New energy high-proportion area voltage stabilization hybrid energy storage supporting method and system

Abstract

The invention discloses a new energy high-proportion regional voltage stabilization hybrid energy storage support method and system, relates to the technical field of voltage stabilization, and provides a high-quality data basis for subsequent analysis by vectorizing power grid data and generating a state sequence and new energy output characteristics. The method can accurately identify weak links and risk points of a power grid in advance, converts an abstract stability problem into a specific quantitative index, analyzes a voltage fault based on the accurate demand index and a real-time energy storage state, and dynamically generates an optimal energy storage support strategy according to an analysis result of the voltage fault, thereby improving the reliability of the power grid. Guiding the hybrid energy storage system to perform cooperative charging and discharging; according to the method, the new energy power fluctuation can be quickly stabilized, the node voltage stability is actively maintained, the voltage out-of-limit accident is effectively prevented, and the comprehensive advantages of the hybrid energy storage in response speed and duration are fully played.

Description

Technical Field

[0001] This application relates to the field of voltage stabilization technology, specifically to a method and system for supporting hybrid energy storage for voltage stabilization in areas with a high proportion of new energy sources. Background Technology

[0002] With the large-scale integration of intermittent and fluctuating renewable energy sources such as wind and solar power into the power grid, the randomness and anti-peak-shaving characteristics of their output pose a severe challenge to the voltage stability of the power system, especially at local nodes. Traditional power grids rely on synchronous generators for inertial support and voltage regulation, while renewable energy units are connected to the grid through power electronic devices. When renewable energy output fluctuates drastically, it can easily cause voltage over-limits at critical nodes of the grid, and even trigger cascading failures, threatening power supply security. Therefore, there is an urgent need for an integrated solution that can deeply integrate grid operating status, renewable energy output characteristics, and energy storage system control.

[0003] Existing technology, such as the invention application patent with announcement number CN117220296A, discloses a method and system for analyzing grid voltage stability considering new energy source control switching, belonging to the field of power system stability analysis technology. This invention can be used for voltage stability analysis of power systems containing new energy sources, and can indicate the voltage instability risk of the power system under the action of new energy source control switching, including but not limited to voltage instability or new energy source disconnection risk caused by continuous new energy source crossing without recovery, voltage and power oscillations or new energy source disconnection risk caused by repeated new energy source entry / exit crossings, etc., providing guidance for power system planning, operation mode arrangement, and stability control.

[0004] Regarding the above-mentioned solutions, the inventors of this application have found that the above-mentioned technologies have at least the following technical problems: 1. Currently, there is a lack of composite indicators that integrate the current state, historical trends and future predictions. The abstract "voltage stability demand" has not been transformed into specific and quantifiable power support values. The control target cannot be made very clear, and the demand cannot be identified before the voltage actually drops dangerously. Therefore, it is impossible to actively call on energy storage resources to intervene and prevent the situation from deteriorating.

[0005] 2. Currently, the lack of an intelligent execution system that integrates "strategic synergy optimization and precise closed-loop control" prevents seamless transitions from decision-making to action. This not only fails to respond to the grid's voltage support requirements but also fails to deeply integrate the differentiated states and characteristics within hybrid energy storage, hindering optimal power and energy allocation while simultaneously prioritizing equipment lifespan and response speed. Furthermore, the inability to leverage the synergistic advantages of hybrid energy storage prevents the achievement of a "fast-slow combination, maximizing strengths and minimizing weaknesses" approach, thus hindering improved support efficiency and economic viability. The lack of an automated closed-loop system encompassing "sensing-decision-execution-correction" not only fails to enhance the grid's resilience and stability in the face of disturbances but also prevents the effective extension of energy storage equipment's lifespan through optimized charging and discharging strategies, ultimately hindering the dual improvement of grid security and asset efficiency. Summary of the Invention

[0006] To address the aforementioned technical shortcomings, the purpose of this application is to provide a method and system for supporting voltage stability and hybrid energy storage in areas with a high proportion of new energy sources.

