Power flow control method for direct-current bus connected with standby battery

Abstract

The invention provides a power flow control method for a direct-current bus connected with a standby battery, and relates to the technical field of power system control, and the method comprises the steps: defining a topological structure of a direct-current micro-grid, and constructing a mathematical model frame comprising state parameters, operation states and constraint conditions; filtering, interpolation and sparseness, time sequence matching, combinatorial calculation, dimensional transformation and normalization processing are carried out on the measurement parameters obtained by the micro-grid to obtain processed data; according to the processed data, analyzing current system state parameters; future state change is predicted through a genetic algorithm, and a prediction result of the future state is obtained. According to the intelligent control scheme, the direct-current bus power flow is predicted and optimized, the system stability is effectively improved on the premise of meeting the operation requirement of the direct connection standby battery on the quality bus, and the optimal scheduling of the energy consumption cost is supported.

Description

Technical Field

[0001] The present invention relates to the technical field of power system control, and in particular to a direct current bus current control method connected with a backup battery. Background Art

[0002] The monitoring and control unit of the traditional uninterruptible DC power supply system is mainly designed to manage the charging and discharging process of the backup battery, and it is only achieved by controlling the output of the AC rectifier. When the DC bus is connected to the renewable energy power generation power supply and energy storage device, this single control logic cannot effectively manage the coordinated operation of multiple power supplies and energy storage devices, which may cause negative impacts on the safety and service life of the backup battery. The existing monitoring and control unit cannot fully monitor the operating mode of each device connected to the DC bus, including the status and demand of renewable energy power generation power supplies, energy storage devices and loads. Therefore, it is impossible to achieve flexible scheduling and utilization of energy based on different costs or priorities, which limits the energy efficiency and economic benefits of the system.

[0003] Traditional systems lack the ability to predict future trends in renewable energy sources, energy storage devices, and electricity loads. This means that the system cannot make scheduling plans in advance to optimize energy distribution and reduce operating costs. The lack of such forward-looking data support makes the system powerless when dealing with emergencies or energy price fluctuations. The power flow control algorithm of the DC microgrid is usually designed for a constant voltage DC bus, while the DC bus of the traditional uninterruptible DC power supply system must be connected to a backup battery, and its voltage will change with the battery status. This makes it impossible for the existing power flow control algorithm to be directly applied to this new type of DC power supply system and unable to meet its special control requirements. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a DC bus current control method connected to a backup battery, which effectively solves the problem of operability of a microgrid.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] In a first aspect, a method for controlling a DC bus current connected to a backup battery is provided, the method comprising:

[0007] Define the topology of the DC microgrid and build a mathematical model framework including state parameters, operating status and constraints;

[0008] The measured parameters obtained by the microgrid are filtered, interpolated and sparse, time series matched, combined and calculated, dimensionally transformed, and normalized to obtain processed data;

[0009] Analyze the current system status parameters based on the processed data;

[0010] By randomly generating an initial population and iteratively performing mutation, crossover, and selection operations, the final solution is finally determined to predict future state changes and obtain the prediction results of the future state;

[0011] According to the prediction results of future status, power flow and energy cost, a control plan is formulated and sent to each controller;

[0012] The control scheme is continuously optimized based on the execution feedback procedure to respond to changes in system status and environment.

[0013] Furthermore, the topology of the DC microgrid is defined, and a mathematical model framework including state parameters, operating states and constraints is constructed, including:

[0014] The DC microgrid is divided into a primary circuit topology and a secondary circuit topology with the DC bus as the center. The primary circuit includes the DC bus, the distribution branch, the intermediate control layer and the branch equipment for the flow of electric energy; the secondary circuit includes a virtual converged data bus, a branch virtual measurement device and an equipment communication port for data collection and transmission; the branch equipment includes power generation, energy storage and load equipment, and at least one backup battery pack directly connected to the bus, and finally the topology of the DC microgrid is obtained;

[0015] According to the topological structure of the DC microgrid, a mathematical model framework of the microgrid is constructed, including state parameters, defined operating states and defined constraint sets. The state parameters include bus voltage, branch current and direction, branch power, controller efficiency, device connection port voltage and current, inflow and outflow electric power, inflow and outflow electric energy price, energy storage margin and value, and energy storage loop loss rate per unit time; the operating state includes various states of the bus, distribution branch, intermediate controller and branch equipment; the constraint set includes various operating restrictions of the bus, distribution branch, intermediate controller and branch equipment.

[0016] Furthermore, the measured parameters obtained by the microgrid are filtered, interpolated and sparse, time series matched, combined and calculated, dimensionally transformed, and normalized to obtain processed data, including:

[0017] Obtain raw measurement parameter data from sensors and monitoring systems in the microgrid;

[0018] Process the original measurement parameter data, remove noise and outliers, and obtain filtered data;

[0019] Interpolate the missing values ​​in the filtered data and perform sparse processing on the overly dense data points to obtain interpolated and sparsely processed data;

[0020] Align the interpolated and sparsely processed data to a unified time series to obtain the time series matched data;

[0021] According to the matched data of time series, combined calculation is performed to obtain new derivative data;

[0022] Convert the measurement data and new derived data of different dimensions into unified dimensions to obtain dimension-transformed data;

[0023] The data after dimension transformation is normalized to obtain the processed data.

[0024] Furthermore, according to the processed data, the current system status parameters are analyzed, including:

[0025] Using the preset neural network model, the extracted system status-related features are used as input data to identify the current status of the system, including normal operation, overload, undervoltage, overvoltage, and frequency deviation;

[0026] Based on the identified current state of the system, state parameters are calculated, including the system's stability index, load level, and failure probability.

[0027] Furthermore, a genetic algorithm is used to randomly generate an initial population and iteratively perform mutation, crossover, and selection operations to ultimately determine the final solution to predict future state changes and obtain the prediction results of the future state, including:

[0028] An initial population is randomly generated, each individual represents a set of state parameter prediction values;

[0029] For each individual in the population, three different individuals are identified to generate a new variant individual;

[0030] Cross the new variant individuals with the original individuals to generate test individuals;

[0031] Use the fitness function to evaluate each individual in the test individual set and the original individual set, calculate the fitness value of each individual, and determine whether the individual will enter the next generation population based on the size of the fitness value;

[0032] Repeat the mutation, crossover and selection operations until the preset number of iterations is reached, and determine the final solution from the final population based on the individual fitness values;

[0033] Based on the final solution, the future state of the DC microgrid is analyzed, including state change trend and anomaly detection;

[0034] Based on the final solution and the analysis of the future state of the DC microgrid, the prediction results of the future state are generated, including the predicted value, prediction error range, and confidence key information.

[0035] Furthermore, the fitness value calculation formula for each individual is:

[0036]

[0037] Where F represents the fitness value; w e represents the prediction error weight; n represents the number of observations; y i represents the actual observed value; represents the predicted value; w s represents the stability weight; Represents the mean of the predicted values.

