Polyanion sodium battery microgrid energy scheduling method, system, medium and product

Abstract

The application discloses a kind of polyanion sodium battery microgrid energy scheduling method, system, medium and product, and it relates to data processing technical field.In the method, first, microgrid source load power, voltage deviation and battery terminal voltage, current and temperature are obtained;Current is input into the preset electrochemical mapping relationship to obtain the predicted voltage, and the polarization state mismatch degree is determined by combining voltage deviation and current change rate;When the mismatch degree is out of limit, the compensation coefficient is calculated to correct the diffusion coefficient and transfer resistance, and the target mapping relationship is obtained;At the same time, the power support urgency is constructed according to the source load power and voltage deviation;Based on the target mapping relationship, the basic polarization boundary is determined, and the dynamic polarization boundary is expanded by combining the urgency and temperature;Finally, the optimal power command is calculated and the battery is controlled to operate with the dynamic boundary as the constraint.The technical scheme provided by the application can improve the accuracy of polyanion sodium battery microgrid energy scheduling in complex dynamic scenarios.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method, system, medium, and product for energy dispatching of a polyanion sodium battery microgrid. Background Technology

[0002] With the deepening of energy transition and power system reform, the proportion of distributed renewable energy in the power supply system is constantly increasing. Microgrids, as regional energy networks integrating distributed power sources, energy storage systems, and local loads, play an increasingly important role in improving energy efficiency and enhancing power supply reliability. Microgrids typically consist of fluctuating power sources such as photovoltaic and wind power, energy storage devices, and various electrical loads. Their operation is characterized by constantly changing source and load power and frequent voltage fluctuations. To adapt to the energy management needs of different application scenarios, microgrid energy dispatch technology is showing a trend towards refinement and intelligence. Its accuracy and response speed under complex operating conditions are of great significance for ensuring the safe and stable operation of microgrids and improving economic efficiency.

[0003] Polyanion sodium batteries (PACs) are gradually becoming an important technology for microgrid energy storage due to their abundant resources, low cost, and excellent safety performance. Compared with lithium-ion batteries, PACs exhibit unique advantages in cycle life and temperature adaptability, attracting widespread attention in the field of microgrid energy storage. Currently, microgrid energy dispatching methods based on PACs mainly rely on offline calibration technology. This involves obtaining the battery's electrochemical parameters through laboratory testing and calculating the battery's power operating boundaries and charge / discharge dispatching strategies based on these parameters. This fixed-parameter-based dispatching method reflects the battery's fundamental electrochemical characteristics and has been widely applied in microgrid energy management.

[0004] However, due to the dynamic nature of the operation and the variability of electrochemical characteristics, the actual state of polyanion sodium batteries is becoming increasingly complex and variable. In practical applications, scheduling methods based on fixed parameters are insufficient to cope with the dynamic evolution of battery characteristics caused by factors such as charge / discharge rate, temperature changes, and cycle aging, and the accuracy of power boundary settings is difficult to guarantee. In particular, when the internal diffusion characteristics and polarization behavior of the battery change after long-term operation, the actual electrochemical state of the battery exhibits dynamic evolution characteristics under different conditions. Existing methods using simple fixed-parameter scheduling are prone to deviations between the calculated power boundary and the battery capacity, or even judgment lags, thus reducing the accuracy of polyanion sodium battery microgrid energy scheduling in complex dynamic scenarios. Summary of the Invention

[0005] This application provides a method, system, medium, and product for energy dispatching of polyanion sodium battery microgrids, which can improve the accuracy of energy dispatching of polyanion sodium battery microgrids in complex dynamic scenarios.

[0006] The first aspect of this application provides a method for energy dispatching in a polyanion sodium battery microgrid, comprising:

[0007] The source-load power and voltage deviation of the microgrid, as well as the terminal voltage, charging and discharging current and surface temperature of the polyanion sodium battery, are obtained.

[0008] The charging and discharging current is input into a preset electrochemical mapping relationship to determine the predicted terminal voltage. The polarization mismatch is determined based on the deviation between the predicted terminal voltage and the terminal voltage and the rate of change of the charging and discharging current in a preset period.

[0009] When the polarization mismatch is greater than the preset mismatch threshold, a compensation coefficient is calculated based on the polarization mismatch. The diffusion coefficient and transfer resistance in the preset electrochemical mapping relationship are corrected using the compensation coefficient to obtain the updated target electrochemical mapping relationship.

[0010] Based on the source load power and the voltage deviation, a power support urgency level is constructed;

[0011] The basic polarization boundary is determined based on the target electrochemical mapping relationship. The basic polarization boundary is then extended according to the power support urgency and the surface temperature to obtain the dynamic polarization boundary.

[0012] Using the dynamic polarization boundary as a constraint, the optimal power command is calculated, and the polyanion sodium battery is controlled to operate according to the optimal power command.

[0013] By adopting the above technical solution, after obtaining the source-load power and voltage deviation of the microgrid, as well as the terminal voltage, charge-discharge current, and surface temperature of the polyanion sodium battery, the charge-discharge current is input into a preset electrochemical mapping relationship to determine the predicted terminal voltage. The polarization mismatch is then determined based on the deviation between the predicted and actual terminal voltages and the rate of change of the charge-discharge current over a preset period. This accurately captures the dynamic evolution of the battery's electrochemical characteristics. When the polarization mismatch exceeds a preset mismatch threshold, a compensation coefficient is calculated based on the polarization mismatch, and the diffusion coefficient and transfer resistance in the preset electrochemical mapping relationship are corrected to obtain the target electrochemical mapping relationship. This allows the electrochemical model parameters to adaptively adjust to the actual battery state, avoiding the cumulative deviations caused by long-term operation, temperature changes, or cyclic aging in fixed-parameter models. Based on this, a power support urgency level is constructed according to source-load power and voltage deviation to quantify the microgrid's demand for battery power response. Then, a basic polarization boundary is determined based on the target electrochemical mapping relationship. This basic polarization boundary is then extended according to the power support urgency and surface temperature to obtain a dynamic polarization boundary. This allows for flexible adjustment of the power operating range based on the actual needs of the microgrid and the battery temperature state, while ensuring battery electrochemical safety. This avoids both overly conservative fixed boundaries that could lead to insufficient power support and overcharge / discharge risks caused by improper boundary settings. Finally, the optimal power command is calculated and the operation of the polyanion sodium battery is controlled using the dynamic polarization boundary as a constraint. This ensures that power scheduling decisions are optimized based on accurate boundaries that match the actual battery state, thereby improving the accuracy of polyanion sodium battery microgrid energy scheduling in complex dynamic scenarios.

[0014] In a second aspect, embodiments of this application provide a polyanion sodium battery microgrid energy dispatching system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions, and the one or more processors call the computer instructions to cause the polyanion sodium battery microgrid energy dispatching system to perform the method described in the first aspect and any possible implementation thereof.

[0015] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a polyanion sodium battery microgrid energy dispatching system, cause the polyanion sodium battery microgrid energy dispatching system to perform the method described in the first aspect and any possible implementation thereof.

[0016] Fourthly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a polyanion sodium battery microgrid energy dispatching system, cause the polyanion sodium battery microgrid energy dispatching system to perform the method described in the first aspect and any possible implementation thereof.

