A hardware-in-the-loop simulation test method for energy storage power station control and protection system

Abstract

The application relates to the technical field of digital simulation, and discloses a hardware-in-the-loop simulation test method for an energy storage power station control and protection system. The method comprises the following steps: obtaining energy storage power station operation data, establishing an SOC-power coupling constraint mechanism by using a state space decomposition algorithm; establishing a current-voltage-SOC three-dimensional protection criterion based on battery state parameters; calculating multi-energy storage power station power balance distribution by using a distributed coordination control algorithm; configuring a hardware interface signal synchronization protocol to establish a test platform; loading multiple scene fault conditions to perform automatic verification and output performance evaluation results. The application solves the technical problem that the energy storage power station control and protection system lacks a high-precision, multi-scene, hardware-in-the-loop integrated simulation test verification method, and improves the precision, efficiency and safety of the energy storage power station control and protection system test verification.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital simulation, in particular to a hardware-in-the-loop simulation test method for energy storage power station control and protection system. BACKGROUND

[0002] With the rapid advancement of new power system construction, energy storage power station as an important technical means of grid frequency modulation, peak shaving and new energy consumption, the reliability and response performance of its control and protection system directly affect the safe and stable operation of the power grid. In the prior art, the verification test of the control and protection system of the energy storage power station mainly adopts traditional hardware test method and simple digital simulation technology, and the correctness of the control and protection logic is verified by repeated debugging on the actual energy storage equipment. This method can verify the basic functions of the energy storage power station control and protection system to a certain extent, and provides technical support for the safe operation of the energy storage power station.

[0003] However, the prior art has significant deficiencies: on the one hand, the traditional hardware test method needs to be repeatedly debugged on the energy storage power station site, which is high in cost, time-consuming and has safety risks, and it is difficult to cover all possible fault scenarios; on the other hand, the existing digital simulation technology lacks a special modeling method for the special characteristics of the energy storage system, and cannot accurately simulate the complex behaviors of the SOC-power coupling characteristics of the energy storage power station, the multi-time scale dynamic response and the coordinated control of the energy storage power station group, resulting in insufficient simulation accuracy and large deviation between the test results and the actual operating conditions. At the same time, the existing simulation test method generally adopts pure software simulation mode, lacks effective combination with real control and protection hardware, and cannot verify the time sequence synchronization performance of hardware and simulation system.

[0004] Based on the above technical status analysis, it can be inferred that the progressive technical problems faced by the verification test of the energy storage power station control and protection system are: first, how to establish a high-precision simulation model that can accurately reflect the physical characteristics of the energy storage system, especially the modeling of the coupling constraint mechanism of SOC state and power output; second, how to construct special protection criteria and test verification methods for fault modes specific to the energy storage system; third, how to solve the group simulation modeling and verification problem of multi-energy storage power station coordinated control; and finally, how to realize accurate synchronization between the simulation system and the real control and protection hardware, and construct a hardware-in-the-loop test and verification platform. SUMMARY

[0005] The present application provides a hardware-in-the-loop simulation test method for energy storage power station control and protection system, which solves the technical problem of lack of high-precision, multi-scenario, hardware-in-the-loop integrated simulation test and verification method for energy storage power station control and protection system, and improves the accuracy, efficiency and safety of the test and verification of the energy storage power station control and protection system.

[0006] The application provides a hardware-in-the-loop simulation test method of an energy storage power station control and protection system.

[0007] In the S1 step, energy storage power station operation data are acquired, an energy storage unit state space decomposition algorithm is used to establish an SOC-power coupling constraint mechanism, and an energy storage power station dynamic response model is constructed.

[0008] In the S2 step, based on the battery state parameters in the energy storage power station dynamic response model, a current-voltage-SOC three-dimensional protection criterion is established, and an energy storage special protection action model is generated.

[0009] In the S3 step, power distribution parameters in the energy storage special protection action model are extracted, a distributed coordinated control algorithm is used to calculate power balance distribution among multiple energy storage power stations, and an energy storage power station group coordinated control simulation model is formed.

[0010] In the S4 step, simulation data of the energy storage power station group coordinated control simulation model are called, a hardware interface signal synchronization protocol is configured, and an energy storage control and protection hardware-in-the-loop test platform is established.

[0011] In the S5 step, multiple scene fault conditions are loaded in the energy storage control and protection hardware-in-the-loop test platform, an energy storage control and protection system automatic verification process is executed, and an energy storage control and protection system performance evaluation result is output.

[0012] In the technical scheme provided by the application, the SOC-power coupling constraint mechanism is established by using the energy storage unit state space decomposition algorithm, which effectively solves the technical problem that the traditional simulation method cannot accurately describe the dynamic characteristics of the energy storage system. The algorithm can decompose the complex dynamic behavior of the energy storage power station into the mathematical relationship among the state variables, input variables and output variables, especially by establishing the product constraint relationship of the power change rate, the SOC value and the temperature compensation coefficient, the accurate mathematical description of the power regulation response characteristics of the energy storage power station under different SOC states is realized. At the same time, the energy storage special protection action model based on the current-voltage-SOC three-dimensional protection criterion breaks through the limitation of the traditional protection system considering only a single electrical quantity, and through the comprehensive judgment of the three-dimensional parameter space, the specific fault modes of the energy storage system such as overcharge and overdischarge, thermal runaway and the like can be more accurately identified, and the reliability and selectivity of the protection action are significantly improved. In addition, the introduction of the distributed coordinated control algorithm solves the technical challenge of power balance distribution among multiple energy storage power stations, and through consistent iterative calculation and SOC balance control rules, the coordinated and optimized operation of the energy storage power station group is realized, and the phenomenon of single station overcharge and discharge is avoided. The configuration of the hardware interface signal synchronization protocol ensures the accurate timing synchronization between the simulation system and the control and protection hardware, overcomes the test error problem caused by the timing deviation in the traditional hardware-in-the-loop system, and provides a high-fidelity test and verification environment for the energy storage control and protection system.

