Battery energy storage secondary frequency modulation method based on dynamic allocation factor, medium and terminal

Abstract

The application discloses a battery energy storage secondary frequency modulation method based on a dynamic allocation factor, a medium and a terminal, wherein the method comprises the following steps: acquiring a sensitivity factor of a regional frequency deviation control model and load fluctuation of super-short-term load prediction; taking the sensitivity factor as a feedback signal, and adjusting the size of the energy storage allocation factor in real time according to a regulation strategy; judging whether the load fluctuation of the super-short-term load prediction meets a preset condition for correcting the energy storage allocation factor; if the preset condition is not met, no correction is performed; if the preset condition is met, a correction amount of the energy storage allocation factor is acquired through a fuzzy correction strategy, and then a corrected energy storage allocation factor is obtained to perform secondary frequency modulation. The method can perceive load change trends in multiple scenes in advance, and the power allocation factor is secondarily corrected, so that sufficient capacity of the energy storage is reserved to cope with continuous large disturbances of the load.

Description

Technical Field

[0001] This invention relates to the application of energy storage in power systems, and more particularly to a battery energy storage secondary frequency regulation method, medium, and terminal based on dynamic allocation factors. Background Technology

[0002] Under the "dual carbon" background, renewable energy sources such as solar and wind power have developed rapidly. However, due to the large-scale grid connection of renewable energy, its volatility and uncertainty pose a severe challenge to grid frequency regulation. Relying solely on conventional generating units for frequency regulation cannot meet regional frequency regulation needs. Battery energy storage, as a high-quality frequency regulation resource, has developed rapidly in recent years. Its rapid response characteristics can not only reduce the output of conventional generating units but also improve the quality of frequency regulation. However, how to fully leverage the advantages of conventional generating units with battery energy storage, research their coordination and cooperation schemes, and effectively address the frequency regulation needs of the power system still require further exploration.

[0003] Although research has been conducted on the participation of energy storage in the secondary frequency regulation of the power system, two important problems remain: (1) the relationship between the processing depth of battery energy storage and regional frequency deviation has not been considered, and the contradiction between battery energy storage SOC reconfiguration and grid frequency regulation requirements has not been considered; (2) the contradiction between continuous deep output of battery energy storage and SOC reconfiguration under continuous large load disturbance scenarios has not been considered. Summary of the Invention

[0004] To address the problems in the background art, the present invention provides a battery energy storage secondary frequency regulation method, medium, and terminal based on dynamic allocation factors.

[0005] In a first aspect, the present invention provides a secondary frequency regulation method for battery energy storage based on a dynamic allocation factor, comprising:

[0006] S1: Obtain the sensitivity factor of the regional frequency deviation control model and the load fluctuation of the ultra-short-term load forecast;

[0007] S2: Use the sensitivity factor as a feedback signal to adjust the magnitude of the energy storage allocation factor in real time according to the control strategy;

[0008] S3: Determine whether the load fluctuation of the ultra-short-term load forecast meets the preset conditions for correcting the energy storage allocation factor: if the preset conditions are not met, no correction is performed; if the preset conditions are met, the correction amount of the energy storage allocation factor is obtained through the fuzzy correction strategy, and then the corrected energy storage allocation factor is obtained to perform secondary frequency regulation.

[0009] Furthermore, the regional frequency deviation control model is specifically as follows:

[0010] A CE =ΔP tie +BΔf

[0011] P bess =aA CE

[0012] P gen =(1-a)A CE

[0013] Among them, A CE For regional power deviation; ΔP tie The power exchanged via the inter-regional tie line; B is the frequency deviation coefficient; Δf is the regional frequency deviation; P gen For the total output of conventional units with frequency regulation; P bess 1 represents the total output of battery energy storage frequency regulation; 'a' represents the allocation factor of battery energy storage in the regional frequency deviation signal; 1-a represents the allocation factor of conventional units in the regional frequency deviation signal.

[0014] Furthermore, in S2, the control strategy is specifically as follows:

[0015]

[0016] Where a(t) is the adjusted energy storage allocation factor; a max The preset maximum energy storage allocation factor; a min The preset minimum energy storage allocation factor; S a (t-t0) is the sensitivity factor calculated at the previous sampling time; t is the current time; t0 is one sampling period.