[0017] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages:

[0018] By adopting the above technical solution, after obtaining the source-load power and voltage deviation of the microgrid, as well as the terminal voltage, charge-discharge current, and surface temperature of the polyanion sodium battery, the charge-discharge current is input into a preset electrochemical mapping relationship to determine the predicted terminal voltage. The polarization mismatch is then determined based on the deviation between the predicted and actual terminal voltages and the rate of change of the charge-discharge current over a preset period. This accurately captures the dynamic evolution of the battery's electrochemical characteristics. When the polarization mismatch exceeds a preset mismatch threshold, a compensation coefficient is calculated based on the polarization mismatch, and the diffusion coefficient and transfer resistance in the preset electrochemical mapping relationship are corrected to obtain the target electrochemical mapping relationship. This allows the electrochemical model parameters to adaptively adjust to the actual battery state, avoiding the cumulative deviations caused by long-term operation, temperature changes, or cyclic aging in fixed-parameter models. Based on this, a power support urgency level is constructed according to source-load power and voltage deviation to quantify the microgrid's demand for battery power response. Then, a basic polarization boundary is determined based on the target electrochemical mapping relationship. This basic polarization boundary is then extended according to the power support urgency and surface temperature to obtain a dynamic polarization boundary. This allows for flexible adjustment of the power operating range based on the actual needs of the microgrid and the battery temperature state, while ensuring battery electrochemical safety. This avoids both overly conservative fixed boundaries that could lead to insufficient power support and overcharge / discharge risks caused by improper boundary settings. Finally, the optimal power command is calculated and the operation of the polyanion sodium battery is controlled using the dynamic polarization boundary as a constraint. This ensures that power scheduling decisions are optimized based on accurate boundaries that match the actual battery state, thereby improving the accuracy of polyanion sodium battery microgrid energy scheduling in complex dynamic scenarios. Attached Figure Description

[0019] Figure 1 This is a schematic flowchart of the energy dispatching method for polyanion sodium battery microgrids disclosed in the embodiments of this application;

[0020] Figure 2 This is another schematic flowchart of the energy dispatching method for polyanion sodium battery microgrids disclosed in the embodiments of this application;

[0021] Figure 3 This is a schematic diagram of the structure of a system provided in an embodiment of this application.

[0022] Explanation of reference numerals in the attached drawings: 301, Central Processing Unit; 302, Read-Only Memory; 303, Random Access Memory; 304, Bus; 305, Input / Output Interface; 306, Input Section; 307, Output Section; 308, Storage Section; 309, Communication Section; 310, Driver; 311, Removable Media. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0024] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0025] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0026] This application provides a method for energy dispatching in a microgrid using polyanion sodium battery, referring to... Figure 1 , Figure 1 This is a flowchart illustrating a polyanion sodium battery microgrid energy dispatching method provided in an embodiment of this application. The method is applied to a system, which refers to a hardware and software integrated platform capable of executing a dynamic mapping program for safe production risks. The system can execute a polyanion sodium battery microgrid energy dispatching program. The method includes steps 101 to 106, as follows:

[0027] Step 101: Obtain the source-load power and voltage deviation of the microgrid, as well as the terminal voltage, charging and discharging current and surface temperature of the polyanion sodium battery.

[0028] In this embodiment, source-load power represents the power generation of distributed power sources and the power consumption of local loads in a microgrid. Distributed power sources include photovoltaic power generation units and wind power generation units, and local loads include residential and industrial power loads. For example, if at a certain moment the photovoltaic power generation is 500 kW, the wind power generation is 300 kW, and the local load power is 600 kW, then the net source-load power is a surplus of 200 kW. Voltage deviation refers to the difference between the actual measured value of the microgrid bus voltage and the rated voltage value. The rated voltage value is usually set to 380 volts or a per-unit value of 1.0. For example, when the actual measured voltage is 370 volts, the voltage deviation is -10 volts. Terminal voltage represents the potential difference between the positive and negative electrodes of the polyanion sodium battery, and is directly measured by a voltage sensor. Charging and discharging current refers to the magnitude and direction of the current flowing through the polyanion sodium battery. The current is positive during charging and negative during discharging, and is measured by a current sensor. Surface temperature represents the temperature value of the surface of the polyanion sodium battery casing, and is collected by a temperature sensor.

[0029] Specifically, the microgrid energy management system's data acquisition module reads the real-time output power of each power generation unit from the power metering device of the distributed power source, and simultaneously reads the real-time power consumption of each power consumption unit from the electricity meter on the load side. The sum of the generated power and the sum of the load power are then algebraically calculated to obtain the source-load power. Three-phase voltage values ​​are collected by voltage transformers installed at the microgrid bus, and the average value of the three-phase voltage is calculated as the actual bus voltage. The difference between this actual value and the preset rated voltage value is calculated to obtain the voltage deviation. For polyanion sodium batteries, a voltage acquisition chip is configured in the battery management system to monitor the terminal voltage of each battery cell in real time, with a sampling frequency set to 10 to 100 times per second. A high-precision Hall current sensor is connected in series in the battery's charging and discharging circuit to collect the instantaneous values ​​of the charging and discharging current at a frequency of 10 to 100 times per second. Thermocouples or thermistors are placed at multiple locations on the battery pack casing to collect the temperature at each measuring point, and the average value is taken as the surface temperature, with a sampling frequency of 1 to 10 times per second. All collected data is transmitted to the central control unit via a communication bus for further processing.

[0030] Step 102: Input the charge / discharge current into the preset electrochemical mapping relationship to determine the predicted terminal voltage. Based on the deviation between the predicted terminal voltage and the terminal voltage and the rate of change of the charge / discharge current in the preset period, determine the polarization mismatch.

[0031] In this embodiment, the preset electrochemical mapping relationship refers to a mathematical model describing the correspondence between the charge / discharge current and the internal electrochemical state and terminal voltage of the battery. This model is based on the battery's diffusion kinetics and electrochemical polarization principle, and includes key parameters such as diffusion coefficient and transfer resistance. The predicted terminal voltage represents the theoretical value of the battery terminal voltage calculated based on the preset electrochemical mapping relationship and the current charge / discharge current. The rate of change of the charge / discharge current over a preset period is used to represent how fast the current changes with time. It is obtained by calculating the difference between the current values ​​at adjacent sampling times and dividing by the time interval. The preset period is typically 1 to 10 seconds. The polarization state mismatch is a comprehensive evaluation index of the deviation between the predicted terminal voltage and the actual measured terminal voltage and the rate of change of the charge / discharge current, used to determine the degree of agreement between the preset electrochemical mapping relationship and the actual state of the battery.

[0032] Specifically, the currently collected charge / discharge current values ​​are substituted into a preset electrochemical mapping relationship, which includes an open-circuit voltage term, an ohmic polarization term, and a concentration polarization term. The open-circuit voltage term is determined based on the battery's state of charge. The ohmic polarization term is calculated by multiplying the charge / discharge current by the transfer internal resistance. The concentration polarization term is calculated based on the diffusion equation by integrating the ratio of the charge / discharge current to the diffusion coefficient. The three terms are added together to obtain the predicted terminal voltage. The difference between the predicted terminal voltage and the actual measured terminal voltage is calculated to obtain the absolute value of the voltage deviation. A preset period, such as 5 seconds, is selected as the observation window. All charge / discharge current samples within this time window are extracted. The difference between the latest and earliest samples is calculated and then divided by the preset period to obtain the rate of change of the charge / discharge current over the preset period. The absolute value of the voltage deviation and the absolute value of the rate of change of the charge / discharge current are weighted and summed. The weighting coefficients are pre-calibrated according to the battery characteristics. The result of the weighted sum is the polarization mismatch. The larger the mismatch value, the more serious the deviation between the preset electrochemical mapping relationship and the actual electrochemical state of the battery.

[0033] In one possible implementation, the polarization mismatch is determined based on the deviation between the predicted terminal voltage and the actual terminal voltage, and the rate of change of the charging and discharging current over a preset period. Specifically, this includes steps 1021-1022, as follows:

[0034] Step 1021: Calculate the voltage residual between the terminal voltage and the predicted terminal voltage; calculate the absolute value of the rate of change of the charging and discharging current at the current time step.

[0035] In this embodiment, the terminal voltage represents the actual potential difference between the positive and negative electrodes of the polyanion sodium battery, acquired in real time by a voltage sensor. The predicted terminal voltage refers to the theoretically predicted value of the battery terminal voltage calculated by substituting the charging and discharging current into a preset electrochemical mapping relationship. The voltage residual represents the deviation between the terminal voltage and the predicted terminal voltage, reflecting the accuracy of the electrochemical mapping relationship. The charging and discharging current refers to the real-time current flowing through the polyanion sodium battery; it is positive during charging and negative during discharging. The current time step refers to the current sampling moment in the discrete-time sampling system where data processing is underway. The absolute value of the current change rate represents the rate at which the charging and discharging current changes with time. It is obtained by calculating the difference between the current at the current time step and the previous time step, dividing by the time interval, and taking the absolute value. For example, if the current changes from -10 amps to -15 amps in 0.1 seconds, the absolute value of the current change rate is 50 amps per second.

