Energy storage converter control method and system for direct current microgrid

Abstract

The invention discloses an energy storage converter control method and system for a direct current micro-grid, and relates to the technical field of energy storage control, and the method comprises the steps: monitoring the power demand of the direct current micro-grid, and carrying out the segmentation to determine a high-frequency demand and a low-frequency demand; energy storage conversion control analysis is carried out according to the high-frequency requirement, and a high-frequency control strategy is determined; energy storage conversion control analysis is carried out according to low-frequency requirements, and a low-frequency control strategy is determined; and with the energy balance of the direct-current micro-grid as a target, performing equalization configuration on the high-frequency control strategy and the low-frequency control strategy to obtain a control equalization strategy of the energy storage converter. The technical problems that an existing energy storage control system cannot effectively distinguish high-frequency and low-frequency requirements and is poor in response speed and stability when coping with complex fluctuations are solved, the energy storage converter control strategy with energy balance is constructed through analysis of the high-frequency and low-frequency requirements, and the energy storage converter control strategy is optimized. And the technical effects of response speed and stability of the system in coping with complex power demand fluctuation are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage control, and particularly to a control method and system for an energy storage converter for a DC microgrid. Background Art

[0002] In modern power systems, with the wide application of renewable energy sources (such as solar energy and wind energy), DC microgrids, as a flexible distributed energy management method, are developing rapidly. DC microgrids integrate various energy forms and energy storage devices to provide users with efficient and stable power. However, in DC microgrids, the fluctuations of loads and power generation include both fast instantaneous high-frequency fluctuations and slow low-frequency fluctuations. Traditional energy storage control systems usually cannot effectively distinguish and independently process these two types of demands, resulting in inflexible responses when dealing with complex fluctuations and affecting the response speed and stability of the system. Summary of the Invention

[0003] This application provides a control method and system for an energy storage converter for a DC microgrid, which are used to solve the technical problem that the existing energy storage control system cannot effectively distinguish high-frequency and low-frequency demands and has poor response speed and stability when dealing with complex fluctuations.

[0004] In the first aspect of this application, a control method for an energy storage converter for a DC microgrid is provided. The method includes: monitoring the power demand of the DC microgrid, segmenting and determining high-frequency demand and low-frequency demand. The high-frequency demand is the instantaneous power change caused by the rapid fluctuations of loads or power generation, and the low-frequency demand is the long-term power change caused by the slow fluctuations of loads or power generation; performing energy storage conversion control analysis according to the high-frequency demand to determine a high-frequency control strategy; performing energy storage conversion control analysis according to the low-frequency demand to determine a low-frequency control strategy; aiming at the energy balance of the DC microgrid, performing balanced configuration on the high-frequency control strategy and the low-frequency control strategy to obtain a control balance strategy for the energy storage converter, including a high-frequency balance strategy and a low-frequency balance strategy.

[0005] The second aspect of the present application provides an energy storage converter control system for a DC microgrid. The system includes: a power demand segmentation module for monitoring the power demand of the DC microgrid and segmenting and determining high-frequency demand and low-frequency demand, where the high-frequency demand is the instantaneous power change caused by the rapid fluctuations of the load or power generation, and the low-frequency demand is the long-term power change caused by the slow change and fluctuation of the load or power generation; a high-frequency control strategy determination module for performing energy storage conversion control analysis according to the high-frequency demand and determining a high-frequency control strategy; a low-frequency control strategy determination module for performing energy storage conversion control analysis according to the low-frequency demand and determining a low-frequency control strategy; and a control strategy balanced configuration module for taking the energy balance of the DC microgrid as the goal and performing balanced configuration on the high-frequency control strategy and the low-frequency control strategy to obtain a control balanced strategy for the energy storage converter, including a high-frequency balanced strategy and a low-frequency balanced strategy.

[0006] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0007] The energy storage converter control method and system for a DC microgrid provided in the present application relate to the technical field of energy storage control. By monitoring the power demand of the DC microgrid and segmenting it into high-frequency demand and low-frequency demand, formulating a high-frequency control strategy and a low-frequency control strategy respectively according to the high-frequency demand and the low-frequency demand, and taking the energy balance of the DC microgrid as the goal, performing balanced configuration on the high-frequency control strategy and the low-frequency control strategy to obtain a control balanced strategy for the energy storage converter, it solves the technical problem that the existing energy storage control system cannot effectively distinguish high-frequency and low-frequency demands and has poor response speed and stability when dealing with complex fluctuations, and realizes the technical effect of constructing an energy balance energy storage converter control strategy through the analysis and control of high-frequency and low-frequency demands, ensuring that the system has good response speed and stability when dealing with complex power demand fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0009] Figure 1 It is a schematic flowchart of the energy storage converter control method for a DC microgrid provided by an embodiment of the present application;

[0010] Figure 2It is a schematic flow chart for obtaining the control equilibrium strategy of the energy storage converter through balanced configuration in the energy storage converter control method for a DC microgrid provided by an embodiment of the present application;

[0011] Figure 3 It is a schematic structural diagram of the energy storage converter control system for a DC microgrid provided by an embodiment of the present application.

[0012] Explanation of reference numerals: Power demand segmentation module 11, high-frequency control strategy determination module 12, low-frequency control strategy determination module 13, control strategy balanced configuration module 14. Detailed implementation manners

[0013] The present application provides an energy storage converter control method and system for a DC microgrid, which are used to solve the technical problems that the existing energy storage control system cannot effectively distinguish high-frequency and low-frequency demands, and has poor response speed and stability when dealing with complex fluctuations.

