Multi-scene networking control method and device suitable for modular energy storage system

Abstract

The invention discloses a multi-scene networking control method and device suitable for a modular energy storage system, and belongs to the technical field of modular energy storage management.The multi-scene networking control method comprises the following steps that test data of various energy storage unit modules are obtained, and unit models of the energy storage unit modules are established based on the test data; obtaining working condition data of the energy storage unit modules, performing simulation evaluation on each unit model through the working condition data, and determining a target energy storage unit module; identifying energy storage system information, and setting a target comprehensive energy storage module according to the energy storage system information; the target comprehensive energy storage module is composed of target energy storage unit modules; performing simulation analysis according to the target comprehensive energy storage module, and setting an energy storage analysis model; establishing a load prediction model and a condition prediction model, and generating a prediction information graph based on the load prediction model and the condition prediction model; performing real-time energy storage analysis adjustment based on the energy storage analysis model and the prediction information graph; and when a switching instruction is received, scene switching is carried out.

Description

Technical Field

[0001] The present invention belongs to the technical field of modular energy storage management, and specifically relates to a multi-scenario networking control method and device suitable for a modular energy storage system. Background Art

[0002] With the widespread use of renewable energy, such as solar and wind power generation, the stability of the power system faces new challenges; modular energy storage systems have become an important means to balance supply and demand and improve power quality due to their flexibility and scalability; modular energy storage systems integrate multiple independent energy storage units and modular energy storage inverters to achieve efficient energy storage and flexible scheduling, which is of great significance for improving grid stability and promoting the consumption of renewable energy.

[0003] However, in the actual operation of modular energy storage systems, the performance of energy storage inverters is directly related to the overall efficiency and stability of the system. Especially in the frequent switching between grid-connected and off-grid modes, the response speed, control accuracy and system stability of energy storage inverters face severe challenges. Traditional energy storage system control strategies are often based on fixed parameters or empirical values ​​for regulation, which makes it difficult to adapt to complex and changeable load demands and grid conditions, resulting in energy fluctuations, voltage flicker and other adverse phenomena when switching between grid-connected and off-grid, affecting the reliability and safety of the system. Therefore, how to effectively control modular energy storage systems to adapt to different grid scenarios, especially smooth switching between multiple working modes, has become an urgent problem to be solved.

[0004] Based on this, the present invention provides a multi-scenario networking control method and device suitable for a modular energy storage system. Summary of the invention

[0005] In order to solve the problems existing in the above-mentioned scheme, the present invention provides a multi-scenario networking control method and device applicable to a modular energy storage system.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A multi-scenario networking control method applicable to a modular energy storage system comprises the following steps:

[0008] Step 1: Acquire test data of various energy storage unit modules, and establish a unit model of the energy storage unit module based on each of the test data;

[0009] Step 2: Acquire the working condition data of the energy storage unit module, simulate and evaluate each unit model through the working condition data, and determine the target energy storage unit module;

[0010] Step 3: Identify the energy storage system information, and set a target comprehensive energy storage module according to the energy storage system information; the target comprehensive energy storage module is composed of various target energy storage unit modules;

[0011] Step 4: Perform simulation analysis according to the target comprehensive energy storage module and set up an energy storage analysis model;

[0012] Step 5: Establish a load forecasting model and a conditional forecasting model, and generate a forecasting information graph based on the load forecasting model and the conditional forecasting model;

[0013] Step 6: Perform real-time energy storage analysis adjustments based on the energy storage analysis model and the prediction information graph; when a switching instruction is received, perform scene switching.

[0014] Furthermore, the working condition data is composed of various working conditions and working condition values ​​corresponding to various working conditions; the working conditions are occurrence probabilities of the working conditions.