[0036] Specifically, the system reads the measured terminal voltage value and the calculated predicted terminal voltage value from the data acquisition system at the current time step. The difference between the measured and calculated terminal voltage values ​​is calculated to obtain a signed value for the voltage residual, which may be positive or negative. The system then reads the charging / discharging current value from the data storage area at the current time step and the charging / discharging current value from the previous time step. The difference between the current and previous charging / discharging current values ​​is calculated to obtain the current change. The time interval between two sampling time steps is obtained, typically set to 0.01 to 0.1 seconds. The current change is divided by the time interval to obtain the current change rate. The absolute value of the current change rate is calculated, converting any positive or negative value to a non-negative value to obtain the absolute value of the current change rate. The calculated voltage residual and the absolute value of the current change rate are stored in the data buffer for subsequent calculations.

[0037] Step 1022: Multiply the voltage residual with the absolute value of the current change rate to obtain the instantaneous polarization characteristic value; integrate the instantaneous polarization characteristic value within the preset sliding time window to obtain the polarization mismatch degree.

[0038] In this embodiment, the voltage residual represents the difference between the terminal voltage and the predicted terminal voltage, and the magnitude of this difference reflects the degree of deviation from the electrochemical model's prediction. The absolute value of the current change rate is used to represent the drastic change in charging and discharging current; the larger the value, the more drastic the current fluctuation. The product operation refers to the mathematical operation of multiplying the voltage residual by the absolute value of the current change rate. The instantaneous polarization characteristic value represents a feature parameter reflecting the degree of deviation in polarization state obtained at the current time step through the product of the voltage residual and the absolute value of the current change rate. The preset sliding time window refers to a fixed time interval that slides forward over time; the window length is typically set to 10 to 60 seconds. For example, a window length of 30 seconds includes all sampled data within the most recent 30 seconds. Integration processing represents the numerical calculation process of accumulating and summing the instantaneous polarization characteristic values ​​within the time window. The polarization state mismatch degree is a comprehensive evaluation index obtained through integration processing, which quantifies the overall deviation between the electrochemical model and the actual state of the battery.

[0039] Specifically, the voltage residual value and the absolute value of the current rate of change calculated at the current time step are read from the data buffer. These two values ​​are multiplied to obtain the instantaneous polarization characteristic value for the current time step, which reflects the combined effect of voltage deviation and current change. The calculated instantaneous polarization characteristic value is added to a data queue with a preset sliding time window, which uses a first-in, first-out storage method. The timestamps stored in the data queue are checked, and all historical data with timestamps earlier than the current time minus the window length are removed, keeping only valid data within the preset sliding time window in the queue. All instantaneous polarization characteristic values ​​in the data queue are summed. If the queue contains 300 sampling points, these 300 instantaneous polarization characteristic values ​​are summed one by one. The sum is multiplied by the sampling time interval to complete the discrete integration calculation, and the integral result is the polarization mismatch. The polarization mismatch value is output and stored for subsequent determination of whether the electrochemical mapping relationship needs to be corrected.

[0040] Step 103: When the polarization mismatch is greater than the preset mismatch threshold, calculate the compensation coefficient based on the polarization mismatch, and use the compensation coefficient to correct the diffusion coefficient and transfer resistance in the preset electrochemical mapping relationship to obtain the updated target electrochemical mapping relationship.

[0041] In this embodiment, the preset mismatch threshold represents a critical value for determining whether the preset electrochemical mapping relationship needs correction. This threshold is determined through experimental calibration based on the battery type and application scenario, and is typically set to a value between 0.05 and 0.15. The compensation coefficient represents the factor by which parameters in the preset electrochemical mapping relationship are corrected, including the diffusion coefficient compensation coefficient and the transfer resistance compensation coefficient. The diffusion coefficient is a physical parameter describing the diffusion rate of sodium ions in the solid and liquid phases inside the battery. The transfer resistance represents a parameter characterizing the impedance of the charge transfer process in the electrochemical mapping relationship. The target electrochemical mapping relationship refers to the new electrochemical mapping relationship obtained after correction by the compensation coefficient, which more accurately reflects the current true electrochemical state of the battery.

[0042] Specifically, the calculated polarization mismatch is compared with a preset mismatch threshold. When the polarization mismatch value is greater than the preset mismatch threshold, the parameter correction process is initiated. A pre-established compensation coefficient lookup table is consulted based on the polarization mismatch value. This lookup table stores the compensation coefficient values ​​corresponding to different mismatch intervals. The diffusion coefficient compensation coefficient and transfer resistance compensation coefficient corresponding to the current mismatch are determined using linear interpolation. If the polarization mismatch is 0.12 and the corresponding diffusion coefficient compensation coefficient is 0.85 and the transfer resistance compensation coefficient is 1.15, then the original diffusion coefficient in the preset electrochemical mapping relationship is multiplied by 0.85 to obtain the corrected diffusion coefficient, and the original transfer resistance is multiplied by 1.15 to obtain the corrected transfer resistance. The corrected diffusion coefficient and transfer resistance are used to replace the corresponding parameters in the preset electrochemical mapping relationship, keeping other parameters in the mapping relationship unchanged, thereby constructing the target electrochemical mapping relationship. The target electrochemical mapping relationship is stored and used for subsequent battery state prediction and power boundary calculation. When the polarization mismatch is less than or equal to the preset mismatch threshold, the preset electrochemical mapping relationship is kept unchanged and used.

[0043] In one possible implementation, a compensation coefficient is calculated based on the polarization mismatch, and the diffusion coefficient and transfer resistance in the preset electrochemical mapping relationship are corrected using the compensation coefficient to obtain the updated target electrochemical mapping relationship. This specifically includes steps 1031-1034, as follows:

[0044] Step 1031: Obtain the current direction of the charging and discharging current, and determine the working state of the polyanion sodium battery based on the current direction. The working state includes sodium insertion charging state and sodium removal discharging state.

[0045] In this embodiment, the charge / discharge current represents the real-time current value flowing between the positive and negative electrodes of the polyanion sodium battery, which is acquired by a current sensor. The current direction refers to the direction of the charge / discharge current flow, determined by the sign of the current value: positive indicates charging, negative indicates discharging. The operating state indicates the current operating mode of the polyanion sodium battery. A polyanion sodium battery refers to a sodium-ion battery using a polyanion-type positive electrode material with a stable three-dimensional framework structure. The sodium-intercalation charging state refers to the charging process where sodium ions are extracted from the electrolyte and intercalated into the lattice structure of the positive electrode material; in this state, the current is positive, for example, a current of +5 amperes indicates the battery is in the sodium-intercalation charging state. The sodium-depletion discharging state refers to the discharging process where sodium ions are extracted from the lattice of the positive electrode material and enter the electrolyte; in this state, the current is negative, for example, a current of -8 amperes indicates the battery is in the sodium-depletion discharging state.

[0046] Specifically, the charging / discharging current value at the current time step is read from the data acquisition interface, and the sign of this value is determined. When the charging / discharging current value is greater than zero, the current direction is determined to be the charging direction, and the operating status flag is set to sodium insertion charging state, indicating that sodium ions are inserting into the positive electrode material. When the charging / discharging current value is less than zero, the current direction is determined to be the discharging direction, and the operating status flag is set to sodium removal discharging state, indicating that sodium ions are removing from the positive electrode material. When the charging / discharging current value is equal to zero, the battery is determined to be in a static state, and the operating status flag is set to the static state, at which point no sodium ion insertion or removal process occurs. The determined operating status flag is stored in the status register, and an operating status flag bit is generated. This flag bit uses a binary encoding method, with a value of 1 corresponding to sodium insertion charging state and a value of 0 corresponding to sodium removal discharging state. The operating status flag and flag bit are output to the data bus for subsequent parameter query and compensation calculation.

[0047] Step 1032: Compare the polarization state mismatch with multiple preset discrete mismatch intervals to determine the target mismatch interval hit by the polarization state mismatch.

