Parameter acquisition method and device, storage medium, program product and system

Abstract

The parameter collection method and device, storage medium, program product and system provided by the embodiments of the present application comprise: in the case that a multi-source heterogeneous energy system is running, electrical parameters corresponding to the multi-source heterogeneous energy system are continuously acquired according to an initial interval duration; based on the electrical parameters collected each time, multiple groups of operation information of a user end are predicted; in the case that the operation information indicates that the multi-source heterogeneous energy system has operation fluctuation, a jitter duration is determined based on a pre-set coefficient jitter range and the initial interval duration; a reduction duration is determined based on the number of groups of operation information indicating that the multi-source heterogeneous energy system has no operation fluctuation, the initial interval duration and a pre-set reference duration; an adjusted interval duration for collecting electrical parameters next time is determined based on the jitter duration and the reduction duration; and after waiting for the adjusted interval duration, the electrical parameters are acquired again, otherwise, the electrical parameters are continuously acquired according to the initial interval duration. The adaptive optimization of the system parameter collection frequency is realized.

Description

Technical Field

[0001] This application relates to the field of power electronics technology, and in particular to a parameter acquisition method, device, storage medium, program product and system. Background Technology

[0002] The core of common anti-backflow technology lies in relying on load data, energy production data, and charged storage data for prediction, and the acquisition frequency of parameters such as load data, energy production data, and charged storage data directly determines the effectiveness of anti-backflow control.

[0003] However, under normal circumstances, the load power data in a multi-source heterogeneous energy system is collected at a set sampling frequency. If the sampling frequency is too high, although it can achieve timely response of anti-backflow control, it will also cause the anti-backflow control of the multi-source heterogeneous energy system to change too frequently, resulting in unnecessary waste of energy. If the sampling frequency is too low, it may cause the anti-backflow control of the multi-source heterogeneous energy system to be lagging and untimely. Summary of the Invention

[0004] This application provides a parameter acquisition method, device, storage medium, program product, and system to ensure the timeliness of anti-backflow control while avoiding frequent control fluctuations and response lags caused by excessively high or low acquisition frequencies.

[0005] In a first aspect, embodiments of this application provide a parameter acquisition method for a multi-source heterogeneous energy system, wherein the multi-source heterogeneous energy system includes a grid side, a user-side load, at least one energy storage terminal, and at least one energy production terminal. The method includes:

[0006] When the multi-source heterogeneous energy system is in operation, the electrical parameters corresponding to the multi-source heterogeneous energy system are continuously acquired at initial intervals; the electrical parameters include at least the load power information of the load, the charge information of the energy storage end, and the energy production information of the energy production end;

[0007] Based on the electrical parameters collected each time, multiple sets of operating information at the user end are predicted;

[0008] When the operation information indicates that the multi-source heterogeneous energy system is experiencing operational fluctuations, the fluctuation duration is determined based on the preset coefficient fluctuation range and the initial interval duration.

[0009] The reduction duration is determined based on the number of groups of operating information indicating that the multi-source heterogeneous energy system has not experienced operational fluctuations, the initial interval duration, and the preset reference duration;

[0010] Based on the jitter duration and the reduction duration, the adjustment interval for the next acquisition of the electrical parameters is determined; and after waiting for the adjustment interval duration, the electrical parameters are acquired again; otherwise, the electrical parameters are acquired again according to the initial interval duration; the adjustment interval duration is less than the initial interval duration.

[0011] In one possible implementation, the operating information includes at least the predicted load power information of the load, the predicted charge information of the energy storage terminal, and the predicted energy production information of the energy production terminal.

[0012] After predicting multiple sets of operational information from the user terminal based on the electrical parameters collected each time, the method further includes:

[0013] Based on the predicted load power information, predicted charge information, and predicted energy production information contained in each set of operating information, the operating parameters of the load, the energy storage terminal, and the energy production terminal are dynamically adjusted to achieve anti-backflow control of the multi-source heterogeneous energy system.

[0014] In one possible implementation, the operating information includes at least the predicted load power information of the load, the predicted charge information of the energy storage terminal, and the predicted energy production information of the energy production terminal.

[0015] The method further includes:

[0016] Based on the predicted load power information, predicted charge information, and predicted production energy information in at least two sets of adjacent operating information, the fluctuation information corresponding to the multi-source heterogeneous energy system is determined; the fluctuation information includes at least the power difference of the load, the charge difference of the energy storage end, and the production energy difference of the energy production end in at least two sets of adjacent operating information.

[0017] If the power difference reaches a preset power difference threshold, and / or the charge difference reaches a preset charge difference threshold, and / or the production energy difference reaches a preset production energy difference threshold, the operation information indicates that the multi-source heterogeneous energy system is experiencing operational fluctuations; otherwise, the operation information indicates that the multi-source heterogeneous energy system is not experiencing operational fluctuations.

[0018] In one possible implementation, the step of continuously acquiring the electrical parameters corresponding to the multi-source heterogeneous energy system according to an initial interval further includes:

[0019] According to the initial interval duration, the interactive power of the common connection point between the grid side and the user side is continuously acquired;

[0020] The method of determining the adjustment interval for the next collection of electrical parameters based on the operational information, when the operational information indicates that the multi-source heterogeneous energy system is experiencing operational fluctuations, further includes:

[0021] If the interactive power indicates a reverse current at the user side, and / or the operation information indicates that the multi-source heterogeneous energy system is experiencing operational fluctuations, the adjustment interval for the next collection of the electrical parameters is determined based on the operation information.

[0022] In one possible implementation, determining the adjustment interval for the next collection of the electrical parameters based on the operational information includes:

[0023] The jitter duration is determined based on the pre-set jitter range and the initial interval duration;

[0024] The reduction duration is determined based on the number of groups of operating information indicating that the multi-source heterogeneous energy system has not experienced operational fluctuations, the initial interval duration, and the preset reference duration;

[0025] Based on the jitter duration and the reduction duration, the adjustment interval for the next collection of the electrical parameters is determined.

[0026] In one possible implementation, determining the jitter duration based on a pre-set coefficient jitter range and the initial interval duration includes:

[0027] From the preset coefficient jitter range, a value is randomly selected, and the value is multiplied by the initial interval duration to obtain the jitter duration.