[0015] Furthermore, the simulating and evaluating each unit model through the working condition data to determine the target energy storage unit module includes:

[0016] Simulating each working condition in the working condition data through the unit model to obtain simulation results corresponding to each working condition;

[0017] Establishing a result judgment model, evaluating each of the simulation results through the result judgment model, and obtaining a simulation judgment value of each of the working conditions; identifying a working condition value corresponding to each working condition in the working condition data;

[0018] According to the formula Calculate the corresponding unit simulation value;

[0019] Where: PA is the unit simulation value; τi is the working condition value of the corresponding working condition; PD(si) is the simulation judgment value, i = 1, 2, ..., n, n is a positive integer;

[0020] Mark each energy storage unit module whose unit simulation value is not greater than the threshold value X1 as a unit module to be selected;

[0021] The candidate unit modules are displayed to the user, and the user selects the candidate unit module to be applied, and the selected candidate unit module is marked as the target energy storage unit module.

[0022] Furthermore, the expression of the result judgment model is:

[0023]

[0024] In the formula: si is the input data, the input data is the simulation result, i=1, 2, ..., n, n is a positive integer; the output data is the simulation judgment value PD(si).

[0025] Furthermore, the setting of the energy storage analysis model specifically includes:

[0026] Identify each target energy storage unit module in the target comprehensive energy storage module, match a corresponding unit model according to each target energy storage unit module, and establish a comprehensive model corresponding to the target comprehensive energy storage module according to each unit model;

[0027] The working condition data is acquired, and simulation analysis is performed on the working condition data through the comprehensive model to obtain energy storage analysis materials, and an energy storage analysis model is established based on the energy storage analysis materials.

[0028] Furthermore, generating a prediction information graph based on the load prediction model and the condition prediction model includes:

[0029] Identify a current scene mode, where the scene mode includes a grid-connected mode and an off-grid mode, and mark the current scene mode as a scene application mode;

[0030] Based on the scenario application mode, real-time analysis is performed through the load prediction model and the condition prediction model to obtain corresponding load prediction curves and condition prediction data; the horizontal axis of the load prediction curve is time, and the vertical axis is the predicted load; the condition prediction data is composed of prediction conditions corresponding to each time;

[0031] Identify the time corresponding to each prediction condition in the conditional prediction data, add each prediction condition to the load prediction curve according to the corresponding time, and mark the load prediction curve as a prediction information graph.

[0032] Furthermore, the method for performing real-time energy storage analysis adjustment based on the energy storage analysis model and the prediction information graph includes:

[0033] Analyzing the prediction conditions and the prediction load corresponding to each time in the prediction information graph by the energy storage analysis model to obtain the corresponding impact value;

[0034] Performing scene switching evaluation according to each of the impact values, obtaining a time period that does not meet the scene switching requirements, and marking it as an energy storage adjustment period; marking the energy storage adjustment period accordingly in the prediction information graph;

[0035] Perform energy storage adjustment simulation, and after the simulation adjustment, update the prediction information graph based on the load prediction model and the condition prediction model to obtain the prediction information graph corresponding to each energy storage adjustment mode, and mark it as a prediction simulation information graph;

[0036] The energy storage analysis model is used to analyze the prediction conditions and the prediction load corresponding to each time in the prediction simulation information diagram to obtain the corresponding impact value; and the energy storage adjustment method is judged to be qualified according to each impact value;

[0037] Summarize the qualified energy storage adjustment methods to obtain the energy storage simulation adjustment results;

[0038] Energy storage is adjusted according to the energy storage simulation adjustment results.

[0039] Furthermore, the method for performing real-time energy storage analysis adjustment based on the energy storage analysis model and the prediction information graph includes:

[0040] Obtain energy storage targets and real-time energy storage values ​​in real time; determine energy storage adjustment segments based on real-time energy storage values ​​and energy storage targets;

[0041] In the energy storage adjustment section, the prediction conditions and predicted loads corresponding to each time in the prediction information diagram are analyzed by the energy storage analysis model to obtain the influence value curve corresponding to each energy storage adjustment value, where the horizontal axis of the influence value curve is time and the vertical axis is the influence value;

[0042] The influence value curves are sorted according to the priority of each energy storage adjustment value to obtain a first sequence; the influence value curves in the first sequence are sorted and adjusted and evaluated in sequence to determine a target adjustment method, and energy storage is adjusted according to the target adjustment method.