[0017] Furthermore, the final energy storage allocation factor a'(t) obtained in S3 is expressed as:

[0018]

[0019] Among them, a max The preset maximum energy storage allocation factor; a min The preset minimum energy storage allocation factor; S a (t-t0) is the sensitivity factor calculated at the previous sampling time; t is the current time; t0 is one sampling period; |dP| represents the load fluctuation in ultra-short-term load forecasting, dP max This represents the maximum allowable disturbance value of the load within the region; w is the correction amount of the energy storage allocation factor obtained through the fuzzy correction strategy.

[0020] Furthermore, the specific process of the fuzzy correction strategy in S3 is as follows:

[0021] The energy storage allocation factor at the current sampling time and the load fluctuation of the ultra-short-term load forecast are used as the input of the fuzzy controller, and the correction of the energy storage allocation factor is used as the output of the fuzzy controller. A fuzzy subset of the energy storage allocation factor integrated over a preset time is set; a fuzzy subset of the load fluctuation of the ultra-short-term forecast integrated over a preset time is set; a fuzzy subset of the corrected energy storage allocation factor is set; a membership function is set; and the correction is obtained according to the input of the fuzzy controller and the fuzzy control rules.

[0022] Furthermore, the membership function adopts a Mamdani-type triangular membership function.

[0023] Furthermore, the fuzzy subset of the energy storage allocation factor within a preset time period is set to two dimensions: {NB(0.3), PB(0.9)}; the fuzzy subset of the ultra-short-term predicted load fluctuation within a preset time period is set to seven dimensions: {NB(negative large), NM(negative medium), NS(negative small), ZO(zero), PS(positive small), PM(positive medium), PB(positive large)}; and the fuzzy subset of the correction amount is set to five dimensions: {NM(0), NS(0.15), ZO(0.3), PS(0.45), PM(0.6)}.

[0024] Furthermore, the fuzzy control rule is specifically as follows: if the energy storage allocation factor a is PB and the load fluctuation dP of the ultra-short-term load forecast is NB, then the correction amount is NS;

[0025] If the energy storage allocation factor a is PB and the load fluctuation dP of the ultra-short-term load forecast is NM, then the correction amount is ZO.

[0026] If the energy storage allocation factor a is PB and the load fluctuation dP of the ultra-short-term load forecast is NS, then the correction amount is PS.

[0027] If the energy storage allocation factor a is PB and the load fluctuation dP of the ultra-short-term load forecast is ZO, then the correction amount is PM.

[0028] If the energy storage allocation factor a is PB and the load fluctuation dP of the ultra-short-term load forecast is PS, then the correction amount is PS.

[0029] If the energy storage allocation factor a is PB and the load fluctuation dP of the ultra-short-term load forecast is PM, then the correction amount is ZO.

[0030] If the energy storage allocation factor a is PB, and the load fluctuation dP of the ultra-short-term load forecast is PB, then the correction amount is NS;

[0031] If the energy storage allocation factor a is NB and the load fluctuation dP of the ultra-short-term load forecast is NB, then the correction amount is NM.

[0032] If the energy storage allocation factor a is NB and the load fluctuation dP of the ultra-short-term load forecast is NM, then the correction amount is NM.

[0033] If the energy storage allocation factor a is NB and the load fluctuation dP of the ultra-short-term load forecast is NS, then the correction amount is NM.

[0034] If the energy storage allocation factor a is NB and the load fluctuation dP of the ultra-short-term load forecast is ZO, then the correction amount is NM.

[0035] If the energy storage allocation factor a is NB and the load fluctuation dP of the ultra-short-term load forecast is PS, then the correction amount is NM.

[0036] If the energy storage allocation factor a is NB and the load fluctuation dP of the ultra-short-term load forecast is PM, then the correction amount is NM.

[0037] If the energy storage allocation factor a is NB and the load fluctuation dP of the ultra-short-term load forecast is PB, then the correction amount is NM.

[0038] In a second aspect, the present invention provides a readable storage medium storing a computer program that, when invoked by a processor, performs the steps of the method described above.