[0014] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0015] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned accompanying drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0016] Embodiment 1

[0017] As Figure 1 shown, the present application provides an energy storage converter control method for a DC microgrid, and the method includes:

[0018] P10: Monitor the power demand of the DC microgrid, and segment and determine the high-frequency demand and the low-frequency demand. The high-frequency demand is the instantaneous power change caused by the rapid fluctuations of the load or power generation, and the low-frequency demand is the long-term power change caused by the low-speed change fluctuations of the load or power generation.

[0019] Further, step P10 of the embodiment of the present application further includes:

[0020] P11: Obtain the cut-off frequency, where the cut-off frequency is determined by analyzing the typical change frequencies of the natural fluctuation period of the system load and the power generation fluctuation through historical samples; P12: Configure a high-pass filter channel and a low-pass filter channel based on the cut-off frequency, where the high-pass filter channel is used to extract high-frequency components higher than the cut-off frequency, and the low-pass filter channel is used to extract low-frequency components lower than the cut-off frequency; P13: Use the monitored DC microgrid power demand as an input variable, and extract high-frequency components and low-frequency components through the high-pass filter channel and the low-pass filter channel to determine the high-frequency demand and the low-frequency demand.

[0021] It should be understood that, first, the power demand of the DC microgrid is monitored, and the purpose is to divide the demand into high-frequency demand and low-frequency demand. High-frequency demand refers to the instantaneous power change caused by the rapid fluctuation of the load or power generation, usually occurring in a short period of time; while low-frequency demand is the long-term power change caused by the slow change and fluctuation of the load or power generation, manifested as a stable adjustment over a long period of time.

[0022] Specifically, before dividing the power demand, a key parameter - the cut-off frequency needs to be determined first. The cut-off frequency is obtained by deeply analyzing historical sample data, which covers the natural fluctuation period of the system load and the typical change frequencies of the power generation fluctuation. Through statistical analysis of these data, such as using data processing and statistical analysis techniques, including but not limited to time series analysis, spectrum analysis, etc., the key frequency characteristics are refined from the complex and changeable power data, that is, the demarcation point between the high-frequency and low-frequency components in the system power fluctuation is identified, namely the cut-off frequency.

[0023] Next, based on the cut-off frequency, a high-pass filter channel and a low-pass filter channel are configured. The high-pass filter channel is designed to allow signal components higher than the cut-off frequency to pass through, so as to extract the high-frequency part of the power demand. These high-frequency components usually correspond to the instantaneous changes caused by the rapid fluctuation of the load or power generation, which are the parts that the system needs to respond quickly to. The low-pass filter channel allows signal components lower than the cut-off frequency to pass through, and is used to extract the low-frequency part. These low-frequency components reflect the long-term stable demand of the system, which usually needs to be met through long-term adjustment. The design of these two filter channels is based on the filtering theory in digital signal processing. By selecting appropriate filter types and parameters (such as cut-off frequency, filter order, etc.), precise separation of different frequency components in the signal can be achieved.

[0024] Finally, the real-time monitored DC microgrid power demand is used as an input variable and sent to the high-pass filter channel and the low-pass filter channel for processing respectively. The high-pass filter channel outputs the high-frequency components above the cut-off frequency, that is, the instantaneous power changes caused by the rapid fluctuations of the load or power generation, namely the high-frequency demand; while the low-pass filter channel outputs the low-frequency components below the cut-off frequency, that is, the long-term power changes caused by the slow changes and fluctuations of the load or power generation, namely the low-frequency demand. These two demands are respectively used for formulating high-frequency control strategies and low-frequency control strategies in subsequent steps. By extracting these frequency components, the system can perform precise energy management for high-frequency and low-frequency demands, ensuring the overall stability and efficient operation of the system.

[0025] Furthermore, step P11 of the embodiment of the present application further includes:

[0026] P11-1: Collect load power fluctuation data and power generation power fluctuation data respectively, store them in the form of time series, and construct a historical sample; P11-2: Perform moving average smoothing processing on the historical sample to remove random fluctuations and fit the power change curve over time; P11-3: Perform time-domain fluctuation analysis according to the power change curve over time to determine short-term fluctuations and long-term fluctuations; P11-4: Perform frequency-domain analysis according to the power change curve over time to determine high-frequency components and low-frequency components; P11-5: Use the short-term fluctuations and long-term fluctuations, high-frequency components and low-frequency components as double verification to obtain the cut-off frequency.

[0027] Optionally, the specific process of obtaining the cut-off frequency can be as follows: First, collect load power fluctuation data and power generation power fluctuation data respectively, and store them in the form of time series to form a historical sample. These historical sample data include the power changes of the microgrid system under different loads and power generations, serving as the basic data for subsequent analysis.

[0028] In order to remove random fluctuations and noise in the historical sample, the moving average smoothing processing method is used to preprocess the data. Moving average processing is a technique for smoothing time series data, which can eliminate short-term random fluctuations, thereby more clearly showing the power change trend over time. On the smoothed data, the system fits the power change curve over time, providing a basis for further analysis.

