Voltage fluctuation rapid suppression method based on composite energy storage system

Abstract

The invention relates to the technical field of power grid energy storage, in particular to a voltage fluctuation rapid suppression method based on a composite energy storage system, which realizes targeted response to voltage fluctuation of different frequencies by setting a dual-time scale power distribution strategy, improves the improvement efficiency of the energy storage system, and improves the suppression efficiency of the composite energy storage system. Through a composite control structure of an adaptive PI controller and a feedforward compensation link, the voltage control precision and the harmonic suppression rate are improved, and according to peak-valley electricity price characteristics and load fluctuation rules of different scenes, the power grid line voltage fluctuation type is monitored in real time, and a dynamic parameter library and an online identification algorithm are combined. The system can automatically adapt to factors such as hardware aging and environment change, a long-term control effect is maintained, and the charging and discharging frequency of the lithium iron battery is reduced by setting the high-frequency power buffer effect of the super capacitor, so that the cycle life of the lithium iron battery is prolonged; meanwhile, the overall energy conversion efficiency of the system is improved through cooperative control of the modular multilevel converter topology and the double energy storage elements.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid energy storage, and particularly relates to a method for quickly suppressing voltage fluctuations based on a composite energy storage system. Background Art

[0002] With the continuous expansion of China's power grid capacity, the peak-valley difference is increasing continuously. With the booming development of renewable energy, distributed energy supply, and smart grid, the demand for large-scale development of battery energy storage technology is increasing day by day.

[0003] However, in the prior art, traditional energy storage systems mostly adopt single-type energy storage elements. Although lithium batteries have a high energy density, their power response speed is relatively slow, making it difficult to cope with high-frequency voltage fluctuations. Frequent high-frequency charge and discharge will also cause rapid decay of the battery life. Although supercapacitors can achieve microsecond-level response, their energy density is low, and they cannot independently undertake the task of low-frequency power regulation. Moreover, the overall efficiency of a single-element system is relatively low. And traditional energy storage inverters generally adopt a proportional-integral controller with fixed parameters and a feedback control structure, which cannot dynamically adapt to the changes in the power grid operating conditions, resulting in low voltage control accuracy and poor harmonic suppression effect, so as to meet the real-time requirements of the power grid. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the technical solution adopted by the present invention to solve its technical problems is: a method for quickly suppressing voltage fluctuations based on a composite energy storage system, including the following steps:

[0005] Step S1: Real-time collect the grid line voltage fluctuation signal through a grid voltage sensor, and identify the voltage fluctuation type based on the instantaneous frequency detection algorithm; real-time collect the line voltage fluctuation signal through a grid voltage sensor, and use FFT spectrum analysis to identify the voltage fluctuation type, providing a data basis for the subsequent control strategy, accurately identifying the fluctuation type, avoiding ineffective actions of the energy storage system caused by "misjudgment", providing a decision basis for the power distribution strategy, with high-frequency fluctuations responded by the supercapacitor and low-frequency fluctuations responded by the lithium iron phosphate battery.

[0006] Step S2: Perform coordinate rotation transformation on the collected grid voltage and current signals to separate the active current component and the reactive current component; perform coordinate rotation transformation on the voltage and current signals to separate the active current component and the reactive current component, and adjust the reactive current reference value through an adaptive controller to make the energy storage system output a reverse reactive compensation current, realizing the decoupling control of active and reactive power, specifically suppressing the core cause of voltage fluctuations, and the feed-forward compensation link cancels the grid voltage disturbance in advance, shortening the control delay and improving the dynamic response speed.

[0007] Step S3: When it is detected that the grid voltage deviation exceeds the threshold, trigger the fast charge and discharge mode of the supercapacitor, and complete the power output switching through the sub-DC / DC converter; when the voltage deviation exceeds ±5% of the rated voltage of the threshold, trigger the fast charge and discharge mode of the supercapacitor, and complete the power switching through the sub-module DC / DC converter, giving priority to suppressing high-frequency voltage fluctuations. Utilize the characteristics of the supercapacitor of "high power density and fast response" to achieve "instantaneous buffering" of sudden voltage fluctuations, reduce the high-frequency charge and discharge burden of the lithium iron phosphate battery, and extend its cycle life.