[0039] Thirdly, the present invention provides an electronic terminal, including a processor and a memory, wherein the memory stores a computer program, and the processor invokes the computer program to execute the steps of the method described in the claims above.

[0040] Beneficial effects

[0041] This invention proposes a secondary frequency regulation method, medium, and terminal for battery energy storage based on dynamic allocation factors. The method resolves the contradiction between regional frequency regulation requirements and battery energy storage SOC reconfiguration, while fully leveraging the rapid frequency regulation characteristics of battery energy storage. The relationship between the optimal energy storage participation depth in frequency regulation (i.e., the allocation factor value) and the regional frequency deviation is analyzed. Specifically, in the initial stage of power system load fluctuations, the energy storage participation depth needs to be increased to leverage the advantages of rapid frequency regulation. In the later stage of load fluctuations, conventional generating units undertake the main frequency regulation task, thereby maintaining the state of charge of the energy storage. Furthermore, a storage control strategy is proposed that dynamically adjusts the allocation factor by calculating its sensitivity, solving the problem of previous studies requiring reconfiguration strategies to maintain the energy storage SOC. Moreover, considering that continuous energy storage output under continuous large load disturbances may cause the SOC to exceed limits, this invention utilizes ultra-short-term load forecasting technology to detect load change trends in advance and perform secondary corrections on the energy storage allocation factor, ensuring sufficient capacity is reserved for continuous large load disturbances. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a frequency response model diagram based on regional frequency deviation control provided by the present invention;

[0044] Figure 2 This is a graph showing the relationship between the energy storage allocation factor and the regional frequency deviation provided by the present invention.

[0045] Figure 3 This is a graph showing the relationship between the energy storage allocation factor and the sensitivity factor provided by the present invention.

[0046] Figure 4 This is a flowchart of a battery energy storage secondary frequency regulation method based on dynamic allocation factors provided in an embodiment of the present invention;

[0047] Figure 5 This is a block diagram of battery energy storage participating in the secondary frequency regulation control of the power system provided in an embodiment of the present invention;

[0048] Figure 6 This is a membership function diagram of the allocation factor and ultra-short-term load variation provided in an embodiment of the present invention; wherein, Figure 6 (a) is a graph of the membership function of the energy storage allocation factor; Figure 6 (b) is a membership function diagram of load fluctuations in ultra-short-term load forecasting;

[0049] Figure 7 This is a membership function graph of the correction amount provided in the embodiments of the present invention;

[0050] Figure 8 These are regional frequency response diagrams under different methods for step disturbance provided in embodiments of the present invention;

[0051] Figure 9 This is a schematic diagram of the battery energy storage SOC value under different methods under step disturbance provided in the embodiments of the present invention;

[0052] Figure 10 This is a graph showing the changes in sensitivity and allocation factor under step conditions provided in an embodiment of the present invention; wherein, Figure 10 (a) shows the variation of the energy storage allocation factor under step conditions; Figure 10 (b) shows the change of the sensitivity factor under step conditions;

[0053] Figure 11These are regional frequency response diagrams under different methods during continuous perturbation provided in embodiments of the present invention;

[0054] Figure 12 This is a schematic diagram of the battery energy storage SOC value under different methods under continuous disturbance provided in the embodiments of the present invention;

[0055] Figure 13 This is a schematic diagram of the energy storage allocation factor before and after continuous large step correction provided in an embodiment of the present invention;

[0056] Figure 14 This is a schematic diagram of the regional frequency response before and after the correction of the energy storage allocation factor under continuous large step changes provided in the embodiments of the present invention;

[0057] Figure 15 This is a schematic diagram of the battery energy storage SOC value before and after the correction of the energy storage allocation factor under continuous large step changes provided in the embodiments of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0059] This invention establishes a secondary frequency regulation model for a regional power system. The model adopts a constant frequency and tie-line deviation control mode from automatic generation control, considering the participation of battery energy storage in the secondary frequency regulation of the grid by conventional generating units. It calculates the Area Control Error (ACE) signal and distributes the ACE signal to the battery energy storage system and conventional generating units respectively according to preset principles. The transfer function model is as follows: Figure 1 As shown. The energy storage allocation factor is determined by acquiring and analyzing the sensitivity factor. The specific process for acquiring the sensitivity factor is as follows:

