A method and system for coordinated load control of ship power generation units based on real-time power.

Abstract

This invention discloses a method and system for coordinated load control of ship power generation units based on real-time power, specifically relating to the power regulation field of ship hybrid power systems. It addresses the problem of control disorder in energy storage systems caused by conflicts between two types of commands: maintaining the load of the power generation unit and suppressing power fluctuations in the grid. By acquiring and analyzing the real-time evolution trend of transient power demand data, it predicts high-risk operating conditions. Under these conditions, it simultaneously evaluates the matching degree between the energy storage system and the power demand, and analyzes the rate of change of the internal structure of the power demand. Based on the evaluation results, it dynamically determines the recovery priority of the two types of control commands, setting the command with higher priority as the dominant power command. It then performs coordination arbitration between the dominant command and the other command based on real-time power margin to generate the final executable power command. This achieves precise and orderly coordinated control of the energy storage system's power, improving the overall operational stability of the ship's power system under complex transient conditions.

Description

Technical Field

[0001] This invention relates to the field of power regulation in marine hybrid power systems, and more specifically, to a method and system for coordinated load control of marine power generation units based on real-time power. Background Technology

[0002] In hybrid-powered marine electrical systems, a shaft generator driven by the main engine, auxiliary diesel or dual-fuel generator sets, and a battery energy storage system are typically installed. To optimize system operation, a common control method is to utilize the energy storage system to regulate the output of the generator units, aiming to maintain their load within a specific preset operating range. Simultaneously, to ensure grid stability during sudden high-power load changes, the system is usually designed to provide rapid power buffering using the energy storage system to suppress fluctuations in grid frequency and voltage. Both of these control functions rely on rapid and precise control of the charging and discharging power of the energy storage system.

[0003] However, under typical operating conditions such as ship entry and exit from port, the two types of functional modules that generate power commands based on different control objectives will be activated simultaneously. Due to the inherent differences in response time and adjustment logic between the two, their power commands output to the energy storage system will directly conflict in the dynamic process, causing disorder in the power execution logic of the energy storage system. This makes it impossible for the system to effectively maintain the dynamic quality of the power grid while ensuring that the power generation unit operates as planned, thus limiting the overall performance and reliability of the ship's integrated power system under complex operating conditions. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for coordinated load control of ship power generation units based on real-time power to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: The method for coordinated load control of ship power generation units based on real-time power includes the following steps: S1. Obtain transient power demand data for the ship's electrical grid; S2. Analyze the real-time evolution trend of transient power demand data to assess the possibility that the power grid will enter a high dynamic risk state within a preset period of time in the future. Based on the comparison between the possibility and the preset possibility threshold, determine whether the power grid is currently in a strong transient power demand condition. S3. When under conditions of strong transient power demand, assess the matching degree between the energy storage system and the transient power demand data in terms of time and space. S4. Analyze the rate of change of the energy ratio of low-frequency trend component and high-frequency fluctuation component in transient power demand data within a preset time window. S5. Based on the matching degree and the rate of change, determine the recovery priority of the first control function command for restoring the load of the power generation unit within the preset operating range and the second control function command for suppressing power fluctuations in the power grid, and set the control function command with higher recovery priority as the dominant power command for the energy storage system. S6. Coordinate and arbitrate the dominant power command with another control function command to generate and output the final power control command to the energy storage system.

[0006] Furthermore, S1 includes: Obtain raw sampled data of the total load power of the ship's electrical network; The raw sampled data of total load power is low-pass filtered to obtain filtered power data that reflects the trend of load base power change. Calculate the real-time deviation of the original sampled data of total load power relative to the filtered power data, and take the absolute value of the real-time deviation to obtain the fluctuating power data that reflects the intensity of instantaneous power fluctuations. The filtered power data is combined with the fluctuating power data to form transient power demand data.

[0007] Furthermore, S2 includes: Calculate the acceleration of the average rate of change of transient power demand data within the first time window prior to the current moment and the average rate of change within the second time window; The acceleration of change is compared with a benchmark parameter that reflects the grid’s inertial response capability to assess the possibility that the grid may lose its stability margin due to the continuous acceleration of power demand changes in the future within a preset period. The estimated probability is compared with a preset probability threshold. If the probability exceeds a preset probability threshold, the power grid is determined to be in a strong transient power demand condition.

[0008] Furthermore, S3 includes: Based on the current available energy capacity of the energy storage system and the power integral of transient power demand data over a preset future period, the ability of the energy storage system to continuously support the power demand over time is evaluated. Based on the current available power capacity of the energy storage system and the peak power of transient power demand data in a preset future period, assess the ability of the energy storage system to cope with the peak power demand in the spatial dimension. The ability of an integrated energy storage system to continuously support the power demand over time and to cope with the peak power demand over space is used to determine the matching degree between the energy storage system and transient power demand data in time and space.

[0009] Furthermore, S4 includes: The transient power demand data is filtered through a low-pass filter to separate the low-frequency trend component; Subtract the low-frequency trend component from the transient power demand data to obtain the high-frequency fluctuation component; Calculate the energy of the low-frequency trend component and the energy of the high-frequency fluctuation component within the preset time window, and calculate the proportion of the energy of the high-frequency fluctuation component in the total energy. Calculate the change of this proportion over multiple consecutive preset time windows, and determine the change per unit time as the rate of change of the energy proportion of the low-frequency trend component and the high-frequency fluctuation component within the preset time window.

