Network construction type energy storage PCS control method based on virtual synchronous machine

Abstract

The invention relates to the technical field of network construction type energy storage, in particular to a network construction type energy storage PCS control method based on a virtual synchronous machine. Comprising the following steps: establishing a plurality of equipment nodes according to parameters of the network construction type energy storage system, and generating a virtual synchronous machine of each equipment node; establishing a plurality of adjustment periods, and setting an operation resource allocation strategy in each adjustment period; generating a sub-control strategy of each virtual synchronous machine, and generating a primary control strategy of the networking type energy storage system according to all the sub-control strategies; according to the method, a plurality of virtual synchronous machines are established based on equipment parameters of the network-building type energy storage PCS in the network-building type energy storage system, so that a control strategy of each network-building type PCS is established, and meanwhile, the control efficiency of the network-building type energy storage PCS is improved by establishing a correlation model. And the operation resource allocation strategy is dynamically adjusted according to the overall operation resource quantity and the operation parameters of each virtual synchronous machine, so that the operation precision of each virtual synchronous machine is ensured, and the overall operation and maintenance cost of the system is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of grid-type energy storage, and in particular to a grid-type energy storage PCS control method based on a virtual synchronous machine. Background Art

[0002] The grid-forming energy storage system is mainly composed of energy storage media (such as lithium batteries, hydrogen production power sources, etc.) and grid-forming power conversion system (GFPCS). Among them, the grid-forming PCS is the core of the grid-forming energy storage system. It converts the energy in the energy storage system into the inertia required by the system through the energy storage elements, provides inertia support for the load, and completes the energy interaction between the energy storage medium and the load. There are multiple PCSs in the microgrid. In order to achieve a better grid-forming effect, the PCSs need to operate in parallel. However, microgrids have problems such as insufficient inertia support capacity and poor frequency support capacity.

[0003] At present, the grid-type energy storage PCS can be adjusted through virtual synchronous machine technology. However, in the adjustment process, in order to pursue the accuracy of regulation, a large amount of computing resources will be used, and dynamic adjustment cannot be performed for each virtual synchronous machine, which increases the overall operation and maintenance cost of the system. Summary of the invention

[0004] The purpose of this application is: to solve the above technical problems, this application provides a grid-type energy storage PCS control method based on a virtual synchronous machine, aiming to improve the control efficiency of the grid-type energy storage PCS and reduce the overall operation and maintenance costs.

[0005] In some embodiments of the present application, multiple virtual synchronous machines are established based on the equipment parameters of the grid-type energy storage PCS in the grid-type energy storage system, so as to construct the control strategy of each grid-type PCS. At the same time, by establishing an association model, corresponding amplitude pre-synchronization and phase pre-synchronization are set when multiple grid-type PCSs are connected in parallel, so that the current in the parallel process is impact-free, thereby improving the control efficiency of the grid-type energy storage PCS.

[0006] In some embodiments of the present application, the computing resource allocation strategy is dynamically adjusted according to the overall computing resource amount and the operating parameters of each virtual synchronous machine to ensure the operating accuracy of each virtual synchronous machine and the overall control efficiency of the grid-type energy storage PCS, thereby reducing the overall operation and maintenance cost of the system.

[0007] In some embodiments of the present application, a virtual synchronous machine-based grid-type energy storage PCS control method is provided, including:

[0008] Establish multiple device nodes according to the parameters of the grid-type energy storage system, and generate virtual synchronous machines for each device node;

[0009] Establish multiple adjustment cycles and set computing resource allocation strategies within each adjustment cycle;

[0010] Generate sub-control strategies for each virtual synchronous machine, and generate a primary control strategy for the grid-type energy storage system based on all sub-control strategies;

[0011] When multiple device subnodes are established, it includes:

[0012] Establish a device node array A, A = (a1, a2...ai...an), where ai is the i-th device node and n is the number of device nodes.