[0043] Furthermore, the method of sequentially adjusting and evaluating according to the order of the influence value curves in the first sequence includes:

[0044] Identify energy storage adjustment periods based on the impact value curve;

[0045] When there is no energy storage adjustment period, the energy storage adjustment value corresponding to the corresponding impact value curve is marked as the target adjustment value, and the target adjustment method is determined according to the target adjustment value;

[0046] When there is an energy storage adjustment period, an energy storage adjustment simulation is performed, and after the simulation adjustment, the prediction information graph is updated based on the load prediction model and the condition prediction model to obtain the corresponding prediction information graph under each energy storage adjustment method, which is marked as a prediction simulation information graph; the prediction conditions and prediction loads corresponding to each time in the prediction simulation information graph are analyzed by the energy storage analysis model to obtain the corresponding impact values; whether the energy storage adjustment method is qualified is judged according to each impact value; when there is no qualified energy storage adjustment method, the next impact value curve is evaluated; when there is a qualified energy storage adjustment method, the target adjustment method is determined according to the qualified energy storage adjustment method and the energy storage adjustment value.

[0047] A multi-scenario networking control device suitable for a modular energy storage system, comprising:

[0048] Memory for storing computer programs;

[0049] A processor is used to implement a multi-scenario networking control method applicable to a modular energy storage system as described above when executing the computer program.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] By establishing a mathematical model based on the test data of the modular energy storage inverter and setting the influence curve of grid-connected / off-grid simulated switching under different loads, combined with load forecasting technology and adjustment strategy, it is an effective way to achieve efficient, stable operation and smooth switching of modular energy storage systems. This method not only improves the control accuracy and response speed of the system, but also significantly enhances the adaptability and reliability of the system, providing a strong guarantee for the large-scale access of renewable energy and the widespread application of distributed energy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 This is a flow chart of a multi-scenario networking control method applicable to a modular energy storage system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] like Figure 1 As shown, a multi-scenario networking control method applicable to a modular energy storage system includes the following steps:

[0056] Step 1: Obtain test data of various energy storage unit modules. The energy storage unit module is the modular energy storage device of the energy storage system. It can be targeted test data or usage data in historical applications, which is used to understand the working conditions and impacts of different types of energy storage unit modules in various environments, such as power grid environment, natural environment, etc.; establish a unit model of each energy storage unit module based on the test data; unit simulation is a mathematical model established based on existing technology, which can simulate the working conditions and impacts of the corresponding energy storage unit module under different conditions; the unit model can also be established based on digital twin technology;

[0057] Step 2: Obtain working condition data of the energy storage unit module, including grid adjustment data, natural environment condition data, and other data that have an impact on the work of the energy storage unit module, which are summarized based on historical related data; that is, the working condition data includes various working conditions that the energy storage working unit may encounter and the probability of occurrence of each working condition, and the probability of occurrence of the working condition is marked as the working condition value; simulate and evaluate each unit model through the working condition data, determine each energy storage unit module that meets the working requirements, and mark it as a candidate unit module; display each candidate unit module to the user, and the user selects the candidate unit module to be applied, and marks the applied candidate unit module as the target energy storage unit module;

[0058] In one embodiment, the method for determining the target energy storage unit module may be: manual evaluation and selection by professionals throughout the process.