[0010] Furthermore, S5 includes: Determine whether the rate of change exceeds the first rate threshold; If the rate of change exceeds the first rate threshold, it is determined whether the matching degree meets the requirements for rapid power regulation to support the suppression of grid power fluctuations. If the matching degree meets the requirements for rapid power regulation needed to suppress grid power fluctuations, the recovery priority of the second control function command is set to be higher than that of the first control function command. Otherwise, the recovery priority of the first control function command is set to be higher than that of the second control function command; Set the control function commands with higher recovery priority as the dominant power commands for the energy storage system.

[0011] Furthermore, determining whether the matching degree meets the rapid power regulation requirements needed to suppress grid power fluctuations includes: If the energy storage system's ability to continuously support the power demand in the time dimension exceeds the first capability threshold, and the energy storage system's ability to cope with the peak power demand in the spatial dimension exceeds the second capability threshold, then the matching degree is determined to meet the rapid power regulation requirements required to support the suppression of grid power fluctuations. Otherwise, the matching degree is determined to be insufficient to support the rapid power regulation required to suppress grid power fluctuations.

[0012] Furthermore, S6 includes: Obtain the first target power value corresponding to the dominant power command and the second target power value corresponding to another control function command; Compare the magnitude and direction of the first target power value and the second target power value to determine whether the two target power values ​​conflict with each other; If two target power values ​​conflict with each other, the power margin is calculated based on the current power output capacity of the energy storage system, and the second target power value is dynamically limited according to the power margin. The first target power value is combined with the second target power value after dynamic limiting to generate the final power control command; The final power control command is output to the energy storage system to control its charging and discharging power.

[0013] Furthermore, a power margin is calculated based on the current power output capacity of the energy storage system, and the second target power value is dynamically limited according to this power margin, including: The difference between the maximum discharge power that the energy storage system can currently output and the current power command value is calculated as the positive power margin, and the difference between the maximum charging power that the energy storage system can currently accept and the current power command value is calculated as the negative power margin. If the second target power value is positive, then it is limited to a range not exceeding the positive power margin; If the second target power value is negative, then it is limited to a range not less than the negative power margin.

[0014] On the other hand, the present invention provides a ship power generation unit load coordination control system based on real-time power, comprising the following modules: The data acquisition module is used to acquire transient power demand data of the ship's electrical network; The operating condition determination module is used to analyze the real-time evolution trend of transient power demand data to assess the possibility that the power grid will enter a high dynamic risk state within a preset period of time in the future. Based on the comparison between the probability and the preset probability threshold, it determines whether the power grid is currently in a strong transient power demand operating condition. The matching evaluation module is used to evaluate the degree of matching between the energy storage system and the transient power demand data in the time and space dimensions when under strong transient power demand conditions. The demand analysis module is used to analyze the rate of change of the energy ratio of low-frequency trend components and high-frequency fluctuation components in transient power demand data within a preset time window. The decision arbitration module is used to determine the recovery priority of the first control function command for restoring the load of the power generation unit within the preset operating range and the second control function command for suppressing power fluctuations in the grid based on the matching degree and the rate of change, and to set the control function command with higher recovery priority as the dominant power command for the energy storage system. The command output module is used to coordinate and arbitrate the dominant power command with another control function command, and generate and output the final power control command to the energy storage system.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. By conducting real-time assessment and risk prediction of power demand evolution trends, high-risk operating conditions that are about to trigger serious command conflicts can be accurately identified, providing accurate triggering opportunities for subsequent refined collaborative control. In-depth assessments are conducted from two dimensions: the energy storage system's supply capacity and the internal structure of power demand, providing quantitative basis for decision-making on whether it can support the demand and what the demand characteristics are. This enables the system to have a clear understanding of its own capabilities and external demands, laying a solid data foundation for the final intelligent arbitration.

[0016] 2. Based on the aforementioned multidimensional evaluation results, a dynamic decision-making process is creatively implemented using the concept of restoration priority. The decision-making basis is the real-time changing matching degree and the rate of change of demand structure, thus making the selection of the dominant control objective adaptive and conditional. The coordination and arbitration mechanism further ensures the executability of the decision. When command conflicts are unavoidable, by dynamically limiting the secondary commands based on real-time power margin, the needs of the other objective are taken into account to the greatest extent while prioritizing the dominant objective. This generates a safe, feasible, and comprehensively optimized final power command, ultimately achieving precise and orderly scheduling of the energy storage system, a key execution resource. This significantly improves the overall coordination, operational stability, and control reliability of the marine hybrid power system when dealing with complex and transient operating conditions. Attached Figure Description

[0017] Figure 1 This is a flowchart of the ship power generation unit load coordination control method based on real-time power according to the present invention; Figure 2 This is a schematic diagram of the load coordination control system for ship power generation units based on real-time power according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1: Figure 1 The present invention provides a method for coordinated load control of ship power generation units based on real-time power, which includes the following steps: S1. Obtain transient power demand data for the ship's electrical grid; S2. Analyze the real-time evolution trend of transient power demand data to assess the possibility that the power grid will enter a high dynamic risk state within a preset period of time in the future. Based on the comparison between the possibility and the preset possibility threshold, determine whether the power grid is currently in a strong transient power demand condition. S3. When under conditions of strong transient power demand, assess the matching degree between the energy storage system and the transient power demand data in terms of time and space. S4. Analyze the rate of change of the energy ratio of low-frequency trend component and high-frequency fluctuation component in transient power demand data within a preset time window. S5. Based on the matching degree and the rate of change, determine the recovery priority of the first control function command for restoring the load of the power generation unit within the preset operating range and the second control function command for suppressing power fluctuations in the power grid, and set the control function command with higher recovery priority as the dominant power command for the energy storage system. S6. Coordinate and arbitrate the dominant power command with another control function command to generate and output the final power control command to the energy storage system.