[0013] The energy storage unit state space decomposition algorithm is particularly suitable for modeling the multi-time scale characteristics of the energy storage system, and its algorithm features can simultaneously process millisecond-level electromagnetic transient processes and second-level power regulation processes, thereby providing the energy storage power station control protection system with unprecedented modeling accuracy. The three-dimensional protection criterion algorithm is specially optimized for the physical constraint characteristics of the energy storage system, and its multi-dimensional judgment mechanism can effectively distinguish between the normal operating state and various abnormal states of the energy storage system, thereby having stronger adaptability and accuracy than the traditional single threshold protection method. The consistency feature of the distributed coordination control algorithm makes it have good scalability and robustness in the application of the energy storage power station group, and can adapt to the control requirements of energy storage power stations of different scales and configurations. The distributed architecture avoids the single point failure risk of centralized control. The automatic verification process of multiple scene fault conditions ensures the reliability verification of the energy storage control protection system under various complex conditions through systematic test scene coverage, and the automatic feature greatly improves the test efficiency and reduces the uncertainty of manual operation. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0015] Figure 1 An embodiment of the hardware-in-the-loop simulation test method of the energy storage power station control protection system in the present application is shown in the figure.

[0016] Figure 2 An embodiment of the hardware-in-the-loop simulation test method of the energy storage power station control protection system in the present application is shown in the figure. DETAILED DESCRIPTION

[0017] The embodiment of the present application provides a hardware-in-the-loop simulation test method of an energy storage power station control protection system. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily mean a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the term "includes" or "has" and any variation thereof is intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0018] For ease of understanding, the specific process of the embodiments of the present application is described below. Please refer to Figure 1 One embodiment of the hardware-in-the-loop simulation test method of the energy storage power station control and protection system in the embodiments of the present application includes:

[0019] S1, obtaining energy storage power station operation data, using a state space decomposition algorithm of the energy storage unit to establish an SOC-power coupling constraint mechanism, and constructing an energy storage power station dynamic response model;

[0020] S2, based on the battery state parameters in the energy storage power station dynamic response model, establishing a current-voltage-SOC three-dimensional protection criterion, and generating an energy storage special protection action model;

[0021] S3, extracting the power distribution parameters in the energy storage special protection action model, using a distributed coordinated control algorithm to calculate the power balance distribution among multiple energy storage power stations, and forming an energy storage power station group coordinated control simulation model;

[0022] S4, calling the simulation data of the energy storage power station group coordinated control simulation model, configuring a hardware interface signal synchronization protocol, and establishing an energy storage control and protection hardware-in-the-loop test platform;

[0023] S5, loading multiple scene fault conditions in the energy storage control and protection hardware-in-the-loop test platform, executing an energy storage control and protection system automatic verification process, and outputting an energy storage control and protection system performance evaluation result.

[0024] It can be understood that the execution subject of the present application can be a hardware-in-the-loop simulation test system of the energy storage power station control and protection system, and can also be a terminal or a server, and the specific place is not limited. The embodiments of the present application take a server as an execution subject for example.

[0025] Specifically, the battery voltage, current, temperature and SOC data in the operation process of the energy storage power station are collected, wherein SOC represents the state of charge of the battery, reflecting the ratio of the current storage capacity to the full capacity of the battery. The four-dimensional state vector collected contains voltage, current, temperature and SOC values, forming an energy storage unit basic state data set. Then the data set is processed by state space decomposition, and a linear relationship between the derivative of the state variable with respect to time and the state variable and the control input is established, wherein the state space decomposition algorithm decomposes the complex dynamic characteristics of the energy storage system into multiple interrelated state variables, the change rate of each state variable is mathematically related to the current state and external control input, so as to obtain a dynamic coefficient matrix that changes with SOC. Based on the SOC related parameters in the dynamic coefficient matrix, a power change rate constraint relationship is established between the product of the SOC value and the temperature compensation coefficient, and this constraint mechanism ensures that the power output of the energy storage power station strictly follows the physical characteristic limit of the battery. Finally, an energy storage power station dynamic response model is constructed.

[0026] A three-dimensional protection criterion of current-voltage-SOC is established based on the battery state parameters in the dynamic response model of energy storage power station. The battery voltage value, current value and SOC value are extracted from the dynamic response model of energy storage power station to construct a three-dimensional parameter space, where each dimension corresponds to the numerical range of current, voltage and SOC respectively, forming a three-dimensional protection basic parameter set. Based on this parameter set, the correlation calculation relationship between the protection current threshold and the battery voltage deviation, SOC deviation and temperature rise rate is established. The protection current threshold is dynamically adjusted according to the current battery state. When the battery voltage deviates from the rated value, the SOC exceeds the safe range or the temperature changes too fast, the protection current threshold is correspondingly reduced to ensure system safety. According to the dynamic protection current threshold, the corresponding trigger conditions of the hierarchical protection logic in different SOC intervals and voltage intervals are set. The hierarchical protection logic includes a first-level protection for slight abnormalities, a second-level protection for moderate faults and a third-level protection for serious faults. The hierarchical protection logic of energy storage power station is combined with the protection action time sequence to generate a special protection action model for energy storage.

[0027] The power allocation parameters are extracted from the special protection action model for energy storage. The rated power value and current SOC state value of each energy storage power station are extracted from the protection action model to construct a power-SOC state matrix of energy storage power station, which describes the available power range of each energy storage power station under different SOC states. The consistency iterative calculation is performed on the power allocation parameter set. The algorithm calculates the power adjustment amount of each energy storage power station based on the power difference between energy storage power stations and the adjacent communication weight. The consistency algorithm makes the power output of all energy storage power stations tend to be consistent through iterative calculation, while considering the SOC state and communication topology relationship of each power station. The SOC equalization control rules and power constraint conditions between energy storage power stations are established based on the target power allocation value. The SOC equalization control rules ensure that the SOC levels of each energy storage power station remain relatively balanced, avoiding excessive charging and discharging of some power stations. The power constraint conditions limit the power change range and change rate of each power station. The distributed coordination control framework containing communication topology and control logic is constructed based on the power equalization allocation strategy of energy storage power station group, forming a coordination control simulation model of energy storage power station group.

