Direct-current power supply and energy storage system suitable for various household appliances

Abstract

The invention relates to the technical field of energy storage modules, in particular to a direct-current power supply energy storage system suitable for various household appliances. According to the method, the current change value and the power fluctuation range of the energy storage unit are analyzed, statistics of the power change amplitude and the time interval is combined, the work cycle rule of the energy storage unit is defined, the power difference between the partitions is extracted through the Markov chain model, and real-time balance of the partition power supply state is achieved by gradually optimizing path distribution. And resource waste caused by power fluctuation is effectively reduced. Through gradual correction of a real-time output power value and a target power value, dynamic power matching of an energy storage unit is realized, the flexibility and accuracy of energy distribution are improved, voltage fluctuation trend is monitored and adjusted through a variational mode decomposition technology, voltage stability control of an energy storage module is realized, the reliability of an energy storage system is optimized, and the energy storage efficiency is improved. And the overall stability and the energy utilization efficiency are improved by matching the output power and the frequency adjustment range.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage modules, and in particular to a direct current power supply energy storage system suitable for a variety of household appliances. Background Art

[0002] The field of energy storage module technology aims to research and develop systems and technologies for electric energy storage, management and efficient release, solve the problem of imbalance between electricity supply and demand and optimize energy utilization efficiency. Energy storage devices can be used to store energy when there is excess electricity and release energy when there is insufficient electricity, thereby achieving stable power supply, peak shaving and valley filling, supporting distributed energy access and improving overall electricity efficiency.

[0003] The purpose of the DC power supply energy storage system suitable for a variety of household appliances is to provide a stable and efficient DC power supply for a variety of electrical appliances in the home, reduce the loss caused by multiple energy conversions in the traditional AC power supply system, and improve the efficiency of home energy management. It aims to solve the grid fluctuations, uninterrupted power supply needs and new energy access challenges that may occur in the home power environment through the introduction of energy storage devices and intelligent power supply management, thereby achieving energy saving and consumption reduction, improving power supply stability and optimizing the use of power resources.

[0004] The existing technology lacks in-depth analysis of the power fluctuations of the energy storage unit and its time laws in the process of processing the charging and discharging of the energy storage module, resulting in insufficient response speed and adjustment ability when the power supply demand changes, and cannot effectively quantify the trend and weight of the state transition. There is a problem of low prediction accuracy, which causes misjudgment of the operating status of the energy storage unit, and does not fully consider the dynamic changes in power demand between regions, resulting in uneven resource allocation or reduced system power supply efficiency, resulting in voltage instability during the operation of the energy storage module, affecting equipment safety, causing a decrease in the operating efficiency of the energy storage equipment, and affecting the reliability of power supply to household appliances and the level of energy efficiency utilization. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a DC power supply energy storage system suitable for a variety of household appliances.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A DC power supply energy storage system suitable for a variety of household appliances comprises:

[0007] Energy storage behavior monitoring module: Based on the charge and discharge status of the energy storage module, collect the current change value of the energy storage unit, analyze the power fluctuation range, record the time interval of power change, count the continuous cycle law of the charge and discharge status, extract the relationship between the power change amplitude and time interval of each unit, classify the cycle and divide the time according to the working status, and generate the energy storage status timing information;

[0008] Charging behavior prediction module: based on the energy storage state timing information, extract the power change value and state transition sequence of the energy storage unit, analyze the change of the transfer trend, calculate the probability weight by combining the transfer sequence with the power change value, adjust the initial transfer weight parameter, update the transfer relationship between states in turn, correct the transfer weight by comparing the state duration, and generate a behavior prediction transfer matrix;

[0009] Partition dynamic control module: Based on the behavior prediction transfer matrix, the Markov chain model is used to analyze the real-time power demand of the energy storage unit, extract the power difference between partitions, sort the regional power and demand deviations, correct the partition power supply status by gradually allocating the power path with the smallest difference, extract the power allocation result of the matching path, and generate the partition power adjustment strategy;

[0010] Energy storage power regulation module: Based on the partition power adjustment strategy, the real-time output power value and target power value of the energy storage unit are extracted, the difference between the two is matched with the adjustment range, the power distribution path of the energy storage unit is corrected in turn, and the output power is gradually optimized to meet the target value, thereby generating an energy storage dynamic power configuration plan;

[0011] Voltage stability control module: Based on the energy storage dynamic power configuration scheme, the voltage fluctuation amplitude of the energy storage unit is monitored by using variational mode decomposition, the voltage deviation of each unit is calculated, the regulation demand is analyzed in combination with the fluctuation amplitude trend, the fluctuation trend is compared with the deviation value, and the appropriate voltage correction parameters are gradually selected to adjust the voltage output value of the energy storage module and generate a voltage regulation control signal;

[0012] Power supply output coordination module: Based on the voltage regulation control signal, extract the output frequency adjustment range of the high-demand area, analyze the matching of the frequency and voltage correction parameters, gradually adjust the power frequency, calculate the power output of each adjustment frequency, select the optimal solution by comparing the power values ​​output at each frequency, and generate an output power coordination signal.

[0013] As a further solution of the present invention, the energy storage behavior monitoring module includes a current change acquisition submodule, a power fluctuation analysis submodule, and a periodic law statistics submodule, wherein:

[0014] Current change acquisition submodule: Based on the charge and discharge status of the energy storage module, the current value of the energy storage unit at each time point during the charge and discharge process is collected, and the current data is timestamped to ensure that each data point can accurately reflect the current change of the energy storage unit and obtain the current change data;

[0015] Power fluctuation analysis submodule: based on the current change data, calculate the power fluctuation corresponding to the current value, analyze the amplitude and frequency of the power fluctuation according to the time interval, record the fluctuation range of the power change, and extract the amplitude change information of the power fluctuation to generate the power fluctuation feature;

[0016] Periodic law statistics submodule: Based on the power fluctuation characteristics, by counting the continuous cycles of power changes during the charging and discharging process of the energy storage unit, analyzing the time intervals between the cycles, combining the relationship between the current and the power change amplitude, classifying the cycles and dividing the time periods, and generating the energy storage state timing information;

[0017] The energy storage state timing information includes the start and end time of the charge and discharge cycle, the power variation amplitude, the time interval sequence and the cycle working state classification.

