A method and device for intelligent power distribution and control based on load group identification

Abstract

The present invention discloses an intelligent power distribution and control method and device based on load group identification. The method obtains load node information and establishes a network topology diagram, and uses a community discovery algorithm to divide load nodes with close physical connections and similar power consumption characteristics into groups to reduce the complexity of the optimization problem. A quadratic programming model for power distribution between groups is constructed, and power distribution within the group is executed in a time-sharing progressive manner. Finally, power distribution control is achieved through a hierarchical progressive control strategy. The device includes a data acquisition module, a load classification module, a power distribution module, and a control execution module. The present invention significantly reduces the complexity of optimization calculations through load group management, reduces cross-group power transmission losses, and improves system stability and scalability.

Description

Technical Field

[0001] The present invention relates to the technical field of power system load management, and in particular to an intelligent power distribution and control method and device based on load group identification. Background Art

[0002] As power systems continue to expand and loads diversify, load management and power distribution control in smart grids are becoming increasingly complex. This is especially true in scenarios like large-scale industrial parks and smart cities, where the number of load nodes is enormous and the power consumption characteristics and power requirements of different loads vary significantly. To achieve efficient and economical operation of the power system, reasonable power allocation and refined management of each load node are necessary.

[0003] Existing technologies typically use a centralized optimization approach, treating all load nodes as independent optimization targets for power allocation and control. While this approach performs well in small-scale systems, it suffers from the following problems in large-scale, complex systems: First, when the number of load nodes is large, the relationships between nodes that need to be processed grow exponentially, resulting in excessive computational complexity in the optimization problem, making it difficult to meet the requirements of real-time control. Second, traditional approaches ignore the physical connection characteristics and power usage similarities between load nodes, and do not consider the geographical distance between nodes and power transmission conditions, resulting in large cross-regional power transmission losses and reduced system efficiency. Third, due to the lack of systematic analysis of load characteristics, it is difficult to achieve unified management and control of loads with similar characteristics, and it is impossible to formulate differentiated control strategies for different types of load groups. Finally, existing methods have poor system scalability. Each additional load node requires the entire optimization problem to be reconstructed, resulting in high system maintenance costs. Furthermore, the addition of new load nodes causes the scale of the optimization problem to increase dramatically, affecting the stable operation of the system.

[0004] Furthermore, with the large-scale integration of renewable energy and the dynamic changes in electricity demand, traditional power allocation methods struggle to adapt to the time-varying and uncertain nature of load characteristics, and are unable to fully leverage the complementary nature of loads to improve system efficiency. Therefore, there is an urgent need for an intelligent power allocation and control method that can effectively reduce optimization complexity, minimize power transmission losses, and improve system scalability. Summary of the Invention

[0005] The problem to be solved by the present invention is how to provide an intelligent power distribution and control method and device with high computational efficiency, low transmission loss and strong system scalability.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: an intelligent power distribution and control method based on load group identification, characterized by comprising the following steps:

[0007] Obtaining load node information, the load node information including static parameter information, real-time operating parameter information, and historical power data information, wherein the static parameter information includes rated power parameters and geographic location parameters, and the real-time operating parameter information includes real-time power demand parameters, available power capacity parameters, and inter-node power transmission parameters;

[0008] Establish a load relationship network topology diagram based on the load nodes and the power transmission parameters between the nodes, wherein the load nodes are set as the vertices of the network diagram, and the power transmission connection edges are established based on the power transmission parameters between the nodes;

[0009] Based on the load relationship network topology, a community discovery algorithm is used to identify load groups. A load group is a collection of multiple load nodes with frequent power transmission relationships, close physical connections, and similar power consumption characteristics. The community discovery algorithm uses the power transmission intensity parameter as the dominant factor to calculate the modularity gain, and combines the connection density parameter and the power distribution similarity parameter to optimize group division. When the modularity gain is positive, the group is merged.

