoltaic grid connection capacity per unit investment for each alternative scheme is calculated based on the overall investment and the increased photovoltaic grid connection capacity, specifically as follows: ; In the formula, G i Indicates the first i Each alternative plan increases the unit investment in photovoltaic power consumption capacity. P i Indicates the first i The alternative scheme increases the photovoltaic consumption capacity.

[0024] As can be seen from the above description, the increase in photovoltaic absorption capacity per unit investment for each alternative scheme is calculated based on the comprehensive investment and the increased photovoltaic absorption capacity, which effectively ensures the economic efficiency of load optimization.

[0025] Furthermore, obtaining the distributed photovoltaic scale and user electricity demand of each node in the distribution network node cluster includes: Construct the topology diagram of the distribution network based on the network structure; The node set of the distribution network is obtained based on the topology diagram; Based on the topology diagram, the distributed photovoltaic scale and user electricity demand of each node in the node set are obtained.

[0026] As described above, constructing a topology diagram of the distribution network and obtaining the distributed photovoltaic scale and user electricity demand of each node in the node set based on the topology diagram can effectively match the actual situation and facilitate subsequent accurate analysis of the spatiotemporal matching relationship between source and load.

[0027] Furthermore, after optimizing the distributed photovoltaic load capacity of each key node using the optimal solution, the process also includes: Determine whether the distributed photovoltaic load optimization has eliminated all photovoltaic consumption problems. If not, return to the step of obtaining the distributed photovoltaic scale and user electricity demand of each node in the distribution network node set.

[0028] As described above, if the distributed photovoltaic load optimization does not eliminate all photovoltaic consumption problems, a dynamic update and feedback mechanism is adopted to reacquire basic data, recalculate the node source-load ratio and matching degree, update the key node library, and dynamically adjust the strategy according to the source-load development to ensure the long-term adaptability of the system.

[0029] Please refer to Figure 2 Another embodiment of the present invention provides a distributed photovoltaic load optimization system for a distribution network, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the above-described distributed photovoltaic load optimization method for a distribution network.

[0030] The above-described method and system for optimizing the distributed photovoltaic (PV) load in a distribution network are applicable to distribution networks connected to distributed PV systems. The specific implementation methods described below illustrate this. Please refer to Figure 1 One embodiment of the present invention is as follows: A method for optimizing the distributed photovoltaic (PV) carrying capacity of a distribution network includes the following steps: S1. Obtain the distributed photovoltaic scale and user electricity demand of each node in the distribution network node set, and perform source-load prediction for each node to obtain the power output curve and load curve, such as... Figure 5 As shown, specifically including S11-S14: S11. Construct the topology diagram of the distribution network based on its network structure, such as... Figure 3 As shown.

[0031] The relationships between nodes in the topology diagram are determined based on the layout of medium-voltage lines and distribution transformers.

[0032] S12. Obtain the node set of the distribution network based on the topology diagram.

[0033] like Figure 3 As shown, the node set is represented as In the formula, n i,j,k Indicates that it is located on a 10kV line i Line load access points and branch points j The branch line is connected to the distribution transformer k The node uses a three-level naming system, with the first level being the 10kV line number. i The second level consists of the line load access point and branch point numbering. j The third level is the transformer number to which the branch line is connected. k There are hierarchical relationships between the nodes, for example, n 1,5,0 yes n1,4,0 subordinates, n 1,3,1 yes n 1,3,0 The subordinate level, and some nodes have communication relationships, for example n 1,3,1 and n 2,1,1 There is a connection between them.

[0034] Distributed photovoltaic power mainly connects to 0.4 / 10kV voltage levels, connecting to corresponding nodes based on the development location, and operating in a mode of self-consumption, surplus power to the grid, or full grid connection. The grid connection method and topology are as follows: Figure 3 As shown. Meanwhile, the scale of distributed photovoltaic installations connected to each node... S n,PV The rated capacity of the node should not be exceeded. Q n ,Right now: Q n S n,PV ; Maximum output of distributed photovoltaic power at each node P n,PV No more than the installed capacity of this node S n,PV ,Right now: P n,PV S n,PV 0.

[0035] S13. Obtain the distributed photovoltaic scale and user electricity demand of each node in the node set according to the topology diagram.

[0036] The distributed photovoltaic scale refers to the photovoltaic capacity of this node and its subordinate connected units, and the user electricity demand refers to all loads of this node and its subordinate connected units.