[0007] To solve the above-mentioned technical problems, this application adopts the following technical solution: In the first aspect, this application provides a method for voltage stability and hybrid energy storage support in areas with high proportion of new energy sources. The method includes the following steps: Step 1, Vector generation: Based on the pre-acquired grid data and new energy output data, generate the grid state vector and the new energy output vector.

[0008] 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.

[0009] Step 3: Demand index generation: Based on the state sequence and the characteristics of new energy output, voltage stability demand index is generated.

[0010] Step 4: Energy storage support strategy generation: Based on the voltage stability demand index and the pre-acquired hybrid energy storage status data, an energy storage support strategy is generated.

[0011] Step 5: Control of hybrid energy storage devices: Control the charging and discharging operations of hybrid energy storage devices based on the energy storage support strategy.

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

[0013] Preferably, the step of generating the grid state vector and the new energy output vector based on the pre-acquired grid data and new energy output data includes: calculating the grid state estimate using a preset state estimation algorithm based on the pre-acquired grid data; and vector encoding the grid state estimate to obtain the grid state vector; calculating the new energy output estimate using a preset state estimation algorithm based on the pre-acquired new energy output data; and vector encoding the new energy output estimate to obtain the new energy output vector.

[0014] Preferably, the power output characteristics of the new energy source include basic statistical characteristics, fluctuation characteristics, intermittency and uncertainty characteristics, and time sequence and shape characteristics.

[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 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 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: Based on the energy storage support strategy, control commands for the hybrid energy storage device are parsed to obtain the energy output commands for the battery and flywheel. Simultaneously, real-time voltage measurements are collected. Then, based on the real-time voltage measurements and reference voltage values, a closed-loop control algorithm is used to calculate the voltage error to obtain the voltage adjustment amount. Finally, the output of the hybrid energy storage device is adjusted based on the voltage adjustment amount to stabilize the voltage.

[0020] In its second aspect, this application provides a system for a hybrid energy storage support method for voltage stability in areas with a high proportion of new energy sources, comprising: preferably, a vector generation module, which generates a state vector of the power grid and a power output vector of the new energy sources based on pre-acquired power grid data and new energy output data.

[0021] The data generation module generates a state sequence of the power grid based on the power grid's state vector; and generates a power output sequence of the new energy source based on the power output vector of the new energy source, thereby generating the power output characteristics of the new energy source.

[0022] The demand index generation module generates voltage stability demand indexes based on the state sequence and the output characteristics of new energy sources.

[0023] The energy storage support strategy generation module generates an energy storage support strategy based on the voltage stability requirement index and the pre-acquired hybrid energy storage status data.

[0024] The hybrid energy storage device control module controls the hybrid energy storage device to perform charging and discharging operations based on the energy storage support strategy.

[0025] The beneficial effects of this application are as follows: 1. The hybrid energy storage support method and system for voltage stability in areas with a high proportion of new energy provided by this application, by vectorizing grid data and generating state sequences and new energy output characteristics, provides a high-quality data foundation for subsequent analysis. It can identify weak links and risk points in the grid in advance and accurately, transforming abstract stability problems into specific quantitative indicators. At the same time, based on the precise demand indicators and real-time energy storage status, voltage faults are analyzed, and the optimal energy storage support strategy is dynamically generated based on the analysis results of voltage faults to guide the hybrid energy storage system to carry out coordinated charging and discharging. This can not only quickly smooth out new energy power fluctuations, actively maintain node voltage stability, and effectively prevent voltage over-limit accidents, but also give full play to the comprehensive advantages of hybrid energy storage in response speed and duration.

[0026] 2. 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 time-series vectors into state and output sequences. 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.