[0029] After obtaining the smoothed power change curve, time-domain fluctuation analysis is performed. Through time series analysis, trend recognition algorithms, etc., analyze the power fluctuations over time. By identifying the fluctuation characteristics on the curve, the power fluctuations can be divided into short-term fluctuations and long-term fluctuations. Short-term fluctuations usually correspond to high-frequency demands, while long-term fluctuations are related to low-frequency demands. The technical support for this step includes.

[0030] After completing the time-domain analysis, frequency-domain analysis is carried out to more deeply understand the frequency characteristics of power fluctuations. Frequency-domain analysis uses mathematical tools such as Fourier transform to convert the time-domain signal into a frequency-domain signal, thereby revealing the distribution of different frequency components in the signal and determining the frequency ranges of high-frequency components and low-frequency components. High-frequency components correspond to rapidly fluctuating power changes, while low-frequency components reflect long-term power change trends.

[0031] Finally, using the short-term fluctuations and long-term fluctuations obtained from the time-domain analysis, as well as the high-frequency components and low-frequency components obtained from the frequency-domain analysis as double verification, jointly determine the cut-off frequency. The cut-off frequency is the demarcation point for distinguishing high-frequency demands and low-frequency demands, and its selection should ensure that it can accurately reflect the actual power fluctuation characteristics of the system. By comparing the short-term fluctuations and long-term fluctuations in the time-domain fluctuation analysis, as well as the high-frequency components and low-frequency components in the frequency-domain analysis, through the method of double verification, the accuracy and reliability of determining the cut-off frequency can be improved. It provides a solid technical support for the subsequent configuration of high-pass and low-pass filters, and helps the system to achieve precise power demand control under complex load and power generation conditions.

[0032] Furthermore, step P11-3 of the embodiment of the present application further includes:

[0033] P11-31: Set the determination node at a unit time point, and the node characteristics are the power value at this moment and the context time interval information; P11-32: Use the time interval and the power change amplitude as growth characteristics, and based on the node characteristics, perform feature growth to construct adjacent nodes, and repeatedly use the time interval and the power change amplitude for feature growth to construct the Nth layer of adjacent nodes; P11-33: Start aggregating the adjacent node characteristics from the Nth layer of adjacent nodes to obtain the change characteristics of the time point; P11-34: According to the change characteristics of the time point, perform time-domain fluctuation type discrimination, and evaluate and converge the loss value of the discrimination result and the labeled sample through a binary classification loss function until the convergence target is met, and determine the short-term fluctuation and the long-term fluctuation.

[0034] In a possible embodiment of the present application, in order to more accurately identify the short-term fluctuations and long-term fluctuations in the power fluctuations of the DC microgrid, the embodiment of the present application introduces a time-domain fluctuation analysis method based on feature growth and aggregation.

[0035] First of all, it is necessary to set the determination node at a unit time point. The characteristics of the node include the power value at this moment and the context time interval information between this moment and the previous and subsequent times. These characteristics not only record the instantaneous value of the power, but also reflect the power change situation of this moment relative to the surrounding time points, providing a basis for subsequent fluctuation analysis.

[0036] Next, using the time interval and the power change amplitude as growth features, feature growth is performed based on the node features. This process is similar to constructing a time tree, where each node grows new nodes through the information of its adjacent time points. By repeatedly using the time interval and the power change amplitude for feature growth, the Nth layer of adjacent nodes is constructed, and these nodes contain the power change information on all paths from the original node to the current node. The feature growth technique plays a role in expanding the data dimension and mining potential patterns here. This process can be understood as starting from the initial time point and gradually expanding along the time axis to capture the fluctuation features of more time points related to this node.

[0037] After constructing multiple layers of adjacent nodes, adjacent node feature aggregation starts from the Nth layer of adjacent nodes, that is, the time interval and the power change amplitude features of adjacent nodes are summarized and generalized to obtain more comprehensive and accurate time point change features. These change features not only reflect the instantaneous value of power but also contain its change trend and fluctuation pattern. Feature aggregation techniques such as weighted average, maximum value, minimum value, etc. are used here to refine and integrate node information.

[0038] Finally, time-domain fluctuation type discrimination is performed according to the change features of time points, that is, to determine whether the change feature belongs to short-term fluctuation or long-term fluctuation. This step is realized through machine learning or deep learning models. The model receives the change features as input and outputs the prediction result of the fluctuation type. To evaluate the performance of the model, a binary classification loss function is used to evaluate the loss value of the discrimination result and the labeled samples, and the loss value is gradually converged through iterative optimization. When the convergence target is met, the division criteria for short-term fluctuations and long-term fluctuations are determined. The time-domain fluctuation type discrimination technique depends on the design of classification algorithms and loss functions, which jointly determine the accuracy and efficiency of discrimination.

[0039] P20: Analyze the energy storage conversion control according to the high-frequency demand to determine the high-frequency control strategy.

[0040] Furthermore, step P20 of the embodiment of the present application further includes:

[0041] P21: Analyze the load demand and the demand fluctuation amplitude of the high-frequency demand; P22: Perform charge and discharge balancing strategies and charge and discharge rate matching according to the load demand change amount and the demand fluctuation amplitude respectively to obtain the high-frequency control strategy; P23: Among them, set the protection threshold of the high-frequency demand device, and use the protection threshold as a constraint condition to match and constrain the high-frequency control strategy.

