An intelligent scheduling method for flywheel energy storage system

Abstract

The present invention relates to the field of energy storage system scheduling, and in particular to an intelligent scheduling method for a flywheel energy storage system, comprising: periodically obtaining the number of task execution devices and the device task conflict degree in a target management area to determine the device execution status of the target management area; determining a setting strategy for a key area according to the device execution status of the target management area, the setting strategy comprising determining the key area according to the distribution status of target task points and the task completion degree, and determining the key area according to the task correlation coefficient of the key node and the related task conflict degree; determining whether to perform energy storage mobilization for the corresponding charging area according to the proportion of the key area and the key area variation coefficient; determining the number of flywheel energy storage nodes that perform energy storage mobilization according to the proportion of the key area, and determining a target mobilization node set according to the speed coefficient of each flywheel energy storage node. The present invention improves the power grid stability of the target management area.

Description

Technical Field

[0001] The present invention relates to the field of energy storage system scheduling, and in particular to an intelligent scheduling method for a flywheel energy storage system. Background Art

[0002] With the rise of new energy technologies, the proportion of new energy work vehicles in different work scenarios has gradually increased. Therefore, charging devices need to be arranged in the work area to ensure the continuous operation of each work vehicle. However, in the actual operation process, the increase in power supply demand in a certain period of time can easily lead to fluctuations in the power grid load. Therefore, the existing technology often schedules the flywheel energy storage system to ensure the frequency stability of the power grid. Among them, for large-scale operation areas, the route arrangement of each work vehicle affects the power supply demand of different charging areas. Therefore, how to effectively predict the fluctuation of power supply demand in different areas based on the route arrangement of work vehicles in actual operation scenarios, and pre-schedule the flywheel energy storage system to further ensure the stability of the power grid is a problem that needs to be urgently solved by technical personnel in this field.

[0003] Patent publication number CN106532814A discloses a flywheel energy storage electric vehicle charging station system, which includes a charging system, a power distribution system, a power storage system, an auxiliary power system, a central control system and a self-protection system. The AC distribution cabinet is connected to the power grid, the auxiliary power system and at least two flywheel energy storage systems respectively. Each flywheel energy storage system is connected to a DC / DC converter and then to a DC distribution cabinet. The DC distribution cabinet is connected to at least two charging devices. The central control system is connected to an energy storage system controller and a device controller. The device controller is respectively connected to the energy storage system controller, the DC distribution cabinet and each charging device. The energy storage system controller is respectively connected to each flywheel energy storage system and the DC / DC converter. Patent publication number CN114123413A discloses a charging method, charging device, and AGV system for an AGV transport vehicle. The method includes: determining a target AGV transport vehicle, where the target AGV transport vehicle is one of the AGV transport vehicles that has not yet performed a task; determining a target charge amount based on at least a first power consumption and a second power consumption, where the first power consumption is the power required for the target AGV transport vehicle to travel from a first predetermined position to a second predetermined position, and the second power consumption is the average power consumed by the target AGV transport vehicle when performing the task, the first predetermined position being the center of a map, and the second predetermined position being the location of a charging station; and controlling the target AGV transport vehicle to charge based on the target charge amount. However, the above solution has the following problems: in the charging scenario of AGV transport vehicles, especially in large-scale scenarios such as smart ports, although a flywheel energy storage system can be used to adjust the output power when the power supply demand changes to ensure the stability of the power grid, it does not take into account the impact of unstable power consumption and high environmental impact on power demand during actual use of the vehicle, resulting in a failure to timely schedule the flywheel energy storage system, which in turn leads to poor power grid stability. Summary of the Invention

[0004] To this end, the present invention provides an intelligent scheduling method for a flywheel energy storage system to overcome the problem in the prior art of failing to effectively predict the fluctuations in power supply demand in different charging areas based on the route arrangements of various operating vehicles in actual scenarios, resulting in the failure to timely schedule the flywheel energy storage system and, in turn, causing poor stability of the power grid.