[0027] 3. The indicator in this application is not a traditional single voltage limit judgment, but a composite indicator that integrates the current state, historical trends, and future predictions. It may be a quantified "demand value," representing how much reactive / active power the system needs to absorb or generate to maintain voltage stability in the next few minutes. Transforming the abstract "voltage stability demand" into a specific, quantifiable power support value makes the control objective very clear. Because the indicator is forward-looking, the system can identify the demand before a dangerous voltage drop actually occurs, thereby proactively calling upon energy storage resources to intervene and prevent the situation from worsening.

[0028] 4. This application constructs an intelligent execution system of "strategy synergistic optimization and precise control closed loop," achieving seamless integration from decision-making to action. This strategy not only responds to the voltage support requirements of the power grid but also deeply integrates the differentiated states and characteristics within hybrid energy storage (such as battery and flywheel energy storage), achieving optimal power and energy allocation while considering equipment lifespan and response speed. By leveraging the synergistic advantages of hybrid energy storage, it achieves a "combination of fast and slow, maximizing strengths and minimizing weaknesses," significantly improving support efficiency and economy. A complete automated closed loop of "sensing-decision-execution-correction" is constructed, which not only greatly enhances the resilience and stability of the power grid in the face of disturbances but also effectively extends the lifespan of energy storage equipment through optimized charging and discharging strategies, achieving a dual improvement in grid security and asset efficiency. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1This is a flowchart illustrating the steps involved in implementing the method described in this application.

[0031] Figure 2 This is a schematic diagram of the system structure connection of this application. Detailed Implementation

[0032] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] Please see Figure 1 As shown, this application provides a method for supporting voltage stability and hybrid energy storage in areas with a high proportion of new energy sources in the first aspect, including: Step 1, vector generation: Based on the pre-acquired grid data and new energy output data, generate the grid state vector and the new energy output vector.

[0034] In one specific instance, the power grid data includes voltage, current, power, and power frequency; the renewable energy output data includes power output, frequency, temperature, pressure, and efficiency.

[0035] It should be noted that the new energy output data includes new energy power generation equipment such as solar and wind power.

[0036] In a specific example, generating the grid state vector and the new energy output vector based on pre-acquired grid data and new energy output data includes: calculating the grid state estimate using a preset state estimation algorithm based on the pre-acquired grid data; and vector encoding the grid state estimate to obtain the grid state vector; calculating the new energy output estimate using a preset state estimation algorithm based on the pre-acquired new energy output data; and vector encoding the new energy output estimate to obtain the new energy output vector.

[0037] It should be noted that, based on the pre-acquired power grid data, a preset state estimation algorithm is used to calculate the power grid state estimate. The specific process is as follows: Using the calculation formula... The conclusion is Power grid state estimate at time 1 ,in Represented as The estimated power grid state at time t. Represented as a correction matrix for power grid data, Represented as The vector of power grid measurements at time t, This is represented as the observation matrix of the power grid data; the formula for calculating the correction matrix of the power grid data 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.

[0038] Furthermore, The power grid state estimate at time t represents the power 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.

[0039] 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.

[0040] 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.

[0041] It should be noted that vector encoding is performed on the estimated power output of new energy sources to obtain a 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] It should be noted that the basic statistical characteristics describe the overall level and distribution of power output, the fluctuation characteristics describe the severity and speed of power output changes, the intermittency and uncertainty characteristics describe the reliability and predictability of power generation, and the time series and shape characteristics describe the distribution pattern of power output on the time axis.

[0047] In a specific example, 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: concatenating the power grid state vectors into a time series 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; concatenating the new energy power output vectors into a time series 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 generating new energy power output features.

[0048] It should be noted that the power grid state sequence is equal to the input state matrix multiplied by the power 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. Furthermore, the principal component analysis algorithm maps high-dimensional data to a low-dimensional space through linear transformation, eliminating redundant information and highlighting the main change patterns. The input state matrix is ​​a concatenated matrix of state vectors from multiple time points, and the input output matrix is ​​a concatenated matrix of output vectors from multiple time points. The column vectors of the power grid principal component transformation matrix are the eigenvectors of the covariance matrix of the power 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.

