Power grid dispatching strategy set construction regulation and control priority ranking method and system

Abstract

The invention discloses a power grid dispatching strategy set construction regulation and control priority ranking method and system, and belongs to the technical field of power grid dispatching. The method comprises the following steps: collecting data to construct a power grid dispatching strategy set, and classifying and formulating regulation and control measures of different overload conditions; performing priority ranking on the constructed strategy set, and determining the priority of each strategy in combination with sensitivity analysis; the power grid state is monitored in real time, and the strategy set and the priority are dynamically adjusted through a feedback mechanism and a machine learning algorithm. According to the power grid dispatching strategy set construction regulation and control priority ranking method provided by the invention, various resources in the power grid are reasonably configured and dispatched, so that the operation efficiency of a power system is improved to the maximum extent, and the operation cost is reduced. Through effective monitoring and regulation measures, the occurrence frequency of faults of a power system is reduced, continuity and safety of power supply are guaranteed, and in the aspect of supporting renewable energy consumption, the fluctuation of new energy can be effectively managed, and it is ensured that the new energy is stably connected to a power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching, and in particular to a method and system for constructing a control priority ranking system for a power grid dispatching strategy set. Background Art

[0002] With the continuous development of power systems and the continuous increase in electricity demand, grid dispatching and optimization have become key technical issues in power systems. In recent years, with the rapid development of smart grids, the Internet of Things and big data technologies, the dispatching mode of power grids has gradually shifted from traditional centralized dispatching to a more flexible and efficient distributed dispatching mode. Modern power grid dispatching systems can not only monitor the load status of the power grid in real time, but also automatically generate and adjust dispatching strategies according to different operating conditions and power grid equipment conditions. Especially when dealing with large-scale power load fluctuations, the power grid dispatching system faces more complex tasks, which requires the dispatching strategy to be sufficiently flexible, real-time and efficient. Therefore, the development of intelligent power grid dispatching strategies and priority sorting methods has become one of the key technologies to improve the operating efficiency of the power system and ensure the security of power supply.

[0003] However, the existing power grid dispatching system still has many shortcomings when dealing with overload problems. Traditional dispatching methods mostly rely on manual operation or rule-driven, lack flexibility, and are difficult to cope with sudden power grid load fluctuations. Especially in the face of large-scale complex power grids, the construction and priority sorting of dispatching strategies often cannot be adjusted in time according to actual conditions, resulting in insufficient dispatching response and low efficiency. The existing dispatching strategies are generally fixed, and fail to dynamically adjust the strategy set and priority, and fail to make full use of real-time feedback information to make optimal decisions. In addition, most traditional systems fail to effectively combine intelligent technologies, such as machine learning and data analysis, to perform strategy optimization and priority sorting. In the overload dispatching process, the existing technology often cannot flexibly handle the control measures of different overload conditions, and it is difficult to make reasonable dispatching decisions according to the actual load status, resulting in unstable power grid operation and even the risk of power supply interruption. Therefore, the present invention can automatically construct a power grid dispatching strategy set and optimize priority sorting by introducing real-time data monitoring, sensitivity analysis and machine learning algorithms, thereby overcoming the shortcomings of the existing technology and improving the efficiency and flexibility of power grid dispatching. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing power grid dispatching strategy construction and priority sorting methods have the problems of insufficient flexibility in strategy execution, lack of dynamic adjustment capability of priority sorting, and failure to fully utilize intelligent algorithms to optimize decisions, as well as how to dynamically adjust dispatching strategies and priorities according to real-time power grid load and equipment status.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for constructing a control priority ranking method for a power grid dispatching strategy set, comprising collecting data to construct a power grid dispatching strategy set, classifying and formulating control measures for different overload conditions; prioritizing the constructed strategy set, and determining the priority of each strategy in combination with sensitivity analysis; monitoring the power grid status in real time, and dynamically adjusting the strategy set and priority through a feedback mechanism and a machine learning algorithm.

[0007] As a preferred solution of the method for constructing a control priority ranking method of a power grid dispatching strategy set according to the present invention, wherein: the data collected to construct a power grid dispatching strategy set includes real-time monitoring data, collecting real-time data in power grid operation, including power flow, load status, and equipment status;

[0008] Analyze historical load data to identify potential overload patterns and trends.