[0013] In some embodiments of the present application, when generating a virtual synchronization machine for each device node, the process includes:

[0014] According to the device node sequence A, the i-th device node is set as the target device node in sequence;

[0015] Get the historical operating parameters of the target device node;

[0016] Generate a feature data training package based on historical operating parameters;

[0017] Construct a virtual synchronization machine of the target device node according to the iterative training results of the characteristic data training package;

[0018] Construct the virtual synchronization machine of each device node in turn;

[0019] A virtual synchronization machine sequence B is established, B = (b1, b2 ... bi ... bn), where bi is the virtual synchronization machine of the i-th device node.

[0020] In some embodiments of the present application, when generating a computing resource allocation strategy in each adjustment cycle, it includes:

[0021] Generate a fluctuation evaluation value of each virtual synchronous machine according to a preset adjustment time node;

[0022] Establish a fluctuation evaluation value sequence F, F = (f1, f2...fi...fn), where fi is the fluctuation evaluation value of the i-th virtual synchronous machine;

[0023] According to the fluctuation evaluation value series F, the primary allocation strategy within the current adjustment period is set;

[0024] According to the primary allocation strategy, the primary computing resource amount, the secondary computing resource amount and the backup computing resource amount in the current adjustment cycle are obtained;

[0025] The sub-computing resource amount of each virtual synchronous machine in the current adjustment cycle is set according to the primary computing resources and the fluctuation evaluation value sequence F.

[0026] In some embodiments of the present application, when generating the fluctuation evaluation value of each virtual synchronous machine, it includes:

[0027] According to the virtual synchronous machine sequence B, the i-th virtual synchronous machine is sequentially set as the target virtual synchronous machine;

[0028] Obtaining the operating parameters of the target virtual synchronous machine in the last adjustment cycle, and generating reference values ​​of various evaluation indicators in the last adjustment cycle;

[0029] Generate a fluctuation evaluation value f of the target virtual synchronous machine;

[0030]

[0031] Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; U is the historical evaluation value of the target virtual synchronous machine; θ is the number of evaluation indicators; βi is the influencing factor of the i-th evaluation indicator; pi is the reference value of the i-th evaluation indicator in the previous adjustment cycle;

[0032] The fluctuation evaluation value of each virtual synchronous machine is generated sequentially.

[0033] In some embodiments of the present application, when setting the primary allocation strategy in the current adjustment period, it includes:

[0034] Generate the adjustment evaluation value g within the current adjustment period according to the fluctuation evaluation value sequence F;

[0035]

[0036] Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; Δf is the average value of all data in the fluctuation evaluation value series F;

[0037] According to the adjustment evaluation value g, the proportion of primary computing resources, the proportion of secondary computing resources and the proportion of standby computing resources in the current adjustment period are set;

[0038] Generate a first-level allocation strategy.

[0039] In some embodiments of the present application, the amount of sub-computing resources of each virtual synchronous machine in the current adjustment period includes:

[0040] According to the fluctuation evaluation value f of the target virtual synchronous machine, the fluctuation evaluation value sequence F and the primary computing resource amount, the sub-computing resource amount of the target virtual synchronous machine in the current adjustment cycle is set;

[0041] According to the fluctuation evaluation value f, multiple feedback time nodes of multiple target virtual synchronous machines are set;

[0042] Generate the deviation evaluation values for each feedback time node;

[0043] Judge whether to correct the sub - operation resource amount of the target virtual synchronous machine according to the deviation evaluation value;

[0044] Set the sub - operation resource amounts of each sub - virtual synchronous machine in the current adjustment cycle in sequence.