[0028] The simulation data of the simulation model of the energy storage power station group coordinated control is called, and a hardware interface signal synchronization protocol is configured. Voltage and current signal data and protection action instruction data are extracted from the simulation model to construct a simulation output signal data set, which includes various electrical quantities and control instructions obtained by simulation calculation. The signal format conversion processing is performed on the hardware interface input data source, and the digital simulation signal is converted into an analog signal and a digital signal. The analog signal represents the continuous change of the physical quantity in the form of voltage or current, and the digital signal represents the switch state or protection signal in the form of high and low level. The clock synchronization protocol is configured based on the standardized hardware interface signal, and the timing synchronization mechanism between the simulation system and the control and protection hardware is established. The clock synchronization protocol ensures that the simulation calculation step and the hardware response time are strictly synchronized, and avoids the test error caused by the timing deviation. The hardware connection platform including the signal conditioning circuit and the communication interface is constructed according to the hardware interface signal synchronization protocol, and the energy storage control and protection hardware-in-the-loop test platform is established.

[0029] The multi-scenario fault working conditions are loaded in the energy storage control and protection hardware-in-the-loop test platform. A multi-scenario fault library is constructed in the test platform, including power grid faults, device faults and operating conditions. The power grid faults include voltage drop, frequency deviation, phase-to-phase short circuit and other abnormal conditions. The device faults include battery over-temperature, converter over-current, DC side grounding and other device abnormalities. The operating conditions include normal charging and discharging, power regulation, SOC management and other typical operation scenarios. The test case execution sequence is generated based on the energy storage power station test scenario database, and the fault working conditions are automatically switched according to the preset timing and the control and protection system response data are recorded. The test sequence is sorted according to the fault severity and the occurrence probability to ensure that all key scenarios are covered. The performance index calculation is performed on the verification data set of the energy storage control and protection system, and the protection action time, control response accuracy and power regulation deviation are analyzed. The protection action time is obtained by recording the time difference between the fault occurrence time and the protection action completion time. The control response accuracy is calculated by comparing the deviation between the power instruction value and the actual output value. The performance parameters of the energy storage control and protection system are compared with the preset threshold value, and the comprehensive evaluation report including the test coverage and the improvement suggestions is generated, and the performance evaluation result of the energy storage control and protection system is output.

[0030] In a specific embodiment, S1 step, comprising:

[0031] The battery voltage, current, temperature and SOC data of the energy storage power station are collected, a four-dimensional state vector including voltage, current, temperature and SOC is constructed, and the energy storage unit basic state data set is obtained;

[0032] The state space decomposition is performed on the energy storage unit basic state data set, the linear relationship between the time derivative of the state variable and the state variable and the control input is established, and the dynamic coefficient matrix varying with the SOC is obtained;

[0033] The SOC-related parameters in the dynamic coefficient matrix are used to establish a power change rate and SOC value and temperature compensation coefficient product constraint relationship, and a SOC-power coupling constraint mechanism is obtained.

[0034] According to the SOC-power coupling constraint mechanism, the power regulation response characteristics of the energy storage power station under different SOC states are mathematically described, and a dynamic response model of the energy storage power station is obtained.

[0035] Specifically, the battery voltage, current, temperature and SOC data in the operation process of the energy storage power station are collected, wherein the battery voltage reflects the current potential state of the energy storage unit, the current represents the charge and discharge power size, the temperature affects the battery performance and safety characteristics, and the SOC, i.e. the state of charge, represents the ratio of the current storage capacity to the full capacity. The four types of parameters are obtained in real time by a sensor array, which includes a voltage sensor for monitoring the battery terminal voltage, a current sensor for measuring the charge and discharge current, a temperature sensor for detecting the battery temperature, and a SOC estimator for calculating the state of charge according to the current integration and voltage characteristics. The four types of data collected are arranged in time sequence to construct a four-dimensional state vector containing voltage, current, temperature and SOC. Each state vector at a time point contains four components, respectively corresponding to the voltage value, current value, temperature value and SOC value at the time point, and a plurality of state vectors form a basic state data set of the energy storage unit.

[0036] The state space decomposition algorithm is used to mathematically model the basic state data set of the energy storage unit. State space decomposition is a method of representing a complex dynamic system as a mathematical relationship between state variables, input variables and output variables. The algorithm defines the battery voltage, current, temperature and SOC as state variables, and the power command and ambient temperature as input variables, and establishes a linear relationship between the derivative of the state variable with respect to time and the state variable and the control input. During the state space decomposition process, the rate of change of the battery voltage is related to the current voltage value, charge and discharge current and SOC state, the rate of change of the current is related to the power command and voltage state, the rate of change of the temperature is related to the current temperature, ambient temperature and current size, and the rate of change of the SOC is related to the charge and discharge current and battery capacity. The coefficients in the state transition matrix are determined by least squares fitting of historical data. Since these coefficients change with the SOC, a dynamic coefficient matrix that changes with the SOC is obtained.

[0037] The dynamic coefficient matrix contains key parameters describing the influence of SOC on the dynamic characteristics of the system, which reflect the variation law of the internal resistance, open-circuit voltage and thermal characteristics of the battery under different SOC states. Based on the SOC-related parameters in the dynamic coefficient matrix, a constraint relationship between the power change rate and the product of the SOC value and the temperature compensation coefficient is established, which describes the variation characteristics of the power regulation capability of the energy storage power station with SOC and temperature. The power change rate constraint mechanism is calculated by extracting the power-related coefficients in the dynamic coefficient matrix, combining the current SOC value and the temperature compensation coefficient. The temperature compensation coefficient is calculated according to the deviation of the current battery temperature from the standard temperature. When the temperature is too high, the compensation coefficient is less than one to reduce the upper limit of the power change rate. When the temperature is too low, the compensation coefficient is greater than one to increase the upper limit of the power change rate. The SOC value directly affects the calculation of the power change rate. When the SOC is low, the power change rate is limited to avoid over-discharge. When the SOC is high, the power change rate is also limited to avoid overcharge. In the medium SOC range, the power change rate reaches the maximum value.