[0042] Optionally, perform energy storage conversion control analysis for high-frequency demands. For example, through time series analysis, statistical modeling, etc., extract key demand characteristics from complex power fluctuation data to determine appropriate high-frequency control strategies. First, conduct in-depth analysis of the high-frequency demands, including analysis of load quantity demands and demand fluctuation amplitudes. Among them, the load quantity demand analysis aims to analyze the high-frequency load demand of the system and determine the total power load of the high-frequency demand within a specific time period. This analysis helps the system understand the actual magnitude of the load demand and provides a data basis for formulating subsequent charge and discharge strategies. The demand fluctuation amplitude analysis, on the other hand, analyzes the fluctuation amplitude of the high-frequency demand to determine the rapid change range of the load or generation power. Through this analysis, the system can identify the instantaneous fluctuation characteristics of the high-frequency demand, thereby accurately matching the response ability of the energy storage device.

[0043] Next, based on the change in load demand and the demand fluctuation amplitude, formulate charge and discharge balancing strategies and match the charge and discharge rates. The charge and discharge balancing strategy aims to balance the charge and discharge states of the energy storage system to ensure that the system can continuously and stably meet high-frequency demands. The matching of the charge and discharge rates is to dynamically adjust the charge and discharge speeds of the energy storage system according to the demand fluctuation amplitude to achieve a rapid response to high-frequency demands.

[0044] Specifically, the formulation of the charge and discharge balancing strategy includes formulating a charge and discharge balancing strategy for high-frequency demands according to the change in load demand to ensure that when high-frequency fluctuations occur, the energy storage device can respond quickly, meet the load demand, and not cause over-discharge or over-charge. Exemplarily, when the high-frequency demand shows an increase in load, the system voltage will drop, and the supercapacitor needs to immediately discharge to provide additional power to maintain the stability of the DC bus voltage; when the load rapidly decreases, the voltage will rise, and the supercapacitor should quickly charge to absorb the excess energy and prevent the voltage from being too high.

[0045] The matching of the charge and discharge rates includes matching the charge and discharge rates according to the demand fluctuation amplitude to adapt to the rapid changes in high-frequency demands. The reasonable matching of the charge and discharge rates helps to maintain the efficient operation of the energy storage device and reduce system stress. Exemplarily, if the high-frequency demand fluctuation amplitude is large, the supercapacitor should quickly adjust the charge and discharge rates to provide high-power output or absorb excess energy; for small fluctuations, the charge and discharge rates can be appropriately reduced to prevent the supercapacitor from being over-consumed and extend its service life.

[0046] Exemplarily, the corresponding charge-discharge balancing strategy can be determined by fitting based on historical data. First, based on historical operation data, continuously collect the change amount and fluctuation characteristics of the load demand, including the rapid fluctuation characteristics of the load, such as the maximum fluctuation amplitude and fluctuation period in high-frequency fluctuations, as well as the charge-discharge characteristics of the energy storage device, such as the charge-discharge rate, response time, and power output upper limit. Further, statistically analyze and fit the collected historical data to determine the charge-discharge strategy model for high-frequency demand. Specifically, by fitting and analyzing the fluctuation amplitude and frequency of the load, determine the optimal charge-discharge rate required by the energy storage device at different fluctuation amplitudes and frequencies. And by fitting the fluctuation frequency with the response time, analyze the response time of the energy storage device under different load fluctuation frequencies to ensure that the system can charge and discharge quickly at the optimal time when the load demand suddenly changes. Based on the fitting results, generate a fitting function for describing the charge-discharge relationship. For example, the charge-discharge rate P(t) can be expressed as a function of the fluctuation amplitude A and the fluctuation frequency f: P(t) = αA + βf; where α and β are the coefficients after fitting the historical data, used to describe the charge-discharge response characteristics under different fluctuation conditions. Construct a charge-discharge strategy model according to the described fitting function. During actual operation, through high-frequency monitoring equipment, the system can capture the rapid fluctuation of the load demand in real time, and based on the real-time monitored fluctuation amplitude and frequency, quickly call the charge-discharge strategy model fitted with historical data to determine the charge-discharge balancing strategy and the optimal charge-discharge rate at the current moment.

[0047] Finally, to ensure the safety of the high-frequency control strategy, professionals set the protection thresholds for high-frequency demand devices according to empirical data, and use these protection thresholds as constraint conditions to match and constrain the high-frequency control strategy. The protection thresholds include but are not limited to the maximum allowable charge-discharge power of the device, the upper limit of the battery pack temperature, the voltage range, etc., which together constitute the boundary conditions for the safe operation of the energy storage system. When formulating the high-frequency control strategy, it is necessary to ensure that these constraint conditions are met to prevent potential risks such as equipment overload and overheating. Exemplarily, set the upper and lower limits of the SOC of the supercapacitor to 20% - 80%. If the SOC is close to the upper or lower limit, the system should dynamically adjust the charge-discharge behavior to ensure that the device operates within a safe range. This protection mechanism ensures the long-term reliability of high-frequency demand devices.

[0048] P30: Analyze and resolve the energy storage conversion control according to the low-frequency demand to determine the low-frequency control strategy.

[0049] Specifically, for low-frequency demand, the system conducts energy storage conversion control analysis and formulates the corresponding low-frequency control strategy. Low-frequency demand is mainly the long-term power demand fluctuation caused by the slow change of the load or power generation. Therefore, a stable and continuous energy management strategy is required to address these demands.