[0020] S1. Obtain transient power demand data for the ship's electrical network. The specific implementation is as follows: Acquire raw sampling data of the total load power of the ship's electrical network. This raw data is collected using a power transmitter installed on the ship's main switchboard bus. For example, a Hall effect power sensor with an accuracy level meeting ship electrical specifications or a power measurement unit obtained by multiplying the signals from a current transformer and a voltage transformer is used. The acquisition process is performed at a fixed sampling period, which must follow Shannon's sampling theorem, meaning the sampling frequency must be at least twice the frequency of the highest-frequency power fluctuation component of interest in the ship's electrical network. For example, when the highest frequency fluctuation to be monitored is 50 Hz, the sampling frequency should be set to no less than 100 Hz. The acquired raw data is in time-series format, with each data point containing a timestamp and the corresponding instantaneous value of the total active power, typically in kilowatts.

[0021] The raw sampled data of total load power is low-pass filtered to obtain filtered power data reflecting the trend of load base power changes. This low-pass filtering process is executed in the digital signal processing unit of the control system, using a specific digital filter algorithm, such as a first-order infinite impulse response low-pass filter or a finite impulse response low-pass filter with a specific window function. The cutoff frequency parameter of the filter needs to be set according to the physical characteristics of the load power change trend. The setting is based on filtering out rapid power changes caused by short-term impact loads, while retaining power components that characterize the slow changes in the overall operation mode of the ship. For example, by analyzing the power mutation spectrum characteristics caused by the operation of the side thrusters in the ship's historical operating data, the cutoff frequency is set to a value significantly lower than the main component of the mutation frequency, such as a specific value between 0.5 Hz and 5 Hz. After calculation by this digital filter, a smooth, slowly changing power data sequence corresponding to the time series of the raw sampled data is output. This sequence is the filtered power data.

[0022] The real-time deviation of the raw total load power sampling data relative to the filtered power data is calculated, and the absolute value of this real-time deviation is taken to obtain the fluctuating power data reflecting the intensity of instantaneous power fluctuations. This calculation process operates on the data at each synchronous time point; at each sampling moment, the specific value of the currently collected raw total load power sampling data is subtracted from the value of the corresponding filtered power data at the same moment to obtain the power deviation value at that moment. This deviation value can be positive or negative, indicating the degree to which the instantaneous power is higher or lower than the baseline trend. Subsequently, the absolute value of this deviation value is taken, that is, negative values ​​are converted to their opposite positive numbers, while positive values ​​remain unchanged. After this operation, a new time series data is obtained, where the value of each data point represents the magnitude of the instantaneous power fluctuation at that moment, in kilowatts. This series is the fluctuating power data.

[0023] The filtered power data and fluctuating power data are combined to form transient power demand data. Logically, this combination involves constructing a data structure with multi-dimensional fields. For each identical sampling time point, the filtered power data value and the fluctuating power data value at that moment are associated, forming a data pair. In the program implementation, this pair can be stored in a two-element data array or as two attributes of a data object. This data structure is transmitted and processed internally, allowing subsequent steps to simultaneously access the basic trend and instantaneous fluctuation intensity of the load power at any given time. The overall data generated and structured through these steps constitutes the transient power demand data used for subsequent collaborative control analysis.

[0024] S2. Analyze the real-time evolution trend of transient power demand data to assess the likelihood of the power grid entering a high-dynamic-risk state within a preset time period. Based on the comparison between the likelihood and a preset likelihood threshold, determine whether the power grid is currently under strong transient power demand conditions. The specific implementation is as follows: Analyzing the real-time evolution trend of transient power demand data to assess the likelihood of the power grid entering a high-dynamic-risk state within a predetermined time period is based on the transient power demand data acquired and constructed in step S1, specifically focusing on the filtered power data component representing the fundamental trend. The average rate of change of the transient power demand data within the first time window prior to the current moment is calculated. The length of the first time window is a parameter that needs to be preset, based on covering a statistically significant power trend change cycle. For example, the length of the first time window can be determined based on the duration of typical ship maneuvers, typically set to the order of several seconds to tens of seconds, such as 10 seconds. The method for calculating the average rate of change is to take all consecutive filtered power data points within the time period of the first time window prior to the current moment, subtract the power value of the first data point from the power value of the last data point within the time window, and then divide by the length of the first time window. The result is the average power change rate within that time window. The physical meaning of the average power change rate is the average rate of change of power within that time period, expressed in kilowatts per second.

[0025] Calculate the average rate of change within the second time window. The second time window is another time period immediately adjacent to the current moment and shorter than the first time window; for example, the length of the second time window can be set to 5 seconds. The purpose of setting the second time window is to capture the most recent power change trend. Calculate the average power change rate within the second time window using the same method as calculating the average rate of change within the first time window. Calculate the acceleration of change. The acceleration of change is defined as the difference between the average rate of change within the second time window and the average rate of change within the first time window, divided by the starting time difference between the first and second time windows. The starting time difference can be approximated as half the difference in length between the two time windows. The acceleration of change reflects the acceleration or deceleration of the power change trend itself, and the unit is kilowatts per square second.

[0026] The calculated acceleration change is compared with a benchmark parameter reflecting the grid's inertial response capability to assess the likelihood that the grid will lose its stability margin due to continuously accelerating changes in power demand within a predetermined future period. The benchmark parameter reflecting the grid's inertial response capability is a dynamic variable characterizing the overall tolerance of the current grid to power changes. This benchmark parameter can be obtained through a comprehensive calculation based on the total inertial time constant of online generating units and the total capacity of currently online generators. For example, the benchmark parameter can be directly proportional to the total inertial time constant of the generating units and inversely proportional to the square root of the total capacity of online generators. The comparison process involves calculating the ratio of the absolute value of the acceleration change to the benchmark parameter. By calculating this ratio, a physical quantity is mapped to a quantitative value representing the probability of risk occurrence, such as a value between 0 and 1. This value represents the assessed probability that the grid will enter a high-dynamic-risk state within a predetermined future period. The length of the predetermined future period can be determined based on the response cycle of the control system; for example, the predetermined future period can be set to three control cycles.