[0028] Secondly, embodiments of this application provide a parameter acquisition device for a multi-source heterogeneous energy system, wherein the multi-source heterogeneous energy system includes a grid side, a user-side load, at least one energy storage terminal, and at least one energy production terminal, and the device includes:

[0029] The acquisition module is used to continuously acquire electrical parameters corresponding to the multi-source heterogeneous energy system at initial intervals when the multi-source heterogeneous energy system is in operation; the electrical parameters include at least the load power information of the load, the charge information of the energy storage end, and the production energy information of the energy production end;

[0030] The prediction module is used to predict multiple sets of operating information at the user end based on the electrical parameters collected each time.

[0031] The determination module is used to determine the duration of fluctuation based on a pre-set coefficient fluctuation range and the initial interval duration when the operation information indicates that the multi-source heterogeneous energy system has experienced operational fluctuations.

[0032] The determining module is also used to determine the reduction duration based on the number of groups of operating information indicating that the multi-source heterogeneous energy system has not experienced operational fluctuations, the initial interval duration, and the preset reference duration;

[0033] The determining module is further configured to determine the adjustment interval for the next acquisition of the electrical parameters based on the jitter duration and the reduction duration; and to acquire the electrical parameters again after waiting for the adjustment interval duration; otherwise, to continue acquiring the electrical parameters according to the initial interval duration; wherein the adjustment interval duration is less than the initial interval duration.

[0034] In one possible implementation, the operating information includes at least the predicted load power information of the load, the predicted charge information of the energy storage terminal, and the predicted energy production information of the energy production terminal.

[0035] The device also includes: an adjustment module;

[0036] The adjustment module is used to dynamically adjust the operating parameters of the load, the energy storage terminal, and the energy production terminal based on the predicted load power information, predicted charge information, and predicted energy production information contained in each set of operating information, so as to realize the anti-backflow control of the multi-source heterogeneous energy system.

[0037] In one possible implementation, the operating information includes at least the predicted load power information of the load, the predicted charge information of the energy storage terminal, and the predicted energy production information of the energy production terminal.

[0038] The determining module is further configured to determine the fluctuation information corresponding to the multi-source heterogeneous energy system based on the predicted load power information, predicted charge information, and predicted production energy information in at least two sets of adjacent operating information; the fluctuation information includes at least the power difference of the load, the charge difference of the energy storage end, and the production energy difference of the energy production end in at least two sets of adjacent operating information.

[0039] The determining module is further configured to determine that the operation information indicates that the multi-source heterogeneous energy system has experienced operational fluctuations when the power difference reaches a preset power difference threshold, and / or the charge difference reaches a preset charge difference threshold, and / or the production energy difference reaches a preset production energy difference threshold; otherwise, it is configured to determine that the operation information indicates that the multi-source heterogeneous energy system has not experienced operational fluctuations.

[0040] In one possible implementation, the acquisition module is further configured to continuously acquire the interaction power of the common connection point between the grid side and the user side according to the initial interval duration;

[0041] The determination module is also used to determine the adjustment interval for the next collection of electrical parameters based on the operation information when the interactive power indicates that a reverse current has occurred on the user side and / or the operation information indicates that the multi-source heterogeneous energy system has experienced operational fluctuations.

[0042] In one possible implementation, the determining module is further configured to randomly select a value from a pre-set coefficient jitter range and multiply the value by the initial interval duration to obtain the jitter duration.

[0043] Thirdly, this application provides a multi-source heterogeneous energy system, including a grid side, a user-side load, at least one energy storage terminal, and at least one energy production terminal;

[0044] The multi-source heterogeneous energy system is configured to employ the parameter acquisition method described in any one of the first aspects and / or various possible implementations of the first aspect.

[0045] Fourthly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0046] The memory stores computer-executed instructions;

[0047] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0048] Fifthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0049] In a sixth aspect, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0050] The parameter acquisition method, device, storage medium, program product, and system provided in this application, through continuous sensing of the operating status of the energy system, prediction of user-end operating information, and dynamic sampling adjustment based on operating fluctuations, achieve adaptive optimization of the parameter acquisition frequency of multi-source heterogeneous energy systems. This enables the sampling strategy to change in real time with changes in system state, reducing acquisition, transmission, and processing overhead during stable phases and improving the granularity of state perception and the ability to capture key state transition points during fluctuating phases. This, in turn, improves the tracking accuracy of dynamic changes in multi-source heterogeneous energy systems and the reliability of subsequent prediction and scheduling. Attached Figure Description

[0051] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0052] Figure 1 Flowchart of the parameter acquisition method provided in this application Figure 1 ;

[0053] Figure 2 Flowchart of the parameter acquisition method provided in this application Figure 2 ;

[0054] Figure 3 A schematic diagram of the parameter acquisition device provided in this application;

[0055] Figure 4 A schematic diagram of the structure of the electronic device provided in this application.

[0056] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0057] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0058] Existing parameter acquisition schemes for multi-source heterogeneous energy systems mostly employ a fixed-interval polling method. This involves continuously acquiring electrical parameters within the system according to a preset sampling period and generating an operational status record based on the acquisition results. This method is simple to implement and suitable for typical scenarios where the system operates relatively smoothly and load changes are not drastic. However, in multi-source heterogeneous energy systems, load power, energy storage state of charge, and power generation output are often affected by weather changes, user behavior, and dispatch instructions, resulting in highly random and sudden parameter fluctuations. While fixed-interval acquisition can ensure data continuity, it struggles to capture detailed changes before and after operational fluctuations, leading to the dilution or missing of critical state transition points, thus affecting the accurate prediction of user-end operational information.

[0059] This application addresses the problem of delayed response or excessive energy consumption in anti-backflow control caused by fixed parameter acquisition frequency in multi-source heterogeneous energy systems. It proposes an approach that adaptively adjusts the electrical parameter acquisition interval based on system operating status and predictive information. When the system is stable, the initial long interval is used to save energy and reduce consumption. When operating fluctuations or backflow are detected, the acquisition interval is automatically shortened to improve response speed. At the same time, dynamic anti-backflow control is achieved by combining predictive operating information, thus achieving a balance between control timeliness, system stability, and energy economy.