[0008] Step S4: Establish a dynamic parameter library including hardware selection parameters and control parameters, and update the dynamic and static model parameters of the energy storage system in real time through an online identification algorithm. Establish a dynamic parameter library including the switching frequency of power devices and the PWM modulation coefficient, and update the model parameters in real time through the recursive least squares method to adapt to parameter drifts caused by factors such as hardware aging and environmental changes of the energy storage system, maintain control accuracy, provide real-time data support for machine learning algorithms, and optimize the dynamic adaptability of control strategies.

[0009] The present invention is further configured such that the step S1 further includes calculating the required reactive power compensation amount under the current working condition according to a preset grid connection criterion; based on a dual-time-scale power distribution strategy, distributing high-frequency power fluctuations to the supercapacitor and low-frequency power fluctuations to the lithium iron phosphate battery. Give play to the characteristics of fast response of the supercapacitor and high energy density of the lithium iron phosphate battery, reduce the "function overlap" between different components, and improve the overall efficiency of the system.

[0010] The present invention is further configured such that the specific method of the dual-time-scale power distribution strategy is as follows:

[0011] Set a frequency cut-off threshold Fc. When the power fluctuation frequency F > Fc, the supercapacitor undertakes 90% of the power regulation tasks; when F ≤ Fc, the lithium iron phosphate battery undertakes the main power regulation tasks, and the supercapacitor only provides auxiliary support.

[0012] The present invention is further configured such that the step S2 adjusts the reactive current reference value of the energy storage converter in real time through an adaptive proportional-integral controller, so that the composite energy storage system outputs a compensation current equal in magnitude and opposite in direction to the grid reactive power fluctuation to suppress voltage fluctuations; and a feed-forward compensation link is introduced to eliminate the influence of grid voltage disturbances on the control loop. Compared with the traditional fixed-parameter PI control, the voltage control accuracy under non-linear working conditions is improved, and the feed-forward compensation shortens the response time of the system to voltage surges / dips.

[0013] In the method for rapid suppression of voltage fluctuations based on a composite energy storage system, after the grid voltage and current signals are collected by the grid voltage sensor, the signals need to be subjected to coordinate rotation transformation to separate the active current component and the reactive current component. At this time, by introducing a feedforward compensation link, the disturbance signal of the grid voltage can be detected in real time, and it can be directly superimposed on the control input of the energy storage converter as a feedforward quantity. The role of this link is to offset the impact of grid voltage fluctuations on the control loop in advance, avoid the adjustment delay caused by the hysteresis of traditional feedback control, thereby improving the dynamic response speed of the composite energy storage system to voltage fluctuations, and ensure that accurate compensation current is quickly output during the reactive power decoupling control process to maintain the stability of the grid line voltage.

[0014] The present invention is further configured such that step S4 also includes establishing a voltage fluctuation feature database based on historical operation data, predicting the optimal control parameter combination under different working conditions through a machine learning algorithm, and adjusting the response strategy of the energy storage system. The LSTM algorithm is used to predict the voltage fluctuation trend, and the reinforcement learning algorithm DRL optimizes the control parameters to realize the "prediction-optimization-control" closed loop, thereby predicting the voltage fluctuation type and amplitude in advance, so that the energy storage system can adjust the power distribution strategy in advance, and the multi-objective optimization model that takes into account voltage stability, life, and efficiency improves the overall performance of the system.

[0015] The present invention is further configured that the steps of establishing the voltage fluctuation characteristic database are:

[0016] Step A1: Use the grid voltage sensor to collect the original data of voltage fluctuations in the operation of the construction site grid in real time; the data includes but is not limited to the real-time monitoring values of voltage amplitude, frequency, and phase; the type of voltage fluctuation and the corresponding time domain / frequency domain characteristic parameters, and the response data of the composite energy storage system under different working conditions;

[0017] Step A2: De-noise and normalize the original data, remove outliers and interference signals, classify and label the data according to the voltage fluctuation type and time scale, and establish a standardized data format; classify and label the data according to the voltage fluctuation type and time scale, and establish a standardized data format.