[0018] As a further solution of the present invention, the charging behavior prediction module includes a transfer sequence extraction submodule, a probability weight calculation submodule, and a behavior prediction update submodule, wherein:

[0019] Transfer sequence extraction submodule: based on the energy storage state timing information, extract the state transition sequence of the energy storage unit during the charging and discharging process, analyze the transfer time and power change characteristics of each state, record the transfer trajectory of each state in turn, and generate transfer sequence characteristics;

[0020] Probability weight calculation submodule: based on the transition sequence characteristics, calculate the transition probability between each state and the next state, combine the time information of state transition, update the initial transition weight, and generate the transition probability weight by analyzing the historical transition data;

[0021] Behavior prediction update submodule: based on the transition probability weight, updates the transition relationship between each state in turn, and makes corrections according to the duration of each state, finally forming a new state transition rule and generating a behavior prediction transition matrix;

[0022] The behavior prediction transfer matrix includes a state transfer starting point, a transfer end point, a transfer weight and a time adjustment parameter.

[0023] As a further solution of the present invention, the partition dynamic control module includes a power demand analysis submodule, a power difference sorting submodule, and a power adjustment strategy submodule, wherein:

[0024] Power demand analysis submodule: Based on the behavior prediction transfer matrix, the Markov chain model is used to extract the real-time power demand data of the energy storage unit, and the power demand deviation of the current area is calculated by comparing the historical power demand with the predicted value. In combination with the working status of the equipment in the area, the power demand difference in different areas is further analyzed to generate power demand difference data;

[0025] Power difference sorting submodule: based on the power demand difference data, sort the power demand deviations of each area, first determine the area with the smallest power difference, then calculate the power difference between other areas and the target area, and sort them by priority according to the difference value to generate a power difference sorting result;

[0026] Power adjustment strategy submodule: based on the power difference sorting results, gradually allocate the power path with the smallest difference in order of priority, adjust the power output path of the battery unit by analyzing the power demand difference between regions, correct the power supply status of each region, and generate a partition power adjustment strategy;

[0027] The partition power adjustment strategy includes partition load range, power allocation path, and regional demand priority sorting.

[0028] As a further solution of the present invention, the Markov chain model is according to the formula:

[0029] P t+1 =(P t +W)·(T+ΔT)·F

[0030] Where: P t+1 is the power demand forecast value of the energy storage unit in the next time period, P t is the power demand vector of the energy storage unit in the current time period, W is the current regional environmental weight factor, T is the behavior prediction transfer matrix, ΔT is the dynamic correction of the transfer matrix, and F is the equipment working state adjustment factor.

[0031] As a further solution of the present invention, the energy storage power regulation module includes a power difference extraction submodule, a power matching correction submodule, and a dynamic configuration scheme submodule, wherein:

[0032] Power difference extraction submodule: based on the partition power adjustment strategy, extract the real-time power output and target power value of the energy storage unit, identify the deviation between the power output and the target by calculating the power difference between the two, and obtain the power difference data in combination with the system demand adjustment strategy;

[0033] Power matching correction submodule: Based on the power difference data, by matching the difference between the real-time power output of the energy storage unit and the target power value, the power distribution path is gradually corrected to ensure that the output power of the energy storage unit is adjusted toward the target value, and a power correction path is generated;

[0034] Dynamic configuration scheme submodule: Based on the power correction path, the output power of the energy storage unit is adjusted according to predetermined steps, the power allocation of each unit is optimized, and the final power output is ensured to meet the target demand through step-by-step adjustment, thereby generating a dynamic power configuration scheme for energy storage;

[0035] The energy storage dynamic power configuration scheme includes a target power value, a power adjustment range, and output path optimization parameters.

[0036] As a further solution of the present invention, the voltage stability control module includes a voltage fluctuation monitoring submodule, a voltage deviation calculation submodule, and a voltage correction control submodule, wherein:

[0037] Voltage fluctuation monitoring submodule: Based on the energy storage dynamic power configuration scheme, variational mode decomposition is used to extract the real-time voltage value of the energy storage unit, the fluctuation amplitude is calculated by recording the voltage data at different time points, and the upper and lower limits of the fluctuation range are marked, and the voltage fluctuation trend in the continuous time period is further compared to generate voltage fluctuation trend data;

[0038] Voltage deviation calculation submodule: based on the voltage fluctuation trend data, extract the difference between the real-time voltage value of the energy storage unit and the standard voltage, evaluate the voltage change of each energy storage unit by calculating the deviation value, and confirm the unit that needs to be adjusted in combination with the fluctuation trend data to generate voltage deviation data;

[0039] Voltage correction control submodule: Based on the voltage deviation data, the correction parameters are gradually selected by analyzing the relationship between the deviation value and the fluctuation trend, the voltage is corrected by adjusting the output voltage of the energy storage module, and the corrected output value is recorded to generate a voltage regulation control signal;

[0040] Wherein, the voltage regulation control signal includes a voltage deviation value, a voltage output adjustment parameter and a correction amplitude limit value.