[0037] S14. Perform source load prediction on each node to obtain the power output curve and load curve.

[0038] Specifically, based on the scale of the distributed photovoltaic system and the electricity demand of the users, source load prediction is performed on each node to obtain the power output curve and load curve.

[0039] The core constraints affecting the absorption capacity of distributed photovoltaic (PV) power grids mainly include two aspects: the scale of distributed PV grid connection and the matching relationship between distributed PV and load. The factors affecting its carrying capacity are mainly the grid capacity level and the interconnection and transfer level.

[0040] S2. Based on the distributed photovoltaic scale, the user electricity demand, the power output curve, and the load curve, calculate the source-load ratio and source-load matching degree of each node, such as... Figure 5 As shown, specifically including S21-S22: S21. Calculate the source-load ratio of each node based on the distributed photovoltaic scale and the user's electricity demand, specifically as follows: ; In the formula, Indicates the source-to-charge ratio of a node. P z Indicates the scale of photovoltaic installations. P f This indicates the maximum load in the area.

[0041] A high source-to-load ratio indicates that the scale of power supply access is relatively large compared to the load. Considering the grid's carrying capacity, The value range is [0,2]. When the source-load ratio is equal to 1, the regional photovoltaic installed capacity is basically matched with the maximum load; when the source-load ratio is less than 1, the regional photovoltaic installed capacity is less than the maximum load; when the source-load ratio is greater than 1, the regional photovoltaic installed capacity is greater than the maximum load.

[0042] S22. Calculate the source-load matching degree of each node using the Pearson correlation coefficient based on the power output curve and the load curve, specifically: ; In the formula, This represents the source-load matching degree of a node. N This indicates the total number of time points in a day when data is collected. For example, if a day has 24 hours, and one data point is collected every hour, then... N =24, if data is collected every 15 minutes, then N =96, x i Indicating the power output curve of the first... i Make an effort at that moment. y i Indicating the first load curve i The load at any given moment.

[0043] A high source-load matching degree indicates that photovoltaic power generation can be more effectively absorbed by local loads. The value range is [-1, 1]. When When >0, it indicates that the output of new energy sources is positively correlated with the load; when When <0, it indicates that the output of new energy sources is negatively correlated with the load; when When = 0, it indicates that the output of new energy sources is unrelated to the load; when When the value is 1 or -1, it means that the output of new energy sources is completely positively correlated with the load and completely negatively correlated with the load.

[0044] The source-charge ratio of the final node set can be expressed as: { 1,1,0 , 1,2,0 , 1,3,0 , 1,4,0 , 1,5,0 , 1,3,1 ,..., i,j,k}, i,j,k Represents a node n i,j,k The source-load ratio and the source-load matching degree of the node set can be expressed as: { 1,1,0 , 1,2,0 , 1,3,0 , 1,4,0 , 1,5,0 , 1,3,1 ,..., i,j,k}, i,j,k Represents a node n i,j,k The source-load matching degree.

[0045] S3. Based on the source-load ratio and the source-load matching degree, identify key nodes from the node set according to the preset key node identification rules to obtain a key node library, such as... Figure 5 As shown, specifically including S31-S34: S31. Based on the preset key node identification rules, generate a four-quadrant absorption capacity distribution map of the node set according to the source-load ratio and the source-load matching degree, such as... Figure 4 As shown.

[0046] The preset key node identification rule is as follows: If the source-load ratio of a node is lower, it means that the scale of distributed photovoltaic power connected to this node is relatively low compared to the load. Furthermore, if the source-load matching degree of this node is high, it means that this node and its downstream photovoltaic power can achieve local consumption. Such nodes will not become critical nodes. If the source-load matching degree of this node is low, it means that there may be a short-term back-feeding problem in the photovoltaic power consumption of this node. Such nodes are unlikely to become critical nodes. A higher source-load ratio at a node indicates a higher scale of distributed photovoltaic (PV) power connected to that node relative to the load. Furthermore, a high source-load matching degree at a node indicates that the node and its downstream PV power can achieve local consumption. However, due to the large scale of PV power, large-scale backflow is likely to occur, and such nodes are likely to become critical nodes. Conversely, a low source-load matching degree at a node indicates that local PV power consumption at that node is difficult, with significant backflow and a high probability of reverse overload. Such nodes are also critical nodes.

