Micro-market hierarchical balancing resource allocation method and system

Abstract

The application provides a micro-market hierarchical balance resource configuration method and system, relates to the technical field of power distribution network resource configuration, and comprises the following steps: dividing a power distribution network into multiple micro-market units according to nodes; identifying the supply-demand attributes of the distributed resources in each micro-market unit according to the operation state data of the distributed resources in a preset time period, and determining the supply areas and demand areas; determining the priority matching objects based on the spatial positional relationship between the supply areas and the demand areas, establishing the intra-unit association relationship for the priority matching objects, and establishing the convergence relationship of the supply areas or the demand areas that are not matched to the upper nodes; and generating the configuration instructions for the distributed resources according to the intra-unit association relationship and the convergence relationship of the upper nodes. The application realizes the hierarchical balance of the power distribution network by preferentially establishing the resource association relationship in the micro-market units and converging the resources that cannot be matched to the upper nodes, and improves the local consumption capacity of the distributed new energy.

Description

Technical Field

[0001] This application relates to the field of distribution network resource allocation technology, and in particular to a micro-market hierarchical balancing resource allocation method and system. Background Technology

[0002] With the advancement of the construction of new power systems based on new energy sources, the distribution network has gradually evolved from a traditional unidirectional power supply network into an active distribution network with bidirectional power flow characteristics. Distributed photovoltaic, distributed wind power, and other new energy power generation devices have been extensively integrated into the distribution network. These new energy power generation devices, along with power consumption devices, energy storage devices, and other distributed resources, have formed a diversified supply and demand structure. Because distributed resources are located in dispersed locations and are numerous in number within the distribution network, it is necessary to establish effective resource allocation methods to achieve a balance between power supply and demand.

[0003] Existing technologies typically employ a centralized dispatching approach. This involves a provincial or municipal-level dispatch center collecting operational status data from all distributed resources, then using optimization algorithms to calculate the appropriate power generation or consumption plan for each resource. Finally, dispatch instructions are generated and sent to each distributed resource for execution. In this centralized dispatching method, the dispatch center needs to simultaneously process data from all distributed resources in the distribution network, calculate a configuration scheme that satisfies the overall network's supply and demand balance constraints, and finally, each distributed resource executes its power generation or consumption operations according to the instructions issued by the dispatch center.

[0004] However, distributed resources in the distribution network actually exhibit local clustering characteristics in space. This means that distributed resources located at the same distribution network node tend to have closer electrical connections. Therefore, there is a possibility of localized consumption between distributed renewable energy generation and electricity load within a specific local area. However, centralized dispatching concentrates all resource allocation decisions at the higher-level dispatch center. This fails to fully utilize the local supply and demand matching potential of the distribution network, resulting in a relatively low local consumption rate of distributed renewable energy and increasing the balancing pressure on the higher-level dispatch center. Summary of the Invention

[0005] This application provides a micro-market hierarchical resource allocation method and system to solve the problem of how to achieve spatial priority allocation and hierarchical balance coordination of distributed resources under the hierarchical structure of distribution networks.

[0006] A first aspect of this application provides a micro-market hierarchical balanced resource allocation method, comprising: dividing the distribution network into multiple micro-market units by nodes according to the topology data of the distribution network, wherein each micro-market unit includes a base node and multiple distributed resources connected to the lower level of the base node;

[0007] Based on the operational status data of each distributed resource within a preset time period, the supply and demand attributes of the distributed resources in each micro-market unit are identified, and the supply and demand areas are determined based on the supply and demand attribute identification.

[0008] Based on the spatial relationship between supply and demand areas, priority matching targets are determined, and intra-unit associations are established for the priority matching targets. For unmatched supply or demand areas within the micro-market unit, aggregation relationships with higher-level nodes are established.

[0009] Based on the intra-unit associations and the aggregation relationships with the superior nodes, configuration instructions are generated for each distributed resource.

[0010] Optionally, in one possible implementation of the first aspect, the step of identifying the supply and demand attributes of distributed resources within each micro-market unit based on the operational status data of each distributed resource within a preset time period includes:

[0011] The preset time period is divided into multiple time slices;

[0012] Statistically analyze the power generation and power consumption data of each distributed resource in each time slice to determine the power time series data of each distributed resource;

[0013] The power fluctuation characteristic value of each distributed resource is calculated based on the power time series data.

[0014] The power fluctuation characteristic values ​​of all distributed resources within the micro-market unit are sorted in descending order, and the top N distributed resources are selected as adjustable resources.

[0015] Distributed resources in the adjustable resources whose power generation is greater than their power consumption are assigned a supply attribute identifier, and distributed resources in the adjustable resources whose power consumption is greater than their power generation are assigned a demand attribute identifier.

[0016] Optionally, in one possible implementation of the first aspect, determining the supply region and demand region based on the supply and demand attribute identifier includes:

[0017] Spatial clustering is performed based on the positional relationship of adjustable resources in the distribution network topology to obtain supply resource groups and demand resource groups;

[0018] The summation of power time-series data of adjustable resources within each resource group is calculated to obtain the group supply curve and group demand curve.

[0019] Calculate the curve correlation coefficient between the group supply curve and the group demand curve, and select the resource group with a negative curve correlation coefficient as the supply region and demand region.

[0020] Optionally, in one possible implementation of the first aspect, determining the priority matching object based on the spatial relationship between the supply area and the demand area includes:

[0021] Construct a topology tree structure for the micro-market unit, wherein the topology tree structure has a base node as the root node and the nodes where each distributed resource is located as leaf nodes;

[0022] Each node in the topology tree structure is labeled with a level, with the level label of the base node being 0 and the level labels of the lower-level nodes increasing sequentially.

[0023] Combine each supply region and each demand region into multiple supply-demand combinations, and identify the nearest common ancestor node of the supply region and demand region in the topology tree structure for each supply-demand combination;

[0024] The topological matching level of each supply and demand combination is determined based on the hierarchical label of the nearest common ancestor node;

[0025] The supply and demand combination with the deepest topology matching level is selected as the priority matching target.

[0026] Optionally, in one possible implementation of the first aspect, determining the topological matching level of each supply-demand combination based on the hierarchical label of the nearest common ancestor node includes:

[0027] Obtain the hierarchical label value of the nearest common ancestor node;

[0028] Obtain the hierarchical label value of the node where the supply region is located and the hierarchical label value of the node where the demand region is located;

[0029] Calculate the first-level difference between the node where the supply region is located and the nearest common ancestor node, and the second-level difference between the node where the demand region is located and the nearest common ancestor node;

[0030] Based on the state of the first-level difference and the second-level difference, the topology matching level of each supply and demand combination is determined.

[0031] Optionally, in one possible implementation of the first aspect, determining the topology matching level of each supply-demand combination based on the state of the first level difference and the second level difference includes:

[0032] When both the first-level difference and the second-level difference are in the initial level state, the supply and demand combination is identified as a direct connection type at the same level, and the topology matching level of the supply and demand combination is determined to be the level label value of the nearest common ancestor node.

[0033] When the sum of the first-level difference and the second-level difference is in a single-level progressive state, the supply and demand combination is identified as a single-hop connection type, and the topology matching level of the supply and demand combination is determined to be the value of the level label of the nearest common ancestor node reduced by one level.

[0034] When the sum of the first-level difference and the second-level difference is in a multi-level progressive state, the supply and demand combination is identified as a multi-hop connection type, and the topological matching level of the supply and demand combination is determined to be the value after deducting the sum of the level differences from the level label value of the nearest common ancestor node.

[0035] Optionally, in one possible implementation of the first aspect, establishing intra-unit associations for the preferred matching objects includes:

[0036] Retrieve the group supply curves for the supply region and the group demand curves for the demand region within the priority matching object;

[0037] The supply and demand of the group supply curves and the group demand curves are compared in each time slice to identify the supply and demand balance in each time slice.

[0038] Based on the supply and demand balance status of each time slice, determine the correlation type and correlation time period distribution between the supply region and the demand region;

[0039] Based on the association type and the distribution of association time periods, intra-unit association relationships between supply areas and demand areas are established.

[0040] Optionally, in one possible implementation of the first aspect, determining the association type and association time period distribution between supply and demand regions based on the supply and demand balance state of each time slice includes:

[0041] For each time slice, compare the power value of the group supply curve with the power value of the group demand curve, and identify the resource group whose power value exceeds that of the other as the power leader of the time slice.

[0042] The power dominance of each time slice is statistically analyzed to form a power dominance sequence;

[0043] A power dominance transition analysis was performed on the power dominance sequence to identify the first time period set where the supply region is the power dominance and the second time period set where the demand region is the power dominance.

