New energy high proportion area voltage stability hybrid energy storage support method and system

Abstract

The application discloses a new energy high-occupancy regional voltage stability hybrid energy storage support method and system, relates to the technical field of voltage stability, and provides a high-quality data basis for subsequent analysis by vectorizing power grid data and generating state sequences and new energy output characteristics, can accurately identify weak links and risk points of the power grid in advance, converts abstract stability problems into specific quantitative indexes, analyzes voltage faults based on the accurate demand indexes and real-time energy storage states, and dynamically generates optimal energy storage support strategies according to the analysis results of the voltage faults to guide the collaborative charging and discharging of the hybrid energy storage system. This not only can quickly suppress new energy power fluctuation, actively maintain node voltage stability, and effectively prevent voltage out-of-limit accidents, but also fully plays the comprehensive advantages of the hybrid energy storage in response speed and duration.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of voltage stabilization, in particular to a new energy high-occupancy regional voltage stabilization hybrid energy storage support method and system. BACKGROUND

[0002] With large-scale access of intermittent and volatile new energy such as wind power and photovoltaic to the power grid, the randomness and anti-peaking characteristics of the output of the new energy pose a serious challenge to the voltage stability of the power system, especially local nodes. The traditional power grid relies on synchronous generators to provide inertia support and voltage regulation, while new energy units are connected to the grid through power electronic devices. When the output of new energy fluctuates sharply, it is easy to cause voltage out-of-limit of key nodes of the power grid, and even cause cascading failures, threatening power supply safety. Therefore, an integrated solution that can deeply integrate the operation state of the power grid, the output characteristics of new energy and the control of the energy storage system is urgently needed.

[0003] The prior art such as the invention patent application with the publication number CN117220296A discloses a power grid voltage stability analysis method and system considering new energy control switching, which belongs to the technical field of power system stability analysis. The present application can be used for voltage stability analysis of power systems containing new energy, and can indicate the voltage instability risk of the power system under the action of new energy control switching, including but not limited to voltage instability or new energy off-grid risk caused by new energy continuous ride-through without recovery, voltage and power oscillation or new energy off-grid risk caused by new energy repeated entry / exit ride-through, etc., providing guidance for power system planning, operation mode arrangement, stability control, etc.

[0004] For the above-mentioned scheme, the present inventors find that the above-mentioned technology at least has the following technical problems: 1. There is currently a lack of composite indicators that integrate current state, historical trends and future forecasts, and there is no way to convert abstract "voltage stability demand" into specific, quantifiable power support values, which cannot make the control target very clear, cannot identify the demand before the voltage actually falls in danger, and thus cannot actively call on energy storage resources for intervention, and cannot avoid the deterioration of the situation.

[0005] 2. There is currently a lack of an intelligent execution system that optimizes strategy coordination and precisely closes the control loop, which cannot achieve seamless connection from decision-making to action. Not only can it not respond to the voltage support demand of the power grid, but it also cannot deeply integrate the differentiated state and characteristics of hybrid energy storage, cannot achieve the best allocation of power and energy, and cannot take into account the service life and response speed of the equipment; by not being able to take advantage of the synergy of hybrid energy storage, it cannot achieve "fast and slow combination, strengths and weaknesses", cannot improve support efficiency and economy. Lack of construction of "perception-decision-making-execution-correction" automated closed loop, not only cannot enhance the resilience and stability of the power grid in response to disturbances, but also cannot effectively prolong the service life of the energy storage equipment by optimizing the charging and discharging strategy, and cannot achieve the dual improvement of power grid safety and asset efficiency. SUMMARY

[0006] In view of the above technical deficiencies, the purpose of the present application is to provide a new energy high proportion area voltage stability hybrid energy storage support method and system.

[0007] To solve the above technical problems, the present application adopts the following technical solutions: the present application provides a new energy high proportion area voltage stability hybrid energy storage support method in the first aspect, which comprises the following steps: step one, vector generation: based on the pre-acquired power grid data and new energy output data, the state vector of the power grid and the output vector of the new energy are generated.

[0008] Step two, data generation: based on the state vector of the power grid, the state sequence of the power grid is generated; and based on the output vector of the new energy, the new energy output sequence is generated, and then the new energy output feature is generated.

[0009] Step three, demand index generation: based on the state sequence and the new energy output feature, the voltage stability demand index is generated.

[0010] Step four, energy storage support strategy generation: based on the voltage stability demand index and the pre-acquired hybrid energy storage state data, the energy storage support strategy is generated.

[0011] Step five, hybrid energy storage device control: based on the energy storage support strategy, the hybrid energy storage device is controlled to perform charging and discharging operation.

[0012] Preferably, the power grid data includes voltage, current, electric power and power frequency; the new energy output data includes power output, frequency, temperature, pressure and efficiency.

[0013] Preferably, based on the pre-acquired power grid data and new energy output data, the state vector of the power grid and the output vector of the new energy are generated, including: based on the pre-acquired power grid data, using a preset state estimation algorithm to calculate the power grid state estimation value; and the power grid state estimation value is vector encoded to obtain the power grid state vector; based on the pre-acquired new energy output data, using a preset state estimation algorithm to calculate the new energy output estimation value; and the new energy output estimation value is vector encoded to obtain the new energy output vector.