[0049] It should be noted that the basic statistical features are extracted from the new energy output sequence using a statistical calculation algorithm, and the specific calculation formula is as follows: in, Expressed as the average output, Expressed as output variance, Represented as the first in the new energy output sequence Output value at each point in time , This is represented as the total number of time points. Furthermore, the basic statistical characteristics also include the maximum, minimum, and quantile output values, which are calculated in real time through an embedded statistical module to quantify the central tendency and range of variation of the output. The output mean describes the overall level of output; the output variance describes the degree of dispersion in the output distribution.

[0050] It should be noted that the volatility characteristics are extracted from the new energy output sequence using a volatility quantification algorithm, and the specific calculation formula is as follows: in This is expressed as the average fluctuation range. The standard deviation of the fluctuation. for The output value at each time point; further, the average fluctuation amplitude describes the drasticness of the output change; the standard deviation of the fluctuation describes the speed stability of the output change; the volatility characteristics capture the first difference and second moment of the output time series, and calculate the difference between consecutive time points through a hardware accelerator to evaluate the dynamic behavior of new energy output.

[0051] It should be noted that the intermittency and uncertainty characteristics are extracted from the renewable energy output sequence through a reliability assessment algorithm; the output availability rate is calculated as the ratio of the number of time points in a specified time window where the output value is greater than a preset threshold to the total number of time points, used to describe the reliability of power generation; the output uncertainty index is calculated as the average of the sum of squares of the differences between the actual output value and the predicted output value based on historical data, used to describe the predictability of power generation; furthermore, the reliability assessment algorithm uses a sliding window to analyze the frequency of output interruptions and prediction errors, and quantifies the intermittent mode of renewable energy power generation through a probability model, wherein the threshold is dynamically adjusted according to the renewable energy type and operating environment to accurately reflect the availability and fluctuation risk of power generation.

[0052] It should be noted that the temporal and shape features are extracted from the new energy output sequence using a shape analysis algorithm. The output-time correlation coefficient is calculated as the sum of the products of the deviation between the time point and the average time point and the deviation between the output value and the average output value, divided by the square root of the product of the sum of squares of time deviations and the sum of squares of output deviations, used to describe the distribution pattern of output on the time axis. The output skewness is calculated as the average of the cubes of the standardized deviations between the output value and the average output value, used to describe the asymmetry of the sequence shape. Furthermore, the shape analysis algorithm extracts the periodic components through Fourier transform and uses morphological operators to identify the peak and valley patterns of the output curve to reflect the temporal evolution law of new energy output. The standardized deviation is calculated based on the standard deviation of the output sequence to eliminate the influence of dimensions.

[0053] 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.

[0054] Step 3: Demand index generation: Based on the state sequence and the characteristics of new energy output, voltage stability demand index is generated.

[0055] 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.

[0056] 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 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 represents the weighting factor corresponding to the power output fluctuation.

[0057] It should be noted that the voltage stability demand index quantifies the scalar value of the grid voltage stability demand, calculated by weighted average voltage deviation and output fluctuation. The larger the value, the higher the voltage stability demand. The voltage value is a real-time voltage measurement extracted from the state sequence, reflecting the instantaneous voltage state of the grid. The reference value of the voltage value is a preset rated voltage used to assess voltage deviation. The fluctuating output value is a real-time output value extracted from the output characteristics of new energy sources, representing the power generation of new energy sources. The average value of the output fluctuation value is the arithmetic mean of the fluctuation characteristics of the output characteristics of new energy sources, used to standardize the output fluctuation.

[0058] It should be noted that the weighting factors corresponding to voltage and output fluctuation are obtained by factor analysis. First, the information of voltage and output fluctuation is condensed, and then the variance explained after rotation is obtained. The weights are obtained by dividing the cumulative variance explained.