[0018] Step A3: extract key characteristic parameters as the core fields of the database for different types of voltage fluctuations, and supplement the characteristic parameters in combination with the energy storage system operation data; the supplementary characteristic parameters are the real-time charge and discharge power distribution ratio of the energy storage element and the control parameters of the energy storage converter;

[0019] Step A4: Use structured storage to establish a data table and index mechanism; basic information includes data collection time, grid operation conditions and weather conditions; fluctuation characteristics include fluctuation type, characteristic parameter value and severity level; system response includes energy storage system control strategy, output compensation, response time and control parameter combination;

[0020] Step A5: Through long-term operation monitoring of the energy storage system, continuously collect new data and supplement it to the database to achieve dynamic update. Regularly verify the validity of the historical data in the database, eliminate redundant or incorrect records, ensure the accuracy and timeliness of the data, verify the model accuracy through the historical data in the database, adjust the algorithm parameters, and achieve the prediction and recommendation of the optimal control strategy under different working conditions.

[0021] The present invention is further configured such that the establishment steps of the machine learning algorithm are as follows:

[0022] Step B1: Through historical voltage fluctuation data and energy storage system response data, predict the voltage fluctuation types and characteristic parameters under different working conditions;

[0023] Step B2: Adopt a time series prediction algorithm to capture the time series characteristics of the voltage signal and predict the voltage fluctuation; for control parameter optimization, adopt deep reinforcement learning DRL to search for the optimal parameter combination based on a multi-objective optimization function;

[0024] Step B2: Extract historical data from the voltage fluctuation feature database, divide the data into a training set, a validation set, and a test set, and perform normalization processing on the numerical features; the input features are voltage fluctuation type, amplitude, frequency, duration, energy storage element SoC, and grid working conditions, and the output features are optimal control parameters;

[0025] The input layer is used to receive the preprocessed voltage fluctuation feature sequence, the hidden layer contains multiple LSTM units to capture the long-term dependence relationship in the time series, and the output layer designs the activation function according to the prediction target;

[0026] Step B3: Use the training set for iterative training, minimize the loss function through the backpropagation algorithm, and then gradually learn the optimal control strategy through the interaction between the intelligent agent and the energy storage system simulation environment; adopt the grid search or random search method to optimize the model hyperparameters, evaluate the model performance through the validation set, and select the parameter combination with the best generalization ability.

[0027] Step B4: Use the test set to evaluate the model prediction accuracy, and embed the trained model into the control unit of the composite energy storage system; the root mean square error, mean absolute error, and classification accuracy of the time series prediction, embed the trained model into the control unit of the composite energy storage system, realize the online prediction of the voltage fluctuation trend, adjust the power distribution strategy of the energy storage element in advance, automatically optimize the control parameters according to the real-time working conditions, and dynamically improve the voltage suppression effect.

[0028] Step B5: With the real-time update of the voltage fluctuation feature database, regularly use the new data to perform incremental training on the model to adapt to the changes in the grid working conditions.

[0029] The beneficial effects of the present invention are as follows:

[0030] 1. By setting a dual-time-scale power distribution strategy, the present invention realizes targeted response to voltage fluctuations of different frequencies, shortens the response time, improves the efficiency of the energy storage system, and improves the voltage control accuracy and harmonic suppression rate through the composite control structure of an adaptive PI controller + a feed-forward compensation link, effectively solving the hysteresis problem of traditional fixed-parameter control. In view of the peak-valley electricity price characteristics and load fluctuation rules of different scenarios, by real-time monitoring the type of grid line voltage fluctuations and combining a dynamic parameter library with an online identification algorithm, the system can automatically adapt to factors such as hardware aging and environmental changes, and maintain long-term control effects.

[0031] 2. By setting the high-frequency power buffering function of the super capacitor, the present invention reduces the charge and discharge frequency of the lithium iron phosphate battery, prolonging its cycle life; at the same time, the modular multilevel converter topology and the coordinated control of the dual energy storage elements improve the overall energy conversion efficiency of the system, taking into account voltage stability, state of charge balance, and operation loss through a multi-objective optimization algorithm, avoiding the singularity of traditional single-objective control, and further prolonging the comprehensive life of the energy storage device. Specific embodiments

[0032] The following further describes the present invention in detail in conjunction with specific embodiments. The embodiments of the present invention are given for purposes of illustration and description, and are not exhaustive or limit the present invention to the disclosed form. Many modifications and variations are obvious to those of ordinary skill in the art. The embodiments are selected and described to better illustrate the principles and practical applications of the present invention, and enable those of ordinary skill in the art to understand the present invention and thus design various embodiments with various modifications suitable for specific purposes.

