Flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling method

Abstract

The application provides a flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling method, comprising: collecting multi-mode signals in parallel, and fusing and packaging the multi-mode signals into a unified time sequence multi-mode data vector. The multi-mode data vector is preprocessed and normalized to obtain a preprocessing sequence, and multi-dimensional grid-connected features are extracted from the preprocessing sequence. A feature correlation graph is constructed based on the multi-dimensional grid-connected features, and the node risk of the feature correlation graph is fused through a linear diffusion model to determine a grid-connected readiness score. The grid-connected readiness score is mapped to a preset interval to determine a ramp start time window. Based on the ramp start time window, a ramp parameterization process is performed to construct a power step sequence, and a plurality of control packets issuing instructions according to the power steps are generated according to the power step sequence. The method generates a gradual grid-connected power ramp to realize phased injection of short-circuit current and avoid one-time large current impact.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of distributed energy and micro-grid technology, and more particularly relates to a multi-mode cross data fusion scheduling method for a flywheel and battery hybrid energy storage system. BACKGROUND

[0002] With the rapid development of distributed energy and micro-grid technology, off-grid micro-grid plays an increasingly important role in grid maintenance, remote area power supply and emergency rescue scenarios. In order to better smooth the switching between micro-grid and public grid, autonomous energy storage system becomes a key support. Flywheel energy storage can instantly absorb or release large power impact due to its millisecond power response, super long cycle life and high frequency energy release characteristics; lithium battery takes the task of continuous energy balance with high energy density and minute-level endurance. The mixed network of the two can be "fast-slow" complementary: flywheel responds to transient impact of grid connection, battery completes subsequent steady-state compensation, and together guarantees the smooth transition of voltage, frequency and power of micro-grid in the process of switching and grid connection. The multi-mode cross data fusion scheduling method further realizes the gradual power ramp injection and absorption through the joint evaluation of grid frequency, bus phase, flywheel power, SOC and thermal management state, and provides intelligent protection for micro-grid grid connection.

[0003] Currently, in the switching and grid connection moment of micro-grid island and main grid, the existing technology includes:

[0004] (1) Fixed dead zone soft start: by setting a fixed power dead zone for grid connection, the current impact of the moment of closing is reduced, but since the dead zone parameter is difficult to adjust online once it is set, it cannot take into account the impact amplitude difference caused by different bus impedance and load changes;

[0005] (2) Static reactive power / active power compensation device: STATCOM, SVC and other devices can provide continuous reactive power support and partial active power balance, but their response speed is mostly in tens to hundreds of milliseconds, which is difficult to cover the millisecond-level over-limit short-circuit pulse in micro-grid grid connection;

[0006] (3) Single energy storage unit isolated buffer: pure flywheel or pure battery system respectively undertakes impact or endurance, but a single system is difficult to meet the high-rate short-time and long-period energy demand at the same time, and lacks multi-source data fusion scheduling capability, and cannot dynamically segment manage pulse amplitude and duration.

[0007] From the above prior art, it can be seen that the existing scheme either responds too slowly, or the energy capacity is insufficient, or it lacks segmented scheduling logic for grid-connected transient impact, and it is difficult to balance the pulse suppression effect and energy storage unit health maintenance. The data acquisition and fusion evaluation technology of multi-mode cross is still in the exploratory stage, and there is a lack of mature solutions with high maturity, low integration difficulty and adaptability to the variable grid-connected environment of microgrid. Therefore, at the moment of microgrid island switching and grid connection, due to the phase and frequency deviation between the grid and the bus, the short-circuit current peak generated by the closing of the bus in an instant far exceeds the rated capacity. If not through the multi-mode cross fusion scheduling of the flywheel and the battery, the phased progressive power injection and absorption, the over-limit pulse will tear the bus fuse and trigger the protection trip, resulting in the loss of power of the entire microgrid. SUMMARY

[0008] In order to solve the problems in the prior art, the purpose of the present application is to solve the above-mentioned defects, and a multi-mode cross data fusion scheduling method for a flywheel and battery hybrid energy storage system is proposed.

[0009] The technical scheme adopted by the present application is as follows.

[0010] The first aspect of the present application discloses a multi-mode cross data fusion scheduling method for a flywheel and battery hybrid energy storage system, the method comprising:

[0011] Parallelly acquiring multi-mode signals, and fusing and packaging the multi-mode signals into a unified time sequence multi-mode data vector;

[0012] Preprocessing and normalizing the multi-mode data vector to obtain a preprocessing sequence, and extracting multi-dimensional grid-connected features from the preprocessing sequence;

[0013] Constructing a feature correlation graph based on the multi-dimensional grid-connected features, and fusing the node risk of the feature correlation graph through a linear diffusion model to determine a grid-connected readiness score;

[0014] Mapping the grid-connected readiness score to a preset interval to determine a ramp start time window;

[0015] Based on the ramp start time window, performing ramp parameterization processing to construct a power ladder sequence, and generating a plurality of control packets according to the power ladder sequence according to the power ladder sequence;

[0016] The multi-mode signals are the bus frequency and phase signals on the public AC bus, the flywheel output power of each flywheel energy storage converter AC side, the battery state of charge and maximum discharge power, and the cooler output power. The upper limit value of the preset interval is the longest ramp start delay time, and the lower limit value is the shortest ramp start delay time.