[0060] Step 1: Employing the constant frequency and tie-line deviation control mode in automatic generation control, and considering the participation of battery energy storage in the secondary frequency regulation of the grid by conventional generating units, a control model based on the regional frequency deviation signal is constructed. The model is represented as follows:

[0061] A CE =ΔP tie +BΔf

[0062] Among them, A CE For regional power deviation; ΔP tie Δf represents the power exchanged between regional tie lines; B represents the frequency deviation coefficient; and Δf represents the regional frequency deviation.

[0063] The regional frequency deviation signal is allocated to the battery energy storage system and conventional units, and the allocation method is expressed as follows:

[0064] P bess =aA CE

[0065] P gen =(1-a)A CE

[0066] Among them, P bess For battery energy storage frequency regulation total output; P gen 1 represents the total output of the conventional generating unit in frequency regulation; 'a' represents the allocation factor of battery energy storage in the regional frequency deviation signal; 1-a represents the allocation factor of the conventional generating unit in the regional frequency deviation signal.

[0067] Step 2: Determine the equivalent frequency regulation model for conventional units. In this invention, a conventional unit refers to a conventional thermal power unit, including a speed governor G. gov and reheat turbine G s The two parts are represented by the transfer functions as follows:

[0068]

[0069]

[0070] Among them, T g T is the time constant of the speed controller; RH F is the reheater time constant; HP For reheater gain; T CH The time constant of the steam turbine

[0071] The transfer function of a conventional unit in series is expressed as:

[0072] G g (s)=G gov (s)·G s (s)

[0073] Step 3: Determine the equivalent frequency regulation model for battery energy storage. In this invention, a simplified general model is adopted for the battery energy storage model. The influence of a first-order inertial element on the secondary frequency regulation of the power grid is analyzed, thereby reflecting the power of the regional frequency deviation signal and the energy storage response. The transfer function of the battery energy storage model is expressed as:

[0074]

[0075] Among them, T b The time constant for energy storage;

[0076] The state of charge (SOC) of a battery is expressed as:

[0077]

[0078] Where t is the current time; t0 is a sampling period; P(t) is the power charged / discharged from (t-t0) to t; and E is the total capacity of the energy storage device.

[0079] Step 4: Establish a frequency response model based on regional frequency deviation (ACE) signal control. ACE signal control refers to the control method for distributing the power demand generated in a region between battery storage and conventional generating units when load fluctuations occur. To enable battery storage to respond quickly to frequency changes, this invention only uses PI control in the conventional generating units. The conventional generating units participate in the primary and secondary frequency regulation of the region, while battery storage only participates in the secondary frequency regulation. The transfer function of the frequency response model is obtained by considering the given load fluctuation, regional frequency deviation, and the output of the conventional generating units and battery storage, and is expressed as:

[0080]

[0081] Wherein, ΔP G Indicates the output of a conventional unit; ΔP b Power output for battery energy storage; ΔP L (s) represents the load disturbance; sM+D is the moment of inertia and damping coefficient;

[0082] conventional unit output ΔP G for:

[0083]

[0084] Among them, K G The unit frequency regulation power of a conventional unit; K p K i All are coefficients for PI control; G g (s) is the transfer function of the conventional unit; 1-a is the allocation factor of the conventional unit in the ACE signal; B is the frequency deviation coefficient.

[0085] Battery energy storage output ΔP b for:

[0086] ΔP b (s)=-a·BG b (s)Δf(s)

[0087] Where, a is the energy allocation factor of battery storage in the ACE signal, i.e., the energy allocation factor; G b (s) is the transfer function of the battery energy storage model.