[0059] In one embodiment, the method for simulating and evaluating each unit model using working condition data includes:

[0060] Through the unit model, each working condition in the working condition data is simulated to obtain the working status and influence under the working conditions, such as the impact on the power grid, the impact on its own work, etc., and the integration is marked as the simulation result; the standard result is set according to the relevant regulations, standards, requirements, etc., that is, the simulation result is not lower than the standard result to be regarded as meeting the working requirements under the working conditions; according to the standard result, a result judgment model is established, which is mainly based on data recognition and judgment technology to compare the simulation results with the standard results to judge whether the simulation results meet the standard results; the expression of the result judgment model is Where: si is the input data, the input data is the simulation result, i = 1, 2, ..., n, n is a positive integer; the output data is the simulation judgment value PD (si);

[0061] Each simulation result is evaluated through the result judgment model to obtain the simulation judgment value corresponding to each working condition;

[0062] According to the formula Calculate the corresponding unit simulation value;

[0063] Where: PA is the unit simulation value; τi is the working condition value of the corresponding working condition; PD(si) is the simulation judgment value, i = 1, 2, ..., n, n is a positive integer;

[0064] A threshold value X1 is set according to the user's requirements for the energy storage unit module, such as 0; and each energy storage unit module whose unit simulation value is not greater than the threshold value X1 is marked as a unit module to be selected.

[0065] Step 3: Integrate and mark each target energy storage unit module used in the energy storage system or the target energy storage unit module subsequently expanded as a target comprehensive energy storage module, that is, dynamically update the target comprehensive energy storage module according to the energy storage system information.

[0066] Step 4: Perform simulation analysis based on the target comprehensive energy storage module and set up an energy storage analysis model. The energy storage analysis model is used to analyze the impact of scenario mode switching of the target comprehensive energy storage module under different loads under current working conditions. The scenario modes include grid-connected mode and off-grid mode (island mode). The subsequent scenario switching is switching from grid-connected mode to off-grid mode or from off-grid mode to grid-connected mode. In order to facilitate analysis and intuitive observation, the switching impact is generally digitized and expressed as an impact value. The greater the adverse impact, the greater the impact value. The conversion can be based on the existing numerical conversion method. For example, the various switching impacts that the target comprehensive energy storage module may have are determined based on the simulation situation. , sort the impact data in order from large to small or from small to large, set the endpoints of the impact value range for the first and last in the sorting, such as 0 or 100, and then set the impact values ​​corresponding to different impacts one by one according to the impact differences, and match them during analysis; illustratively, the impact value is mainly used for subsequent scene mode switching impact evaluation. If the user's requirements for the impact of scene mode switching remain unchanged for a long time, it can be represented by 1 or 0, and the value that meets the switching impact requirements is 1, otherwise it is 0; that is, it is mainly used to ensure that the scene switching meets the user-defined scene smooth switching requirements; however, if more frequent standard changes are required, it is more convenient to set the impact value from 0 to 100, and then adjust the preset value.

[0067] The setting methods of energy storage analysis model include:

[0068] Identify the unit model corresponding to the target energy storage unit module in the target comprehensive energy storage module, and establish the comprehensive model corresponding to the target comprehensive energy storage module according to the unit model corresponding to each target energy storage unit module. The comprehensive model is used to simulate the working condition of the target comprehensive energy storage module under different working conditions, obtain the working condition data, simulate the working condition data through the comprehensive model, obtain the impact condition of the target comprehensive energy storage module under various working conditions and different loads, convert the impact condition into an impact value, integrate the above data into energy storage analysis materials, and establish an energy storage analysis model based on the energy storage analysis materials; the energy storage analysis model can be established based on the existing matching technology, and then matched based on the energy storage analysis materials; it can also be established based on a neural network such as a DNN network, and a training set is set for training with the energy storage analysis materials.

[0069] Step 5: Establish a load prediction model and a condition prediction model. The load prediction model is used to predict the energy storage load, that is, to predict the load of the current energy storage system and the load situation after a period of time. Specifically, the load prediction model is established using existing load prediction technology; the condition prediction model is used to predict subsequent working conditions based on existing power grid prediction, environmental prediction and other technologies;

[0070] Identify the current scene mode and mark the current scene mode as the scene application mode;

[0071] In the scenario application mode, real-time analysis is performed through the load forecasting model to obtain the load forecast curve, with the horizontal axis being time and the vertical axis being the predicted load; real-time analysis is performed through the conditional forecasting model to obtain conditional forecasting data, which is added to the load forecasting curve at the corresponding time, and the current load forecasting curve is marked as a forecasting information graph.