[0038] According to the SOC-power coupling constraint mechanism, the power regulation response characteristics of the energy storage power station under different SOC states are mathematically described, and a dynamic response model of the energy storage power station is established. The dynamic response model takes the SOC-power coupling constraint mechanism as the core constraint condition to describe the actual power output process of the energy storage power station after receiving the power instruction. The model includes four parts: power instruction processing module, SOC state judgment module, temperature compensation module and power output calculation module. The power instruction processing module receives external power instructions and performs preliminary processing. The SOC state judgment module determines the allowed range of power regulation according to the current SOC value. The temperature compensation module modifies the power regulation capability according to the current temperature. The power output calculation module considers the power instruction, SOC constraint and temperature compensation to calculate the actual power output.

[0039] In a specific embodiment, the S2 step includes:

[0040] The battery voltage value, current value and SOC value are extracted from the dynamic response model of the energy storage power station to construct a current-voltage-SOC three-dimensional parameter space, and a three-dimensional protection basic parameter set is obtained;

[0041] Based on the three-dimensional protection basic parameter set, a correlation calculation relationship between the protection current threshold and the battery voltage deviation, SOC deviation and temperature rise rate is established, and a dynamic protection current threshold is obtained;

[0042] According to the dynamic protection current threshold, the corresponding hierarchical protection triggering conditions of different SOC intervals and voltage intervals are set, and a hierarchical protection logic of the energy storage power station is obtained;

[0043] The hierarchical protection logic of the energy storage power station is combined with the protection action time sequence to obtain a protection action model special for energy storage.

[0044] Specifically, the complete construction process of the energy storage special protection action model is constructed. The process of extracting battery voltage value, current value and SOC value from the dynamic response model of the energy storage power station needs to analyze and screen the output data of the dynamic response model. The dynamic response model continuously outputs battery state parameters during simulation running. The data extraction module samples these parameter values at fixed time intervals and stores them in the cache area. The battery voltage value reflects the current potential state of the energy storage unit, the current value represents the charge and discharge power size, and the SOC value represents the state of charge percentage. These three parameters together describe the operating state of the energy storage power station. The three-dimensional parameter space construction process establishes a three-dimensional coordinate system with current as the X-axis, voltage as the Y-axis, and SOC as the Z-axis. The state of the battery at each time corresponds to a space point in the coordinate system, and the state points at multiple times form a trajectory in the three-dimensional parameter space. The three-dimensional protection basic parameter set includes current, voltage and SOC data points at all sampling times. These data points cover the state distribution range of the energy storage power station under various operating conditions, forming the basic data source for protection criterion design.

[0045] Based on the three-dimensional protection basic parameter set, the correlation calculation relationship between the protection current threshold and the battery voltage deviation, SOC deviation and temperature rise rate is established. The correlation relationship represents the protection current threshold as a function of the three deviation quantities through mathematical modeling. The battery voltage deviation is calculated using the difference between the current voltage value and the rated voltage value. A positive voltage deviation indicates a high voltage, a negative voltage deviation indicates a low voltage, and a larger absolute value of the voltage deviation indicates a more serious voltage anomaly. The SOC deviation is calculated using the distance between the current SOC value and the safe SOC range boundary. When the SOC is lower than the safe lower limit, the SOC deviation is negative, and when the SOC is higher than the safe upper limit, the SOC deviation is positive. The absolute value of the SOC deviation reflects the severity of overcharging and overdischarging. The temperature rise rate is calculated by dividing the temperature difference between two consecutive time points by the time interval. A positive temperature rise rate indicates temperature rise, a negative temperature rise rate indicates temperature drop, and a larger absolute value of the temperature rise rate indicates a more severe temperature change. The dynamic protection current threshold is calculated in real time according to the three deviation quantities. When any deviation quantity increases, the protection current threshold decreases accordingly. When all deviation quantities are within the normal range, the protection current threshold remains at the rated value.

[0046] According to the dynamic protection current threshold setting different SOC interval and voltage interval corresponding to the hierarchical protection trigger condition, the hierarchical protection logic adopts hierarchical protection strategy to cope with different severity of abnormal situation. SOC interval division divides the whole SOC range into three levels of normal interval, warning interval and dangerous interval, the normal interval corresponds to the state of SOC in the safe range, the warning interval corresponds to the state of SOC close to the safe boundary, and the dangerous interval corresponds to the state of SOC exceeding the safe range. Voltage interval division also adopts three-layer structure, normal voltage interval corresponds to the state of voltage in the rated range, warning voltage interval corresponds to the state of voltage deviation, and dangerous voltage interval corresponds to the state of voltage deviation. The hierarchical protection trigger condition determines the protection level according to the combination of SOC interval and voltage interval, when the operation state of energy storage power station is located in the normal interval combination, no protection action is triggered, when the operation state is located in the warning interval combination, the first level protection is triggered, and when the operation state is located in the dangerous interval combination, the second or third level protection is triggered. The hierarchical protection logic of energy storage power station establishes a mapping relationship between the trigger condition and the protection level, and forms a protection decision table.

[0047] The hierarchical protection logic of energy storage power station is combined with the protection action time sequence to configure, and the protection action time sequence defines the action time requirement corresponding to different protection levels. The first level protection action time sequence is set as detection delay, judgment delay and execution delay, the detection delay is the response time of the protection device detecting abnormal signal, the judgment delay is the calculation time of the protection logic analyzing the severity of the abnormality, and the execution delay is the transmission time of the protection action output control signal. The action time sequence of the second and third level protection shortens the delay of each stage on the basis of the first level protection, the second level protection requires faster response speed to cope with moderate severity abnormality, and the third level protection requires the fastest response speed to cope with severe abnormality. The protection action model combines the hierarchical protection logic with the time sequence, executes the protection action according to the corresponding time sequence when a certain level of protection is triggered, and the protection action includes power limitation, circuit breaker tripping, system shutdown and other measures of different severity.