[0010] Implementing power optimization allocation, including using the load group as a basic unit of power allocation, constructing a quadratic programming mathematical model for power allocation between groups, and performing power allocation within the group in a time-sharing progressive manner, wherein time-sharing progressive means performing power allocation and adjustment in sequence according to a time series;

[0011] A hierarchical progressive control strategy is adopted to implement power allocation control, where hierarchical progressive refers to generating group power allocation instructions through the macro-scheduling control module, including generating group power allocation instructions through the macro-scheduling control module and executing load node power regulation through the local scheduling control module.

[0012] A further technical solution is that the construction of the load relationship map includes:

[0013] Determine the load node as a graph vertex, record the rated power and the geographic location information, establish an edge connection based on the power transmission data between the nodes, and determine the power transmission data as the edge weight;

[0014] Calculate the correlation between load nodes, including node connection density, power transmission intensity and power distribution similarity, where the node connection density is the ratio of the actual number of node connections to the total number of load nodes, the power transmission intensity is the ratio of the power transmission data between the nodes to the rated power, and the power distribution similarity is based on the time series correlation calculated based on the power change pattern;

[0015] Determine an inter-group connection relationship, wherein the inter-group power transmission strength is the sum of the power transmission strengths between nodes in different groups;

[0016] A further technical solution is that the process of acquiring the power change pattern includes:

[0017] The power variation characteristics of each load node are calculated based on the historical power data. The power variation characteristics include a daily maximum power value, a daily minimum power value and a power variation rate, so as to obtain a typical power variation pattern of the load node.

[0018] A further technical solution is that the execution process of the community discovery algorithm includes:

[0019] Based on the topological structure, edge weights, and node associations of the load relationship graph, load groups are divided using the Louvain algorithm to form load groups with frequent power transmission, close physical connections, and similar power consumption characteristics. The Louvain algorithm initially treats each load node as an independent group and calculates the modularity gain before and after merging adjacent groups. The modularity gain is based on the power transmission intensity, node connection density, and power distribution similarity of the nodes within the group. Groups are merged when the modularity gain is positive, and the merging process is repeated until the modularity no longer increases.

[0020] Calculating the group power characteristics of the load group using a weighted average method based on the typical power change patterns of the load nodes in the group, where the weight is the ratio of the rated power of each node to the total rated power of the group, to obtain the daily maximum power value, daily minimum power value, and power change rate of the group;

[0021] The maximum power transmission capability between groups is calculated. The calculation is based on the group division result, and the available power capacities of all power transmission connections between the same group pair are accumulated to obtain the maximum power transmission capability between the group pair.

[0022] A further technical solution is that the group power characteristic calculation process includes:

[0023] The group weighted total power is calculated based on the power data of the load node, and the daily maximum power value, the daily minimum power value, and the power change rate are obtained by analyzing the time series distribution of the group weighted total power.

[0024] A further technical solution is that the power optimization allocation based on group characteristics includes:

[0025] The load group is used as a basic unit for power allocation. The upper and lower power capacity constraints of the group are calculated based on the daily maximum power value and the daily minimum power value of the group. The maximum power adjustment amplitude of the group in adjacent time periods is calculated based on the power change rate of the group. The power demand curve of the group is predicted based on the typical power change pattern of the group using a time series prediction method.

[0026] Establishing a quadratic programming model for inter-group power allocation, wherein the objective function is to minimize the total inter-group power transmission loss, wherein the loss is proportional to the square of the power transmission amount, and the proportionality coefficient is determined by the inter-group power transmission intensity. The constraints of the quadratic programming model include the group's power balance constraint, power capacity constraint, power regulation constraint, and inter-group power transmission constraint. The power balance constraint requires that the allocated power of the group be equal to the group's predicted power demand. The power capacity constraint requires that the allocated power of the group do not exceed the upper and lower limits of the group's power capacity. The power regulation constraint requires that the power change of the group in adjacent time periods do not exceed the group's maximum power regulation amplitude. The power transmission constraint requires that the power transmission amount between groups do not exceed the maximum inter-group transmission power.

[0027] Power is distributed within the group in a time-sharing progressive manner, including determining an initial distribution coefficient based on the rated power ratio of each load node in the group, calculating a weighted sum of the deviation size and the accumulated time according to the real-time power demand deviation of each load node in the group, and dynamically adjusting the distribution coefficient of each load node based on the weighted sum. When the distributed power of a load node exceeds its available power capacity, the distributed power is proportionally reduced according to the rated power ratio of each node.