[0060] Figure 1 Flowchart of the parameter acquisition method provided in this application Figure 1 .like Figure 1 As shown, this embodiment provides a parameter acquisition method for a multi-source heterogeneous energy system. The multi-source heterogeneous energy system includes a grid side, a user-side load, at least one energy storage terminal, and at least one energy production terminal. The method includes:

[0061] S101. When the multi-source heterogeneous energy system is in operation, continuously acquire the electrical parameters corresponding to the multi-source heterogeneous energy system according to the initial interval duration.

[0062] The electrical parameters include at least the load power information of the load, the charge information of the energy storage terminal, and the energy production information of the energy production terminal. The initial interval duration is used as the basic sampling period during the normal operation phase of the system, and the electrical parameters are used as a data set to describe the system's electrical state. The load power information can be the total active power, reactive power, branch power, or time-of-use power curve; the charge information can be the state of charge percentage (SOC), remaining capacity, or equivalent discharge time of the energy storage battery; and the energy production information can be the output power of the photovoltaic inverter, the output of the wind power unit, or the cumulative power generation.

[0063] In this embodiment of the disclosure, a multi-source heterogeneous energy system refers to an energy operation system composed of a grid side, a user-side load, at least one energy storage terminal, and at least one energy production terminal. As the object of parameter acquisition and operation status analysis, its system boundary covers the point of common connection, user electrical equipment, energy storage unit, and distributed generation equipment, so that the subsequent acquisition results can reflect the internal supply and demand relationship and power flow changes of the system. In a multi-source heterogeneous energy system, the grid side is the public power grid, serving as the system's backup power source and standard grid connection interface. Examples include public distribution networks and transformers. The user-side load consists of various electrical devices in commercial and industrial parks, industrial parks, or residential scenarios, including continuous loads, intermittent loads, and fluctuating loads, such as production line equipment, air conditioners, and charging piles. The energy storage side consists of user-side energy storage battery systems, which may include energy storage battery packs and energy storage converters or battery energy storage systems (BESS), used to smooth power fluctuations and absorb surplus electricity. The energy production side consists of user-side self-consumption distributed generation equipment, such as distributed photovoltaic modules in industrial parks, small wind turbines, and micro gas turbines, which are user-side local power generation units.

[0064] In one specific implementation, the executing entity can be, for example, a master station controller, edge computing gateway, industrial computer, or cloud-edge collaborative server deployed in the park's energy management system. After the system is put into operation, the executing entity first completes equipment modeling and point mapping, connecting the load-side smart meters, energy storage converters, battery management systems, photovoltaic inverters, and common connection point measurement devices to the same data acquisition bus. Electrical parameters can be acquired through Modbus, Controller Area Network (CAN), IEC 104, Message Queuing Telemetry Transport (MQTT), or dedicated industrial Ethernet protocols. Alternatively, voltage and current signals can be sampled by an analog sampling module and converted to obtain the corresponding parameters; this application does not impose any restrictions on this.

[0065] For load power information, the active and reactive power output from the smart meter can be read directly, or the total load power on the user side can be generated by summing the data collected from multiple load branches. For charge information, the state of charge, average cell voltage, and remaining capacity can be read from the battery management system and then converted into energy storage charge information after consistency verification. For energy production information, instantaneous output power and cumulative electricity can be collected from the photovoltaic inverter, combiner box monitoring unit, or wind turbine controller to reflect the current production capacity of the energy production end.

[0066] Understandably, to ensure the stability of continuous acquisition, a scheduling thread with an initial interval is established when the sampling task is started. The initial interval can be set to a fixed value of 1 second, 5 seconds, 10 seconds, or minutes, depending on the system scale and communication bandwidth. At each sampling moment, the execution entity sends polling requests to each acquisition node or receives data frames actively reported by each node, and aligns the timestamps uniformly. If there is a millisecond to second-level deviation in the reports from different devices at the same time, nearest neighbor alignment or time window aggregation can be used to integrate the data within the current sampling window into a complete set of electrical parameters. Subsequently, the acquisition results undergo anomaly removal, missing value compensation, and dimensional unification processing. For example, single-point missing values ​​caused by communication interruptions are replaced by maintaining the previous time, linear interpolation, or redundant signals from the device side; data that significantly exceeds the rated range is truncated with a threshold and its quality status is marked. The processed parameters are written to the real-time database and buffer, providing structured input for subsequent operational information prediction.

[0067] In some embodiments, in order to improve the ability of the acquisition results to reflect the true state of the system, the interactive power, bus voltage, frequency and branch switch status at the common connection point can also be acquired simultaneously as auxiliary variables to participate in subsequent analysis, thereby enhancing the system's ability to perceive changes in grid connection status and local operational anomalies.

[0068] S102. Based on the electrical parameters collected each time, predict multiple sets of operating information at the user end.

[0069] The operational information includes at least the predicted load power information of the load, the predicted charge information of the energy storage end, and the predicted energy production information of the energy production end.

[0070] Operational information refers to the system's future or current operational status information predicted based on collected electrical parameters. Multiple sets of operational information refer to multiple sets of system operational status results obtained at different sampling times or prediction windows, used to compare changes in adjacent states and identify any operational fluctuations. Based on the changing trends of historical load power information, predicted load power information for future periods is generated to understand potential fluctuations in user electricity demand; combined with current charge information and its charging and discharging trends, predicted charge information for future moments is extrapolated to assess the energy storage capacity of energy storage devices; and based on historical energy production information from the energy production end, predicted energy production information for subsequent cycles is generated to predict the output efficiency of distributed energy resources.

[0071] Specifically, after completing this round of electrical parameter acquisition, the electrical parameters at the current moment and several historical moments can be input into the prediction model to generate operational information for one or more subsequent time periods. The prediction model can be a rule-based trend extrapolation model, a time series model, or a machine learning model, such as a time series analysis model, a regression analysis model, or a deep learning model.

[0072] For predicting energy storage charge information, charging and discharging power, rated capacity, and efficiency parameters can be introduced, and constraints can be applied according to the energy conservation relationship to make the prediction results meet the physical feasibility requirements.