[0048] In this embodiment, the polarization state mismatch represents a comprehensive evaluation index of the deviation between the quantitative electrochemical model obtained through integration and the actual state of the battery. The preset discrete mismatch intervals refer to a series of pre-defined numerical ranges. These intervals divide the value space of the polarization state mismatch into several non-overlapping continuous segments. For example, the intervals can be set to five discrete intervals: 0 to 10, 10 to 20, 20 to 30, 30 to 50, and above 50. Comparison represents the process of determining the magnitude relationship between the polarization state mismatch value and the boundary values ​​of each discrete mismatch interval. The target mismatch interval refers to the specific discrete mismatch interval into which the polarization state mismatch value falls. A hit indicates that the polarization state mismatch value satisfies the boundary condition of a certain discrete mismatch interval, i.e., the value is greater than or equal to the lower bound of the interval and less than the upper bound.

[0049] Specifically, the polarization mismatch value obtained from integration processing is read from the data storage area; this value is a non-negative real number. Multiple preset discrete mismatch interval boundary parameters are loaded from the configuration file; each interval contains two boundary parameters: a lower bound and an upper bound. The comparison operation is performed sequentially according to the interval order. First, it is determined whether the polarization mismatch is greater than or equal to the lower bound of the first interval and less than the upper bound of that interval. If the condition is met, the first interval is determined as the target mismatch interval, and the comparison process terminates. If the condition of the first interval is not met, it is continued to determine whether it falls into the second interval, and this process is repeated until an interval that meets the conditions is found. When the polarization mismatch is 25, after sequential comparison, it is determined that it hits the target mismatch interval of 20 to 30. The index number of the target mismatch interval is recorded in the query cache; the index number is in integer form, starting from 0 and increasing sequentially. The index number and boundary value of the target mismatch interval are output for matrix addressing.

[0050] Step 1033: Based on the working status and target mismatch interval, perform address lookup in the preset polyanion polarization compensation matrix to extract the corresponding diffusion compensation coefficient and internal resistance compensation coefficient.

[0051] In this embodiment, the operating state indicates the current operating mode of the polyanion sodium battery, indicating whether it is in a sodium-intercalation charging state or a sodium-depletion discharging state. The target mismatch interval refers to a specific discrete mismatch interval matched after polarization state mismatch degree comparison. The preset polyanion polarization compensation matrix is ​​a pre-established two-dimensional data table. The row index of this matrix corresponds to the operating state type, the column index corresponds to the discrete mismatch interval number, and the matrix elements store the compensation coefficient pairs under the corresponding conditions. Address lookup is used to represent the operation process of locating a specific element position in the matrix and reading data based on the row and column indices. The diffusion compensation coefficient is a correction factor used to correct the diffusion rate of sodium ions in the cathode material, typically ranging from 0.8 to 1.2. The internal resistance compensation coefficient is a correction factor used to correct the charge transfer internal resistance, typically ranging from 0.9 to 1.1.

[0052] Specifically, the operating status identifier is read from the status register, and the row index of the matrix is ​​determined based on the identifier. Sodium insertion charging corresponds to row index 0, and sodium removal discharging corresponds to row index 1. The index number of the target mismatch interval is read from the query cache and used as the column index value of the matrix. Addressing and locating the target element in the preset polyanion polarization compensation matrix is ​​performed based on the row and column index values. The physical address of the target element in the matrix storage space is calculated by multiplying the row index value by the number of columns in the matrix and then adding the column index value. The calculated physical address is accessed, and the data structure stored at that location is read. This data structure contains two numerical fields: diffusion compensation coefficient and internal resistance compensation coefficient. The diffusion compensation coefficient and internal resistance compensation coefficient values ​​are extracted from the data structure. For example, when the operating status is sodium insertion charging and the target mismatch interval index is 2, the extracted diffusion compensation coefficient is 1.05 and the internal resistance compensation coefficient is 0.98. The extracted diffusion compensation coefficient and internal resistance compensation coefficient are written to the parameter cache for subsequent multiplication operations.

[0053] Step 1034: Multiply the initial diffusion coefficient and initial transfer resistance in the preset electrochemical mapping relationship with the diffusion compensation coefficient and the internal resistance compensation coefficient respectively to obtain the target diffusion coefficient and the target transfer resistance. Update the preset electrochemical mapping relationship according to the target diffusion coefficient and the target transfer resistance to obtain the updated target electrochemical mapping relationship.

[0054] In this embodiment, the preset electrochemical mapping relationship refers to a mathematical model describing the conversion law between charge / discharge current and terminal voltage, which is based on electrochemical principles. The initial diffusion coefficient represents the sodium ion diffusion rate parameter initially set in the preset electrochemical mapping relationship, reflecting the transport capability of sodium ions in the solid phase of the cathode material. The initial transfer resistance refers to the impedance parameter of the charge transfer process initially set in the preset electrochemical mapping relationship. The diffusion compensation coefficient represents the correction factor extracted from the polyanion polarization compensation matrix for correcting the diffusion coefficient. The internal resistance compensation coefficient represents the correction factor extracted from the polyanion polarization compensation matrix for correcting the transfer internal resistance. Multiplication operation refers to the mathematical calculation operation of multiplying two values ​​to obtain the result. The target diffusion coefficient represents the sodium ion diffusion rate parameter obtained after compensation and correction. The target transfer internal resistance represents the charge transfer internal resistance parameter obtained after compensation and correction. Update refers to the operation process of replacing the original parameter value with the newly calculated parameter value. The target electrochemical mapping relationship refers to the new electrochemical mapping relationship obtained after updating the parameters using the target diffusion coefficient and target transfer internal resistance.

[0055] Specifically, the initial diffusion coefficient and initial transfer resistance values ​​stored in the preset electrochemical mapping relationship are read from the model parameter storage area. The extracted diffusion compensation coefficient and internal resistance compensation coefficient values ​​are read from the parameter cache area. The initial diffusion coefficient and diffusion compensation coefficient are multiplied; for example, when the initial diffusion coefficient is 0.0001 m² / s and the diffusion compensation coefficient is 1.05, the target diffusion coefficient is calculated to be 0.000105 m² / s. The initial transfer resistance is multiplied with the internal resistance compensation coefficient; for example, when the initial transfer resistance is 0.05 ohms and the internal resistance compensation coefficient is 0.98, the target transfer resistance is calculated to be 0.049 ohms. The calculated target diffusion coefficient and target transfer resistance are written to the model parameter storage area, overwriting the original initial diffusion coefficient and initial transfer resistance values. The parameter configuration of the preset electrochemical mapping relationship is reloaded, and the internal calculation structure of the electrochemical mapping model is reconstructed using the updated target diffusion coefficient and target transfer resistance. The updated target electrochemical mapping relationship is activated, so that subsequent terminal voltage prediction calculations use the new parameters.

[0056] Step 104: Construct the power support urgency based on the source load power and voltage deviation.

[0057] In this embodiment, the power support urgency level is used to represent the urgency of the microgrid's current demand for the energy storage system's power support capability. This index comprehensively considers the degree of imbalance between source and load power and the severity of voltage deviation, and its value ranges from 0 to 1, with a higher value indicating a more urgent demand. The power support urgency level is constructed based on a nonlinear mapping relationship between source and load power deficit and voltage deviation. When there is a large gap in source and load power or the voltage deviation exceeds the allowable range, the power support urgency level rises rapidly. For example, when the voltage deviation exceeds 5% of the rated voltage and the source and load power deficit reaches 50% of the rated power of the energy storage system, the power support urgency level may reach a high value of 0.8 or higher.

[0058] Specifically, the algebraic sum of source and load power is calculated. A negative value indicates a power deficit when the generated power is less than the load power, and a positive value indicates a power surplus when the generated power is greater than the load power. The absolute value of the power deficit or surplus is taken as the source-load power imbalance. The source-load power imbalance is normalized by dividing the source-load power imbalance by the rated power of the energy storage system to obtain the power imbalance degree. The absolute value of the voltage deviation is calculated and normalized by dividing it by the rated voltage value to obtain the voltage deviation degree. A nonlinear transformation is applied to the power imbalance degree using a piecewise function. When the power imbalance degree is less than 0.3, the mapped value is small and grows slowly; when the power imbalance degree is greater than 0.3, the mapped value grows rapidly, yielding the power emergency component. A similar nonlinear transformation is applied to the voltage deviation degree. When the voltage deviation degree is less than 0.03, the mapped value is close to zero; when the voltage deviation degree is greater than 0.05, the mapped value increases rapidly, yielding the voltage emergency component. The power emergency component and the voltage emergency component are weighted and summed. The weight of the power emergency component is set to 0.4 to 0.6, and the weight of the voltage emergency component is set to 0.4 to 0.6. The sum of the two weights is equal to 1. The result of the weighted sum is the power support emergency level, and this value is limited to the range of 0 to 1.

