Hardware-in-the-loop simulation test method of energy storage power station control and protection system

Abstract

The invention relates to the technical field of digital simulation, and discloses a hardware-in-the-loop simulation test method for a control and protection system of an energy storage power station. The method comprises the following steps: acquiring operation data of an energy storage power station, and establishing an SOC-power coupling constraint mechanism by adopting a state space decomposition algorithm; establishing a current-voltage-SOC three-dimensional protection criterion based on the battery state parameters; calculating power balance distribution of the multiple energy storage power stations by adopting a distributed coordination control algorithm; configuring a hardware interface signal synchronization protocol to establish a test platform; and loading a multi-scene fault working condition to execute automatic verification and outputting a performance evaluation result. The technical problem that the energy storage power station control and protection system lacks a high-precision, multi-scene and hardware-in-the-loop integrated simulation test verification method is solved, and the precision, efficiency and safety of test verification of the energy storage power station control and protection system are improved.

Description

Technical Field

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

[0002] With the rapid advancement of new power system construction, energy storage power stations, as a key technical means for grid frequency and peak regulation and renewable energy absorption, face a direct impact on the safe and stable operation of the grid. The reliability and responsiveness of their control and protection systems directly impact the safety and stability of the grid. Existing technologies primarily verify the control and protection systems of energy storage power stations using traditional hardware testing methods and simple digital simulation techniques. These methods verify the correctness of the control and protection logic through repeated debugging on actual energy storage equipment. These methods can, to a certain extent, verify the basic functionality of energy storage power station control and protection systems, providing technical support for their safe operation.

[0003] However, existing technologies have significant shortcomings. Traditional hardware testing methods require repeated debugging at the energy storage power station site, which is costly, time-consuming, and carries safety risks, making it difficult to cover all possible failure scenarios. Furthermore, existing digital simulation technologies lack specialized modeling methods tailored to the unique characteristics of energy storage systems, making it impossible to accurately simulate complex behaviors such as the SOC-power coupling characteristics, multi-timescale dynamic responses, and coordinated control of energy storage power station clusters. This results in insufficient simulation accuracy and significant deviations between test results and actual operating conditions. Furthermore, existing simulation testing methods generally employ a purely software-based simulation model, lacking effective integration with real-world control and maintenance hardware, making it impossible to verify the timing synchronization performance between the hardware and simulation systems.

[0004] Based on the above analysis of the current state of the art, we can infer the progressive technical challenges facing the verification and testing of energy storage power station control and protection systems: 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 between the SOC state and power output; second, how to construct dedicated protection criteria and test verification methods for the unique failure modes of energy storage systems; then, how to solve the group simulation modeling and verification problems of coordinated control of multiple energy storage power stations; and finally, how to achieve precise synchronization between the simulation system and the actual control and protection hardware to build a hardware-in-the-loop test and verification platform. Summary of the Invention

[0005] The present application provides a hardware-in-the-loop simulation test method for an energy storage power station control and protection system, which is used to solve the technical problem that the energy storage power station control and protection system lacks a high-precision, multi-scenario, hardware-in-the-loop integrated simulation test and verification method, thereby improving the accuracy, efficiency and safety of the test and verification of the energy storage power station control and protection system.

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

[0007] Step S1: Obtain the operating data of the energy storage power station, use the energy storage unit state space decomposition algorithm to establish the SOC-power coupling constraint mechanism, and build the dynamic response model of the energy storage power station;

[0008] Step S2: establishing a current-voltage-SOC three-dimensional protection criterion based on the battery state parameters in the dynamic response model of the energy storage power station, and generating a dedicated energy storage protection action model;

[0009] Step S3: extract the power distribution parameters in the energy storage dedicated protection action model, use a distributed coordinated control algorithm to calculate the balanced power distribution among multiple energy storage power stations, and form a coordinated control simulation model for the energy storage power station group;

[0010] Step S4: calling the simulation data of the energy storage power station group coordinated control simulation model, configuring the hardware interface signal synchronization protocol, and establishing an energy storage control and protection hardware-in-the-loop test platform;

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

[0012] The technical solution provided in this application effectively addresses the technical difficulty of traditional simulation methods in accurately describing the dynamic characteristics of energy storage systems by establishing a SOC-power coupling constraint mechanism using a state-space decomposition algorithm for energy storage units. This algorithm can decompose the complex dynamic behavior of energy storage power stations into mathematical relationships between state variables, input variables, and output variables. In particular, by establishing a constraint relationship between the power change rate and the product of the SOC value and the temperature compensation coefficient, it achieves an accurate mathematical description of the power regulation response characteristics of the energy storage station under different SOC states. At the same time, a dedicated energy storage protection action model based on the three-dimensional protection criteria of current, voltage, and SOC breaks through the limitations of traditional protection systems that only consider a single electrical quantity. Through comprehensive judgment in the three-dimensional parameter space, it can more accurately identify failure modes unique to energy storage systems, such as overcharge, over-discharge, and thermal runaway, significantly improving the reliability and selectivity of protection actions. In addition, the introduction of a distributed coordinated control algorithm solves the technical challenge of balanced power distribution among multiple energy storage power stations. Through consistent iterative calculation and SOC balancing control rules, it achieves coordinated and optimized operation of energy storage power station groups, avoiding the phenomenon of excessive charging and discharging at a single station. The configuration of the hardware interface signal synchronization protocol ensures precise timing synchronization between the simulation system and the control and protection hardware, overcoming the test error problem caused by timing deviation in traditional hardware-in-the-loop systems and providing 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 well-suited for modeling the multi-timescale characteristics of energy storage systems. Its algorithmic features can simultaneously handle millisecond-level electromagnetic transients and second-level power regulation processes, providing unprecedented modeling accuracy for energy storage power station control and protection systems. The three-dimensional protection criterion algorithm is specifically optimized for the physical constraints of energy storage systems. Its multi-dimensional judgment mechanism can effectively distinguish between normal operating conditions and various abnormal conditions of energy storage systems, offering greater adaptability and accuracy than traditional single-threshold protection methods. The consistent nature of the distributed coordinated control algorithm ensures excellent scalability and robustness in energy storage power station cluster applications, adapting to the control requirements of energy storage power station clusters of varying sizes and configurations. Its distributed architecture avoids the single-point failure risk of centralized control. The automated verification process for multi-scenario fault conditions ensures the reliability of the energy storage control and protection system under various complex operating conditions through systematic test scenario coverage. Its automated features significantly improve testing efficiency and reduce the uncertainty associated with manual operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 This is a schematic diagram of an embodiment of a hardware-in-the-loop simulation test method for an energy storage power station control and protection system in an embodiment of the present application;