[0045] In some embodiments of the present application, when judging whether to correct the sub - operation resource amount of the target virtual synchronous machine, it includes:

[0046] Generate the deviation evaluation value d of the target virtual synchronous machine at the current feedback time node;

[0047]

[0048] Wherein, e5 is a preset fifth weight coefficient; e6 is a preset sixth weight coefficient; Q5 is a preset fifth fixed coefficient; Q6 is a preset sixth fixed coefficient; m is the number of monitoring indicators; ri is the influence factor of the i - th monitoring indicator; ki is the reference value of the i - th monitoring indicator between the previous feedback time node and the current feedback time node; H is the cumulative deviation amount in the current adjustment cycle;

[0049] Judge whether to generate a correction instruction at the current feedback time node according to the deviation evaluation value d.

[0050] In some embodiments of the present application, when judging whether to generate a correction instruction at the current feedback time node, it includes:

[0051] Preset a first deviation evaluation value threshold D1 and a second deviation evaluation value threshold D2;

[0052] If d < D1, no correction instruction is generated at the current feedback time node;

[0053] If D1 ≤ d < D2, a first - level correction instruction is generated at the current feedback time node;

[0054] If d ≥ D2, a second - level correction instruction is generated at the current feedback time node.

[0055] In some embodiments of the present application, generating the primary control strategy of the network - forming energy storage system includes:

[0056] Construct an association model according to the device parameters and historical operation parameters of all device nodes;

[0057] Generate a mapping relation table of each virtual synchronous machine according to the association model;

[0058] Obtain the sub - control strategies of all virtual synchronous machines;

[0059] Generate correction parameters for each sub-control strategy according to the mapping relationship table;

[0060] Generate a primary control strategy based on the correction results.

[0061] Compared with the prior art, the grid-type energy storage PCS control method based on a virtual synchronous machine in the embodiment of the present application has the following beneficial effects:

[0062] Based on the equipment parameters of the grid-type energy storage PCS in the grid-type energy storage system, multiple virtual synchronous machines are established to construct the control strategy of each grid-type PCS. At the same time, by establishing an association model, the corresponding amplitude pre-synchronization and phase pre-synchronization are set when multiple grid-type PCSs are connected in parallel, so that there is no impact on the current during the parallel process, thereby improving the control efficiency of the grid-type energy storage PCS.

[0063] The computing resource allocation strategy is dynamically adjusted according to the overall computing resource volume and the operating parameters of each virtual synchronous machine to ensure the operating accuracy of each virtual synchronous machine and the overall control efficiency of the grid-type energy storage PCS, thereby reducing the overall operation and maintenance cost of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a flow chart of a grid-type energy storage PCS control method based on a virtual synchronous machine in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0065] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0066] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0067] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0068] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0069] like Figure 1 As shown, a preferred embodiment of the present application embodiment is a grid-type energy storage PCS control method based on a virtual synchronous machine, comprising:

[0070] S101: Establish multiple device nodes according to the parameters of the grid-type energy storage system, and generate a virtual synchronous machine for each device node;

[0071] S102: establishing multiple adjustment cycles, and setting a computing resource allocation strategy within each adjustment cycle;

[0072] S103: Generate sub-control strategies for each virtual synchronous machine, and generate a primary control strategy for the grid-connected energy storage system based on all sub-control strategies;

[0073] When multiple device subnodes are established, it includes:

[0074] Establish a device node array A, A = (a1, a2...ai...an), where ai is the i-th device node and n is the number of device nodes.

[0075] Specifically, a single grid-forming PCS is a device node, and a device node array is established according to the grid-forming PCS parameters in the grid-forming energy storage system.

[0076] Specifically, when generating a virtual synchronization machine for each device node, it includes:

[0077] According to the device node sequence A, the i-th device node is set as the target device node in sequence;

[0078] Get the historical operating parameters of the target device node;

[0079] Generate a feature data training package based on historical operating parameters;

[0080] Construct a virtual synchronization machine of the target device node according to the iterative training results of the characteristic data training package;

[0081] Construct the virtual synchronization machine of each device node in turn;

[0082] A virtual synchronization machine sequence B is established, B = (b1, b2 ... bi ... bn), where bi is the virtual synchronization machine of the i-th device node.