[0038] Furthermore, based on the prediction results of the future state, power flow and energy cost, a control plan is formulated and sent to each controller, including:

[0039] Collect relevant forecast data of future status, mainly including the forecast of power load demand of each branch, and obtain current power flow information, including the power supply status of the power grid, voltage and current of each node; collect energy cost data, including electricity prices in different time periods and energy consumption characteristics of equipment;

[0040] Construct a prediction model to predict the future state, and use the collected prediction data of the future state, mainly the power load prediction of each branch, as input data, and input it into the prediction model to obtain the prediction result of the future state;

[0041] Develop control plans based on the predicted results of future states and information on power flow and energy cost, including adjusting the operating power of equipment, optimizing power consumption time periods, and starting or shutting down equipment;

[0042] The developed control scheme is encoded into instructions recognized by the controller, and the control instructions are sent to each controller.

[0043] In a second aspect, a DC bus power flow control system connected to a backup battery comprises:

[0044] The acquisition module is used to define the topological structure of the DC microgrid and build a mathematical model framework including state parameters, operating status and constraints; filter, interpolate and sparse, time series match, combine calculations, transform dimensions, and normalize the measured parameters obtained by the microgrid to obtain processed data; and analyze the current system state parameters based on the processed data;

[0045] The processing module is used to randomly generate an initial population and iteratively perform mutation, crossover, and selection operations to ultimately determine the final solution in order to predict future state changes and obtain prediction results of future states; formulate a control plan based on the prediction results of future states and the power flow and energy cost, and send it to each controller; and continuously optimize the control plan based on the execution feedback program to cope with changes in system states and environment.

[0046] According to a third aspect, a computing device includes:

[0047] one or more processors;

[0048] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0049] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0050] The above solution of the present invention includes at least the following beneficial effects:

[0051] By defining the topological structure of the DC microgrid and constructing a mathematical model framework including state parameters, operating status and constraints, a solid theoretical foundation is provided for the stable operation of the system. This helps to accurately describe the dynamic behavior of the system and timely discover and deal with potential unstable factors. The measurement parameters obtained by the microgrid are filtered, interpolated and sparse, time series matched, combined calculated, dimension transformed, and normalized, which effectively improves the accuracy and reliability of the data. Furthermore, by randomly generating the initial population and iteratively performing mutation, crossover, and selection operations, the final solution is finally determined to predict future state changes. This data processing and prediction method based on intelligent algorithms can more accurately grasp the future trend of the system and provide strong support for the formulation of control schemes. According to the prediction results of the future state and the power flow and energy cost, the control scheme is formulated and sent to each controller. This control strategy that comprehensively considers the power flow and energy cost helps to achieve efficient use of energy, reduce the operating cost of the system, and improve economic benefits.

[0052] By continuously optimizing the control scheme according to the execution feedback program to cope with changes in system status and environment, the system's adaptability and flexibility are enhanced. This enables the system to better cope with various emergencies and uncertainties, ensuring the stable operation of the system and the quality of power supply. The entire control process is based on intelligent algorithms and data processing technology, realizing intelligent management of microgrids. This helps reduce manual intervention, improve management efficiency, reduce operation and maintenance costs, and provide strong guarantees for the long-term and stable operation of microgrids. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of a DC bus power flow control method connected to a backup battery provided by an embodiment of the present invention.

[0054] Figure 2 It is a schematic diagram of a DC bus power flow control system connected to a backup battery provided by an embodiment of the present invention.

[0055] Figure 3 This is a primary circuit topology diagram of a DC microgrid provided by an embodiment of the present invention.

[0056] Figure 4 This is an example diagram of a primary circuit topology diagram of a DC microgrid provided in an embodiment of the present invention.

[0057] Figure 5 It is a secondary circuit topology diagram of a DC microgrid provided by an embodiment of the present invention.

[0058] Figure 6 This is an example diagram based on a primary circuit topology diagram of a DC microgrid provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0060] like Figure 1 As shown, an embodiment of the present invention provides a DC bus current control method connected to a backup battery, the method comprising the following steps:

[0061] Step 11, defining the topology of the DC microgrid and constructing a mathematical model framework including state parameters, operating states and constraints;

[0062] Step 12, filtering, interpolation and sparseness, time series matching, combined calculation, dimension transformation, and normalization are performed on the measurement parameters obtained by the microgrid to obtain processed data;

[0063] Step 13, analyzing the current system state parameters according to the processed data;

[0064] Step 14, using a genetic algorithm, by randomly generating an initial population and iteratively performing mutation, crossover, and selection operations, finally determining a final solution to predict future state changes and obtain a prediction result of the future state;

[0065] Step 15: formulate a control plan based on the prediction results of the future state, the power flow and the energy cost, and send it to each controller;

[0066] Step 16, continuously optimize the control scheme according to the execution feedback program to cope with changes in system status and environment.

[0067] In an embodiment of the present invention, by clarifying the topological structure of the microgrid, a clear foundation is provided for flow calculation and control strategy formulation. A mathematical model framework including state parameters, operating status and constraints is constructed, so that the system can more accurately describe and simulate the actual operating conditions of the microgrid, providing a solid theoretical basis for optimization control. By performing a series of preprocessing on the measurement parameters, noise and outliers are effectively removed, and the accuracy and reliability of the data are improved. At the same time, through dimensional transformation and normalization processing, parameters of different dimensions and value ranges can be compared and calculated, providing a unified data basis for state analysis and prediction. By analyzing the processed data, the current state parameters of the microgrid, such as voltage, current, power, etc., can be obtained in real time, providing real-time and accurate information support for the formulation of the control scheme.

[0068] The use of intelligent optimization algorithms to predict future states can fully consider the uncertainty and nonlinear characteristics of the system and obtain more accurate and reliable prediction results. This helps to discover potential problems and risks in advance and provide forward-looking guidance for the formulation of control plans. Combining the prediction results of future states with power flow and energy costs, the formulated control plan is more in line with the actual operation needs and economic benefit goals of the system. By sending it to each controller for execution, precise control of the microgrid is achieved, and the operating efficiency and stability of the system are improved. By collecting execution feedback in real time, the control plan is continuously optimized and adjusted, so that the system can better adapt to state changes and environmental fluctuations. This closed-loop control strategy improves the adaptability and robustness of the system and ensures the long-term stable operation of the microgrid.