[0033] Embodiment:

[0034] The present invention provides a technical solution: a method for quickly suppressing voltage fluctuations based on a composite energy storage system, including the following steps:

[0035] Step S1: Real-time collect the grid line voltage fluctuation signal through a grid voltage sensor, and identify the voltage fluctuation type based on the instantaneous frequency detection algorithm;

[0036] Step S2: Perform coordinate rotation transformation on the collected grid voltage and current signals to separate the active current component and the reactive current component;

[0037] Step S3: When it is detected that the grid voltage deviation exceeds the threshold, trigger the fast charge and discharge mode of the super capacitor, and complete the power output switching through the sub-DC / DC converter;

[0038] Step S4: Establish a dynamic parameter library including hardware selection parameters and control parameters, and update the dynamic and static model parameters of the energy storage system in real time through an online identification algorithm.

[0039] Step S1 also includes calculating the reactive power compensation required under the current working conditions according to the preset grid connection criteria; based on the dual time scale power allocation strategy, allocating high frequency power fluctuations to supercapacitors and low frequency power fluctuations to iron-lithium batteries.

[0040] The specific method of the dual time scale power allocation strategy is:

[0041] Set the frequency cutoff threshold Fc. When the power fluctuation frequency F>Fc, the supercapacitor will take on 90% of the power regulation task. When F≤Fc, the lithium iron battery will take on the main power regulation task, and the supercapacitor will only provide auxiliary support.

[0042] Step S2 adjusts the reactive current reference value of the energy storage converter in real time through an adaptive proportional-integral controller, so that the composite energy storage system outputs a compensation current that is equal to and opposite to the reactive power fluctuation of the power grid, thereby suppressing voltage fluctuations; and introduces a feedforward compensation link to eliminate the influence of power grid voltage disturbances on the control loop.

[0043] Step S4 also includes establishing a voltage fluctuation characteristic database based on historical operating data, predicting the optimal control parameter combination under different operating conditions through a machine learning algorithm, and adjusting the response strategy of the energy storage system.

[0044] The steps to establish the voltage fluctuation characteristic database are:

[0045] Step A1, using a power grid voltage sensor to collect raw data of voltage fluctuations in the operation of the construction site power grid in real time;

[0046] Step A2: De-noise and normalize the original data, remove outliers and interference signals, classify and label the data according to voltage fluctuation type and time scale, and establish a standardized data format;

[0047] Step A3: for different types of voltage fluctuations, extract key characteristic parameters as core fields of the database, and supplement the characteristic parameters in combination with the energy storage system operation data;

[0048] Step A4: Use structured storage to establish data tables and index mechanisms;

[0049] Step A5: Through long-term operation monitoring of the energy storage system, new data is continuously collected and added to the database to achieve dynamic updates.

[0050] The steps to build a machine learning algorithm are:

[0051] Step B1: Predict the voltage fluctuation types and characteristic parameters under different working conditions based on historical voltage fluctuation data and energy storage system response data;

[0052] Step B2: Adopt a time series prediction algorithm to capture the time series characteristics of the voltage signal and predict the voltage fluctuation;

[0053] Step B2: Extract historical data from the voltage fluctuation feature database, divide the data into a training set, a validation set, and a test set, and perform normalization processing on numerical features;

[0054] Step B3: Use the training set for iterative training, minimize the loss function through the backpropagation algorithm, and then gradually learn the optimal control strategy through the interaction between the agent and the energy storage system simulation environment;

[0055] Step B4: Use the test set to evaluate the prediction accuracy of the model, and embed the trained model into the control unit of the composite energy storage system;

[0056] Step B5: As the voltage fluctuation feature database is updated in real time, regularly use new data to perform incremental training on the model to adapt to the changes in the grid working conditions.