[0083] Specifically, the historical operating parameters of the meshed PCS corresponding to the target device node are preprocessed to extract the characteristic data, including but not limited to: synchronous generator frequency, voltage regulation characteristics, etc., to generate the corresponding characteristic data training package, and the mathematical model of the synchronous generator of the target device node is constructed based on the characteristic data training package combined with the mechanical equation of the synchronous generator rotor, the stator voltage equation, etc., so as to construct the virtual synchronous machine of the target device node and generate the corresponding virtual synchronous machine control strategy.

[0084] In a preferred embodiment of the present application, when generating a computing resource allocation strategy in each adjustment cycle, it includes:

[0085] Generate a fluctuation evaluation value of each virtual synchronous machine according to a preset adjustment time node;

[0086] Establish a fluctuation evaluation value sequence F, F = (f1, f2...fi...fn), where fi is the fluctuation evaluation value of the i-th virtual synchronous machine;

[0087] According to the fluctuation evaluation value series F, the primary allocation strategy within the current adjustment period is set;

[0088] According to the primary allocation strategy, the primary computing resource amount, the secondary computing resource amount and the backup computing resource amount in the current adjustment cycle are obtained;

[0089] The sub-computing resource amount of each virtual synchronous machine in the current adjustment cycle is set according to the primary computing resources and the fluctuation evaluation value sequence F.

[0090] Specifically, the primary computing resources refer to the sum of the computing resources allocated to each virtual synchronous machine, the secondary computing resources refer to the computing resources occupied when correcting and integrating the sub-control strategies of all virtual synchronous machines, and the spare computing resources refer to the computing resources occupied when adjusting resources to cope with the operating fluctuations of each virtual synchronous machine.

[0091] Specifically, the larger the fluctuation evaluation value is, the more unstable the operation of the meshed PCS corresponding to the target virtual synchronous machine is, and the greater the computing resources required by the virtual synchronous machine to maintain the simulation accuracy is.

[0092] Specifically, when generating the fluctuation evaluation value of each virtual synchronous machine, it includes:

[0093] According to the virtual synchronous machine sequence B, the i-th virtual synchronous machine is sequentially set as the target virtual synchronous machine;

[0094] Obtaining the operating parameters of the target virtual synchronous machine in the last adjustment cycle, and generating reference values ​​of various evaluation indicators in the last adjustment cycle;

[0095] Generate a fluctuation evaluation value f of the target virtual synchronous machine;

[0096]

[0097] Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; U is the historical evaluation value of the target virtual synchronous machine; θ is the number of evaluation indicators; βi is the influencing factor of the i-th evaluation indicator; pi is the reference value of the i-th evaluation indicator in the previous adjustment cycle;

[0098] The fluctuation evaluation value of each virtual synchronous machine is generated sequentially.

[0099] Specifically, all parameters in the model are normalized by presetting the first fixed coefficient and the second fixed coefficient, so that each parameter is in the same value range.

[0100] Specifically, the greater the historical fluctuation of the PCS of the device node corresponding to the target virtual synchronous machine, the greater the corresponding historical evaluation value. Its evaluation indicators include but are not limited to power change, switching frequency, voltage, current and other parameters. The larger the reference value, the greater the fluctuation of the corresponding evaluation indicator in the previous adjustment cycle.

[0101] Specifically, the corresponding impact factor is set according to the interference degree of each evaluation index on the operation of the networking PCS. The greater the interference degree, the greater the corresponding impact factor.

[0102] Specifically, when setting the primary allocation strategy within the current adjustment cycle, it includes:

[0103] Generate the adjustment evaluation value g within the current adjustment period according to the fluctuation evaluation value sequence F;

[0104]

[0105] Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; Δf is the average value of all data in the fluctuation evaluation value series F;

[0106] According to the adjustment evaluation value g, the proportion of primary computing resources, the proportion of secondary computing resources and the proportion of standby computing resources in the current adjustment period are set;

[0107] Generate a first-level allocation strategy.