[0059] It should be noted that factor analysis is a well-known technique. It is a multivariate statistical analysis method that starts by studying the internal dependencies of variables and reduces some variables with complex relationships to a few comprehensive factors. Information condensation is expressed as the calculation of the median. The variance explained rate is the amount of information extracted by the factors. Variance explained rate = eigenvalues ​​ / total number of analysis terms. The rotated variance explained rate is expressed as the variance explained by the factors after maximum variance rotation.

[0060] It should be noted that the extraction of the voltage stability sequence is based on the state sequence. The embedded data selection module filters voltage-related dimensions from the state sequence to generate a voltage value time series. Furthermore, the extraction of the voltage stability sequence utilizes a hardware-level indexer to access the state sequence storage area in real time, isolates the voltage data stream, and optimizes data read latency through cache alignment to ensure the continuity and consistency of the voltage sequence.

[0061] It should be noted that the extraction of volatility features is based on the aforementioned new energy output features. A subset of volatility features, such as average volatility amplitude or volatility standard deviation, is selected from the new energy output features using a feature selection algorithm. Furthermore, the physical implementation of the extraction of volatility features relies on an embedded processor to perform parallel feature extraction, calculate volatility indices using a sliding window, and dynamically adjust feature weights through a numerical comparator to accurately reflect the impact of new energy output on voltage stability.

[0062] This application's indicator is not a traditional, single-factor voltage limit judgment, but a composite indicator that integrates the current state, historical trends, and future forecasts. It may be a quantified "demand value," representing how much reactive / active power the system needs to absorb or generate to maintain voltage stability over the next few minutes. Transforming the abstract "voltage stability demand" into a specific, quantifiable power support value makes the control objective very clear. Because the indicator is forward-looking, the system can identify the demand before a truly dangerous voltage drop occurs, thus proactively calling upon energy storage resources to intervene and prevent the situation from worsening.

[0063] Step 4: Energy storage support strategy generation: Based on the voltage stability demand index and the pre-acquired hybrid energy storage status data, an energy storage support strategy is generated.

[0064] In a specific example, generating an energy storage support strategy based on the voltage stability demand index and 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 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.

[0065] It should be noted that the voltage is determined to be abnormal by comparing the voltage stability requirement index with the voltage stability requirement index threshold. When the voltage stability requirement index is greater than the voltage stability requirement index threshold, the voltage is determined to be abnormal and hybrid energy storage support is required. When the voltage stability requirement index is less than or equal to the voltage stability requirement index threshold, the voltage is determined to be normal and hybrid energy storage support is not required.

[0066] Furthermore, the voltage stability demand threshold is a scalar value determined through statistical analysis based on historical power grid operation data. It is used to ensure that the voltage is stable within a safe range and is the critical point for the power grid voltage stability demand. If this value is exceeded, energy storage intervention is required to prevent voltage instability.

[0067] It should be noted that the preset optimization algorithm is a linear programming algorithm, and its mathematical expression is as follows: in, The decision variable vector includes the battery output value. and flywheel energy storage output value ; Represented as the coefficient vector of the objective function. For the constraint matrix, The constraint vector represents the energy storage output target; 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 battery's output power during the dispatch cycle, which is the battery's power compensation to the grid; the flywheel energy storage output value represents the flywheel energy storage's output power during the dispatch cycle, which is the flywheel energy storage's rapid power response to the grid; the constraint matrix is ​​determined based on the hybrid energy storage state data; the constraint vector is determined based on the hybrid energy storage state data.

[0068] Furthermore, the decision variable vector objective function coefficient vector ,in and The weighting coefficients are calculated based on the voltage stability demand index and are used to reflect the relative importance of battery and flywheel energy storage to voltage stability. They represent the degree of preference for different energy storage types during the optimization process. The constraint matrix and constraint vector are constructed based on the hybrid energy storage state data, including battery state of charge limits, charge and discharge power limits, and flywheel energy storage capacity limits. They represent the physical operating boundaries of the energy storage devices and ensure the feasibility and safety of the strategy.