[0069] In a preferred embodiment of the present invention, the above step 11, defining the topology of the DC microgrid and constructing a mathematical model framework including state parameters, operating states and constraints, may include:

[0070] Step 112, the DC microgrid is divided into a primary loop topology and a secondary loop topology with the DC bus as the center, wherein the primary loop includes the DC bus, the distribution branch, the intermediate control layer and the branch equipment for the flow of electric energy; the secondary loop includes a virtual converged data bus, a branch virtual measurement device and an equipment communication port for data collection and transmission; the branch equipment includes power generation, energy storage and load equipment, and at least one backup battery pack directly connected to the bus, and finally the topology of the DC microgrid is obtained;

[0071] Step 113, according to the topological structure of the DC microgrid, a microgrid mathematical model framework is constructed, including state parameters, defined operating states, and defined constraint condition sets, wherein the state parameters include bus voltage, branch current and direction, branch power, controller efficiency, device connection port voltage and current, inflow and outflow electric power, inflow and outflow electric energy price, energy storage margin and value, and energy storage loop loss rate per unit time; the operating state includes various states of the bus, distribution branch, intermediate controller, and branch equipment; and the constraint condition set includes various operation restrictions of the bus, distribution branch, intermediate controller, and branch equipment.

[0072] In the embodiment of the present invention, the primary circuit and the secondary circuit are divided:

[0073] Primary circuit: With the DC bus as the center, it connects the distribution branches, intermediate control layer and branch equipment and is responsible for the flow and distribution of electric energy.

[0074] Secondary circuit: Build a virtual converged data bus to achieve data collection, transmission and monitoring through branch virtual measurement devices and equipment communication ports.

[0075] Branch equipment includes power generation equipment (such as solar panels, wind turbines, diesel generator sets, etc.), energy storage equipment (such as smart lithium batteries), load equipment (such as transmission, 4G / 5G equipment, lighting equipment), etc. Make sure to include at least one backup battery branch to provide backup power when necessary. Based on the above elements, draw a topological structure diagram of the DC microgrid, clearly showing the connection relationship between the primary and secondary circuits, as well as the distribution of branch equipment.

[0076] Define the status parameters:

[0077] Bus voltage: Indicates the voltage level of the DC bus.

[0078] Branch current and direction: Describes the magnitude and direction of the current in each branch.

[0079] Branch Power: Calculates the power flow on a branch.

[0080] Controller efficiency: reflects the energy conversion efficiency of the intermediate controller.

[0081] Device connection port voltage and current: Record the voltage and current values ​​at the device access point.

[0082] Inflow and outflow electrical power: represent the input and output power of a device or branch respectively.

[0083] Inflow and outflow electricity prices: price factors considered when electricity is traded.

[0084] Energy storage surplus and value: represents the remaining energy of the energy storage equipment and its economic value.

[0085] Energy storage circuit loss rate per unit time: describes the loss of energy storage equipment during the process of storing and releasing energy.

[0086] Define various possible states for busbars, distribution branches, intermediate controllers and branch devices, such as normal operation, fault, maintenance, standby, etc. According to equipment characteristics, system requirements and safety specifications, formulate a series of operating restrictions and constraints, such as busbar voltage fluctuation range, branch current limit, and energy storage device charge and discharge rate limit.

[0087] Assume a simple DC microgrid with the following elements:

[0088] DC bus: voltage level is 48V.

[0089] DC distribution branch: more than 6 branches, connecting the solar controller backup battery, energy storage lithium battery and load equipment respectively.

[0090] Intermediate control equipment: includes a solar controller bidirectional DC / DC, AC rectifier, etc., used for power conversion and regulation of DC output voltage and current.

[0091] Power generation equipment: One solar panel with a rated power of 2kW.

[0092] Backup battery: 2 sets of lead-acid batteries, rated voltage 48V, capacity 500Ah.

[0093] Energy storage equipment: 2 smart lithium batteries, each with a capacity of 5kWh.

[0094] Load equipment: A group of 4G / 5G communication equipment occupies 2 distribution branches with a rated power of 5kW.

[0095] DC bus voltage: 54V (under normal operation).

[0096] Branch current and direction: solar controller branch current I1 (direction is positive), load branch current I2 (direction is negative).

[0097] Branch power: solar controller output power P1, load input power P2.

[0098] Controller efficiency: Solar controller converter efficiency η.

[0099] Device connection port voltage and current: solar controller port voltage V1 and current I1, load port voltage V2 and current I2.

[0100] Inflow and outflow of electrical power: Solar inflow power P i , load outflow power P o .

[0101] Energy storage margin and value: remaining power Qs of smart lithium battery, economic value V of battery b .

[0102] The remaining energy storage capacity of the backup battery is QB, and the economic value of the battery is V. b .

[0103] Energy storage and backup power circuit loss rate per unit time: battery loss rate r.

[0104] Busbar: normal operation / fault. Distribution branch: on / off. Controller: working / standby / fault. Backup power and energy storage battery: charging / discharging / standby / protection. Load equipment: running / stopping (power-off protection).

[0105] The constraint condition set is bus voltage fluctuation range: 42V-58V, voltage follows the backup lead-acid battery port voltage. Branch current limit: I1≤15A, I2≤10A. Energy storage device charge and discharge rate limit: charge rate ≤0.1C, discharge rate ≤0.15. Battery discharge protection: low voltage protection mode.

[0106] Dividing the DC microgrid into primary and secondary circuits clarifies the paths of power flow and data collection and transmission, providing a clear basis for system design and optimization. By defining state parameters including bus voltage, branch current and direction, branch power, etc., the operating status of the system can be fully reflected, providing accurate data support for system monitoring, analysis and optimization. Defining various states of the bus, distribution branch, intermediate controller and branch equipment helps to accurately judge the operating status of the system and promptly discover and handle abnormal states. By defining a set of constraints including various operating restrictions of the bus, distribution branch, intermediate controller and branch equipment, it can ensure that the system operates within a safe and reasonable range, avoiding system instability or failure caused by improper operation. Based on the mathematical model framework, a more accurate and optimized control strategy can be formulated to achieve precise control of the system state and improve the stability and reliability of the system.

[0107] Including the inflow and outflow electricity prices into the state parameters will help to fully consider economic factors when formulating control strategies, and achieve efficient use of electricity and minimize costs. By defining state parameters such as energy storage margin and value, and energy storage circuit loss rate per unit time, energy storage equipment can be managed more scientifically, energy storage strategies can be optimized, and the utilization efficiency of energy storage equipment and the overall energy efficiency of the system can be improved. The setting of secondary circuits and the application of virtual measuring devices and equipment communication ports realize the automatic collection and transmission of data, providing a basis for the intelligence and automation of the system. Based on the mathematical model framework, an intelligent solution support system can be developed to realize real-time monitoring, analysis and solutions of the system status, and improve the intelligence level and operation efficiency of the system. The topology of the DC microgrid can be flexibly adjusted according to actual needs to adapt to microgrid systems of different scales and complexities.