[0016] Figure 2 This is a schematic diagram of the protection action time performance analysis of the energy storage power station control and protection system in the embodiment of the present application. DETAILED DESCRIPTION

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

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

[0019] Step S1: Obtain the operating data of the energy storage power station, use the energy storage unit state space decomposition algorithm to establish the SOC-power coupling constraint mechanism, and build the dynamic response model of the energy storage power station;

[0020] Step S2: Based on the battery state parameters in the dynamic response model of the energy storage power station, a current-voltage-SOC three-dimensional protection criterion is established to generate a dedicated energy storage protection action model;

[0021] Step S3: extract the power distribution parameters in the energy storage dedicated protection action model, use the distributed coordinated control algorithm to calculate the balanced power distribution among multiple energy storage power stations, and form a coordinated control simulation model for the energy storage power station group;

[0022] Step S4: Call the simulation data of the coordinated control simulation model of the energy storage power station group, configure the hardware interface signal synchronization protocol, and establish the energy storage control and protection hardware-in-the-loop test platform;

[0023] In step S5, multiple fault scenarios are loaded into the energy storage control and protection hardware-in-the-loop test platform, the energy storage control and protection system automated verification process is executed, and the energy storage control and protection system performance evaluation results are output.

[0024] It is understandable that the execution subject of this application can be a hardware-in-the-loop simulation test system of the energy storage power station control and protection system, or a terminal or server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0025] Specifically, the battery voltage, current, temperature and SOC data are collected during the operation of the energy storage power station. SOC represents the battery state of charge, reflecting the ratio of the current stored power of the battery to the full power. The collected four-dimensional state vector contains voltage, current, temperature and SOC values, forming a basic state data set of the energy storage unit. The data set is then subjected to state space decomposition processing to establish a linear relationship between the time derivative of the state variable and the state variable and control input. The state space decomposition algorithm decomposes the complex dynamic characteristics of the energy storage system into multiple interrelated state variables. The rate of change of each state variable has a mathematical relationship with the current state and the external control input, thereby obtaining a dynamic coefficient matrix that changes with SOC. Based on the SOC-related parameters in the dynamic coefficient matrix, a constraint relationship is established between the power change rate and the product of the SOC value and the temperature compensation coefficient. This constraint mechanism ensures that the power output of the energy storage power station strictly follows the physical characteristics of the battery, and finally constructs the dynamic response model of the energy storage power station.

[0026] A three-dimensional protection criterion based on current, voltage, and SOC is established based on the battery state parameters in the energy storage power station's dynamic response model. Battery voltage, current, and SOC values ​​are extracted from the energy storage power station's dynamic response model to construct a three-dimensional parameter space, where each dimension corresponds to a range of current, voltage, and SOC values, forming a three-dimensional protection basic parameter set. Based on this parameter set, a correlation calculation relationship is established between the protection current threshold and the battery voltage deviation, SOC deviation, and temperature rise rate. The protection current threshold is dynamically adjusted based on the current battery state. When the battery voltage deviates from the rated value, the SOC exceeds the safe range, or the temperature changes rapidly, the protection current threshold is lowered accordingly to ensure system safety. Based on the dynamic protection current threshold, hierarchical protection trigger conditions are set for different SOC and voltage ranges. The hierarchical protection logic includes a hierarchical protection strategy with level 1 protection for minor anomalies, level 2 protection for moderate faults, and level 3 protection for severe faults. The hierarchical protection logic of the energy storage power station is combined with the protection action time series to generate a dedicated energy storage protection action model.

[0027] Power allocation parameters are extracted from the energy storage-specific protection action model. The rated power and current SOC value of each energy storage station are extracted from the protection action model. A power-SOC matrix is ​​constructed to describe the available power range of each energy storage station at different SOC states. A consistency algorithm is iteratively calculated for the power allocation parameter set. This algorithm calculates the power adjustment for each energy storage station based on the power difference between energy storage stations and the adjacent communication weight. This consistency algorithm iteratively ensures that the power output of all energy storage stations converges, while also considering the SOC state and communication topology of each station. Based on the target power allocation value, SOC balancing control rules and power constraints are established between energy storage stations. The SOC balancing control rules ensure that the SOC levels of each energy storage station remain relatively balanced, preventing excessive charging and discharging at some stations. The power constraints limit the range and rate of power change for each station. Based on the power balancing allocation strategy for the energy storage station group, a distributed coordinated control framework, including communication topology and control logic, is constructed to form a coordinated control simulation model for the energy storage station group.