[0017] Further, the parallel acquisition of multi-mode signals, and the fusion and packaging of the multi-mode signals into a unified time sequence multi-mode data vector, comprise:

[0018] A frequency transmitter and a synchronous phase measurement unit are arranged on the public AC bus, and a synchronous sampling method is used to acquire the bus frequency and phase signals with time stamps in combination with a GPS clock;

[0019] A voltage sensor and a current sensor are arranged on the AC side of each flywheel energy storage converter, and a power calculation formula is executed by a power calculation unit to acquire the flywheel output power with time stamps.

[0020] Further, the parallel acquisition of multi-mode signals, and the fusion and packaging of the multi-mode signals into a unified time sequence multi-mode data vector, further comprise:

[0021] The SOC is updated in real time in the battery management unit by Coulomb counting, and the maximum discharge power of the battery is estimated based on a battery temperature and internal resistance lookup table, and is acquired by being issued through a CAN or EtherCAT bus in combination with the battery state of charge and the maximum discharge power;

[0022] Flow meters and temperature difference sensors are arranged in the cooling circuits of the flywheel cabins and the battery cabinets to calculate the cooling power in real time, and the cooler output power is determined based on the cooling power;

[0023] The main control machine is used to synchronize the clocks of the multi-mode signals, and the multi-mode signals are resampled and interpolated to be aligned at a preset frequency, and the multi-mode signals are fused and packaged to obtain the multi-mode data vector.

[0024] Further, the preprocessing and normalization of the multi-mode data vector to obtain a preprocessing sequence, and the extraction of multi-dimensional grid-connected features from the preprocessing sequence, comprise:

[0025] The central control unit is used to perform mutation threshold testing on each component in the multi-mode data vector to mark and remove abnormal data, and to perform linear interpolation to fill in missing values;

[0026] The multi-mode data vector after the abnormal data and the missing value filling are subjected to noise filtering and smoothing processing, and are resampled using a unified time grid to normalize the multi-mode data vector to obtain a normalized preprocessing sequence.

[0027] Further, the preprocessing and normalization of the multi-mode data vector to obtain a preprocessing sequence, and the extraction of multi-dimensional grid-connected features from the preprocessing sequence, further comprise:

[0028] The bus frequency sequence is extracted from the preprocessing sequence, and the frequency change rate is calculated at a fixed time interval in the real-time processor to obtain a frequency change rate sequence.

[0029] extracting a phase signal sequence from the pre-processing sequence, and calculating a phase difference absolute value of each sampling point in the phase signal sequence to construct an absolute phase deviation sequence.

[0030] Further, the pre-processing and normalization of the multi-mode data vector to obtain a pre-processing sequence, and extracting a multi-dimensional grid-connected feature from the pre-processing sequence, further comprises:

[0031] extracting a flywheel output power sequence from the pre-processing sequence, and calculating a flywheel output power increment through a controller at the same time interval;

[0032] extracting a battery state of charge and maximum discharge power sequence from the pre-processing sequence, and fusing the battery state of charge and maximum discharge power in the BMS to obtain a battery remaining available power indicator;

[0033] extracting a cooler output power sequence from the pre-processing sequence, and calculating a cooling margin coefficient according to the rated refrigeration power of the cooling unit.

[0034] Further, the constructing a feature correlation graph based on the multi-dimensional grid-connected feature, and fusing the node risk of the feature correlation graph through a linear diffusion model to determine a grid-connected readiness score, comprises:

[0035] constructing a multi-dimensional feature vector based on the multi-dimensional grid-connected feature, and perturbing each dimension feature in a microgrid digital simulation platform to determine a weight coefficient corresponding to each dimension feature;

[0036] calculating an initial risk of each dimension feature according to a set threshold corresponding to each dimension feature to construct an initial risk vector, and constructing the feature correlation graph based on the weight coefficient and the initial risk vector.

[0037] Further, the constructing a feature correlation graph based on the multi-dimensional grid-connected feature, and fusing the node risk of the feature correlation graph through a linear diffusion model to determine a grid-connected readiness score, further comprises:

[0038] calling the linear diffusion model to iteratively update the initial risk of each dimension feature, and outputting a fused node risk after convergence;

[0039] calculating a total risk index based on the fused node risk corresponding to each dimension feature, and calculating the grid-connected readiness score according to the total risk index;

[0040] wherein the total risk index is a weighted average of the fused node risk corresponding to each dimension feature.

[0041] Further, the slope parameterization processing based on the slope start time window is performed to construct a power step sequence, and a plurality of control packets according to the power step sequence are generated according to the power step sequence, including:

[0042] The bus rated power capacity is acquired, the rated power peak upper limit is calculated based on the grid-connected readiness score and the bus rated power capacity, and the rated power peak upper limit is the product of the grid-connected readiness score and the bus rated power capacity.

[0043] The ratio of the primary grid-connected injection power to the rated power peak upper limit is set as the primary slope amplitude ratio, and the total number of slopes is calculated in combination with the sampling period to perform slope parameterization processing.

[0044] Further, the slope parameterization processing based on the slope start time window is performed to construct a power step sequence, and a plurality of control packets according to the power step sequence are generated according to the power step sequence, including:

[0045] The primary power is calculated based on the primary slope amplitude ratio and the rated power peak upper limit, and the primary time length and the primary step number are calculated according to the slope start time window;

[0046] The power increment of each step in the remaining slope steps other than the primary step number is calculated, and a step is generated based on the power increment of each step in the remaining slope steps to construct the power step sequence according to the generated step;

[0047] Wherein, each power step in the power step sequence corresponds to a control packet of a step downlink instruction.