[0028] A further technical solution is that the hierarchical progressive control strategy includes macro-scheduling control and local scheduling control, wherein the macro-scheduling control is used to receive the secondary planning results of the power distribution between the groups and generate power distribution instructions for each group, and the local scheduling control is used to decompose the group power instructions based on the power distribution strategy within the group and issue and execute power adjustment instructions to the load node.

[0029] The present invention also discloses an intelligent power distribution and control device based on load group identification, which is characterized by comprising:

[0030] Data acquisition module, used to collect static data, real-time operation data and historical power data of load nodes;

[0031] A load classification module, configured to identify load groups based on the collected data;

[0032] A power allocation module, used to generate power allocation strategies between and within groups;

[0033] The control execution module is used to realize power distribution control through a hierarchical progressive control strategy, including a macro scheduling control unit and a local scheduling control unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The flowchart of the intelligent power distribution and control method based on load group identification is shown in FIG.

[0035] Figure 2 This is a schematic diagram of the load relationship network topology.

[0036] Figure 3 Flowchart for community discovery algorithm implementation.

[0037] Figure 4 This is the structure diagram of the hierarchical progressive control strategy. DETAILED DESCRIPTION

[0038] The following is a clear and complete description of the technical solutions in the embodiments of the invention in conjunction with the drawings in the embodiments of the invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0039] Reference Figure 1 The main steps of the method provided by the present invention are as follows:

[0040] (1) Obtain load node information, including static parameter information, real-time operating parameter information, and historical power data information;

[0041] (2) Establish a load relationship network topology diagram based on load nodes and power transmission parameters between nodes;

[0042] (3) Using community discovery algorithm to identify load groups based on the load relationship network topology;

[0043] (4) Implement power optimization allocation;

[0044] (5) A hierarchical progressive control strategy is used to achieve power distribution control.

[0045] As a preferred embodiment of the present invention, in step (1), the process of acquiring load node information includes three aspects: collecting static parameter information, acquiring real-time operating parameter information, and collecting historical power data information. The static parameter information mainly includes the rated power parameters of each load node and its geographical location parameters, which reflect the basic characteristics of the load node. The real-time operating parameter information includes real-time power demand parameters, available power capacity parameters, and inter-node power transmission parameters, which are used to reflect the current operating status of the load node. The historical power data information is used to analyze the power consumption characteristics and power change patterns of the load node, providing data support for subsequent group identification.

[0046] As a preferred embodiment of the present invention, the specific process for calculating power variation characteristics based on historical power data first involves preprocessing the collected historical power data. This preprocessing process includes normalizing the data according to a uniform time interval (e.g., 15 minutes), replacing outliers with local means, and supplementing missing values ​​with linear interpolation between adjacent time points. After preprocessing, the data is segmented according to natural days to facilitate the subsequent calculation of daily characteristic values.

[0047] After data preprocessing, the power variation characteristics of each load node are calculated. The first step is to calculate the daily maximum power value. By summarizing the power data at all sampling points within a 24-hour period, the maximum value is selected as the maximum power value for that day. Similarly, the minimum value is selected as the minimum power value for that day. The power change rate is calculated by taking the ratio of the power change between adjacent sampling points to the sampling interval. This ratio is then divided by the node's rated power to obtain the power change rate as a percentage.

[0048] As a preferred embodiment of the present invention, Figure 2 As shown, in step (2), the process of constructing the load relationship network topology diagram first sets each load node as a vertex of the network diagram, and records the rated power and geographical location information of each vertex. Then, a connection edge is established based on the power transmission data between nodes, and the power transmission data is set as the edge weight. Then, the correlation between nodes is calculated, including node connection density, power transmission intensity and power distribution similarity. Among them, the node connection density refers to the ratio of the actual number of connections to the total number of nodes, the power transmission intensity refers to the ratio of the power transmission data between nodes to the rated power, and the power distribution similarity is obtained by analyzing the time series correlation of the power change pattern.