[0073] For example, at least one set of short-term operational information for the next sampling period can be generated for each acquisition result, or multi-step prediction results for multiple future times can be generated simultaneously. For instance, after acquiring load power, state of charge, and photovoltaic output at the current time T, respectively, [the following can be generated]... , , The predicted load power information, predicted charge information, and predicted production energy information at three time points together constitute a set of multi-step operation information. As subsequent sampling continues, a new set of prediction results will be formed at each sampling time, thus forming a continuously updated sequence of multiple sets of operation information.

[0074] S103. When the operation information indicates that the multi-source heterogeneous energy system is experiencing operational fluctuations, determine the fluctuation duration based on the pre-set coefficient fluctuation range and initial interval duration.

[0075] S104. Based on the number of groups of operation information indicating that the multi-source heterogeneous energy system has not experienced operational fluctuations, the initial interval duration, and the preset reference duration, determine the reduction duration.

[0076] S105. Based on the jitter duration and reduction duration, determine the adjustment interval duration for the next acquisition of electrical parameters; and after waiting for the adjustment interval duration, acquire electrical parameters again; otherwise, continue to acquire electrical parameters according to the initial interval duration.

[0077] The adjustment interval is shorter than the initial interval.

[0078] Operational fluctuations refer to significant changes or unstable trends in the operating status of a multi-source heterogeneous energy system. These fluctuations can be identified by comparing changes in power, charge, or generation in adjacent operational data to see if they exceed thresholds. Adjustment intervals refer to shortened sampling periods used for the next sampling after an operational fluctuation is detected. These intervals are shorter than the initial intervals, used to increase the monitoring frequency of fluctuations.

[0079] Specifically, after obtaining multiple sets of operational information, differential calculations can first be performed on adjacent sets of operational information to obtain the predicted changes in load power, predicted changes in charge information, and predicted changes in production energy information. Then, each change is compared with its corresponding fluctuation threshold. When any change exceeds its corresponding threshold, or when the weighted composite index of multiple changes exceeds the composite threshold, it is determined that the system is experiencing operational fluctuations.

[0080] After confirming system fluctuations, the executing entity calculates the adjustment interval based on the operational information. The adjustment interval can be determined by proportional shortening, for example, multiplying the initial interval by a scaling factor less than 1 based on the deviation of the fluctuation index from the threshold to obtain the next sampling period; alternatively, it can be determined by a tiered mapping method, for example, adjusting the sampling interval to half the initial interval when the fluctuation level is level one, and to one-quarter of the initial interval when the fluctuation level is level two. To ensure controlled system resource usage, a lower limit can also be set for the adjustment interval to avoid excessively frequent sampling and communication congestion in extreme fluctuation scenarios.

[0081] When the operational information does not indicate any fluctuations in system performance, the execution entity maintains the default sampling strategy and continues to acquire electrical parameters at the initial interval. In other words, during stable operation, sampling encryption is not triggered, and the system maintains a low frequency of data acquisition, thereby reducing the processing burden on edge devices, minimizing network communication overhead, and reducing the accumulation of invalid data.

[0082] Understandably, during the stable phase of the system, the initial interval can be relatively long (e.g., 10 seconds), but the response mechanism for dynamically adjusting operating parameters can also be independent of the acquisition frequency. For example, when an increase in user-side load demand is predicted, the system can immediately issue load adjustment commands (such as increasing absorption capacity) through the edge computing gateway without waiting for the next electrical parameter acquisition.

[0083] In one possible implementation, continuously acquiring electrical parameters corresponding to the multi-source heterogeneous energy system according to an initial interval duration, further includes: continuously acquiring the interaction power of the common connection point between the grid side and the user side according to the initial interval duration.

[0084] When operational information indicates fluctuations in the operation of a multi-source heterogeneous energy system, the determination of the adjustment interval for the next collection of electrical parameters based on the operational information also includes:

[0085] When the interactive power indicates a reverse current on the user side, and / or the operation information indicates that the multi-source heterogeneous energy system is experiencing operational fluctuations, the adjustment interval for the next collection of electrical parameters is determined based on the operation information.

[0086] The Point of Common Coupling (PCC) is the point where the grid and the user connect. Interaction power characterizes the direction and magnitude of power exchange at this point. If it is reverse power, it indicates a reverse current flow from the user to the grid. In other words, when the interaction power is negative (i.e., the user is supplying power to the grid), it is considered reverse power, thus indicating a reverse current flow. Operational information is a state representation based on collected electrical parameters, reflecting the interconnected trends of load, energy storage, and generation. The adjustment interval refers to the interval obtained by shortening the next electrical parameter acquisition cycle after detecting reverse current or operational fluctuations.

[0087] Following an initial interval consistent with the electrical parameter acquisition, the interactive power at the common connection point between the grid and the user side is synchronously collected to comprehensively understand the energy interaction status between the user and grid sides. The adjusted interval can be calculated based on the reverse current intensity, fluctuation amplitude, and communication capacity, and is usually a time value shorter than the initial interval, with a lower limit set to avoid over-sampling.

[0088] After the controller adjusts the output interval, it re-triggers electrical parameter acquisition at that interval, thereby increasing the monitoring frequency when backflow or operational fluctuations occur on the user side, and maintaining the initial interval when the system is stable, so as to reduce the burden of data transmission and processing.

[0089] In some optional embodiments, the method further includes:

[0090] Based on the predicted load power information, predicted charge information, and predicted energy production information from at least two sets of adjacent operating information, the fluctuation information corresponding to the multi-source heterogeneous energy system is determined; the fluctuation information includes at least the power difference of the load, the charge difference of the energy storage end, and the energy production difference of the energy production end from at least two sets of adjacent operating information.

[0091] If the power difference reaches a preset power difference threshold, and / or the charge difference reaches a preset charge difference threshold, and / or the production energy difference reaches a preset production energy difference threshold, the operation information indicates that the multi-source heterogeneous energy system has experienced operational fluctuations; otherwise, the operation information indicates that the multi-source heterogeneous energy system has not experienced operational fluctuations.

[0092] The operational information characterizes the state changes of the multi-source heterogeneous energy system at adjacent prediction times. Predicted load power information reflects user-side load demand trends, predicted charge information reflects changes in available energy in energy storage units, and predicted energy production information reflects changes in power generation output. Fluctuation information is a set of change characteristics obtained by comparing two adjacent sets of operational information. Power difference, charge difference, and energy production difference can be implemented as absolute differences, relative differences, or normalized differences. Thresholds for power difference, charge difference, and energy production difference can be preset based on historical operational data, equipment rated parameters, and scenario fluctuation sensitivity, and can be written into the threshold table of the controller or edge gateway.