[0005] To achieve the above objectives, the present invention provides an intelligent scheduling method for a flywheel energy storage system, comprising:

[0006] Periodically obtain the number of task execution devices and device task conflict degree in the target management area to determine the device execution status of the target management area;

[0007] Determine the key area setting strategy based on the equipment execution status of the target management area. The setting strategy includes determining the key area based on the distribution status of the target task points and the task completion degree, and determining the key area based on the task correlation coefficient of the key node and the conflict degree of related tasks;

[0008] Determine whether to deploy energy storage to the corresponding charging area based on the key area proportion and key area variation coefficient;

[0009] The number of flywheel energy storage nodes to be mobilized is determined based on the proportion of key areas, and the target mobilization node set is determined based on the speed coefficient of each flywheel energy storage node.

[0010] Further, the device execution status of the target management area is determined according to the number of task execution devices in the target management area and the device-task conflict degree;

[0011] If the number of task execution devices in the target management area is less than or equal to the preset number of task execution devices and the device-task conflict degree is less than or equal to the preset task conflict degree, then the device execution state is determined to be the first preset device execution state;

[0012] If the number of task execution devices in the target management area is greater than the preset number of task execution devices or the device-task conflict degree is greater than the preset task conflict degree, the device execution state is determined to be the second preset device execution state.

[0013] Furthermore, the setting strategy of the key area is determined according to the device execution status of the target management area, including:

[0014] If the target management area is in the first preset device execution state, determine the key area according to the distribution state of the target task points and the task completion degree;

[0015] If the target management area is in the second preset device execution state, the key area is determined according to the task correlation coefficient of the key node and the conflict degree of the related tasks.

[0016] Furthermore, the distribution state of the target task points is determined according to the number of target task points in the preset distribution area;

[0017] If the number of target task points in the preset distribution area of ​​a target task point is greater than the preset number of target task points, the target task point is determined to be in a dense state;

[0018] The target task point is the end point of the task route of each task execution device in the target management area at the current moment.

[0019] Furthermore, the completion difference values ​​of each dense area are tested;

[0020] The completion difference value of any dense area is determined according to the task completion degree of the task execution equipment corresponding to each target task point in the dense area. The dense area within the target management area where the completion difference value is less than the preset completion difference value is recorded as a key area;

[0021] The dense task points are target task points in a dense state;

[0022] The dense area is a collection of preset distribution areas of at least two dense task points, and any dense task point in the dense area has an overlapping task point.

[0023] Furthermore, when determining the key area based on the task correlation coefficient of the key node and the conflict degree of the related tasks, the task routes of each task execution device in the target management area at the current moment are obtained, and the route nodes whose number of related task routes is greater than the preset number of related task routes are recorded as key nodes;

[0024] For any route node, the task route passing through the route node is recorded as the relevant task route;

[0025] For any key node, the task execution devices corresponding to each task route passing through the key node are recorded as the associated devices of the key node, and the task association coefficient of the key node is the number of its associated devices.

[0026] Furthermore, the key length of the corresponding key node is set according to the task correlation coefficient of each key node and the conflict degree of related tasks;

[0027] The conflict degree of related tasks of any key node is determined based on the proportion of overlapping routes of its related task routes and the reference interval distance;

[0028] The critical length is positively correlated with the task association coefficient and the related task conflict degree respectively.

[0029] Furthermore, if the relevant key parameter of a key node is greater than the preset relevant key parameter, the key length of the key node is increased and adjusted according to the number of relevant key nodes;

[0030] The increase in the key length is positively correlated with the number of relevant key nodes.

[0031] Furthermore, if the critical area ratio of a charging area is greater than the preset critical area ratio or the critical area variation coefficient is greater than the preset key area variation coefficient, energy storage mobilization is performed for the charging area, and the number of flywheel energy storage nodes performing energy storage mobilization is determined based on the critical area ratio;

[0032] The number of flywheel energy storage nodes is positively correlated with the proportion of key areas.