[0027] The assessed probability is compared with a preset probability threshold. The preset probability threshold is a pre-defined threshold used to delineate the boundary between high risk and acceptable risk. The preset probability threshold can be adjusted according to different ship operating modes. It can be based on the statistical lower limit of the maximum probability value corresponding to a stable system in historical operating statistics. For example, by analyzing probability data from the past 100 times under the same operating conditions, the 95th percentile value can be used as the preset probability threshold. The comparison operation directly determines whether the assessed probability value is greater than the preset probability threshold.

[0028] If the probability exceeds a preset probability threshold, the power grid is determined to be in a strong transient power demand condition. Determining that the power grid is currently in a strong transient power demand condition means that the control system has identified a high risk in the current power demand evolution trend, requiring the initiation of the multi-dimensional assessment and collaborative arbitration control logic described in subsequent steps S3 to S6.

[0029] When it is determined that the power grid is not currently under conditions of strong transient power demand, the control command aimed at suppressing power grid fluctuations serves as the final power control command for the energy storage system. The logic behind generating this control command involves continuously monitoring the instantaneous frequency deviation of the power grid and calculating the power value that the energy storage system needs to absorb or release using a proportional-integral (PI) controller. The PI controller's proportional gain and integral time constant are adjusted according to the frequency characteristics of the power grid to quickly smooth out instantaneous power fluctuations.

[0030] S3. When under conditions of strong transient power demand, assess the matching degree between the energy storage system and the transient power demand data in terms of time and space. The specific implementation is as follows: The assessment of the matching degree between the energy storage system and transient power demand data in time and space is triggered after step S2 determines that the power grid is currently under a strong transient power demand condition. The assessment process requires two core inputs. The first core input is the real-time status information of the energy storage system, including its current available energy capacity and current available power capacity. The current available energy capacity is obtained by periodically reading the current state of charge (SOC) of the energy storage system through the battery management system. The current available energy capacity is calculated by multiplying the current SOC by the rated total energy capacity of the energy storage system, and then multiplying by a discount factor that considers depth of discharge limitations and battery health status. The unit of the current available energy capacity is typically kilowatt-hours (kWh). The current available power capacity is determined by the smaller of the maximum continuous discharge power and the maximum continuous charge power provided by the battery management system in real time. The unit of the current available power capacity is typically kilowatts (kW). The second core input required for the assessment process is future power demand information. This information is obtained through short-term prediction based on the transient power demand data acquired in step S1. The prediction information includes the power integral of the transient power demand data within a preset future time period and the peak power of the transient power demand data within the preset future time period. The length of the preset future time period is associated with the preset future time period used for risk assessment in step S2; for example, the preset future time period can be set to 5 seconds.

[0031] Based on the current available energy capacity of the energy storage system and the power integral of transient power demand data over a preset future period, the system's ability to continuously support this power demand over time is assessed. Calculating the power integral of the transient power demand data over the preset future period requires numerical integration of the predicted power sequence over that period. The predicted power sequence is obtained through trend extrapolation, for example, assuming that the filtered power data component in the transient power demand data maintains a linear change at its current average rate over the preset future period, while simultaneously superimposing recent statistical characteristics of the fluctuating power data component, such as the root mean square value. The predicted power value at each sampling moment is multiplied by the sampling time interval, and all multiplications are summed to obtain the power integral value of the transient power demand data over the preset future period, expressed in kilowatt-hours. The ability of the energy storage system to continuously support this power demand over time is assessed by calculating a time-dimensional capacity ratio. This ratio is calculated by dividing the current available energy capacity of the energy storage system by the power integral of the transient power demand data over the preset future period. The time-dimensional capacity ratio is a dimensionless number. A time-dimensional capacity ratio greater than or equal to 1 indicates that the energy storage system's energy is theoretically sufficient to cover the total demand over a predetermined future period, while a time-dimensional capacity ratio less than 1 indicates that the energy may be insufficient. The magnitude of the time-dimensional capacity ratio directly reflects the energy storage system's continuous support capability over time.

[0032] Based on the current available power capacity of the energy storage system and the peak power of transient power demand data within a preset future time period, the spatial capability of the energy storage system to cope with the peak power demand is assessed. The peak power of transient power demand data within the preset future time period is obtained by finding the power value with the largest absolute value in the predicted power sequence for the preset future time period. The spatial capability ratio is calculated by dividing the current available power capacity of the energy storage system by the peak power of transient power demand data within the preset future time period. The spatial capability ratio is a dimensionless number; a ratio greater than or equal to 1 indicates that the energy storage system's power output capacity can meet the maximum instantaneous demand, while a ratio less than 1 indicates that the power capacity may be insufficient to cope with the peak. The magnitude of the spatial capability ratio directly reflects the energy storage system's spatial capability.