[0048] In a specific embodiment, the S3 step comprises:

[0049] The rated power value and the current SOC state value of each energy storage power station are extracted from the energy storage special protection action model, an energy storage power station power-SOC state matrix is constructed, and a power distribution parameter set is obtained;

[0050] The power distribution parameter set is calculated by consistency iteration, the power adjustment amount of each energy storage power station is calculated based on the power difference between the energy storage power stations and the adjacent communication weight, and the target power distribution value of each energy storage power station is obtained;

[0051] Based on the target power distribution value, the SOC equalization control rule and the power constraint condition between the energy storage power stations are established, and the energy storage power station group power equalization distribution strategy is obtained;

[0052] According to the energy storage power station group power balance allocation strategy, a distributed coordination control framework including a communication topology and control logic is constructed, and a simulation model of the energy storage power station group coordination control is obtained.

[0053] Specifically,

[0054] In a specific embodiment, the S4 step includes:

[0055] The voltage and current signal data and the protection action instruction data are extracted from the simulation model of the energy storage power station group coordination control, a simulation output signal data set is constructed, and a hardware interface input data source is obtained.

[0056] The hardware interface input data source is subjected to signal format conversion processing, digital simulation signals are converted into analog signals and digital signals, and standardized hardware interface signals are obtained.

[0057] Based on the standardized hardware interface signals, a clock synchronization protocol is configured, a timing synchronization mechanism between the simulation system and the control and protection hardware is established, and a hardware interface signal synchronization protocol is obtained.

[0058] According to the hardware interface signal synchronization protocol, a hardware connection platform including a signal conditioning circuit and a communication interface is constructed, and a hardware-in-the-loop test platform for the energy storage control and protection hardware is obtained.

[0059] Specifically, the rated power value and the current SOC state value of each energy storage power station are extracted from the energy storage special protection action model by analyzing the data structure of the protection action model. The protection action model internally stores the basic parameters and real-time state information of each energy storage power station, and the data extraction module obtains these parameter values by accessing the data interface of the model. The rated power value represents the maximum charging and discharging power capability determined during the design of the energy storage power station, which reflects the capacity scale and technical level of the energy storage power station. The current SOC state value represents the real-time state of charge percentage of the energy storage power station, which reflects the current available capacity and charging and discharging capability of the energy storage power station. In the process of constructing the energy storage power station power-SOC state matrix, the rated power value is used as the row index of the matrix, and the current SOC state value is used as the column index of the matrix. Each element in the matrix represents the actual available power of the corresponding energy storage power station at the current SOC state, which is calculated by multiplying the rated power value by the SOC-related correction coefficient. The power allocation parameter set includes the rated power value, the current SOC state value, and the actual available power value of all energy storage power stations, which constitute the basic data for the power allocation calculation of multiple energy storage power stations.

[0060] The consistency iterative calculation algorithm coordinates and optimizes the power distribution parameter set. The consistency algorithm is a classic algorithm in the field of distributed control, and the system reaches a globally consistent state through information exchange between multiple nodes. The power difference between energy storage stations is calculated by the difference between the current power output and the average power output of each energy storage station. A positive power difference indicates that the power output of the energy storage station is higher than the average level, and a negative power difference indicates that the power output of the energy storage station is lower than the average level. The adjacent communication weight reflects the communication connection strength and information transmission capacity between energy storage stations. The weight value is determined according to the physical distance, communication delay and connection reliability between energy storage stations. The greater the weight value, the stronger the communication connection, and the smaller the weight value, the weaker the communication connection. The power adjustment amount of each energy storage station is calculated by multiplying the current power difference by the adjacent communication weight and then by the consistency gain coefficient. The consistency gain coefficient controls the convergence speed and stability of power adjustment. An excessively large gain coefficient leads to system oscillation, and an excessively small gain coefficient leads to slow convergence. The target power distribution value of each energy storage station is calculated by adding the current power output to the power adjustment amount. The iterative calculation process is repeated until the power difference of all energy storage stations converges to a preset threshold range.

[0061] Based on the target power distribution value, the SOC equalization control rule and the power constraint condition between energy storage stations are established. The SOC equalization control rule ensures that the SOC levels of multiple energy storage stations remain relatively balanced. The SOC equalization control rule uses an SOC deviation compensation mechanism. When the SOC of a certain energy storage station is significantly higher than that of other energy storage stations, the energy storage station undertakes more discharging tasks. When the SOC of a certain energy storage station is significantly lower than that of other energy storage stations, the energy storage station undertakes more charging tasks. The power constraint condition includes single-station power constraint and total power constraint. The single-station power constraint limits the power output of each energy storage station to be less than the smaller value of its rated power and current available power. The total power constraint limits the sum of the power outputs of all energy storage stations to equal the external power instruction. The energy storage station group power equalization distribution strategy combines the SOC equalization control rule and the power constraint condition to form a solution strategy for the multi-objective optimization problem. The strategy tries to achieve SOC equalization while meeting the power constraint.

[0062] A distributed coordination control framework is constructed according to the power balancing allocation strategy of the energy storage power station group, which includes two core components of communication topology and control logic. The communication topology describes the information transmission network structure between energy storage power stations. The topology structure adopts a directed graph representation, where each node represents an energy storage power station, and the directed edge represents the communication link between the energy storage power stations. The weight of the edge represents the strength of the communication connection. The control logic defines the algorithm flow of each energy storage power station to calculate its own power output according to the received neighbor node information. The control logic includes three parts: information receiving module, local calculation module and output execution module. The information receiving module obtains the power and SOC information of the neighbor energy storage power station. The local calculation module calculates the target power according to the consistency algorithm and the balancing strategy. The output execution module converts the target power into the actual power control instruction. The coordination control simulation model of the energy storage power station group integrates the communication topology and the control logic to form an executable simulation program.