[0049] As a preferred embodiment of the present invention, refer to Figure 3In step (3), the execution process of the community discovery algorithm mainly includes three stages. The first stage is to use the Louvain algorithm to divide the load into groups. When the algorithm is executed, each load node is first initialized as an independent group unit. Then, the algorithm begins to calculate the modularity gain when adjacent groups are merged. The calculation formula is:

[0050]

[0051] Where ΔQ is the modularity gain, Σ in represents the total power transfer within the group, Σ tot represents the total power transfer associated with the group, k i，in represents the power transfer amount of node i connected to the internal group, k i represents the total power transfer of node i, and m is the total power transfer in the network. During the calculation process, the power transfer intensity parameter is used as the dominant factor, and is comprehensively considered in combination with the connection density parameter and the power distribution similarity parameter. When the calculated modularity gain ΔQ is positive, a group merge operation is performed. This merging process is repeated until the modularity of the entire network no longer increases.

[0052] The second stage is the calculation process of the group power characteristics. After completing the group division, it is necessary to calculate the overall power characteristics of each group. Based on the typical power change pattern of all load nodes in the group, the weighted average method is used for calculation. During the calculation process, the weight of each node is determined by the ratio of the rated power of the node to the total rated power of the group. This ensures that nodes with higher power have a greater influence on the group characteristics. Through this weighted average method, the three key characteristic parameters of each group are finally obtained: the daily maximum power value, the daily minimum power value, and the power change rate.

[0053] The third stage is to calculate the maximum power transmission capacity between groups. This calculation is based on the completed group division results, and mainly examines all power transmission connections between the same group pair. Specifically, the available power capacity of these power transmission connections is accumulated to obtain the maximum power transmission capacity between the group pairs. This parameter has important guiding significance for the subsequent power optimization allocation. As a preferred embodiment of the present invention, the calculation process of the group power characteristics is an important link after the group division is completed. First, it is necessary to calculate the weighted total power of the group, and the calculation adopts a weighted method based on the rated power ratio. For each moment, the real-time power value of each load node in the group is weighted and summed according to the ratio of its rated power to the total rated power of the group. This weighted method ensures that nodes with larger power capacity have a more significant impact on the group characteristics.

[0054] After obtaining the time series data for the group's weighted total power, a time series distribution analysis is required. This analysis first divides the time series data by calendar day. For each calendar day, the system records the group's weighted total power value at each sampling point within a 24-hour period. By performing statistical analysis on this time series data, key parameters reflecting the group's overall power usage characteristics can be obtained. The daily maximum power value is obtained by selecting the maximum group weighted total power value across all sampling points within the day, while the daily minimum power value is obtained by selecting the minimum value. These two characteristic values, respectively, reflect the peak and valley power usage characteristics of the group within a day.

[0055] To determine the group power change rate, the system analyzes the change in the group's weighted total power between adjacent sampling moments. By calculating the ratio of the power change between adjacent moments to the time interval, the group-level power change rate is derived. This parameter reflects the dynamic characteristics of the group's overall power regulation and provides important guidance for subsequent power allocation and control.

[0056] As a preferred embodiment of the present invention, in step (4), the power optimization allocation process is implemented in three main stages. The first stage is to determine the constraints based on the group characteristics. First, based on the daily maximum power value and daily minimum power value of the group, the upper and lower limit constraints of the group power capacity are determined. At the same time, the maximum power adjustment amplitude of adjacent time periods is determined according to the power change rate of the group to limit the speed of power adjustment. In addition, a time series prediction method is used to predict the power demand curve of the group in the future time period based on historical data, providing a reference basis for optimal allocation.

[0057] The second stage is to establish a quadratic programming model for power allocation between groups. The objective function of this model is set to minimize the total power transmission loss between groups, and the formula is:

[0058]

[0059] Where J is the total power transmission loss, k ij is the power transmission intensity coefficient between groups i and j, P ij is the power transferred from group i to group j, and n is the total number of groups. Constraints primarily include power balance constraints, power capacity constraints, power regulation constraints, and inter-group power transfer constraints. Power balance constraints ensure that power supply and demand within each group are balanced; power capacity constraints ensure that allocated power does not exceed the group capacity limit; power regulation constraints control the rate of power change; and power transfer constraints ensure that inter-group power transfers do not exceed the maximum transmission capacity.