[0033] The matching degree between the energy storage system and transient power demand data is determined by integrating the system's ability to continuously support power demand over time and its ability to cope with peak power demand over space. The integration process involves assigning weighting coefficients to the time-dimensional and spatial-dimensional capability ratios, then combining the weighted ratios to obtain a comprehensive score as the matching degree. The weighting coefficients for the time and space dimensions reflect the relative importance of time continuity and power peaks in a specific scenario. These weighting coefficients can be determined based on statistical analysis of historical ship operation data, such as analyzing the frequency of energy and power shortage events under different operating conditions; alternatively, they can be preset to a set of fixed values ​​by operators based on experience. The combined weighted capability ratio can be calculated using multiplication or addition; for example, the matching degree equals the time-dimensional weighting coefficient multiplied by the time-dimensional capability ratio, plus the spatial dimension weighting coefficient multiplied by the spatial dimension capability ratio. The matching degree is ultimately expressed as a numerical value. The higher the value, the better the overall capability of the energy storage system matches the current transient power demand.

[0034] S4. Analyze the rate of change of the energy ratio of low-frequency trend components and high-frequency fluctuation components in transient power demand data within a preset time window. The specific implementation is as follows: The analysis uses the transient power demand data obtained in step S1 as input to measure the rate of change of the energy proportion of low-frequency trend components and high-frequency fluctuation components within a preset time window. The length of the preset time window is a parameter that needs to be set in advance to define the statistical period for energy calculation. The setting of the preset time window length should take into account the ability to capture changes in power fluctuation characteristics. For example, the preset time window can be set to 1 second. The length of the preset time window is usually set much shorter than the length of the first time window in step S2. If the sampling frequency of the data acquisition system is 100 Hz, then a 1-second preset time window corresponds to 100 consecutive sampling data points.

[0035] The transient power demand data is passed through a low-pass filter to separate the low-frequency trend component. The low-pass filter used in this step is independent of the one used in step S1 and has a different cutoff frequency parameter. The cutoff frequency parameter of this low-pass filter is set according to the need to separate the long-term trend component of the power demand; for example, the cutoff frequency can be set to 1 Hz to filter out fluctuations caused by rapid load switching while retaining slow power changes caused by changes in ship operation modes. The low-pass filter is implemented using a digital filter algorithm; for example, a first-order infinite impulse response low-pass filter is used. The calculation of this filter is completed in a digital signal processor through recursive difference equations. The transient power demand data sequence is used as the input of this filter, and the output sequence obtained after point-by-point recursive calculation is the low-frequency trend component.

[0036] Subtracting the low-frequency trend component from the transient power demand data yields the high-frequency fluctuation component. This subtraction operation is performed synchronously at each sampling time point; specifically, for each identical sampling time, the transient power demand data value at that time is taken, and the low-frequency trend component value at the same time is subtracted. The difference is the high-frequency fluctuation component value at that time. If the phase delay of the low-pass filter causes a time shift in the low-frequency trend component sequence, phase compensation needs to be performed on the low-frequency trend component sequence before the subtraction operation to realign the time axes of the two sequences. Phase compensation can be achieved by shifting the low-frequency trend component sequence forward by several sampling points, the number of shift points being determined by the group delay characteristics of the filter. The high-frequency fluctuation component sequence obtained after subtraction and time alignment represents the rapidly changing portion of the power demand.

[0037] Calculate the energy of the low-frequency trend component and the energy of the high-frequency fluctuation component within a preset time window, and then calculate the proportion of the high-frequency fluctuation component's energy in the total energy. The method for calculating the energy of the low-frequency trend component is to square the low-frequency trend component values ​​corresponding to all sampling points within the current preset time window, then sum all the squared results, and multiply the sum by the sampling time interval; the sampling time interval is equal to the reciprocal of the sampling frequency, for example, 0.01 seconds when the sampling frequency is 100 Hz; the unit of the calculated low-frequency trend component energy is kilowatt-seconds. The method for calculating the energy of the high-frequency fluctuation component is to square the high-frequency fluctuation component values ​​corresponding to all sampling points within the same preset time window, then sum all the squared results, and multiply the sum by the same sampling time interval; the unit of the calculated high-frequency fluctuation component energy is also kilowatt-seconds. The total energy is the sum of the energy of the low-frequency trend component and the energy of the high-frequency fluctuation component. The calculation of the proportion of the high-frequency fluctuation component's energy in the total energy is to divide the energy of the high-frequency fluctuation component by the total energy; the result is a dimensionless value between 0 and 1, representing the proportion of the high-frequency fluctuation component in the total energy.

[0038] The change in this proportion over multiple consecutive preset time windows is calculated, and the change per unit time is determined as the rate of change of the energy proportion of the low-frequency trend component and the high-frequency fluctuation component within the preset time windows. Multiple consecutive preset time windows refer to selecting multiple consecutive, non-overlapping preset time windows backward from the current analysis time; the number of consecutive preset time windows is another parameter that needs to be set, for example, it can be set to 5. For each of these 5 consecutive preset time windows, the proportion of the energy of a high-frequency fluctuation component in the total energy is calculated using the aforementioned method, thus forming a proportion sequence containing 5 values. The method for calculating the change is to subtract the first value from the last value in this proportion sequence to obtain the difference; this difference is the change in proportion over multiple consecutive preset time windows. The change is divided by the total time length covered by these multiple consecutive preset time windows to obtain the change per unit time; the total time length is equal to the number of multiple consecutive preset time windows multiplied by the length of a single preset time window; for example, if the preset time window length is 1 second and the number of consecutive windows is 5, then the total time length is 5 seconds. The calculated change per unit time, measured in seconds, is determined as the rate of change of the energy ratio of the low-frequency trend component and the high-frequency fluctuation component within a preset time window. A positive rate of change indicates that the energy ratio of high-frequency fluctuations is increasing in the near term, while a negative rate of change indicates a decreasing trend. This rate of change parameter is then passed to the subsequent step S5 for decision-making.