[0028] Call simulation data from the coordinated control simulation model for the energy storage power station group and configure the hardware interface signal synchronization protocol. Extract voltage and current signal data and protection action instruction data from the simulation model to construct a simulation output signal dataset containing various electrical quantities and control instructions obtained from the simulation calculations. Perform signal format conversion on the hardware interface input data source, converting digital simulation signals into analog and digital signals. Analog signals use voltage or current to represent continuously changing physical quantities, while digital signals use high and low levels to represent switch states or protection signals. Configure a clock synchronization protocol based on standardized hardware interface signals to establish a timing synchronization mechanism between the simulation system and the control and protection hardware. The clock synchronization protocol ensures strict synchronization between the simulation calculation step size and the hardware response time, avoiding test errors caused by timing deviations. Based on the hardware interface signal synchronization protocol, construct a hardware connection platform containing signal conditioning circuits and communication interfaces, and establish an energy storage control and protection hardware-in-the-loop test platform.

[0029] Multiple fault scenarios are loaded into the energy storage control and protection hardware-in-the-loop (HIL) test platform. A multi-scenario fault library, encompassing grid faults, equipment faults, and operating conditions, is constructed within the test platform. Grid faults include abnormalities such as voltage drop, frequency deviation, and phase-to-phase short circuits. Equipment faults include battery overtemperature, converter overcurrent, and DC-side grounding. Operating conditions encompass typical operational scenarios such as normal charging and discharging, power regulation, and SOC management. Test case execution sequences are generated based on the energy storage power station test scenario database. Fault conditions are automatically switched according to a preset sequence, and the control and protection system response data is recorded. Test sequences are sorted by fault severity and probability to ensure coverage of all key scenarios. Performance metrics are calculated for the energy storage control and protection system validation dataset, analyzing protection action time, control response accuracy, and power regulation deviation. Protection action time is calculated by recording the time difference between fault occurrence and protection action completion. Control response accuracy is calculated by comparing the deviation between power command values ​​and actual output values. Energy storage control and protection system performance parameters are compared and analyzed against preset thresholds. A comprehensive evaluation report is generated, including test coverage and improvement recommendations, and the performance evaluation results of the energy storage control and protection system are output.

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

[0031] Collect battery voltage, current, temperature, and SOC data from the energy storage power station, construct a four-dimensional state vector including voltage, current, temperature, and SOC, and obtain a basic state data set for the energy storage unit;

[0032] Perform state space decomposition on the basic state data set of the energy storage unit, establish a linear relationship between the time derivative of the state variable and the state variable and control input, and obtain a dynamic coefficient matrix that changes with SOC;

[0033] 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 to obtain the SOC-power coupling constraint mechanism;

[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 the dynamic response model of the energy storage power station is obtained.

[0035] Specifically, battery voltage, current, temperature, and SOC data are collected during the operation of the energy storage power station. Battery voltage reflects the current potential state of the energy storage unit, current indicates the charge and discharge power, and temperature affects battery performance and safety characteristics. SOC (State of Charge) represents the ratio of the battery's current stored power to its full capacity. Data acquisition acquires these four parameters in real time through a sensor array. The sensor array includes a voltage sensor to monitor the battery terminal voltage, a current sensor to measure the charge and discharge current, a temperature sensor to detect the battery temperature, and an SOC estimator to calculate the state of charge based on the current integral and voltage characteristics. The four types of data collected are arranged in a time series to construct a four-dimensional state vector containing voltage, current, temperature, and SOC. The state vector at each moment contains four components, corresponding to the voltage, current, temperature, and SOC values ​​at that moment. The state vectors at multiple moments constitute the basic state data set of the energy storage unit.

[0036] The state-space decomposition algorithm mathematically models the energy storage unit's basic state data set. State-space decomposition represents a complex dynamic system as a mathematical relationship between state variables, input variables, and output variables. The algorithm defines battery voltage, current, temperature, and SOC as state variables, and power command and ambient temperature as input variables. It establishes a linear relationship between the time derivatives of the state variables and the state variables and control inputs. During the state-space decomposition process, the rate of change of battery voltage is related to the current voltage value, charge / discharge current, and SOC; the rate of change of current is related to the power command and voltage state; the rate of change of temperature is related to the current temperature, ambient temperature, and current; and the rate of change of SOC is related to the charge / discharge current and battery capacity. The coefficients in the state transition matrix are determined by fitting historical data using the least squares method. Since these coefficients vary with SOC, a dynamic coefficient matrix that varies with SOC is obtained.

[0037] The dynamic coefficient matrix contains key parameters that describe the impact of SOC on the system's dynamic characteristics. These parameters reflect the variations in battery internal resistance, open-circuit voltage, and thermal characteristics at different SOC states. Based on the SOC-related parameters in the dynamic coefficient matrix, a constraint relationship is established between the power rate of change and the product of the SOC value and the temperature compensation coefficient. This constraint describes the power regulation capability of the energy storage power station as it varies with SOC and temperature. The power rate of change constraint mechanism extracts the power-related coefficients from the dynamic coefficient matrix and calculates them based on the current SOC value and the temperature compensation coefficient. The temperature compensation coefficient is calculated based on the deviation between the current battery temperature and the reference temperature. When the temperature is high, the compensation coefficient is less than -, lowering the upper limit of the power rate of change. When the temperature is low, the compensation coefficient is greater than -, raising the upper limit of the power rate of change. The SOC value directly influences the calculation of the power rate of change. At low SOC, the power rate of change is limited to prevent over-discharge. At high SOC, the power rate of change is also limited to prevent over-charging. The power rate of change reaches its maximum value in the medium SOC range.