[0009] As a preferred solution of the control priority sorting method for constructing the power grid dispatching strategy set described in the present invention, wherein: the classification and formulation of control measures for different overload conditions include classifying the control measures into high-urgency measures, medium-urgency measures and low-urgency measures according to the operating state and load conditions of the power grid;

[0010] When identified as a high-urgency measure, part of the load is transferred from overloaded equipment to equipment with greater carrying capacity to balance the grid load. When the load peaks, renewable energy generation is reduced and the output of traditional generators is increased;

[0011] Identify as medium emergency measures and reduce the operation of loads that do not affect the power consumption of users;

[0012] Identify as low-urgency measures and optimize the operating efficiency of the equipment by adjusting operating parameters and time.

[0013] As a preferred solution of the method for constructing and prioritizing control of the power grid dispatching strategy set described in the present invention, wherein: the priority sorting of the constructed strategy set includes high emergency situation, priority execution of load transfer, second-best choice of power generation adjustment, and load shedding when load transfer and power generation adjustment bureau cannot be implemented;

[0014] In case of emergency, load reduction is given priority. If load reduction does not meet the adjustment requirements, equipment dispatch is performed to carry the load.

[0015] In low emergency situations, load optimization is performed and preventive maintenance is carried out.

[0016] As a preferred solution of the method for constructing and prioritizing control of the power grid dispatching strategy set described in the present invention, wherein: the prioritizing of the constructed strategy set also includes giving priority to load-related adjustments when equipment failure or load fluctuation occurs in the operation of the power grid;

[0017] After determining the priority ranking, the priorities were further optimized through sensitivity analysis.

[0018] As a preferred solution of the method for constructing a control priority ranking method for the power grid dispatching strategy set described in the present invention, the method of determining the priority of each strategy in combination with sensitivity analysis includes calculating sensitivity parameters, calculating the sensitivity of different measures, and evaluating the impact of each measure on power flow, which is expressed as:

[0019]

[0020] in, The new power flow monitored for the transmission line connecting substations i and j, L ij is the raw power monitored on the transmission line connecting substations i and j, is the new power flow of transformer i in substation, T i is the original power flow of transformer i in substation, ΔP is the adjusted load, W is the importance parameter of abandoned load, and different weights are taken according to the Delphi method, ranging from 0 to 1, where 1 means completely important and 0 means unimportant;

[0021] Measures of the same type are sorted according to the sensitivity parameters. The higher the sensitivity parameter, the higher the priority.

[0022] As a preferred solution of the method for constructing a control priority ranking method of the power grid dispatching strategy set described in the present invention, wherein: the dynamic adjustment of the strategy set and priority through the feedback mechanism and the machine learning algorithm includes using a reinforcement learning algorithm to automatically optimize the power grid dispatching strategy during the dynamic adjustment process, and optimizing the decision path by analyzing the long-term impact of different strategies on the power grid load;

[0023] During the operation of the power grid, the system determines whether to add standby control measures based on different loads and equipment status, adjust the equipment operation mode and increase the output of the generator set;

[0024] Based on the analysis results of the machine learning model, the system provides real-time feedback and optimizes the priority of regulatory measures.

[0025] Another object of the present invention is to provide a power grid dispatching strategy set to build a control priority sorting system, which can dynamically adjust the strategy set and priority by adopting real-time data monitoring and sensitivity analysis combined with machine learning algorithms, thereby solving the problem of lack of flexibility and real-time responsiveness of current power grid dispatching strategies.

[0026] As a preferred solution for constructing a control priority sorting system for a power grid dispatching strategy set according to the present invention, it includes: a data acquisition module, a priority sorting module, and a feedback optimization module;

[0027] The data acquisition module is used to collect data to construct a power grid dispatch strategy set, classify and formulate control measures for different overload conditions;

[0028] The prioritization module is used to prioritize the constructed policy set and determine the priority of each policy in combination with sensitivity analysis;

[0029] The feedback optimization module is used to monitor the power grid status in real time and dynamically adjust the strategy set and priority through feedback mechanism and machine learning algorithm.