[0059] In one possible implementation, a power support urgency level is constructed based on the source load power and voltage deviation, specifically including steps 1041-1042, as follows:

[0060] Step 1041: Calculate the net power fluctuation rate of the microgrid based on the source and load power; normalize the net power fluctuation rate and voltage deviation respectively to obtain the normalized fluctuation rate and normalized voltage deviation.

[0061] In this embodiment, source-load power represents the combined power output of all generating devices and the power consumed by all load devices in the microgrid. A microgrid is a small power system composed of distributed generation units, energy storage devices, loads, and control systems, which can operate independently or be connected to the main grid. Net power volatility represents the degree of fluctuation in the net power of the microgrid over time, obtained by dividing the standard deviation of the net power over a preset time period by the average value. Voltage deviation refers to the difference between the actual terminal voltage and the rated voltage of the microgrid, reflecting the degree of deviation in voltage quality; for example, when the rated voltage is 400 volts and the actual voltage is 390 volts, the voltage deviation is 10 volts. Normalization refers to a data preprocessing method that maps data with different dimensions or numerical ranges to a unified interval of 0 to 1 or -1 to +1. Normalized volatility represents the dimensionless standardized value obtained after normalizing the net power volatility. Normalized voltage deviation represents the dimensionless standardized value obtained after normalizing the voltage deviation.

[0062] Specifically, the system reads the source-load power value sequence for all time steps within a preset time period, typically 5 to 15 minutes, from the data acquisition system. It calculates the average value of all sampled points in the source-load power value sequence, squares the difference between each value and the average, sums all squared values, divides by the number of sampled points, and then takes the square root of the result to obtain the standard deviation. Dividing the standard deviation by the average value yields the net power fluctuation rate, a dimensionless ratio. The system reads the actual terminal voltage value of the current microgrid and the rated voltage value stored in the system configuration from the voltage monitoring module, calculating the absolute value of the difference between the actual and rated voltage values ​​to obtain the voltage deviation. It obtains the normalization processing configuration parameters, including the maximum expected value of the net power fluctuation rate and the maximum allowable value of the voltage deviation. Dividing the net power fluctuation rate by its maximum expected value yields the normalized fluctuation rate, ranging from 0 to 1. Dividing the voltage deviation by its maximum allowable value yields the normalized voltage deviation, also ranging from 0 to 1. The normalized fluctuation rate and normalized voltage deviation are stored in the feature data cache for subsequent weighted calculations.

[0063] Step 1042: Obtain the preset volatility weighting coefficient and the preset voltage deviation weighting coefficient; sum the product of normalized volatility and the preset volatility weighting coefficient, and the product of normalized voltage deviation and the preset voltage deviation weighting coefficient to obtain the power support urgency.

[0064] In this embodiment, the preset volatility weighting coefficient refers to a pre-configured weighting parameter used to quantify the importance of net power volatility in the comprehensive evaluation, typically ranging from 0 to 1. The preset voltage deviation weighting coefficient refers to a pre-configured weighting parameter used to quantify the importance of voltage deviation in the comprehensive evaluation, typically ranging from 0 to 1. Normalized volatility represents the standardized value obtained after normalizing the net power volatility. Normalized voltage deviation represents the standardized value obtained after normalizing the voltage deviation. Multiplication operation refers to the mathematical calculation operation of multiplying two values ​​to obtain the result. Summation operation is used to represent the mathematical calculation process of accumulating multiple values ​​to obtain a sum. Power support urgency refers to an evaluation index calculated after comprehensively considering power volatility and voltage deviation, reflecting the urgency of the microgrid's need for power support from polyanion sodium batteries; a larger value indicates a higher urgency.

[0065] Specifically, the system reads the preset volatility weighting coefficient and preset voltage deviation weighting coefficient from the system configuration file. The sum of the two weighting coefficients is usually equal to 1 to maintain the normalization characteristics of the evaluation; for example, the volatility weighting coefficient is 0.7 and the voltage deviation weighting coefficient is 0.3. The normalized volatility value and normalized voltage deviation value are read from the feature data cache. The normalized volatility is multiplied by the preset volatility weighting coefficient; for example, when the normalized volatility is 0.6 and the preset volatility weighting coefficient is 0.7, the first product is calculated to be 0.42. The normalized voltage deviation is multiplied by the preset voltage deviation weighting coefficient; for example, when the normalized voltage deviation is 0.8 and the preset voltage deviation weighting coefficient is 0.3, the second product is calculated to be 0.24. The first and second products are added together, i.e., 0.42 plus 0.24, resulting in 0.66. This summation result is the power support urgency level. The calculated power support urgency level value is written to the decision parameter storage area and the data validity flag is set. This power support urgency value is used to determine whether the power support mode of the polyanion sodium battery needs to be activated and to determine the specific support power level.

[0066] Step 105: Determine the basic polarization boundary based on the target electrochemical mapping relationship, and extend the basic polarization boundary according to the power support urgency and surface temperature to obtain the dynamic polarization boundary.

[0067] In this embodiment, the basic polarization boundary represents the upper limit of the allowable polarization voltage of the battery calculated based on the target electrochemical mapping relationship under standard operating conditions. This boundary ensures that the battery's performance will not degrade due to excessive polarization during normal operation. The dynamic polarization boundary refers to the upper limit of the polarization voltage obtained by adaptively adjusting the basic polarization boundary according to the power support urgency and surface temperature. This boundary allows for moderate overshooting in emergency situations to provide greater power support capability, while being subject to safety constraints from the surface temperature. The extent of the expansion of the basic polarization boundary depends on the magnitude of the power support urgency and the margin between the surface temperature and the safe temperature limit.

[0068] Specifically, based on the diffusion coefficient and transfer resistance parameters in the target electrochemical mapping relationship, combined with the battery's state of charge and rated capacity, the polarization voltage distribution under different charge and discharge currents is calculated using an electrochemical model. The current value corresponding to the polarization voltage exceeding the safety threshold as the current increases is taken as the limiting current, and the polarization voltage corresponding to this limiting current is the basic polarization boundary, typically set to 0.3V to 0.6V. The current power support urgency value is obtained. When the power support urgency is greater than a preset urgency threshold, such as 0.5, the excess amount of the urgency exceeding the threshold is calculated. The excess amount is multiplied by a preset expansion coefficient, which is typically calibrated to 1.5 to 2.5 based on battery characteristics, to obtain the preliminary expansion amount. The current surface temperature value is obtained, and the temperature margin between the surface temperature and the battery's safe upper temperature limit, such as 45 degrees Celsius, is calculated. The maximum allowable expansion amount is determined based on the temperature margin; the smaller the temperature margin, the smaller the maximum allowable expansion amount. When the temperature margin is less than 3 degrees Celsius, the maximum expansion amount is set to 10% of the basic polarization boundary. The smaller of the initial extension and the maximum extension is taken as the actual extension. The dynamic polarization boundary is obtained by adding the basic polarization boundary and the actual extension.

[0069] Step 106: Using the dynamic polarization boundary as a constraint, calculate the optimal power command and control the polyanion sodium battery to operate according to the optimal power command.

[0070] In this embodiment, constraints are used to represent the boundary conditions and limitations that need to be met during the optimization calculation process, including multiple constraints such as dynamic polarization boundaries, state of charge range, and current magnitude limits. The optimal power command refers to the energy storage system's charge and discharge power command value calculated by the optimization algorithm, which optimizes the objective function while satisfying all constraints. This command comprehensively considers minimizing both energy costs and battery aging costs. The calculation of the optimal power command employs a rolling time-domain optimization method, performing power scheduling planning for a future time window within each control cycle.