[0063] In a specific embodiment, the S5 step comprises:

[0064] A multi-scenario fault library containing power grid faults, device faults and operating conditions is constructed in the energy storage control and protection hardware-in-the-loop test platform to obtain an energy storage power station test scenario database;

[0065] Test case execution sequences are generated based on the energy storage power station test scenario database, the fault operating conditions are automatically switched according to the preset timing, and the control and protection system response data are recorded to obtain an energy storage control and protection system verification data set;

[0066] The performance index calculation is performed on the energy storage control and protection system verification data set to analyze the protection action time, control response accuracy and power regulation deviation, and the performance parameters of the energy storage control and protection system are obtained;

[0067] The performance parameters of the energy storage control and protection system are compared with the preset threshold value for comparative analysis, and a comprehensive evaluation report containing test coverage and improvement suggestions is generated to obtain the performance evaluation results of the energy storage control and protection system.

[0068] Specifically, constructing a multi-scenario fault library in the energy storage control and protection hardware-in-the-loop test platform requires systematic classification and modeling of various abnormal situations encountered during the operation of the energy storage power station. Grid faults include voltage sag, frequency deviation, phase-to-phase short circuit, single-phase grounding, and other grid-side abnormalities. Each fault type needs to define parameters such as fault duration, fault severity, and fault location. Device faults include battery over-temperature protection, converter over-current protection, DC side grounding fault, communication interruption fault, and other device-level abnormalities. The modeling of device faults needs to consider the physical mechanism of fault occurrence and the range of fault influence. Operating conditions include normal charging and discharging process, power regulation response, SOC management strategy, and multi-power station coordinated operation, etc. The modeling of operating conditions needs to reflect the dynamic characteristics of the energy storage power station under different external conditions. The multi-scenario fault library uses a database structure to store the parameter configurations of these faults and operating conditions. Each scenario corresponds to a record in the database, which contains fields such as scenario type, parameter settings, and expected response. The energy storage power station test scenario database generates specific test scenarios by querying and combining these records.

[0069] Based on the energy storage power station test scenario database, test case execution sequences are generated. The test case execution sequences use a time-driven approach to organize multiple test scenarios. The sequence generation algorithm selects appropriate test scenarios from the scenario database based on test coverage requirements and priority strategies. The test case generation process first analyzes the importance and probability of each test scenario. High-priority scenarios include severe faults that may cause safety risks, medium-priority scenarios include general faults that affect system performance, and low-priority scenarios include rare but need to be verified special operating conditions. The sequence arrangement algorithm arranges the selected test scenarios in order from simple to complex and from common to rare, ensuring the logicality and progressiveness of the test process. The preset timing defines the execution time and switching interval of each test scenario. Timing design needs to consider the response time of the control and protection system and the completeness of data recording. If the execution time is too short, the response process cannot be observed, and if the execution time is too long, the test efficiency will be affected. Automatic switching of fault conditions sends scene switching instructions to the simulation model through the test platform control interface. The control interface triggers the update of scene parameters according to the preset timing, and the simulation model recalculates the operating state of the energy storage power station after receiving the new scene parameters. Recording the response data of the control and protection system includes the input signals, output signals, internal state variables, and action timestamps of the control and protection devices. The data recording module collects these data at a fixed sampling frequency and stores them in the verification data set.

[0070] The performance index calculation algorithm extracts key information from the verification dataset and performs statistical analysis. The protection action time analysis extracts the timestamps of the fault occurrence time and the protection action completion time from the dataset, calculates the protection response time by calculating the time difference, and analyzes the process of identifying the trigger conditions and completion flags of different types of protection actions. The action times of the first-level protection, the second-level protection, and the third-level protection are counted separately. The control response accuracy analysis extracts the power instruction value and the actual power output value from the dataset, evaluates the accuracy performance of the control system by calculating the instruction tracking error, and calculates the error using absolute error and relative error. The absolute error reflects the absolute size of the power deviation, and the relative error reflects the proportion of the power deviation to the instruction value. The power regulation deviation analysis extracts the dynamic response curve of the power regulation process from the dataset, evaluates the power regulation performance by analyzing the overshoot, regulation time, and steady-state error, and reflects the overshoot degree of the power response, the speed of the power reaching steady state, and the final tracking accuracy of the power.

[0071] According to the comparison and analysis of the performance parameters of the energy storage control and protection system and the preset threshold, the comparison and analysis algorithm compares the measured performance parameters with the threshold values specified by the design requirements or industry standards one by one. The preset threshold values include protection action time threshold values, control accuracy threshold values, power regulation performance threshold values, and other aspects. These threshold values are determined according to the technical specifications and safety requirements of the energy storage power station. The comparison and analysis process calculates the compliance rate of each performance index, which is equal to the number of test cases that meet the threshold requirements divided by the total number of test cases. The compliance rate reflects the reliability level of the control and protection system under different working conditions. The test coverage rate statistical analysis covers the types of test scenarios and parameter ranges in the verification dataset. The coverage rate calculation uses the number of scenario coverage divided by the total number of scenarios. High coverage rate indicates the comprehensiveness and sufficiency of the test verification. The improvement suggestion generation algorithm proposes targeted optimization suggestions based on the non-compliance items and deviation levels of the performance parameters. The suggestion content includes parameter adjustment direction, algorithm improvement idea, hardware configuration optimization, and other aspects. The comprehensive evaluation report integrates the comparison and analysis results, coverage rate statistics, and improvement suggestions to form a structured evaluation document. The performance evaluation results of the energy storage control and protection system provide decision-making basis for the engineering application and continuous improvement of the energy storage power station.