[0093] Understandably, predicted load power information is used to assess user-side load demand trends. When an increase in load demand is predicted and the energy storage system has the capacity to absorb it, the system sends power boosting commands to adjustable loads (such as air conditioners and charging piles) through the edge computing gateway to match the output of the energy production end. For example, when a sudden increase in production line load is predicted, the system can immediately increase the power level of some non-critical loads to avoid reverse power supply.

[0094] Optionally, the power difference threshold, charge difference threshold, and energy production difference threshold can be preset based on historical operating data, equipment rated parameters, and scenario fluctuation sensitivity. For example, the power difference threshold can refer to the typical fluctuation range of the load, the charge difference threshold can be combined with the charging and discharging rate of the energy storage system, and the energy production difference threshold can be configured according to the output stability of the energy production end.

[0095] Specifically, for example, in an industrial park microgrid, the power difference threshold can be set to 50kW to match the load fluctuations of the production line, while in a residential scenario it can be set to 5kW to adapt to changes in the start and stop of home appliances.

[0096] After completing continuous data acquisition, the system performs differential calculations on the predicted load power information of the same object at two adjacent sampling times to obtain the load power difference; it also performs differential calculations on the predicted charge information at two adjacent sampling times to obtain the charge difference at the energy storage end; and it performs differential calculations on the predicted energy production information at two adjacent sampling times to obtain the energy production difference at the energy production end. Subsequently, the controller compares these differences with corresponding thresholds one by one. When any difference reaches the threshold, the adjacent operating information pair is marked as having fluctuation characteristics, and an operating fluctuation flag is output; when all differences do not reach the threshold, an operating fluctuation flag is output. Furthermore, to ensure the stability of the judgment, the difference calculations can be completed under the same time base. If necessary, time alignment and interpolation compensation can be performed on the reporting times of different devices to avoid the impact of communication delays on the comparison results.

[0097] By comparing adjacent prediction results across three dimensions—load, energy storage, and power generation—the system transforms previously dispersed state changes into directly identifiable fluctuation information. This enables the system to promptly identify operational fluctuations during sudden load increases, energy storage charging / discharging switching, or abrupt changes in power generation output, and to stably output stable, fluctuation-free results during periods of calm. This approach provides a clear threshold for fluctuation detection, facilitating coordinated execution with subsequent sampling interval adjustments, load coordination control, and backflow prevention control, thereby improving the accuracy and timeliness of state perception in multi-source heterogeneous energy systems.

[0098] The parameter acquisition method provided in this application, through continuous sensing of the operating status of the energy system, prediction of user-end operating information, and dynamic sampling adjustment based on operating fluctuations, achieves adaptive optimization of the parameter acquisition frequency of multi-source heterogeneous energy systems. This enables the sampling strategy to change in real time with changes in system status, reducing acquisition, transmission, and processing overhead during stable phases and improving the granularity of state perception and the ability to capture key state transition points during fluctuating phases. This, in turn, improves the tracking accuracy of dynamic changes in multi-source heterogeneous energy systems and the reliability of subsequent prediction and scheduling.

[0099] Figure 2 Flowchart of the parameter acquisition method provided in the embodiments of this application Figure 2 .like Figure 2 As shown, this embodiment is... Figure 1 Based on the embodiments, a parameter acquisition method for multi-source heterogeneous energy systems is described in detail. The method includes:

[0100] S201. When the multi-source heterogeneous energy system is in operation, continuously acquire the electrical parameters corresponding to the multi-source heterogeneous energy system according to the initial interval duration.

[0101] S202. Based on the predicted load power information, predicted charge information and predicted production energy information contained in each set of operating information, the operating parameters of the load, energy storage end and energy production end are dynamically adjusted to realize the anti-backflow control of the multi-source heterogeneous energy system.

[0102] Specifically, predicted load power information is used to characterize changes in power demand of user-side loads in the future; predicted charge information is used to characterize changes in the state of charge of energy storage in the future; and predicted energy production information is used to characterize changes in power generation or supply of energy by energy producers in the future. Dynamic adjustment refers to online correction of the operating parameters of each object based on the prediction results. Specific adjustment parameters include, for example, the operating parameters of the load (start / stop status, power level, or operating period); the operating parameters of the energy storage end (charging / discharging power, charging / discharging direction, or current limit); and the operating parameters of the energy production end (active power output setpoint, power limiting threshold, or inverter control commands).

[0103] Understandably, after collecting load power information, charge information, and energy production information each time, the system inputs the current sampled values ​​and their historical trends into the prediction model to form a corresponding set of operational information. The prediction model can be, for example, a time series regression model, a neural network model, or a power balance model based on physical constraints, to output the load demand, energy storage availability, and power generation output for one or more future control cycles.

[0104] The system inputs the currently collected sampled values ​​and their historical trends into the prediction model, outputting predicted values ​​for load demand, energy storage availability, and power generation output for one or more future control cycles. The controller performs joint analysis on each set of operational information and assesses reverse flow risk by combining the power balance relationship at the point of common coupling (PCC). When the predicted local power generation capacity exceeds user load demand and the energy storage absorption capacity is insufficient, the controller reduces the output at the energy production end, increases the charging power at the energy storage end, or increases the adjustable load absorption power, enabling the system power to be absorbed internally and suppressing reverse power transmission to the grid.

[0105] In other words, the adaptive change in the data acquisition interval directly corresponds to the synchronous change in the frequency of the operating parameter adjustment: when the power consumption on the user side fluctuates, shortening the data acquisition interval and increasing the adjustment frequency can significantly improve control accuracy and achieve more refined anti-reverse current control; when the user side is running smoothly, maintaining the normal data acquisition and adjustment frequency avoids frequent system actions and balances control timeliness and operational stability.

[0106] Through the above-mentioned coordinated adjustment, the system performs coordinated pre-adjustment of the load, energy storage and generation terminals based on multiple sets of operating information, so that the operating status of each unit matches the future power relationship in advance, and the power flow direction at the common connection point can be actively corrected before the reverse flow occurs, thereby maintaining the stability of the power direction at the grid connection point.