[0050] First, monitor and identify low-frequency demands, that is, long-term power demand fluctuations caused by slow changes in load or generation. Compared with high-frequency demands, low-frequency demands have a smaller change amplitude but a longer duration. Therefore, through time series analysis, trend prediction models, etc., detailed analysis of low-frequency demand data can be carried out, including but not limited to analyzing the change trend of demand volume, periodic characteristics, and possible seasonal changes, etc., to extract useful information from complex data and obtain the basic characteristics of low-frequency demands.

[0051] After grasping the basic characteristics of low-frequency demands, the next step is to formulate a low-frequency control strategy. The core goal of this strategy is to maximize the satisfaction of low-frequency demands while optimizing the charge and discharge efficiency and economy of the energy storage system on the premise of ensuring the safe and stable operation of the energy storage system. To achieve this goal, multiple factors need to be considered, such as the capacity limit of energy storage devices, charge and discharge efficiency, maintenance cost, and market electricity price fluctuations. Based on these factors, technical means such as optimization control algorithms and energy management strategies are used to formulate a scientific and reasonable low-frequency control strategy.

[0052] In addition, when formulating a low-frequency control strategy, special attention needs to be paid to the protection of energy storage devices. Since low-frequency demands may last for a long time and change relatively slowly, energy storage devices may be in a charge and discharge state for a long time. To prevent adverse situations such as overheating, overcharging, or over-discharging of the devices, reasonable protection thresholds need to be set, and corresponding protection mechanisms need to be incorporated into the control strategy. These protection mechanisms can dynamically adjust control parameters based on the real-time operation data of the devices to ensure that the energy storage devices always work in a safe and efficient operating state.

[0053] P40: Taking the energy balance of the DC microgrid as the goal, evenly allocate the high-frequency control strategy and the low-frequency control strategy to obtain the control balance strategy of the energy storage converter, including a high-frequency balance strategy and a low-frequency balance strategy.

[0054] Furthermore, as Figure 2 shown, step P40 of the embodiment of the present application further includes:

[0055] P41: Taking energy balance as the goal, construct an equilibrium optimization model, and the equilibrium optimization model is embedded with an objective function. Among them, the objective function is constructed based on minimizing the fluctuation amplitude of high-frequency demands and low-frequency demands and maximizing the charge and discharge efficiency of energy storage devices. The objective function is: min∑ i (P hf,i (t)) 2 +∑ i (P lf,i (t)) 2 -α∑ i f(μ1,μ2), where P hf,i(t) is the high-frequency demand response power of the i-th energy storage unit, P lf,i (t) is the low-frequency demand response power of the i-th energy storage unit, f(μ1, μ2) is the charge-discharge efficiency function of the energy storage device, and α is the weight coefficient used to balance the minimization of power output and the maximization of charge-discharge efficiency; P42: Obtain the protection thresholds of high-frequency demand devices and low-frequency demand devices, and fit the protection thresholds into the equilibrium optimization model as constraint conditions; P43: Align the time series according to the high-frequency control strategy and the low-frequency control strategy to establish a control strategy equilibrium time zone; P44: Perform equilibrium allocation on the control strategy equilibrium time zone through the equilibrium optimization model to obtain the equilibrium strategy of the control strategy equilibrium time zone; P45: Piece together the equilibrium strategies of the control strategy equilibrium time zone according to the time sequence and perform smooth fitting with the minimization of power fluctuation to obtain the control equilibrium strategy.

[0056] It should be understood that the goal of the embodiments of this application is to ensure the energy balance of the DC microgrid through the equilibrium allocation of high-frequency control strategies and low-frequency control strategies, so as to obtain the control equilibrium strategy of the energy storage converter.

[0057] To achieve energy balance, the system needs to construct an equilibrium optimization model. The core of this model is an embedded objective function, whose purpose is to minimize the fluctuation amplitudes of high-frequency and low-frequency demands and maximize the charge-discharge efficiency of the energy storage device. The form of the objective function is as follows:

[0058]

[0059] Among them, is the high-frequency demand response power of the i-th energy storage unit, P lf,i (t) is the low-frequency demand response power of the i-th energy storage unit, f(μ1, μ2) is the charge-discharge efficiency function of the energy storage device, and α is the weight coefficient used to balance the minimization of power output and the maximization of charge-discharge efficiency. Through this optimization model, the system can ensure the minimum of high-frequency and low-frequency power fluctuations while maximizing the overall efficiency of the energy storage device as much as possible.

[0060] To ensure the safety of the energy storage device, the system also needs to set the protection thresholds of the energy storage device according to the basic attributes of the device. These protection thresholds may include the maximum / minimum power limit, temperature limit, voltage / current range, etc. of the device. The respective protection thresholds of high-frequency demand devices and low-frequency demand devices are regarded as constraint conditions and are embedded into the optimization model by fitting to ensure that the safe operating range of the device will not be exceeded during the optimization process.

[0061] After obtaining the control strategies for high-frequency and low-frequency demands, the system needs to align the time series of both to establish a balanced time zone for the control strategies that includes high-frequency and low-frequency demands. This time zone ensures that the strategies for high-frequency control and low-frequency control can be executed in a coordinated manner, avoiding policy conflicts at different time points. Time series alignment can be achieved through time series analysis, data synchronization techniques, etc.