[0044] The association type is determined based on the distribution patterns of the first and second time period sets. When the two time period sets are distributed alternately, it is identified as a complementary matching association. When a single time period set is continuously occupied, it is identified as a biased matching association. When the two time period sets are evenly distributed, it is identified as a perfect matching association.

[0045] Optionally, in one possible implementation of the first aspect, establishing the intra-unit correlation between the supply region and the demand region based on the correlation type and correlation time period distribution includes:

[0046] Determine whether each time slice is used as a time period for association configuration based on the association type;

[0047] For each associated configuration period, the configuration capacity for that period is determined based on the power values ​​of the group supply curve and the power values ​​of the group demand curve.

[0048] Based on each associated configuration time period and the corresponding time period configuration capacity, configuration relationship data is generated, and the configuration relationship data is used as the intra-unit association between the supply area and the demand area; the configuration relationship data includes time period information and capacity information.

[0049] Optionally, in one possible implementation of the first aspect, establishing a convergence relationship between unmatched supply or demand areas within a micro-market unit and higher-level nodes includes:

[0050] Identify supply and demand areas within a micromarket unit that lack intra-unit relationships and mark them as completely mismatched areas.

[0051] For supply and demand combinations with established inter-unit relationships, calculate the supply surplus between the total power of the group supply curve in the supply region and the cumulative value of the time-period configuration capacity corresponding to each associated configuration period, and the demand gap between the total power of the group demand curve in the demand region and the cumulative value of the time-period configuration capacity corresponding to each associated configuration period.

[0052] Supply areas with surplus supply are marked as partially unmatched supply areas, and demand areas with demand gaps are marked as partially unmatched demand areas.

[0053] For completely unmatched areas and partially unmatched supply or demand areas, establish aggregation relationships with higher-level nodes.

[0054] Optionally, in one possible implementation of the first aspect, establishing a convergence relationship with the superior node for completely unmatched regions and partially unmatched supply or demand regions includes:

[0055] Extract the total power of the group supply curves of completely mismatched supply regions as the upward convergence supply capacity, and extract the total power of the group demand curves of completely mismatched demand regions as the upward convergence demand capacity.

[0056] The remaining supply from some unmatched supply areas will be used as the supply capacity for upward aggregation;

[0057] The demand gap in some unmatched demand areas will be used as the upward aggregated demand capacity.

[0058] Based on the supply capacity aggregated upwards from each supply region and the demand capacity aggregated upwards from each demand region, aggregated data of micro-market units to higher-level nodes is generated, forming aggregated relationships with higher-level nodes.

[0059] Optionally, in one possible implementation of the first aspect, generating configuration instructions for each distributed resource based on the intra-unit association and the aggregation relationship with the superior node includes:

[0060] To establish the supply and demand combination with intra-unit relationships, configuration instructions are generated for each distributed resource in the supply area and each distributed resource in the demand area; the intra-unit configuration instructions include source resource identifier, target resource identifier, and configuration capacity value.

[0061] For supply or demand regions that have established upward aggregation relationships with higher-level nodes, an upward aggregation instruction is generated for each distributed resource; the upward aggregation instruction includes a resource identifier and an aggregation capacity value;

[0062] When a distributed resource is determined to be an energy storage device, configuration instructions are generated as supply resources for time slices in the discharging state and as demand resources for time slices in the charging state, based on the charging and discharging state of the energy storage device in each time slice.

[0063] A second aspect of this application provides a micro-market tiered balanced resource allocation system, including:

[0064] The partitioning module is used to divide the distribution network into multiple micro-market units based on the topology data of the distribution network. Each micro-market unit includes a base node and multiple distributed resources connected to the base node.

[0065] The identification module is used to identify the supply and demand attributes of distributed resources in each micro-market unit based on the operational status data of each distributed resource within a preset time period, and to determine the supply area and demand area based on the supply and demand attribute identification.

[0066] The planning module determines priority matching targets based on the spatial relationship between supply and demand areas, establishes intra-unit relationships for priority matching targets, and establishes aggregation relationships to higher-level nodes for unmatched supply or demand areas within the micro-market unit.

[0067] The generation module is used to generate configuration instructions for each distributed resource based on the relationships within the unit and the aggregation relationships with the superior nodes.

[0068] A third aspect of this application provides an electronic device comprising: a memory, a processor, and a computer program, wherein the computer program is stored in the memory, and the processor executes the computer program to perform the methods described in the first aspect of this application and various possible methods related to the first aspect.

[0069] The micro-market tiered balanced resource allocation method provided in this application has the following beneficial effects:

[0070] 1. This application constructs a topology tree structure for the distribution network and identifies the nearest common ancestor (LCA) node of the supply and demand areas within the topology tree. This allows for accurate determination of the topology matching level between supply and demand areas, prioritizing the selection of the supply and demand combination with the deepest topology matching level to establish intra-unit relationships. Consequently, the allocation decision for distributed resources is prioritized within the micro-market unit, as aggregation to higher-level nodes only occurs when matching cannot be achieved within the unit, thus realizing hierarchical balancing of the distribution network. Compared to existing centralized dispatching methods that require a higher-level dispatch center to handle all resource allocation decisions, this application effectively reduces the balancing pressure on the higher-level dispatch center and improves the efficiency of distribution network resource allocation.

[0071] 2. This application analyzes the power time-series data of various distributed resources to identify adjustable resources with adjustment capabilities. Furthermore, it determines the supply and demand regions based on the correlation coefficient between the group supply and demand curves of these adjustable resources. When establishing intra-unit relationships, this application determines the relationship type and time period distribution based on the supply-demand balance status of the supply and demand regions within each time slice. This allows for the generation of corresponding configuration relationship data for different supply-demand matching situations. Therefore, this application can fully utilize the potential for localized consumption of distributed renewable energy generation and electricity load within the local area of ​​the distribution network, thereby improving the local consumption rate of distributed renewable energy. Attached Figure Description

[0072] Figure 1 This is a flowchart illustrating the micro-market tiered resource allocation method provided in the embodiments of this application;

[0073] Figure 2 This is a schematic diagram of the topology tree structure and topology matching hierarchy of the micro-market hierarchical resource allocation method provided in the embodiments of this application;

[0074] Figure 3 This is a schematic diagram a of the topology matching hierarchy of the micro-market tiered balanced resource allocation method provided in the embodiments of this application;

[0075] Figure 4 This is a topology matching hierarchy diagram (b) of the micro-market tiered resource allocation method provided in this application embodiment;

[0076] Figure 5 This is a topology matching hierarchy diagram c of the micro-market tiered balanced resource allocation method provided in this application embodiment;

[0077] Figure 6 This is a power-dominant sequence and complementary matching correlation diagram of the micro-market hierarchical balanced resource allocation method provided in the embodiments of this application;

[0078] Figure 7 This is a schematic diagram of the structure of the micro-market tiered balanced resource allocation system provided in the embodiments of this application;

[0079] Figure 8 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0080] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0081] The technical solutions of this application will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0082] Existing technologies typically employ a centralized dispatching approach, where a higher-level dispatch center centrally handles the configuration decisions for all distributed resources in the distribution network and calculates configuration schemes that satisfy the overall network's supply and demand balance constraints. However, distributed resources in the distribution network exhibit localized clustering characteristics, and there is a possibility of localized consumption between distributed renewable energy generation and electricity load within a specific local area. The centralized dispatching approach fails to fully utilize the local supply and demand matching potential of the distribution network, resulting in a low local consumption rate of distributed renewable energy and increasing the balancing pressure on the higher-level dispatch center. Therefore, this application identifies the spatial location relationships between supply and demand areas by constructing a topology tree structure, prioritizing the establishment of supply and demand configuration relationships within micro-market units to achieve hierarchical balance.

[0083] See Figure 1 This is a flowchart illustrating the micro-market tiered resource allocation method provided in this application embodiment. Figure 1The execution entity of the method shown can be a software and / or hardware device. The execution entity of this application can include, but is not limited to, at least one of the following: user equipment, network equipment, etc. User equipment can include, but is not limited to, computers, smartphones, personal digital assistants (PDAs), and the aforementioned electronic devices. Network equipment can include, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of computers or network servers. Cloud computing is a type of distributed computing, consisting of a super virtual computer composed of a group of loosely coupled computers. This embodiment does not limit this. Steps 100 to 400 are detailed as follows:

[0084] Step 100: Divide the distribution network into multiple micro-market units according to the topology data of the distribution network. Each micro-market unit includes a base node and multiple distributed resources connected to the base node.

[0085] Distributed resources include power generation devices, power consumption devices, and energy storage devices.

[0086] It should be noted that the topology data of the distribution network is acquired. This topology data records the voltage level information of each node in the distribution network, the electrical connection relationships between nodes, and the location information of the access nodes for each distributed resource.