[0060] The third stage is to use a time-sharing progressive method to distribute power within the group. First, the initial distribution coefficient is determined based on the rated power ratio of each load node. The formula is:

[0061]

[0062] Among them, α i is the power distribution coefficient of load node i, P i , rated is the rated power of node i, n is the total number of nodes in the group, and the denominator represents the sum of the rated powers of all nodes in the group. The allocation coefficient of each load node is then dynamically adjusted based on the real-time power demand deviation. When it is found that the allocated power of a load node exceeds its available power capacity, it is proportionally reduced according to the rated power ratio of each node to ensure that the power allocation result meets the capacity constraints of each node. This time-sharing progressive allocation method not only ensures the overall optimization of power allocation, but also can be dynamically adjusted according to real-time demand, realizing refined management of load nodes within the group.

[0063] As a preferred embodiment of the present invention, Figure 4 As shown in step (5), the hierarchical progressive control strategy achieves coordinated control of power distribution through two levels: macro-dispatching control and local dispatching control. The macro-dispatching control module first receives the secondary planning results from the power optimization and allocation module and generates power allocation instructions for each load group based on these planning results. These instructions contain the target power values ​​of each group at different time periods, providing specific guidance for achieving coordinated control between groups.

[0064] After receiving the group power allocation instructions from the macro-dispatching control module, the local scheduling control module decomposes the instructions according to the power allocation strategy within the group. The decomposed power adjustment instructions contain the specific power adjustments required for each load node. The local scheduling control module is responsible for issuing the instructions to these load nodes and executing the power adjustments.

[0065] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, can make equivalent replacements or changes based on the technical solution and inventive concept of the present invention, which should be within the scope of protection of the present invention.

Claims

1. An intelligent power distribution and control method based on load group identification, characterized in that: The method comprises the following steps: Obtaining load node information, the load node information including static parameter information, real-time operating parameter information, and historical power data information, wherein the static parameter information includes rated power parameters and geographic location parameters, and the real-time operating parameter information includes real-time power demand parameters, available power capacity parameters, and inter-node power transmission parameters; Establish a load relationship network topology diagram based on the load nodes and the power transmission parameters between the nodes, wherein the load nodes are set as the vertices of the network diagram, and the power transmission connection edges are established based on the power transmission parameters between the nodes; Based on the load relationship network topology, a community discovery algorithm is used to identify load groups. A load group is a collection of multiple load nodes with frequent power transmission relationships, close physical connections, and similar power consumption characteristics. The community discovery algorithm uses the power transmission intensity parameter as the dominant factor to calculate the modularity gain, and combines the connection density parameter and the power distribution similarity parameter to optimize group division. When the modularity gain is positive, the group is merged. Implementing power optimization allocation, including using the load group as a basic unit of power allocation, constructing a quadratic programming mathematical model for power allocation between groups, and performing power allocation within the group in a time-sharing progressive manner, wherein time-sharing progressive means performing power allocation and adjustment in sequence according to a time series; A hierarchical progressive control strategy is adopted to implement power allocation control, where hierarchical progressive refers to generating group power allocation instructions through the macro-scheduling control module, including generating group power allocation instructions through the macro-scheduling control module and executing load node power regulation through the local scheduling control module.

2. The intelligent power distribution and control method based on load group identification according to claim 1, characterized in that: The construction of the load relationship network topology diagram includes: Determine the load nodes as vertices of the network graph, record the rated power parameters and the geographic location parameters, establish edge connections based on the power transmission parameters between the nodes, and determine the power transmission parameters as edge weights; Calculate the correlation between load nodes, including node connection density, power transmission intensity and power distribution similarity, where the node connection density is the ratio of the actual number of node connections to the total number of load nodes, the power transmission intensity is the ratio of the power transmission data between the nodes to the rated power, and the power distribution similarity is based on the time series correlation calculated based on the power change pattern; An inter-group connection relationship is determined, wherein the inter-group power transmission strength is the sum of the power transmission strengths between nodes in different groups.