[0071] Specifically, an objective function for the optimization problem is established, including an energy cost term and an aging cost term. The energy cost term is calculated by accumulating the product of the electricity price and the charging / discharging power at each time period. The aging cost term is calculated using empirical formulas on the impact of polarization voltage and charging / discharging current on battery life. Constraints for the optimization problem are set. First, the dynamic polarization boundary is transformed into upper and lower limits for charging / discharging power. The maximum allowable charging power and maximum allowable discharging power satisfying the dynamic polarization boundary are determined through inverse calculation of the target electrochemical mapping relationship. A state of charge constraint is added, limiting the state of charge to between 10% and 90%. A charging / discharging current constraint is added, limiting the absolute value of the current to no more than twice the rated current. A power balance constraint is added, requiring that the algebraic sum of the energy storage power and the source load power can meet the power demand of the microgrid. A quadratic programming or mixed-integer programming algorithm is used to solve the objective function. The solver searches for the power sequence that minimizes the objective function value within the feasible region that satisfies all constraints. The power value at the first moment in the optimized power sequence is taken as the optimal power command for the current control cycle. The optimal power command is sent to the energy storage converter through the communication interface. The converter adjusts the output current and voltage according to the power command, driving the polyanion sodium battery to charge or discharge according to the optimal power command.

[0072] In one possible implementation, the optimal power command is calculated using the dynamic polarization boundary as a constraint, and the polyanion sodium battery is controlled to operate according to the optimal power command. Specifically, this includes steps 1061-1063, as follows:

[0073] Step 1061: Obtain real-time electricity price information for the microgrid, aging cost indicators for polyanion sodium batteries, and charge / discharge power variables.

[0074] In this embodiment, a microgrid refers to a small power system composed of distributed generation units, energy storage devices, loads, and a control system. Real-time electricity price information represents the unit price of electricity trading at any given moment in the electricity market. This price changes in real time according to supply and demand; for example, the peak-hour price is 0.8 yuan per kilowatt-hour, and the off-peak-hour price is 0.3 yuan per kilowatt-hour. A polyanion sodium battery refers to a sodium-ion energy storage battery that uses a polyanion compound as the positive electrode material, possessing high cycle life and safety performance. The aging cost index is used to represent the quantified economic loss caused by capacity decay and performance degradation during battery charging and discharging, typically expressed in yuan per kilowatt-hour. The charge / discharge power variable refers to the power output or input value sequence of the polyanion sodium battery at different times; positive values ​​indicate discharging, and negative values ​​indicate charging.

[0075] Specifically, a communication connection is established with the electricity market data exchange interface to obtain electricity price data for the current moment and each time step within the predicted future time period from the electricity trading platform. The electricity price data includes timestamps, price values, and data validity identifiers. The price values ​​are stored in chronological order in a real-time electricity price information array, with the array length corresponding to the number of time steps within the prediction time window. The cycle number, current capacity, and initial capacity data of the polyanion sodium battery are read from the battery management system. Based on the battery capacity decay model, the difference between the current capacity and the initial capacity is calculated to determine the capacity decay. The capacity decay is multiplied by the battery unit capacity cost coefficient and then divided by the cumulative discharge amount to obtain the aging cost index value. The predicted charge and discharge power value sequence for each time step within the prediction time window is obtained from the power prediction module. The range of each power prediction value is checked to ensure that the charging power does not exceed the maximum charging power limit and the discharging power does not exceed the maximum discharging power limit. The constrained predicted charge and discharge power value sequence is stored as a charge and discharge power variable in the optimization calculation input data area.

[0076] Step 1062: Construct microgrid operating cost indicators based on real-time electricity price information, aging cost indicators, and charging and discharging power variables.

[0077] In this embodiment, real-time electricity price information represents the unit price data sequence of electricity transactions at each moment in the electricity market. The aging cost index is used to represent the quantitative value of the economic loss due to capacity decay caused by battery charge-discharge cycles. The charge-discharge power variable refers to the power input or output value sequence of the polyanion sodium battery at different moments. The microgrid operating cost index refers to the total economic cost incurred by the microgrid operating within the predicted time window, including electricity purchase cost and battery aging cost. Construction refers to the process of establishing a mathematical expression relationship based on input parameters and cost calculation rules. Electricity purchase cost represents the total cost required for the microgrid to purchase electricity from the main grid. Battery aging cost represents the economic loss corresponding to the capacity decay of the polyanion sodium battery during charge-discharge processes.

[0078] Specifically, the real-time electricity price information array, aging cost index value, and charge / discharge power variable sequence are read from the optimization calculation input data area. For each time step within the prediction time window, the sign of the charge / discharge power variable is determined. When the charge / discharge power variable is positive, it indicates that the battery is discharging and supplying power to the microgrid, and no electricity purchase cost is incurred at this time step. When the charge / discharge power variable is negative or the microgrid load power is greater than the sum of the generation power and the battery discharge power, the electricity power that needs to be purchased from the main grid at this time step is calculated. The purchased power is multiplied by the real-time electricity price and time step length at this time step to obtain the electricity purchase cost at this time step. The electricity purchase costs of all time steps within the prediction time window are summed to obtain the total electricity purchase cost. The sum of the absolute values ​​of the charge / discharge power variables within the prediction time window is calculated, and this sum is multiplied by the aging cost index value to obtain the battery aging cost. The total electricity purchase cost is added to the battery aging cost to obtain the microgrid operating cost index value. This operating cost index is stored as the objective function in the optimization problem definition data structure.

[0079] Step 1063: Limit the predicted polarization voltage corresponding to the charging and discharging power variable within the dynamic polarization boundary as a constraint; under the constraint, minimize the microgrid operating cost index based on the preset prediction time window to determine the optimal power command at the current moment.

[0080] In this embodiment, the charge / discharge power variable refers to the sequence of power input or output values ​​of the polyanion sodium battery at different times. The predicted polarization voltage represents the battery polarization voltage value predicted based on the charge / discharge power variable; this voltage reflects the dynamic response characteristics of the electrochemical reaction inside the battery. The dynamic polarization boundary refers to the upper and lower limits of the allowable polarization voltage range calculated in real-time based on the battery state, used to protect the battery from excessive polarization. Constraints refer to the conditions or inequalities that must be satisfied during the optimization calculation process. The preset prediction time window represents the time range extending into the future from the current moment, such as the next 15 or 30 minutes. The microgrid operating cost index refers to the total economic cost incurred by the microgrid operating within the prediction time window. Minimization processing refers to the calculation process of finding the variable values ​​that minimize the objective function through an optimization algorithm. The optimal power command represents the charging or discharging power command value that the battery should execute at the current moment, obtained through optimization calculation.

[0081] Specifically, the charging / discharging power variable sequence and dynamic polarization boundary data are read from the optimization problem definition data structure. For each time step within the prediction time window, the corresponding predicted polarization voltage value is calculated based on the charging / discharging power variables and the battery equivalent circuit model at that time step. The predicted polarization voltage is compared with the upper limit of the dynamic polarization boundary to generate inequality constraints, requiring that the predicted polarization voltage not exceed the upper limit value. The predicted polarization voltage is compared with the lower limit of the dynamic polarization boundary to generate inequality constraints, requiring that the predicted polarization voltage not be less than the lower limit value. The polarization voltage constraints for all time steps are summarized to form a constraint set. The nonlinear optimization solver is invoked, using the microgrid operating cost index as the objective function, the charging / discharging power variable sequence as the decision variable, and the constraint set and battery power boundary as constraints, setting a preset prediction time window as the optimization time domain. The optimization solution process is initiated, iteratively searching for the charging / discharging power variable sequence that satisfies all constraints and minimizes the operating cost index. The charging / discharging power variable value corresponding to the first time step of the prediction time window is extracted from the optimization solution; this value is the optimal power command for the current moment. The optimal power command is sent to the battery power control module.

[0082] In the above embodiments, the power state of the microgrid and its urgency level are quantitatively assessed and determined through power support urgency calculation and normalization. To further improve the power response capability of polyanion sodium batteries under emergency conditions and enhance the stability support level of the microgrid under voltage deviation and power fluctuation, this application also provides a polyanion sodium battery microgrid energy dispatch method. This method establishes a dynamic adjustment model of the polarization boundary through the correlation mapping between power support urgency and over-limit value, constructs a multi-level boundary expansion constraint system by combining the safety margin information of battery surface temperature, and uses a temperature compensation limiting algorithm to achieve safe and controllable expansion of the polarization boundary, enabling the system to maximize the utilization of the battery's dynamic power response potential to meet the emergency power demand of the microgrid while maintaining battery thermal safety. The following section will combine... Figure 2 Another energy dispatching method for polyanion sodium battery microgrids is described in the embodiments of this application:

[0083] Please see Figure 2 This is another flowchart illustrating a different energy dispatching method for a polyanion sodium battery microgrid in this application embodiment:

[0084] Step 201: Determine the permissible over-limit value of polarization voltage based on the urgency of power support.