[0048] The second aspect of the application discloses a flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling system, which is used to realize the flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling method of any one of the first aspect, and the system comprises:

[0049] The data acquisition and fusion module is used for acquiring multi-mode signals in parallel, and fusing and packaging the multi-mode signals into a unified time sequence multi-mode data vector;

[0050] The feature extraction module is used for preprocessing and normalizing the multi-mode data vector to obtain a preprocessing sequence, and extracting multi-dimensional grid-connected features from the preprocessing sequence;

[0051] The grid-connected readiness score module is used for constructing a feature correlation graph based on the multi-dimensional grid-connected features, and fusing the node risk of the feature correlation graph through a linear diffusion model to determine the grid-connected readiness score;

[0052] A slope window determination module is configured to map the grid-connected readiness score to a preset interval to determine a slope start time window;

[0053] An instruction issuing module is configured to perform slope parameterization processing based on the slope start time window to construct a power step sequence, and generate a plurality of control packets according to the power step sequence, wherein the control packets are configured to issue instructions according to power steps.

[0054] The multi-mode signal comprises bus frequency and phase signals on a common AC bus, flywheel output power on an AC side of each flywheel energy storage converter, battery state of charge and maximum discharge power, and cooler output power. The upper limit of the preset interval is a longest slope start delay time, and the lower limit is a shortest slope start delay time.

[0055] The third aspect of the present application discloses a terminal comprising a processor and a storage medium.

[0056] The processor is configured to operate according to the instructions to perform the steps of the method of the first aspect.

[0057] The fourth aspect of the present application discloses a computer-readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the steps of the method of the first aspect.

[0058] Compared with the prior art, the present application has the following advantages:

[0059] (1) The present application collects multi-mode signals comprising bus frequency and phase signals on a common AC bus, flywheel output power on an AC side of each flywheel energy storage converter, battery state of charge and maximum discharge power, and cooler output power in parallel, and then fuses and packs the multi-mode signals into a multi-mode data vector with unified timing, thereby realizing the collection of multiple key signals related to grid-connected transients, and providing a data basis for subsequent multi-dimensional feature extraction and generation of a gradual grid-connected power ramp.

[0060] (2) The present application pre-processes and normalizes the collected multi-mode data vector, and extracts multi-dimensional grid-connected features from the obtained pre-processed sequence, thereby eliminating the bias caused by different physical dimensions and value ranges, making the subsequent direct weighted summation of risk indicators (such as the overall risk index) have comparability and physical meaning on data, and more conveniently reflecting the equal contribution of each dimensional feature to the overall risk under the equal weight assumption.

[0061] (3) The application constructs a feature correlation graph based on multi-dimensional grid connection characteristics, and fuses the node risks of the feature correlation graph through a linear diffusion model to determine a grid preparation degree score, and then determines a ramp starting time window, and finally performs ramp parameterization processing based on the ramp starting time window, constructs a power ladder sequence, and generates multiple control packages according to the power ladder issuing instructions. At the moment of microgrid island switching and grid connection, the designed power ladder control scheme is gradually executed, and the grid preparation degree score and the ramp window form a closed-loop scheduling together, through the multi-mode cross fusion scheduling of the flywheel and the battery, the phased progressive power injection and absorption, the problem that the super-limit pulse will tear the bus fuse and trigger the protection trip, resulting in the power loss of the entire microgrid, is solved. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 A flowchart of a flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling method provided by the application is shown in the figure.

[0063] Figure 2 A system architecture diagram of a flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling provided by the application is shown in the figure. DETAILED DESCRIPTION

[0064] The application will be further described below in conjunction with the drawings. The following examples are only used to more clearly illustrate the technical solutions of the application, and cannot be used to limit the protection scope of the application.

[0065] As shown in the figure, in one embodiment, a flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling method includes the following steps: Figure 1 Step S110, parallelly collect multi-mode signals, and fuse and package the multi-mode signals into a unified time sequence multi-mode data vector.

[0066] Among them, the multi-mode signal is the bus frequency and phase signal on the public AC bus, the flywheel output power of each flywheel energy storage converter AC side, the battery state of charge and maximum discharge power, and the cooler output power.

[0067] In some embodiments, the flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling method provided by the application includes the following steps:

[0068] Step S111, set a frequency transmitter and a synchronous phase measurement unit on the public AC bus, and collect bus frequency and phase signals with time stamps by using a synchronous sampling method combined with a GPS clock.

[0069]

[0070] ​Step S112, voltage sensors and current sensors are arranged on the AC side of each flywheel energy storage converter, and a power calculation formula is executed by a power calculation unit to collect flywheel output power with a time stamp.

[0071] In some embodiments, the flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling method provided by the application further comprises the following steps:

[0072] Step S113, the SOC is updated in real time in the battery management unit by the Coulomb counting method, and the maximum discharge power of the battery is estimated based on the battery temperature and internal resistance lookup table, combined with CAN or EtherCAT bus distribution, to obtain the battery state of charge and maximum discharge power.

[0073] Step S114, flow meters and temperature difference sensors are arranged in the cooling circuits of the flywheel cabin and the battery cabinet to calculate the cooling power in real time, and the cooler output power is determined based on the cooling power.

[0074] Step S115, the multi-mode signals are clock-synchronized by the host computer, and resampled and interpolated to align at a preset frequency, and the multi-mode signals are fused and packaged to obtain a multi-mode data vector.

[0075] In specific embodiments, the flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling method provided by the application comprises steps 1-4:

[0076] Step 1, parallel collection and preprocessing of multi-mode signals.

[0077] Comprising the following steps:

[0078] Step 1.1, bus frequency and phase signal collection.