[0069] It should be noted that the energy storage support strategy is generated based on the energy storage output target. The output value is converted into control commands, including charging and discharging time and power setting value, through a strategy mapping algorithm.

[0070] Furthermore, the strategy mapping algorithm uses a lookup table or rule engine to discretize the continuous output value into an executable control sequence, and sends it to the hybrid energy storage device through a communication interface. Its physical essence is that the embedded processor performs numerical-to-instruction conversion and generates real-time control signals through pre-stored scheduling rules to drive the energy storage device to perform charging and discharging operations.

[0071] Step 5: Control of hybrid energy storage devices: Control the charging and discharging operations of hybrid energy storage devices based on the energy storage support strategy.

[0072] In a specific example, the control of the hybrid energy storage device for charging and discharging operations based on the energy storage support strategy includes: parsing control commands for the hybrid energy storage device based on the energy storage support strategy to obtain the energy output commands of the battery and flywheel; simultaneously collecting real-time voltage measurement values; then calculating the voltage error based on the real-time voltage measurement values ​​and reference voltage values ​​through a closed-loop control algorithm to obtain the voltage adjustment amount; and finally adjusting the output of the hybrid energy storage device based on the voltage adjustment amount to stabilize the voltage.

[0073] It should be noted that the preset closed-loop control algorithm is based on the proportional-integral control algorithm for voltage error, and the specific process is as follows: Through the calculation formula... Calculate the voltage adjustment amount , Represented as proportional gain, Represented as integral gain, The reference value is represented as the voltage value. Represented as real-time voltage measurement value, It is expressed as the continuous duration of the control process.

[0074] Furthermore, the voltage adjustment amount represents the adjusted power value of the hybrid energy storage device's output, which is a power command to compensate for voltage deviations by controlling the charging and discharging of the energy storage device; the proportional gain represents the instantaneous response coefficient of the voltage error to the adjustment amount, which is the sensitivity of the adjustment amount to changes in voltage deviation, and the larger the value, the faster the response; the integral gain represents the long-term correction coefficient of the accumulated voltage error to the adjustment amount, which is to eliminate the cumulative effect of steady-state error, and the larger the value, the higher the steady-state accuracy; the continuous duration of the control process is the time base for integral calculation. The physical implementation of the preset closed-loop control algorithm relies on the embedded processor to perform discrete calculations, acquire voltage signals through an analog-to-digital converter, and accumulate errors using a digital integrator to generate continuous adjustment commands to ensure rapid voltage stabilization.

[0075] It should be noted that the energy storage output target is analyzed based on the energy storage support strategy. The battery output value and flywheel energy storage output value are extracted by the strategy analysis module and converted into an instruction format that can be executed by the device.

[0076] Furthermore, the essence of analyzing the energy storage output target is to use an embedded processor to perform data decoding, discretize the continuous values ​​output by the optimization algorithm into pulse width modulation signals or digital instructions, and send them to the energy storage device controller through the communication interface.

[0077] It should be noted that the output of the hybrid energy storage device is adjusted based on the voltage adjustment amount. The charging and discharging power of the battery and flywheel energy storage is modified through the output control module. The battery output adjustment focuses on slow compensation, while the flywheel energy storage output adjustment focuses on fast response.

[0078] Furthermore, the physical realization of adjusting the output power relies on the power electronic switch to perform pulse width modulation, convert DC power into AC power through the inverter, and use the feedback circuit to monitor the output power effect in real time to maintain voltage stability in a closed-loop manner.