[0047] S32. Select the node with the highest percentage of absorption difficulty from the four-quadrant absorption capacity distribution map and add it to the candidate library.

[0048] Specifically, such as Figure 4 As shown, since the nodes in the four-quadrant absorption capacity distribution map are closer to the upper left corner (higher source-load ratio and lower source-load matching degree), the local absorption of distributed photovoltaic power is more difficult. Nodes with a preset percentage closer to the upper left corner are selected from the four-quadrant absorption capacity distribution map as nodes with the highest absorption difficulty and added to the candidate pool.

[0049] In one alternative implementation, the preset percentage is 20%.

[0050] S33. Sort the nodes in the candidate library based on the source-load ratio and the source-load matching degree to obtain the sorted candidate library.

[0051] Specifically, for any two nodes in the candidate library, if the source-load ratio of the first node is greater than that of the second node, and the source-load matching degree of the first node is less than that of the second node, then the first node is considered to have a higher degree of difficulty in absorption than the second node, and the first node is ranked before the second node. If the source load ratio of the first node is less than that of the second node, and the source load matching degree of the first node is greater than that of the second node, then the absorption difficulty of the first node is considered to be lower than that of the second node, and the first node is ranked after the second node. If the source-load ratio of the first node is greater than that of the second node, and the source-load matching degree of the first node is greater than that of the second node, or if the source-load ratio of the first node is less than that of the second node, and the source-load matching degree of the first node is less than that of the second node, then the product of the source-load ratio and the source-load matching degree of the first and second nodes is calculated. If the product of the first node is greater than that of the second node, then the first node is considered to have a higher degree of absorption difficulty than the second node, and the first node is ranked before the second node. Conversely, if the product of the first node is less than that of the second node, then the first node is considered to have a lower degree of absorption difficulty than the second node, and the first node is ranked after the second node.

[0052] Where, product M i,j,k Specifically: .

[0053] For example, using nodes n 1,1,0 and n 1,2,0 For example, if the source-to-charge ratio 1,1,0 Greater than 1,2,0 And source-load matching degree 1,1,0 Less than 1,2,0 When, then the node is considered n 1,1,0 The difficulty of absorption is higher than that of nodes. n 1,2,0 If the source-to-charge ratio 1,1,0 Less than 1,2,0 And source-load matching degree 1,1,0 Greater than 1,2,0 When, then the node is considered n 1,1,0 The difficulty of absorption is lower than that of the node. n 1,2,0 If the source-to-charge ratio 1,1,0 Greater than 1,2,0 And source-load matching degree 1,1,0 Greater than 1,2,0 , or source-to-charge ratio 1,1,0 Less than 1,2,0 And source-load matching degree 1,1,0 Less than 1,2,0When, calculate the product of the source-load ratio and the source-load matching degree, when M 1,1,0 Greater than M 1,2,0 Then the node is considered n 1,1,0 The difficulty of absorption is higher than that of nodes. n 1,2,0 Conversely, nodes n 1,1,0 The difficulty of absorption is lower than that of the node. n 1,2,0 .

[0054] S34. Analyze the hierarchical relationships between nodes in the sorted candidate library. If two nodes with a hierarchical relationship are adjacent in sorting order, adjust the sorting order of the lower-level node to be before the sorting order of the upper-level node to obtain the key node library.

[0055] Key nodes are those where distributed photovoltaic power generation is difficult to absorb within a region, and are often the crux of the problem restricting the development of distributed photovoltaic power generation in the region.

[0056] S4. Generate a set of alternative solutions for each key node in the key node library, and calculate the increase in photovoltaic grid integration capacity per unit investment for each alternative solution in the set of alternative solutions, such as... Figure 5 As shown, specifically including S41-S44: S41. Based on the type of each key node in the key node library, and the source-load ratio and source-load matching degree, select typical technical means from the preset photovoltaic absorption capacity improvement strategy tree to obtain a set of alternative solutions.

[0057] The preset strategy tree for improving photovoltaic absorption capacity is shown in Table 1.

[0058] Table 1. Strategies for Improving Distributed Photovoltaic Grid Integration Capacity

[0059] Specifically, if the source-load ratio of each key node in the key node library is greater than a first preset value, then the source-load ratio of the key node is considered high; otherwise, the source-load ratio of the key node is considered low. If the source-load matching degree of each key node in the key node library is greater than a second preset value, then the source-load matching degree of the key node is considered high; otherwise, the source-load matching degree of the key node is considered low. Based on the type, source-load ratio, and source-load matching degree of each key node, typical technical means are selected from the preset photovoltaic absorption capacity improvement strategy tree to obtain a set of alternative solutions.