[0107] S203. When the operation information indicates that the multi-source heterogeneous energy system is experiencing operational fluctuations, determine the fluctuation duration based on the pre-set coefficient fluctuation range and initial interval duration.

[0108] The coefficient jitter range is a pre-configured coefficient interval, with its upper and lower limits used to define the amplitude of random perturbations. The jitter duration is obtained by multiplying the coefficients selected within this interval by the initial interval duration, thus introducing a slight randomness to the sampling period and avoiding long-term fixation. The initial interval duration serves as the baseline sampling period and can be used to unify the time scale of jitter duration and reduction duration. The jitter duration is used to randomly perturb the sampling period, causing the sampling time to vary around the base period, thereby preventing the sampling beat from remaining fixed for a long time and synchronizing with external periodic interference.

[0109] After completing the operation information determination, random coefficients are first generated based on the coefficient jitter range. The random coefficients can be output by a random number generator or a pseudo-random sequence module and multiplied by the initial interval duration to form the jitter duration.

[0110] In one possible implementation, a value is randomly selected from a pre-set coefficient jitter range, and this value is multiplied by the initial interval duration to obtain the jitter duration.

[0111] The randomly selected value can be a real number coefficient located between preset upper and lower bounds. The controller can use a pseudo-random number generator to generate a coefficient within this range and multiply it by the initial interval duration to obtain the corresponding time value. This value can be directly used as the jitter ratio in the calculation. When the coefficient jitter range is set to fluctuate around the base ratio, the resulting jitter duration can vary within the adjacent intervals of the initial interval duration. To ensure randomness and repeatability, the pseudo-random number generator can generate random sequences based on timestamps, device identifiers, or preset seeds. In practical applications, other models of the random number generation module can also be selected, and this embodiment does not limit this.

[0112] Understandably, during operation, the system first reads the current initial interval duration, then generates coefficients based on the preset coefficient jitter range, and subsequently performs multiplication to obtain the jitter duration, which is then used to correct subsequent sampling intervals. In this way, the sampling period maintains its correlation with the base period while also exhibiting random perturbation characteristics, thereby reducing the probability of missing periodic fluctuations at fixed sampling times.

[0113] S204. Based on the number of groups of operating information indicating that the multi-source heterogeneous energy system has not experienced operational fluctuations, the initial interval duration, and the preset reference duration, determine the reduction duration.

[0114] The number of operational information sets without fluctuations characterizes the degree of continuous stability of the system, while the preset reference duration limits the shortening scale in a stable state. Combining these two with the initial interval duration maps to the shortened duration. By mapping the number of continuous operational information sets without fluctuations, the initial interval duration, and the preset reference duration, a shortened duration matching the system's stability is obtained. The more consecutively stable sets the system has, the larger the shortened duration and the greater the reduction in the sampling period, thus improving data acquisition efficiency while ensuring control effectiveness.

[0115] S205. Based on the jitter duration and reduction duration, determine the adjustment interval duration for the next acquisition of electrical parameters; and after waiting for the adjustment interval duration, acquire electrical parameters again; otherwise, continue to acquire electrical parameters according to the initial interval duration.

[0116] The jitter duration can be obtained by randomly selecting a value from the preset jitter range and multiplying it by the initial interval duration, thus introducing slight randomness to avoid the sampling beat being fixed for a long time.

[0117] The reduction time can be calculated based on the number of groups of operation information indicating no operational fluctuations, the initial interval time, and the preset reference time. In other words, it is dynamically calculated based on the number of groups of continuous and stable operation information. The more groups there are, the greater the reduction time, thereby appropriately shortening the interval during the stable phase.

[0118] The final adjustment interval is the smaller of the jitter duration and the reduction duration, to ensure encrypted data collection in fluctuating scenarios, while avoiding excessive shortening due to random jitter.

[0119] Understandably, the jitter duration and the reduction duration are combined to obtain the adjustment interval duration for the next collection of electrical parameters. This adjustment interval duration maintains a moderate shortening of the steady state while retaining necessary random disturbances to avoid a synchronous bias between the sampling time and the equipment control cycle.

[0120] Specifically, the combination operation can be performed by direct addition, weighted summation, etc. In this embodiment, it is preferable to directly add the jitter duration and the reduction duration to obtain the adjustment interval duration. This adjustment interval duration satisfies two requirements at the same time: first, it achieves a moderate shortening of the sampling period based on the stable state of the system, thereby improving the acquisition efficiency under stable conditions; second, it preserves the characteristics of random disturbances and avoids the synchronization bias problem caused by a fixed period.

[0121] The random design of the jitter duration effectively avoids synchronization between the sampling cycle and periodic interference (such as the photovoltaic power output fluctuation cycle), reducing the risk of missed detections. At the same time, the dynamic adjustment of the duration improves the acquisition efficiency and reduces the amount of invalid data during the stable phase. By reasonably configuring the coefficient jitter range and the reference duration, it is ensured that the obtained adjustment interval duration is less than the initial interval duration, so as to realize the encryption of the acquisition frequency in system fluctuation or reverse current scenarios and ensure the timeliness of anti-reverse current control.

[0122] Optionally, if the operation information indicates that the multi-source heterogeneous energy system is experiencing operational fluctuations, or if the interactive power indicates that a reverse current has occurred on the user side, the electrical parameters will be acquired again after waiting for the aforementioned adjustment interval. If the operation information does not indicate that the system is experiencing operational fluctuations, the electrical parameters will continue to be acquired according to the initial interval.

[0123] For example, determining the adjustment interval for the next collection of electrical parameters based on operational information is implemented as follows: The jitter duration is determined based on a pre-set coefficient jitter range and an initial interval duration. The coefficient jitter range is a preset interval less than 1, used to ensure that the adjustment interval duration is less than the initial interval duration. In this embodiment, the initial interval duration... The coefficient jitter range is set to [0.1, 0.9]. A coefficient of 0.5 is randomly selected from this range, and then... Calculate the duration of the jitter. .

[0124] Secondly, the reduction duration is determined based on the number of operation information groups indicating no operational fluctuations, the initial interval duration, and the preset reference duration. In this embodiment, the preset reference duration... The number of consecutive groups without fluctuations is attempt=10, according to the formula Calculate, and Not less than 0, substituting gives The reduction time is negatively correlated with the number of consecutive stable samples; the more samples there are, the shorter the reduction time and the tighter the sampling period.