[0085] In this embodiment, the power support urgency refers to an evaluation index calculated after comprehensively considering power fluctuations and voltage deviations, reflecting the urgency of the microgrid's need for power support from polyanion sodium batteries, with a value range of 0 to 1. The permissible polarization voltage exceedance value represents the additional margin allowed for the battery polarization voltage to exceed the normal operating boundary under emergency conditions. This value is used to temporarily relax polarization voltage constraints to meet power support requirements.

[0086] Specifically, the power support urgency value (ranging from 0 to 1) is read from the decision parameter storage area. The calculation parameters for the polarization voltage allowable over-limit value, including the maximum allowable over-limit value and mapping coefficient, are read from the system configuration file. It is then determined whether the power support urgency is greater than a preset activation threshold (e.g., 0.3). If the power support urgency is not greater than the preset activation threshold, the polarization voltage allowable over-limit value is set to 0. If the power support urgency is greater than the preset activation threshold, the power support urgency is subtracted from the preset activation threshold to obtain the effective urgency component. The effective urgency component is divided by 1 and the result of subtracting the preset activation threshold to obtain the normalized urgency (ranging from 0 to 1). The normalized urgency is multiplied by the mapping coefficient to obtain the initial over-limit value. The initial over-limit value is compared with the maximum allowable over-limit value, and the smaller of the two is taken as the polarization voltage allowable over-limit value. The calculated polarization voltage allowable over-limit value is stored in the dynamic boundary calculation cache.

[0087] In one possible implementation, the permissible over-limit value of the polarization voltage is determined based on the power support urgency, specifically including step 2011, which is as follows:

[0088] Step 2011: Determine whether the power support urgency is greater than the preset urgency threshold; if the power support urgency is less than or equal to the urgency threshold, then set the polarization voltage allowable over-limit value to zero; if the power support urgency is greater than the urgency threshold, then calculate the amount by which the power support urgency exceeds the urgency threshold; multiply the amount by the preset over-limit conversion coefficient to obtain the polarization voltage allowable over-limit value.

[0089] In this embodiment, the power support urgency level refers to an evaluation index calculated after comprehensively considering power fluctuations and voltage deviations, reflecting the urgency of the microgrid's need for power support from polyanion sodium batteries, with a value range of 0 to 1. The preset urgency threshold is a pre-set critical value for determining whether to activate the power support mode; values ​​below this threshold indicate normal microgrid operation without special support, typically set between 0.3 and 0.5. The permissible polarization voltage exceedance value represents the additional margin allowed for the battery polarization voltage to exceed the normal operating boundary under emergency conditions. Judgment refers to the logical operation process of determining the truth or falsity of a condition through comparison. The excess amount represents the portion of the power support urgency level exceeding the preset urgency threshold, calculated as the difference between the two. The preset exceedance conversion coefficient is a proportional coefficient that converts the power support urgency exceedance amount into the permissible polarization voltage exceedance value; this coefficient is in volts and is typically set between 0.1 volts and 0.2 volts. The product operation refers to the mathematical calculation operation of multiplying two values ​​to obtain the result.

[0090] Specifically, the power support urgency value is read from the decision parameter storage area. The preset urgency threshold value and preset over-limit conversion coefficient value are read from the system configuration file. The power support urgency value is compared with the preset urgency threshold value. When the power support urgency value is less than the preset urgency threshold value, the polarization voltage over-limit value is directly set to 0, indicating that the power support mode is not activated and the battery operates according to the basic polarization boundary. When the power support urgency value is equal to the preset urgency threshold value, the polarization voltage over-limit value is also set to 0. When the power support urgency value is greater than the preset urgency threshold value, the over-limit calculation process is executed. The preset urgency threshold value is subtracted from the power support urgency value to obtain the over-limit value. For example, if the power support urgency value is 0.66 and the preset urgency threshold value is 0.3, then the over-limit value is 0.36. The validity of the calculation result is confirmed by checking whether the over-limit value is positive. The over-limit value is multiplied by the preset over-limit conversion coefficient. For example, if the over-limit value is 0.36 and the preset over-limit conversion coefficient is 0.15 volts, the calculation result is 0.054 volts. The result of the multiplication operation is used as the permissible limit for polarization voltage. The calculated permissible limit for polarization voltage is written into the dynamic boundary calculation buffer, and a data update flag is set. This value is subsequently used for the calculation and adjustment of the dynamic polarization boundary.

[0091] Step 202: Obtain the preset safe temperature threshold and calculate the temperature margin between the preset safe temperature threshold and the surface temperature.

[0092] In this embodiment, the preset safe temperature threshold refers to a pre-set upper limit for the battery surface temperature. Exceeding this temperature may lead to a decrease in battery performance or safety risks. It is typically set to 55 to 65 degrees Celsius. Surface temperature represents the real-time temperature value measured at the casing or tab position of the polyanion sodium battery. Temperature margin represents the difference between the preset safe temperature threshold and the actual surface temperature. This value reflects the remaining margin of the battery from the temperature safety boundary; a larger value indicates a more sufficient temperature safety margin. Acquisition refers to the process of reading data from a storage medium or sensor. Calculation refers to the process of performing mathematical operations based on the input values ​​to obtain the result.

[0093] Specifically, the preset safe temperature threshold value is read from the system configuration file. This threshold is pre-calibrated based on the battery model and environmental conditions. The validity flag of the preset safe temperature threshold is checked to confirm that the data is not expired and the value is within a reasonable range, such as 40 to 70 degrees Celsius. The measured surface temperature of the polyanion sodium battery at the current moment is read from the temperature monitoring module of the battery management system. The timestamp of the surface temperature data is checked to confirm that the data is fresh data from the most recent sampling period. The surface temperature measurement value is filtered, and the average of the most recent 5 sampling points is calculated using a moving average method to eliminate the influence of measurement noise. The preset safe temperature threshold is subtracted from the filtered surface temperature value to obtain the temperature margin. The sign of the temperature margin is determined. When the temperature margin is negative, it indicates that the surface temperature has exceeded the safe threshold, and the temperature margin is set to 0. When the temperature margin is positive, the calculation result remains unchanged. For example, if the preset safe temperature threshold is 60 degrees Celsius and the filtered surface temperature is 42 degrees Celsius, the temperature margin is 18 degrees Celsius. The calculated temperature margin value is stored in the dynamic boundary calculation cache for subsequent compensation upper limit calculation.

[0094] Step 203: Determine the dynamic compensation upper limit of the allowable polarization voltage limit based on the temperature margin; superimpose the polarization voltage limit after the limiting processing of the dynamic compensation upper limit onto the basic polarization boundary to obtain the dynamic polarization boundary.

[0095] In this embodiment, temperature margin represents the difference between a preset safe temperature threshold and the actual surface temperature. The permissible polarization voltage exceedance value represents the additional margin allowed for the battery polarization voltage to exceed the normal operating boundary under emergency conditions. The dynamic compensation upper limit refers to the maximum permissible value of the permissible polarization voltage exceedance value dynamically calculated based on the battery temperature state, used to prevent excessive relaxation of polarization constraints when the temperature is too high. Limiting processing refers to the data processing operation that restricts the value within a specified upper and lower limit range. Superposition refers to the mathematical operation of adding two values ​​together. The basic polarization boundary represents the safe operating range of the battery polarization voltage under normal operating conditions, including the upper and lower boundaries. The dynamic polarization boundary refers to the polarization voltage constraint range obtained by dynamically adjusting the basic polarization boundary based on power support requirements and temperature state.