[0108] Specifically, the larger the adjustment evaluation value is, the more difficult it is for each virtual synchronous machine to perform overall regulation, and the corresponding secondary computing resource proportion is larger.

[0109] Specifically, by presetting the third fixed coefficient and the fourth fixed coefficient, all parameters in the model are normalized so that each parameter is within the same value range.

[0110] It can be understood that in the above embodiment, the computing resource allocation strategy is dynamically adjusted according to the overall computing resource amount and the operating parameters of each virtual synchronous machine to ensure the operating accuracy of each virtual synchronous machine and the overall control efficiency of the grid-type energy storage PCS, thereby reducing the overall operation and maintenance cost of the system.

[0111] In a preferred embodiment of the present application, the sub-computing resource amount of each virtual synchronous machine in the current adjustment cycle includes:

[0112] According to the fluctuation evaluation value f of the target virtual synchronous machine, the fluctuation evaluation value sequence F and the primary computing resource amount, the sub-computing resource amount of the target virtual synchronous machine in the current adjustment cycle is set;

[0113] According to the fluctuation evaluation value f, multiple feedback time nodes of multiple target virtual synchronous machines are set;

[0114] Generate deviation evaluation values ​​for each feedback time node;

[0115] Determine whether to modify the sub-computing resource amount of the target virtual synchronous machine according to the deviation evaluation value;

[0116] The sub-computing resource amount of each sub-virtual synchronous machine in the current adjustment cycle is sequentially set.

[0117] Specifically, the greater the fluctuation evaluation value, the greater the corresponding sub-computing resources. By dynamically adjusting the sub-computing resources of the virtual synchronous machine, the overall control efficiency is improved and the overall operation and maintenance cost is reduced.

[0118] Specifically, the determination of whether to modify the sub-computing resource amount of the target virtual synchronous machine includes:

[0119] Generate a deviation evaluation value d of the target virtual synchronous machine at the current feedback time node;

[0120]

[0121] Wherein, e5 is a preset fifth weight coefficient; e6 is a preset sixth weight coefficient; Q5 is a preset fifth fixed coefficient; Q6 is a preset sixth fixed coefficient; m is the number of monitoring indicators; ri is the influence factor of the i-th monitoring indicator; ki is the reference value of the i-th monitoring indicator between the previous feedback time node and the current feedback time node; H is the cumulative deviation amount within the current adjustment period;

[0122] Specifically, its ki is set according to the change parameter of the i-th monitoring indicator between adjacent feedback time nodes. The more剧烈 the change, the larger the corresponding reference value. The monitoring indicators include but are not limited to parameters such as power change, switching frequency, voltage, and current.

[0123] Judge whether to generate a correction instruction at the current feedback time node according to the deviation evaluation value d.

[0124] Specifically, the fifth fixed coefficient and the sixth fixed coefficient are set to normalize all the data in the model, so that each parameter is within the same value range.

[0125] Specifically, the larger the deviation evaluation value, the more unstable the operation of the device node corresponding to the current virtual synchronous machine, and it is necessary to increase the corresponding computing resources to ensure the simulation accuracy of the virtual synchronous machine and improve the control efficiency of the system.

[0126] Specifically, when judging whether to generate a correction instruction at the current feedback time node, it includes:

[0127] Preset a first deviation evaluation value threshold D1 and a second deviation evaluation value threshold D2;

[0128] If d < D1, no correction instruction is generated at the current feedback time node;

[0129] If D1 ≤ d < D2, a first-level correction instruction is generated at the current feedback time node;

[0130] If d ≥ D2, a second-level correction instruction is generated at the current feedback time node.