[0125] Finally, the smaller value between the jitter duration and the reduction duration is determined as the adjustment interval for the next collection of electrical parameters. This enables encrypted data acquisition in fluctuating scenarios and improves the response speed of anti-backflow control.

[0126] In one possible embodiment, the method is applied to an industrial park microgrid. The system hardware includes a 1MW distributed photovoltaic (energy production end), a 500kWh energy storage system (energy storage end), park production line loads (user side), and an edge computing gateway (control core) equipped with control programs, and is connected to the external power grid through a point of common connection (PCC).

[0127] After system startup, the initial data acquisition interval was set to 10 seconds, and the power fluctuation threshold was set to 50kW. In the morning, due to stable sunlight and load, the system performed routine monitoring at 10-second intervals without triggering any special adjustments. After running for a period, weather changes caused drastic fluctuations in photovoltaic output due to cloud cover. The system's built-in predictive model (such as an LSTM neural network) predicted, based on the collected data, that the change in photovoltaic power would exceed the 50kW threshold, and thus determined that the system had entered an "operational fluctuation" state. To accurately capture transient changes, the system can automatically and dynamically shorten the data acquisition interval from 10 seconds to 2 seconds, entering a high-frequency monitoring mode.

[0128] After confirming the risk through high-frequency data acquisition, the system enters the anti-reverse flow regulation phase. The system predicts that the photovoltaic power generation capacity will exceed the load demand and that energy storage has absorption capacity. To prevent excess power from being fed back to the grid, the controller issues a power limiting command to the photovoltaic inverter in advance, lowering the active power output setpoint; simultaneously, it controls the energy storage converter to start forced charging to absorb excess power. This series of regulation actions is completed before the actual reverse flow occurs. Through coordinated pre-regulation of "reducing power generation and increasing storage," the power flow direction at the point of common coupling is proactively corrected.

[0129] Once the photovoltaic output stabilizes, the system automatically restores the data acquisition interval to the energy-saving mode of 10 seconds. This embodiment verifies that the solution effectively suppresses backflow through predictive adjustment while ensuring real-time data acquisition, achieving a balance between system resource consumption and control precision.

[0130] The parameter acquisition method provided in this application collects electrical parameters such as load power, energy storage charge, and energy production at initial intervals during system operation, thereby predicting user-end operating information. It determines whether system fluctuations occur by analyzing the difference between adjacent operating information and monitors for backflow at the common connection point. If fluctuations or backflow occur, the jitter duration is first calculated based on a preset coefficient jitter range and the initial interval duration. Then, the reduced duration is calculated by combining the number of continuous stable operating information sets, the initial interval duration, and the reference duration. These two are combined to obtain an adjustment interval duration smaller than the initial interval, used for more intensive acquisition. If the system is stable, the initial interval acquisition is maintained, and dynamic adjustments are made based on the operating information to achieve backflow prevention control. By adaptively adjusting the acquisition interval, the timeliness of backflow prevention control is ensured while effectively balancing system resource consumption, avoiding response lag or frequent control fluctuations caused by fixed-frequency acquisition.

[0131] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0132] Based on the same inventive concept, this application also provides a parameter acquisition device for implementing the parameter acquisition method described above. The solution provided by this parameter acquisition device is similar to the implementation scheme described in the parameter acquisition method above. Therefore, the specific limitations in one or more device embodiments provided below can be found in the limitations of the parameter acquisition method above, and will not be repeated here.

[0133] In one embodiment, such as Figure 3 As shown, a parameter acquisition device 300 for use in a multi-source heterogeneous energy system is provided. The multi-source heterogeneous energy system includes a grid side, a user-side load, at least one energy storage terminal, and at least one energy production terminal. The parameter acquisition device 300 includes:

[0134] The acquisition module 301 is used to continuously acquire the electrical parameters corresponding to the multi-source heterogeneous energy system according to the initial interval duration when the multi-source heterogeneous energy system is in operation; the electrical parameters include at least the load power information of the load, the charge information of the energy storage end, and the energy production information of the energy production end;

[0135] Prediction module 302 is used to predict multiple sets of operating information at the user end based on the electrical parameters collected each time.

[0136] The determination module 303 is used to determine the duration of fluctuation based on a pre-set coefficient fluctuation range and initial interval duration when the operation information indicates that the multi-source heterogeneous energy system has operation fluctuations.

[0137] The determination module 303 is also used to determine the reduction duration based on the number of groups of operating information indicating that the multi-source heterogeneous energy system has not experienced operational fluctuations, the initial interval duration, and the preset reference duration;

[0138] The determination module 303 is also used to determine the adjustment interval for the next acquisition of electrical parameters based on the jitter duration and the reduction duration; and to acquire electrical parameters again after waiting for the adjustment interval duration, otherwise, to continue acquiring electrical parameters according to the initial interval duration; the adjustment interval duration is less than the initial interval duration.

[0139] In one possible implementation, the operating information includes at least the predicted load power information of the load, the predicted charge information of the energy storage end, and the predicted energy production information of the energy production end; the device also includes: a regulation module;

[0140] The adjustment module is used to dynamically adjust the operating parameters of the load, energy storage end, and energy production end based on the predicted load power information, predicted charge information, and predicted energy production information contained in each set of operating information, so as to realize the anti-backflow control of the multi-source heterogeneous energy system.

[0141] In one possible implementation, the operational information includes at least the predicted load power information of the load, the predicted charge information of the energy storage end, and the predicted energy production information of the energy production end.

[0142] The determination module 303 is also used to determine the fluctuation information corresponding to the multi-source heterogeneous energy system based on the predicted load power information, predicted charge information and predicted production energy information in at least two sets of adjacent operating information; the fluctuation information includes at least the power difference of the load, the charge difference of the energy storage end and the production energy difference of the energy production end in at least two sets of adjacent operating information.

[0143] The determination module 303 is further configured to determine that the operation information indicates that the multi-source heterogeneous energy system has experienced operational fluctuations when the power difference reaches a preset power difference threshold, and / or the charge difference reaches a preset charge difference threshold, and / or the production energy difference reaches a preset production energy difference threshold; otherwise, it determines that the operation information indicates that the multi-source heterogeneous energy system has not experienced operational fluctuations.