[0014] Preferably, the new energy output feature includes basic statistical feature, volatility feature, intermittency and uncertainty feature, and time sequence and shape feature.

[0015] Preferably, the process of generating a power grid state sequence based on the power grid state vector and a new energy power output sequence based on the new energy power output vector, and then generating new energy power output features, includes: performing time series concatenation on the power grid state vector to form a power grid state matrix; processing the power grid state matrix using a preset feature dimensionality reduction algorithm to obtain the power grid state sequence; performing time series concatenation on the new energy power output vector to form a new energy power output matrix; processing the new energy power output matrix using a preset feature dimensionality reduction algorithm to obtain the new energy power output sequence and generate new energy power output features.

[0016] Preferably, generating a voltage stability demand index based on the state sequence and the new energy output characteristics includes: extracting a voltage stability sequence based on the state sequence; extracting volatility characteristics based on the new energy output characteristics; and calculating the voltage stability demand index using a voltage stability demand algorithm based on the voltage stability sequence and the volatility characteristics.

[0017] Preferably, the step of calculating the voltage stability demand index based on the voltage stability sequence and volatility characteristics using a voltage stability demand algorithm includes: calculating the voltage stability demand index using the formula... Determine the voltage stability requirement index , This is represented by the number corresponding to the sampling point. , This represents the total number of sampling points. Represented as the first Voltage values ​​at each sampling point This is a reference value for the voltage. Represented as the first The power fluctuation value at each sampling point This is expressed as the average value of the output fluctuation. This is represented by the weighting factor corresponding to the voltage. This represents the weighting factor corresponding to the power output fluctuation.

[0018] Preferably, the step of generating an energy storage support strategy based on the voltage stability demand index and the pre-acquired hybrid energy storage status data includes: determining whether the voltage is abnormal based on the voltage stability demand index; when the voltage is determined to be abnormal, hybrid energy storage support is required, and the voltage stability demand index and the pre-acquired hybrid energy storage status data are combined and analyzed through a preset optimization algorithm to obtain the energy storage output target and generate the energy storage support strategy.

[0019] Preferably, the step of controlling the hybrid energy storage device to perform charging and discharging operations based on the energy storage support strategy includes:

[0020] The hybrid energy storage device is controlled according to the energy storage support strategy to obtain battery and flywheel energy storage output instructions, and real-time voltage measurement values are collected, and then the voltage error is calculated based on the real-time voltage measurement values and reference values of the voltage values through a closed-loop control algorithm to obtain a voltage adjustment amount, and finally the hybrid energy storage device output is adjusted based on the voltage adjustment amount to stabilize the voltage.

[0021] The application provides a new energy high proportion area voltage stabilization hybrid energy storage support method system in a second aspect, comprising: preferably, a vector generation module, based on pre-acquired power grid data and new energy output data, generating a state vector of the power grid and an output vector of the new energy.

[0022] A data generation module generates a state sequence of the power grid based on the state vector of the power grid, and generates a new energy output sequence based on the output vector of the new energy, and further generates a new energy output feature.

[0023] A demand index generation module generates a voltage stabilization demand index based on the state sequence and the new energy output feature.

[0024] An energy storage support strategy generation module generates an energy storage support strategy based on the voltage stabilization demand index and pre-acquired hybrid energy storage state data.

[0025] A hybrid energy storage device control module controls the hybrid energy storage device to charge and discharge based on the energy storage support strategy.

[0026] The application has the following advantages: 1. The new energy high proportion area voltage stabilization hybrid energy storage support method and system provided by the application vectorizes the power grid data and generates a state sequence and a new energy output feature, providing a high-quality data basis for subsequent analysis, which can accurately identify weak links and risk points of the power grid in advance, convert abstract stability problems into specific quantitative indicators, analyze voltage faults based on accurate demand indicators and real-time energy storage states, and dynamically generate optimal energy storage support strategies to guide the hybrid energy storage system to perform coordinated charging and discharging. This not only can quickly suppress new energy power fluctuations, actively maintain node voltage stability, and effectively prevent voltage out-of-limit accidents, but also fully utilizes the comprehensive advantages of hybrid energy storage in response speed and duration.

[0027] 2、The application abstracts the massive heterogeneous data of the power grid and new energy into structured state vectors and output vectors by fusing and vectorizing the multi-source data, providing a regular input basis for advanced algorithms. Furthermore, it realizes the leap from static "point" data to dynamic "sequence" data by constructing time series vectors into state sequences and output sequences, capturing the trajectory and trend of system evolution. Most importantly, this process deeply excavates the output characteristics of new energy, surpassing the original data to extract key indicators that can represent its volatility, intermittency, and prediction bias. It lays the data foundation for accurate analysis and intelligent decision-making, providing ideal input for machine learning models through data standardization and vectorization; it realizes the transition from passive perception to active insight, enabling the system to understand dynamic laws and the potential impact of new energy through time series analysis and feature extraction.