[0038] Based on the SOC-power coupling constraint mechanism, a mathematical description of the power regulation response characteristics of the energy storage power station under different SOC states is conducted, and a dynamic response model of the energy storage power station is established. The dynamic response model uses the SOC-power coupling constraint mechanism as the core constraint condition and describes the actual power output process of the energy storage power station after receiving the power command. The model consists of four parts: a power command processing module, an SOC state judgment module, a temperature compensation module, and a power output calculation module. The power command processing module receives external power commands and performs preliminary processing. The SOC state judgment module determines the allowable range of power regulation based on the current SOC value. The temperature compensation module corrects the power regulation capability based on the current temperature. The power output calculation module calculates the actual power output by comprehensively considering the power command, SOC constraint, and temperature compensation.

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

[0040] Extract battery voltage, current, and SOC values ​​from the dynamic response model of the energy storage power station, construct a three-dimensional parameter space of current-voltage-SOC, and obtain a three-dimensional protection basic parameter set;

[0041] 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 to obtain the dynamic protection current threshold;

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

[0043] The hierarchical protection logic of energy storage power stations and the protection action time sequence are combined and configured to obtain a dedicated protection action model for energy storage.

[0044] Specifically, the complete construction process of the energy storage-specific protection action model is constructed. Extracting battery voltage, current, and SOC values ​​from the energy storage power station's dynamic response model requires parsing and filtering the dynamic response model's output data. The dynamic response model continuously outputs battery status parameters during simulation. The data extraction module samples these parameter values ​​at regular intervals and stores them in a cache. The battery voltage reflects the current potential of the energy storage unit, the current indicates the charge and discharge power, and the SOC represents the state of charge percentage. These three parameters collectively 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 battery status at each moment corresponds to a spatial point in this coordinate system, and multiple state points at each moment form a trajectory in the three-dimensional parameter space. The three-dimensional protection basic parameter set contains current, voltage, and SOC data points at all sampling moments. These data points cover the state distribution range under various operating conditions of the energy storage power station and form the basic data source for protection criterion design.

[0045] Based on the three-dimensional protection basic parameter set, a correlation is established between the protection current threshold and battery voltage deviation, SOC deviation, and temperature rise rate. This correlation is mathematically modeled to express the protection current threshold as a function of the three deviations. The battery voltage deviation is calculated based on the difference between the current voltage and the rated voltage. A positive voltage deviation indicates an overvoltage, while a negative voltage deviation indicates an undervoltage. A larger absolute value of the voltage deviation indicates a more severe voltage anomaly. The SOC deviation is calculated based on the distance between the current SOC value and the safe SOC range boundary. When the SOC is below the lower limit, the SOC deviation is negative; when the SOC is above the upper limit, the SOC deviation is positive. The absolute value of the SOC deviation reflects the severity of overcharge or overdischarge. The temperature rise rate is calculated by dividing the temperature difference between two consecutive moments by the time interval. A positive temperature rise rate indicates a temperature increase, while a negative temperature rise rate indicates a temperature decrease. The absolute value of the temperature rise rate reflects the severity of the temperature change. The dynamic protection current threshold is calculated in real time based on these three deviations. As any deviation increases, the protection current threshold decreases accordingly. When all deviations are within the normal range, the protection current threshold remains at the rated value.

[0046] Based on dynamic protection current thresholds, trigger conditions for graded protection are set for different SOC and voltage ranges. The graded protection logic employs a hierarchical protection strategy to address abnormal conditions of varying severity. The SOC range is divided into three tiers: normal, warning, and danger. The normal range corresponds to SOC conditions within the safe range, the warning range corresponds to SOC conditions approaching the safety boundary, and the danger range corresponds to SOC conditions exceeding the safe range. The voltage range is similarly divided into three tiers: the normal voltage range corresponds to voltage conditions within the rated range, the warning voltage range corresponds to voltage conditions with small deviations, and the danger range corresponds to voltage conditions with large deviations. The graded protection trigger conditions determine the protection level based on the combination of SOC and voltage ranges. When the energy storage power station operates within the normal range, no protection is triggered. When the operation state is within the warning range, level one protection is triggered. When the operation state is within the danger range, level two or three protection is triggered. The graded protection logic of the energy storage power station maps trigger conditions to protection levels, forming a protection decision table.

[0047] The hierarchical protection logic of the energy storage power station is combined with the protection action time sequence. The protection action time sequence defines the action time requirements corresponding to different protection levels. The first-level protection action time sequence is set to three stages: detection delay, judgment delay, and execution delay. The detection delay is the response time of the protection device detecting the 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-level protection and the third-level protection shortens the delay of each stage based on the first-level protection. The second-level protection requires a faster response speed to deal with moderately severe abnormalities, and the third-level protection requires the fastest response speed to deal with severe abnormalities. The protection action model combines the hierarchical protection logic with the time sequence. When a certain level of protection is triggered, the protection action is executed according to the corresponding time sequence. The protection action includes power limiting, circuit breaker tripping, system shutdown and other measures of different severity.

[0048] In a specific embodiment, step S3 includes:

[0049] Extract the rated power value and current SOC state value of each energy storage power station from the energy storage dedicated protection action model, construct the energy storage power station power-SOC state matrix, and obtain the power allocation parameter set;

[0050] Perform iterative consistency calculations on the power allocation parameter set, calculate the power adjustment amount of each energy storage station based on the power difference between energy storage stations and the adjacent communication weight, and obtain the target power allocation value of each energy storage station;

[0051] Based on the target power allocation value, the SOC balancing control rules and power constraints between energy storage power stations are established to obtain the power balancing allocation strategy for the energy storage power station group.