[0057] Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art and related fields without creative efforts shall fall within the protection scope of the present invention. The structures, devices, and operation methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art without special instructions and limitations.

Claims

1. A method for rapidly suppressing voltage fluctuations based on a composite energy storage system, characterized in that, The following steps are involved: Step S1, collecting grid line voltage fluctuation signals in real time through a grid voltage sensor, and identifying the voltage fluctuation type based on an instantaneous frequency detection algorithm; Step S2, performing coordinate rotation transformation on the collected grid voltage and current signals to separate active current components and reactive current components; Step S3: When it is detected that the grid voltage deviation exceeds the threshold, the fast charge and discharge mode of the supercapacitor is triggered, and the power output switching is completed through the sub-DC / DC converter; Step S4: Establish a dynamic parameter library including hardware selection parameters and control parameters, and update the dynamic and static model parameters of the energy storage system in real time through an online identification algorithm.

2. A method for quickly suppressing voltage fluctuations based on a composite energy storage system according to claim 1, characterized in that: The step S1 also includes calculating the reactive power compensation required under the current working condition according to the preset grid connection criteria; Based on the dual-time-scale power allocation strategy, high-frequency power fluctuations are allocated to supercapacitors, and low-frequency power fluctuations are allocated to iron-lithium batteries.

3. A method for quickly suppressing voltage fluctuations based on a composite energy storage system according to claim 1, characterized in that: The specific method of the dual time scale power allocation strategy is: Set the frequency cutoff threshold Fc. When the power fluctuation frequency F>Fc, the supercapacitor will take on 90% of the power regulation task. When F≤Fc, the lithium iron battery will take on the main power regulation task, and the supercapacitor will only provide auxiliary support.

4. A method for quickly suppressing voltage fluctuations based on a composite energy storage system according to claim 1, characterized in that: The step S2 adjusts the reactive current reference value of the energy storage converter in real time through an adaptive proportional-integral controller, so that the composite energy storage system outputs a compensation current that is equal to and opposite to the reactive power fluctuation of the power grid, thereby suppressing voltage fluctuations; and introduces a feedforward compensation link to eliminate the influence of power grid voltage disturbances on the control loop.

5. A method for quickly suppressing voltage fluctuations based on a composite energy storage system according to claim 1, characterized in that: The step S4 also includes establishing a voltage fluctuation characteristic database based on historical operating data, predicting the optimal control parameter combination under different working conditions through a machine learning algorithm, and adjusting the response strategy of the energy storage system.

6. A method for quickly suppressing voltage fluctuations based on a composite energy storage system according to claim 5, characterized in that: The steps of establishing the voltage fluctuation characteristic database are: Step A1, using a power grid voltage sensor to collect raw data of voltage fluctuations in the operation of the construction site power grid in real time; Step A2: De-noise and normalize the original data, remove outliers and interference signals, classify and label the data according to voltage fluctuation type and time scale, and establish a standardized data format; Step A3: for different types of voltage fluctuations, extract key characteristic parameters as core fields of the database, and supplement the characteristic parameters in combination with the energy storage system operation data; Step A4: Use structured storage to establish data tables and index mechanisms; Step A5: Through long-term operation monitoring of the energy storage system, new data is continuously collected and added to the database to achieve dynamic updates.

7. A method for quickly suppressing voltage fluctuations based on a composite energy storage system according to claim 5, characterized in that: The steps for establishing the machine learning algorithm are: Step B1: predicting voltage fluctuation types and characteristic parameters under different working conditions through historical voltage fluctuation data and energy storage system response data; Step B2: using a time series prediction algorithm to capture the time series characteristics of the voltage signal and predict voltage fluctuations; Step B2: extract historical data from the voltage fluctuation feature database, divide the data into a training set, a validation set, and a test set, and normalize the numerical features; Step B3: Use the training set for iterative training. Minimize the loss function through the backpropagation algorithm, and then gradually learn the optimal control strategy through the interaction between the agent and the energy storage system simulation environment; Step B4: Use the test set to evaluate the model prediction accuracy, and embed the trained model into the control unit of the composite energy storage system; Step B5: As the voltage fluctuation feature database is updated in real time, use new data to perform incremental training on the model regularly to adapt to the changes in the grid operating conditions.