[0062] Next, a balanced configuration is performed on the balanced time zone of the control strategy through a balanced optimization model to obtain the balanced strategy for the balanced time zone of the control strategy. Exemplarily, optimization algorithms such as the gradient descent method, genetic algorithm, etc. are used to solve the objective function, thereby obtaining a control strategy with the smallest fluctuation amplitude and the largest charge-discharge efficiency. During the optimization process, the model will be adjusted according to the set constraints to ensure that the control strategy not only meets the actual requirements but also conforms to the safety requirements of the equipment. The purpose of this configuration is to enable the high-frequency and low-frequency strategies to act together on the basis of mutual coordination within the balanced time zone, ensuring the stable operation of the energy system.

[0063] Finally, the balanced strategies within the balanced time zone are spliced in chronological order and smoothed and fitted according to the principle of minimizing power fluctuations. This can be achieved through signal processing, filtering techniques, etc., to ensure the continuity and smoothness of the control strategy in time, reducing power fluctuations and unnecessary energy losses caused by policy switching. Ensure that the finally generated control balanced strategy can avoid sudden fluctuations in actual operation and ensure the overall stability and balance of the system.

[0064] Furthermore, the embodiment of this application further includes step P40a, and step P40a further includes:

[0065] P41a: Extract all energy storage converters of the DC microgrid and establish a balanced control network for the energy storage converters; P42a: Respectively obtain the high-frequency control strategy and low-frequency control strategy of all energy storage converters, and perform a global balanced configuration based on the balanced control network to obtain the control balanced strategy of the energy storage converters.

[0066] Optionally, in the DC microgrid, in order to maximize the utilization efficiency of energy storage resources and the stability of the system, a global balanced configuration needs to be performed on all energy storage converters. This step aims to achieve system-level energy balance and response efficiency by centrally managing and optimizing the control strategies of each energy storage converter.

[0067] First, extract all energy storage converters from the DC microgrid. The energy storage converter is the core device in the microgrid for responding to energy demands, responsible for handling power variations from high-frequency and low-frequency demands. After extracting these energy storage converters, based on their physical connections, performance parameters, and communication capabilities, construct an equalization control network. This network is a virtual or physical architecture for enabling information interaction and control coordination among energy storage converters, ensuring efficient and stable energy exchange among devices in the microgrid.

[0068] Next, after the equalization control network is constructed, obtain the high-frequency control strategies and low-frequency control strategies of all energy storage converters respectively. These strategies may vary due to factors such as device type, capacity, current state, and historical data. Based on the equalization control network, conduct a global analysis and comparison of these control strategies to find the optimal equalization configuration plan.

[0069] The core of global equalization configuration lies in comprehensively considering the performance characteristics and current demands of each energy storage converter, and adjusting the control strategies of each device through optimization algorithms (such as linear programming, nonlinear optimization, heuristic search, etc.) to achieve energy balance and maximize efficiency of the entire microgrid system. In this process, factors to be considered include but are not limited to: remaining capacity of energy storage devices, charge and discharge efficiency, response speed, maintenance cost, and market electricity price, etc.

[0070] Through global equalization configuration, dynamically adjust the operating states of each energy storage converter so that they can work together to jointly respond to high-frequency and low-frequency demand changes in the microgrid. This can not only improve the utilization rate of energy storage resources, but also enhance the stability and response ability of the system, ensuring that the DC microgrid can operate efficiently and reliably under various working conditions.

[0071] In summary, the embodiments of the present application at least have the following technical effects:

[0072] The present application monitors the power demand of the DC microgrid, divides it into high-frequency demand and low-frequency demand, formulates high-frequency control strategies and low-frequency control strategies respectively according to the high-frequency demand and low-frequency demand, and takes the energy balance of the DC microgrid as the goal to conduct equalization configuration on the high-frequency control strategy and low-frequency control strategy to obtain the control equalization strategy of the energy storage converter.

[0073] It achieves the technical effect of constructing an energy-balanced control strategy for the energy storage converter through the analysis and control of high-frequency and low-frequency demands, ensuring that the system has good response speed and stability when dealing with complex power demand fluctuations.

[0074] Embodiment 2

[0075] Based on the same inventive concept as the energy storage converter control method for the DC microgrid in the foregoing embodiment, asFigure 3 As shown in Figure 3 , the present application provides an energy storage converter control system for a DC microgrid. The system and method embodiments in the present application are based on the same inventive concept. Among them, the system includes:

[0076] A power demand segmentation module 11, which is used to monitor the power demand of the DC microgrid, segment and determine the high-frequency demand and the low-frequency demand. The high-frequency demand is the instantaneous power change caused by the rapid fluctuations of the load or power generation, and the low-frequency demand is the long-term power change caused by the slow changes and fluctuations of the load or power generation.

[0077] A high-frequency control strategy determination module 12, which is used to perform energy storage conversion control analysis according to the high-frequency demand and determine the high-frequency control strategy.

[0078] A low-frequency control strategy determination module 13, which is used to perform energy storage conversion control analysis according to the low-frequency demand and determine the low-frequency control strategy.

[0079] A control strategy balanced configuration module 14, which is used to balance and configure the high-frequency control strategy and the low-frequency control strategy with the energy balance of the DC microgrid as the goal, and obtain the control balance strategy of the energy storage converter, including a high-frequency balance strategy and a low-frequency balance strategy.