[0087] Nodes at a preset voltage level are identified as reference nodes in the distribution network topology data. The preset voltage level is set according to the actual distribution network topology and can be selected from nodes at the high-voltage, medium-voltage, or low-voltage distribution levels.

[0088] For each reference node, based on the electrical connection information between nodes recorded in the distribution network topology data, the lower-level connection paths of the reference node are traced, and the lower-level nodes that have electrical connection with the reference node are identified.

[0089] Obtain the access distributed resource information of each subordinate node and determine the distributed resources connected to the subordinate nodes of the base node.

[0090] Each reference node and the distributed resources connected to its subordinate nodes are combined into a micro-market unit, thus completing the division of the distribution network into micro-market units based on nodes.

[0091] Preferably, step 100 involves acquiring the topology data of the distribution network and identifying the reference node, dividing the distribution network into multiple micro-market units with clear electrical connection boundaries, thus providing a foundation for subsequent identification of the supply and demand attributes of distributed resources and establishment of hierarchical configuration relationships within the micro-market units.

[0092] Step 200: Based on the operational status data of each distributed resource within a preset time period, identify the supply and demand attributes of the distributed resources in each micro-market unit, and determine the supply area and demand area based on the supply and demand attribute identification.

[0093] In some embodiments, step 200 is specifically implemented by steps 210 and 220:

[0094] Step 210: Based on the operational status data of each distributed resource within a preset time period, identify the supply and demand attributes of the distributed resources in each micro-market unit.

[0095] Specifically, step 210 includes steps 211 to 215:

[0096] Step 211: Divide the preset time period into multiple time slices.

[0097] The preset time period is the operational statistics cycle of distributed resources in the distribution network, and the time slice is a time interval unit within the preset time period. Step 211 divides the preset time period into multiple time slices with a fixed time length, and each time slice has the same time length.

[0098] Step 212: Statistically analyze the power generation and power consumption data of each distributed resource in each time slice to determine the power time series data of each distributed resource.

[0099] Understandably, the real-time power values ​​of each distributed resource within each time slice are collected, power generation data and power consumption data are distinguished, and the power generation data and power consumption data of each distributed resource are arranged in the order of time slices to form the power time series data of each distributed resource.

[0100] Step 213: Calculate the power fluctuation characteristic value of each distributed resource based on power time series data.

[0101] The power fluctuation characteristic value is the standard deviation of the power time series data.

[0102] It's easy to understand that by extracting the power values ​​from each time slice of the power time-series data of each distributed resource, and calculating the standard deviation of the power values, the power fluctuation characteristic value of each distributed resource can be obtained. This power fluctuation characteristic value reflects the magnitude of change in the power output or power consumption of the distributed resource over time.

[0103] Step 214: Sort the power fluctuation characteristic values ​​of all distributed resources in the micro-market unit in descending order, and select the top N distributed resources as adjustable resources.

[0104] Wherein, N is a preset adjustable resource quantity threshold, which is set according to the total number of distributed resources within the micro-market unit.

[0105] It should be noted that the regulation capabilities of distributed resources in a distribution network vary. Distributed resources with larger power fluctuations have stronger power regulation potential, while those with smaller power fluctuations have limited regulation space. Therefore, step 214 in this application sorts the power fluctuation characteristic values ​​of all distributed resources within the micro-market unit in descending order of their numerical values, and selects the top N distributed resources as adjustable resources.

[0106] In some embodiments, adjustable resources can also be obtained by: calculating the average power fluctuation characteristic value of all distributed resources within the micro-market unit to obtain the average fluctuation characteristic value; and selecting distributed resources whose power fluctuation characteristic value is greater than the average fluctuation characteristic value as adjustable resources.

[0107] Step 215: Assign supply attribute identifiers to distributed resources in the adjustable resources where power generation is greater than power consumption, and assign demand attribute identifiers to distributed resources in the adjustable resources where power consumption is greater than power generation.

[0108] Understandably, for each adjustable resource, the total power generation data and the total power consumption data of each time slice within the preset time period are statistically analyzed, the relationship between the total power generation data and the total power consumption data is compared, and then step 215 is executed.

[0109] Preferably, step 210 calculates power fluctuation characteristic values ​​from the power time-series data of distributed resources, identifies adjustable resources with adjustment capabilities, and assigns supply and demand attribute identifiers based on the power generation and power consumption relationship of the adjustable resources, laying the foundation for the division and matching of supply and demand areas within the micro-market unit.

[0110] Step 220: Determine the supply region and demand region based on the supply and demand attribute identifiers.

[0111] Specifically, step 220 includes steps 221 to 223:

[0112] Step 221: Perform spatial clustering based on the positional relationship of adjustable resources in the distribution network topology to obtain supply resource groups and demand resource groups.

[0113] It should be noted that, since distributed resources in the distribution network have local clustering characteristics in terms of spatial location, distributed resources located at adjacent distribution network nodes are closer in electrical distance and form regional distribution in physical space. Therefore, step 221 performs spatial clustering based on the positional relationship of adjustable resources in the distribution network topology, and aggregates spatially adjacent adjustable resources into resource groups.

[0114] Step 222: Calculate the sum of the power time-series data of adjustable resources within each resource group to obtain the group supply curve and group demand curve.

[0115] Understandably, for each supply resource group, the power time-series data of each adjustable resource within the supply resource group is extracted, and the power values ​​of each adjustable resource within the supply resource group in the same time slice are accumulated to obtain the power superposition value of each time slice. The power superposition values ​​are arranged in the order of the time slices to form the group supply curve of the supply resource group.

[0116] For each demand resource group, the power time-series data of each adjustable resource within the demand resource group is extracted. The power superposition value of each time slice is obtained by summing the power values ​​of each adjustable resource within the demand resource group in the same time slice. The power superposition values ​​are arranged in the order of time slices to form the group demand curve of the demand resource group.

[0117] Step 223: Calculate the curve correlation coefficient between the group supply curve and the group demand curve, and select the resource group with a negative curve correlation coefficient as the supply region and demand region.

[0118] It is easy to understand that by combining each supply resource group with each demand resource group into multiple supply and demand resource pairs, and for each supply and demand resource pair, the group supply curve of the supply resource group and the group demand curve of the demand resource group are extracted, and the curve correlation coefficient between the group supply curve and the group demand curve is calculated.

[0119] The curve correlation coefficient reflects the relationship between the group supply curve and the group demand curve over time. A negative curve correlation coefficient indicates that the group supply curve and the group demand curve show opposite trends over time, and that the peak power output period of the supply resource group and the peak power consumption period of the demand resource group are complementary in time. This means that the supply resource group and the demand resource group have the conditions to establish a supply-demand matching relationship.

[0120] Furthermore, supply resource groups with negative curve correlation coefficients are selected as supply regions, and demand resource groups with negative curve correlation coefficients are selected as demand regions.

[0121] Preferably, step 220 involves clustering adjustable resources according to spatial location to form resource groups, calculating the group supply curve and group demand curve of the resource groups, and selecting supply and demand regions with time complementarity based on the curve correlation coefficient, thereby realizing the identification and division of supply and demand regions within the micro-market unit.

[0122] Preferably, step 200 identifies adjustable resources based on the power time-series characteristics of distributed resources and assigns supply and demand attribute labels, and determines supply and demand areas through spatial clustering and curve correlation analysis, providing a data foundation for hierarchical balancing and priority matching within micro-market units.

[0123] Step 300: Determine priority matching targets based on the spatial relationship between supply and demand areas, establish intra-unit associations for priority matching targets, and establish aggregation relationships with higher-level nodes for unmatched supply or demand areas within the micro-market unit.

[0124] In some embodiments, step 300 is specifically implemented by steps 310 to 330:

[0125] Step 310: Determine the priority matching targets based on the spatial relationship between the supply area and the demand area.

[0126] Specifically, step 310 includes steps 311 to 315:

[0127] Step 311: Construct the topology tree structure of the micro-market unit. The topology tree structure has the base node as the root node and the nodes where each distributed resource is located as leaf nodes.

[0128] Understandably, the baseline node of the micro-market unit is extracted and set as the root node of the topology tree structure. The location information of the access nodes for each distributed resource within the micro-market unit is obtained. Based on the electrical connection information between nodes recorded in the distribution network topology data, the connection paths from the baseline node to each distributed resource access node are traced. Each distributed resource access node is set as a leaf node of the topology tree structure, and intermediate nodes on the connection path are set as branch nodes, thus completing the construction of the micro-market unit's topology tree structure.

[0129] Step 312: Label each node in the topology tree structure with a level label. The level label of the base node is 0, and the level labels of the lower-level nodes increase sequentially.