[0041] As a further solution of the present invention, the variational mode decomposition is according to the formula:

[0042]

[0043] Where: V′(t) is the real-time voltage value of the improved energy storage unit at time t, is the weighted modal signal after variational mode decomposition, IMF i (t) is the i-th intrinsic mode function generated by variational mode decomposition, w i is the weight coefficient of the i-th mode function, r(t) is the residual term remaining after the signal mode decomposition, k·e(t) is the error correction term, e(t) is the signal measurement error correction value, k is the error correction weight coefficient, δ·D(t) is the dynamic power signal compensation term, D(t) is the compensation signal introduced by the external dynamic power adjustment, and δ is the adjustment coefficient of the dynamic compensation signal.

[0044] As a further solution of the present invention, the correction parameters are gradually selected by analyzing the relationship between the deviation value and the fluctuation trend. The correction amplitude is adjusted based on the comprehensive analysis of the voltage deviation value and the fluctuation trend in combination with the deviation value. The fluctuation trend distinguishes short-term fluctuations from long-term trends to ensure that the correction amplitude matches the demand. At the same time, the output capacity and dynamic response speed of the energy storage unit are considered to prevent excessive adjustment from affecting the stability of the equipment. In combination with the balance requirements of stability and energy consumption, rapid recovery of the target voltage is prioritized in high-load scenarios, and efficiency optimization is emphasized in low-load scenarios.

[0045] As a further solution of the present invention, the power supply output coordination module includes a frequency adjustment range extraction submodule, a frequency matching analysis submodule, and an output power optimization submodule, wherein:

[0046] Frequency adjustment range extraction submodule: based on the voltage regulation control signal, extract the output frequency range of the high-demand area, analyze the upper and lower limits of the regional power supply frequency, record the frequency adjustment range and determine the adjustment target in combination with the current demand situation, gradually screen the feasible frequency range, and generate frequency adjustment range data;

[0047] Frequency matching analysis submodule: based on the frequency adjustment range data, extract the output power value corresponding to each frequency, evaluate by calculating the matching degree of the frequency and voltage correction parameters, and record the power output of each group of matching frequencies, finally arrange the matching result order of all frequencies, and generate frequency matching sorting data;

[0048] Output power optimization submodule: based on the frequency matching sorting data, select the optimal combination of matching frequency and power output, optimize the power supply output scheme by gradually adjusting the power output frequency, correct the frequency value in combination with regional demand, and finally generate an output power coordination signal;

[0049] The output power coordination signal includes a frequency adjustment range, a power output optimization value and an output matching parameter set.

[0050] Compared with the prior art, the advantages and positive effects of the present invention are:

[0051] 1. In the present invention, by analyzing the current change value and power fluctuation range of the energy storage unit, combined with the statistics of the power change amplitude and time interval, the working cycle law of the energy storage unit is clarified, and combined with the energy storage state timing information, the probability weight of the state transition is adjusted and combined with the duration correction, the accuracy and controllability of the energy storage unit state prediction are improved;

[0052] 2. In the present invention, the power difference between partitions is extracted through the Markov chain model, and the real-time balance of the power supply status of the partitions is achieved by gradually optimizing the path allocation, effectively reducing the waste of resources caused by power fluctuations. The gradual correction of the real-time output power value and the target power value realizes the dynamic power matching of the energy storage unit and improves the flexibility and accuracy of energy distribution;

[0053] 3. In the present invention, the voltage fluctuation trend is monitored and adjusted by variational mode decomposition technology, so as to realize voltage stability control of the energy storage module, optimize the reliability of the energy storage system, and calculate and select the optimal frequency scheme by matching the output power and frequency adjustment range, thereby improving the overall stability and energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a system flow chart of the present invention;

[0055] Figure 2 Schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] See also Figure 1 The present invention provides a technical solution: a DC power supply energy storage system suitable for a variety of household appliances comprises:

[0058] Energy storage behavior monitoring module: Based on the charge and discharge status of the energy storage module, collect the current change value of the energy storage unit, analyze the power fluctuation range, record the time interval of power change, count the continuous cycle law of the charge and discharge status, extract the relationship between the power change amplitude and time interval of each unit, classify the cycle and divide the time according to the working status, and generate the energy storage status timing information;

[0059] Charging behavior prediction module: Based on the energy storage state timing information, extract the power change value and state transition sequence of the energy storage unit, analyze the change of the transfer trend, combine the transfer sequence with the power change value to calculate the probability weight, adjust the initial transfer weight parameter, update the transfer relationship between states in turn, correct the transfer weight by comparing the state duration, and generate a behavior prediction transfer matrix;

[0060] Partition dynamic control module: Based on the behavior prediction transfer matrix, the Markov chain model is used to analyze the real-time power demand of the energy storage unit, extract the power difference between partitions, sort the regional power and demand deviations, correct the partition power supply status by gradually allocating the power path with the smallest difference, extract the power allocation results of the matching path, and generate the partition power adjustment strategy;

[0061] Energy storage power adjustment module: Based on the partition power adjustment strategy, the real-time output power value and target power value of the energy storage unit are extracted, the difference between the two is matched with the adjustment range, the power distribution path of the energy storage unit is corrected in turn, and the dynamic power configuration plan of the energy storage is generated by gradually optimizing the output power to meet the target value;

[0062] Voltage stability control module: Based on the energy storage dynamic power configuration scheme, the voltage fluctuation amplitude of the energy storage unit is monitored by using variational mode decomposition, the voltage deviation of each unit is calculated, the regulation demand is analyzed in combination with the fluctuation amplitude trend, the fluctuation trend is compared with the deviation value, the appropriate voltage correction parameters are gradually selected, the voltage output value of the energy storage module is adjusted, and the voltage regulation control signal is generated;

[0063] Power supply output coordination module: Based on the voltage regulation control signal, extract the output frequency adjustment range of the high-demand area, analyze the matching of frequency and voltage correction parameters, gradually adjust the power frequency, calculate the power output of each adjustment frequency, select the optimal solution by comparing the power values ​​output at each frequency, and generate an output power coordination signal.