[0028] 3、The index of the application is not a traditional single voltage limit judgment, but a composite index that integrates the current state, historical trend, and future prediction. It can be a quantitative "demand value" representing how much reactive power / active power the system needs to absorb or emit to maintain voltage stability in the next few minutes. The abstract "voltage stability demand" is converted into specific and quantifiable power support values, making the control target very clear. Since the index is forward-looking, the system can identify the demand before the voltage actually drops, thereby actively invoking energy storage resources for intervention to prevent the situation from deteriorating.

[0029] 4、The application constructs an intelligent execution system of "strategy coordination optimization and control precise closed loop", realizing seamless connection from decision-making to action. The strategy not only responds to the voltage support demand of the power grid, but also deeply integrates the differentiated state and characteristics of hybrid energy storage (such as battery and flywheel energy storage), achieving optimal allocation of power and energy, while considering equipment life and response speed. By leveraging the synergistic advantages of hybrid energy storage, it realizes "fast and slow combination, strengths and weaknesses", significantly improving support efficiency and economy. It builds a complete "perception-decision-execution-correction" automated closed loop, not only greatly enhancing the resilience and stability of the power grid in response to disturbances, but also effectively extending the service life of energy storage devices through optimized charging and discharging strategies, achieving dual improvement of power grid safety and asset efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, a brief introduction will be given below to the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can be obtained without creative labor based on these drawings.

[0031] Figure 1The flowchart of the method embodiment of the present application is shown in the figure.

[0032] Figure 2 The schematic diagram of the system structure connection of the present application is shown in the figure. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present application will be clearly and completely described in the figure of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0034] Please refer to Figure 1 The present application provides a new energy high-occupancy regional voltage stability hybrid energy storage support method in the first aspect, which comprises the following steps: step one, vector generation: based on the pre-acquired power grid data and new energy output data, the state vector of the power grid and the output vector of the new energy are generated.

[0035] In a specific example, the power grid data includes voltage, current, electric power and power frequency; the new energy output data includes power output, frequency, temperature, pressure and efficiency.

[0036] It should be noted that the new energy output data relates to solar energy, wind energy and other new energy power generation equipment.

[0037] In a specific example, based on the pre-acquired power grid data and new energy output data, the state vector of the power grid and the output vector of the new energy are generated, which comprises: based on the pre-acquired power grid data, using a preset state estimation algorithm to calculate the power grid state estimation value; and vector encoding the power grid state estimation value to obtain the power grid state vector; based on the pre-acquired new energy output data, using a preset state estimation algorithm to calculate the new energy output estimation value; and vector encoding the new energy output estimation value to obtain the new energy output vector.

[0038] It should be noted that based on the pre-acquired power grid data, the power grid state estimation value is calculated using a preset state estimation algorithm, and the specific process is as follows: through the calculation formula The power grid state estimation value at time t is obtained The power grid state estimation value at time t is obtained Wherein The power grid state estimation value at time t is obtained The power grid state estimation value at time t is obtained The power grid data correction matrix is represented as The power grid state estimation value at time t is obtained The power grid measurement value vector at time t is obtained The power grid data observation matrix is represented as; wherein the calculation formula of the power grid data correction matrix is , Represented as The state error covariance matrix at time t. This is the transpose of the observation matrix of the power grid data. This is the covariance matrix of the actual measurements of the power grid data.

[0039] Furthermore, The grid state estimate at time t represents the grid's state at... The estimated state vectors of voltage, current, electric power, and electric frequency at time t; The power grid state estimate at time t represents the power grid's state at... The estimated state vector at time step; the power grid measurement vector includes pre-acquired voltage, current, power, and power frequency data. The observation matrix of the power grid data is used to map the state vector to the measurement space; the observation matrix of the power grid data is defined as follows: The state variable (such as voltage and current) directly corresponds to the measured variable, simplifying the state estimation process; the state error covariance matrix represents the uncertainty of the state estimation and quantifies the error covariance between the estimated value and the true value; the transpose of the observation matrix of the power grid data adjusts the dimensions in the calculation to match the state and measurement space; the covariance matrix of the actual measurement of the power grid data represents the statistical characteristics of the measurement error, such as the error caused by the sensor.

[0040] It should be noted that the power grid state estimate is vector-encoded to obtain the power grid state vector. The specific process is as follows: ,in This is the power grid state vector; This is a voltage state estimate; This is an estimate of the current state; This is an estimate of the electrical power state. This is an estimate of the power frequency state.

[0041] It should be noted that, based on the pre-acquired renewable energy output data, a preset state estimation algorithm is used to calculate the estimated renewable energy output value. The specific process is as follows: Using the calculation formula... The conclusion is Estimated output of new energy sources at any given time , Represented as Estimated output of new energy sources at any given time; Correction matrix for new energy output data, for Vector of measured new energy output at any given time. The observation matrix for new energy output data; the formula for calculating the correction matrix for power grid data is as follows: , Represented as The covariance matrix of the new energy output error at time t, Transpose of the observation matrix for new energy output data. The covariance matrix of the actual measured power output data of new energy sources.

[0042] It should be noted that vector encoding is performed on the estimated value of new energy power output to obtain the vector of new energy power output. The specific process is as follows: through the calculation formula... Determine the power output vector of the new energy source. , This is expressed as an estimated value of the power output from new energy sources. Expressed as the frequency-based estimated output value of new energy sources, This is expressed as an estimated value for the output of new energy sources based on temperature. This is expressed as an estimated value of the output of the new energy source under pressure. This is expressed as an estimated value for the output of new energy sources.