[0060] In one optional implementation, the first preset value is 1, and the second preset value is 0.3.

[0061] S42. Obtain the equipment purchase cost, construction and installation cost, operation and maintenance management cost, other costs, and increased photovoltaic consumption scale for each alternative scheme in the alternative scheme set.

[0062] S43. Calculate the comprehensive investment of each alternative scheme based on the equipment purchase cost, the construction and installation cost, the operation and maintenance management cost, and the other costs, specifically as follows: ; In the formula, S i Indicates the first i The total investment of the alternative options S i,设备 This indicates the cost of purchasing the equipment. S i,施工 This indicates the construction and installation costs. S i,运维 This indicates the operation and maintenance management costs. S i,其他 This indicates other expenses.

[0063] S44. Calculate the increase in photovoltaic grid connection capacity per unit investment for each alternative scheme based on the comprehensive investment and the increased photovoltaic grid connection capacity, specifically as follows: ; In the formula, G i Indicates the first i Each alternative plan increases the unit investment in photovoltaic power consumption capacity. P i Indicates the first i The alternative scheme increases the photovoltaic consumption capacity.

[0064] S5. With the goal of maximizing the increase in photovoltaic consumption per unit investment, select the optimal solution for each key node from the set of alternative solutions.

[0065] For example, each key node has a set of alternative solutions, including solution 1 to solution N. The optimal solution is selected from the set of alternative solutions that increases the photovoltaic consumption scale by the unit investment.

[0066] S6. Optimize the distributed photovoltaic load capacity of each key node using the optimal solution.

[0067] In one alternative implementation, such as Figure 5 As shown, it also includes: S7. Determine whether the distributed photovoltaic load optimization has eliminated all photovoltaic grid connection problems. If not, return to execute S1; if yes, end.

[0068] Returning to execute S1 involves updating the basic data and key nodes.

[0069] The aforementioned method for optimizing the carrying capacity of distributed photovoltaic (PV) power grids, through hierarchical analysis of the spatiotemporal matching relationship between sources and loads, combined with the grid topology and node correlation, achieves a shift from localized governance to system optimization, thereby enhancing the overall carrying capacity of the distribution network for distributed PV. Based on a dual-dimensional index of source-load ratio (spatial matching) and source-load matching degree (temporal matching), combined with a four-quadrant absorption capacity distribution map, it accurately locates key nodes limiting PV absorption, avoiding resource waste. Through a pre-constructed PV absorption capacity enhancement strategy tree and a two-layer scheme comparison method (technical feasibility + economic optimization), it aims to maximize the increase in PV absorption per unit investment, significantly reducing governance costs and improving investment efficiency. Through absorption capacity analysis and a key node update mechanism, it tracks source-load development changes in real time, dynamically adjusts optimization strategies, and ensures the long-term effectiveness of the scheme, thus effectively and reliably improving the carrying capacity of the regional distribution network for distributed PV.

[0070] According to another aspect of the invention, Figure 2 This is a schematic diagram illustrating a distributed photovoltaic load optimization system for a distribution network according to an embodiment of the present invention. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the distributed photovoltaic load optimization method for a distribution network as described above.

[0071] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A power distribution network distributed photovoltaic load optimization method, characterized in that, The method comprises the steps of: obtaining distributed photovoltaic scales of nodes in a node set of a power distribution network and user electricity demand of the nodes, and performing source-load prediction on the nodes to obtain power supply output curves and load curves; calculating source-load ratios and source-load matching degrees of the nodes based on the distributed photovoltaic scales, the user electricity demand, the power supply output curves and the load curves; identifying key nodes from the node set based on the source-load ratios and the source-load matching degrees according to a preset key node identification rule to obtain a key node library; generating a set of alternative schemes for each key node in the key node library, and calculating photovoltaic consumption scale per unit investment of each alternative scheme in the set of alternative schemes; selecting optimal schemes for the key nodes from the set of alternative schemes with the maximum photovoltaic consumption scale per unit investment as the target; performing distributed photovoltaic load bearing optimization on the key nodes using the optimal schemes.