[0079] This application constructs an intelligent execution system of "strategic collaborative optimization and precise closed-loop control," achieving seamless integration from decision-making to action. This strategy not only responds to the voltage support requirements of the power grid but also deeply integrates the differentiated states and characteristics within hybrid energy storage (such as battery and flywheel energy storage), achieving optimal power and energy allocation while considering equipment lifespan and response speed. By leveraging the synergistic advantages of hybrid energy storage, it achieves a "combination of fast and slow, maximizing strengths and minimizing weaknesses," significantly improving support efficiency and economy. A complete automated closed loop of "sensing-decision-execution-correction" is constructed, which not only greatly enhances the resilience and stability of the power grid in the face of disturbances but also effectively extends the lifespan of energy storage equipment through optimized charging and discharging strategies, achieving a dual improvement in grid security and asset efficiency.

[0080] Please see Figure 2 As shown, this application provides a system for a hybrid energy storage support method for voltage stability in areas with a high proportion of new energy sources in its second aspect.

[0081] The system 100 of the hybrid energy storage support method for voltage stabilization in areas with a high proportion of new energy sources, as described in this invention, can be installed in an electronic device. Depending on the functions implemented, the system 100 may include a vector generation module 101, a data generation module 102, a demand index generation module 103, an energy storage support strategy generation module 104, and a hybrid energy storage device control module 105. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0082] 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 source based on the pre-acquired power grid data and new energy output data.

[0083] The data generation module generates a state sequence of the power grid based on the power grid's state vector; and generates a power output sequence of the new energy source based on the power output vector of the new energy source, thereby generating the power output characteristics of the new energy source.

[0084] The demand index generation module generates voltage stability demand indexes based on the state sequence and the output characteristics of new energy sources.

[0085] The energy storage support strategy generation module generates an energy storage support strategy based on the voltage stability requirement index and the pre-acquired hybrid energy storage status data.

[0086] The hybrid energy storage device control module controls the hybrid energy storage device to perform charging and discharging operations based on the energy storage support strategy.

[0087] The hybrid energy storage support method and system for voltage stability in areas with a high proportion of renewable energy provided in this application vectorizes grid data and generates state sequences and renewable energy output characteristics, providing a high-quality data foundation for subsequent analysis. It can proactively and accurately identify weak links and risk points in the grid, transforming abstract stability issues into specific quantitative indicators. Based on these precise demand indicators and real-time energy storage status, it analyzes voltage faults and dynamically generates optimal energy storage support strategies based on the analysis results, guiding the hybrid energy storage system to perform coordinated charging and discharging. This not only rapidly smooths out renewable energy power fluctuations and proactively maintains node voltage stability, effectively preventing voltage over-limit accidents, but also fully leverages the comprehensive advantages of hybrid energy storage in terms of response speed and duration.

[0088] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0089] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0090] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0091] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0092] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for supporting voltage stability and hybrid energy storage in areas with a high proportion of new energy sources, characterized in that, include: Step 1, Vector Generation: Based on the pre-acquired power grid data and renewable energy output data, generate the power grid state vector and the renewable energy output vector; 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 output sequence, thereby generating the new energy output characteristics; Step 3: Demand index generation: Based on the state sequence and the characteristics of new energy output, generate voltage stability demand indexes; Step 4: Energy storage support strategy generation: Based on the voltage stability demand index and the pre-acquired hybrid energy storage state data, an energy storage support strategy is generated; Step 5: Control of hybrid energy storage devices: Control the charging and discharging operations of hybrid energy storage devices based on the energy storage support strategy.

2. The method for supporting voltage stability and hybrid energy storage in areas with a high proportion of new energy sources according to claim 1, characterized in that, The power grid data includes voltage, current, power, and power frequency; the new energy output data includes power output, frequency, temperature, pressure, and efficiency.