[0079] Specifically, a high-precision frequency transmitter and a synchronous phase measurement unit are installed on the public AC bus, a synchronous sampling technology (sampling rate 1 kHz) is used, and an absolute time stamp is given to each measurement by a GPS clock, revealing the frequency difference and phase difference between the grid and the microgrid at the moment of grid connection, and providing a reference for subsequent slope grid connection judgment.

[0080] Step 1.2, flywheel output power measurement.

[0081] Specifically, high-precision voltage and current sensors (sampling rate 2 kHz) are installed on the AC side of each flywheel energy storage converter, a power calculation unit is configured to calculate the flywheel output power according to the formula power=voltage x current, and is time-stamped synchronously. This step quantifies the flywheel's absorption of the bus's electric capacity to determine the available buffer and response speed.

[0082] Step 1.3, battery state of charge and output capacity collection.

[0083] Specifically, the battery management system updates the SOC every second by coulomb counting method and estimates the maximum discharge power based on temperature and internal resistance lookup table, and issues the battery SOC value and the maximum available discharge power of the battery through the vehicle-level CAN or EtherCAT bus to evaluate the battery's continuous output capability and determine the time window and power share of the subsequent battery relay.

[0084] Step 1.4, state acquisition of thermal management system.

[0085] Specifically, flow meters and temperature difference sensors are arranged in the cooling circuits of the flywheel cabin and the battery cabinet, and the cooling power (flow x specific heat capacity x temperature difference) is calculated in real time, sampled at 100 Hz and returned to obtain the cooling power index of the cooler, which is used to determine the heat surplus during high-power pulse and avoid overheating.

[0086] Step 1.5, data fusion and packaging.

[0087] Specifically, the main control machine synchronizes the various dimensions of data collected in steps 1.1~1.4, then resamples and interpolates at a frequency of 100 Hz, and finally forms a 6×N matrix, where the rows correspond to signal types and the columns correspond to time points, i.e. a multi-modal data vector with unified time sequence. This step constructs a high-dimensional, multi-modal raw data set, providing comprehensive and synchronized input for subsequent grid connection shock buffering decisions.

[0088] The 6×N matrix formed is The expression is:

[0089] ;

[0090] In the formula, is the instantaneous frequency value of the bus at time t, is the phase value at time t, is the flywheel output power at time t, is the SOC value at time t, is the maximum available discharge power of the battery at time t, is the cooling power index at time t, T represents the transpose of the matrix, and N represents the total number of time stamps.

[0091] Step 1.6, outlier detection and elimination.

[0092] Specifically, the central control unit composed of FPGA and DSP applies the "mutation threshold" test based on upper and lower limits to each component in the multi-modal data vector after data fusion and packaging:

[0093] (1) The bus frequency exceeds 47-53 Hz;

[0094] (2) Phase mutation amplitude exceeds 10° / ms;

[0095] (3) Flywheel / battery power rate of change is greater than 50% / ms;

[0096] (4) Cooling power mutation exceeds ±30% / s.

[0097] If (1)-(4) are met, the data at the corresponding time is marked as abnormal data and removed.

[0098] At the same time, the linear interpolation of the previous or next moment of the detected missing value is filled to ensure the continuity of the data.

[0099] Step 1.7, noise filtering and smoothing.

[0100] Specifically, the sliding median filter is used for the DSP in the cabinet, the median filter with a window length of 5 points is used for the frequency and phase channels, and the weighted moving average with a window length of 7 points is used for the power and battery SOC, cooling power channels.

[0101] Step 1.8, uniform time grid resampling.

[0102] Specifically, the sampling period is set to 10ms in the main control server, and the data sampled at the original timestamp that is not an integer multiple of 10ms is linearly interpolated to the new timestamp that is an integer multiple of 10ms. The new timestamp after resampling is recorded.

[0103] Step 1.9, amplitude normalization and calibration.

[0104] Specifically, according to the system specifications, the maximum and minimum safe values of each signal are given, and each channel is mapped to the interval [0,1] according to its safe interval, outputting dimensionless signals. All calibration coefficients are saved in the control cabinet EEPROM, supporting online update.

[0105] In this embodiment, data preprocessing and normalization eliminate the bias caused by different physical dimensions and value ranges, making the subsequent direct weighted sum of risk indicators (e.g., overall risk index) mathematically comparable and physically meaningful, so that the equal contribution of each feature to the overall risk can be more conveniently reflected under the assumption of equal weight.

[0106] Step S120, pre-processing and normalization of the multi-mode data vector to obtain a pre-processed sequence, and extracting multi-dimensional grid-connected features from the pre-processed sequence.

[0107] ​​​​In some embodiments, the flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling method provided by the application specifically comprises the following steps in step S120:

[0108] Step S121, the central control unit carries out mutation threshold test on each component in the multi-mode data vector to mark and remove abnormal data, and carries out linear interpolation filling for missing values.

[0109] Step S122, after the multi-mode data vector with abnormal data and missing value filling is subjected to noise filtering and smoothing processing, and resampling is carried out by using a unified time grid, the multi-mode data vector is normalized to obtain a normalized preprocessing sequence.

[0110] In some embodiments, the flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling method provided by the application specifically further comprises the following steps in step S120:

[0111] Step S123, extracting the bus frequency sequence from the preprocessing sequence, and calculating the frequency change rate in the real-time processor according to the fixed time interval to obtain the frequency change rate sequence.

[0112] Step S124, extracting the phase signal sequence from the preprocessing sequence, and calculating the phase difference absolute value of each sampling point in the phase signal sequence to construct the absolute phase deviation sequence.