[0108] In a preferred embodiment of the present invention, the above step 12, filtering, interpolation and sparseness, time series matching, combined calculation, dimension transformation, and normalization processing are performed on the measurement parameters obtained by the microgrid to obtain processed data, which may include:

[0109] Step 123, obtaining raw measurement parameter data from sensors and monitoring systems of the microgrid;

[0110] Step 124, processing the original measurement parameter data to remove noise and outliers to obtain filtered data;

[0111] Step 125, interpolating the missing values ​​in the filtered data and performing sparse processing on the overly dense data points to obtain interpolated and sparse processed data;

[0112] Step 126, aligning the interpolated and sparsely processed data to a unified time series to obtain time series matched data;

[0113] Step 137, performing combined calculations based on the matched time series data to obtain new derivative data;

[0114] Step 128, converting the measurement data and the new derived data of different dimensions into a unified dimension to obtain dimension-converted data;

[0115] Step 129, normalize the data after dimension transformation to obtain processed data.

[0116] In an embodiment of the present invention, raw measurement parameter data are obtained in real time or periodically from sensors and monitoring systems of the microgrid. These data include various physical quantities such as voltage, current, power, frequency, temperature, etc. A filtering algorithm, such as median filtering, is applied to the raw measurement parameter data to remove noise and outliers in the data. The filtered data should be smoother and can more accurately reflect the actual operating status of the microgrid. Median filtering is a nonlinear filtering method that replaces the value of the data point with the median of the data point and its neighborhood. For any data point x j (can be voltage, current, power, etc.), median filtering can be expressed as: filtered data = Median{x j -k, ..., x j , …, x j +k}; where k is the size of the filter window, Median represents the median operation, and j represents the index of the data point. For missing values ​​in the filtered data, interpolation methods (such as linear interpolation) are used to fill them. For data points that are too dense, sparse processing is performed according to actual needs to reduce data redundancy and improve data processing efficiency. Assume that d c is a missing value, and its adjacent non-missing value is d c -1 and d c +1, then the linear interpolation can be expressed as: d j =d j -1+(j+1)-(j-1)(d j +1-d j -1)×(j-(j-1))=2d j -1+d j +1. Align the interpolated and sparsely processed data to a unified time series to ensure that the timestamps of all data points are consistent. This helps with data analysis and processing, especially when data at different time points need to be compared. Based on the matched data in the time series, combined calculations are performed to generate new derivative data.

[0117] For example, the product of voltage and current is calculated to obtain power p = V × I, where V is voltage and I is current. Convert measurement data and new derived data of different dimensions to a unified dimension. For example, convert power from watts to kilowatts, etc., to ensure that all data have the same dimension for easy comparison and analysis. Normalize the data after the dimension transformation to scale the data to a specific range (such as between 0 and 1). Normalization helps eliminate dimensional differences between data and improve the accuracy and efficiency of data processing.

[0118] Assume that the following raw measurement parameter data are obtained from the sensors of the microgrid:

[0119] Voltage (V): [53.5, 53.6, 53.5, ..., 53.7] (unit: volt).

[0120] Current (I): [5, 5.1, 4.9, ..., 5.2] (unit: ampere). Apply the mean filtering algorithm to smooth the voltage and current data to remove noise and outliers. Assume that at a certain point in time, the voltage data is missing and the missing value is filled by linear interpolation. At the same time, if the current data is too dense, we choose to retain some data points according to actual needs and perform sparse processing. Align the filtered and sparse voltage and current data to a unified time series to ensure that each data point has a corresponding timestamp. Calculate the product of voltage and current to obtain power data: [p1, p2, p3..., p m ]. Convert power from watts to kilowatts (P / 1000). Scale the voltage and power data after dimension transformation to between 0 and 1. After the above processing steps, the processed data are obtained, which are smoother, more accurate, and have a unified dimension and normalized range, which is convenient for data analysis and processing.

[0121] Through filtering, the noise and outliers in the original measurement parameter data are removed, making the data smoother and more accurate, reflecting the actual operation status of the microgrid more realistically. The missing values ​​in the filtered data are interpolated to ensure the integrity and continuity of the data and avoid the adverse effects of missing data on subsequent analysis. The overly dense data points are sparsely processed to reduce data redundancy, improve the efficiency of data processing and storage, and reduce computing costs. The interpolated and sparsely processed data are aligned to a unified time series to ensure the time consistency between different measurement parameters, which is convenient for subsequent data analysis and comparison. According to the data after time series matching, combined calculations are performed to generate new derivative data (such as power, energy, impedance, price difference, etc.), providing more information support for the monitoring, control and optimization of the microgrid. The measurement data and new derivative data of different dimensions are converted to a unified dimension to eliminate the comparison obstacles caused by the dimension difference, so that the data can be directly compared and analyzed. The data after dimension transformation is normalized to scale the data to a specific range, enhance the comparability and interpretability between data, and facilitate the mining of laws and trends in the data.

[0122] In a preferred embodiment of the present invention, the above step 13, parsing the current system state parameters according to the processed data, may include:

[0123] Step 131, using a preset neural network model, taking the extracted system state-related features as input data, and identifying the current state of the system, including normal operation, overload, undervoltage, overvoltage, etc.;

[0124] Step 132, based on the identified current state of the system, calculate state parameters, including the stability index, load level and failure probability of the system.

[0125] Assume that the voltage time series data obtained after processing is [V1, V2, ..., V r ], the current time series data is [I1, I2, ..., I b ]. Use the preset neural network model and the extracted features as input data to identify the current state of the system. Build a trained neural network model, such as a long short-term memory network (LSTM). Input the extracted features (voltage level, current size, power factor, energy consumption) as input data into the neural network. The neural network outputs the current state of the system, such as normal operation, overload, undervoltage, overvoltage, etc.

[0126] Assume that a trained LSTM model is used. The extracted features [voltage level, current size, power factor, energy consumption] are used as input vectors and input into the LSTM model. The model outputs a probability distribution, which indicates the probability of the system being in various states. For example, [0.9, 0.05, 0.02, 0.02, 0.01] indicates that the system has a 90% probability of being in normal operation, a 5% probability of being in an overloaded state, and so on. Based on the identified system state, the state parameters are calculated, including the system's load level and failure probability. The load level of the system Among them, I c Indicates the current; I r Indicates rated current; V f represents the voltage correction factor. The failure probability FP = 1-P n ; Among them, P n Indicates the normal operation status of the power system.

[0127] Different stability index thresholds are set according to the system status. The load level is calculated as the ratio of the current load to the rated load. The failure probability is estimated based on historical data and the current status. If the system is in normal operation, the stability index can be set to 1; if it is in an overload or undervoltage state, the stability index can be reduced; if it is in an overvoltage or frequency offset state, the stability index can be further reduced. Assuming the current load is L and the rated load is L r , the load level is Based on the duration of various states and the frequency of failures in historical data, the probability of failure in the current state is estimated. For example, if the system is in an overload state for a long time, the probability of failure will increase.