[0080] Furthermore, the power demand segmentation module 11 is further used to perform the following steps:

[0081] Obtain the cut-off frequency, which is determined by analyzing the typical change frequencies of the natural fluctuation period of the system load and the power generation fluctuation through historical samples; configure a high-pass filter channel and a low-pass filter channel based on the cut-off frequency. Among them, the high-pass filter channel is used to extract the high-frequency components higher than the cut-off frequency, and the low-pass filter channel is used to extract the low-frequency components lower than the cut-off frequency; use the monitored power demand of the DC microgrid as the input variable, and extract the high-frequency components and low-frequency components through the high-pass filter channel and the low-pass filter channel to determine the high-frequency demand and the low-frequency demand.

[0082] Furthermore, the power demand segmentation module 11 is further used to perform the following steps:

[0083] Collect the load power fluctuation data and the power generation power fluctuation data respectively, store them in the form of time series, and construct a historical sample; perform moving average smoothing processing on the historical sample to remove random fluctuations and fit the curve of power change over time; perform time-domain fluctuation analysis according to the curve of power change over time to determine short-term fluctuations and long-term fluctuations; perform frequency-domain analysis according to the curve of power change over time to determine high-frequency components and low-frequency components; use the short-term fluctuations, long-term fluctuations, high-frequency components and low-frequency components as double verification to obtain the cut-off frequency.

[0084] Further, the power demand segmentation module 11 is further configured to perform the following steps:

[0085] Set the node determination at the unit time point, and the node feature is the power value and the context time interval information at this moment; use the time interval and the power change amplitude as the growth features, and perform feature growth based on the node features to construct adjacent nodes, and repeatedly use the time interval and the power change amplitude for feature growth to construct the Nth layer of adjacent nodes; start from the Nth layer of adjacent nodes to perform adjacent node feature aggregation to obtain the change features of the time point; perform time-domain fluctuation type discrimination according to the change features of the time point, and evaluate and converge the loss value of the discrimination result and the labeled sample through the binary classification loss function until the convergence target is met, and determine the short-term fluctuations and long-term fluctuations.

[0086] Further, the high-frequency control strategy determination module 12 is further configured to perform the following steps:

[0087] Perform load demand analysis and demand fluctuation amplitude analysis on the high-frequency demand; perform charge and discharge balance strategy and charge and discharge rate matching according to the load demand change amount and the demand fluctuation amplitude respectively to obtain the high-frequency control strategy; where, set the protection threshold of the high-frequency demand device, and use the protection threshold as a constraint condition to perform matching constraint on the high-frequency control strategy.

[0088] Further, the control strategy equilibrium configuration module 14 is further configured to perform the following steps:

[0089] Build an equilibrium optimization model with the goal of energy balance. The equilibrium optimization model has an objective function embedded. Among them, the objective function is constructed based on minimizing the fluctuation amplitudes of high-frequency demand and low-frequency demand and maximizing the charge and discharge efficiency of energy storage devices. The objective function is: min∑ i (P hf,u (t)) 2 +∑ i (P lf,i (t)) 2 -α∑ i f(μ1,μ2), where, P hf,i(t) is the high-frequency demand response power of the i-th energy storage unit, P lf,i (t) is the low-frequency demand response power of the i-th energy storage unit, f(μ1, μ2) is the charge-discharge efficiency function of the energy storage device, and α is the weight coefficient used to balance the minimization of power output and the maximization of charge-discharge efficiency; obtain the protection thresholds of the high-frequency demand devices and the low-frequency demand devices, and fit the protection thresholds as constraint conditions into the equilibrium optimization model; perform time series alignment according to the high-frequency control strategy and the low-frequency control strategy to establish a control strategy equilibrium time zone; perform equilibrium configuration on the control strategy equilibrium time zone through the equilibrium optimization model to obtain the equilibrium strategy of the control strategy equilibrium time zone; splice the equilibrium strategies of the control strategy equilibrium time zone according to the time sequence and perform smooth fitting with the minimum power fluctuation to obtain the control equilibrium strategy.

[0090] Further, the control strategy equilibrium configuration module 14 is further configured to perform the following steps:

[0091] Extract all the energy storage converters of the DC microgrid and establish an equilibrium control network for the energy storage converters; respectively obtain the high-frequency control strategies and low-frequency control strategies of all the energy storage converters, and perform global equilibrium configuration based on the equilibrium control network to obtain the control equilibrium strategy of the energy storage converters.

[0092] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0093] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0094] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. Energy storage converter control method for DC microgrid, characterized in that, The energy storage converter control method for a DC microgrid includes: Monitoring the power demand of the DC microgrid, and dividing and determining the high-frequency demand and the low-frequency demand. The high-frequency demand is the instantaneous power change caused by the rapid fluctuations of the load or power generation, and the low-frequency demand is the long-term power change caused by the low-speed change fluctuations of the load or power generation; Performing energy storage conversion control analysis according to the high-frequency demand to determine the high-frequency control strategy; Performing energy storage conversion control analysis according to the low-frequency demand to determine the low-frequency control strategy; Taking the energy balance of the DC microgrid as the goal, performing balanced configuration on the high-frequency control strategy and the low-frequency control strategy to obtain the control balance strategy of the energy storage converter, including the high-frequency balance strategy and the low-frequency balance strategy.

2. The energy storage converter control method for a DC microgrid according to claim 1, wherein The monitoring of the power demand of the DC microgrid, and dividing and determining the high-frequency demand and the low-frequency demand, includes: Obtaining the cut-off frequency, which is determined by analyzing the typical change frequencies of the natural fluctuation period of the system load and the power generation fluctuations through historical samples; Configuring a high-pass filter channel and a low-pass filter channel based on the cut-off frequency. Among them, the high-pass filter channel is used to extract the high-frequency components higher than the cut-off frequency, and the low-pass filter channel is used to extract the low-frequency components lower than the cut-off frequency; Taking the monitored power demand of the DC microgrid as the input variable, and extracting the high-frequency components and low-frequency components through the high-pass filter channel and the low-pass filter channel to determine the high-frequency demand and the low-frequency demand.