[0033] Furthermore, if a charging area needs to perform energy storage mobilization and the number of flywheel energy storage nodes that perform energy storage mobilization has been determined, a target mobilization node set is determined based on the speed coefficient of each flywheel energy storage node;

[0034] The collective rotational speed coefficient of the target mobilization node set is greater than a preset collective rotational speed coefficient.

[0035] Compared with the prior art, the beneficial effect of the present invention lies in that, in the technical solution of the present invention, a targeted key area setting strategy is determined according to the equipment execution status of the target management area, and the key areas within the target management area are obtained to effectively estimate the fluctuation of the power supply demand of the target key areas, and whether to mobilize energy storage for the corresponding charging areas is determined according to the proportion of the key areas and the key area variation coefficient, and the flywheel energy storage system is scheduled in time. The present invention improves the stability of the power grid maintained by the flywheel energy storage system.

[0036] Furthermore, the present invention determines the equipment execution status of the target management area based on the number of task execution devices and the equipment task conflict degree in the target management area. The number of task execution devices and the equipment task conflict degree can effectively characterize the number and concentration of operating equipment running in the target management area, thereby determining the equipment execution status, and determining the setting strategy of the key area according to different equipment execution statuses, thereby improving the accuracy of the determined key areas.

[0037] Furthermore, in the present invention, when the target management area is in the first preset equipment execution state, that is, the number of operating equipment running in the target management area is small and the probability of concentrated distribution of operating equipment during operation is low, the dense area in the target management area where the completion difference value is less than the preset completion difference value is recorded as a key area. At this time, more task execution equipment is concentrated in the determined key area, so the possibility of increased charging demand in the determined key area is greater, and the present invention improves the accuracy of the determined key area.

[0038] Furthermore, in the present invention, when the target management area is in the second preset equipment execution state, that is, the number of operating equipment in the target management area is large or the probability of concentrated distribution of operating equipment during operation is high, since the task execution equipment is more likely to be congested when traveling near the key node, it is necessary to adjust the task routes of some task execution equipment, which leads to the possibility of concentration of task execution equipment in the vicinity of the key node. The radius length of the corresponding key area is set according to the task correlation coefficient of each key node and the relevant task conflict degree, so as to determine the key area that is more in line with the actual target management area.

[0039] Furthermore, whether to mobilize energy storage for the corresponding charging area is determined based on the proportion of key areas and the key area variation coefficient. The change in power supply demand in the charging area is estimated based on the proportion of key areas and the key area variation coefficient. When the proportion of key areas is large or the change in key areas is large, energy storage is mobilized for the charging area. The flywheel energy storage system is used to regulate the grid frequency to avoid grid frequency fluctuations caused by changes in power supply demand, thereby avoiding damage to various power equipment and ensuring the stability of the grid in the target management area. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Schematic diagram of the intelligent scheduling method of the flywheel energy storage system of the present invention;

[0041] Figure 2 This is a flow chart of the present invention for determining the device execution status of a target management area according to the number of task execution devices in the target management area and the device-task conflict degree;

[0042] Figure 3 A flowchart of the present invention for determining a setting strategy for a key area according to the device execution status of a target management area;

[0043] Figure 4 This is a flow chart of the present invention for determining whether to mobilize energy storage for corresponding charging areas based on the proportion of key areas and the key area variation coefficient. DETAILED DESCRIPTION

[0044] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0045] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0046] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0047] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0048] See also Figures 1 to 4 As shown, the present invention provides an intelligent scheduling method for a flywheel energy storage system, comprising:

[0049] Periodically obtain the number of task execution devices and device task conflict degree in the target management area to determine the device execution status of the target management area;

[0050] Determine the key area setting strategy based on the equipment execution status of the target management area. The setting strategy includes determining the key area based on the distribution status of the target task points and the task completion degree, and determining the key area based on the task correlation coefficient of the key node and the conflict degree of related tasks;

[0051] Determine whether to deploy energy storage to the corresponding charging area based on the key area proportion and key area variation coefficient;

[0052] The number of flywheel energy storage nodes to be mobilized is determined based on the proportion of key areas, and the target mobilization node set is determined based on the speed coefficient of each flywheel energy storage node.