[0072] Step 6: Perform real-time energy storage analysis adjustments based on the energy storage analysis model and the prediction information graph; when a switching instruction is received, perform scene switching.

[0073] In one embodiment, a method for performing real-time energy storage analysis adjustment based on an energy storage analysis model and a prediction information graph includes:

[0074] The prediction conditions and predicted loads corresponding to each time in the prediction information graph are analyzed through the energy storage analysis model to obtain the corresponding impact values;

[0075] According to the impact value, the scene switching is evaluated, and the time period that does not meet the scene switching requirements is obtained and marked as the energy storage adjustment period; for the impact value of 1 or 0, it can be directly judged, and for the impact value of 0 to 100, it is judged according to the corresponding preset value, and it is greater than the preset value, which means that it does not meet the scene switching requirements; the energy storage adjustment period is marked accordingly in the prediction information diagram;

[0076] Perform energy storage adjustment simulation, that is, perform energy storage adjustment simulation of the energy storage system, such as increasing energy storage, adjusting charging and discharging speed, output power of the inverter, etc., to determine whether the standards can be met in time when receiving the mode switching command to achieve smooth switching of scene modes; such as when switching from grid-connected mode to off-grid mode, it is necessary to quickly adjust the charging and discharging speed of the energy storage system and the output power of the inverter; when switching from off-grid mode to grid-connected mode, it is necessary to adopt a pre-synchronization control strategy to synchronize the output voltage amplitude and phase of the inverter with the large power grid quickly; therefore, when performing energy storage adjustment, it is possible to pre-simulate and adjust to the corresponding conditions for analysis; determine under what circumstances the energy storage adjustment can smoothly switch the scene mode when receiving the scene switching command, and integrate it into the energy storage simulation result;

[0077] Exemplarily, an energy storage adjustment simulation is performed, and the prediction information graph is updated based on the load prediction model and the conditional prediction model. The energy storage adjustment will cause load changes. The specific energy storage adjustment can be adjusted by adjusting adjustable parameters one by one, or a corresponding simulation adjustment strategy can be preset, and then simulated adjustment is performed according to the simulation adjustment strategy. The corresponding prediction information graph under each energy storage adjustment mode is obtained and marked as a prediction simulation information graph.

[0078] The prediction conditions and predicted loads corresponding to each time in the prediction simulation information diagram are analyzed through the energy storage analysis model to obtain the corresponding impact values; and whether the energy storage adjustment method is qualified is determined based on each impact value;

[0079] The qualified energy storage adjustment methods are summarized to obtain the energy storage simulation adjustment results.

[0080] Energy storage adjustment is performed according to the energy storage simulation adjustment results; specifically, the energy storage adjustment can be performed by random selection or other selection criteria or priority evaluation methods to select the energy storage adjustment method to be applied.

[0081] In one embodiment, the energy storage system will manage energy storage according to actual conditions during the application process. Therefore, there will be an energy storage target in the management process, that is, the energy storage target expected to be achieved at each time. As conditions change, the energy storage target is in a changing state. In order to ensure the operation of the energy storage system, corresponding adjustments can be made in combination with the energy storage target; that is, another method for real-time energy storage analysis and adjustment based on the energy storage analysis model and the prediction information graph is:

[0082] Obtain the energy storage target and the current energy storage value in real time, and mark the current energy storage value as the real-time energy storage value; determine the energy storage adjustment segment according to the real-time energy storage value and the energy storage target, that is, from the real-time energy storage value to the energy storage target, including the corresponding endpoints;

[0083] In the energy storage adjustment section, the prediction conditions and predicted loads corresponding to each time in the prediction information diagram are analyzed by the energy storage analysis model to obtain the impact value curve corresponding to each energy storage adjustment value, with the horizontal axis being time and the vertical axis being the impact value;

[0084] The influence value curves are sorted in order from large to small according to the energy storage adjustment value. The larger the energy storage adjustment value, the closer it is to the energy storage target; a first sequence is obtained; specifically, the energy storage adjustment values ​​are sorted according to their priority order. Generally, the closer it is to the energy storage target, the higher its priority. If the priority is not determined according to this principle, the order of the energy storage adjustment values ​​is determined according to the analysis method of the energy storage target. Because each energy storage value will be evaluated accordingly when determining the energy storage target in the prior art, the priority of each energy storage adjustment value can be determined based on its determination method.