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

[0053] Specifically,

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

[0055] Extract voltage and current signal data and protection action instruction data from the coordinated control simulation model of the energy storage power station group, construct a simulation output signal data set, and obtain the hardware interface input data source;

[0056] Perform signal format conversion on the hardware interface input data source, convert the digital simulation signal into analog signal and digital signal, and obtain standardized hardware interface signal;

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

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

[0059] Specifically, extracting the rated power and current SOC value of each energy storage plant from the energy storage-specific protection action model requires parsing the data structure of the protection action model. The protection action model internally stores basic parameters and real-time status information for each energy storage plant. The data extraction module obtains these parameter values ​​by accessing the model's data interface. The rated power value represents the maximum charge and discharge power capability determined during the energy storage plant's design, reflecting its capacity scale and technical level. The current SOC value represents the real-time state of charge percentage of the energy storage plant, reflecting its current available capacity and charge and discharge capabilities. The power-SOC matrix of the energy storage plant is constructed using the rated power value as the matrix's row index and the current SOC value as the matrix's column index. Each element in the matrix represents the actual available power of the corresponding energy storage plant at its current SOC state. This available power is calculated by multiplying the rated power value by a correction factor related to the SOC. The power allocation parameter set contains the rated power, current SOC value, and actual available power values ​​of all energy storage plants. These parameters constitute the basic data for the power allocation calculation of multiple energy storage plants.

[0060] The consistency iterative calculation algorithm coordinates and optimizes the power allocation parameter set. Consensus algorithms are a classic algorithm in the field of distributed control. They achieve a globally consistent system state through information exchange between multiple nodes. The power difference between energy storage plants is calculated by taking the difference between each plant's current power output and its average power output. A positive power difference indicates that the plant's power output is above average, while a negative power difference indicates that the plant's power output is below average. The neighboring communication weight reflects the strength of the communication connection and the information transmission capacity between energy storage plants. The weight is determined based on the physical distance between the plants, communication latency, and connection reliability. A larger weight indicates a stronger communication connection, while a smaller weight indicates a weaker connection. The power adjustment for each energy storage plant is calculated by multiplying the current power difference by the neighboring communication weight, and then by the consistency gain coefficient. The consistency gain coefficient controls the convergence speed and stability of the power adjustment. Excessively large gain coefficients can cause system oscillations, while too small gain coefficients can slow convergence. The target power allocation value for each energy storage plant is calculated by adding the power adjustment coefficient to the current power output. The iterative calculation process is repeated until the power differences of all energy storage plants converge within a preset threshold.

[0061] Based on the target power allocation value, SOC balancing control rules and power constraints are established between energy storage plants. The SOC balancing control rules ensure that the SOC levels of multiple energy storage plants remain relatively balanced. The SOC balancing control rules utilize an SOC deviation compensation mechanism. When the SOC of a particular energy storage plant is significantly higher than that of other energy storage plants, it assumes more discharge tasks. When the SOC of a particular energy storage plant is significantly lower than that of other energy storage plants, it assumes more charging tasks. Power constraints include single-station power constraints and total power constraints. Single-station power constraints limit the power output of each energy storage plant to the smaller of its rated power and currently available power. Total power constraints limit the total power output of all energy storage plants to equal the external power command. The energy storage plant group power balancing allocation strategy combines the SOC balancing control rules and power constraints to form a solution strategy for multi-objective optimization problems. The strategy strives to achieve SOC balancing while satisfying power constraints.

[0062] A distributed coordinated control framework is constructed based on the power balancing allocation strategy of the energy storage power station group. This framework includes two core components: communication topology and control logic. The communication topology describes the information transmission network structure between energy storage power stations. The topology is represented by a directed graph, in which each node represents an energy storage power station, and the directed edges represent the communication links between energy storage power stations. The edge weights represent the strength of the communication connection. The control logic defines the algorithm flow for each energy storage power station to calculate its own power output based on the information received from neighboring nodes. The control logic consists of three parts: an information receiving module, a local calculation module, and an output execution module. The information receiving module obtains the power and SOC information of neighboring energy storage power stations. The local calculation module calculates the target power according to the consistency algorithm and balancing strategy. The output execution module converts the target power into actual power control instructions. The coordinated control simulation model of the energy storage power station group integrates the communication topology and control logic to form an executable simulation program.

[0063] In a specific embodiment, step S5 includes:

[0064] A multi-scenario fault library covering grid faults, equipment faults, and operating conditions is constructed within the energy storage control and protection hardware-in-the-loop test platform to generate a test scenario database for energy storage power stations.

[0065] Generate a test case execution sequence based on the energy storage power station test scenario database, automatically switch to the fault condition according to the preset timing, and record the control and protection system response data to obtain the energy storage control and protection system verification data set;

[0066] Calculate performance indicators for the energy storage control and protection system verification data set, analyze protection action time, control response accuracy, and power regulation deviation, and obtain the energy storage control and protection system performance parameters;

[0067] By comparing and analyzing the performance parameters of the energy storage control and protection system with preset thresholds, a comprehensive evaluation report including test coverage and improvement suggestions is generated to obtain the performance evaluation results of the energy storage control and protection system.

