Intelligent power distribution and control method and device based on load group identification

Abstract

The invention discloses an intelligent power distribution and control method and device based on load group identification. According to the method, load node information is acquired, a network topological graph is established, load nodes with close physical connection and similar power utilization characteristics are divided into groups by adopting a community discovery algorithm so as to reduce the complexity of an optimization problem, an inter-group power distribution quadratic programming model is constructed, and intra-group power distribution is executed in a time-sharing progressive mode, so that the power distribution efficiency is improved. And finally, realizing power distribution control through a hierarchical progressive control strategy. The device comprises a data acquisition module, a load classification module, a power distribution module and a control execution module. Through load grouping management, the optimization calculation complexity is remarkably reduced, the cross-group power transmission loss is reduced, and the system stability and expandability are improved.

Description

Technical Field

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

[0002] As the scale of power systems continues to expand and power loads become more diverse, load management and power distribution control in smart grids are becoming increasingly complex. Especially in application scenarios such as large-scale industrial parks and smart cities, the number of load nodes is huge, and the power characteristics and power requirements of different loads vary significantly. In order to achieve economical and efficient operation of the power system, it is necessary to reasonably allocate power and finely manage each load node.

[0003] Existing technologies usually adopt centralized optimization methods to distribute and control power for all load nodes as independent optimization objects. Although this method performs well in small-scale systems, it has the following problems in large-scale complex systems: First, when the number of load nodes is large, the relationship between nodes that needs to be processed grows exponentially, resulting in excessive computational complexity of the optimization problem, which is difficult to meet the needs of real-time control; second, traditional methods ignore the physical connection characteristics and power similarity between load nodes, and do not consider the geographical distance and power transmission conditions between nodes, resulting in large cross-regional power transmission losses and reducing system operation 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, the existing method has poor system scalability, and each additional load node requires the entire optimization problem to be rebuilt, resulting in high system maintenance costs, and the addition of new load nodes will cause the scale of the optimization problem to increase dramatically, affecting the stable operation of the system.

[0004] In addition, with the large-scale access of renewable energy and the dynamic changes in electricity demand, traditional power allocation methods are difficult to adapt to the time-varying and uncertain characteristics of load characteristics, and cannot fully utilize the complementary characteristics between loads to improve the operating efficiency of the system. Therefore, there is an urgent need for an intelligent power allocation and control method that can effectively reduce optimization complexity, reduce 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 calculation 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 in that it includes the following steps:

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

[0008] A load relationship network topology diagram is established according to 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 diagram, a community discovery algorithm is used to perform load group identification, where a load group refers to a set 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 the group division. When the modularity gain is positive, group merging is performed;

[0010] Implementing power optimization allocation, including taking 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 realize power allocation control, wherein hierarchical progressive refers to generating group power allocation instructions through a macro-dispatching control module, including generating group power allocation instructions through a macro-dispatching control module, and executing load node power regulation through a local dispatching 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 geographical 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, wherein 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 power change mode to calculate the timing correlation;

[0015] Determine the connection relationship between groups, wherein the power transmission strength between groups 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 mode includes:

[0017] The power variation characteristics of each load node are calculated based on the historical power data, and 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 association of the load relationship graph, the load groups are divided by the Louvain algorithm to form load groups with frequent power transmission, close physical connection 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 in the group. When the modularity gain is positive, group merging is performed 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 pattern 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, and 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 of power allocation, the upper and lower limits of the power capacity of the group are calculated based on the daily maximum power value of the group and the daily minimum power value of the group, the maximum power adjustment amplitude of the adjacent time periods of the group is calculated based on the power change rate of the group, and the power demand curve of the group is predicted based on the typical power change mode of the group by using a time series prediction method;

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

[0027] The power distribution within the group is carried out 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, used to complete load group identification based on the collected data;

[0032] A power allocation module, used to generate power allocation strategies between groups 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 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 creative work belong to the protection scope 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, and 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 load relationship network topology;

[0043] (4) Implement power optimization allocation;

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

[0045] As a preferred implementation scheme 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 power transmission parameters between nodes, 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 nodes, and provide data support for subsequent group identification.

[0046] As a preferred embodiment of the present invention, the specific process of calculating power change characteristics based on historical power data is first to preprocess the collected historical power data. The preprocessing process includes regularizing the data according to a uniform time interval (such as 15 minutes), replacing abnormal values ​​with local means, and supplementing missing values ​​with linear interpolation methods at adjacent moments. After the preprocessing is completed, the data is segmented according to natural days to facilitate the subsequent calculation of daily characteristic values.

[0047] After completing data preprocessing, the power change characteristics of each load node are calculated separately. The first is to calculate the maximum daily power value. By counting the power data of all sampling times within 24 hours every day, the maximum value is selected as the maximum power value of the day. Similarly, the minimum value is selected as the minimum power value of the day. For the calculation of the power change rate, the ratio of the power change amount at adjacent sampling times to the sampling time interval is calculated, and the ratio is divided by the node rated power to obtain the power change rate in percentage form.

[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 temporal 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 groups. When the algorithm is executed, each load node is first initialized as an independent group unit. Then, the algorithm starts 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 transmission amount of node i connected to the group, k i represents the total power transmission of node i, and m is the total power transmission in the network. In the calculation process, the power transmission intensity parameter is taken 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 a positive value, the group merging operation is performed. This merging process will be 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, which ensures that nodes with larger power have greater influence in the group characteristics. Through this weighted average method, the three key characteristic parameters of the daily maximum power value, daily minimum power value and power change rate of each group are finally obtained.