[0088] Therefore, the formula for calculating frequency deviation is obtained:

[0089]

[0090] Step 5: Determine the energy storage allocation factor based on sensitivity analysis. Based on the frequency deviation calculation formula obtained in Step 4, take the partial derivative with respect to the energy storage allocation factor to obtain the sensitivity to frequency deviation under ACE control mode, expressed as:

[0091]

[0092] Thus, the dimensionless sensitivity factor S under ACE control mode is obtained. a , is represented as:

[0093]

[0094] Under ACE-based control, given a step signal at 1 second, the relationships between regional frequency deviation, sensitivity, and energy storage allocation factor over time are as follows: Figure 2 , Figure 3 As shown in the figures, based on the analysis of the two figures, in the initial stage of power system load fluctuations, it is necessary to increase the depth of energy storage participation in frequency regulation to leverage the advantages of rapid frequency regulation. In the later stage of load fluctuations, conventional generating units should undertake the main frequency regulation task to maintain the state of charge of the energy storage. This ensures that frequency deviation is minimized at all stages of load fluctuations, while simultaneously adjusting the frequency regulation depth of battery energy storage to maintain its own state.

[0095] Example 1

[0096] like Figure 4 As shown in the figure, this embodiment provides a battery energy storage secondary frequency regulation method based on dynamic allocation factors, whereby battery energy storage participates in power system secondary frequency regulation. The control block diagram for battery energy storage participating in power system secondary frequency regulation is shown below. Figure 5 As shown. The specific steps of the method include:

[0097] S1: Obtain the sensitivity factor of the regional frequency deviation control model and the load fluctuation of the ultra-short-term load forecast.

[0098] S2: The sensitivity factor is used as a feedback signal to adjust the magnitude of the energy storage allocation factor in real time according to the control strategy.

[0099] Specifically, the control strategy is as follows:

[0100]

[0101] Where a(t) is the adjusted energy storage allocation factor; a max The preset maximum energy storage allocation factor; a min The preset minimum energy storage allocation factor; S a (t-t0) is the sensitivity factor calculated at the previous sampling time; t0 is one sampling period. In this embodiment, the maximum energy storage allocation factor amax The preset minimum energy storage allocation factor a is set to 0.9. min The value is set to 0.3, but this can be adjusted according to the actual situation during implementation; no fixed value is imposed. S a As a feedback signal, the energy storage allocation factor 'a' is adjusted in real time. To maximize the role of battery energy storage, this embodiment sets two extreme values ​​for 'a'. max and a min In the sensitivity factor S a When < 0, let the energy storage allocation factor be a. max In sensitivity S a When the value is greater than 0, let the energy storage allocation factor be a. min This minimizes frequency deviation throughout the load fluctuation period, while timely detection of system frequency changes allows for the timely withdrawal of most energy storage, enabling the battery to maintain its SOC value.

[0102] S3: Determine whether the load fluctuation of the ultra-short-term load forecast meets the preset conditions for the modified energy storage allocation factor: if the preset conditions are not met, no modification is performed; if the preset conditions are met, the energy storage allocation factor is modified and calculated through a fuzzy modification strategy, and then the modified energy storage allocation factor is obtained for secondary frequency regulation.

[0103] When predicted load fluctuations increase, sufficient energy storage capacity is required to participate in frequency regulation in future periods. This means the energy storage allocation factor should be reduced at the current time to decrease energy storage output. Conversely, when predicted load fluctuations decrease, it indicates that the energy storage output required for frequency regulation in future periods is smaller. In this case, based on the sensitivity analysis results, the energy storage can continue to play its role in rapid frequency regulation at the current time. The preset condition refers to whether the predicted load fluctuation exceeds the maximum allowable disturbance value of the load in the region.

[0104] Specifically, the final energy storage allocation factor a'(t) obtained in S3 is expressed as:

[0105]

[0106] Among them, a max The preset maximum energy storage allocation factor; a min The preset minimum energy storage allocation factor; S a (t) represents the sensitivity factor calculated at the previous sampling time; t is the current time; t0 is one sampling period; |dP| represents the load fluctuation in ultra-short-term load forecasting, dP max This represents the maximum allowable disturbance value of the load within the region; w is the correction amount of the energy storage allocation factor obtained through the fuzzy correction strategy.