[0039] S5. Based on the matching degree and rate of change, determine the recovery priority of the first control function command for restoring the load of the power generation unit within the preset operating range and the second control function command for suppressing grid power fluctuations, and set the control function command with higher recovery priority as the dominant power command for the energy storage system. The specific implementation is as follows: This step takes into account the temporal and spatial matching degree between the energy storage system and transient power demand data from step S3, and the rate of change of the energy ratio of the low-frequency trend component and the high-frequency fluctuation component from step S4 within a preset time window. The matching degree is a comprehensive score, and the rate of change is a value measured in seconds.

[0040] The system determines whether the rate of change exceeds a first rate threshold. The first rate threshold is a preset critical value used to distinguish whether the power demand structure is undergoing rapid changes. The first rate threshold is set based on statistical analysis of historical operating data; for example, analyzing the statistical distribution of the rate of change of the proportion of high-frequency fluctuation energy during a typical working cycle of a ship's side thruster, and setting the 90th percentile of this distribution as the first rate threshold. The judgment operation directly compares the value of the rate of change with the value of the first rate threshold; if the value of the rate of change is greater than the value of the first rate threshold, then the rate of change is determined to exceed the first rate threshold.

[0041] If the rate of change exceeds a first rate threshold, it is determined whether the matching degree meets the rapid power regulation requirements needed to suppress grid power fluctuations. Determining whether the matching degree meets the rapid power regulation requirements includes two parallel sub-conditions. The first sub-condition is whether the energy storage system's ability to continuously support the power demand over time exceeds a first capacity threshold. The energy storage system's ability to continuously support the power demand over time is the time-dimensional capacity ratio calculated in step S3. The first capacity threshold is a preset threshold value. The first capacity threshold is set to ensure that the energy storage system has a safe energy margin within a preset future period; for example, the first capacity threshold can be set to 1.2, indicating that the available energy of the energy storage system must be at least 1.2 times the predicted energy demand. During the determination, the value of the time-dimensional capacity ratio is compared with the value of the first capacity threshold; if the value of the time-dimensional capacity ratio is greater than the value of the first capacity threshold, the first sub-condition is satisfied. The second sub-condition is whether the energy storage system's ability to cope with the peak power demand over space exceeds a second capacity threshold. The ability of the energy storage system to cope with the peak power demand in the spatial dimension is the spatial dimension capability ratio calculated in step S3. The second capability threshold is a preset threshold value. The second capability threshold is set to ensure that the energy storage system can withstand the load peak impact and leave a certain dynamic margin; for example, the second capability threshold can be set to 1.1. During the judgment, the value of the spatial dimension capability ratio is compared with the value of the second capability threshold; if the value of the spatial dimension capability ratio is greater than the value of the second capability threshold, it is determined that the second sub-condition is met. The matching degree meets the fast power regulation requirements required to support and suppress grid power fluctuations if and only if the first sub-condition and the second sub-condition are met simultaneously, that is, the ability of the energy storage system to continuously support the power demand in the time dimension exceeds the first capability threshold and the ability of the energy storage system to cope with the peak power demand in the spatial dimension exceeds the second capability threshold.

[0042] If the matching degree meets the rapid power regulation requirements needed to suppress grid power fluctuations, the recovery priority of the second control function command is set higher than that of the first control function command. Recovery priority is a sorting identifier. The setting operation involves setting the priority of the second control function command to high and simultaneously setting the priority of the first control function command to low.

[0043] Otherwise, the recovery priority of the first control function command is set higher than that of the second control function command. "Otherwise" here covers two cases: the first is that the rate of change does not exceed the first rate threshold; the second is that the rate of change exceeds the first rate threshold but the matching degree does not meet the rapid power regulation requirements needed to suppress grid power fluctuations. In both cases, the setting operation involves setting the priority state representing the first control function command to high and the priority state representing the second control function command to low.

[0044] The control function command with the higher recovery priority is set as the dominant power command for the energy storage system. The dominant power command is the command that has the dominant position in the subsequent coordination and arbitration process. The setting operation selects the corresponding control function command as the dominant power command based on the comparison result of the recovery priorities; for example, if the recovery priority of the first control function command is higher than that of the second control function command, then the first control function command is set as the dominant power command; otherwise, the second control function command is set as the dominant power command.

[0045] S6. Coordinate and arbitrate the dominant power command with another control function command to generate and output the final power control command to the energy storage system. The specific implementation is as follows: The coordination and arbitration process obtains the first target power value corresponding to the dominant power command and the second target power value corresponding to another control function command. The first and second target power values ​​are active power values, measured in kilowatts (kW); positive values ​​indicate a requirement for the energy storage system to discharge power to the grid, while negative values ​​indicate a requirement for the energy storage system to absorb power from the grid. The first target power value is calculated based on the type of the dominant power command. If the dominant power command is the first control function command, the first target power value is calculated using a control algorithm that maintains the load of the generating unit within a preset operating range. This algorithm continuously measures the actual active power of the generating unit and compares it with the set value of the preset operating range. A proportional-integral controller calculates the power difference that the energy storage system needs to compensate for; this difference is the first target power value. If the dominant power command is the second control function command, the first target power value is calculated using a control algorithm that suppresses grid power fluctuations. This algorithm continuously measures the deviation of the grid frequency from the rated frequency and a proportional-derivative controller calculates the power value that the energy storage system needs to rapidly inject or absorb; this power value is the first target power value. The second target power value is calculated independently based on another control function command that is not designated as dominant, using a different control algorithm corresponding to the above.