[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 of the group weighted total power, it is necessary to perform a time series distribution analysis on it. The analysis process first divides the time series data according to natural days. For the data of each natural day, the system records the group weighted total power value at each sampling time within 24 hours. By performing statistical analysis on these time series data, key parameters reflecting the overall power consumption characteristics of the group can be obtained. Among them, the daily maximum power value is obtained by selecting the maximum group weighted total power value among all sampling times within the day, and the daily minimum power value is the minimum value. These two characteristic values ​​respectively reflect the peak and valley characteristics of the group's power consumption in a day.

[0055] To determine the power change rate of the group, the system analyzes the change of the weighted total power of the group between adjacent sampling moments. By calculating the ratio of the power change at adjacent moments to the time interval, the power change rate at the group level can be obtained. This parameter reflects the dynamic characteristics of the overall power regulation of the group and has important guiding significance 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 the adjacent time period is determined according to the power change rate of the group to limit the speed of power adjustment. In addition, the 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 for the optimization 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 transfer amount from group i to group j, and n is the total number of groups. The constraints mainly include power balance constraint, power capacity constraint, power regulation constraint and power transfer constraint between groups. Power balance constraint ensures the power supply and demand balance of each group; power capacity constraint ensures that the allocated power does not exceed the group capacity limit; power regulation constraint controls the power change rate; and power transfer constraint ensures that the power transfer between groups does not exceed the maximum transmission capacity.

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

[0061]

[0062] Among them, α i is the power allocation 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. Then the allocation coefficient of each load node is 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 reduced in proportion 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 needs, realizing the refined management of load nodes in the group.

[0063] As a preferred embodiment of the present invention, Figure 4 As shown in Figure 5, in step (5), the hierarchical progressive control strategy realizes the 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 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 instruction from the macro-scheduling control module, the local scheduling control module decomposes the instruction according to the power allocation strategy within the group. The decomposed power adjustment instruction contains the specific power adjustment amount that each load node needs to perform. The local scheduling control module is responsible for issuing instructions to these load nodes and performing power adjustment.

[0065] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be within the protection scope 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 operation parameter information and historical power data information, wherein the static parameter information includes rated power parameters and geographic location parameters, and the real-time operation parameter information includes real-time power demand parameters, available power capacity parameters and inter-node power transmission parameters; A load relationship network topology diagram is established according to 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 diagram, a community discovery algorithm is used to perform load group identification, where a load group refers to a set 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 the group division. When the modularity gain is positive, group merging is performed; Implementing power optimization allocation, including taking 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 realize power allocation control, wherein hierarchical progressive refers to generating group power allocation instructions through a macro-dispatching control module, including generating group power allocation instructions through a macro-dispatching control module, and executing load node power regulation through a local dispatching control module.

2. The intelligent power distribution and control method based on load group identification according to claim 1 is characterized in that: The construction of the load relationship map includes: Determine the load node as a graph vertex, record the rated power and the geographical 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; Calculate the correlation between load nodes, including node connection density, power transmission intensity and power distribution similarity, wherein 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 power change mode to calculate the timing correlation; The 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 is 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, and 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.

4. The intelligent power distribution and control method based on load group identification according to claim 1 is characterized in that: The execution process of the community discovery algorithm includes: Based on the topological structure, edge weights and node association of the load relationship graph, the load groups are divided by the Louvain algorithm to form load groups with frequent power transmission, close physical connection 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 in the group. When the modularity gain is positive, group merging is performed and the merging process is repeated until the modularity no longer increases. Calculating the group power characteristics of the load group, using a weighted average method based on the typical power change pattern 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; The maximum power transmission capability between groups is calculated, and 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 is 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 1 is characterized in that: The power optimization allocation based on group characteristics includes: The load group is used as a basic unit of power allocation, the upper and lower limits of the power capacity of the group are calculated based on the daily maximum power value of the group 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, and the power demand curve of the group is predicted based on the typical power change mode of the group by using a time series prediction method; A quadratic programming model for power allocation between groups is established, wherein the objective function is to minimize the total power transmission loss between groups, the loss is proportional to the square of the power transmission amount, and the proportionality coefficient is determined by the power transmission intensity between groups. The constraints of the quadratic programming model include the power balance constraint, power capacity constraint, power regulation constraint and power transmission constraint between groups of the group, wherein the power balance constraint requires that the allocated power of the group is equal to the predicted power demand of the group, the power capacity constraint requires that the allocated power of the group does not exceed the upper and lower limits of the power capacity of the group, the power regulation constraint requires that the power change of the group in adjacent time periods does not exceed the maximum power adjustment amplitude of the group, and the power transmission constraint requires that the power transmission amount between groups does not exceed the maximum transmission power between groups; power allocation within the group is performed in a time-sharing progressive manner, including determining an initial allocation 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 allocation coefficient of each load node based on the weighted sum, and when the allocated power of a load node exceeds its available power capacity, the allocated 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 is 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 allocation between the groups and generate power allocation instructions for each group, and the local scheduling control is used to decompose the group power instructions based on the power allocation 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, characterized in that: include: Data acquisition module, used to collect static data, real-time operation data and historical power data of load nodes; A load classification module, used to complete load group identification based on the collected data; A power allocation module, used to generate power allocation strategies between groups 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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