[0130] It's easy to understand that the root node, or reference node, of the topology tree is set to 0. Starting from the reference node, the tree is traversed downwards along the electrical connection path. The level label of each node directly electrically connected to the reference node is set to 1, and the level label of each node directly electrically connected to a node with a level label of 1 is set to 2. This process continues until all leaf nodes are reached, completing the level labeling of each node in the topology tree.

[0131] See Figure 2 This is a schematic diagram of the topology tree structure provided in an embodiment of this application. Figure 2As shown, the topology tree structure of the micro-market unit has a root node as the base node, with a level label of 0. The base node connects to nodes A and B, with a level label of 1. Node A connects to nodes C and D, and node B connects to nodes E and F, with a level label of 2 for nodes C, D, E, and F. Node C connects to supply region 1 and demand region 1, node D connects to supply region 2, and node E connects to demand region 2. Supply region 1, demand region 1, supply region 2, and demand region 2 serve as leaf nodes, with a level label of 3. The level labels of each node in the topology tree structure increase sequentially, with supply and demand regions distributed at different locations within the topology tree structure.

[0132] Step 313: Combine each supply region and each demand region into multiple supply-demand combinations, and identify the nearest common ancestor node of the supply region and demand region in the topology tree structure for each supply-demand combination.

[0133] The nearest common ancestor node refers to the node that is the parent node of both the supply region and the demand region in the topology tree structure, and has the largest level label value.

[0134] It should be noted that there is a correspondence between the electrical distance between distributed resources in a distribution network and the hierarchical relationship between nodes in the topology tree structure. The larger the hierarchical label value of the nearest common ancestor node (LCA) of the supply and demand regions in the topology tree structure, the closer the electrical distance between the supply and demand regions in the distribution network topology structure, and the fewer distribution network layers need to be traversed to establish a configuration relationship between them. Therefore, by identifying the LCA node, the topological distance between the supply and demand regions can be quantified.

[0135] Each supply region and each demand region is combined into multiple supply-demand combinations. For each supply-demand combination, the position information of the node containing the supply region and the node containing the demand region in the topology tree structure are obtained. The path of the parent node of the supply region is traced upwards along the topology tree structure from the node containing the supply region to the root node, and the path of the parent node of the demand region is also traced upwards along the topology tree structure from the node containing the demand region to the root node. The intersection node of the parent node paths of the supply region and the demand region is identified. Among the intersection nodes, the node with the largest level label value is selected as the least common ancestor node of the supply and demand regions in the topology tree structure.

[0136] Step 314: Determine the topological matching level of each supply and demand combination based on the hierarchical label of the nearest common ancestor node.

[0137] Specifically, step 314 includes steps A1 to A5:

[0138] Step A1: Obtain the hierarchical label value of the nearest common ancestor node.

[0139] Step A2: Obtain the hierarchical label value of the node where the supply area is located and the hierarchical label value of the node where the demand area is located.

[0140] Step A3: Calculate the first-level difference between the node where the supply region is located and the nearest common ancestor node, and the second-level difference between the node where the demand region is located and the nearest common ancestor node.

[0141] Understandably, the first level difference between the node containing the supply region and the node containing the nearest common ancestor is obtained by subtracting the level label value from the level label value of the nearest common ancestor node. Similarly, the second level difference is obtained by subtracting the level label value between the node containing the demand region and the node containing the nearest common ancestor node.

[0142] Step A4: Determine the topology matching level of each supply and demand combination based on the state of the first-level difference and the second-level difference.

[0143] It should be noted that the relative positions of the supply and demand regions within the distribution network topology affect the topological depth at which a configuration relationship is established between them. A greater topological depth indicates that the configuration relationship is established at a deeper level in the topology tree structure, involving fewer higher-level nodes in the distribution network. The first-level difference and the second-level difference reflect the topological distance between the nodes in the supply and demand regions and their nearest common ancestor nodes. By analyzing the values ​​of these two differences, the topological connection type of the supply-demand combination can be determined, thereby identifying the topological matching level.

[0144] Furthermore, step A4 includes three cases: A41, A42, and A43.

[0145] A41: When both the first-level difference and the second-level difference are in the initial level state, the supply and demand combination is identified as a direct connection type at the same level, and the topology matching level of the supply and demand combination is determined to be the level label value of the nearest common ancestor node.

[0146] The initial hierarchical state refers to a state where the difference between the first level and the difference between the second level are both 1.

[0147] Understandably, when both the first-level difference and the second-level difference are 1, it indicates that the node containing the supply region and the node containing the demand region are at the same level in the topology tree structure, and both are directly electrically connected to the nearest common ancestor node. The configuration relationship between the supply region and the demand region can be completed at the nearest common ancestor node level, without needing to converge to higher-level nodes. The supply and demand combination is identified as a direct connection at the same level, and the level label value of the nearest common ancestor node is used as the topology matching level of the supply and demand combination.

[0148] See Figure 3 Figure a is a schematic diagram of the topology matching hierarchy provided in an embodiment of this application, showing a direct-connection topology matching hierarchy at the same layer. For example... Figure 3 As shown, node A has a level label of 1. Node A connects to both the supply and demand regions, both of which have a level label of 2. The nearest common ancestor (LCA) node between the supply and demand regions in the topology tree is identified as node A, with a level label of 1. The first level difference between the node containing the supply region and its LCA is calculated to be 1, and the second level difference between the node containing the demand region and its LCA is also calculated to be 1. When both the first and second level differences are 1, the supply and demand combination is identified as a direct connection at the same level, and the topology matching level of the supply and demand combination is determined to be the level label of the LCA node, which is 1.

[0149] A42: When the sum of the difference between the first level and the difference between the second level is in a single-level progressive state, the supply and demand combination is marked as a single-hop connection type, and the topological matching level of the supply and demand combination is determined to be the value after reducing the level label value of the nearest common ancestor node by one level.

[0150] Among them, the single-level progressive state refers to the state in which the sum of the difference between the first level and the difference between the second level is 2.

[0151] It's easy to understand that when the sum of the first-level difference and the second-level difference is 2, it indicates that at least one node in the supply region and the demand region has an intermediate node with the nearest common ancestor node. The configuration relationship between the supply region and the demand region needs to be completed through a single node jump. The supply-demand combination is marked as a single-hop connection, and the level label value of the nearest common ancestor node is subtracted by 1 to obtain the topology matching level of the supply-demand combination.

[0152] like Figure 4As shown in the figure, this application embodiment provides a topology matching hierarchy diagram b, which is used to illustrate a single-hop connection type topology matching hierarchy. Node A is labeled as level 1. Node A's subordinate nodes are node C and the demand region, and node C's level is labeled as level 2. The demand region's level is also labeled as level 2. Node C's subordinate nodes are the supply region, and the supply region's level is labeled as level 3. The nearest common ancestor (LCA) node between the supply region and the demand region in the topology tree structure is identified as node A, and the LCA node's level label is 1. The first level difference between the node containing the supply region and the LCA node is calculated as 2, and the second level difference between the node containing the demand region and the LCA node is calculated as 1. The sum of the first and second level differences is 2. The supply and demand combination is identified as a single-hop connection type, and the topology matching level of the supply and demand combination is determined to be the LCA node's level label value of 1 minus 1, resulting in a topology matching level of 0.

[0153] A43: When the sum of the first-level difference and the second-level difference is in a multi-level progressive state, the supply and demand combination is marked as a multi-hop connection type, and the topological matching level of the supply and demand combination is determined to be the value after deducting the sum of the level differences from the level label value of the nearest common ancestor node.

[0154] Among them, the multi-level progressive state refers to the state in which the sum of the difference between the first level and the difference between the second level is greater than 2.

[0155] Understandably, when the sum of the first-level difference and the second-level difference is greater than 2, it indicates that there are multiple intermediate nodes between the node containing the supply region and the node containing the demand region and the nearest common ancestor node. The configuration relationship between the supply region and the demand region needs to be completed through multiple node hops. The supply-demand combination is identified as a multi-hop connection type, and the topology matching level of the supply-demand combination is obtained by subtracting the sum of the first-level difference and the second-level difference from the level label value of the nearest common ancestor node.

[0156] like Figure 5The diagram shown is a schematic diagram (c) of the topology matching hierarchy provided in this embodiment of the application, used to illustrate a multi-hop connection type topology matching hierarchy. The hierarchy label of the base node is 0. The base node connects to nodes A and B, and the hierarchy labels of nodes A and B are 1. The supply region is connected to the supply region, and the hierarchy label of the supply region is 2. The demand region is connected to the demand region, and the hierarchy label of the demand region is 2. The nearest common ancestor (LCA) node of the supply region and the demand region in the topology tree structure is identified as the base node, and the LCA node has a hierarchy label of 0. The first hierarchy difference between the node containing the supply region and the LCA node is calculated to be 2, and the second hierarchy difference between the node containing the demand region and the LCA node is also calculated to be 2. The sum of the first hierarchy difference and the second hierarchy difference is 4. The supply and demand combination is identified as a multi-hop connection type, and the topology matching hierarchy of the supply and demand combination is determined to be the LCA node's hierarchy label value of 0 minus the sum of the hierarchy differences of 4, resulting in a topology matching hierarchy of -4.