[0085] According to the sorting of each impact value curve in the first sequence, the adjustment evaluation is carried out in sequence, the target adjustment method is determined, and the energy storage adjustment is carried out according to the target adjustment method.

[0086] The method of sequentially adjusting and evaluating according to the order of each impact value curve in the first sequence includes:

[0087] Identify energy storage adjustment periods based on the impact value curve;

[0088] When there is no energy storage adjustment period, the energy storage adjustment value corresponding to the impact value curve is marked as the target adjustment value, and the target adjustment method is determined according to the target adjustment value;

[0089] When there is an energy storage adjustment period, an energy storage adjustment simulation is performed, and after the simulation adjustment, the prediction information graph is updated based on the load prediction model and the condition prediction model to obtain the corresponding prediction information graph under each energy storage adjustment method, which is marked as a prediction simulation information graph, excluding the adjustment of the energy storage value, and the default fixed energy storage adjustment value; the prediction conditions and predicted loads corresponding to each time in the prediction simulation information graph are analyzed by the energy storage analysis model to obtain the corresponding impact values; whether the energy storage adjustment method is qualified is judged according to each impact value; when there is no qualified energy storage adjustment method, the next impact value curve is evaluated; when there is a qualified energy storage adjustment method, the target adjustment method is determined according to the qualified energy storage adjustment method and the energy storage adjustment value.

[0090] A multi-scenario networking control device suitable for a modular energy storage system, comprising:

[0091] Memory for storing computer programs;

[0092] A processor is used to implement the steps of the above-mentioned embodiment (a multi-scenario networking control method applicable to a modular energy storage system) when executing a computer program.

[0093] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0094] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0095] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, is also included in the patent protection scope of the present invention.

[0096] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0097] For the convenience of description, the above device is described by dividing each unit into functions and describing them separately. Of course, when implementing the present application, the functions of each unit can be implemented in the same one or more software and / or hardware. It should be understood by those skilled in the art that the embodiments of the present invention can be provided as methods, systems or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0098] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 The functionality specified in one or more boxes.

[0099] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A function specified in a process or multiple boxes.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

Claims

1. A multi-scenario network control method suitable for a modular energy storage system, characterized in that: The steps include: Step 1: Acquire test data of various energy storage unit modules, and establish a unit model of the energy storage unit module based on the test data; Step 2: Acquire the working condition data of the energy storage unit module, simulate and evaluate each unit model through the working condition data, and determine the target energy storage unit module; Step 3: Identify the energy storage system information, and set a target comprehensive energy storage module according to the energy storage system information; the target comprehensive energy storage module is composed of various target energy storage unit modules; Step 4: Perform simulation analysis according to the target comprehensive energy storage module and set up an energy storage analysis model; Step 5: Establish a load forecasting model and a conditional forecasting model, and generate a forecasting information graph based on the load forecasting model and the conditional forecasting model; Step 6: Perform real-time energy storage analysis and adjustment based on the energy storage analysis model and prediction information graph; When a switching instruction is received, the scene is switched.

2. A multi-scenario networking control method applicable to a modular energy storage system according to claim 1, characterized in that: The working condition data is composed of various working conditions and working condition values ​​corresponding to various working conditions; the working conditions are the occurrence probabilities of the working conditions.