[0043] Furthermore, the estimated output of new energy sources indicates the capacity of new energy power generation equipment. The estimated renewable energy output vector is generated based on the power output, frequency, temperature, pressure, and efficiency at any given time. A correction matrix for the renewable energy output data is used to adjust for the deviation between measured and predicted values. An observation matrix for the renewable energy output data is used to map the renewable energy output vector to the measurement space; this observation matrix is ​​defined as follows: The variable representing the output of new energy sources directly corresponds to the measured variable, simplifying the state estimation process. The error covariance matrix of new energy output represents the uncertainty of state estimation and quantifies the error covariance between the estimated value and the true value. The transpose of the observation matrix of new energy output data adjusts the dimension in the calculation to match the state and measurement space. The covariance matrix of the actual measurement of new energy output data represents the statistical characteristics of measurement error, such as the error caused by the sensor.

[0044] Furthermore, vector encoding serializes multidimensional estimates into vectors with a fixed format using an embedded processor, facilitating subsequent data concatenation and dimensionality reduction processing, where vector elements are arranged sequentially to maintain data consistency.

[0045] Step 2, Data Generation: Based on the power grid's state vector, generate the power grid's state sequence; and based on the new energy's output vector, generate the new energy's output sequence, thereby generating the new energy's output characteristics.

[0046] In a specific example, the output characteristics of the new energy source include basic statistical characteristics, volatility characteristics, intermittency and uncertainty characteristics, and time series and shape characteristics.

[0047] It should be noted that the basic statistical characteristics describe the overall level and distribution of the output, the volatility characteristics describe the intensity and speed of the output change, the intermittency and uncertainty characteristics describe the reliability and predictability of the power generation, and the timing and shape characteristics describe the distribution mode of the output on the time axis.

[0048] In one specific example, the grid-based state vector generates a state sequence of the grid; and based on the output vector of the new energy, an output sequence of the new energy is generated, and then the output characteristics of the new energy are generated, including: time series splicing is performed on the state vector of the grid to form a grid state matrix; the grid state matrix is processed using a preset feature dimension reduction algorithm to obtain the state sequence of the grid; time series splicing is performed on the output vector of the new energy to form a new energy output matrix; the new energy output matrix is processed using a preset feature dimension reduction algorithm to obtain the new energy output sequence, and the output characteristics of the new energy are generated.

[0049] It should be noted that the state sequence of the grid is equal to the input state matrix multiplied by the grid principal component transformation matrix; the output sequence matrix is equal to the input output matrix multiplied by the new energy principal component transformation matrix; further, the principal component analysis algorithm maps high-dimensional data to low-dimensional space through linear transformation, eliminates redundant information and highlights the main change mode, wherein the input state matrix is a splicing matrix of state vectors at multiple time points, and the input output matrix is a splicing matrix of output vectors at multiple time points; the column vectors of the grid principal component transformation matrix are the eigenvectors of the covariance matrix of the grid data, arranged in descending order of eigenvalues, to maximize the variance contribution of the sequence; the column vectors of the new energy principal component transformation matrix are the eigenvectors of the covariance matrix of the new energy output data, arranged in descending order of eigenvalues, to maximize the variance contribution of the sequence.

[0050] It should be noted that the basic statistical characteristics are extracted from the new energy output sequence by a statistical calculation algorithm, and the specific calculation formula is as follows: wherein, represents the output mean, represents the output variance, represents the output value of the i-th time point in the new energy output sequence, , , represents the total number of time points; further, the basic statistical characteristics also include the maximum and minimum values and quantiles of the output, which are calculated in real time by an embedded statistical module to quantify the concentration trend and variation range of the output. The output mean describes the overall level of the output; the output variance describes the distribution dispersion degree of the output.

[0051] It should be noted that the volatility characteristics are extracted from the new energy output sequence by a volatility quantification algorithm, and the specific calculation formula is as follows: wherein is the average fluctuation amplitude, is the standard deviation of fluctuation, is the output value at the time point; further, the average fluctuation amplitude describes the intensity of output change; the standard deviation of fluctuation describes the speed stability of output change; the fluctuation feature captures the first-order difference and the second-order moment of the output time series, and the difference between consecutive time points is calculated by a hardware accelerator to evaluate the dynamic behavior of new energy output.

[0052] It should be noted that the intermittency and uncertainty feature is extracted from the new energy output sequence by a reliability evaluation algorithm; the output availability is calculated as the ratio of the number of time points with output values greater than a preset threshold to the total number of time points within a specified time window, for describing the reliability of power generation; the output uncertainty index is calculated as the average of the sum of squares of the difference between the actual output value and the predicted output value based on historical data, for describing the predictability of power generation; further, the reliability evaluation algorithm analyzes the output interruption frequency and prediction error by a sliding window, and quantifies the intermittent mode of new energy power generation by a probability model, wherein the threshold is dynamically adjusted according to the type of new energy and the operating environment, to accurately reflect the availability and fluctuation risk of power generation.