[0157] Preferably, step 314 analyzes the value status of the first-level difference and the second-level difference to divide the supply and demand combination into three topology connection types: same-level direct connection, single-hop connection, and multi-hop connection. Based on the different topology connection types, the topology matching level of the supply and demand combination is determined to achieve a quantitative assessment of the topology distance between the supply area and the demand area.

[0158] Step 315: Select the supply and demand combination with the deepest topology matching level as the priority matching object.

[0159] It's easy to understand that by sorting the topology matching levels of each supply and demand combination in descending order, the supply and demand combination with the highest topology matching level value is selected as the priority matching target. The supply and demand combination with the deepest topology matching level corresponds to the supply area and demand area with the shortest electrical distance in the distribution network topology. The configuration relationship between the two can be established at a deeper topology level, reducing the balancing pressure on the upper-level nodes and realizing priority configuration within the micro-market unit.

[0160] Preferably, step 310 involves constructing a topology tree structure for the micro-market unit and labeling each node hierarchically. This identifies the nearest common ancestor node of the supply and demand regions of each supply-demand combination in the topology tree structure. Based on the difference between the first and second levels, the topology matching level of the supply-demand combination is determined. The supply-demand combination with the deepest topology matching level is selected as the priority matching object, thereby realizing priority matching selection of supply and demand based on spatial location relationships and laying the foundation for hierarchical balance within the micro-market unit.

[0161] Step 320: Establish intra-unit associations for priority matching objects.

[0162] In some embodiments, step 320 is specifically implemented by steps 321 to 324:

[0163] Step 321: Obtain the group supply curve of the supply region and the group demand curve of the demand region in the priority matching object.

[0164] It is easy to understand that the supply region and demand region are extracted from the priority matching object, and the group supply curve generated in step 222 for the supply region and the group demand curve generated in step 222 for the demand region are retrieved.

[0165] Step 322: Compare the supply and demand of the group supply curve and the group demand curve in each time slice to identify the supply and demand balance in each time slice.

[0166] Understandably, for each time slice, the power values ​​of the group supply curve and the group demand curve are extracted, and their relative strengths are compared. If the power value of the group supply curve is greater than that of the group demand curve, the supply-demand balance state of each time slice is marked as a state of excess supply. If the power value of the group supply curve is less than that of the group demand curve, the supply-demand balance state of each time slice is marked as a state of insufficient supply. If the power value of the group supply curve is equal to that of the group demand curve, the supply-demand balance state of each time slice is marked as a state of supply-demand equilibrium.

[0167] Step 323: Based on the supply and demand balance of each time slice, determine the type of association and the distribution of associated time periods between the supply and demand regions.

[0168] Specifically, step 323 includes steps B1 to B4:

[0169] Step B1: For each time slice, compare the power value of the group supply curve with the power value of the group demand curve, and identify the resource group whose power value exceeds that of the other as the power leader of the time slice.

[0170] Understandably, for each time slice, if the power value of the group supply curve is greater than the power value of the group demand curve, the supply region is identified as the power dominant region for that time slice. Conversely, if the power value of the group demand curve is greater than the power value of the group supply curve, the demand region is identified as the power dominant region for that time slice.

[0171] Step B2: Analyze the power dominance of each time slice to form a power dominance sequence.

[0172] It is easy to understand that the power dominance identifiers of each time slice are arranged in chronological order to form a power dominance sequence. The power dominance sequence records the power advantage distribution of the supply and demand regions in each time slice within a preset time period.

[0173] Step B3: Perform a power dominance shift analysis on the power dominance sequence to identify the first time period set where the supply region is the power dominance and the second time period set where the demand region is the power dominance.

[0174] It should be noted that the attribution of power dominance in the supply and demand regions at different time slices reflects the temporal complementarity of their power changes. By analyzing the transformation pattern of power dominance attribution in the power dominance sequence, the time distribution in which the supply and demand regions each occupy power dominance can be identified.

[0175] Traverse the time slice identifiers in the power dominance sequence, extract the time slices where the power dominance belongs to the supply region, and aggregate these time slices into the first time period set where the supply region is the power dominance. Extract the time slices where the power dominance belongs to the demand region, and aggregate these time slices into the second time period set where the demand region is the power dominance.

[0176] Step B4: Determine the association type based on the distribution patterns of the first time period set and the second time period set. If the two time period sets are distributed alternately, it is identified as a complementary matching association. If a single time period set is continuously occupied, it is identified as a biased matching association. If the two time period sets are evenly distributed, it is identified as a perfect matching association.

[0177] Understandably, we analyze the distribution characteristics of the first and second time period sets along the time dimension. If the first and second time period sets exhibit an alternating distribution pattern along the time dimension, it indicates a temporal switching relationship between the power advantage periods of the supply region and the power advantage periods of the demand region, classifying the association type as a complementary matching association. If a single time period set within either the first or second time period set consistently occupies a majority of the time slices, it indicates that one of the supply or demand regions holds a long-term power advantage, classifying the association type as a biased matching association. If the distribution ratios of the first and second time period sets along the time dimension are similar, it indicates a relatively balanced temporal distribution of power advantage periods between the supply and demand regions, classifying the association type as a perfectly matching association.

[0178] like Figure 6As shown, the horizontal axis represents time slices t1 to t9, and the vertical axis represents power values. The power values ​​of the group supply curve and group demand curve exhibit an alternating relationship across time slices. In time slices t1, t3, t5, t7, and t9, the power value of the group demand curve is greater than that of the group supply curve, indicating that the demand region is the power dominant party. In time slices t2, t4, t6, and t8, the power value of the group supply curve is greater than that of the group demand curve, indicating that the supply region is the power dominant party. The power dominant party attribution for each time slice is statistically analyzed in sequence, forming a power dominance sequence of demand-supply-demand-supply-demand-supply-demand-supply-demand. The first time period set identifying the supply region as the power dominant party is t2, t4, t6, and t8, and the second time period set identifying the demand region as the power dominant party is t1, t3, t5, t7, and t9. When the first and second time period sets exhibit an alternating distribution pattern, the association type is identified as a complementary matching association.

[0179] Preferably, steps B1 to B4 identify the power dominance of each time slice by comparing the power values ​​of the group supply curve and the power values ​​of the group demand curve for each time slice, forming a power dominance sequence, analyzing the distribution patterns of the first time period set and the second time period set, determining the correlation type between the supply region and the demand region, and realizing the identification and classification of the time characteristics of supply and demand matching.

[0180] Step 324: Based on the association type and the distribution of association time periods, establish intra-unit association relationships between supply areas and demand areas.

[0181] Specifically, step 324 includes steps C1 to C3:

[0182] Step C1: Determine whether each time slice is used as a time period for association configuration based on the association type.

[0183] It should be noted that the supply and demand matching time characteristics differ for different association types. The determination of the association configuration period needs to select a suitable time slice for establishing the configuration relationship based on the supply and demand matching characteristics of the association type.

[0184] It should be noted that when the association type is determined to be a complementary matching association, the time slice position where the power dominance of the power dominance changes in the power dominance sequence is extracted as the power dominance change boundary, the time slice segment between two adjacent power dominance change boundaries is identified, and each time slice segment is used as the association configuration period.

[0185] When the association type is determined to be a biased matching association, the time period set with fewer time slices in the first and second time period sets is identified. If the number of time slices in the first time period set is less than the number of time slices in the second time period set, the demand region that is continuously in a power-dominant state is identified as the advantageous resource group, and the supply region is identified as the restricted resource group. The time slices in the first time period set are then used as the association configuration time periods. If the number of time slices in the second time period set is less than the number of time slices in the first time period set, the supply region that is continuously in a power-dominant state is identified as the advantageous resource group, and the demand region is identified as the restricted resource group. The time slices in the second time period set are then used as the association configuration time periods.

[0186] When the association type is determined to be a perfect match, all time slices within the preset time period are used as the association configuration time period.

[0187] Step C2: For each associated configuration time period, determine the time period configuration capacity based on the power value of the group supply curve and the power value of the group demand curve.

[0188] It is understandable that, for each associated configuration period of the association type of complementary matching, the power values ​​of the group supply curve and the power values ​​of the group demand curve for each time slice within each associated configuration period are extracted, the average power value of the group supply curve and the average power value of the group demand curve within each associated configuration period is calculated, and the average power value is used as the time period configuration capacity for each associated configuration period.