[0107] In this embodiment, the maximum allowable disturbance value dP of the load within the region is... maxThe maximum disturbance value is set to 0.3 pu. If this value is exceeded, the energy storage allocation factor will begin to adjust. In practical implementation, the maximum perturbation value allowed by the system is dP. max Adjustments can be made according to the actual situation; no restrictions are imposed.

[0108] Specifically, the process of the fuzzy correction strategy is as follows:

[0109] The energy storage allocation factor at the current sampling time and the load fluctuation of the ultra-short-term load forecast are used as the input of the fuzzy controller, and the correction is used as the output of the fuzzy controller.

[0110] Set a fuzzy subset of the energy storage allocation factor within a preset time period, wherein the fuzzy subset of the energy storage allocation factor within the preset time period is set to two dimensions: {NB(0.3), PB(0.9)}.

[0111] Set a fuzzy subset of load fluctuations in ultra-short-term forecasts within a preset time period, wherein the fuzzy subset of load fluctuations in ultra-short-term forecasts within the preset time period is set to seven dimensions: {NB (negative large), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (positive medium), PB (positive large)}.

[0112] Set a fuzzy subset of the correction amount; the fuzzy subset of the correction amount is set to five dimensions: {NM(0), NS(0.15), ZO(0.3), PS(0.45), PM(0.6)}.

[0113] Set a membership function, wherein the membership function adopts a Mamdani type triangular membership function, such as... Figure 5 , Figure 6 As shown;

[0114] The corrected energy storage allocation factor is obtained based on the fuzzy input and fuzzy control rules. The fuzzy control rules are obtained through ultra-short-term load forecasting technology. When the predicted load fluctuation increases, the energy storage allocation factor at the current moment is reduced; when the predicted load fluctuation decreases, the energy storage allocation factor calculated by sensitivity is maintained.

[0115] Specifically, the fuzzy control rules are as follows:

[0116] If the energy storage allocation factor a is PB and the ultra-short-term load forecast result dP is NB, then the correction amount is NS;

[0117] If the energy storage allocation factor a is PB and the ultra-short-term load forecast result dP is NM, then the correction amount is ZO.

[0118] If the energy storage allocation factor a is PB and the ultra-short-term load forecast result dP is NS, then the correction amount is PS;

[0119] If the energy storage allocation factor a is PB and the ultra-short-term load forecast result dP is ZO, then the correction amount is PM;

[0120] If the energy storage allocation factor a is PB and the ultra-short-term load forecast result dP is PS, then the correction amount is PS;

[0121] If the energy storage allocation factor a is PB and the ultra-short-term load forecast result dP is PM, then the correction amount is ZO.

[0122] If the energy storage allocation factor a is PB and the ultra-short-term load forecast result dP is PB, then the correction amount is NS;

[0123] If the energy storage allocation factor a is NB and the ultra-short-term load forecast result dP is NB, then the correction amount is NM;

[0124] If the energy storage allocation factor a is NB and the ultra-short-term load forecast result dP is NM, then the correction amount is NM;

[0125] If the energy storage allocation factor a is NB and the ultra-short-term load forecast result dP is NS, then the correction amount is NM;

[0126] If the energy storage allocation factor a is NB and the ultra-short-term load forecast result dP is ZO, then the correction amount is NM;

[0127] If the energy storage allocation factor a is NB and the ultra-short-term load forecast result dP is PS, then the correction amount is NM;

[0128] If the energy storage allocation factor a is NB and the ultra-short-term load forecast result dP is PM, then the correction amount is NM;

[0129] If the energy storage allocation factor a is NB and the ultra-short-term load forecast result dP is PB, then the correction amount is NM.

[0130] To verify feasibility, a dual-zone interconnection system was adopted, with load disturbances set in one of the zones. The rated power of the conventional unit frequency regulation was set to 1000MW, the adjustable frequency capacity to be (-60~60)MW, the ramp rate to be 30MW / min, and the rated capacity of the battery energy storage system to be 12.5MW / 2.5MWh. The system was per-unitized based on a rated power of 1000MW and a rated frequency of 50Hz, and the initial SOC of the battery energy storage was set to 0.5.