[0072] In a specific embodiment, the performance index calculation process of the energy storage control and protection system verification dataset can specifically include the following steps:

[0073] Extract the timestamps of the protection trigger time and the protection action completion time from the energy storage control and protection system verification dataset, calculate the time difference, and obtain the protection action time data;

[0074] Based on the statistical analysis of the protection action time data, the average response time, the maximum response time and the response time standard deviation are calculated to obtain the protection time performance index;

[0075] The deviation between the power instruction value and the actual power output value in the energy storage control and protection system verification data set is calculated to analyze the steady-state error and dynamic overshoot, and the control accuracy performance index is obtained;

[0076] The protection time performance index and the control accuracy performance index are comprehensively analyzed to obtain the performance parameters of the energy storage control and protection system.

[0077] Specifically, the timestamp data of the protection trigger time and the protection action completion time extracted from the energy storage control and protection system verification data set need to analyze and identify the time sequence data of the verification data set. The verification data set contains all signal changes and state transition information recorded during the test process, and the timestamp data records the absolute time of each event occurrence using a high-precision timer. The protection trigger time identification algorithm determines the trigger moment by monitoring the state change of the protection criterion signal. When the protection criterion changes from normal state to abnormal state, the time is recorded as the protection trigger time. The identification of the trigger time needs to consider the influence of signal noise and filtering delay. The protection action completion time identification algorithm determines the action completion moment by monitoring the state change of the protection output signal. When the protection output changes from standby state to action state, the time is recorded as the protection action completion time. The action completion determination criteria include circuit breaker tripping signal, power limitation instruction, system shutdown instruction and other types of protection measures. The time difference value calculation uses the simple subtraction operation of the protection action completion time minus the protection trigger time. The time difference value reflects the total time consumption of the protection device from detecting abnormalities to executing protection measures. The protection action time data contains the response time records of various protection types in all test cases.

[0078] Based on the statistical analysis of the protection action time data, the statistical analysis algorithm performs mathematical processing on the collected multiple protection action time values. The average response time calculation adopts the arithmetic mean of all protection action time values, and the calculation process adds all time values and divides by the total number. The average response time reflects the typical response speed level of the protection device. The maximum response time calculation adopts the maximum value of all protection action time values, and the maximum response time reflects the response speed of the protection device under the most unfavorable conditions. This index is used to evaluate the lower limit of the reliability of the protection device. The response time standard deviation calculation adopts the square root of variance method. First, the square of the difference between each time value and the average value is calculated. Then, the sum of all squared differences is added to the total number to obtain the variance. Finally, the square root of the variance is obtained to obtain the standard deviation. The standard deviation reflects the dispersion and consistency level of the protection action time. The protection time performance index integrates the average response time, the maximum response time and the response time standard deviation into a comprehensive index set describing the protection performance. These indexes reflect the time characteristics of the protection device from different angles.

[0079] Referring to Figure 2 , it is a schematic diagram of protection action time performance analysis of the energy storage power station control protection system. As shown in Figure 2 (a), the box plot shows the protection response time distribution characteristics of five fault types including voltage drop, overcurrent trigger, temperature anomaly, SOC overrun and communication interruption, and the 200ms performance threshold is marked with a red dashed line. The statistical results show that the response times of various faults differ significantly: voltage drop and overcurrent trigger respond the fastest (60-110ms), temperature anomaly and SOC overrun are in the middle (100-180ms), and communication interruption is the slowest (250-420ms). As shown in Figure 2 (b), the compliance rate statistics show that the 200ms compliance rates of voltage drop, overcurrent trigger, temperature anomaly and SOC overrun are all above 95%, verifying the effectiveness of the protection time performance index calculation method.

[0080] The deviation calculation algorithm synchronously extracts the time series data of the power instruction signal and the power output signal from the verification data set. The power instruction value represents the power control command issued by the control system to the energy storage device, which reflects the expected output of the control system. The actual power output value represents the actual power output of the energy storage device, which is collected in real time by the power measurement device. The deviation calculation uses the actual power output value minus the power instruction value. A positive deviation indicates that the actual output is higher than the instruction value, and a negative deviation indicates that the actual output is lower than the instruction value. The absolute value of the deviation reflects the size of the control error. The steady-state error analysis extracts the error value after the power regulation process reaches a stable state from the deviation data. The steady-state error calculation uses the average value of the deviation at multiple sampling points in the stable state. The steady-state error reflects the final tracking accuracy of the control system. The dynamic overshoot analysis extracts the maximum deviation value in the power regulation process from the deviation data. The overshoot calculation uses the maximum value of the absolute value of the deviation minus the absolute value of the steady-state error. The overshoot reflects the overshoot degree of the dynamic response of the control system. The control accuracy performance index integrates the steady-state error and the dynamic overshoot into a comprehensive index set that describes the control performance.

[0081] The protection time performance index and the control accuracy performance index are comprehensively analyzed. The correlation analysis and weight distribution algorithm is used to analyze the correlation between the two types of performance indicators. The correlation analysis checks the relationship between the protection time index and the control accuracy index. The correlation coefficient is calculated to evaluate the correlation degree of the two types of indicators. A positive correlation indicates that the longer the protection time, the worse the control accuracy. A negative correlation indicates that the protection time and the control accuracy are inversely related. No correlation indicates that the two indicators are independent of each other. The weight distribution assigns importance weights to different indicators according to the application requirements and safety requirements of the energy storage power station. In safety-critical application scenarios, the protection time index has a higher weight, and in application scenarios with strict control accuracy requirements, the control accuracy index has a higher weight. The comprehensive score calculation uses the weighted average method to integrate the indicators into a single comprehensive performance score. The calculation process multiplies each indicator value by the corresponding weight and adds them together to get the total score. The performance parameters of the energy storage control and protection system include the protection time performance index, the control accuracy performance index, and the comprehensive performance score, forming a performance description system.