[0189] For each association configuration period with a biased matching association type, the power value of the restricted resource group for each time slice within each association configuration period is extracted, and the power value of the restricted resource group is used as the time period configuration capacity for each association configuration period.

[0190] For each associated configuration period with a complete match association type, extract the group supply curve power value and group demand curve power value of each time slice within each associated configuration period, calculate the average power value of the group supply curve power value and group demand curve power value within each associated configuration period, and use the average power value as the time period configuration capacity for each associated configuration period.

[0191] Step C3: Generate configuration relationship data based on each associated configuration time period and the corresponding time period configuration capacity, and use the configuration relationship data as the intra-unit association between the supply area and the demand area.

[0192] The configuration relationship data includes time period information and capacity information.

[0193] It's easy to understand that the process involves extracting the start and end positions of time slices for each associated configuration period to generate time slice information. Then, the time slice configuration capacity for each associated configuration period is extracted to generate capacity information. Finally, the time slice information and capacity information are combined to generate configuration relationship data. This data records the configuration capacity values ​​of the supply and demand regions for each associated configuration period, serving as the intra-unit association between the supply and demand regions.

[0194] Preferably, step 324 determines the associated configuration time period based on the association type, adopts different time period configuration capacity calculation methods for different association types, generates configuration relationship data, realizes the establishment of intra-unit association relationship between supply area and demand area, and completes the supply and demand configuration within the micro-market unit.

[0195] Preferably, step 320 obtains the group supply curve of the supply region and the group demand curve of the demand region in the priority matching object, compares the supply and demand balance status of each time slice, analyzes the power-dominant sequence to determine the association type, determines the association configuration period and period configuration capacity according to the association type, generates configuration relationship data, establishes the intra-unit association relationship between the supply region and the demand region, and realizes the time-dimensional fine matching and configuration of supply and demand resources within the micro-market unit.

[0196] Step 330: Establish aggregation relationships with higher-level nodes for unmatched supply or demand areas within the micro-market unit.

[0197] In some embodiments, step 330 includes steps 331 to 334:

[0198] Step 331: Identify supply and demand areas within a micro-market unit that have not established intra-unit relationships and mark them as completely unmatched areas.

[0199] Understandably, the process involves traversing each supply and demand region within a micro-market unit and checking whether corresponding configuration relationship data exists for each supply and demand region. If it is determined that no corresponding configuration relationship data exists for a supply or demand region, it indicates that no intra-unit association relationship was established for the supply or demand region in step 320, and the supply or demand region is marked as a completely unmatched region.

[0200] Step 332: For the supply and demand combinations with established intra-unit relationships, calculate the supply surplus between the total power of the group supply curve in the supply region and the cumulative value of the time-period configuration capacity corresponding to each associated configuration period, and the demand gap between the total power of the group demand curve in the demand region and the cumulative value of the time-period configuration capacity corresponding to each associated configuration period.

[0201] It should be noted that in supply and demand combinations with established intra-unit relationships, the total power of the group supply curve in the supply region may exceed the cumulative value of the time-period configuration capacity corresponding to each associated configuration period, and the total power of the group demand curve in the demand region may exceed the cumulative value of the time-period configuration capacity corresponding to each associated configuration period. The excess power cannot be configured within the micro-market unit and needs to be aggregated to the upper-level node.

[0202] For each supply-demand combination with established intra-unit relationships, the group supply curve of the supply region within the supply-demand combination is extracted. The sum of the power values ​​of each time slice of the group supply curve within a preset time period is calculated to obtain the total power of the group supply curve. The time-period configuration capacity corresponding to each associated configuration period in the configuration relationship data of the supply-demand combination is extracted, and the time-period configuration capacities corresponding to each associated configuration period are summed to obtain the cumulative value of the time-period configuration capacity. The difference between the total power of the group supply curve and the cumulative value of the time-period configuration capacity is calculated. If the difference is greater than zero, the difference is taken as the supply surplus of the supply region.

[0203] Extract the group demand curve of the demand region in the supply-demand combination, calculate the sum of the power values ​​of each time slice of the group demand curve within a preset time period, and obtain the total power of the group demand curve. Calculate the difference between the total power of the group demand curve and the cumulative value of the allocated capacity for the time period. If the difference is greater than zero, the difference is taken as the demand gap of the demand region.

[0204] Step 333: Mark supply areas with supply surpluses as partially unmatched supply areas, and mark demand areas with demand gaps as partially unmatched demand areas.

[0205] It's easy to understand that when determining if a supply region has a supply surplus, it is marked as a partially unmatched supply region. Conversely, when determining if a demand region has a demand deficit, it is marked as a partially unmatched demand region.

[0206] Step 334: Establish aggregation relationships with higher-level nodes for completely unmatched regions and partially unmatched supply or demand regions.

[0207] Specifically, step 334 includes steps D1 to D4:

[0208] Step D1: Extract the total power of the group supply curves of completely mismatched supply regions as the upward convergence supply capacity, and extract the total power of the group demand curves of completely mismatched demand regions as the upward convergence demand capacity.

[0209] Understandably, for supply areas marked as completely unmatched, the group supply curve of the completely unmatched supply area is extracted, the sum of the power values ​​of each time slice of the group supply curve within a preset time period is calculated, the total power of the group supply curve is obtained, and the total power of the group supply curve is used as the supply capacity that the completely unmatched supply area converges upward.

[0210] For demand regions marked as completely unmatched, extract the group demand curves of the completely unmatched demand regions, calculate the sum of the power values ​​of each time slice of the group demand curve within a preset time period, obtain the total power of the group demand curve, and use the total power of the group demand curve as the demand capacity that is aggregated upward from the completely unmatched demand regions.

[0211] Step D2: Use the remaining supply from some unmatched supply areas as the supply capacity to be aggregated upwards.

[0212] It is easy to understand that, for the supply areas marked as partially unmatched, the supply surplus calculated in step 332 for the partially unmatched supply areas is extracted, and the supply surplus is used as the supply capacity that is aggregated upward from the partially unmatched supply areas.

[0213] Step D3: Use the demand gap in some unmatched demand areas as the upward converged demand capacity.

[0214] It is understandable that, for the partially unmatched demand areas, the demand gap amount calculated in step 332 for the partially unmatched demand areas is extracted, and the demand gap amount is used as the demand capacity that is aggregated upward from the partially unmatched demand areas.

[0215] Step D4: Based on the supply capacity aggregated upwards from each supply region and the demand capacity aggregated upwards from each demand region, generate aggregation data of the micro-market unit to the upper-level node, and form an aggregation relationship to the upper-level node.

[0216] It's easy to understand that the aggregated supply capacity of each supply region within a micro-market unit, including the aggregated supply capacity of completely mismatched supply regions and the aggregated supply capacity of partially mismatched supply regions, is used to generate the aggregated supply capacity of the micro-market unit. Similarly, the aggregated demand capacity of each demand region within a micro-market unit, including the aggregated demand capacity of completely mismatched demand regions and the aggregated demand capacity of partially mismatched demand regions, is used to generate the aggregated demand capacity of the micro-market unit. The aggregated supply capacity and aggregated demand capacity of the micro-market unit are then combined to generate the aggregated data from the micro-market unit to its superior nodes. This aggregated data records the power capacity that cannot be configured within the micro-market unit and serves as the aggregated relationship between the micro-market unit and its superior nodes.

[0217] Preferably, step 334 extracts the total power of the group supply curve or the total power of the group demand curve in the completely unmatched area as the supply capacity or demand capacity to be aggregated upwards, and uses the supply surplus in the partially unmatched supply area or the demand gap in the demand area as the supply capacity or demand capacity to be aggregated upwards, generating aggregated data of the micro-market unit to the upper-level node, thereby realizing the aggregation of power capacity that cannot be configured within the micro-market unit to the upper-level node.

[0218] Preferably, step 330 identifies completely unmatched areas and partially unmatched supply or demand areas within the micro-market unit, calculates the supply or demand capacity that each area converges upwards, generates convergence data from the micro-market unit to the upper-level node, establishes the convergence relationship with the upper-level node, realizes hierarchical coordination between the micro-market unit and the upper-level node, and provides data support for the hierarchical balance of the distribution network.

[0219] Preferably, step 300 involves constructing a topology tree structure and identifying the nearest common ancestor node, determining the topology matching level based on the difference between the first and second levels, selecting the supply and demand combination with the deepest topology matching level as the priority matching object, analyzing the supply and demand balance status of each time slice to determine the association type, establishing intra-unit association relationships based on the association type, and establishing aggregation relationships to upper-level nodes for unmatched supply or demand areas, thereby achieving priority allocation and hierarchical balance within the micro-market unit and completing the spatial priority allocation and hierarchical balance coordination of distributed resources in the distribution network.

[0220] Step 400: Generate configuration instructions for each distributed resource based on the intra-unit association and the aggregation relationship with the upper-level node.