[0053] Among them, the application scenario of the present invention is a large port, and the area in the port where task execution equipment can pass or charge is recorded as a target management area, and the target management area is divided into several charging areas, and the number of charging devices in each divided charging area is the same. The operating equipment in the present invention is a movable device for performing port operation tasks, and each operating equipment uses electricity to provide operating power. The types of operating equipment include but are not limited to: electric-driven mobile cranes, tractors and automatic guided vehicles. The port operation tasks performed by the operating equipment in the present invention include but are not limited to: cargo loading and unloading, cargo stacking, cargo transshipment and personnel transshipment. There are several driving routes in the target management area for the operating equipment to travel, and the locations where the driving routes intersect are recorded as route nodes; multiple flywheel energy storage nodes are connected in parallel to form a flywheel energy storage array system, and each flywheel energy storage unit operates in a coordinated manner. When the grid frequency fluctuates, the flywheel energy storage array system can immediately adjust its output or input power to stabilize the grid frequency;

[0054] The present invention applies a cyclic management cycle, the duration of which can be determined by the user. The higher the user's requirements for the grid stability of the target management area, the shorter the management cycle. A management cycle of 30 minutes is provided. At the end of each management cycle, the number of task execution devices and the device task conflict degree in the target management area at that moment are obtained to determine the device execution status. Based on the device execution status, the setting strategy of the key area is determined to determine the key area in the target management area, so as to further determine whether to mobilize energy storage for each charging area.

[0055] The present invention applies several historical management records, and any historical management record records the number of task execution devices, task conflict degree, number of related task routes, node distance, related key parameters, key area ratio and key area variation coefficient in at least one management record for the target management area, and each historical management record corresponds to a qualified mark, which records whether the power grid stability requirements for the target management area meet user needs.

[0056] Specifically, the device execution status of the target management area is determined according to the number of task execution devices in the target management area and the device-task conflict degree;

[0057] If the number of task execution devices in the target management area is less than or equal to the preset number of task execution devices and the device-task conflict degree is less than or equal to the preset task conflict degree, then the device execution state is determined to be the first preset device execution state;

[0058] If the number of task execution devices in the target management area is greater than the preset number of task execution devices or the device-task conflict degree is greater than the preset task conflict degree, the device execution state is determined to be the second preset device execution state.

[0059] Among them, at the end moment of any management cycle, the task execution equipment and the task routes of each task execution equipment in the target management area at that moment are obtained. The task execution equipment is the operation equipment that is performing any port operation task at that moment. When any task execution equipment obtains the port operation task it needs to perform, it also obtains the driving route it needs to pass through in the process of completing the port operation task, and the set of driving routes that need to be passed is recorded as the task route of the task execution equipment. The equipment task conflict degree is determined according to the task routes of each task execution equipment obtained at that moment. The equipment task conflict degree = the sum of the lengths of the overlapping routes in the detected task routes / the sum of the lengths of the detected task routes. If there are at least two task routes that need to pass through the same part of the driving route, this part of the driving route is recorded as an overlapping route;

[0060] The values ​​of the preset task execution device number and the preset task conflict degree can be determined by the user according to the actual working scenario. For example, the user can set them according to historical monitoring records. The higher the user's requirements for the grid stability of the target management area, the smaller the value of the preset task execution device number and the smaller the value of the preset task conflict degree. A method for determining the value of the preset task execution device number is provided, and the historical management records of the target management area in the first preset device execution state are recorded as state reference records. The maximum value of the number of task execution devices in the target management area obtained from each detection in the state reference records that meet the user's requirements for the grid stability of the target management area is recorded as the preset task execution device number. A method for determining the preset task conflict degree is provided, and the maximum value of the task conflict degree of the target management area obtained from each detection in the state reference records that meet the user's requirements for the grid stability of the target management area is recorded as the preset task conflict degree.