[0128] Based on the identified system status, the calculated stability index can quantify the stability level of the system. This helps operators to promptly identify potential unstable factors and take appropriate measures to prevent system crashes or failures, thereby improving the overall stability of the system. The calculation of the load level provides the ratio between the current system load and the rated load or maximum load capacity. This helps operators understand the load situation of the system, reasonably adjust the load distribution, and avoid overload operation, thereby extending the life of the equipment and improving the efficiency of the system. The calculation of the failure probability is based on the historical data and current status of the system, and can predict the possibility of failure of the system in the future. This helps operators to formulate maintenance plans in advance, prevent failures, and reduce the risk of power outages and equipment damage caused by failures. Taking into account the system's stability indicators, load levels, and failure probabilities, operators can have a more comprehensive understanding of the system's operating status and risk situation. This provides them with strong data support for formulating plans, helps optimize system operation strategies, and improves system reliability and economy.

[0129] In a preferred embodiment of the present invention, the above step 14 uses a genetic algorithm to randomly generate an initial population and iteratively perform mutation, crossover, and selection operations to finally determine a final solution to predict future state changes and obtain a prediction result of the future state, which may include:

[0130] Step 145, randomly generating an initial population, each individual representing a set of state parameter prediction values;

[0131] Step 146, for each individual in the population, three different individuals are determined to generate a new variant individual;

[0132] Step 147, cross the new variant individual with the original individual to generate a test individual;

[0133] Step 148, using the fitness function to evaluate each individual in the test individual set and the original individual set, calculating the fitness value of each individual, and determining whether the individual enters the next generation population according to the size of the fitness value;

[0134] Step 149, repeating the mutation, crossover and selection operations until a preset number of iterations is reached, and determining a final solution from the final population based on individual fitness values;

[0135] Step 150, analyzing the future state of the DC microgrid according to the final solution, including state change trend and anomaly detection;

[0136] Step 151, based on the final solution and the analysis of the future state of the DC microgrid, a prediction result of the future state is generated, including a prediction value, a prediction error range, and confidence key information.

[0137] In an embodiment of the present invention, a population size (such as 100 individuals) is set. Each individual represents a set of predicted values ​​of state parameters of a DC microgrid (such as voltage, current, power, etc.), which can be randomly generated or initialized based on historical data. For example, each individual in the initial population is a vector containing predicted values ​​of voltage, current and power. For each individual in the population, three different individuals are determined as parents. A new variant individual is generated by a certain mutation strategy (such as randomly changing the value of a certain parameter). The mutation operation aims to increase the diversity of the population and explore new solution spaces. The new variant individual is crossed with the original individual to generate a test individual. The crossover operation can be achieved by exchanging some parameter values ​​or adopting other crossover strategies. The test individual inherits some characteristics of the parent individual and introduces new mutations. The fitness function is used to evaluate each individual in the test individual set and the original individual set. The fitness function can be defined according to the actual operation of the DC microgrid and the prediction target, such as the sum of squares of the prediction error, energy efficiency index, etc. According to the size of the fitness value, individuals with higher fitness are selected to enter the next generation population.

[0138] Repeat the mutation, crossover, and selection operations until the preset number of iterations is reached or other stop conditions are met. In each iteration, the population will evolve in a better direction and gradually approach the final solution. The final solution, that is, a set of state parameter prediction values ​​with the best prediction effect, is determined from the final population based on the individual fitness values. According to the final solution, the future state of the DC microgrid is analyzed, including state change trends, anomaly detection, etc. Data analysis tools or visualization methods can be used to display the prediction results and analysis results. Based on the final solution and the analysis of the future state of the DC microgrid, the prediction results of the future state are generated. The prediction results can include key information such as the prediction value, prediction error range, and confidence. This information can help operators understand the future operating state of the DC microgrid and formulate corresponding plans and plans.

[0139] Assume that you want to predict the voltage change of the DC microgrid in the next day, and randomly generate an initial population: generate 100 individuals, each of which is a vector containing 24-hour voltage prediction values. For each individual, randomly select three different individuals as parents, and randomly change the voltage prediction value of one hour to generate a variant individual. Cross the variant individual with the original individual to generate a test individual. For example, the voltage prediction values ​​of a few hours in two individuals can be exchanged. Use a fitness function (such as the sum of squares of the prediction error) to evaluate the test individual and the original individual, and select the individual with lower fitness to enter the next generation population. Repeat the above operation 100 times (or until other stop conditions are met). Select the individual with the lowest fitness from the final population as the final solution, that is, a set of voltage prediction values ​​with the best prediction effect. According to the final solution, analyze the voltage change trend and possible abnormal points of the DC microgrid in the next day. Generate a prediction result report containing the predicted voltage value, prediction error range and confidence. For example, the voltage at a certain hour can be predicted to be the backup battery voltage ±0.1V (confidence 95%).

[0140] By simulating the biological evolution process, it is possible to search for a solution close to the final solution in a complex solution space. By iteratively performing mutation, crossover and selection operations, the algorithm can continuously optimize individuals (i.e., the predicted values ​​of state parameters) so that the final solution is closer to the actual value. This improves the accuracy of the prediction of the future state of the DC microgrid. In the prediction of the DC microgrid, the algorithm can adaptively adjust the predicted values ​​of the state parameters to cope with various uncertainties and changes in the operation of the power grid. In the analysis of the future state of the DC microgrid, the changing trends and abnormal points of the state parameters can be identified. This helps operators to promptly identify potential problems in the power grid, such as equipment failure, overload, etc., and take corresponding preventive measures. Through early warning and intervention, the losses and impacts caused by power grid failures can be avoided or reduced. The prediction results include key information such as the predicted value, prediction error range and confidence, providing comprehensive solution support for operators. Operators can formulate more reasonable power grid operation plans, maintenance plans and emergency plans based on the prediction results. This helps to improve the operating efficiency and reliability of the power grid and reduce operating costs and risks.

[0141] In a preferred embodiment of the present invention, the fitness value calculation formula for each individual is:

[0142]

[0143] Where F represents the fitness value; w e represents the prediction error weight; n represents the number of observations; y i represents the actual observed value; represents the predicted value; w s represents the stability weight; Represents the mean of the predicted values.

[0144] In the embodiment of the present invention, w e is the prediction error weight, which is used to adjust the importance of the prediction error in the fitness value calculation. s is the stability weight, which is used to adjust the importance of prediction stability (i.e., the degree of change of the predicted value) in the calculation of the fitness value. n is the number of observations, i.e., how many actual observations are compared with the predicted values. i (for i=1 to n) is the array of actual observations. (for i = 1 to n) is an array of predicted values. Calculate the square of the difference between the actual observed value and the predicted value for each observation point and sum these squared values: Calculate the square of the error for each point (for i=1 to n). Sum all squared errors The sum of squared errors is divided by the number of observations n to get the average prediction error. Compute the square of the difference between each predicted value and the mean: (for i=1 to n). Sum the squares of all differences and divide by n-1, and we get Take the square root of the obtained variance to get the standard deviation of the predicted value. Finally, the fitness value F is obtained.