[0046] The system compares the magnitude and direction of the first and second target power values ​​to determine if they conflict. The judgment logic is based on algebraic signs and numerical relationships. It checks if the algebraic signs of the first and second target power values ​​are the same; if both are positive or both are negative, the directions are considered consistent; if one is positive and the other negative, the directions are considered opposite. If the directions are consistent, the absolute difference between the first and second target power values ​​is further calculated; if this absolute difference is less than or equal to a preset power tolerance threshold, the two target power values ​​are considered not conflicting; if the absolute difference is greater than the preset power tolerance threshold, the two target power values ​​are considered to conflict in amplitude. The preset power tolerance threshold is set according to the system control accuracy requirements, for example, it can be set to 1% of the rated power of the energy storage system. If the directions are opposite, the two target power values ​​are directly determined to conflict.

[0047] If two target power values ​​conflict, a power margin is calculated based on the current power output capability of the energy storage system, and the second target power value is dynamically limited according to this power margin. The current power output capability of the energy storage system is obtained from the battery management system, including the maximum discharge power currently output by the energy storage system and the maximum acceptable charging power. The maximum discharge power currently output by the energy storage system is a positive value, and the maximum acceptable charging power is a negative value; both are in kilowatts. Simultaneously, the current power command value of the energy storage system is obtained; this value is the final power control command value sent to the energy storage system in the previous control cycle. The positive power margin is calculated by subtracting the current power command value from the current maximum discharge power output by the energy storage system. This subtraction considers the sign; if the current power command value is negative, subtracting a negative number is equivalent to adding its absolute value. The negative power margin is calculated by subtracting the current power command value from the current maximum acceptable charging power of the energy storage system. A positive power margin represents the additional discharge capacity that the energy storage system can add at the current operating point, while a negative power margin represents the additional charging capacity that can be added.

[0048] The second target power value is dynamically limited based on this power margin. If the second target power value is positive, it is limited to a range no greater than the positive power margin. Specifically, the original value of the second target power value is compared with the value of the positive power margin, and the smaller of the two values ​​is taken as the second target power value after dynamic limiting. If the second target power value is negative, it is limited to a range no less than the negative power margin. Specifically, the original value of the second target power value is compared with the value of the negative power margin, and the larger of the two values ​​is taken as the second target power value after dynamic limiting. In the comparison of negative values, the larger value represents its smaller absolute value.

[0049] The first target power value and the second target power value after dynamic limiting are combined to generate the final power control command. The combination is an algebraic addition; the value of the final power control command is equal to the value of the first target power value plus the value of the second target power value after dynamic limiting. If it was previously determined that the two target power values ​​do not conflict, the original second target power value is used directly during the combination, without needing to use the second target power value after dynamic limiting.

[0050] The final power control command is output to the energy storage system to control its charging and discharging power. This output is achieved through a communication link between the control system and the energy storage system's power converter; for example, the voltage signal of the final power control command is sent to the power converter's power setting port via an analog output module, or the command value is sent to the power converter's controller via a digital communication protocol. The energy storage system's power converter adjusts its AC-side output current based on the received final power control command value, thereby controlling the active power exchanged with the ship's power grid.

[0051] Example 2: Figure 2 A schematic diagram of the load coordination control system for ship power generation units based on real-time power is provided. The load coordination control system for ship power generation units based on real-time power includes the following modules: The data acquisition module is used to acquire transient power demand data of the ship's electrical network; The operating condition determination module is used to analyze the real-time evolution trend of transient power demand data to assess the possibility that the power grid will enter a high dynamic risk state within a preset period of time in the future. Based on the comparison between the probability and the preset probability threshold, it determines whether the power grid is currently in a strong transient power demand operating condition. The matching evaluation module is used to evaluate the degree of matching between the energy storage system and the transient power demand data in the time and space dimensions when under strong transient power demand conditions. The demand analysis module is used to analyze the rate of change of the energy ratio of low-frequency trend components and high-frequency fluctuation components in transient power demand data within a preset time window. The decision arbitration module is used to determine the recovery priority of the first control function command for restoring the load of the power generation unit within the preset operating range and the second control function command for suppressing power fluctuations in the grid based on the matching degree and the rate of change, and to set the control function command with higher recovery priority as the dominant power command for the energy storage system. The command output module is used to coordinate and arbitrate the dominant power command with another control function command, and generate and output the final power control command to the energy storage system.

[0052] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0053] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0054] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0055] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0056] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0057] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0058] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for coordinated load control of ship power generation units based on real-time power, characterized in that, Includes the following steps: S1. Obtain transient power demand data for the ship's electrical grid; S2. Analyze the real-time evolution trend of transient power demand data to assess the possibility that the power grid will enter a high dynamic risk state within a preset period of time in the future. Based on the comparison between the possibility and the preset possibility threshold, determine whether the power grid is currently in a strong transient power demand condition. S3. When under conditions of strong transient power demand, assess the degree of matching between the energy storage system and the transient power demand data in terms of time and space, including: Based on the current available energy capacity of the energy storage system and the power integral of transient power demand data over a preset future period, the ability of the energy storage system to continuously support the power demand over time is evaluated. Based on the current available power capacity of the energy storage system and the peak power of transient power demand data in a preset future period, assess the ability of the energy storage system to cope with the peak power demand in the spatial dimension. The ability of an integrated energy storage system to continuously support the power demand over time and to cope with the peak power demand over space are used to determine the matching degree between the energy storage system and transient power demand data in time and space. S4. Analyze the rate of change of the energy ratio of low-frequency trend component and high-frequency fluctuation component in transient power demand data within a preset time window. S5. Based on the matching degree and the rate of change, determine the recovery priority of the first control function command for restoring the load of the power generation unit within the preset operating range and the second control function command for suppressing power fluctuations in the power grid, and set the control function command with higher recovery priority as the dominant power command for the energy storage system. S6. Coordinate and arbitrate the dominant power command with another control function command to generate and output the final power control command to the energy storage system.