[0068] Specifically, building a multi-scenario fault library within the energy storage control and protection hardware-in-the-loop test platform requires systematic classification and modeling of various abnormal conditions encountered during energy storage power station operation. Grid faults include grid-side anomalies such as voltage sag, frequency deviation, phase-to-phase short circuit, and single-phase ground fault. Each fault type requires definition of parameters such as fault duration, severity, and location. Equipment faults include device-level anomalies such as battery overtemperature protection, converter overcurrent protection, DC-side ground fault, and communication interruption. Modeling of equipment faults requires consideration of the physical mechanism and impact of the fault. Operating conditions encompass typical scenarios such as normal charging and discharging, power regulation response, SOC management strategies, and coordinated multi-station operation. The modeling of these operating conditions must reflect the dynamic characteristics of the energy storage power station under varying external conditions. The multi-scenario fault library uses a database structure to store parameter configurations for these faults and operating conditions. Each scenario corresponds to a record in the database, containing 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] A test case execution sequence is generated based on the energy storage power plant test scenario database. This sequence organizes multiple test scenarios in a time-driven manner. The sequence generation algorithm selects appropriate test scenarios from the scenario database based on test coverage requirements and a prioritization strategy. The test case generation process first analyzes the importance and probability of each test scenario. High-priority scenarios include serious faults that could pose safety risks, medium-priority scenarios include general faults that impact system performance, and low-priority scenarios include rare but special operating conditions that require verification. A sequence arrangement algorithm arranges the selected test scenarios from simple to complex and from common to rare to ensure a logical and progressive testing process. A preset timing defines the execution time and switching interval for each test scenario. Timing design must consider the response time of the control and maintenance system and the integrity of data records. Too short an execution time makes it impossible to observe the response process, while too long an execution time affects test efficiency. The automatic switching fault condition sends a scenario switching command to the simulation model via the test platform's control interface. The control interface triggers an update of the scenario parameters according to the preset timing. The simulation model receives the new scenario parameters and recalculates the energy storage power plant's operating status. The response data of the control and protection system are recorded, including the input signal, output signal, internal state variables and action timestamp of the control and protection device. The data recording module collects this data at a fixed sampling frequency and stores it in the verification data set.

[0070] Performance indicators were calculated for the energy storage control and protection system validation dataset. The performance indicator calculation algorithm extracted key information from the validation dataset and performed statistical analysis. The protection action time analysis extracted timestamps for the fault occurrence and protection action completion times from the dataset, calculating the time difference to obtain the protection response time. This analysis process identified the triggering conditions and completion markers for different types of protection actions, and statistically analyzed the action times for primary, secondary, and tertiary protection. The control response accuracy analysis extracted power command values ​​and actual power output values ​​from the dataset and evaluated the control system's accuracy by calculating the command tracking error. Errors were calculated using both absolute and relative error methods. Absolute error reflects the absolute magnitude of the power deviation, while relative error reflects the proportion of the power deviation to the command value. The power regulation deviation analysis extracted the dynamic response curve of the power regulation process from the dataset and evaluated the power regulation performance by analyzing dynamic characteristics such as overshoot, regulation time, and steady-state error. Overshoot reflects the degree of overshoot in the power response, regulation time reflects how quickly the power reaches steady state, and steady-state error reflects the final power tracking accuracy. The energy storage control and protection system performance parameters aggregate these analysis results into comprehensive performance indicators encompassing response time, control accuracy, and dynamic performance.

[0071] The performance parameters of the energy storage control and protection system are compared against preset thresholds. The comparative analysis algorithm compares each measured performance parameter against thresholds specified by design requirements or industry standards. Preset thresholds include protection action time thresholds, control accuracy thresholds, and power regulation performance thresholds. These thresholds are determined based on the technical specifications and safety requirements of the energy storage power plant. The comparative analysis process calculates the compliance rate for each performance indicator. The compliance rate is calculated by dividing the number of test cases that meet the threshold requirements by the total number of test cases. The compliance rate reflects the reliability of the control and protection system under different operating conditions. Test coverage statistics verify the types of test scenarios and parameter ranges covered by the dataset. Coverage is calculated by dividing the number of scenarios covered by the total number of scenarios. A high coverage rate indicates comprehensiveness and adequacy of test verification. The improvement suggestion generation algorithm provides targeted optimization suggestions based on the non-compliance items and the degree of deviation of performance parameters. Suggestions include parameter adjustment directions, algorithm improvement ideas, hardware configuration optimization, and other aspects. The comprehensive evaluation report integrates the comparative analysis results, coverage statistics, and improvement suggestions into a structured evaluation document. The performance evaluation results of the energy storage control and protection system provide a decision-making basis for the engineering application and continuous improvement of energy storage power plants.

[0072] In a specific embodiment, the process of performing the step of calculating the performance index of the energy storage control and protection system verification data set may specifically include the following steps:

[0073] Extract the timestamp data of the protection triggering moment and the protection action completion moment from the energy storage control and protection system verification data set, calculate the time difference, and obtain the protection action time data;

[0074] Perform statistical analysis based on protection action time data, calculate the average response time, maximum response time and standard deviation of response time, and obtain protection time performance indicators;

[0075] Calculate the deviation between the power command value and the actual power output value in the energy storage control and protection system verification data set, analyze the steady-state error and dynamic overshoot, and obtain the control accuracy performance index;

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

[0077] Specifically, extracting timestamps for protection trigger and action completion from the energy storage control and protection system validation dataset requires parsing and identifying the validation dataset's time series data. The validation dataset contains all signal changes and state transitions recorded during the test. The timestamp data uses a high-precision timer to record the absolute time of each event. The protection trigger moment identification algorithm monitors state changes in the protection criterion signal to determine the trigger instant. When the protection criterion transitions from a normal state to an abnormal state, this moment is recorded as the protection trigger moment. The identification of the trigger moment must account for the effects of signal noise and filtering delay. The protection action completion moment identification algorithm monitors state changes in the protection output signal to determine the action completion instant. When the protection output transitions from a standby state to an action state, this moment is recorded as the protection action completion moment. Action completion criteria include different types of protection measures, such as circuit breaker trip signals, power limit commands, and system shutdown commands. The time difference is calculated by simply subtracting the protection action completion moment from the protection trigger moment. This time difference reflects the total time taken by the protection device from detecting an anomaly to executing the protection action. The protection action time data includes response time records for various protection types across all test cases.