[0082] The above examples are only used to illustrate the technical solutions of the present application, and are not limited thereto. Although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that they can modify the technical solutions described in the foregoing examples, or make equivalent substitutions for part of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A hardware-in-the-loop simulation test method for a control and protection system of an energy storage power station, characterized in that, The method comprises: S1 step, obtaining energy storage power station operation data, using energy storage unit state space decomposition algorithm to establish SOC-power coupling constraint mechanism, constructing energy storage power station dynamic response model; S2 step, based on the battery state parameters in the energy storage power station dynamic response model, a current-voltage-SOC three-dimensional protection criterion is established, and a special protection action model for energy storage is generated; S3 step, extracting the power distribution parameters in the special protection action model for energy storage, using distributed coordinated control algorithm to calculate the power balance distribution among multiple energy storage power stations, forming an energy storage power station group coordinated control simulation model; S4 step, calling the simulation data of the energy storage power station group coordinated control simulation model, configuring hardware interface signal synchronization protocol, and establishing an energy storage control and protection hardware-in-the-loop test platform; S5 step, loading multiple scene fault conditions in the energy storage control and protection hardware-in-the-loop test platform, executing energy storage control and protection system automatic verification process, and outputting energy storage control and protection system performance evaluation results.

2. The hardware-in-the-loop simulation test method of the energy storage power station protection system according to claim 1, characterized in that, The S1 step comprises: Collecting battery voltage, current, temperature and SOC data of the energy storage power station, constructing a four-dimensional state vector containing voltage, current, temperature and SOC, and obtaining an energy storage unit basic state data set; State space decomposition is performed on the energy storage unit basic state data set to establish a linear relationship between the time derivative of the state variable and the state variable and the control input, and a dynamic coefficient matrix varying with SOC is obtained; Based on the SOC related parameters in the dynamic coefficient matrix, a power change rate is established with the product of the SOC value and the temperature compensation coefficient, and a SOC-power coupling constraint mechanism is obtained; According to the SOC-power coupling constraint mechanism, the power regulation response characteristics of the energy storage power station under different SOC states are mathematically described, and an energy storage power station dynamic response model is obtained.

3. The hardware-in-the-loop simulation test method of the energy storage power station protection system according to claim 1, characterized in that, The S2 step comprises: Extracting battery voltage, current and SOC value from the energy storage power station dynamic response model, constructing a current-voltage-SOC three-dimensional parameter space, and obtaining a three-dimensional protection basic parameter set; Based on the three-dimensional protection basic parameter set, a protection current threshold is established with the associated calculation relationship of battery voltage deviation, SOC deviation and temperature rise rate, and a dynamic protection current threshold is obtained; According to the dynamic protection current threshold, the trigger conditions of different SOC intervals and voltage intervals are set, and the energy storage power station grading protection logic is obtained; The energy storage power station grading protection logic and the protection action time sequence are combined and configured to obtain a special protection action model for energy storage.

4. The hardware-in-the-loop simulation test method of the energy storage power station protection system according to claim 1, characterized in that, The S3 step comprises: From the special protection action model for energy storage, the rated power value and the current SOC state value of each energy storage power station are extracted, a power-SOC state matrix of energy storage power station is constructed, and a power distribution parameter set is obtained; Iterative calculation is performed on the power distribution parameter set, and the power adjustment amount of each energy storage power station is calculated based on the power difference value and the adjacent communication weight between energy storage power stations, and the target power distribution value of each energy storage power station is obtained; Based on the target power distribution value, SOC equalization control rules and power constraint conditions between energy storage power stations are established, and energy storage power station group power balance distribution strategy is obtained; According to the energy storage power station group power balance allocation strategy, a distributed coordination control framework including a communication topology and control logic is constructed to obtain a simulation model of the energy storage power station group coordination control.

5. The hardware-in-the-loop simulation test method of the energy storage power station protection system according to claim 1, characterized in that, The S4 step includes: Extracting voltage and current signal data and protection action instruction data from the energy storage power station group coordination control simulation model, constructing a simulation output signal data set, and obtaining a hardware interface input data source; Performing signal format conversion processing on the hardware interface input data source to convert digital simulation signals into analog and digital signals, and obtaining standardized hardware interface signals; Based on the standardized hardware interface signals, a clock synchronization protocol is configured to establish a timing synchronization mechanism between the simulation system and the protection hardware, and a hardware interface signal synchronization protocol is obtained; According to the hardware interface signal synchronization protocol, a hardware connection platform including a signal conditioning circuit and a communication interface is constructed to obtain a hardware-in-the-loop test platform for energy storage protection hardware.

6. The hardware-in-the-loop simulation test method of the energy storage power station protection system according to claim 1, characterized in that, The S5 step includes: In the energy storage protection hardware-in-the-loop test platform, a multi-scenario fault library including power grid faults, device faults and operating conditions is constructed to obtain an energy storage power station test scenario database; Based on the energy storage power station test scenario database, a test case execution sequence is generated, the fault conditions are automatically switched according to the preset timing, and the protection system response data is recorded to obtain a verification data set of the energy storage protection system; Performance index calculation is performed on the energy storage protection system verification data set to analyze protection action time, control response accuracy and power regulation deviation to obtain performance parameters of the energy storage protection system; According to the comparison analysis of the performance parameters of the energy storage protection system and the preset threshold, a comprehensive evaluation report including test coverage and improvement suggestions is generated to obtain performance evaluation results of the energy storage protection system.

7. The hardware-in-the-loop simulation test method of the energy storage power station protection system according to claim 6, characterized in that, The performance index calculation on the energy storage protection system verification data set, the analysis of protection action time, control response accuracy and power regulation deviation, and the performance parameters of the energy storage protection system include: From the energy storage protection system verification data set, timestamp data of protection trigger time and protection action completion time are extracted, time difference is calculated, and protection action time data is obtained; Statistical analysis is performed based on the protection action time data to calculate average response time, maximum response time and response time standard deviation to obtain protection time performance indicators; The deviation between the power instruction value and the actual power output value in the energy storage protection system verification data set is calculated to analyze steady-state error and dynamic overshoot to obtain control accuracy performance indicators; The protection time performance indicators and the control accuracy performance indicators are comprehensively analyzed to obtain performance parameters of the energy storage protection system.

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