[0030] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a method for constructing a control priority ranking method for a power grid dispatching strategy set.

[0031] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for constructing a control priority ranking method for a power grid dispatching strategy set.

[0032] Beneficial effects of the present invention: The grid dispatching strategy set construction and control priority sorting method provided by the present invention maximizes the operating efficiency of the power system and reduces operating costs by reasonably allocating and dispatching various resources within the power grid. Through effective monitoring and control measures, the frequency of power system failures is reduced, the continuity and safety of power supply are guaranteed, and in terms of supporting the consumption of renewable energy, the volatility of new energy can be effectively managed to ensure its smooth access to the power grid. Through sensitivity analysis and dynamic adjustment mechanisms, a scientific basis is provided for power grid dispatchers to help optimize decisions and improve the overall management level. By reducing power outages and failures, the user's electricity demand is guaranteed and the user's satisfaction with power services is enhanced. The systematic strategy set construction and priority sorting method enhances the power grid's ability to respond to emergencies and improves the overall safety and stability of the power grid. The present invention achieves better results in terms of stability, continuity and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0034] Figure 1 An overall flow chart of a method for constructing a control priority ranking method for a power grid dispatching strategy set provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0036] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a method for constructing a control priority ranking of a power grid dispatching strategy set, comprising:

[0037] S1: Collect data to build a grid dispatch strategy set, classify and formulate control measures for different overload situations.

[0038] Furthermore, data collection to construct a grid dispatch strategy set includes real-time monitoring data, collecting real-time data on grid operation, including power flow, load conditions, and equipment status.

[0039] Analyze historical load data to identify potential overload patterns and trends.

[0040] It should be noted that the classification and formulation of control measures for different overload situations includes dividing the control measures into high-emergency measures, medium-emergency measures and low-emergency measures according to the operating status and load conditions of the power grid.

[0041] When identified as a high-urgency measure, part of the load is transferred from overloaded equipment to equipment with greater carrying capacity, balancing the grid load. During peak load periods, renewable energy generation is reduced and the output of conventional generators is increased.

[0042] Identify as medium emergency measures to reduce the operation of loads that do not affect users' electricity consumption.

[0043] Identify as low-urgency measures and optimize the operating efficiency of the equipment by adjusting operating parameters and time.

[0044] Machine learning algorithms are used to train historical power grid data, automatically predict possible overload risks through pattern recognition, and generate corresponding strategies.

[0045] Based on real-time data on grid operation, the proportion of various control measures in the strategy set is automatically adjusted. For example, the proportion of power generation adjustment measures is increased during peak load periods to ensure the reliability of power supply.

[0046] Based on changes in real-time data, the system automatically updates and reclassifies the priorities of control strategies to ensure timely response in the face of emergencies.

[0047] S2: Prioritize the constructed strategy set and determine the priority of each strategy based on sensitivity analysis.

[0048] Furthermore, the constructed strategy set is prioritized to include high urgency, prioritizing load shifting, second-best choice of generation adjustment, and load shedding when load shifting and generation adjustment cannot be implemented.

[0049] In case of emergency, load reduction is given priority. If load reduction does not meet the adjustment requirements, equipment scheduling is carried out to carry the load.

[0050] In low emergency situations, load optimization is performed and preventive maintenance is carried out.

[0051] It should be noted that prioritizing the constructed strategy set also includes giving priority to load-related adjustments when equipment failure or load fluctuation occurs in the power grid operation.

[0052] After determining the priority ranking, the priorities were further optimized through sensitivity analysis.

[0053] S3: Monitor the grid status in real time and dynamically adjust the strategy set and priority through feedback mechanisms and machine learning algorithms.

[0054] Furthermore, the priority of each strategy is determined by combining sensitivity analysis, including calculating sensitivity parameters, calculating the sensitivity of different measures, and evaluating the impact of each measure on power flow, which is expressed as:

[0055]

[0056] in, The new power flow monitored for the transmission line connecting substations i and j, L ij is the raw power monitored on the transmission line connecting substations i and j, is the new power flow of transformer i in substation, T i is the original power flow of transformer i in substation, ΔP is the adjusted load, and W is the importance parameter of abandoned load, which takes different weights according to the Delphi method, ranging from 0 to 1, where 1 means completely important and 0 means unimportant.