[0061] Specifically, the key area setting strategy is determined based on the device execution status of the target management area, including:

[0062] If the target management area is in the first preset device execution state, determine the key area according to the distribution state of the target task points and the task completion degree;

[0063] If the target management area is in the second preset device execution state, the key area is determined according to the task correlation coefficient of the key node and the conflict degree of the related tasks.

[0064] Among them, determining the key area according to the distribution status of the target task points and the task completion degree is recorded as the first setting strategy, and determining the key area according to the task correlation coefficient of the key node and the related task conflict degree is recorded as the second setting strategy.

[0065] Specifically, the distribution state of the target task points is determined according to the number of target task points in the preset distribution area;

[0066] If the number of target task points in the preset distribution area of ​​a target task point is greater than the preset number of target task points, the target task point is determined to be in a dense state;

[0067] The target task point is the end point of the task route of each task execution device in the target management area at the current moment.

[0068] Specifically, the completion difference values ​​of each dense area are tested;

[0069] The completion difference value of any dense area is determined according to the task completion degree of the task execution equipment corresponding to each target task point in the dense area. The dense area within the target management area where the completion difference value is less than the preset completion difference value is recorded as a key area;

[0070] The dense task points are target task points in a dense state;

[0071] The dense area is a collection of preset distribution areas of at least two dense task points, and any dense task point in the dense area has an overlapping task point.

[0072] When executing the first setting strategy, the distribution status of each target task point is obtained and the dense area is determined. The completion difference value of each dense area is detected, and the dense area with a completion difference value less than the preset completion difference value in the target management area is recorded as a key area;

[0073] The distribution state includes a dense state and a discrete state. If the number of target task points in the preset distribution area of ​​a target task point is less than or equal to the preset number of target task points, the target task point is determined to be in a discrete state. When judging the distribution state of each target task point, the distribution length of the preset distribution area of ​​each target task point is positively correlated with the number of related equipment of the corresponding target task point. For a single target task point, its preset distribution area is a circular area with the target task point as the center and the distribution length as the radius; the value of the preset number of target task points can be determined by the user according to the actual working scenario. For example, the user can set it according to historical monitoring records. The higher the user's requirements for the grid stability of the target management area, the smaller the value of the preset number of target task points. A method for determining the value of the preset number of target task points is provided, which will meet the user's requirements for the grid stability of the target management area and the minimum value of the number of target task points in the preset distribution area of ​​each dense task point in the historical management records.

[0074] If a target task point exists in two different preset distribution areas of dense task points, the target task point is recorded as an overlapping task point. For any dense area, m is the number of task execution devices corresponding to the target task point in the dense area, Dv is the task completion degree of the vth task execution device, D0 is the average task completion degree of each task execution device, and for a single task execution device, the task completion degree = the length of the task route that the task execution device has traveled / the length of the task route of the task execution device;

[0075] The value of the preset completion difference value can be determined by the user according to the actual working scenario. For example, the user can set it according to historical monitoring records. The higher the user's requirements for the grid stability of the target management area, the larger the value of the preset completion difference value. A method for determining the preset completion difference value is provided. The historical management records of the key areas determined according to the distribution status of the target task points and the task completion are recorded as completion reference records, and the average value of the completion difference values ​​of each key area in the completion reference records that meet the user's requirements for the grid stability of the target management area is recorded as the preset completion difference value.