3. The energy storage converter control method for a DC microgrid according to claim 2, wherein The obtaining of the cut-off frequency includes: Respectively collecting the load power fluctuation data and the power generation power fluctuation data, storing them in the form of a time series, and constructing a historical sample; Performing moving average smoothing processing on the historical sample to remove random fluctuations and fitting the power change curve over time; Performing time-domain fluctuation analysis according to the power change curve over time to determine the short-term fluctuations and long-term fluctuations; Performing frequency-domain analysis according to the power change curve over time to determine the high-frequency components and low-frequency components; Using the short-term fluctuations and long-term fluctuations, high-frequency components and low-frequency components as double verification to obtain the cut-off frequency.

4. The energy storage converter control method for a DC microgrid according to claim 3, characterized in that The performing of time-domain fluctuation analysis according to the power change curve over time to determine the short-term fluctuations and long-term fluctuations includes: Setting the unit time point to determine the node, and the node characteristics are the power value at this moment and the context time interval information; Taking the time interval and the power change amplitude as the growth characteristics, performing feature growth based on the node characteristics to construct adjacent nodes, and repeatedly using the time interval and the power change amplitude for feature growth to construct the Nth layer of adjacent nodes; Starting from the Nth layer of adjacent nodes, performing adjacent node feature aggregation to obtain the change characteristics of the time point; Performing time-domain fluctuation type discrimination according to the change characteristics of the time point, and evaluating and converging the loss value of the discrimination result and the labeled sample through a binary classification loss function until the convergence target is met, to determine the short-term fluctuations and long-term fluctuations.

5. The energy storage converter control method for a DC microgrid according to claim 1, wherein Performing energy storage conversion control analysis according to the high-frequency demand to determine the high-frequency control strategy, including: Performing load demand analysis and demand fluctuation amplitude analysis on the high-frequency demand; Perform charge and discharge balancing strategies and charge and discharge rate matching respectively according to the change amount of load demand and the amplitude of demand fluctuation to obtain the high-frequency control strategy; Among them, set the protection threshold of high-frequency demand equipment, and use the protection threshold as a constraint condition to perform matching constraint on the high-frequency control strategy.

6. The energy storage converter control method for a DC microgrid according to claim 1, characterized in that With the energy balance of the DC microgrid as the goal, perform balanced configuration on the high-frequency control strategy and the low-frequency control strategy to obtain the control balance strategy of the energy storage converter, including: With the goal of energy balance, an equilibrium optimization model is constructed. The equilibrium optimization model incorporates an objective function. Among them, the objective function is constructed based on minimizing the fluctuation amplitudes of high-frequency demand and low-frequency demand, and maximizing the charge-discharge efficiency of energy storage devices. The objective function is: min∑ i (P hf,i (t)) 2 +∑ i (P lf,i (t)) 2 -α∑ i f(μ1,μ2), where P hf,i (t) is the high-frequency demand response power of the i-th energy storage unit, P lf,i (t) is the low-frequency demand response power of the i-th energy storage unit, f(μ1,μ2) is the charge-discharge efficiency function of the energy storage device, and α is a weight coefficient used to balance the minimization of power output and the maximization of charge-discharge efficiency; Obtain the protection thresholds of high-frequency demand equipment and low-frequency demand equipment, and fit the protection thresholds into the equilibrium optimization model as constraint conditions; Perform time series alignment according to the high-frequency control strategy and the low-frequency control strategy to establish a control strategy balance time zone; Perform balanced configuration on the control strategy balance time zone through the equilibrium optimization model to obtain the balance strategy of the control strategy balance time zone; Stitch the balance strategies of the control strategy balance time zone according to the time sequence and perform smooth fitting with the minimum power fluctuation to obtain the control balance strategy.

7. The energy storage converter control method for a DC microgrid according to claim 6, wherein, It also includes: Extract all the energy storage converters of the DC microgrid and establish an equilibrium control network for the energy storage converters; Obtain the high-frequency control strategy and the low-frequency control strategy of all the energy storage converters respectively, and perform global balanced configuration based on the equilibrium control network to obtain the control balance strategy of the energy storage converters.

8. Energy storage converter control system for DC microgrid, characterized in that, The system includes: A power demand segmentation module, which is used to monitor the power demand of the DC microgrid, segment and determine high-frequency demand and low-frequency demand. The high-frequency demand is the instantaneous power change caused by the rapid fluctuation of load or power generation, and the low-frequency demand is the long-term power change caused by the low-speed change and fluctuation of load or power generation; A high-frequency control strategy determination module, which is used to perform energy storage conversion control analysis according to the high-frequency demand to determine the high-frequency control strategy; A low-frequency control strategy determination module, which is used to perform energy storage conversion control analysis according to the low-frequency demand to determine the low-frequency control strategy; A control strategy balance configuration module, which is used to perform balanced configuration on the high-frequency control strategy and the low-frequency control strategy with the energy balance of the DC microgrid as the goal to obtain the control balance strategy of the energy storage converter, including a high-frequency balance strategy and a low-frequency balance strategy.

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