[0057] Measures of the same type are ranked according to the sensitivity parameter. Measures with higher sensitivity parameters have higher priority. In high and medium emergency situations, measures with greater impact on power flow are preferred. In low emergency situations, measures with less impact on the power system are selected.

[0058] It should be noted that dynamically adjusting the strategy set and priority through feedback mechanisms and machine learning algorithms includes using reinforcement learning algorithms to automatically optimize the grid dispatch strategy during the dynamic adjustment process, and optimizing the decision path by analyzing the long-term impact of different strategies on the grid load.

[0059] During the operation of the power grid, the system determines whether it is necessary to increase backup control measures, adjust the equipment operation mode and increase the output of the generator set based on different loads and equipment status.

[0060] Based on the analysis results of the machine learning model, the system provides real-time feedback and optimizes the priority of regulatory measures.

[0061] Example 2, an embodiment of the present invention, provides a method for constructing a control priority ranking method for a power grid dispatching strategy set. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0062] First, a series of experiments were conducted on the construction and priority sorting methods of power grid dispatching strategy sets to verify the effectiveness of this method in dealing with different power grid load conditions and emergencies. The experimental objects were 6 different test points in the power system, involving different load conditions and equipment dispatching measures. The parameters of each test point include voltage, current, load, generated power, overload degree, and response time of adjustment measures. During the experiment, the real-time monitoring system was used to collect power grid operation data, such as voltage, load, equipment status, etc. Based on these real-time and historical data, the system uses a sensitivity analysis method to identify overload patterns of the power grid and formulate appropriate control measures according to different overload conditions. In order to further verify the effect of the invention, the experiment dynamically adjusted the strategy set and priority sorting of the test point, and combined the reinforcement learning algorithm to optimize the dispatching strategy under different load and equipment conditions.

[0063] Table 1 Experimental data table

[0064]

[0065] Referring to Table 1, it can be seen that through load transfer and power generation adjustment measures (such as the implementation of "load transfer" corresponding to test points 1 to 3), the overload situation can be effectively reduced and the power grid operation can be ensured to be more stable. For example, the "load transfer" measures of test points 1 to 3 are relatively rapid (response time is 5 minutes), which effectively alleviates the risk of overload. At test point 4, the "power generation adjustment" measure is adopted, which enables the power grid load to be handled in a timely manner, with the shortest response time (3 minutes), and significantly improves the response speed of scheduling compared to traditional methods. Combined with the sensitivity analysis method, the present invention ensures that the optimal scheduling measures are executed first by optimizing the priority sorting according to the overload situation and equipment load fluctuations. For example, test point 5 adopts the "load reduction" measure, and when the load reduction does not meet the adjustment requirements, the equipment is further scheduled. This shows that sensitivity analysis can adjust the priority of measures in real time according to load fluctuations to ensure that the most effective control measures are implemented in a timely manner.

[0066] In test point 6, "load optimization" measures were adopted. Through machine learning algorithms, the system can adaptively optimize the dispatching strategy, realize real-time feedback of load and equipment operating status, and further adjust the priority, thereby effectively improving the operating efficiency of the power grid. Especially when the equipment load is high, the system can intelligently judge and adopt appropriate strategies, thus avoiding the problem of failure to respond to load fluctuations in time in traditional methods.

[0067] Embodiment 3, an embodiment of the present invention, provides a power grid dispatching strategy set construction control priority sorting system, including a data acquisition module, a priority sorting module, and a feedback optimization module.

[0068] Among them, the data acquisition module is used to collect data to construct a power grid dispatching strategy set, classify and formulate control measures for different overload conditions. The priority sorting module is used to prioritize the constructed strategy set and determine the priority of each strategy in combination with sensitivity analysis. The feedback optimization module is used to monitor the power grid status in real time and dynamically adjust the strategy set and priority through feedback mechanism and machine learning algorithm.

[0069] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0070] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0071] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0072] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0073] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for constructing a control priority ranking method for a power grid dispatching strategy set, characterized in that: include: Collect data to build a grid dispatch strategy set, classify and formulate control measures for different overload situations; Prioritize the constructed strategy set and determine the priority of each strategy in combination with sensitivity analysis; Monitor the power grid status in real time and dynamically adjust the strategy set and priority through feedback mechanisms and machine learning algorithms.