[0076] Specifically, when determining the key area based on the task correlation coefficient of the key node and the conflict degree of the related tasks, the task routes of each task execution device in the target management area at the current moment are obtained, and the route nodes whose number of related task routes is greater than the preset number of related task routes are recorded as key nodes;

[0077] For any route node, the task route passing through the route node is recorded as the relevant task route;

[0078] For any key node, the task execution devices corresponding to each task route passing through the key node are recorded as the associated devices of the key node, and the task association coefficient of the key node is the number of its associated devices.

[0079] Among them, when executing the second setting strategy, the key nodes are determined according to the number of related task routes, the task correlation coefficient and the related task conflict degree of each key node are obtained to determine the critical length of the corresponding key node, and the key area of ​​the corresponding key node is determined according to the critical length of each key node. For a single key node, its key area is a circular area formed with the key node as the center and the critical length of the key node as the radius;

[0080] The value of the preset number of related task routes can be determined by the user according to the actual working scenario. For example, the user can set it according to historical monitoring records. The higher the user's requirements for the grid stability of the target management area, the smaller the value of the preset number of related task routes. A method for determining the value of the preset number of related task routes is provided. The historical management records of the key areas of the key nodes determined according to the task correlation coefficient and task overlap of the key nodes are recorded as relevant reference records. The minimum number of related task routes of each key node in the relevant reference records that meets the user's requirements for the grid stability of the target management area is recorded as the value of the preset number of related task routes. A value of the preset related task route is provided, and the value of the preset related task route is 3.

[0081] Specifically, the key length of the corresponding key node is set according to the task correlation coefficient of each key node and the conflict degree of related tasks;

[0082] The conflict degree of related tasks of any key node is determined based on the proportion of overlapping routes of its related task routes and the reference interval distance;

[0083] The critical length is positively correlated with the task association coefficient and the related task conflict degree respectively.

[0084] Among them, for a single key node, the proportion of overlapping routes = the sum of the lengths of all overlapping routes in the relevant task routes of the key node / the sum of the lengths of the relevant task routes of the key node, the reference interval distance is the average of the interval distances between the key node and all overlapping routes, the shortest distance required for the key node to reach an overlapping route is recorded as the interval distance between the key node and the overlapping route, the relevant task conflict degree = ln (proportion of overlapping routes / reference interval distance), the task correlation coefficient and the sum of the relevant task conflict degree are positively correlated with the key length corresponding to the key node.

[0085] Specifically, if the relevant key parameter of a key node is greater than the preset relevant key parameter, the key length of the key node is increased and adjusted according to the number of relevant key nodes;

[0086] The increase in the key length is positively correlated with the number of relevant key nodes.

[0087] Among them, for a single key node, the relevant key parameter is determined according to the number of relevant key nodes of the key node and the reference node distance, the relevant key parameter = ln (number of relevant key nodes / reference node distance), the reference node distance is the average value of the node distance between the key node and its relevant key nodes, if there is a relevant task route between the two key nodes and the node distance is less than the preset node distance, then the two key nodes are determined to be relevant key nodes to each other, and the length of the relevant task route between the two key nodes is recorded as the node distance, and the value of the preset node distance can be determined by the user according to the actual working scenario. For example, the user can set it according to historical monitoring records. The higher the user's requirements for the grid stability of the target management area, the smaller the value of the preset node distance. A method for determining the value of the preset node distance is provided, and the minimum value of the node distance between two nodes that are relevant key nodes in the historical management records that meet the user's requirements for the grid stability of the target management area is recorded as the preset node distance;

[0088] The values ​​of the preset relevant key parameters can be determined by the user according to the actual working scenario. For example, the user can set them according to historical monitoring records. The higher the user's requirements for the grid stability of the target management area, the smaller the values ​​of the preset relevant key parameters. A method for setting the values ​​of the preset relevant key parameters is provided, and the historical management records adjusted for the length of the key area are recorded as key parameter records. The average value of the relevant key parameters of each key node in the key parameter records that meet the user's requirements for the grid stability of the target management area is recorded as the preset relevant key parameters.