[0221] In some embodiments, step 400 is specifically implemented by steps 410 to 440:

[0222] Step 410: For supply and demand combinations that establish intra-unit relationships, generate intra-unit configuration instructions for each distributed resource in the supply area and each distributed resource in the demand area within the supply and demand combination.

[0223] The configuration instructions within a unit include the source resource identifier, the target resource identifier, and the configuration capacity value.

[0224] Understandably, for each supply and demand combination that establishes a relationship within the unit, the configuration relationship data of the supply and demand combination is extracted, and the time period information and capacity information in the configuration relationship data are obtained.

[0225] Extract the identifiers of each distributed resource within the supply region of the supply-demand combination as source resource identifiers, and extract the identifiers of each distributed resource within the demand region of the supply-demand combination as target resource identifiers. Determine the time slice range for each associated configuration time period based on the time period information in the configuration relationship data. Obtain the time period configuration capacity corresponding to each associated configuration time period based on the capacity information in the configuration relationship data.

[0226] The power values ​​of each distributed resource within the supply area are proportionally allocated to the time-period configuration capacity during each associated configuration period to obtain the configuration capacity value of each distributed resource within the supply area during each associated configuration period. Similarly, the power values ​​of each distributed resource within the demand area are proportionally allocated to the time-period configuration capacity during each associated configuration period to obtain the configuration capacity value of each distributed resource within the demand area during each associated configuration period.

[0227] The source resource identifier, target resource identifier, time slice range of each associated configuration period, and configuration capacity value are combined to generate a configuration instruction within the unit. The configuration instruction within the unit indicates the specific capacity of power configuration from each distributed resource in the supply area to each distributed resource in the demand area during each associated configuration period.

[0228] Step 420: For supply or demand regions that have established upward aggregation relationships with higher-level nodes, generate upward aggregation instructions for each distributed resource.

[0229] The upward aggregation instruction includes the resource identifier and the aggregation capacity value.

[0230] It is easy to understand that, for the supply area marked as a completely unmatched area, the resource identifiers of each distributed resource in the completely unmatched supply area are extracted, the upward aggregation supply capacity calculated in step D1 for the completely unmatched supply area is extracted, the power values ​​of each distributed resource in the completely unmatched supply area within a preset time period are proportionally allocated to the upward aggregation supply capacity, the aggregation capacity value of each distributed resource in the completely unmatched supply area is obtained, and the resource identifiers and aggregation capacity values ​​are combined to generate an upward aggregation instruction.

[0231] For the partially unmatched supply areas, extract the resource identifiers of each distributed resource in the partially unmatched supply areas, extract the upward aggregation supply capacity determined in step D2 for the partially unmatched supply areas, allocate the power values ​​of each distributed resource in the partially unmatched supply areas within the unconfigured time slice to the upward aggregation supply capacity proportionally, obtain the aggregation capacity value of each distributed resource in the partially unmatched supply areas, and combine the resource identifiers and aggregation capacity values ​​to generate an upward aggregation instruction.

[0232] For demand areas marked as completely unmatched, extract the resource identifiers of each distributed resource within the completely unmatched demand area, extract the upward aggregation demand capacity calculated in step D1 for the completely unmatched demand area, allocate the power values ​​of each distributed resource within the completely unmatched demand area to the upward aggregation demand capacity proportionally within a preset time period, obtain the aggregation capacity value of each distributed resource within the completely unmatched demand area, and combine the resource identifiers and aggregation capacity values ​​to generate an upward aggregation instruction.

[0233] For the partially unmatched demand areas, extract the resource identifiers of each distributed resource within the partially unmatched demand areas, extract the upward aggregation demand capacity determined in step D3 for the partially unmatched demand areas, allocate the power values ​​of each distributed resource within the partially unmatched demand areas to the upward aggregation demand capacity proportionally within the unconfigured time slices, obtain the aggregation capacity value of each distributed resource within the partially unmatched demand areas, and combine the resource identifiers and aggregation capacity values ​​to generate an upward aggregation instruction.

[0234] Step 430: When the distributed resource is determined to be an energy storage device, based on the charging and discharging state of the energy storage device in each time slice, generate configuration instructions as supply resources for time slices in the discharging state, and generate configuration instructions as demand resources for time slices in the charging state.

[0235] It should be noted that energy storage devices in the distribution network can act as both a supply resource (providing electricity to power-consuming devices) and a demand resource (absorbing electricity from power generation devices). The role of the energy storage device switches between supply and demand resources depending on its charging and discharging state. Therefore, when generating configuration instructions for energy storage devices, it is necessary to distinguish whether the energy storage device is configured as a supply or demand resource based on its charging and discharging state in each time slice.

[0236] Understandably, the process involves identifying energy storage devices within a micro-market unit and acquiring their charging / discharging status data for each time slice within a preset time period. When a device is determined to be discharging in any time slice, it is identified as a supply resource. Based on configuration instructions within the supply region or upward aggregation instructions, a configuration instruction is generated for the device as a supply resource. Conversely, when a device is determined to be charging in any time slice, it is identified as a demand resource. Based on configuration instructions within the demand region or upward aggregation instructions, a configuration instruction is generated for the device as a demand resource.

[0237] Preferably, step 400 generates intra-unit configuration instructions for each distributed resource in the supply and demand combination that establishes intra-unit relationships based on the intra-unit correlation, generates upward aggregation instructions for each distributed resource in the unmatched supply or demand area based on the aggregation relationship with the upper-level node, and generates corresponding configuration instructions for the energy storage device based on the charging and discharging status, thereby realizing the generation of configuration instructions for each distributed resource and completing the configuration decision of distributed resources within the micro-market unit.

[0238] In summary, this application constructs a topology tree structure for the distribution network and identifies the nearest common ancestor (LCA) node of the supply and demand areas within the topology tree. Based on the difference between the first and second levels, it determines the topology matching level of the supply and demand combinations and selects the supply and demand combination with the deepest topology matching level as the priority matching object. This achieves priority matching of supply and demand based on spatial location relationships, ensuring that the allocation decisions for distributed resources are completed preferentially within the micro-market unit, only converging to higher-level nodes when matching cannot be completed within the unit, thus achieving hierarchical balancing of the distribution network. Compared to centralized dispatching methods, which require a higher-level dispatch center to handle all resource allocation decisions, this application reduces the balancing pressure on the higher-level dispatch center and improves the efficiency of distribution network resource allocation. Meanwhile, this application analyzes the power time-series data of distributed resources to identify adjustable resources with adjustment capabilities. Based on the curve correlation coefficient between the group supply curve and the group demand curve of the adjustable resources, the supply area and demand area are determined. When establishing the intra-unit correlation, the correlation type and correlation time period distribution are determined based on the supply and demand balance status of the supply area and demand area in each time slice. Corresponding configuration relationship data are generated for different supply and demand matching situations, making full use of the local consumption potential between distributed new energy power generation and electricity load within the local area of ​​the distribution network to improve the local consumption rate of distributed new energy.

[0239] Based on the above steps, this application also includes the following embodiments:

[0240] See Figure 7 This is a schematic diagram of the structure of a micro-market hierarchical balanced resource allocation system provided in an embodiment of this application. The micro-market hierarchical balanced resource allocation system includes:

[0241] The partitioning module is used to divide the distribution network into multiple micro-market units based on the topology data of the distribution network. Each micro-market unit includes a base node and multiple distributed resources connected to the base node.

[0242] The identification module is used to identify the supply and demand attributes of distributed resources in each micro-market unit based on the operation status data of each distributed resource within a preset time period, and to determine the supply area and demand area based on the supply and demand attribute identification.

[0243] The planning module determines priority matching targets based on the spatial relationship between supply and demand areas, establishes intra-unit relationships for priority matching targets, and establishes aggregation relationships with higher-level nodes for unmatched supply or demand areas within the micro-market unit.

[0244] The generation module is used to generate configuration instructions for each distributed resource based on the relationships within the unit and the aggregation relationships with the superior nodes.

[0245] Figure 7 The system of the illustrated embodiment can be used to perform corresponding operations. Figure 1 The steps in the method embodiments shown are implemented in a similar manner and have similar technical effects, and will not be repeated here.

[0246] See Figure 8 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. The electronic device 50 includes: a processor 51, a memory 52, and a computer program; wherein,

[0247] The memory 52 is used to store the computer program, and the memory may also be flash memory. The computer program is, for example, an application program or functional module that implements the above method.

[0248] The processor 51 is configured to execute the computer program stored in the memory to implement the various steps performed by the device in the above method. For details, please refer to the relevant descriptions in the preceding method embodiments.

[0249] Alternatively, the memory 52 can be either standalone or integrated with the processor 51.