[0113] In some embodiments, the flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling method provided by the application specifically further comprises the following steps in step S120:

[0114] Step S125, extracting the flywheel output power sequence from the preprocessing sequence, and calculating the flywheel output power increment by the controller according to the same time interval.

[0115] Step S126, extracting the battery state of charge and maximum discharge power sequence from the preprocessing sequence, and fusing the battery state of charge and maximum discharge power in the BMS to obtain the battery remaining available power index.

[0116] Step S127, extracting the cooler output power sequence from the preprocessing sequence, and calculating the cooling margin coefficient according to the rated refrigeration power of the cooling unit.

[0117] In a specific embodiment, the flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling method provided by the application, step 2, key grid-connected feature extraction.

[0118] Comprises the following steps:

[0119] Step 2.1, frequency change rate Extraction.

[0120] Specifically, in the real-time processor, the normalized frequency value at the nth time interval is calculated according to the fixed time interval Calculation:

[0121] ;

[0122] In the formula, respectively, are the normalized frequency values at the nth and n-1 time intervals.

[0123] Then, the sampling processor compares the frequency slope at the threshold value (such as 0.02 / s) and records the frequency slope moment exceeding the threshold value. This step quantifies the frequency jump speed at the grid-connected moment and is used to evaluate the response speed of the system to suppress the short-circuit pulse.

[0124] Step 2.2, phase deviation quantification.

[0125] Specifically, the absolute value of the phase difference of each sampling point in the normalized phase difference sequence is calculated to obtain the corresponding non-negative value. In the phase measurement unit, when the non-negative value exceeds the set safety angle (such as 0.1, i.e., 36°), a high deviation flag is triggered, which can directly reflect the degree of phase asynchronization of the microgrid and the main grid voltage waveform and is used to determine the grid-connected phase tolerance.

[0126] Step 2.3, flywheel power increment calculation.

[0127] Specifically, the flywheel power increment is calculated by the controller according to the same time difference of the normalized flywheel power sequence. If the flywheel power increment is greater than 0.05 (corresponding to 5% rated power / 10ms), it is marked as "large power jump", which is used to capture the flywheel transient charge and discharge rate and evaluate the acceptable acceleration and deceleration amplitude in the slope grid connection.

[0128] Step 2.4, battery available power evaluation.

[0129] Specifically, in the BMS, the normalized SOC sequence and the SOC in the battery output power are combined with the nominal maximum output power (constant), i.e., multiplied by each other, to obtain the battery remaining available power index. When the battery remaining available power index is lower than 10% of the nominal maximum output power, the battery capacity is marked as insufficient.

[0130] Step 2.5, cooler heat surplus calculation.

[0131] Specifically, the cooling system rated refrigeration power is taken as the reference to calculate the cooling surplus coefficient , the expression is:

[0132] ;

[0133] wherein, denotes the nth timestamp in the normalized chiller output power sequence corresponding chiller output power.

[0134] If is lower than 0.2, a warning of thermal management approaching limit is given, which can be used to reflect the remaining cooling capacity of the thermal management system during grid-connection impact to ensure that the flywheel and battery do not reduce power due to overheating.

[0135] Finally, based on the frequency slope of the grid-connection transient state obtained in steps 2.1-2.5 , phase deviation , flywheel power jump , available battery power and cooling margin , a multi-dimensional grid-connection feature vector is constructed, which is represented as:

[0136] ;

[0137] Step S130, based on the multi-dimensional grid-connection feature, a feature correlation graph is constructed, and the node risk of the feature correlation graph is fused through a linear diffusion model to determine the grid-connection readiness score.

[0138] In some embodiments, the flywheel and battery hybrid energy storage system multi-modal cross data fusion scheduling method provided by the present application specifically comprises the following steps:

[0139] Step S131, based on the multi-dimensional grid-connection feature, a multi-dimensional feature vector is constructed, and each dimension of the feature is perturbed in the microgrid digital simulation platform to determine the weight coefficient corresponding to each dimension of the feature.

[0140] Step S132, the initial risk of each dimension of the feature is calculated according to the set threshold value corresponding to each dimension of the feature, to construct an initial risk vector, and a feature correlation graph is constructed based on the weight coefficient and the initial risk vector.

[0141] In some embodiments, the flywheel and battery hybrid energy storage system multi-modal cross data fusion scheduling method provided by the present application specifically further comprises the following steps:

[0142] Step S133, the initial risk of each dimension of the feature is iteratively updated by calling a linear diffusion model, and the fused node risk is output after convergence.

[0143] Step S134, the overall risk index is calculated based on the fused node risk corresponding to each dimension of the feature, and the grid-connection readiness score is calculated according to the overall risk index.

[0144] The overall risk index is the weighted average of the risks of the fusion nodes corresponding to each dimension of features.

[0145] Step S140: Map the grid connection readiness score to a preset interval to determine the ramp start time window.

[0146] The upper limit of the preset interval is the longest ramp start delay time, and the lower limit is the shortest ramp start delay time.

[0147] In a specific embodiment, the multi-mode cross-data fusion scheduling method for a flywheel and battery hybrid energy storage system provided by the present invention includes step 3, multi-mode cross-fusion evaluation.

[0148] Includes the following steps:

[0149] Step 3.1: Construct a feature association graph.