[0053] It should be noted that the timing and shape features are extracted from the new energy output sequence by a shape analysis algorithm; the output-time correlation coefficient is calculated as the sum of the product of the deviation of time points and the deviation of output values from the average time point and the average output value, divided by the square root of the product of the square sum of time deviation and the square sum of output deviation, for describing the distribution mode of output on the time axis; the output skewness is calculated as the average of the cube of the standardized deviation of output values from the average output value, for describing the asymmetry of sequence shape; further, the shape analysis algorithm extracts periodic components by Fourier transform, and identifies the peak and valley modes of the output curve by morphological operators, to reflect the timing evolution law of new energy output, wherein the standardized deviation is calculated based on the standard deviation of the output sequence to eliminate the dimension influence.

[0054] This application integrates and vectorizes multi-source data, unifying and abstracting massive heterogeneous data from power grids and new energy sources into structured state and output vectors, providing a standardized input foundation for advanced algorithms. Furthermore, it achieves a leap from static "point" data to dynamic "sequence" data, capturing the trajectory and trends of system evolution by constructing state and output sequences from time-series vectors. Most importantly, this process deeply mines the output characteristics of new energy sources, going beyond raw data to extract key indicators characterizing their volatility, intermittency, and prediction bias. This lays the data foundation for accurate analysis and intelligent decision-making, providing ideal input for machine learning models through data standardization and vectorization; it realizes a shift from passive perception to proactive insight, enabling the system to understand dynamic patterns and the potential impact of new energy sources through time-series analysis and feature extraction.

[0055] Step 3: Demand index generation: Based on the state sequence and the characteristics of new energy output, generate voltage stability demand indexes.

[0056] In a specific example, generating a voltage stability demand index based on the state sequence and the new energy output characteristics includes: extracting a voltage stability sequence based on the state sequence; extracting volatility characteristics based on the new energy output characteristics; and calculating the voltage stability demand index using a voltage stability demand algorithm based on the voltage stability sequence and the volatility characteristics.

[0057] In a specific example, the step of calculating the voltage stability demand index based on the voltage stability sequence and volatility characteristics using a voltage stability demand algorithm includes: calculating the voltage stability demand index using the formula... Determine the voltage stability requirement index , This is represented by the number corresponding to the sampling point. , This represents the total number of sampling points. Represented as the first Voltage values ​​at each sampling point This is a reference value for the voltage. Represented as the first The power fluctuation value at each sampling point This is expressed as the average value of the output fluctuation. This is represented by the weighting factor corresponding to the voltage. This represents the weighting factor corresponding to the power output fluctuation.

[0058] It should be noted that the voltage stability demand index quantifies the scalar value of the power grid voltage stability demand, which is calculated by weighted average voltage deviation and output fluctuation, and the greater the value, the higher the voltage stability demand; the voltage value is the real-time voltage measurement value extracted from the state sequence, reflecting the instantaneous voltage state of the power grid; the reference value of the voltage value is the preset rated voltage, which is used to evaluate the voltage deviation; the fluctuation output value is the real-time output value extracted from the new energy output characteristics, indicating the new energy power generation power; the average value of the output fluctuation value is the arithmetic mean of the fluctuation characteristics of the new energy output characteristics, which is used to standardize the output fluctuation.

[0059] It should be noted that the weight factor corresponding to the voltage and the weight factor corresponding to the output fluctuation are obtained by factor analysis method. First, the information condensation of voltage and output fluctuation is carried out, and then the variance explained rate after rotation is obtained, and the weight is obtained by accumulating the variance explained rate.

[0060] It should be noted that the factor analysis method is a known technology, which is a multivariate statistical analysis method that starts from the dependent relationship of internal correlation of variables and concludes that some variables with complex relationship are summarized as a few comprehensive factors; information condensation is represented as the median in calculation; variance explained rate is the amount of information extracted by factor; the variance explained rate after rotation represents the variance explained rate of the factor after maximum variance rotation.

[0061] It should be noted that the extraction of voltage stability sequence is based on the state sequence, and the voltage related dimension is selected from the state sequence by the embedded data selection module to generate the voltage value time sequence; further, the extraction of voltage stability sequence is to access the state sequence storage area in real time by using hardware level indexer, isolate voltage data stream, and optimize data reading delay by cache alignment to ensure the continuity and consistency of voltage sequence.

[0062] It should be noted that the extraction of fluctuation characteristics is based on the new energy output characteristics, and the fluctuation characteristic subset is selected from the new energy output characteristics by the feature selection algorithm, such as average fluctuation amplitude or fluctuation standard deviation; further, the physical implementation of extracting fluctuation characteristics depends on the embedded processor to execute parallel feature extraction, calculate fluctuation index by using sliding window, and dynamically adjust feature weight by using numerical comparator, to accurately reflect the influence of new energy output on voltage stability.

[0063] The index of the present application is not a traditional single voltage over-limit judgment, but a composite index integrating current state, historical trend and future prediction. It can be a quantitative "demand value" indicating how much reactive power / active power the system needs to absorb or emit in order to maintain voltage stability in the next few minutes. The abstract "voltage stability demand" is converted into specific and quantifiable power support values, making the control target very clear. Since the index is forward-looking, the system can identify the demand before the voltage actually falls dangerously, so as to actively call on energy storage resources for intervention and avoid deterioration.