3. The method for supporting voltage stability and hybrid energy storage in areas with a high proportion of new energy sources according to claim 1, characterized in that, The process of generating a grid state vector and a new energy output vector based on pre-acquired grid data and new energy output data includes: Based on the pre-acquired power grid data, a preset state estimation algorithm is used to calculate the power grid state estimate; and the power grid state estimate is vector-encoded to obtain the power grid state vector. Based on the pre-acquired renewable energy output data, a preset state estimation algorithm is used to calculate the renewable energy output estimate; and the renewable energy output estimate is vector-encoded to obtain the renewable energy output vector.

4. The method for supporting voltage stability and hybrid energy storage in areas with a high proportion of new energy sources according to claim 1, characterized in that, The characteristics of the new energy output include basic statistical characteristics, volatility characteristics, intermittent and uncertain characteristics, and time-series and shape characteristics.

5. The method for supporting voltage stability and hybrid energy storage in areas with a high proportion of new energy sources according to claim 1, characterized in that, The process involves generating a power grid state sequence based on the power grid's state vector, and generating a new energy power output sequence based on the new energy power output vector, thereby generating new energy power output characteristics, including: The state vectors of the power grid are concatenated over time to form a power grid state matrix; the power grid state matrix is ​​processed using a preset feature dimensionality reduction algorithm to obtain a power grid state sequence; the output vectors of the new energy sources are concatenated over time to form a new energy output matrix; the new energy output matrix is ​​processed using a preset feature dimensionality reduction algorithm to obtain a new energy output sequence and generate new energy output features.

6. The method for supporting voltage stability and hybrid energy storage in areas with a high proportion of new energy sources according to claim 1, characterized in that, The generation of voltage stability demand indicators based on the state sequence and the characteristics of new energy output includes: Based on the state sequence, a voltage stability sequence is extracted; based on the new energy output characteristics, volatility characteristics are extracted; and based on the voltage stability sequence and volatility characteristics, a voltage stability demand index is calculated using a voltage stability demand algorithm.

7. The method for supporting voltage stability and hybrid energy storage in areas with a high proportion of new energy sources according to claim 6, characterized in that, 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: 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 represents the weighting factor corresponding to the power output fluctuation.

8. The method for supporting voltage stability and hybrid energy storage in areas with a high proportion of new energy sources according to claim 1, characterized in that, The generation of energy storage support strategies based on the voltage stability demand index and pre-acquired hybrid energy storage state data includes: Based on the voltage stability requirement index, it is determined whether the voltage is abnormal. When it is determined that the voltage is abnormal, hybrid energy storage support is required. The voltage stability requirement 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.

9. The method for supporting voltage stability and hybrid energy storage in areas with a high proportion of new energy sources according to claim 1, characterized in that, The method of controlling the charging and discharging operation of hybrid energy storage devices based on energy storage support strategies includes: Based on the energy storage support strategy, control commands for the hybrid energy storage device are parsed to obtain the energy output commands for the battery and flywheel. Simultaneously, real-time voltage measurements are collected. Then, based on the real-time voltage measurements and reference voltage values, a closed-loop control algorithm is used to calculate the voltage error to obtain the voltage adjustment amount. Finally, the output of the hybrid energy storage device is adjusted based on the voltage adjustment amount to stabilize the voltage.

10. A system for implementing the hybrid energy storage support method for voltage stability in areas with a high proportion of new energy sources as described in any one of claims 1-9, characterized in that, include: The vector generation module generates the grid state vector and the new energy output vector based on the pre-acquired grid data and new energy output data. The data generation module generates a power grid state sequence based on the power grid state vector; and generates a new energy power output sequence based on the new energy power output vector, thereby generating new energy power output characteristics. The demand index generation module generates voltage stability demand indexes based on the state sequence and the characteristics of new energy output. The energy storage support strategy generation module generates an energy storage support strategy based on the voltage stability requirement index and the pre-acquired hybrid energy storage state data. The hybrid energy storage device control module controls the hybrid energy storage device to perform charging and discharging operations based on the energy storage support strategy.

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