3. The intelligent power distribution and control method based on load group identification according to claim 2, characterized in that: The process of acquiring the power change mode includes: The power variation characteristics of each load node are calculated based on the historical power data information. The power variation characteristics include the daily maximum power value, the daily minimum power value and the power variation rate, so as to obtain the typical power variation pattern of the load node.

4. The intelligent power distribution and control method based on load group identification according to claim 3 is characterized in that: The execution process of the community discovery algorithm includes: Based on the topological structure, edge weights, and inter-node associations of the load relationship network topology graph, load groups are divided using the Louvain algorithm to form load groups with frequent power transmission, close physical connections, and similar power consumption characteristics. The Louvain algorithm initially treats each load node as an independent group and calculates the modularity gain before and after merging adjacent groups. The modularity gain is based on the power transmission intensity, node connection density, and power distribution similarity of the nodes within the group. Groups are merged when the modularity gain is positive, and the merging process is repeated until the modularity no longer increases. Calculating group power characteristics of the load group using a weighted average method based on the typical power variation patterns of the load nodes in the group, where the weights in the weighted average method are the ratios of the rated power of each node to the total rated power of the group, to obtain the daily maximum power value, daily minimum power value, and power variation rate of the group; The maximum power transmission capability between groups is calculated. The calculation is based on the group division result, and the available power capacities of all power transmission connections between the same group pair are accumulated to obtain the maximum power transmission capability between the group pair.

5. The intelligent power distribution and control method based on load group identification according to claim 4, characterized in that: The group power characteristic calculation process includes: The group weighted total power is calculated based on the power data of the load node, and the daily maximum power value, the daily minimum power value, and the power change rate are obtained by analyzing the time series distribution of the group weighted total power.

6. The intelligent power distribution and control method based on load group identification according to claim 5, characterized in that: Optimal allocation based on group power characteristics includes: The load group is used as a basic unit for power allocation. The upper and lower power capacity constraints of the group are calculated based on the daily maximum power value and the daily minimum power value of the group. The maximum power adjustment amplitude of the group in adjacent time periods is calculated based on the power change rate of the group. The power demand curve of the group is predicted based on the typical power change pattern of the group using a time series prediction method. Establishing a quadratic programming model for inter-group power allocation, wherein the objective function is to minimize the total inter-group power transmission loss, wherein the loss is proportional to the square of the inter-group power transmission amount, and the proportionality coefficient is determined by the inter-group power transmission intensity. The constraints of the quadratic programming model include the group's power balance constraint, power capacity constraint, power regulation constraint, and inter-group power transmission constraint, wherein the power balance constraint requires that the group's allocated power equals the group's predicted power demand, the power capacity constraint requires that the group's allocated power does not exceed the group's power capacity upper and lower limit constraints, the power regulation constraint requires that the power change of the group in adjacent time periods does not exceed the group's maximum power regulation amplitude, and the power transmission constraint requires that the inter-group power transmission amount does not exceed the inter-group maximum transmission power. Power is distributed within the group in a time-sharing progressive manner, including determining the initial distribution coefficient based on the rated power ratio of each load node in the group, calculating the weighted sum of the deviation size and the accumulated time according to the real-time power demand deviation of each load node in the group, and dynamically adjusting the distribution coefficient of each load node based on the weighted sum. When the distributed power of a load node exceeds its available power capacity, the distributed power is proportionally reduced according to the rated power ratio of each node.

7. The intelligent power distribution and control method based on load group identification according to claim 1, characterized in that: The hierarchical progressive control strategy includes macro-scheduling control and local scheduling control, wherein the macro-scheduling control is used to receive the secondary planning results of the power distribution between the groups and generate power distribution instructions for each group, and the local scheduling control is used to decompose the group power instructions based on the power distribution strategy within the group and issue and execute power adjustment instructions to the load node.

8. An intelligent power distribution and control device based on load group identification for implementing the method according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, used to collect static data, real-time operation data and historical power data of load nodes; Load classification module, used to complete load group identification based on collected data; A power allocation module, used to generate power allocation strategies between and within groups; The control execution module is used to realize power distribution control through a hierarchical progressive control strategy, including a macro scheduling control unit and a local scheduling control unit.

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