[0064] Step four, energy storage support strategy generation: based on the voltage stability demand index and pre-acquired hybrid energy storage state data, an energy storage support strategy is generated.

[0065] In a specific example, the generation of the energy storage support strategy based on the voltage stability demand index and pre-acquired hybrid energy storage state data includes: judging whether the voltage is abnormal based on the voltage stability demand index; when judging whether the voltage is abnormal, hybrid energy storage support is needed, and the voltage stability demand index and pre-acquired hybrid energy storage state data are combined to analyze through a preset optimization algorithm to obtain an energy storage output target, and the energy storage support strategy is generated.

[0066] It should be noted that whether the voltage is abnormal is determined by comparing the voltage stability demand index with a voltage stability demand index threshold value; when the voltage stability demand index is greater than the voltage stability demand index threshold value, it is determined that the voltage is abnormal and hybrid energy storage support is needed; when the voltage stability demand index is less than or equal to the voltage stability demand index threshold value, it is determined that the voltage is not abnormal, and hybrid energy storage support is not needed.

[0067] Further, the voltage stability demand index threshold value is a scalar value determined based on historical grid operation data through statistical analysis, which is used to ensure that the voltage stability is within a safe range and is a critical point of the grid voltage stability demand. If the value is exceeded, energy storage intervention is needed to prevent voltage instability.

[0068] It should be noted that the preset optimization algorithm is a linear programming algorithm, and its mathematical expression is as follows: wherein, is a decision variable vector, including a battery output value and a flywheel energy storage output value ; is a target function coefficient vector, is a constraint matrix, is a constraint vector; the decision variable vector represents the energy storage output target; the objective function coefficient vector is determined based on the voltage stability demand index; the battery output value represents the output power of the battery in the scheduling period, which is the power compensation amount of the battery to the power grid; the flywheel energy storage output value represents the output power of the flywheel energy storage in the scheduling period, which is the rapid power response amount of the flywheel energy storage to the power grid; the constraint matrix is determined based on the hybrid energy storage state data; and the constraint vector is determined based on the hybrid energy storage state data.

[0069] Further, the decision variable vector , the objective function coefficient vector , wherein and are weight coefficients, calculated based on the voltage stability demand index, used to reflect the relative importance of the battery and the flywheel energy storage to the voltage stability, and are the preference degree of the optimization process to the output of different energy storage types; the constraint matrix and the constraint vector are constructed based on the hybrid energy storage state data, including the battery state of charge limit, the charge and discharge power limit and the flywheel energy storage capacity limit, which are the physical operation boundaries of the energy storage equipment, ensuring the feasibility and safety of the strategy.

[0070] It should be noted that the generation of the energy storage support strategy is based on the energy storage output target, and the output value is converted into control instructions, including charge and discharge time and power set value, through a strategy mapping algorithm.

[0071] Further, the strategy mapping algorithm discretizes the continuous output value into executable control sequences using a lookup table or a rule engine, and sends it to the hybrid energy storage equipment through a communication interface. The physical essence is that an embedded processor performs numerical-instruction conversion to generate real-time control signals through pre-stored scheduling rules to drive the energy storage equipment to perform charge and discharge operations.

[0072] Step five, hybrid energy storage equipment control: control the hybrid energy storage equipment to perform charge and discharge operations based on the energy storage support strategy.

[0073] In a specific example, the control of the hybrid energy storage equipment to perform charge and discharge operations based on the energy storage support strategy includes: control instruction analysis of the hybrid energy storage equipment based on the energy storage support strategy to obtain battery and flywheel energy storage output instructions, while collecting real-time voltage measurement values, and then calculating voltage error based on real-time voltage measurement values and reference values of voltage values through a closed-loop control algorithm to obtain voltage adjustment amount, and finally adjusting the output of the hybrid energy storage equipment based on the voltage adjustment amount to stabilize the voltage.

[0074] It should be noted that the pre-set closed-loop control algorithm is a proportional-integral control algorithm based on voltage error, and the specific process is as follows: the voltage adjustment amount is obtained by the calculation formula , represents the proportional gain, denoted as integral gain, denoted as reference value of voltage, denoted as real-time voltage measurement, denoted as continuous duration of control process.

[0075] Further, the voltage adjustment amount denotes the adjustment power value of the hybrid energy storage device output, which is the power instruction for compensating the voltage deviation by controlling the charge and discharge of the energy storage device; the proportional gain denotes the immediate response coefficient of the voltage error to the adjustment amount, which is the sensitivity of the adjustment amount to the voltage deviation, the greater the value, the faster the response; the integral gain denotes the long-term correction coefficient of the voltage error accumulation to the adjustment amount, which is the cumulative effect of eliminating steady-state error, the greater the value, the higher the steady-state accuracy; the continuous duration of the control process is the time reference of integral operation. The physical implementation of the preset closed-loop control algorithm depends on the embedded processor to perform discrete calculation, the voltage signal is collected through the analog-to-digital converter, and the error is accumulated by using the digital integrator to generate continuous adjustment instructions, to ensure the rapid stability of the voltage.

[0076] It should be noted that the analysis of the energy storage output target is based on the energy storage support strategy, the battery output value and the flywheel energy storage output value are extracted through the strategy analysis module, and are converted into an executable instruction format of the device.