[0064] See also Figure 2 The energy storage behavior monitoring module includes a current change acquisition submodule, a power fluctuation analysis submodule, and a periodic law statistics submodule, among which:

[0065] Current change acquisition submodule: Based on the charge and discharge status of the energy storage module, the current value of the energy storage unit at each time point during the charge and discharge process is collected, and the current data is timestamped to ensure that each data point can accurately reflect the current change of the energy storage unit and obtain the current change data;

[0066] Power fluctuation analysis submodule: Based on the current change data, calculate the power fluctuation corresponding to the current value, analyze the amplitude and frequency of the power fluctuation according to the time interval, record the fluctuation range of the power change, and extract the amplitude change information of the power fluctuation to generate the power fluctuation characteristics;

[0067] Periodic law statistics submodule: Based on the power fluctuation characteristics, by counting the continuous cycles of power changes during the charging and discharging process of the energy storage unit, analyzing the time intervals between cycles, and combining the relationship between the current and power change amplitude, the cycles are classified and divided into time periods to generate energy storage status timing information;

[0068] Current change acquisition submodule: Based on the charge and discharge status of the energy storage module, the current value of the energy storage unit at each time point during the charge and discharge process is collected using a time-sharing sampling method. The collected current value is segmented according to the set interval using a fixed time interval segmentation method. The current data at each time point is marked using a timestamp algorithm. Timestamp generation is completed by calling the timer function inside the system, and data is recorded in a format. The current value is associated with the time point and stored in a data structure. A dynamic array storage structure is used to ensure dynamic allocation of storage capacity, and integrity checks are performed on the collected data. The difference between the array length and the expected number of collection points is calculated to determine whether data is lost, and current change data is generated;

[0069] Power fluctuation analysis submodule: Based on the current change data, the Ohm's law formula is used to calculate the product of the current value and the internal impedance of the energy storage unit to obtain the power value. The power change difference algorithm is used to perform differential processing on the power values ​​at adjacent time points to obtain the fluctuation value. The Fourier transform method is used to extract the frequency component of the power fluctuation. The specific process is to apply the fast Fourier transform algorithm to the differential power data to decompose it into frequency components, extract the frequency value and amplitude value corresponding to the main component, calculate the energy contribution of the frequency component by calculating the square of the amplitude, use the moving average method to smooth the amplitude of the power fluctuation, record the upper and lower limits of the power change, and generate the power fluctuation characteristics;

[0070] Periodic law statistics submodule: Based on the power fluctuation characteristics, the peak detection algorithm is used to extract the period of the power fluctuation data. Specifically, the continuous period is obtained by finding the time intervals between adjacent peaks in the power fluctuation curve, and the period time intervals are analyzed by the quantile statistics method to extract the duration distribution of each period. The period distribution data is divided into different time intervals, and the least squares method is used to perform regression analysis on the period data and the time interval data to extract the relationship between the period and the power amplitude change. The K-means clustering algorithm is used to classify the period according to the time interval characteristics, and the time period is generated according to the classification results to generate the energy storage state timing information;

[0071] Among them, the energy storage state timing information includes the start and end time of the charge and discharge cycle, the power change amplitude, the time interval sequence and the cycle working state classification.

[0072] See also Figure 2 The charging behavior prediction module includes a transfer sequence extraction submodule, a probability weight calculation submodule, and a behavior prediction update submodule, wherein:

[0073] Transfer sequence extraction submodule: Based on the energy storage state timing information, extract the state transition sequence of the energy storage unit during the charging and discharging process, analyze the transfer time and power change characteristics of each state, record the transfer trajectory of each state in turn, and generate the transfer sequence characteristics;

[0074] Probability weight calculation submodule: Based on the characteristics of the transfer sequence, the transfer probability between each state and the next state is calculated, and the initial transfer weight is updated in combination with the time information of the state transfer. The transfer probability weight is generated by analyzing the historical transfer data;

[0075] Behavior prediction update submodule: Based on the transition probability weight, the transition relationship between each state is updated in sequence, and corrections are made according to the duration of each state, and finally a new state transition law is formed to generate a behavior prediction transition matrix;

[0076] Transfer sequence extraction submodule: Based on the energy storage state timing information, the finite state machine algorithm is used to extract the state changes of the energy storage unit during the charging and discharging process. Each timing point is regarded as a state node of the finite state machine. A unique identifier is assigned to each node using the state encoding method. The transition between adjacent nodes is marked using the state transition rule. The state transition time is recorded and the power difference between adjacent states is calculated. A directed graph data structure is constructed through the state transition table to store the state trajectory information, and the state and transition relationship is stored in the form of an adjacency matrix of the graph to generate transfer sequence features.

[0077] Probability weight calculation submodule: Based on the characteristics of the transfer sequence, the Markov chain model is used to calculate the transfer probability. The state transfer matrix is ​​used to record the historical transfer frequency between each state. The transfer frequency of each state is normalized to generate the initial transfer probability matrix. Combined with the state transfer time data, the transfer probability is adjusted by the time weighting method. The time weighting is calculated by combining the state transfer time difference with the time attenuation factor to generate the transfer probability weight.