[0144] In one possible implementation, the acquisition module 301 is further configured to continuously acquire the interaction power of the common connection point between the grid side and the user side according to the initial interval duration.

[0145] The determination module 303 is also used to determine the adjustment interval of the next electrical parameter acquisition based on the operation information when a reverse current occurs on the user side of the interactive power indication and / or the operation information indicates that the multi-source heterogeneous energy system has operation fluctuations.

[0146] In one possible implementation, the determining module 303 is further configured to randomly select a value from a preset coefficient jitter range and multiply the value by the initial interval duration to obtain the jitter duration.

[0147] Each module in the above-mentioned device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0148] Figure 4 A schematic diagram of the structure of the electronic device provided in this application. Figure 4 As shown, the electronic device 400 provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the electronic device 400 further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0149] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0150] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0151] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0152] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0153] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0154] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0155] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0156] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0157] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0158] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units 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 indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0159] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0160] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0161] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0162] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0163] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A parameter collection method applied to a multi-source heterogeneous energy system, characterized in that, The multi-source heterogeneous energy system includes a grid side, a user-side load, at least one energy storage terminal, and at least one energy production terminal; The method includes: When the multi-source heterogeneous energy system is in operation, the electrical parameters corresponding to the multi-source heterogeneous energy system are continuously acquired at initial intervals; the electrical parameters include at least the load power information of the load, the charge information of the energy storage end, and the energy production information of the energy production end; Based on the electrical parameters collected each time, multiple sets of operating information at the user end are predicted; When the operation information indicates that the multi-source heterogeneous energy system is experiencing operational fluctuations, a value is randomly selected from a pre-set coefficient fluctuation range, and this value is multiplied by the initial interval duration to obtain the fluctuation duration; The reduced duration is obtained by subtracting the product of the number of sets of operating information indicating that the multi-source heterogeneous energy system has not experienced operational fluctuations and the preset reference duration from the initial interval duration; The smaller value between the jitter duration and the reduction duration is determined as the adjustment interval duration for the next collection of the electrical parameters; Alternatively, the jitter duration and the reduction duration can be combined to obtain the adjustment interval duration for the next acquisition of the electrical parameters. The combination operation includes addition or weighted summation. After waiting for the adjustment interval duration, the electrical parameters are acquired again. Otherwise, the electrical parameters are acquired again according to the initial interval duration. The adjustment interval duration is less than the initial interval duration.

2. The method according to claim 1, characterized in that, The operational information includes at least the predicted load power information of the load, the predicted charge information of the energy storage terminal, and the predicted energy production information of the energy production terminal. After predicting multiple sets of operational information from the user terminal based on the electrical parameters collected each time, the method further includes: Based on the predicted load power information, predicted charge information, and predicted energy production information contained in each set of operating information, the operating parameters of the load, the energy storage terminal, and the energy production terminal are dynamically adjusted to achieve anti-backflow control of the multi-source heterogeneous energy system.

3. The method according to claim 1, characterized in that, The operational information includes at least the predicted load power information of the load, the predicted charge information of the energy storage terminal, and the predicted energy production information of the energy production terminal. The method further includes: Based on the predicted load power information, predicted charge information, and predicted production energy information in at least two sets of adjacent operating information, the fluctuation information corresponding to the multi-source heterogeneous energy system is determined; the fluctuation information includes at least the power difference of the load, the charge difference of the energy storage end, and the production energy difference of the energy production end in at least two sets of adjacent operating information. If the power difference reaches a preset power difference threshold, and / or the charge difference reaches a preset charge difference threshold, and / or the production energy difference reaches a preset production energy difference threshold, the operation information indicates that the multi-source heterogeneous energy system is experiencing operational fluctuations; otherwise, the operation information indicates that the multi-source heterogeneous energy system is not experiencing operational fluctuations.

4. The method according to claim 1, characterized in that, The step of continuously acquiring the electrical parameters corresponding to the multi-source heterogeneous energy system according to the initial interval duration also includes: According to the initial interval duration, the interactive power of the common connection point between the grid side and the user side is continuously acquired; The step of determining the adjustment interval for the next collection of electrical parameters based on the operational information, when the operational information indicates that the multi-source heterogeneous energy system is experiencing operational fluctuations, further includes: If the interactive power indicates a reverse current at the user side, and / or the operation information indicates that the multi-source heterogeneous energy system is experiencing operational fluctuations, the adjustment interval for the next collection of the electrical parameters shall be determined based on the operation information.

5. A parameter acquisition device for a multi-source heterogeneous energy system, characterized in that, The multi-source heterogeneous energy system includes a grid side, a user-side load, at least one energy storage terminal, and at least one energy production terminal; The device includes: The acquisition module is used to continuously acquire electrical parameters corresponding to the multi-source heterogeneous energy system at initial intervals when the multi-source heterogeneous energy system is in operation; the electrical parameters include at least the load power information of the load, the charge information of the energy storage end, and the production energy information of the energy production end; The prediction module is used to predict multiple sets of operating information at the user end based on the electrical parameters collected each time. The determination module is used to randomly select a value from a pre-set coefficient fluctuation range when the operation information indicates that the multi-source heterogeneous energy system has operation fluctuations, and multiply the value by the initial interval duration to obtain the fluctuation duration; The determining module is also used to subtract the product of the number of sets of operating information indicating that the multi-source heterogeneous energy system has not experienced operational fluctuations and the preset reference duration from the initial interval duration to obtain the reduced duration; The determining module is further configured to determine the smaller value between the jitter duration and the reduction duration as the adjustment interval duration for the next collection of the electrical parameters; Alternatively, the jitter duration and the reduction duration can be combined to obtain the adjustment interval duration for the next acquisition of the electrical parameters. The combination operation includes addition or weighted summation. After waiting for the adjustment interval duration, the electrical parameters are acquired again. Otherwise, the electrical parameters are acquired again according to the initial interval duration. The adjustment interval duration is less than the initial interval duration.

6. A multi-source heterogeneous energy system, characterized in that, This includes loads on the grid side and the user side, at least one energy storage terminal, and at least one energy production terminal; The multi-source heterogeneous energy system is configured to use the parameter acquisition method as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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