[0089] Specifically, if the critical area ratio of a charging area is greater than the preset critical area ratio or the key area variation coefficient is greater than the preset key area variation coefficient, energy storage mobilization is performed for the charging area, and the number of flywheel energy storage nodes performing energy storage mobilization is determined based on the critical area ratio;

[0090] The number of flywheel energy storage nodes is positively correlated with the proportion of key areas.

[0091] For a single charging area, the critical area ratio = the sum of the areas of the critical areas within the charging area / the area of ​​the charging area. The critical area variation coefficient is negatively correlated with the overlapping area of ​​the key areas determined at the end of the current management cycle and the key areas determined at the end of the previous management cycle. The overlapping area is the area of ​​the portion of the target management area that was twice identified as a critical area. The values ​​of the preset critical area ratio and the preset key area variation coefficient can be determined by the user based on actual work scenarios. For example, the user can set them based on historical monitoring records. The higher the user's grid stability requirements for the target management area, the smaller the value of the preset critical area ratio and the smaller the value of the preset key area variation coefficient. A method for determining the preset key area ratio is provided. The minimum value of the critical area ratio of each charging area undergoing energy storage mobilization in the historical management records that meets the user's grid stability requirements for the target management area is recorded as the preset key area ratio. A method for determining the preset key area variation coefficient is provided. The minimum value of the critical area variation coefficient of each charging area undergoing energy storage mobilization in the historical management records that meets the user's grid stability requirements for the target management area is recorded as the preset key area variation coefficient.

[0092] Specifically, if a charging area needs to perform energy storage mobilization and the number of flywheel energy storage nodes that have completed energy storage mobilization is determined, the target mobilization node set is determined according to the speed coefficient of each flywheel energy storage node;

[0093] The collective rotational speed coefficient of the target mobilization node set is greater than a preset collective rotational speed coefficient.

[0094] Among them, for a charging area that needs to perform energy storage mobilization, the power supply demand of the charging area is monitored in real time. When the power supply demand of the charging area changes, the output or input power of each flywheel energy storage node in the target mobilization node set is adjusted to ensure the stability of the grid frequency. For a single flywheel energy storage node, the flywheel angular velocity at the current moment, as well as the maximum flywheel angular velocity and the minimum flywheel angular velocity of the flywheel energy storage node are detected. The maximum flywheel angular velocity is the maximum value of the flywheel angular velocity reached during the historical operation of the flywheel energy storage node, and the minimum flywheel angular velocity is the minimum value of the flywheel angular velocity reached during the historical operation of the flywheel energy storage node. The speed coefficient = (flywheel angular velocity at the current moment - minimum flywheel angular velocity) × (flywheel angular velocity at the current moment + minimum flywheel angular velocity) / (maximum flywheel angular velocity + minimum flywheel angular velocity) × (maximum flywheel angular velocity - minimum flywheel angular velocity). The number of flywheel energy storage nodes in the determined target mobilization node set is the same as the number of flywheel energy storage nodes that have completed energy storage mobilization. n is the number of flywheel energy storage nodes in the target mobilization node set, mi is the flywheel angular velocity of the i-th flywheel energy storage node at the current moment, li is the minimum flywheel angular velocity of the i-th flywheel energy storage node, fi is the maximum flywheel angular velocity of the i-th flywheel energy storage node, and a preset set speed coefficient value is provided, and the preset set speed coefficient value is 0.2.