[0131] The design was validated under two typical operating conditions: step load disturbance and continuous load disturbance, and compared with conditions without energy storage (energy storage allocation factor is always 0), energy storage allocation factor is always 0.5, and energy storage allocation factor is always 0.9. Operating condition 1: Step load disturbance. A step load disturbance with an amplitude of 0.05 pu is added at 2s. Operating condition 2: Continuous load disturbance. Continuous measured load fluctuation data is used, with a sampling interval of 1s. Operating condition 3: Continuous large step load disturbance. A step load disturbance with an amplitude of 0.35 pu is set at 1s, and a step load disturbance with an amplitude of 0.35 pu is superimposed at 2s.

[0132] like Figure 8-13 As shown in the experimental results, the method proposed in this embodiment dynamically adjusts the energy storage control by calculating the sensitivity of the energy storage allocation factor. It analyzes that the energy storage allocation factor should reach its maximum and minimum values ​​respectively at the beginning and end of load fluctuations to minimize the regional frequency deviation. A dynamic adjustment of the energy storage allocation factor based on sensitivity analysis is proposed. Simulation results show that under step load and continuous load scenarios, the regional frequency deviation is controlled within a small range while maintaining the stability of the energy storage state of charge, thus resolving the contradiction between frequency regulation and SOC reconfiguration. Furthermore, a method for correcting the energy storage allocation factor using ultra-short-term load forecasting and fuzzy control is proposed for scenarios with continuous large load disturbances. Simulation results show that this method, after a second correction of the energy storage allocation factor under continuous large load disturbance scenarios, can balance the contradiction between current SOC maintenance and subsequent frequency regulation requirements, achieving efficient utilization of battery energy storage. This study addresses the previous need to develop reconfiguration methods to ensure the maintenance of the energy storage's State of Charge (SOC). Building upon this, considering that continuous energy storage output may cause the SOC to exceed limits under continuous large load disturbances, a secondary correction method for the energy storage power allocation factor based on ultra-short-term load forecasting and fuzzy control is proposed. This method can detect load change trends in multiple scenarios in advance and perform secondary correction on the power allocation factor, enabling the energy storage to reserve sufficient capacity to cope with continuous large load disturbances.

[0133] Example 2

[0134] A readable storage medium storing a computer program that, when invoked by a processor, performs the steps of the method described above.

[0135] Example 3

[0136] An electronic terminal includes a processor and a memory, the memory storing a computer program, the processor invoking the computer program to perform the steps of the method described above.

[0137] It should be understood that, in the embodiments of the present invention, the processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.

[0138] The readable storage medium is a computer-readable storage medium, which can be an internal storage unit of the controller described in any of the foregoing embodiments, such as the controller's hard drive or memory. The readable storage medium can also be an external storage device of the controller, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the controller. Further, the readable storage medium can include both the controller's internal storage unit and external storage devices. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium can also be used to temporarily store data that has been output or will be output.

[0139] Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0140] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0141] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A secondary frequency regulation method for battery energy storage based on dynamic allocation factors, characterized in that, include: S1: Obtain the sensitivity factor of the regional frequency deviation control model and the load fluctuation of the ultra-short-term load forecast; S2: Use the sensitivity factor as a feedback signal to adjust the magnitude of the energy storage allocation factor in real time according to the control strategy; S3: Determine whether the load fluctuation of the ultra-short-term load forecast meets the preset conditions for correcting the energy storage allocation factor: if the preset conditions are not met, no correction is performed; if the preset conditions are met, the correction amount of the energy storage allocation factor is obtained through the fuzzy correction strategy, and then the corrected energy storage allocation factor is obtained to perform secondary frequency regulation. In S2, the control strategy is specifically as follows: in, The adjusted energy storage allocation factor; The preset maximum energy storage allocation factor; This is the preset minimum energy storage allocation factor; The sensitivity factor was calculated at the previous sampling time; t is the current time. One sampling period; The final energy storage allocation factor obtained in S3 Represented as: in, The preset maximum energy storage allocation factor; The preset minimum energy storage allocation factor; The sensitivity factor was calculated at the previous sampling time; t is the current time. One sampling period; This represents the load fluctuations in ultra-short-term load forecasting. This indicates the maximum allowable disturbance value of the load within the area; This represents the correction amount of the energy storage allocation factor obtained through the fuzzy correction strategy; The specific process of the fuzzy correction strategy in S3 is as follows: The energy storage allocation factor at the current sampling time and the load fluctuation of the ultra-short-term load forecast are used as the input of the fuzzy controller, and the correction of the energy storage allocation factor is used as the output of the fuzzy controller. A fuzzy subset of the energy storage allocation factor within a preset time is set; a fuzzy subset of the load fluctuation of the ultra-short-term forecast within a preset time is set; a fuzzy subset of the correction is set; a membership function is set; and the correction is obtained according to the input of the fuzzy controller and the fuzzy control rules. The fuzzy subset of the energy storage allocation factor within a preset time period is set to two dimensions: {NB(0.3), PB(0.9)}. The fuzzy subset of the ultra-short-term predicted load fluctuation within a preset time period is set to seven dimensions: {NB(negative large), NM(negative medium), NS(negative small), ZO(zero), PS(positive small), PM(positive medium), PB(positive large)}. The fuzzy subset of the correction amount is set to five dimensions: {NM(0), NS(0.15), ZO(0.3), PS(0.45), PM(0.6)}.