[0150] Specifically, firstly, based on the grid connection feature vector obtained in step 2... Constructing a five-dimensional grid-connected feature vector :

[0151] ;

[0152] Again Each component in the graph is used as a feature node to construct a corresponding node set, and then the influence weight of each node is determined based on empirical values. , representing the strength of the influence of node i on node j. In this example, the influence weight can be determined by simulating sensitivity. In the microgrid digital simulation platform, a small perturbation (±5%~10%) is applied to each characteristic parameter, and the rate of change in response to other characteristics or overall grid-connected impact indicators (such as peak current) is observed. Parameters with higher sensitivity are assigned greater weight. Alternatively, hardware-in-the-loop (HIL) testing can be used: on a flywheel-battery system test bench, a phase or frequency step is actually applied, and the changes in power and thermal management for each path are recorded. The quantification effect is compared to determine... .

[0153] After determining the influence weights between each node, the risk of each feature is calculated using the threshold method. The expression is:

[0154] ;

[0155] In the formula, Let be the security threshold corresponding to the i-th feature (i.e., the i-th element / node). For the limit value, express The first in There are elements, among which... Therefore, the initial risk .

[0156] Step 3.2, risk propagation fusion.

[0157] Specifically, according to the correlation graph obtained in step 3.1 and the initial risk, the linear diffusion model is used to iteratively update the risk, and the expression is:

[0158] ;

[0159] In the formula, is the node risk of the t-th iteration, is the attenuation factor, and 0.85 is taken; is the degree matrix, and the diagonal element value of the degree matrix is the sum of the element values corresponding to the row of the weight matrix; is the initial risk, and the iteration converges when , and the fusion node risk is output after convergence.

[0160] Step 3.3, comprehensive risk and grid connection preparation degree score.

[0161] Specifically, the weighted average value of the five-way risk in the fusion node risk is calculated, that is, the overall risk index , and then the grid preparation score is calculated according to the overall risk index : .

[0162] Among them, the average of the fused node risks can reflect the overall system risk in the closing transient state, and the grid preparation is the complement of it, the higher the value, the safer the system.

[0163] Step 3.4, slope start time window mapping.

[0164] Specifically, the grid preparation degree score obtained in step 3.3 is mapped to the interval [50ms, 500ms] to output the slope start time window , and the expression is:

[0165] ;

[0166] In the formula, , .

[0167] It should be noted that respectively represent the shortest ramp-up delay and the longest ramp-up delay. Since the short-circuit current pulse during the microgrid closing often reaches the peak value within milliseconds to tens of milliseconds. If the delay is too long, the bus and the protection device may experience multiple overshoots in an uncontrolled manner. Usually, 50 ms is taken as the shortest ramp-up delay, which can not only ensure that the control system completes signal acquisition, calculation and instruction issuance, but also quickly open the ramp schedule before the first short-circuit impact has not fully arrived, avoiding the impact amplitude exceeding the limit. In addition, 500 ms is within the upper limit range of the buffer window commonly used in UPS and flywheel-battery hybrid energy storage systems, which can smooth the transition and will not cause the main grid or load to affect the standby power generation or regrid operation due to too long isolation.

[0168] In the embodiment, according to the current security score, the flywheel and battery power stage injection / absorption can be provided with accurate ramp-up time length, which can ensure that the short-circuit pulse is borne step by step and instantaneous over-limit is avoided.

[0169] In step S150, the ramp parameterization processing is performed based on the ramp start time window to construct a power step sequence, and a plurality of control packets issuing instructions according to the power steps are generated according to the power step sequence.

[0170] In some embodiments, the flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling method provided by the present application specifically includes the following steps:

[0171] In step S151, the bus rated power capacity is obtained, the rated power peak upper limit is calculated based on the grid-connected preparation degree score and the bus rated power capacity, and the rated power peak upper limit is the product of the grid-connected preparation degree score and the bus rated power capacity.

[0172] In step S152, the ratio of the primary grid injection power to the rated power peak upper limit is set as the primary ramp amplitude ratio, the total number of steps is calculated in combination with the sampling period, and the ramp parameterization processing is performed.

[0173] In some embodiments, the flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling method provided by the present application specifically includes the following steps:

[0174] In step S153, the primary power is calculated based on the primary ramp amplitude ratio and the rated power peak upper limit, and the primary time length and the primary step number are calculated according to the ramp start time window.

[0175] In step S154, the power increment of each step in the remaining ramp steps except the primary step number is calculated, and the steps are generated based on the power increment of each step in the remaining ramp steps to construct a power step sequence according to the generated steps.

[0176] In the power step sequence, each power step corresponds to a control packet issuing an instruction of one step.

[0177] In a specific embodiment, the flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling method provided by the application comprises the following steps:

[0178] comprising the following steps:

[0179] Step 4.1, slope parameterization.

[0180] Specifically, first, according to the bus rated power capacity and the safety margin, the set power peak value upper limit is determined:

[0181] ;

[0182] In the formula, when , , high safety allows high amplitude grid connection.

[0183] It should be noted that is the maximum output (or energy absorption) power of the flywheel + battery hybrid energy storage system allowed at the grid connection moment, which is used to limit the highest power of the slope sequence and ensure that the bus rated capacity and the system limit can be tolerated.

[0184] Secondly, the proportion of the primary small amplitude grid injection power to the peak value upper limit is determined as , wherein the lower limit 0.05 (5% of the peak value) ensures that even a low score can have a minimum response, and the upper limit 0.2 (20% of the peak value) avoids excessive impact in the initial stage.

[0185] Finally, according to the fastest response ability of the control system, the value period is 10ms, so the total number of slopes is the ratio between the slope starting time window and 10ms.

[0186] Step 4.2, primary small amplitude grid power calculation.

[0187] Specifically, first, the primary power , the primary time length and the corresponding primary step length are calculated:

[0188] ;

[0189] ;

[0190] ;

[0191] In the formula, if =0.1, =500kW, then = 50kW, when = 200ms, = 20ms.