[0145] The mean square error (MSE) between the predicted value and the actual observed value is calculated in part, which is a common indicator of forecast accuracy. e (Prediction Error Weight) adjustment can emphasize or weaken the importance of prediction accuracy in fitness calculation. The standard deviation of the predicted value is calculated in part, reflecting the degree of fluctuation or stability of the predicted results. s (Stability Weight) adjustment can emphasize or weaken the importance of predicted stability in fitness calculation. e and w s As a weight parameter, it allows users to adjust the relative importance of prediction accuracy and stability in fitness calculation according to specific application scenarios and requirements. This flexibility enables the formula to be applicable to different optimization objectives and problem contexts.

[0146] In a preferred embodiment of the present invention, the above step 15, formulating a control plan and sending it to each controller according to the prediction result of the future state and the power flow and energy cost, may include:

[0147] Step 151, collect relevant prediction data of future status, including weather conditions, user demand prediction, and obtain current power flow information, including power supply status of the power grid, voltage and current of each node; collect energy cost data, including electricity prices in different time periods and energy consumption characteristics of equipment;

[0148] Step 152, constructing a prediction model to predict the future state, and using the collected prediction data of the future state, including weather conditions and user demand prediction, as input data, to input into the prediction model to obtain the prediction result of the future state;

[0149] Step 153, formulating a control plan based on the prediction results of the future state and the power flow and energy cost information, including adjusting the operating power of the equipment, optimizing the power consumption period, and starting or shutting down the equipment;

[0150] Step 154 ​​, encode the formulated control scheme into instructions recognized by the controller, and send the control instructions to each controller.

[0151] In an embodiment of the present invention, weather data for a period of time in the future (such as 24 hours, 48 ​​hours) is obtained from the meteorological department or weather forecast API, including temperature, humidity, wind speed, irradiance, etc. These data are crucial for predicting the power generation of new energy (such as solar energy and wind energy). Based on historical electricity consumption data, user behavior patterns, holiday effects and other factors, a prediction model (such as time series analysis, machine learning algorithm) is used to predict the user's power demand in each time period in the future. Monitor the total power supply of the power grid, the load of each power supply line, whether there is a fault or maintenance plan, etc. The voltage and current data of each node are collected in real time through sensors or smart meters in the power grid to evaluate the stability and security of the power grid. The time-of-use electricity price information of the power market is obtained, which usually includes the electricity prices during peak hours, normal hours, and valley hours, as well as possible special electricity price policies (such as holiday electricity prices). Collect parameters such as energy consumption curves, power factors, and efficiency of each power-consuming device to calculate the energy consumption cost under different operating modes. Select a suitable prediction method (such as a neural network) and train the model based on historical data and collected future state prediction data. Verify and tune the model to ensure its prediction accuracy. The collected data such as weather conditions and user demand forecasts are used as inputs into the prediction model. The model outputs the prediction results for each future time period, including renewable energy power generation, user power demand, grid load, etc.

[0152] Combine the prediction results of future states with the current power flow information to analyze the supply and demand balance of the power grid, the fluctuation of renewable energy power generation, the peak and valley periods of user demand, etc. Calculate the energy consumption cost of each device in different time periods according to the electricity prices and energy consumption characteristics of the equipment in different time periods. Adjust the operating power of the equipment: According to the load of the power grid and the renewable energy power generation, adjust the operating power of the adjustable equipment to balance the supply and demand. Arrange high-energy-consuming equipment to operate during periods with lower electricity prices to reduce electricity costs. According to the prediction results and actual needs, decide whether to start the backup power supply, energy storage equipment or shut down some non-essential equipment. Convert the formulated control scheme into an instruction format that the controller can recognize, including equipment number, control action (such as start, stop, adjust power), execution time, etc. Send the control command to each controller through a communication network (such as wired network, wireless network, Internet of Things, etc.). After receiving the command, the controller executes the corresponding control action to adjust the operating state of the equipment.

[0153] Assume that a microgrid system includes a solar power station, a wind power station, a storage battery, and multiple user loads. It is predicted that it will be cloudy and the wind speed will be low in the next day, resulting in a decrease in the amount of power generated by renewable energy. At the same time, it is predicted that the user's electricity demand will increase significantly at night. Based on this information, the following control plan is formulated:

[0154] Reduce the operating power of non-essential equipment to reduce the load on the power grid. Encourage users to use electricity during the day when electricity prices are lower, and reduce electricity consumption at night when electricity prices are higher. During periods when renewable energy generation is insufficient, start discharging energy storage batteries to supplement electricity; during periods when renewable energy generation is sufficient, charge energy storage batteries for later use.

[0155] By collecting the power supply status of the power grid, the voltage and current information of each node in real time, and combining the prediction results of future states (such as load fluctuations caused by weather changes), it is possible to identify and prevent power grid overload or failure in advance, and ensure the stable operation of the microgrid system. By adjusting the operating power and power consumption period of the equipment according to the prediction results, the electric energy can be allocated more efficiently. By collecting the electricity price information of different time periods and combining the load demand prediction, a strategy for optimizing the power consumption period can be formulated, and high energy consumption tasks can be arranged in the period with lower electricity prices, thereby significantly reducing the user's electricity cost. Adjusting the operating power of the equipment according to its energy consumption characteristics can make the equipment operate under more efficient working conditions, reduce energy waste, and further reduce energy costs. By predicting weather conditions (such as light intensity, wind speed, etc.), the power generation of new energy (such as solar energy and wind energy) can be more accurately predicted, so as to formulate a more reasonable new energy utilization plan and improve the grid connection rate and utilization rate of new energy.

[0156] By optimizing the power consumption period and equipment operating power, we can reduce dependence on traditional fossil energy (such as coal and oil), reduce carbon emissions, and promote sustainable energy utilization and environmental protection. The formulated control plan can be dynamically adjusted according to actual conditions, such as responding quickly to weather changes, changes in user demand, or power grid failures, to enhance the flexibility and adaptability of the system. By building a prediction model, collecting and analyzing a large amount of data to formulate a control plan, and encoding it into instructions for controller recognition and sending it to each controller, the intelligent management of the power system is realized, and the management efficiency and accuracy are improved.