[0077] Further, the essence of analyzing the energy storage output target is to use an embedded processor to perform data decoding, to discretize the continuous value output by the optimization algorithm into a pulse width modulation signal or a digital instruction, and to send it to the energy storage device controller through a communication interface.

[0078] It should be noted that the adjustment of the output of the hybrid energy storage device is based on the voltage adjustment amount, and the charge and discharge power of the battery and the flywheel energy storage is modified through the output control module, wherein the battery output adjustment focuses on slow compensation, and the flywheel energy storage output adjustment focuses on fast response.

[0079] Further, the physical implementation of adjusting the output depends on the power electronic switch to perform pulse width modulation, to convert the direct current power into alternating current power through the inverter, and to use the feedback circuit to monitor the output effect in real time, to maintain voltage stability in a closed-loop manner.

[0080] The application constructs an intelligent execution system of "strategy coordination optimization and control precise closed loop", realizes seamless connection from decision to action. The strategy not only responds to the voltage support demand of the power grid, but also deeply integrates the differentiated state and characteristics of the internal mixed energy storage (such as battery and flywheel energy storage), realizes the optimal allocation of power and energy, and takes into account the equipment life and response speed. By exerting the synergistic advantages of mixed energy storage, the "fast and slow combination, strengths and weaknesses" are realized, which greatly improves the support efficiency and economy. A complete "perception-decision-execution-correction" automatic closed loop is constructed, which not only greatly enhances the resilience and stability of the power grid in response to disturbances, but also effectively prolongs the service life of the energy storage equipment through optimizing the charging and discharging strategy, realizes the double improvement of power grid safety and asset benefit.

[0081] Please refer to Figure 2 The application provides a system for voltage stabilization mixed energy storage support in a new energy high proportion area in the second aspect.

[0082] The system 100 for voltage stabilization mixed energy storage support in a new energy high proportion area can be installed in an electronic device. According to the functions realized, the system 100 for voltage stabilization mixed energy storage support in a new energy high proportion area can include a vector generation module 101, a data generation module 102, a demand index generation module 103, a energy storage support strategy generation module 104, and a mixed energy storage device control module 105. The modules of the application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0083] In this embodiment, the functions of each module / unit are as follows: the vector generation module generates the state vector of the power grid and the output vector of the new energy based on the pre-acquired power grid data and new energy output data.

[0084] The data generation module generates the state sequence of the power grid based on the state vector of the power grid, and generates the new energy output sequence based on the output vector of the new energy, and further generates the new energy output feature.

[0085] The demand index generation module generates the voltage stabilization demand index based on the state sequence and the new energy output feature.

[0086] The energy storage support strategy generation module generates the energy storage support strategy based on the voltage stabilization demand index and the pre-acquired mixed energy storage state data.

[0087] The mixed energy storage device control module controls the mixed energy storage device to perform charging and discharging operation based on the energy storage support strategy.

[0088] The new energy high proportion regional voltage stability hybrid energy storage support method and system provided by the application can provide a high-quality data basis for subsequent analysis by vectorizing power grid data and generating state sequences and new energy output characteristics, can accurately identify weak links and risk points of the power grid in advance, convert abstract stability problems into specific quantitative indexes, analyze voltage faults based on the accurate demand indexes and real-time energy storage states, and dynamically generate optimal energy storage support strategies to guide the collaborative charging and discharging of the hybrid energy storage system; this can not only quickly suppress new energy power fluctuations, actively maintain node voltage stability, and effectively prevent voltage out-of-limit accidents, but also fully utilizes the comprehensive advantages of hybrid energy storage in response speed and duration.

[0089] In several embodiments provided in the present application, it should be understood that the disclosed method and system can be implemented in other ways. For example, the system embodiments described above are only illustrative, and for example, the division of the modules is only a logical functional division, and another division mode can be used in actual implementation.

[0090] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical units, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs.

[0091] In addition, the functional modules in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software functional modules.

[0092] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0093] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. Artificial intelligence is a theory, method, technology and application system for simulating, extending and expanding human intelligence by using a digital computer or a machine controlled by a digital computer, perceiving an environment, acquiring knowledge and using knowledge to obtain optimal results.

[0094] Finally, it should be noted that the above examples are merely intended to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the present application.

Claims

1. A new energy high proportion area voltage stability hybrid energy storage support method, characterized in that, The method comprises the following steps: Step 1, vector generation: based on the pre-acquired power grid data and new energy output data, the state vector of the power grid and the output vector of the new energy are generated; Step 2, data generation: based on the state vector of the power grid, the state sequence of the power grid is generated; and based on the output vector of the new energy, the new energy output sequence is generated, and then the new energy output feature is generated; Step 3, demand index generation: based on the state sequence and the new energy output feature, the voltage stability demand index is generated; The generation of the voltage stability demand index based on the state sequence and the new energy output feature comprises: Based on the state sequence, the voltage stability sequence is extracted; based on the new energy output feature, the volatility feature is extracted; and based on the voltage stability sequence and the volatility feature, the voltage stability demand index is calculated through a voltage stability demand algorithm; The calculation of the voltage stability demand index based on the voltage stability sequence and the volatility feature through the voltage stability demand algorithm comprises: The voltage stability demand index is obtained by the following formula The number corresponding to the sampling point is represented by The total number of sampling points is represented by The voltage value of the th sampling point is represented by The reference value of the voltage value is represented by The output fluctuation value of the th sampling point is represented by The average value of the output fluctuation value is represented by The weight factor corresponding to the voltage is represented by The weight factor corresponding to the output fluctuation is represented by​​​ Step 4, energy storage support strategy generation: based on the voltage stability demand index and the pre-acquired hybrid energy storage state data, the energy storage support strategy is generated; Step 5, hybrid energy storage device control: based on the energy storage support strategy, the hybrid energy storage device is controlled to perform charging and discharging operations.