[0192] This step injects / absorbs power in small amplitude in a very short time, withstands the initial pulse, and gains buffer time for subsequent larger power cut-in.

[0193] Step 4.3, incremental power step generation.

[0194] Specifically, first, for the remaining ramp except the initial step, the number of steps is M = N- The power increased at each step is:

[0195] ;

[0196] Second, generate steps based on the power increased at each step of the remaining ramp:

[0197] ;

[0198] In the formula, represents the power increased at the i-th step, N represents the total number of steps of the ramp, and finally it is necessary to ensure .

[0199] This step gradually increases the power from a small amplitude to a peak value through equal-amplitude incremental power steps, which can not only smooth the transition, but also fully utilize the scoring and grid readiness.

[0200] Step 4.4, instruction issuing and closed-loop monitoring.

[0201] Specifically, first, generate the control package for each step in the step according to the incremental power steps obtained in step 4.3, denoted as:

[0202] ;

[0203] In the formula, represents the timestamp, represents the timestamp of grid connection, is the interval time for sending instructions regularly.

[0204] Then, send the first small-amplitude injection instruction {0, } to the flywheel and battery converter through the EtherCAT bus, and then send the power instructions in turn according to Synchronously collect the bus voltage and actual output power, and if a sudden change or protection action warning occurs, trigger an emergency stop or back up to the previous step of the ladder.

[0205] The step gradually executes the designed power step program, and the execution effect can be ensured through real-time monitoring, and the grid-connected preparation degree score and the ramp window form a closed-loop scheduling together.

[0206] The flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling system provided by the application is described below, and the flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling system described below can be correspondingly referred to the flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling method described above.

[0207] As shown in Figure 2 In one embodiment, a flywheel and battery hybrid energy storage system multi-mode cross data fusion scheduling system includes a data acquisition and fusion module, a feature extraction module, a grid-connected preparation degree scoring module, a ramp window determination module, and an instruction issuing module.

[0208] The data acquisition and fusion module is used to acquire multiple mode signals in parallel, and fuse and package the multiple mode signals into a unified time sequence multi-mode data vector.

[0209] The feature extraction module is used to preprocess and normalize the multi-mode data vector to obtain a preprocessed sequence, and extract multi-dimensional grid-connected features from the preprocessed sequence.

[0210] The grid-connected preparation degree scoring module is used to construct a feature correlation graph based on the multi-dimensional grid-connected features, and fuse the node risk of the feature correlation graph through a linear diffusion model to determine the grid-connected preparation degree score.

[0211] The ramp window determination module is used to map the grid-connected preparation degree score to a preset interval to determine a ramp start time window.

[0212] The instruction issuing module is used to perform ramp parameterization processing based on the ramp start time window to construct a power step sequence, and generate a plurality of control packets according to the power step sequence according to the power step issuing instructions.

[0213] The multi-mode signal is the bus frequency and phase signal on the public AC bus, the flywheel output power of each flywheel energy storage converter AC side, the battery state of charge and maximum discharge power, and the cooler output power. The upper limit value of the preset interval is the longest ramp start delay time, and the lower limit value is the shortest ramp start delay time.

[0214] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium having computer readable program instructions loaded thereon for causing a processor to implement various aspects of the present disclosure.

[0215] Computer readable storage media can be tangible storage media which can retain and store instructions for use by an instruction execution device. Computer readable storage media can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer readable storage media include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0216] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0217] Computer readable program instructions for carrying out operations of the present disclosure can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0218] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0219] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other data storage device. When the computer readable program instructions are loaded into the computer and other programmable data processing apparatus, a series of operational steps are implemented that provide processes such that the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0220] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

Claims

1. A multi-mode cross-data fusion scheduling method for a flywheel and battery hybrid energy storage system, characterized in that, The method includes: Parallel acquisition of multi-mode signals, and fusion and packaging of the multi-mode signals into a unified time-series multi-mode data vector; the multi-mode signals are the bus frequency and phase signals on the common AC bus, the flywheel output power on the AC side of each flywheel energy storage converter, the battery state of charge and maximum discharge power, and the cooler output power; The multi-mode data vector is preprocessed and normalized to obtain a preprocessed sequence, and multi-dimensional grid-connected features are extracted from the preprocessed sequence. Based on the multidimensional grid connection characteristics, a feature association graph is constructed, and the node risks of the feature association graph are fused through a linear diffusion model to determine the grid connection readiness score. The grid connection readiness score is mapped to a preset interval to determine the ramp start time window; the upper limit of the preset interval is the longest ramp start delay time, and the lower limit is the shortest ramp start delay time. Based on the slope start time window, slope parameterization is performed to construct a power step sequence, and multiple control packets are generated according to the power step sequence to issue commands according to the power step.

2. The multi-mode cross-data fusion scheduling method for a flywheel and battery hybrid energy storage system according to claim 1, characterized in that, The parallel acquisition of multi-mode signals, and the fusion and packaging of the multi-mode signals into a unified time-series multi-mode data vector, includes: A frequency transmitter and a synchronous phase measurement unit are installed on the common AC bus, and the bus frequency and phase signals with timestamps are collected by using a synchronous sampling method combined with a GPS clock. Voltage and current sensors are installed on the AC side of each flywheel energy storage converter. The power calculation unit executes the power calculation formula and collects the flywheel output power with timestamps.