[0131] Specifically, the first-level correction instruction means shortening the feedback cycle duration of the target virtual synchronous machine, and the second-level correction instruction means setting the compensated sub-computing resource amount according to its deviation evaluation value and calling the standby computing resources according to the compensated sub-computing resource amount.

[0132] In the preferred embodiment of this application, generating the primary control strategy of the network-forming energy storage system includes:

[0133] Construct an association model according to the device parameters and historical operation parameters of all device nodes;

[0134] Generate a mapping relationship table of each virtual synchronous machine according to the association model;

[0135] Get the sub-control strategies of all virtual synchronous machines;

[0136] Generate correction parameters for each sub-control strategy according to the mapping relationship table;

[0137] Generate a primary control strategy based on the correction results.

[0138] Specifically, when multiple PCSs are connected in parallel, there are problems such as uneven output power distribution and power oscillation due to line impedance. Therefore, by constructing an associated model and considering the pre-synchronization process, including amplitude pre-synchronization and phase pre-synchronization, each sub-control strategy is corrected and integrated to make the current in the parallel process impact-free, thereby improving the overall control efficiency of the system.

[0139] According to the first concept of the present application, multiple virtual synchronous machines are established based on the equipment parameters of the grid-type energy storage PCS in the grid-type energy storage system, so as to construct the control strategy of each grid-type PCS. At the same time, by establishing an association model, corresponding amplitude pre-synchronization and phase pre-synchronization are set when multiple grid-type PCSs are connected in parallel, so that the current in the parallel process is impact-free, thereby improving the control efficiency of the grid-type energy storage PCS.

[0140] According to the second concept of the present application, the computing resource allocation strategy is dynamically adjusted according to the overall computing resource amount and the operating parameters of each virtual synchronous machine to ensure the operating accuracy of each virtual synchronous machine and the overall control efficiency of the grid-type energy storage PCS, thereby reducing the overall operation and maintenance cost of the system.

[0141] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.

Claims

1. A grid-type energy storage PCS control method based on a virtual synchronous machine, characterized in that: include: Establish multiple device nodes according to the parameters of the grid-type energy storage system, and generate virtual synchronous machines for each device node; Establish multiple adjustment cycles and set computing resource allocation strategies within each adjustment cycle; Generate sub-control strategies for each virtual synchronous machine, and generate a primary control strategy for the grid-type energy storage system based on all sub-control strategies; When multiple device subnodes are established, it includes: Establish a device node array A, A = (a1, a2...ai...an), where ai is the i-th device node and n is the number of device nodes.

2. The grid-type energy storage PCS control method based on virtual synchronous machine according to claim 1, characterized in that: When generating a virtual synchronization machine for each device node, include: According to the device node sequence A, the i-th device node is set as the target device node in sequence; Get the historical operating parameters of the target device node; Generate a feature data training package based on historical operating parameters; Construct a virtual synchronization machine of the target device node according to the iterative training results of the characteristic data training package; Construct the virtual synchronization machine of each device node in turn; A virtual synchronization machine sequence B is established, B = (b1, b2 ... bi ... bn), where bi is the virtual synchronization machine of the i-th device node.

3. The PCS control method for grid-type energy storage based on virtual synchronous machine according to claim 2, characterized in that: When generating computing resource allocation strategies in each adjustment cycle, it includes: Generate a fluctuation evaluation value of each virtual synchronous machine according to a preset adjustment time node; Establish a fluctuation evaluation value sequence F, F = (f1, f2...fi...fn), where fi is the fluctuation evaluation value of the i-th virtual synchronous machine; According to the fluctuation evaluation value series F, the primary allocation strategy within the current adjustment period is set; According to the primary allocation strategy, the primary computing resource amount, the secondary computing resource amount and the backup computing resource amount in the current adjustment cycle are obtained; The sub-computing resource amount of each virtual synchronous machine in the current adjustment cycle is set according to the primary computing resources and the fluctuation evaluation value sequence F.