[0157] In a preferred embodiment of the present invention, the above step 16, continuously optimizing the control scheme according to the execution feedback program to cope with changes in the system state and environment, may include:

[0158] In an embodiment of the present invention, a series of key performance indicators are set, such as microgrid stability, equipment energy efficiency, electricity cost, and new energy utilization rate, to evaluate the execution effect of control decisions. Monitoring equipment such as sensors and smart meters are deployed in the microgrid system to collect information such as grid status, equipment operating status, and energy consumption data in real time. Ensure that data communication between the monitoring equipment and the central control system is unimpeded so that execution information can be fed back in a timely manner. According to the previously formulated control scheme, the control instructions are encoded and issued to each controller, and the instructions include equipment adjustment, power consumption time period optimization, etc. The data during the execution process is collected in real time through the monitoring equipment, such as the actual operating power of the equipment, the changes in grid voltage and current, and the actual power consumption of users. According to the collected data, the actual values ​​of various feedback indicators are calculated and compared with the expected values ​​to evaluate the execution effect of the control decision. The collected feedback data is deeply analyzed to identify problems, deviations, or deficiencies in the execution process, such as equipment response delays, energy consumption exceeding expectations, etc. Further diagnosis is performed for the analyzed problems to find out the root causes of the problems, such as equipment aging, inaccurate prediction models, etc. According to the problem diagnosis results, adjust and optimize the original control decisions, such as modifying the prediction model, adjusting the equipment control parameters, optimizing the power consumption period, etc. Integrate the adjusted control decisions into the new control plan to ensure that the plan can more accurately reflect the system status and environmental changes.

[0159] The updated control scheme is sent to each controller for execution again, and the above feedback collection, analysis and adjustment process is repeated to form a closed-loop control. The operation status of the microgrid system is tracked and monitored for a long time, and data and experience are continuously accumulated to provide a more accurate basis for future control decisions. With the continuous advancement of technology and the development of the power system, new monitoring technologies, control algorithms and intelligent means are introduced in a timely manner to improve the overall performance and efficiency of the control system.

[0160] like Figure 2As shown, an embodiment of the present invention further provides a DC bus power flow control system 20 connected to a backup battery, comprising:

[0161] The acquisition module 21 is used to define the topological structure of the DC microgrid and construct a mathematical model framework including state parameters, operating status and constraint conditions; filter, interpolate and sparse, time series match, combine calculations, dimension transformation, and normalize the measurement parameters obtained by the microgrid to obtain processed data; and analyze the current system state parameters according to the processed data;

[0162] The processing module 22 is used to randomly generate an initial population and iteratively perform mutation, crossover, and selection operations to ultimately determine a final solution, so as to predict future state changes and obtain prediction results of future states; formulate a control plan based on the prediction results of future states and the power flow and energy cost, and send it to each controller; and continuously optimize the control plan according to the execution feedback program to cope with changes in system states and environment.

[0163] like Figure 3 As shown, an embodiment of the present invention also provides a primary circuit topology including:

[0164] The DC bus is located at the top of the entire topology and is the central aggregation node of the system, responsible for providing DC power. It has a single voltage state and provides stable DC power for all connected branches and devices.

[0165] Multiple distribution branches extend from the DC bus, which are used to distribute DC power to different devices or loads. The distribution branches are mainly composed of various switches (such as B1, B2, B3, ..., Bn) and relays to ensure the safe distribution of power and isolation of circuits. The branch status parameters are mainly branch currents. By monitoring these currents, the load and operating status of the branches can be understood.

[0166] The intermediate control layer is located in the middle of the distribution branch and contains power controller equipment, such as power electronic converters. These controllers are responsible for monitoring and controlling parameters such as current and voltage in the branch to ensure stable operation of the system and efficient energy conversion. The converter is generally a dual-port or multi-port device, and its important parameter is the conversion efficiency, which determines the energy loss during the power conversion process.

[0167] Branch equipment is connected to the end of the distribution branch, including power generation equipment, energy storage equipment and load equipment (such as C1, C2, C3, ..., Cn). These devices are connected to the system according to actual needs to meet different power needs and energy storage requirements. Each device has its own specific parameters, including cost parameters.

[0168] like Figure 4 As shown, an example of a DC microgrid primary circuit topology diagram is shown:

[0169] The DC bus is the core of the entire system. As the main power supply trunk, it provides stable DC power to each distribution branch. It reflects a single voltage state and is the center of power gathering and distribution in the system.

[0170] The distribution branch extends from the DC bus and is used to distribute DC power to different devices or loads. The branch mainly includes air switches (smart air switches can also be used) and contactors to control the on and off of the circuit and protect the safety of the equipment. In addition, the branch also includes a DC / DC converter to ensure voltage conversion and maintain the stability of the DC bus voltage. The energy storage battery is also directly connected to the bus and connected through switches and protective contactors without an intermediate controller, providing the system with energy storage and enhancing the stability of power supply.

[0171] The middle control layer is the "brain" of the system, responsible for monitoring and managing the operation status of the entire DC microgrid. The controllers include MPPT (photovoltaic controller) to maximize the power generation efficiency of photovoltaic modules; AC / DC (AC / DC converter, rectifier) ​​to convert AC power to DC power; DC / DC (bidirectional DC converter, DC energy storage controller) for bidirectional conversion and control of DC power; DC / AC (DC / AC converter, inverter) to convert DC power to AC power to meet the needs of AC loads. These controllers work together to ensure stable operation of the system and efficient energy conversion.

[0172] Branch equipment includes various electrical equipment and protection devices connected to the distribution branch. These devices are connected to the system according to actual needs to meet different power needs and protection requirements. Electrical equipment may include DC loads and AC loads, which are connected to the DC bus and the converted AC power supply respectively.

[0173] like Figure 5 As shown, the secondary circuit topology includes:

[0174] The virtual busbar is a central node, which is the convergence point of various signal lines. This virtual busbar does not actually exist, but is a logical concept for signal convergence and processing, used to centralize and manage signals from various branches. It is connected to the smart edge gateway (microgrid controller) through a dotted line, which means that the busbar status information is collected and processed in the controller.

[0175] The branch virtual measurement device is a concept proposed to facilitate the modeling and analysis of branches. The key state parameters of the branches are concentrated to form a unified and standardized state parameter feature vector for each branch. The branch virtual measurement is also connected to the smart edge gateway through a dotted line, indicating that these measurement data are collected in the controller for analysis and processing.

[0176] The device communication port is the physical port that the device connects to the gateway, such as RS-232, RS-485, CAN bus port, etc. The seven modules (m1 to m7) are connected to the smart edge gateway through the communication port (n1 to n7). These connections are represented by solid or dotted lines, symbolizing the communication and control relationship between them. Through these communication ports, devices can transmit and interact with the smart edge gateway to achieve system coordination and control.

[0177] like Figure 6 As shown in the figure, an example of a microgrid secondary circuit: the smart edge gateway (microgrid controller) serves as the control center of the entire microgrid. It is responsible for coordinating and managing various devices and components in the microgrid to achieve centralized data processing and intelligent control.

[0178] Key equipment and components include:

[0179] Virtual electricity meter: used to monitor and record electricity data in the microgrid and provide basic information for energy management.

[0180] AC / DC converter: realizes the conversion of AC to DC to meet the energy requirements of different devices in the microgrid.