2. The power distribution network distributed photovoltaic load optimization method of claim 1, wherein, The method comprises the steps of: calculating source-load ratios of the nodes based on the distributed photovoltaic scales and the user electricity demand; calculating source-load matching degrees of the nodes using Pearson correlation coefficients according to the power supply output curves and the load curves.

3. The power distribution network distributed photovoltaic load optimization method of claim 2, wherein, The method comprises the steps of: ； In the formula, denotes the source load ratio of the node, P z denotes the installed capacity of photovoltaic, P f denotes the maximum load of the region; calculating source-load matching degrees of the nodes using Pearson correlation coefficients according to the power supply output curves and the load curves. ； In the formula, This represents the source-load matching degree of a node. N This represents the total number of time points in a day when data was collected. x i Indicating the power output curve of the first... i Make an effort at that moment. y i Indicating the first load curve i The load at any given moment.

4. The power distribution network distributed photovoltaic load optimization method of claim 1, wherein, The method comprises the steps of: generating a four-quadrant consumption capacity distribution diagram of the node set based on the source-load ratios and the source-load matching degrees according to the preset key node identification rule; selecting a preset percentage of nodes with the highest consumption difficulty from the four-quadrant consumption capacity distribution diagram to form an alternative library; sorting the nodes in the alternative library based on the source-load ratios and the source-load matching degrees to obtain a sorted alternative library; analyzing an existing superior-inferior relationship between the nodes in the sorted alternative library, and if the sorting sequence positions of two nodes with the superior-inferior relationship are adjacent, adjusting the sorting sequence position of the inferior node to be in front of the sorting sequence position of the superior node to obtain a key node library.

5. The power distribution grid distributed photovoltaic hosting capacity optimization method of claim 1, wherein, The method comprises the steps of: selecting typical technical means from a preset photovoltaic consumption capacity improvement strategy tree based on the source-load ratios and the source-load matching degrees according to the types of the key nodes in the key node library to obtain a set of alternative schemes.

6. The power distribution network distributed photovoltaic load optimization method of claim 1, wherein, The method comprises the steps of: obtaining equipment procurement costs, construction and installation costs, operation and maintenance costs, other costs and increased photovoltaic consumption scales of each alternative scheme in the set of alternative schemes; calculating comprehensive investments of the alternative schemes according to the equipment procurement costs, the construction and installation costs, the operation and maintenance costs and the other costs; and calculating photovoltaic consumption scale per unit investment of each alternative scheme in the set of alternative schemes according to the comprehensive investments. According to the comprehensive investment and the increased photovoltaic consumption scale, a unit investment of each alternative solution is calculated to increase the photovoltaic consumption scale.

7. The power distribution network distributed photovoltaic load optimization method of claim 6, wherein, According to the equipment purchase cost, the construction and installation cost, the operation and maintenance cost and the other cost, the comprehensive investment of each alternative solution is calculated, in particular: ； In the formula, S i Indicates the first i The total investment of the alternative options S i,设备 This indicates the cost of purchasing the equipment. S i,施工 This indicates the construction and installation costs. S i,运维 This indicates the operation and maintenance management costs. S i,其他 Other expenses; According to the comprehensive investment and the increased photovoltaic consumption scale, a unit investment of each alternative solution is calculated to increase the photovoltaic consumption scale, in particular: ； wherein G i represents the unit investment of the i alternative solution to increase the scale of photovoltaic consumption, P i represents the scale of photovoltaic consumption increased by the i alternative solution.

8. The method of claim 1, wherein, The distributed photovoltaic scale and the user electricity demand of each node in the node set of the power distribution network are obtained. According to the network structure of the power distribution network, a topological structure diagram of the power distribution network is constructed. According to the topological structure diagram, the node set of the power distribution network is obtained. According to the topological structure diagram, the distributed photovoltaic scale and the user electricity demand of each node in the node set are obtained.

9. The method of claim 1, wherein, After the distributed photovoltaic carrying optimization of each key node using the optimal solution, the method further comprises: It is judged whether all photovoltaic consumption problems are eliminated after the distributed photovoltaic carrying optimization, and if not, the step of obtaining the distributed photovoltaic scale and the user electricity demand of each node in the node set of the power distribution network is returned to be executed.

10. A distributed photovoltaic load optimization system for power distribution grid, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize each step in the power distribution network distributed photovoltaic carrying optimization method of any one of claims 1 to 9.

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