[0078] Behavior prediction update submodule: Based on the transfer probability weight, the transfer relationship between each state is adjusted in turn using the matrix update method, and the initial transfer matrix is ​​updated using the iterative weighted method. During the update, the observed transfer probability is combined with the current transfer probability value, and the transfer relationship is corrected using the weighted method. The duration of each state is introduced into the weighted calculation, and the duration is obtained by accumulating the state existence time. Finally, the transfer matrix is ​​re-standardized using the normalization method to generate the behavior prediction transfer matrix;

[0079] Among them, the behavior prediction transfer matrix includes the state transfer starting point, transfer end point, transfer weight and time adjustment parameter.

[0080] See also Figure 2 The partition dynamic control module includes a power demand analysis submodule, a power difference sorting submodule, and a power adjustment strategy submodule, wherein:

[0081] Power demand analysis submodule: Based on the behavior prediction transfer matrix, the Markov chain model is used to extract the real-time power demand data of the energy storage unit. By comparing the historical power demand with the predicted value, the power demand deviation of the current area is calculated. Combined with the working status of the equipment in the area, the power demand differences in different areas are further analyzed to generate power demand difference data;

[0082] Power difference sorting submodule: Based on the power demand difference data, the power demand deviation of each area is sorted. First, the area with the smallest power difference is determined, and then the power difference between other areas and the target area is calculated. The power difference is sorted according to the difference value to generate the power difference sorting result.

[0083] Power adjustment strategy submodule: Based on the power difference sorting results, the power path with the smallest difference is gradually allocated in order of priority. By analyzing the power demand differences between regions, the power output path of the battery unit is adjusted, the power supply status of each region is corrected, and the partition power adjustment strategy is generated;

[0084] Power demand analysis submodule: Based on the behavior prediction transfer matrix, the Markov chain model is used to extract the real-time power demand data of the energy storage unit, and the power value of each state in the state transfer matrix is ​​used for weighted calculation. The current power prediction value is calculated through the probability data of historical state transfer, and the predicted power is compared and analyzed in combination with the real-time power data. The current regional power demand deviation is calculated, and the power demand of each device is further accumulated into the regional power demand value using the equipment working status table. The power demand difference is analyzed through the difference between the regional power demands to generate power demand difference data;

[0085] Power difference sorting submodule: Based on the power demand difference data, the quick sorting algorithm is used to sort the power demand deviation of each area. The area with the smallest power deviation is used as the reference area, and the power difference between other areas and the reference area is calculated. The power difference value is stored in the sorting queue, and the sorting queue is arranged according to the difference value using quick sorting. The queue is dynamically optimized and sorted by adjusting the recursive partitioning point to generate the power difference sorting result;

[0086] Power adjustment strategy submodule: Based on the power difference sorting results, the area with the smallest power deviation is selected as the starting adjustment area according to the sorting priority. The shortest path algorithm is used to calculate the power adjustment path between areas. The path planning algorithm is used to optimize the power output path of the battery unit. The power supply status of each area is gradually corrected through the dynamic planning method. The power output value of each path after adjustment is recorded, and the power supply adjustment plan is stored in the dynamic data table to generate the partition power adjustment strategy;

[0087] Among them, the partition power adjustment strategy includes partition load range, power allocation path, and regional demand priority sorting.

[0088] Markov chain model, according to the formula:

[0089] P t+1 =(P t +W)·(T+ΔT)·F

[0090] Where: P t+1 is the power demand forecast value of the energy storage unit in the next time period, P t is the power demand vector of the energy storage unit in the current time period, W is the current regional environmental weight factor, T is the behavior prediction transfer matrix, ΔT is the dynamic correction amount of the transfer matrix, and F is the equipment working state adjustment factor;

[0091] Execution process: First, by real-time monitoring of the power demand of the current energy storage unit, the power demand vector P of the current time period is collected. t The data is then combined with the regional environmental data to calculate the environmental weight factor W. The relationship model between environmental impact and power demand is established through historical data regression analysis, and normalized to the range of [0, 1] to obtain the weight value. Then the behavior prediction transfer matrix T is extracted, and the dynamic collection of operation logs is used to analyze the transition law between the current states. At the same time, the dynamic correction amount ΔT of the transfer matrix is ​​adjusted based on the historical error trend, and the values ​​of each element in the matrix are adjusted. Finally, the equipment working state adjustment factor F is loaded, and the weight coefficient is assigned according to different equipment types. The power demand prediction value P is completed through matrix operation. t+1 The power demand deviation of the current area and the power demand difference of different areas are determined by calculation.

[0092] See also Figure 2 The energy storage power regulation module includes a power difference extraction submodule, a power matching correction submodule, and a dynamic configuration scheme submodule, among which:

[0093] Power difference extraction submodule: Based on the partition power adjustment strategy, extract the real-time power output and target power value of the energy storage unit, identify the deviation between the power output and the target by calculating the power difference between the two, and obtain the power difference data in combination with the system demand adjustment strategy;

[0094] Power matching correction submodule: Based on the power difference data, by matching the difference between the real-time power output of the energy storage unit and the target power value, the power distribution path is gradually corrected to ensure that the output power of the energy storage unit is adjusted toward the target value and generate a power correction path;

[0095] Dynamic configuration scheme submodule: Based on the power correction path, the output power of the energy storage unit is adjusted according to the predetermined steps, the power allocation of each unit is optimized, and the final power output is ensured to meet the target demand through gradual adjustment, thus generating a dynamic power configuration scheme for energy storage;

[0096] Power difference extraction submodule: Based on the partition power adjustment strategy, the difference calculation method is used to compare the real-time power output of the energy storage unit with the target power value, and the real-time power data and the target power value at each time point are extracted. The real-time power data is stored in the time series array, and the target power value is used as the reference array. The power difference between the two is calculated by point-by-point subtraction, and the difference value is recorded and stored in the difference matrix. The power deviation is classified and analyzed through matrix row and column operations, and the power deviation is divided into two categories: higher than the target value and lower than the target value. Combined with the adjustment strategy, the difference data is integrated into a multidimensional array to generate power difference data;

[0097] Power matching correction submodule: Based on the power difference data, the least squares method is used to fit the difference between the real-time power output of the energy storage unit and the target power value. The fitting curve is generated by minimizing the sum of squares of the power deviation. The power output path is gradually matched based on the fitting results. The actual power output value of each energy storage unit is segmented and adjusted to the target value range. The dynamic path allocation algorithm is used to match the optimal power allocation path according to the power difference. The weight parameters of the energy storage unit output path are gradually iterated and corrected. The corrected path and power output value are recorded to generate a power correction path.