[0181] EMU (microgrid control unit): used to analyze, predict, allocate and manage the electric energy in the microgrid to ensure the stable operation of the system.

[0182] DC / DC: It is a DC potential transformer used to monitor and control DC power.

[0183] Smart switches and contactors: used to control the on and off of circuits in microgrids and protect the safety of equipment and systems.

[0184] Lithium battery packs and smart lithium batteries: As energy storage devices, they provide backup power for microgrids and enhance the system's power supply stability and reliability.

[0185] Virtual aggregation data bus: As the key point of virtual data aggregation, it aggregates the power parameter information of each branch to provide information flow guarantee for the microgrid. The smart edge gateway is connected to each key device and component through a dotted line to realize data transmission and the issuance of control instructions. There may also be interconnections between various devices and components, which together constitute the core control network of the microgrid.

[0186] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0187] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0188] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0189] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for controlling the flow of a DC bus connected to a backup battery, characterized in that: The method comprises: Define the topology of the DC microgrid and build a mathematical model framework including state parameters, operating status and constraints; The measured parameters obtained by the microgrid are filtered, interpolated and sparse, time series matched, combined and calculated, dimensionally transformed, and normalized to obtain processed data; Analyze the current system status parameters based on the processed data; Use genetic algorithms to predict future state changes and obtain prediction results of future states; According to the prediction results of future status, power flow and energy cost, a control plan is formulated and sent to each controller; The control scheme is continuously optimized based on the execution feedback procedure to respond to changes in system status and environment.

2. The DC bus power flow control method connected to a backup battery according to claim 1, characterized in that: Define the topology of the DC microgrid and build a mathematical model framework containing state parameters, operating status and constraints, including: The DC microgrid is divided into a primary circuit topology and a secondary circuit topology with the DC bus as the center. The primary circuit includes the DC bus, the distribution branch, the intermediate control layer and the branch equipment for the flow of electric energy; the secondary circuit includes a virtual converged data bus, a branch virtual measurement device and an equipment communication port for data collection and transmission; the branch equipment includes power generation, energy storage and load equipment, and at least one backup battery pack directly connected to the bus, and finally the topology of the DC microgrid is obtained; According to the topological structure of the DC microgrid, a mathematical model framework of the microgrid is constructed, including state parameters, defined operating states and defined constraint sets. The state parameters include bus voltage, branch current and direction, branch power, controller efficiency, device connection port voltage and current, inflow and outflow electric power, inflow and outflow electric energy price, energy storage margin and value, and energy storage loop loss rate per unit time; the operating state includes various states of the bus, distribution branch, intermediate controller and branch equipment; the constraint set includes various operating restrictions of the bus, distribution branch, intermediate controller and branch equipment.

3. The DC bus power flow control method connected to a backup battery according to claim 2, characterized in that: The measured parameters obtained by the microgrid are filtered, interpolated and sparse, time series matched, combined calculated, dimensionally transformed, and normalized to obtain processed data, including: Obtain raw measurement parameter data from sensors and monitoring systems in the microgrid; Process the original measurement parameter data, remove noise and outliers, and obtain filtered data; Interpolate the missing values ​​in the filtered data and perform sparse processing on the overly dense data points to obtain interpolated and sparsely processed data; Align the interpolated and sparsely processed data to a unified time series to obtain the time series matched data; According to the matched data of time series, combined calculation is performed to obtain new derivative data; Convert the measurement data and new derived data of different dimensions into unified dimensions to obtain dimension-transformed data; The data after dimension transformation is normalized to obtain the processed data.

4. A DC bus power flow control method connected to a backup battery according to claim 3, characterized in that: According to the processed data, analyze the current system status parameters, including: Using the preset genetic algorithm neural network model, the extracted system state-related features are used as input data to identify the current state of the system, including normal operation, overload, undervoltage, and overvoltage; Based on the identified current state of the system, state parameters are calculated, including the system's stability index, load level, and failure probability.

5. The DC bus power flow control method connected to a backup battery according to claim 4, characterized in that: Through the genetic algorithm, the initial population is randomly generated and mutation, crossover, and selection operations are iteratively performed to finally determine the final solution, predict future state changes, and obtain the prediction results of the future state, including: An initial population is randomly generated, each individual represents a set of state parameter prediction values; For each individual in the population, three different individuals are identified to generate a new variant individual; Cross the new variant individuals with the original individuals to generate test individuals; Use the fitness function to evaluate each individual in the test individual set and the original individual set, calculate the fitness value of each individual, and determine whether the individual will enter the next generation population based on the size of the fitness value; Repeat the mutation, crossover and selection operations until the preset number of iterations is reached, and determine the final solution from the final population based on the individual fitness values; Based on the final solution, the future state of the DC microgrid is analyzed, including state change trend and anomaly detection; Based on the final solution and the analysis of the future state of the DC microgrid, the prediction results of the future state are generated, including the predicted value, prediction error range, and confidence key information.

6. The DC bus power flow control method connected to a backup battery according to claim 5, characterized in that: The fitness value calculation formula for each individual is: Where F represents the fitness value; w e represents the prediction error weight; n represents the number of observations; y i represents the actual observed value; represents the predicted value; w s represents the stability weight; Represents the mean of the predicted values.

7. The DC bus power flow control method connected to a backup battery according to claim 6, characterized in that: Based on the prediction results of future states, power flow and energy costs, a control plan is developed and sent to each controller, including: Collect relevant forecast data of future status and obtain current power flow information, including power supply status of the power grid, voltage and current of each node; collect energy cost data, including electricity prices in different time periods and energy consumption characteristics of equipment; Construct a prediction model to predict the future state, and use the collected prediction data of the future state, mainly the power load demand prediction, as input data, and input it into the prediction model to obtain the prediction result of the future state; Develop control plans based on the predicted results of future states and information on power flow and energy cost, including adjusting the operating power of equipment, optimizing power consumption time periods, and starting or shutting down equipment; The developed control scheme is encoded into instructions recognized by the controller, and the control instructions are sent to each controller.

8. A DC bus power flow control system connected to a backup battery, the system implementing the method according to any one of claims 1 to 7, characterized in that: include: The acquisition module is used to define the topology of the DC microgrid and build a mathematical model framework including state parameters, operating status and constraints; The measured parameters obtained by the microgrid are filtered, interpolated and sparse, time series matched, combined and calculated, dimensionally transformed, and normalized to obtain processed data; based on the processed data, the current system state parameters are analyzed; The processing module is used to randomly generate an initial population and iteratively perform mutation, crossover, and selection operations to ultimately determine the final solution in order to predict future state changes and obtain prediction results of future states; formulate a control plan based on the prediction results of future states and the power flow and energy cost, and send it to each controller; and continuously optimize the control plan based on the execution feedback program to cope with changes in system states and environment.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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