[0250] When the memory 52 is a device independent of the processor 51, the device may further include:

[0251] Bus 53 is used to connect the memory 52 and the processor 51.

[0252] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A micro-market stratified balanced resource allocation method, characterized in that, include: Based on the topology data of the distribution network, the distribution network is divided into multiple micro-market units by nodes. Each micro-market unit includes a base node and multiple distributed resources connected to the base node. Based on the operational status data of each distributed resource within a preset time period, the supply and demand attributes of the distributed resources in each micro-market unit are identified, and the supply and demand areas are determined based on the supply and demand attribute identification. Priority matching targets are determined based on the spatial relationship between supply and demand areas, including: Construct a topology tree structure for the micro-market unit, wherein the topology tree structure has a base node as the root node and the nodes where each distributed resource is located as leaf nodes; Each node in the topology tree structure is labeled with a level, with the level label of the base node being 0 and the level labels of the lower-level nodes increasing sequentially. Combine each supply region and each demand region into multiple supply-demand combinations, and identify the nearest common ancestor node of the supply region and demand region in the topology tree structure for each supply-demand combination; The topological matching level of each supply and demand combination is determined based on the hierarchical labeling of the nearest common ancestor node, including: Obtain the hierarchical label value of the nearest common ancestor node; Obtain the hierarchical label value of the node where the supply region is located and the hierarchical label value of the node where the demand region is located; Calculate the first-level difference between the node where the supply region is located and the nearest common ancestor node, and the second-level difference between the node where the demand region is located and the nearest common ancestor node; Based on the states of the first-level difference and the second-level difference, the topology matching level of each supply and demand combination is determined, including: When both the first-level difference and the second-level difference are in the initial level state, the supply and demand combination is identified as a direct connection type at the same level, and the topology matching level of the supply and demand combination is determined to be the level label value of the nearest common ancestor node. When the sum of the first-level difference and the second-level difference is in a single-level progressive state, the supply and demand combination is identified as a single-hop connection type, and the topology matching level of the supply and demand combination is determined to be the value of the level label of the nearest common ancestor node reduced by one level. When the sum of the first-level difference and the second-level difference is in a multi-level progressive state, the supply and demand combination is identified as a multi-hop connection type, and the topological matching level of the supply and demand combination is determined to be the value after deducting the sum of the level differences from the level label value of the nearest common ancestor node. The supply and demand combination with the deepest topological matching level is selected as the priority matching target; Establishing intra-unit associations for the priority matching objects includes: Retrieve the group supply curves for the supply region and the group demand curves for the demand region within the priority matching object; The supply and demand of the group supply curves and the group demand curves are compared in each time slice to identify the supply and demand balance in each time slice. Based on the supply and demand balance status of each time slice, determine the correlation type and correlation time period distribution between the supply region and the demand region; Based on the type and time period of association, establish intra-unit association relationships between supply and demand regions; Establish aggregation relationships with higher-level nodes for unmatched supply or demand areas within a micro-market unit; Based on the intra-unit associations and the aggregation relationships with the superior nodes, configuration instructions are generated for each distributed resource.

2. The method according to claim 1, characterized in that, The step of identifying the supply and demand attributes of distributed resources within each micro-market unit based on the operational status data of each distributed resource within a preset time period includes: The preset time period is divided into multiple time slices; Statistically analyze the power generation and power consumption data of each distributed resource in each time slice to determine the power time series data of each distributed resource; The power fluctuation characteristic value of each distributed resource is calculated based on the power time series data. The power fluctuation characteristic values ​​of all distributed resources within the micro-market unit are sorted in descending order, and the top N distributed resources are selected as adjustable resources. Distributed resources in the adjustable resources whose power generation is greater than their power consumption are assigned a supply attribute identifier, and distributed resources in the adjustable resources whose power consumption is greater than their power generation are assigned a demand attribute identifier.

3. The method according to claim 1 or 2, characterized in that, The process of determining the supply region and demand region based on the supply and demand attribute identifiers includes: Spatial clustering is performed based on the positional relationship of adjustable resources in the distribution network topology to obtain supply resource groups and demand resource groups; The summation of power time-series data of adjustable resources within each resource group is calculated to obtain the group supply curve and group demand curve. Calculate the curve correlation coefficient between the group supply curve and the group demand curve, and select the resource group with a negative curve correlation coefficient as the supply region and demand region.

4. The method according to claim 1, characterized in that, The step of determining the correlation type and correlation time period distribution between supply and demand regions based on the supply and demand balance status of each time slice includes: For each time slice, compare the power value of the group supply curve with the power value of the group demand curve, and identify the resource group whose power value exceeds that of the other as the power leader of the time slice. The power dominance of each time slice is statistically analyzed to form a power dominance sequence; A power dominance transition analysis was performed on the power dominance sequence to identify the first time period set where the supply region is the power dominance and the second time period set where the demand region is the power dominance. The association type is determined based on the distribution patterns of the first and second time period sets. When the two time period sets are distributed alternately, it is identified as a complementary matching association. When a single time period set is continuously occupied, it is identified as a biased matching association. When the two time period sets are evenly distributed, it is identified as a perfect matching association.

5. The method according to claim 1, characterized in that, The method of establishing intra-unit correlations between supply and demand regions based on correlation type and correlation time period distribution includes: Determine whether each time slice is used as a time period for association configuration based on the association type; For each associated configuration period, the configuration capacity for that period is determined based on the power values ​​of the group supply curve and the power values ​​of the group demand curve. Based on each associated configuration time period and the corresponding time period configuration capacity, configuration relationship data is generated, and the configuration relationship data is used as the intra-unit association relationship between the supply area and the demand area.

6. The method according to claim 1, characterized in that, The process of establishing aggregation relationships with higher-level nodes for unmatched supply or demand areas within a micro-market unit includes: Identify supply and demand areas within a micromarket unit that lack intra-unit relationships and mark them as completely mismatched areas. For supply and demand combinations with established inter-unit relationships, calculate the supply surplus between the total power of the group supply curve in the supply region and the cumulative value of the time-period configuration capacity corresponding to each associated configuration period, and the demand gap between the total power of the group demand curve in the demand region and the cumulative value of the time-period configuration capacity corresponding to each associated configuration period. Supply areas with surplus supply are marked as partially unmatched supply areas, and demand areas with demand gaps are marked as partially unmatched demand areas. For completely unmatched areas and partially unmatched supply or demand areas, establish aggregation relationships with higher-level nodes.

7. The method according to claim 6, characterized in that, The process of establishing aggregation relationships with higher-level nodes for completely unmatched regions and partially unmatched supply or demand regions includes: Extract the total power of the group supply curves of completely mismatched supply regions as the upward convergence supply capacity, and extract the total power of the group demand curves of completely mismatched demand regions as the upward convergence demand capacity. The remaining supply from some unmatched supply areas will be used as the supply capacity for upward aggregation; The demand gap in some unmatched demand areas will be used as the upward aggregated demand capacity. Based on the supply capacity aggregated upwards from each supply region and the demand capacity aggregated upwards from each demand region, aggregated data of micro-market units to higher-level nodes is generated, forming aggregated relationships with higher-level nodes.

8. The method according to claim 1, characterized in that, The step of generating configuration instructions for each distributed resource based on the intra-unit association and the aggregation relationship with the superior node includes: To establish the supply and demand combination with intra-unit relationships, configuration instructions are generated for each distributed resource in the supply area and each distributed resource in the demand area; the intra-unit configuration instructions include source resource identifier, target resource identifier, and configuration capacity value. For supply or demand regions that have established upward aggregation relationships with higher-level nodes, an upward aggregation instruction is generated for each distributed resource; the upward aggregation instruction includes a resource identifier and an aggregation capacity value; When a distributed resource is determined to be an energy storage device, configuration instructions are generated as supply resources for time slices in the discharging state and as demand resources for time slices in the charging state, based on the charging and discharging state of the energy storage device in each time slice.

9. A micro-market tiered balanced resource allocation system, employing the micro-market tiered balanced resource allocation method as described in any one of claims 1 to 8, characterized in that, include: The partitioning module is used to divide the distribution network into multiple micro-market units by nodes according to the topology data of the distribution network. Each micro-market unit includes a base node and multiple distributed resources connected to the lower level of the base node. The identification module is used to identify the supply and demand attributes of distributed resources in each micro-market unit based on the operating status data of each distributed resource within a preset time period, and to determine the supply area and demand area based on the supply and demand attribute identification. The planning module determines priority matching objects based on the spatial relationship between supply and demand areas, establishes intra-unit association relationships for the priority matching objects, and establishes aggregation relationships to higher-level nodes for unmatched supply or demand areas within the micro-market unit. The generation module is used to generate configuration instructions for each distributed resource based on the association relationship within the unit and the aggregation relationship with the superior node.

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