[0098] Dynamic configuration scheme submodule: Based on the power correction path, the output power of the energy storage unit is gradually adjusted according to the predetermined steps of the path planning algorithm. The difference between the adjusted power value and the target power value is monitored in real time, and the adjusted output power data is stored in the dynamic partition array. The power adjustment steps are updated in real time using the feedback control algorithm. The adjustment process is dynamically optimized in combination with the load response time of each energy storage unit, and the remaining power path is gradually allocated to finally generate a dynamic power configuration scheme for energy storage.

[0099] Among them, the energy storage dynamic power configuration plan includes target power value, power adjustment range, and output path optimization parameters.

[0100] See also Figure 2 The voltage stability control module includes a voltage fluctuation monitoring submodule, a voltage deviation calculation submodule, and a voltage correction control submodule, wherein:

[0101] Voltage fluctuation monitoring submodule: Based on the energy storage dynamic power configuration scheme, variational mode decomposition is used to extract the real-time voltage value of the energy storage unit, calculate the fluctuation amplitude by recording the voltage data at different time points, and mark the upper and lower limits of the fluctuation range. The voltage fluctuation trend in the continuous time period is further compared to generate voltage fluctuation trend data;

[0102] Voltage deviation calculation submodule: Based on the voltage fluctuation trend data, the difference between the real-time voltage value of the energy storage unit and the standard voltage is extracted, the voltage change of each energy storage unit is evaluated by calculating the deviation value, and the unit that needs to be adjusted is confirmed in combination with the fluctuation trend data to generate voltage deviation data;

[0103] Voltage correction control submodule: Based on the voltage deviation data, the correction parameters are gradually selected by analyzing the relationship between the deviation value and the fluctuation trend, the voltage is corrected by adjusting the output voltage of the energy storage module, and the corrected output value is recorded to generate a voltage regulation control signal;

[0104] Voltage fluctuation monitoring submodule: Based on the energy storage dynamic power configuration scheme, the real-time voltage value of the energy storage unit is decomposed and processed by variational mode decomposition, and the voltage value is decomposed into different modal components. The voltage value at each time point is recorded by point-by-point sampling, and the recorded discrete voltage data is processed for continuity by interpolation. The fluctuation amplitude is calculated by the voltage difference method, and the extreme points in the fluctuation data are marked by the upper and lower limit detection algorithm. The voltage fluctuation amplitude of each time period is calculated by the sliding window method, and the voltage change trend in the continuous time period is fitted as a trend line according to the linear fitting method to generate voltage fluctuation trend data;

[0105] Voltage deviation calculation submodule: Based on the voltage fluctuation trend data, the real-time voltage value and the standard voltage of the energy storage unit are extracted, and the difference is calculated by subtracting the standard voltage value from the voltage data at each time point. All the differences are stored as a voltage deviation array, and the deviation values ​​in the array are evaluated using the mean calculation method. The mean and variance of the voltage deviation are calculated, and the discrete degree of the deviation is analyzed using the standard deviation formula. The threshold judgment method is used in combination with the fluctuation trend data to confirm the unit that needs to be adjusted, and the adjusted unit data is marked and stored to generate voltage deviation data;

[0106] Voltage correction control submodule: Based on the voltage deviation data, the difference analysis method is used to extract the relationship between the deviation value and the fluctuation trend, the dynamic parameter optimization algorithm is used to select the correction parameters, the priorities of different adjustment paths are sorted by the weight calculation method, and the correction parameters are selected according to the sorting to perform voltage adjustment. The output voltage of the energy storage module is dynamically corrected using the feedback control algorithm, and the output voltage value at each time point after correction is recorded as a correction output array, and the correction result is stored and a voltage regulation control signal is generated;

[0107] The voltage regulation control signal includes a voltage deviation value, a voltage output adjustment parameter and a correction amplitude limit value.

[0108] Variational mode decomposition, according to the formula:

[0109]

[0110] Where: V′(t) is the real-time voltage value of the improved energy storage unit at time t, is the weighted modal signal after variational mode decomposition, IMF i (t) is the i-th intrinsic mode function generated by variational mode decomposition, w i is the weight coefficient of the i-th mode function, r(t) is the residual term remaining after the signal mode decomposition, k·e(t) is the error correction term, e(t) is the signal measurement error correction value, k is the error correction weight coefficient, δ·D(t) is the dynamic power signal compensation term, D(t) is the compensation signal introduced by the external dynamic power adjustment, and δ is the adjustment coefficient of the dynamic compensation signal;

[0111] Execution process: First, collect the voltage data of the energy storage unit at different time points and decompose the voltage signal to generate the intrinsic mode function IMF i (t) and the residual term r(t), calculate the energy proportion of each mode function and obtain the weight coefficient w i , and then the measurement error e(t) is calculated through repeated sampling and data fitting. The standard deviation of the error distribution is analyzed and normalized to obtain the weight coefficient k, and the possible deviation in the correction signal is then introduced. The external dynamic power adjustment signal D(t) is used to reflect the influence of the signal on the voltage fluctuation through the dynamic compensation coefficient δ. The dynamic compensation coefficient is determined by calculating the deviation range of the historical power fluctuation data and normalizing it. Finally, the weighted sum of all modal signals, the margin term, the error correction term and the dynamic compensation term are combined to calculate the improved real-time voltage value V′(t), so as to realize accurate voltage fluctuation trend monitoring.