[0078] Statistical analysis is performed based on protection action time data. The statistical analysis algorithm mathematically processes the collected multiple protection action time values. The average response time is calculated using the arithmetic mean of all protection action time values. The calculation process adds up all time values ​​and divides them by the total number. The average response time reflects the typical response speed level of the protection device. The maximum response time is calculated using the maximum value of all protection action time values. The maximum response time reflects the response speed of the protection device under the most unfavorable conditions. This indicator is used to assess the lower limit of the protection device's reliability. The response time standard deviation is calculated using the square root of the variance. First, the square of the difference between each time value and the average is calculated. Then, the square of all differences is added and divided by the total number to obtain the variance. Finally, the square root of the variance is used to obtain the standard deviation. The standard deviation reflects the degree of dispersion and consistency of the protection action time. The protection time performance index integrates the average response time, maximum response time, and response time standard deviation into a comprehensive set of indicators that describe protection performance. These indicators reflect the time characteristics of the protection device from different perspectives.

[0079] See also Figure 2 , is a schematic diagram of the performance analysis of the protection action time of the energy storage power station control and protection system. Figure 2 As shown in (a), the box plot shows the distribution characteristics of the protection response time of five fault types: voltage drop, overcurrent trigger, temperature anomaly, SOC limit violation and communication interruption, where the 200ms performance threshold is marked by a red dotted line. The statistical results show that the response time of each type of fault is significantly different: voltage drop and overcurrent trigger have the fastest response (60-110ms), temperature anomaly and SOC limit violation are in the middle (100-180ms), and communication interruption is the slowest (250-420ms). Figure 2 As shown in (b), the statistical results of the compliance rate show that the 200ms compliance rate of the four types of faults, namely voltage drop, overcurrent trigger, temperature anomaly and SOC over-limit, all reached more than 95%, verifying the effectiveness of the protection time performance index calculation method of the present invention.

[0080] The deviation between the power command value and the actual power output value in the energy storage control and maintenance system validation dataset is calculated. The deviation calculation algorithm synchronously extracts time series data of the power command signal and the power output signal from the validation dataset. The power command value represents the power control command issued by the control system to the energy storage device and reflects the control system's desired output. The actual power output value represents the actual power output of the energy storage device and is collected in real time by a power measurement device. The deviation calculation is performed by subtracting the power command value from the actual power output value. A positive deviation indicates that the actual output exceeds the command value, while a negative deviation indicates that the actual output falls below the command value. The absolute value of the deviation reflects the magnitude of the control error. Steady-state error analysis extracts the error value after the power regulation process reaches a steady state from the deviation data. The steady-state error calculation uses the average value of the deviations at multiple sampling points in the steady state. The steady-state error reflects the final tracking accuracy of the control system. Dynamic overshoot analysis extracts the maximum deviation value during the power regulation process from the deviation data. The overshoot calculation is calculated by subtracting the absolute value of the steady-state error from the maximum absolute value of the deviation during the regulation process. The overshoot reflects the degree of overshoot in the dynamic response of the control system. The control accuracy performance index combines the steady-state error and dynamic overshoot into a comprehensive indicator that describes control performance.

[0081] A comprehensive statistical analysis of the protection time performance indicators and control accuracy performance indicators was conducted. The comprehensive statistical analysis algorithm performed correlation analysis and weight assignment on the two performance indicators. The correlation analysis examined the relationship between the protection time and control accuracy indicators. The degree of correlation between the two indicators was assessed by calculating the correlation coefficient. A positive correlation indicates that longer protection time correlates with lower control accuracy, a negative correlation indicates an inverse relationship between protection time and control accuracy, and no correlation indicates that the two indicators are independent. Weight assignment assigns importance to different indicators based on the application needs and safety requirements of the energy storage power station. The protection time indicator carries a higher weight in safety-critical applications, while the control accuracy indicator carries a higher weight in applications with strict control accuracy requirements. The comprehensive score calculation uses a weighted average method to combine each indicator into a single comprehensive performance score. The calculation process multiplies each indicator value by its corresponding weight and then adds them together to obtain the total score. The performance parameters of the energy storage control and protection system include the protection time performance indicator, the control accuracy performance indicator, and the comprehensive performance score, forming a performance description system.