2. The battery energy storage secondary frequency regulation method based on dynamic allocation factor according to claim 1, characterized in that, The specific regional frequency deviation control model is as follows: in, For regional power deviation; Power is exchanged for inter-regional communication lines; This is the frequency deviation coefficient; This refers to the regional frequency deviation. This is the total output of the conventional unit for frequency regulation; For battery energy storage frequency regulation total output; This is the allocation factor for battery energy storage in the regional frequency deviation signal; This is the allocation factor for conventional generating units in the regional frequency deviation signal.

3. The battery energy storage secondary frequency regulation method based on dynamic allocation factor according to claim 1, characterized in that, The membership function adopts the Mamdani type triangular membership function.

4. The battery energy storage secondary frequency regulation method based on dynamic allocation factor according to claim 1, characterized in that, The fuzzy control rule is specifically as follows: If energy storage allocation factor The load fluctuation is PB, and the ultra-short-term load forecast is... When it is NB, the correction amount is NS; If energy storage allocation factor The load fluctuation is PB, and the ultra-short-term load forecast is... When the value is NM, the correction amount is ZO; If energy storage allocation factor The load fluctuation is PB, and the ultra-short-term load forecast is... When the value is NS, the correction amount is PS; If energy storage allocation factor The load fluctuation is PB, and the ultra-short-term load forecast is... When the value is ZO, the correction amount is PM; If energy storage allocation factor The load fluctuation is PB, and the ultra-short-term load forecast is... When the value is PS, the correction amount is PS; If energy storage allocation factor The load fluctuation is PB, and the ultra-short-term load forecast is... When it is PM, the correction amount is ZO; If energy storage allocation factor The load fluctuation is PB, and the ultra-short-term load forecast is... When the value is PB, the correction amount is NS; If energy storage allocation factor For NB, and the load fluctuation of ultra-short-term load forecast. When it is NB, the correction amount is NM; If energy storage allocation factor For NB, and the load fluctuation of ultra-short-term load forecast. When the value is NM, the correction amount is NM; If energy storage allocation factor For NB, and the load fluctuation of ultra-short-term load forecast. When the value is NS, the correction amount is NM; If energy storage allocation factor For NB, and the load fluctuation of ultra-short-term load forecast. When the value is ZO, the correction amount is NM; If energy storage allocation factor For NB, and the load fluctuation of ultra-short-term load forecast. When the value is PS, the correction amount is NM; If energy storage allocation factor For NB, and the load fluctuation of ultra-short-term load forecast. When it is PM, the correction amount is NM; If energy storage allocation factor For NB, and the load fluctuation of ultra-short-term load forecast. When the value is PB, the correction amount is NM.

5. A readable storage medium, characterized in that: A computer program is stored, which, when invoked by a processor, performs the steps of the method according to any one of claims 1-4.

6. An electronic terminal, characterized in that: It includes a processor and a memory, the memory storing a computer program, the processor calling the computer program to perform the steps of the method according to any one of claims 1-4.

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