[0112] See also Figure 2 , by analyzing the relationship between the deviation value and the fluctuation trend, the correction parameters are gradually selected. According to the comprehensive analysis of the voltage deviation value and the fluctuation trend, the correction amplitude is adjusted in combination with the deviation value. The fluctuation trend distinguishes short-term fluctuations from long-term trends to ensure that the correction amplitude matches the demand. At the same time, the output capacity and dynamic response speed of the energy storage unit are considered to prevent excessive adjustment from affecting the stability of the equipment. In combination with the balance requirements of stability and energy consumption, priority is given to quickly restoring the target voltage in high-load scenarios, and efficiency optimization is emphasized in low-load scenarios.

[0113] See also Figure 2 The power supply output coordination module includes a frequency adjustment range extraction submodule, a frequency matching analysis submodule, and an output power optimization submodule, wherein:

[0114] Frequency adjustment range extraction submodule: Based on the voltage regulation control signal, the output frequency range of the high-demand area is extracted. By analyzing the upper and lower limits of the regional power supply frequency, the frequency adjustment range is recorded and the adjustment target is determined in combination with the current demand situation. The feasible frequency range is gradually screened to generate frequency adjustment range data.

[0115] Frequency matching analysis submodule: Based on the frequency adjustment range data, the output power value corresponding to each frequency is extracted, and the matching degree of the frequency and voltage correction parameters is evaluated, and the power output of each group of matching frequencies is recorded. Finally, the matching results of all frequencies are arranged in order to generate frequency matching sorting data;

[0116] Output power optimization submodule: Based on the frequency matching sorting data, select the optimal combination of matching frequency and power output, optimize the power supply output plan by gradually adjusting the power output frequency, correct the frequency value based on regional needs, and finally generate an output power coordination signal;

[0117] Frequency adjustment range extraction submodule: Based on the voltage regulation control signal, the interval analysis method is used to extract the output frequency range of the high-demand area. The power supply frequency interval is analyzed from the control signal by setting the upper and lower frequency limits. The dynamic changes in the frequency interval are gradually scanned using the sliding window algorithm. The current power supply demand data is compared with the upper and lower frequency limits. The adjustment target frequency range is determined by the threshold screening method. The infeasible frequency interval is eliminated using the Boolean screening algorithm. The qualified frequency interval is stored in the frequency interval array to generate the frequency adjustment range data.

[0118] Frequency matching analysis submodule: Based on the frequency adjustment range data, the matching relationship between the frequency and the output power value is analyzed by using a matching degree calculation algorithm. Each frequency value in the frequency adjustment range is read to extract the corresponding output power parameter. The frequency value and the voltage correction parameter are weighted to evaluate the matching degree. The matching score is generated by using the weighted average method. The matching score is combined with the output power value to generate a matching degree matrix. The matching order of each frequency is arranged by a matrix sorting algorithm. The sorting results are stored in a sorting table to generate frequency matching sorting data.

[0119] Output power optimization submodule: Based on the frequency matching sorting data, a dynamic programming algorithm is used to select the optimal combination of frequency and power output. The highest priority frequency in the matching sorting is used as the initial adjustment frequency. The frequency value is gradually adjusted in combination with the regional demand data. The increment and decrement of the output frequency are adjusted using the step size optimization algorithm. The difference between the adjusted power output and the target value is monitored in real time. The adjusted frequency and output power value are stored in the optimization path array. The output plan is corrected through multiple iterations to generate an output power coordination signal.

[0120] The output power coordination signal includes a frequency adjustment range, a power output optimization value and an output matching parameter set.

[0121] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A DC power supply energy storage system suitable for a variety of household appliances, characterized by: The system comprises: Energy storage behavior monitoring module: Based on the charge and discharge status of the energy storage module, collect the current change value of the energy storage unit, analyze the power fluctuation range, record the time interval of power change, count the continuous cycle law of the charge and discharge status, extract the relationship between the power change amplitude and time interval of each unit, classify the cycle and divide the time according to the working status, and generate the energy storage status timing information; Charging behavior prediction module: based on the energy storage state timing information, extract the power change value and state transition sequence of the energy storage unit, analyze the change of the transfer trend, calculate the probability weight by combining the transfer sequence with the power change value, adjust the initial transfer weight parameter, update the transfer relationship between states in turn, correct the transfer weight by comparing the state duration, and generate a behavior prediction transfer matrix; Partition dynamic control module: Based on the behavior prediction transfer matrix, the Markov chain model is used to analyze the real-time power demand of the energy storage unit, extract the power difference between partitions, sort the regional power and demand deviations, correct the partition power supply status by gradually allocating the power path with the smallest difference, extract the power allocation result of the matching path, and generate the partition power adjustment strategy; Energy storage power regulation module: Based on the partition power adjustment strategy, the real-time output power value and target power value of the energy storage unit are extracted, the difference between the two is matched with the adjustment range, the power distribution path of the energy storage unit is corrected in turn, and the output power is gradually optimized to meet the target value, thereby generating an energy storage dynamic power configuration plan; Voltage stability control module: Based on the energy storage dynamic power configuration scheme, the voltage fluctuation amplitude of the energy storage unit is monitored by using variational mode decomposition, the voltage deviation of each unit is calculated, the regulation demand is analyzed in combination with the fluctuation amplitude trend, the fluctuation trend is compared with the deviation value, and the appropriate voltage correction parameters are gradually selected to adjust the voltage output value of the energy storage module and generate a voltage regulation control signal; Power supply output coordination module: Based on the voltage regulation control signal, extract the output frequency adjustment range of the high-demand area, analyze the matching of the frequency and voltage correction parameters, gradually adjust the power frequency, calculate the power output of each adjustment frequency, select the optimal solution by comparing the power values ​​output at each frequency, and generate an output power coordination signal.