2. The method for constructing a control priority ranking method for a power grid dispatching strategy set according to claim 1, characterized in that: The data collection to construct the grid dispatching strategy set includes real-time monitoring data, collecting real-time data in grid operation, including power flow, load conditions, and equipment status; Analyze historical load data to identify potential overload patterns and trends.

3. The method for constructing a control priority ranking method for a power grid dispatching strategy set according to claim 2, characterized in that: The classification and formulation of control measures for different overload situations include classifying the control measures into high-urgency measures, medium-urgency measures and low-urgency measures according to the operation status and load conditions of the power grid; When identified as a high-urgency measure, part of the load is transferred from overloaded equipment to equipment with greater carrying capacity to balance the grid load. When the load peaks, renewable energy generation is reduced and the output of traditional generators is increased; Identify as medium emergency measures and reduce the operation of loads that do not affect the power consumption of users; Identify as low-urgency measures and optimize the operating efficiency of the equipment by adjusting operating parameters and time.

4. The method for constructing a control priority ranking method for a power grid dispatching strategy set according to claim 3, characterized in that: The prioritization of the constructed strategy set includes high emergency, priority execution of load transfer, second-best choice of power generation adjustment, and load shedding when load transfer and power generation adjustment bureau cannot be implemented; In case of emergency, load reduction is given priority. If load reduction does not meet the adjustment requirements, equipment dispatch is performed to carry the load. In low emergency situations, load optimization is performed and preventive maintenance is carried out.

5. The method for constructing a control priority ranking method for a power grid dispatching strategy set according to claim 4, characterized in that: The prioritizing of the constructed strategy set also includes giving priority to load-related adjustments when equipment failure or load fluctuation occurs in the operation of the power grid; After determining the priority ranking, the priorities were further optimized through sensitivity analysis.

6. The method for constructing a control priority ranking method for a power grid dispatching strategy set according to claim 5, characterized in that: The determination of the priority of each strategy in combination with sensitivity analysis includes calculating sensitivity parameters, calculating the sensitivity of different measures, and evaluating the impact of each measure on power flow, which is expressed as: in, The new power flow monitored for the transmission line connecting substations i and j, L ij is the raw power monitored on the transmission line connecting substations i and j, is the new power flow of transformer i in substation, T i is the original power flow of transformer i in substation, ΔP is the adjusted load, W is the importance parameter of abandoned load, and different weights are taken according to the Delphi method, ranging from 0 to 1, where 1 means completely important and 0 means unimportant; Measures of the same type are sorted according to the sensitivity parameters. The higher the sensitivity parameter, the higher the priority.

7. The method for constructing a control priority ranking method for a power grid dispatching strategy set according to claim 6, characterized in that: The dynamically adjusting the strategy set and priority through the feedback mechanism and the machine learning algorithm includes using a reinforcement learning algorithm to automatically optimize the power grid dispatch strategy during the dynamic adjustment process, and optimizing the decision path by analyzing the long-term impact of different strategies on the power grid load; During the operation of the power grid, the system determines whether to add standby control measures based on different loads and equipment status, adjust the equipment operation mode and increase the output of the generator set; Based on the analysis results of the machine learning model, the system provides real-time feedback and optimizes the priority of regulatory measures.

8. A system for constructing a control priority sorting method using a power grid dispatching strategy set as described in any one of claims 1 to 7, characterized in that: Including data collection module, priority sorting module, feedback optimization module; The data acquisition module is used to collect data to construct a power grid dispatch strategy set, classify and formulate control measures for different overload conditions; The prioritization module is used to prioritize the constructed policy set and determine the priority of each policy in combination with sensitivity analysis; The feedback optimization module is used to monitor the power grid status in real time and dynamically adjust the strategy set and priority through feedback mechanism and machine learning algorithm.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for constructing a control priority ranking of a power grid dispatch strategy set described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for constructing a control priority ranking of a power grid dispatch strategy set according to any one of claims 1 to 7 are implemented.

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