[0082] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A hardware-in-the-loop simulation test method for an energy storage power station control and protection system, characterized in that: The method comprises: Step S1: Obtain the operating data of the energy storage power station, use the energy storage unit state space decomposition algorithm to establish the SOC-power coupling constraint mechanism, and build the dynamic response model of the energy storage power station; Step S2: establishing a current-voltage-SOC three-dimensional protection criterion based on the battery state parameters in the dynamic response model of the energy storage power station, and generating a dedicated energy storage protection action model; Step S3: extract the power distribution parameters in the energy storage dedicated protection action model, use a distributed coordinated control algorithm to calculate the balanced power distribution among multiple energy storage power stations, and form a coordinated control simulation model for the energy storage power station group; Step S4: calling the simulation data of the energy storage power station group coordinated control simulation model, configuring the hardware interface signal synchronization protocol, and establishing an energy storage control and protection hardware-in-the-loop test platform; In step S5, multiple fault conditions are loaded into the energy storage control and protection hardware-in-the-loop test platform, an automated verification process of the energy storage control and protection system is executed, and a performance evaluation result of the energy storage control and protection system is output.

2. The hardware-in-the-loop simulation test method for the energy storage power station control and protection system according to claim 1 is characterized in that: The S1 step includes: Collect battery voltage, current, temperature, and SOC data from the energy storage power station, construct a four-dimensional state vector including voltage, current, temperature, and SOC, and obtain a basic state data set for the energy storage unit; Performing state space decomposition on the basic state data set of the energy storage unit, establishing a linear relationship between the time derivative of the state variable and the state variable and the control input, and obtaining a dynamic coefficient matrix that changes with the SOC; Establishing a constraint relationship between the power change rate and the product of the SOC value and the temperature compensation coefficient based on the SOC-related parameters in the dynamic coefficient matrix to obtain an SOC-power coupling constraint mechanism; The power regulation response characteristics of the energy storage power station under different SOC states are mathematically described according to the SOC-power coupling constraint mechanism, and a dynamic response model of the energy storage power station is obtained.

3. The hardware-in-the-loop simulation test method for the energy storage power station control and protection system according to claim 1 is characterized in that: The S2 step comprises: Extracting battery voltage, current, and SOC values ​​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 correlation calculation relationship between the protection current threshold and the battery voltage deviation, SOC deviation and temperature rise rate is established to obtain a dynamic protection current threshold; According to the dynamic protection current threshold, hierarchical protection trigger conditions corresponding to different SOC intervals and voltage intervals are set to obtain the hierarchical protection logic of the energy storage power station; The energy storage power station hierarchical protection logic and the protection action time sequence are combined and configured to obtain an energy storage-specific protection action model.

4. The hardware-in-the-loop simulation test method for the energy storage power station control and protection system according to claim 1 is characterized in that: The S3 step includes: Extracting the rated power value and current SOC state value of each energy storage power station from the energy storage-specific protection action model, constructing the energy storage power station power-SOC state matrix, and obtaining a power allocation parameter set; Performing consistency iterative calculation on the power allocation parameter set, calculating the power adjustment amount of each energy storage station based on the power difference between energy storage stations and the adjacent communication weight, and obtaining the target power allocation value of each energy storage station; Based on the target power distribution value, an SOC balancing control rule and power constraint conditions between energy storage power stations are established to obtain a power balancing distribution strategy for the energy storage power station group; According to the power balanced distribution strategy of the energy storage power station group, a distributed coordinated control framework including communication topology and control logic is constructed to obtain a coordinated control simulation model of the energy storage power station group.

5. The hardware-in-the-loop simulation test method for the energy storage power station control and protection system according to claim 1 is characterized in that: The S4 step comprises: Extracting voltage and current signal data and protection action instruction data from the energy storage power station group coordinated 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, converting the digital simulation signal into an analog signal and a digital signal, and obtaining a standardized hardware interface signal; Based on the standardized hardware interface signal configuration clock synchronization protocol, a timing synchronization mechanism is established between the simulation system and the control and protection hardware to obtain the hardware interface signal synchronization protocol; A hardware connection platform including a signal conditioning circuit and a communication interface is constructed according to the hardware interface signal synchronization protocol to obtain an energy storage control and protection hardware-in-the-loop test platform.

6. The hardware-in-the-loop simulation test method for the energy storage power station control and protection system according to claim 1 is characterized in that: The step S5 comprises: Constructing a multi-scenario fault library including grid faults, equipment faults, and operating conditions in the energy storage control and protection hardware-in-the-loop test platform to obtain an energy storage power station test scenario database; Generate a test case execution sequence based on the energy storage power station test scenario database, automatically switch to the fault condition according to the preset time sequence and record the control and protection system response data to obtain the energy storage control and protection system verification data set; Calculate performance indicators for the energy storage control and protection system verification data set, analyze protection action time, control response accuracy, and power regulation deviation, and obtain performance parameters of the energy storage control and protection system; Based on the comparative analysis of the energy storage control and protection system performance parameters and preset thresholds, a comprehensive evaluation report including test coverage and improvement suggestions is generated to obtain the energy storage control and protection system performance evaluation results.

7. The hardware-in-the-loop simulation test method for the energy storage power station control and protection system according to claim 6 is characterized in that: The energy storage control and protection system verification data set is used to calculate performance indicators, analyze protection action time, control response accuracy, and power regulation deviation, and obtain energy storage control and protection system performance parameters, including: Extracting the timestamp data of the protection triggering moment and the protection action completion moment from the energy storage control and protection system verification data set, calculating the time difference, and obtaining the protection action time data; Perform statistical analysis based on the protection action time data, calculate the average response time, maximum response time and response time standard deviation, and obtain the protection time performance index; Calculate the deviation between the power command value and the actual power output value in the energy storage control and protection system verification data set, analyze the steady-state error and dynamic overshoot, and obtain the control accuracy performance index; The protection time performance index and the control accuracy performance index are subjected to comprehensive statistical analysis to obtain the performance parameters of the energy storage control and protection system.

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