[0096] Specifically, the temperature margin and allowable polarization voltage exceedance values ​​are read from the dynamic boundary calculation buffer. The conversion factor from temperature margin to compensation upper limit is read from the system configuration file; this factor is typically set to 0.005 V / °C to 0.01 V / °C. The temperature margin is multiplied by the conversion factor to obtain the temperature-related compensation upper limit reference value. This compensation upper limit reference value is compared with a preset minimum compensation upper limit, and the larger of the two is taken (e.g., the minimum compensation upper limit is set to 0.02 V). The comparison result is compared with a preset maximum compensation upper limit, and the smaller of the two is taken as the dynamic compensation upper limit (e.g., the maximum compensation upper limit is set to 0.15 V). The allowable polarization voltage exceedance value is compared with the dynamic compensation upper limit. If the allowable polarization voltage exceedance value is less than or equal to the dynamic compensation upper limit, the original value is maintained; if it is greater than the dynamic compensation upper limit, it is limited to the dynamic compensation upper limit value, completing the limiting process. The upper and lower boundary values ​​of the basic polarization boundary are read from the battery parameter database. The limited polarization voltage exceedance value is added to the upper boundary of the basic polarization boundary to obtain the upper boundary of the dynamic polarization boundary. The negative value of the allowed over-limit value of the polarization voltage after the limiting process is added to the lower boundary of the basic polarization boundary to obtain the lower boundary of the dynamic polarization boundary. The upper and lower boundaries of the dynamic polarization boundary are combined to form a boundary data pair and stored in the dynamic polarization boundary data area.

[0097] The following describes a polyanion sodium battery microgrid energy dispatching system from the perspective of hardware processing in an embodiment of this invention. Please refer to [link to relevant documentation]. Figure 3 This is a schematic diagram of the structure of a polyanion sodium battery microgrid energy dispatching system in an embodiment of this application.

[0098] It should be noted that, Figure 3 The structure of the polyanion sodium battery microgrid energy dispatch system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0099] like Figure 3 As shown, a polyanion sodium battery microgrid energy dispatching system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage section 308 into a random access memory (RAM) 303, such as executing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0100] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0101] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0102] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0103] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0104] Specifically, the polyanion sodium battery microgrid energy dispatching system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the polyanion sodium battery microgrid energy dispatching method provided in the above embodiment.

[0105] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the polyanion sodium battery microgrid energy dispatch system described in the above embodiments; or it may exist independently and not assembled into the polyanion sodium battery microgrid energy dispatch system. The storage medium carries one or more computer programs, which, when executed by a processor of the polyanion sodium battery microgrid energy dispatch system, cause the polyanion sodium battery microgrid energy dispatch system to implement the polyanion sodium battery microgrid energy dispatch method based on IoT data encryption transmission provided in the above embodiments.

Claims

1. A method for energy dispatching in a microgrid powered by a polyanion sodium battery, characterized in that, The method includes: The source-load power and voltage deviation of the microgrid, as well as the terminal voltage, charging and discharging current and surface temperature of the polyanion sodium battery, are obtained. The charging and discharging current is input into a preset electrochemical mapping relationship to determine the predicted terminal voltage. The polarization mismatch is determined based on the deviation between the predicted terminal voltage and the terminal voltage and the rate of change of the charging and discharging current in a preset period. When the polarization mismatch is greater than the preset mismatch threshold, a compensation coefficient is calculated based on the polarization mismatch. The diffusion coefficient and transfer resistance in the preset electrochemical mapping relationship are corrected using the compensation coefficient to obtain the updated target electrochemical mapping relationship. Based on the source load power and the voltage deviation, a power support urgency level is constructed; The basic polarization boundary is determined based on the target electrochemical mapping relationship. The basic polarization boundary is then extended according to the power support urgency and the surface temperature to obtain the dynamic polarization boundary. Using the dynamic polarization boundary as a constraint, the optimal power command is calculated, and the polyanion sodium battery is controlled to operate according to the optimal power command.

2. The method according to claim 1, characterized in that, The step of determining the polarization mismatch based on the deviation between the predicted terminal voltage and the actual terminal voltage and the rate of change of the charging and discharging current over a preset period includes: Calculate the voltage residual between the terminal voltage and the predicted terminal voltage; Calculate the absolute value of the rate of change of the charging and discharging current at the current time step; The instantaneous polarization characteristic value is obtained by multiplying the voltage residual with the absolute value of the current rate of change. The instantaneous polarization characteristic values ​​within a preset sliding time window are integrated to obtain the polarization state mismatch.

3. The method according to claim 1, characterized in that, The calculation of the compensation coefficient based on the polarization mismatch, and the use of the compensation coefficient to correct the diffusion coefficient and transfer resistance in the preset electrochemical mapping relationship, yields the updated target electrochemical mapping relationship, including... The current direction of the charging and discharging current is obtained, and the operating state of the polyanion sodium battery is determined based on the current direction. The operating state includes sodium insertion charging state and sodium removal discharging state. The polarization state mismatch is compared with a plurality of preset discrete mismatch intervals to determine the target mismatch interval hit by the polarization state mismatch. Based on the working state and the target mismatch interval, an address lookup is performed in the preset polyanion polarization compensation matrix to extract the corresponding diffusion compensation coefficient and internal resistance compensation coefficient. The initial diffusion coefficient and initial transfer resistance in the preset electrochemical mapping relationship are multiplied by the diffusion compensation coefficient and the internal resistance compensation coefficient, respectively, to obtain the target diffusion coefficient and target transfer resistance. The preset electrochemical mapping relationship is then updated based on the target diffusion coefficient and target transfer resistance to obtain the updated target electrochemical mapping relationship.

4. The method according to claim 1, characterized in that, The step of constructing the power support urgency level based on the source load power and the voltage deviation includes: Calculate the net power fluctuation rate of the microgrid based on the source load power; The net power fluctuation rate and the voltage deviation are normalized respectively to obtain normalized fluctuation rate and normalized voltage deviation; Obtain the preset volatility weighting coefficient and the preset voltage deviation weighting coefficient; The power support urgency is obtained by summing the product of the normalized volatility and the preset volatility weighting coefficient, and the product of the normalized voltage deviation and the preset voltage deviation weighting coefficient.

5. The method according to claim 1, characterized in that, The step of extending the basic polarization boundary based on the power support urgency and the surface temperature to obtain the dynamic polarization boundary includes: The permissible over-limit value of polarization voltage is determined based on the power support urgency. Obtain a preset safe temperature threshold and calculate the temperature margin between the preset safe temperature threshold and the surface temperature; The dynamic compensation upper limit for the allowable over-limit value of the polarization voltage is determined based on the temperature margin. The allowable over-limit value of the polarization voltage after being limited by the dynamic compensation upper limit is superimposed on the basic polarization boundary to obtain the dynamic polarization boundary.

6. The method according to claim 5, characterized in that, The step of determining the permissible over-limit value of polarization voltage based on the power support urgency includes: Determine whether the power support urgency level is greater than a preset urgency threshold; If the power support urgency is less than or equal to the urgency threshold, then the polarization voltage allowable over-limit value is determined to be zero; If the power support urgency is greater than the urgency threshold, then calculate the amount by which the power support urgency exceeds the urgency threshold; The excess amount is multiplied by a preset over-limit conversion coefficient to obtain the allowable over-limit value of the polarization voltage.

7. The method according to claim 1, characterized in that, The step of calculating the optimal power command using the dynamic polarization boundary as a constraint and controlling the polyanion sodium battery to operate according to the optimal power command includes: Obtain real-time electricity price information of the microgrid, aging cost indicators of the polyanion sodium battery, and charge / discharge power variables; Based on the real-time electricity price information, the aging cost index, and the charging and discharging power variable, the microgrid operating cost index is constructed. The predicted polarization voltage corresponding to the charge / discharge power variable is restricted within the dynamic polarization boundary as a constraint. Under the constraints, the microgrid operating cost index is minimized based on a preset prediction time window to determine the optimal power command at the current moment.

8. A polyanion sodium battery microgrid energy dispatching system, characterized in that, The polyanion sodium battery microgrid energy dispatch system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the polyanion sodium battery microgrid energy dispatch system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the polyanion sodium battery microgrid energy dispatch system, the polyanion sodium battery microgrid energy dispatch system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the polyanion sodium battery microgrid energy dispatch system, the polyanion sodium battery microgrid energy dispatch system performs the method as described in any one of claims 1-7.

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