2. The method for coordinated load control of ship power generation units based on real-time power as described in claim 1, characterized in that, S1 includes: Obtain raw sampled data of the total load power of the ship's electrical network; The raw sampled data of total load power is low-pass filtered to obtain filtered power data that reflects the trend of load base power change. Calculate the real-time deviation of the original sampled data of total load power relative to the filtered power data, and take the absolute value of the real-time deviation to obtain the fluctuating power data that reflects the intensity of instantaneous power fluctuations. The filtered power data is combined with the fluctuating power data to form transient power demand data.

3. The method for coordinated load control of ship power generation units based on real-time power according to claim 1, characterized in that, S2 include: Calculate the acceleration of the average rate of change of transient power demand data within the first time window prior to the current moment and the average rate of change within the second time window; The acceleration of change is compared with a benchmark parameter that reflects the grid’s inertial response capability to assess the possibility that the grid may lose its stability margin due to the continuous acceleration of power demand changes in the future within a preset period. The estimated probability is compared with a preset probability threshold. If the probability exceeds a preset probability threshold, the power grid is determined to be in a strong transient power demand condition.

4. The method for coordinated load control of ship power generation units based on real-time power as described in claim 1, characterized in that, S4 includes: The transient power demand data is filtered through a low-pass filter to separate the low-frequency trend component; Subtract the low-frequency trend component from the transient power demand data to obtain the high-frequency fluctuation component; Calculate the energy of the low-frequency trend component and the energy of the high-frequency fluctuation component within the preset time window, and calculate the proportion of the energy of the high-frequency fluctuation component in the total energy. Calculate the change of this proportion over multiple consecutive preset time windows, and determine the change per unit time as the rate of change of the energy proportion of the low-frequency trend component and the high-frequency fluctuation component within the preset time window.

5. The method for coordinated load control of ship power generation units based on real-time power according to claim 1, characterized in that, S5 include: Determine whether the rate of change exceeds the first rate threshold; If the rate of change exceeds the first rate threshold, it is determined whether the matching degree meets the requirements for rapid power regulation needed to suppress grid power fluctuations. If the matching degree meets the requirements for rapid power regulation needed to suppress grid power fluctuations, the recovery priority of the second control function command is set to be higher than that of the first control function command. Otherwise, the recovery priority of the first control function command is set to be higher than that of the second control function command; Set the control function commands with higher recovery priority as the dominant power commands for the energy storage system.

6. The method for coordinated load control of ship power generation units based on real-time power according to claim 5, characterized in that, Determining whether the matching degree meets the rapid power regulation requirements needed to suppress grid power fluctuations includes: If the energy storage system's ability to continuously support the power demand in the time dimension exceeds the first capability threshold, and the energy storage system's ability to cope with the peak power demand in the spatial dimension exceeds the second capability threshold, then the matching degree is determined to meet the rapid power regulation requirements required to support the suppression of grid power fluctuations. Otherwise, the matching degree is determined to be insufficient to support the rapid power regulation required to suppress grid power fluctuations.

7. The method for coordinated load control of ship power generation units based on real-time power according to claim 1, characterized in that, S6 include: Obtain the first target power value corresponding to the dominant power command and the second target power value corresponding to another control function command; Compare the magnitude and direction of the first target power value and the second target power value to determine whether the two target power values ​​conflict with each other; If two target power values ​​conflict with each other, the power margin is calculated based on the current power output capacity of the energy storage system, and the second target power value is dynamically limited according to the power margin. The first target power value is combined with the second target power value after dynamic limiting to generate the final power control command; The final power control command is output to the energy storage system to control its charging and discharging power.

8. The method for coordinated load control of ship power generation units based on real-time power as described in claim 7, characterized in that, Calculate the power margin based on the current power output capacity of the energy storage system, and dynamically limit the second target power value according to this power margin, including: The difference between the maximum discharge power that the energy storage system can currently output and the current power command value is calculated as the positive power margin, and the difference between the maximum charging power that the energy storage system can currently accept and the current power command value is calculated as the negative power margin. If the second target power value is positive, then it is limited to a range not exceeding the positive power margin. If the second target power value is negative, then it is limited to a range not less than the negative power margin.

9. A real-time power-based load coordination control system for ship power generation units, used to implement the real-time power-based load coordination control method for ship power generation units as described in any one of claims 1-8, characterized in that, Includes the following modules: The data acquisition module is used to acquire transient power demand data of the ship's electrical network; The operating condition determination module is used to analyze the real-time evolution trend of transient power demand data to assess the possibility that the power grid will enter a high dynamic risk state within a preset period of time in the future. Based on the comparison between the probability and the preset probability threshold, it determines whether the power grid is currently in a strong transient power demand operating condition. The matching evaluation module is used to evaluate the degree of matching between the energy storage system and the transient power demand data in the time and space dimensions when under strong transient power demand conditions. The demand analysis module is used to analyze the rate of change of the energy ratio of low-frequency trend components and high-frequency fluctuation components in transient power demand data within a preset time window. The decision arbitration module is used to determine the recovery priority of the first control function command for restoring the load of the power generation unit within the preset operating range and the second control function command for suppressing power fluctuations in the grid based on the matching degree and the rate of change, and to set the control function command with higher recovery priority as the dominant power command for the energy storage system. The command output module is used to coordinate and arbitrate the dominant power command with another control function command, and generate and output the final power control command to the energy storage system.

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