[0095] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0096] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. An intelligent scheduling method for a flywheel energy storage system, characterized in that: include: Periodically obtain the number of task execution devices and device task conflict degree in the target management area to determine the device execution status of the target management area; Determine the key area setting strategy based on the equipment execution status of the target management area. The setting strategy includes determining the key area based on the distribution status of the target task points and the task completion degree, and determining the key area based on the task correlation coefficient of the key node and the conflict degree of related tasks; Determine whether to deploy energy storage to the corresponding charging area based on the key area proportion and key area variation coefficient; Determine the number of flywheel energy storage nodes to be deployed based on the proportion of key areas, and determine the target deployment node set based on the speed coefficient of each flywheel energy storage node; Determine the key area configuration strategy based on the device execution status of the target management area, including: If the target management area is in the first preset device execution state, determine the key area according to the distribution state of the target task points and the task completion degree; If the target management area is in the second preset device execution state, the key area is determined according to the task correlation coefficient of the key node and the conflict degree of the relevant tasks; Determine the device execution status of the target management area based on the number of task execution devices in the target management area and the device task conflict degree; If the number of task execution devices in the target management area is less than or equal to the preset number of task execution devices and the device-task conflict degree is less than or equal to the preset task conflict degree, then the device execution state is determined to be the first preset device execution state; If the number of task execution devices in the target management area is greater than the preset number of task execution devices or the device-task conflict degree is greater than the preset task conflict degree, the device execution state is determined to be the second preset device execution state.

2. The intelligent scheduling method for a flywheel energy storage system according to claim 1, characterized in that: The distribution status of the target task points is determined according to the number of target task points in the preset distribution area; If the number of target task points in the preset distribution area of ​​a target task point is greater than the preset number of target task points, the target task point is determined to be in a dense state; The target task point is the end point of the task route of each task execution device in the target management area at the current moment.

3. The intelligent scheduling method for a flywheel energy storage system according to claim 2, characterized in that: Detect the difference in completion values ​​of each dense area; The completion difference value of any dense area is determined according to the task completion degree of the task execution equipment corresponding to each target task point in the dense area. The dense area within the target management area where the completion difference value is less than the preset completion difference value is recorded as a key area; The dense task point is a target task point in a dense state; The dense area is a collection of preset distribution areas of at least two dense task points, and any dense task point in the dense area has an overlapping task point.

4. The intelligent scheduling method for a flywheel energy storage system according to claim 3, characterized in that: When determining the key area based on the task correlation coefficient of the key node and the conflict degree of the related tasks, the task routes of each task execution device in the target management area at the current moment are obtained, and the route nodes whose number of related task routes is greater than the preset number of related task routes are recorded as key nodes; For any route node, the task route passing through the route node is recorded as the relevant task route; For any key node, the task execution devices corresponding to each task route passing through the key node are recorded as the associated devices of the key node, and the task association coefficient of the key node is the number of its associated devices.

5. The intelligent scheduling method for a flywheel energy storage system according to claim 4, characterized in that: The key length of the corresponding key node is set according to the task correlation coefficient of each key node and the conflict degree of related tasks; The conflict degree of related tasks of any key node is determined based on the proportion of overlapping routes of its related task routes and the reference interval distance; The critical length is positively correlated with the task association coefficient and the related task conflict degree respectively.

6. The intelligent scheduling method for a flywheel energy storage system according to claim 5, characterized in that: If the relevant key parameter of a key node is greater than the preset relevant key parameter, the key length of the key node is increased and adjusted according to the number of relevant key nodes; The increase in the key length is positively correlated with the number of relevant key nodes.

7. The intelligent scheduling method for a flywheel energy storage system according to claim 6, characterized in that: If the critical area ratio of a charging area is greater than the preset critical area ratio or the key area variation coefficient is greater than the preset key area variation coefficient, energy storage mobilization is performed for the charging area, and the number of flywheel energy storage nodes performing energy storage mobilization is determined based on the critical area ratio; The number of flywheel energy storage nodes is positively correlated with the proportion of key areas.

8. The intelligent scheduling method for a flywheel energy storage system according to claim 7, characterized in that: If a charging area needs to perform energy storage mobilization and the number of flywheel energy storage nodes that perform energy storage mobilization has been determined, the target mobilization node set is determined according to the speed coefficient of each flywheel energy storage node; The collective rotational speed coefficient of the target mobilization node set is greater than a preset collective rotational speed coefficient.

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