2. The DC power supply energy storage system applicable to various household appliances according to claim 1, characterized in that: The energy storage state timing information includes the start and end time of the charge and discharge cycle, the power change amplitude, the time interval sequence and the cycle working state classification.

3. The DC power supply energy storage system applicable to various household appliances according to claim 1, characterized in that: The behavior prediction transfer matrix includes a state transfer starting point, a transfer end point, a transfer weight and a time adjustment parameter.

4. The DC power supply energy storage system applicable to various household appliances according to claim 1, characterized in that: The partition dynamic control module includes a power demand analysis submodule, a power difference sorting submodule, and a power adjustment strategy submodule, wherein: Power demand analysis submodule: Based on the behavior prediction transfer matrix, the Markov chain model is used to extract the real-time power demand data of the energy storage unit, and the power demand deviation of the current area is calculated by comparing the historical power demand with the predicted value. In addition, the power demand difference of different areas is further analyzed in combination with the working status of the equipment in the area, and the power demand difference data is generated; Power difference sorting submodule: based on the power demand difference data, sort the power demand deviations of each area, first determine the area with the smallest power difference, then calculate the power difference between other areas and the target area, and sort them by priority according to the difference value to generate a power difference sorting result; Power adjustment strategy submodule: based on the power difference sorting results, gradually allocate the power path with the smallest difference in order of priority, adjust the power output path of the battery unit by analyzing the power demand difference between regions, correct the power supply status of each region, and generate a partition power adjustment strategy; The partition power adjustment strategy includes partition load range, power allocation path, and regional demand priority ranking.

5. The DC power supply energy storage system applicable to various household appliances according to claim 1, characterized in that: The Markov chain model is based on the formula: P t+1 =(P t +W)·(T+ΔT)·F Where: P t+1 is the power demand forecast value of the energy storage unit in the next time period, P t is the power demand vector of the energy storage unit in the current time period, W is the current regional environmental weight factor, T is the behavior prediction transfer matrix, ΔT is the dynamic correction of the transfer matrix, and F is the equipment working state adjustment factor.

6. The DC power supply energy storage system applicable to various household appliances according to claim 1, characterized in that: The energy storage dynamic power configuration scheme includes a target power value, a power adjustment range, and output path optimization parameters.

7. The DC power supply energy storage system applicable to various household appliances according to claim 1, characterized in that: The voltage stability control module includes a voltage fluctuation monitoring submodule, a voltage deviation calculation submodule, and a voltage correction control submodule, wherein: Voltage fluctuation monitoring submodule: Based on the energy storage dynamic power configuration scheme, variational mode decomposition is used to extract the real-time voltage value of the energy storage unit, the fluctuation amplitude is calculated by recording the voltage data at different time points, and the upper and lower limits of the fluctuation range are marked, and the voltage fluctuation trend in the continuous time period is further compared to generate voltage fluctuation trend data; Voltage deviation calculation submodule: based on the voltage fluctuation trend data, extract the difference between the real-time voltage value of the energy storage unit and the standard voltage, evaluate the voltage change of each energy storage unit by calculating the deviation value, and confirm the unit that needs to be adjusted in combination with the fluctuation trend data to generate voltage deviation data; Voltage correction control submodule: Based on the voltage deviation data, the correction parameters are gradually selected by analyzing the relationship between the deviation value and the fluctuation trend, the voltage is corrected by adjusting the output voltage of the energy storage module, and the corrected output value is recorded to generate a voltage regulation control signal; Wherein, the voltage regulation control signal includes a voltage deviation value, a voltage output adjustment parameter and a correction amplitude limit value.

8. The DC power supply energy storage system applicable to various household appliances according to claim 1, characterized in that: The variational mode decomposition is according to the formula: Where: V′(t) is the real-time voltage value of the improved energy storage unit at time t, is the weighted modal signal after variational mode decomposition, IMF i (t) is the i-th intrinsic mode function generated by variational mode decomposition, w i is the weight coefficient of the i-th mode function, r(t) is the residual term remaining after the signal mode decomposition, k·e(t) is the error correction term, e(t) is the signal measurement error correction value, k is the error correction weight coefficient, δ·D(t) is the dynamic power signal compensation term, D(t) is the compensation signal introduced by the external dynamic power adjustment, and δ is the adjustment coefficient of the dynamic compensation signal.

9. The DC power supply energy storage system applicable to various household appliances according to claim 7, characterized in that: The correction parameters are gradually selected by analyzing the relationship between the deviation value and the fluctuation trend. The correction amplitude is adjusted based on the comprehensive analysis of the voltage deviation value and the fluctuation trend, and the fluctuation trend distinguishes short-term fluctuations from long-term trends to ensure that the correction amplitude matches the demand. At the same time, the output capacity and dynamic response speed of the energy storage unit are considered to prevent excessive adjustment from affecting the stability of the equipment. In combination with the balance requirements of stability and energy consumption, rapid recovery of the target voltage is prioritized in high-load scenarios, and efficiency optimization is emphasized in low-load scenarios.

10. The DC power supply energy storage system applicable to various household appliances according to claim 1, characterized in that: The output power coordination signal includes a frequency adjustment range, a power output optimization value and an output matching parameter set.

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