2. The new energy high-occupancy regional voltage stabilization hybrid energy support method according to claim 1, characterized in that, The power grid data includes voltage, current, electric power and power frequency; the new energy output data includes power output, frequency, temperature, pressure and efficiency. 3.The new energy high-occupancy regional voltage stabilization hybrid energy support method according to claim 1, characterized in that, The generation of the state vector of the power grid and the output vector of the new energy based on the pre-acquired power grid data and new energy output data comprises: Based on the pre-acquired power grid data, the state estimation value of the power grid is calculated using a preset state estimation algorithm; and the state estimation value of the power grid is vector encoded to obtain the state vector of the power grid; based on the pre-acquired new energy output data, the output estimation value of the new energy is calculated using a preset state estimation algorithm; and the output estimation value of the new energy is vector encoded to obtain the output vector of the new energy.

4. The new energy high-occupancy regional voltage stabilization hybrid energy support method according to claim 1, characterized in that, The new energy output feature includes basic statistical feature, volatility feature, intermittency and uncertainty feature, and time sequence and shape feature.

5. The new energy high-occupancy regional voltage stabilization hybrid energy support method according to claim 1, characterized in that, The generation of the state sequence of the power grid based on the state vector of the power grid, and the generation of the new energy output sequence based on the output vector of the new energy, and then the generation of the new energy output feature, comprises: The state vector of the power grid is time series spliced to form a power grid state matrix; the power grid state matrix is processed using a preset feature dimension reduction algorithm to obtain the state sequence of the power grid; the output vector of the new energy is time series spliced to form a new energy output matrix; the new energy output matrix is processed using a preset feature dimension reduction algorithm to obtain the new energy output sequence, and the new energy output feature is generated. 6.The new energy high-occupancy regional voltage stabilization hybrid energy support method according to claim 1, characterized in that, The generation of the energy storage support strategy based on the voltage stability demand index and the pre-acquired hybrid energy storage state data comprises: Based on the voltage stability demand index, it is judged whether the voltage is abnormal; when it is judged that the voltage is abnormal, hybrid energy storage support is needed, and the voltage stability demand index and the pre-acquired hybrid energy storage state data are combined to analyze through a preset optimization algorithm to obtain an energy storage output target, and the energy storage support strategy is generated.

7. The new energy high-occupancy regional voltage stabilization hybrid energy support method according to claim 1, characterized in that, The hybrid energy storage device is controlled to charge and discharge based on the energy storage support strategy, and the method comprises the steps of: Based on the energy storage support strategy, the control instruction of the hybrid energy storage device is analyzed to obtain the output instruction of the battery and the flywheel energy storage, and the real-time voltage measurement value is collected, and then the voltage error is calculated based on the real-time voltage measurement value and the reference value of the voltage value through the closed-loop control algorithm to obtain the voltage adjustment amount, and finally the output of the hybrid energy storage device is adjusted based on the voltage adjustment amount to stabilize the voltage.

8. A system for performing the new energy high-occupancy regional voltage stability hybrid energy storage support method of any one of claims 1-7, characterized in that, Comprise: A vector generation module generates a state vector of the power grid and an output vector of the new energy based on pre-acquired power grid data and new energy output data; A data generation module generates a state sequence of the power grid based on the state vector of the power grid, generates a new energy output sequence based on the output vector of the new energy, and further generates a new energy output feature; A demand index generation module generates a voltage stability demand index based on the state sequence and the new energy output feature; The voltage stability demand index is generated based on the state sequence and the new energy output feature, comprising: Based on the state sequence, the voltage stability sequence is extracted; based on the new energy output feature, the volatility feature is extracted; and based on the voltage stability sequence and the volatility feature, the voltage stability demand index is calculated through a voltage stability demand algorithm; The voltage stability demand index is calculated based on the voltage stability sequence and the volatility feature through the voltage stability demand algorithm, comprising: Through calculation formula Determine the voltage stability requirement index , This is represented by the number corresponding to the sampling point. , This represents the total number of sampling points. Represented as the first Voltage values ​​at each sampling point The reference value is represented as the voltage value. Represented as the first The power fluctuation value at each sampling point This is expressed as the average value of the output fluctuation. This is represented by the weighting factor corresponding to the voltage. This is represented as the weighting factor corresponding to the power output fluctuation; An energy storage support strategy generation module generates an energy storage support strategy based on the voltage stability demand index and pre-acquired hybrid energy storage state data; A hybrid energy storage device control module controls the hybrid energy storage device to charge and discharge based on the energy storage support strategy.

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