4. The PCS control method for grid-type energy storage based on virtual synchronous machine according to claim 3 is characterized in that: When generating the fluctuation evaluation value of each virtual synchronous machine, it includes: According to the virtual synchronous machine sequence B, the i-th virtual synchronous machine is sequentially set as the target virtual synchronous machine; Obtaining the operating parameters of the target virtual synchronous machine in the last adjustment cycle, and generating reference values ​​of various evaluation indicators in the last adjustment cycle; Generate a fluctuation evaluation value f of the target virtual synchronous machine; Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; U is the historical evaluation value of the target virtual synchronous machine; θ is the number of evaluation indicators; βi is the influencing factor of the i-th evaluation indicator; pi is the reference value of the i-th evaluation indicator in the previous adjustment cycle; The fluctuation evaluation value of each virtual synchronous machine is generated sequentially.

5. The PCS control method for grid-type energy storage based on virtual synchronous machine according to claim 4, characterized in that: When setting the primary allocation strategy within the current adjustment cycle, it includes: Generate the adjustment evaluation value g within the current adjustment period according to the fluctuation evaluation value sequence F; Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; Δf is the average value of all data in the fluctuation evaluation value series F; According to the adjustment evaluation value g, the proportion of primary computing resources, the proportion of secondary computing resources and the proportion of standby computing resources in the current adjustment period are set; Generate a first-level allocation strategy.

6. The PCS control method for grid-type energy storage based on virtual synchronous machine according to claim 4, characterized in that: When determining the sub-computation resource amount of each virtual synchronous machine within the current adjustment period, it includes: Based on the fluctuation evaluation value f of the target virtual synchronous machine, the fluctuation evaluation value sequence F, and the primary computation resource amount, set the sub-computation resource amount of the target virtual synchronous machine within the current adjustment period; Set multiple feedback time nodes of multiple target virtual synchronous machines according to the fluctuation evaluation value f; Generate the deviation evaluation value of each feedback time node; Judge whether to correct the sub-computation resource amount of the target virtual synchronous machine according to the deviation evaluation value; Sequentially set the sub-computation resource amount of each sub-virtual synchronous machine within the current adjustment period.

7. The PCS control method for grid-type energy storage based on virtual synchronous machine according to claim 6, characterized in that: When judging whether to correct the sub-computation resource amount of the target virtual synchronous machine, it includes: Generate the deviation evaluation value d of the target virtual synchronous machine at the current feedback time node; Where, e5 is the preset fifth weight coefficient; e6 is the preset sixth weight coefficient; Q5 is the preset fifth fixed coefficient; Q6 is the preset sixth fixed coefficient; m is the number of monitoring indicators; ri is the influence factor of the i-th monitoring indicator; ki is the reference value of the i-th monitoring indicator between the previous feedback time node and the current feedback time node; H is the cumulative deviation amount within the current adjustment period; Judge whether to generate a correction instruction at the current feedback time node according to the deviation evaluation value d.

8. The PCS control method for grid-type energy storage based on virtual synchronous machine according to claim 7, characterized in that: When judging whether to generate a correction instruction at the current feedback time node, it includes: Preset the first deviation evaluation value threshold D1 and the second deviation evaluation value threshold D2; If d < D1, no correction instruction is generated at the current feedback time node; If D1 ≤ d < D2, a primary correction instruction is generated at the current feedback time node; If d ≥ D2, a secondary correction instruction is generated at the current feedback time node.

9. The PCS control method for grid-type energy storage based on virtual synchronous machine according to claim 8, characterized in that: Generate the primary control strategy of the network-forming energy storage system, including: Construct an association model based on the device parameters and historical operation parameters of all device nodes; Generate the mapping relationship table of each virtual synchronous machine according to the association model; Obtain the sub-control strategies of all virtual synchronous machines; Generate the correction parameters of each sub-control strategy according to the mapping relationship table; Generate the primary control strategy according to the correction result.

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