3. The multi-mode cross-data fusion scheduling method for a flywheel and battery hybrid energy storage system according to claim 2, characterized in that, The parallel acquisition of multi-mode signals, and the fusion and packaging of the multi-mode signals into a unified time-series multi-mode data vector, further includes: The SOC is updated in real time in the battery management unit by coulomb measurement and the maximum discharge power of the battery is estimated by looking up a table based on the battery temperature and internal resistance. Combined with the CAN or EtherCAT bus, the state of charge and maximum discharge power of the battery are obtained. Flow meters and temperature difference sensors are installed in the cooling circuits of the flywheel nacelle and battery cabinet to calculate the cooling power in real time and determine the output power of the cooler based on the cooling power. The host computer synchronizes the multi-mode signal with a clock, resamples and interpolates it at a preset frequency, and fuses and packages the multi-mode signal to obtain the multi-mode data vector.

4. The multi-mode cross-data fusion scheduling method for a flywheel and battery hybrid energy storage system according to claim 1, characterized in that, The preprocessing and normalization of the multi-mode data vector to obtain a preprocessed sequence, and the extraction of multi-dimensional grid-connected features from the preprocessed sequence, include: The central control unit performs a mutation threshold test on each component in the multimodal data vector to mark and remove outlier data, and performs linear interpolation to fill in missing values. The multi-modal data vector after the abnormal data and missing values ​​are filled is subjected to noise filtering and smoothing, and then resampled using a unified time grid to normalize the multi-modal data vector, resulting in a normalized preprocessed sequence.

5. The multi-mode cross-data fusion scheduling method for a flywheel and battery hybrid energy storage system according to claim 4, characterized in that, The step of preprocessing and normalizing the multi-mode data vector to obtain a preprocessed sequence, and extracting multi-dimensional grid-connected features from the preprocessed sequence, further includes: The bus frequency sequence is extracted from the preprocessed sequence, and the frequency change rate is calculated in the real-time processor at fixed time intervals to obtain the frequency change rate sequence. The phase signal sequence is extracted from the preprocessed sequence, and the absolute value of the phase difference at each sampling point in the phase signal sequence is calculated to construct an absolute phase deviation sequence.

6. The multi-mode cross-data fusion scheduling method for a flywheel and battery hybrid energy storage system according to claim 5, characterized in that, The step of preprocessing and normalizing the multi-mode data vector to obtain a preprocessed sequence, and extracting multi-dimensional grid-connected features from the preprocessed sequence, further includes: The flywheel output power sequence is extracted from the preprocessed sequence, and the flywheel output power increment is calculated by the controller at the same time intervals. The battery state of charge and maximum discharge power sequence are extracted from the preprocessed sequence, and the battery state of charge and maximum discharge power are fused in the BMS to obtain the remaining usable power index of the battery. Extract the cooler output power sequence from the preprocessed sequence and calculate the cooling margin coefficient based on the rated cooling power of the cooling unit.

7. The multi-mode cross-data fusion scheduling method for a flywheel and battery hybrid energy storage system according to claim 1, characterized in that, The process of constructing a feature association graph based on the multi-dimensional grid connection characteristics, and fusing the node risks of the feature association graph using a linear diffusion model to determine the grid connection readiness score, includes: Based on the multi-dimensional grid connection characteristics, a multi-dimensional feature vector is constructed, and the features of each dimension are perturbed in the microgrid digital simulation platform to determine the weight coefficients corresponding to each dimension. The initial risk of each feature is calculated according to the set threshold corresponding to each feature, so as to construct an initial risk vector, and the feature association graph is constructed based on the weight coefficient and the initial risk vector.

8. The multi-mode cross-data fusion scheduling method for a flywheel and battery hybrid energy storage system according to claim 7, characterized in that, The step of constructing a feature association graph based on the multi-dimensional grid connection characteristics, and fusing the node risks of the feature association graph using a linear diffusion model to determine the grid connection readiness score, further includes: The linear diffusion model is invoked to iteratively update the initial risk of each feature dimension, and the risk of the fusion node is output after convergence. The overall risk index is calculated based on the risk of the fusion node corresponding to each dimension of the characteristics, and the grid connection readiness score is calculated based on the overall risk index. The overall risk index is the weighted average of the risks of the fusion nodes corresponding to each of the features.

9. The multi-mode cross-data fusion scheduling method for a flywheel and battery hybrid energy storage system according to claim 1, characterized in that, The process of parameterizing the ramp based on the ramp start time window to construct a power step sequence, and generating multiple control packets according to the power step sequence to issue commands according to the power step, includes: Obtain the rated power capacity of the bus, and calculate the upper limit of the rated power peak based on the grid connection readiness score and the rated power capacity of the bus, wherein the upper limit of the rated power peak is the product of the grid connection readiness score and the rated power capacity of the bus; The ratio of the primary grid-injected power to the upper limit of the rated power peak is set as the primary ramp amplitude ratio, and the total number of ramp steps is calculated in combination with the sampling period to perform ramp parameterization processing.

10. The multi-mode cross-data fusion scheduling method for a flywheel and battery hybrid energy storage system according to claim 9, characterized in that, The step of performing slope parameterization processing based on the slope start time window to construct a power step sequence, and generating multiple control packets according to the power step sequence to issue commands according to the power step, further includes: The primary power is calculated based on the primary ramp amplitude ratio and the upper limit of the rated power peak, and the primary duration and primary steps are calculated based on the ramp start time window. The power increment of each step in the remaining ramp steps other than the initial steps is taken, and a step is generated based on the power increment of each step in the remaining ramp steps, so as to construct the power step sequence according to the generated step. In the power ladder sequence, each power ladder corresponds to a control packet that issues a command at one step.

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