3. A multi-scenario networking control method applicable to a modular energy storage system according to claim 2, characterized in that: The simulating and evaluating each unit model through the working condition data to determine the target energy storage unit module includes: Simulating each working condition in the working condition data through the unit model to obtain simulation results corresponding to each working condition; Establishing a result judgment model, evaluating each of the simulation results through the result judgment model, and obtaining a simulation judgment value of each of the working conditions; identifying a working condition value corresponding to each working condition in the working condition data; According to the formula Calculate the corresponding unit simulation value; Where: PA is the unit simulation value; τi is the working condition value of the corresponding working condition; PD(si) is the simulation judgment value, i = 1, 2, ..., n, n is a positive integer; Mark each energy storage unit module whose unit simulation value is not greater than the threshold value X1 as a unit module to be selected; The candidate unit modules are displayed to the user, and the user selects the candidate unit module to be applied, and the selected candidate unit module is marked as the target energy storage unit module.

4. A multi-scenario networking control method applicable to a modular energy storage system according to claim 3, characterized in that: The expression of the result judgment model is: In the formula: si is the input data, the input data is the simulation result, i=1, 2, ..., n, n is a positive integer; the output data is the simulation judgment value PD(si).

5. A multi-scenario networking control method applicable to a modular energy storage system according to claim 1, characterized in that: The energy storage analysis model is set up, specifically including: Identify each target energy storage unit module in the target comprehensive energy storage module, match a corresponding unit model according to each target energy storage unit module, and establish a comprehensive model corresponding to the target comprehensive energy storage module according to each unit model; The working condition data is acquired, and simulation analysis is performed on the working condition data through the comprehensive model to obtain energy storage analysis materials, and an energy storage analysis model is established based on the energy storage analysis materials.

6. A multi-scenario networking control method applicable to a modular energy storage system according to claim 1, characterized in that: The generating of the prediction information graph based on the load prediction model and the condition prediction model includes: Identify a current scene mode, where the scene mode includes a grid-connected mode and an off-grid mode, and mark the current scene mode as a scene application mode; Based on the scenario application mode, real-time analysis is performed through the load prediction model and the condition prediction model to obtain corresponding load prediction curves and condition prediction data; the horizontal axis of the load prediction curve is time, and the vertical axis is the predicted load; the condition prediction data is composed of prediction conditions corresponding to each time; Identify the time corresponding to each prediction condition in the conditional prediction data, add each prediction condition to the load prediction curve according to the corresponding time, and mark the load prediction curve as a prediction information graph.

7. A multi-scenario networking control method applicable to a modular energy storage system according to claim 1, characterized in that: The method for real-time energy storage analysis adjustment based on the energy storage analysis model and the prediction information graph includes: Analyzing the prediction conditions and the prediction load corresponding to each time in the prediction information graph by the energy storage analysis model to obtain the corresponding impact value; Performing scene switching evaluation according to each of the impact values, obtaining a time period that does not meet the scene switching requirements, and marking it as an energy storage adjustment period; marking the energy storage adjustment period accordingly in the prediction information graph; Perform energy storage adjustment simulation, and after the simulation adjustment, update the prediction information graph based on the load prediction model and the condition prediction model to obtain the prediction information graph corresponding to each energy storage adjustment mode, and mark it as a prediction simulation information graph; The energy storage analysis model is used to analyze the prediction conditions and the prediction load corresponding to each time in the prediction simulation information diagram to obtain the corresponding impact value; and the energy storage adjustment method is judged to be qualified according to each impact value; Summarize the qualified energy storage adjustment methods to obtain the energy storage simulation adjustment results; Energy storage is adjusted according to the energy storage simulation adjustment results.

8. A multi-scenario networking control method applicable to a modular energy storage system according to claim 1, characterized in that: The method for real-time energy storage analysis adjustment based on the energy storage analysis model and the prediction information graph includes: Obtain energy storage targets and real-time energy storage values ​​in real time; determine energy storage adjustment segments based on real-time energy storage values ​​and energy storage targets; In the energy storage adjustment section, the prediction conditions and predicted loads corresponding to each time in the prediction information diagram are analyzed by the energy storage analysis model to obtain the influence value curve corresponding to each energy storage adjustment value, where the horizontal axis of the influence value curve is time and the vertical axis is the influence value; The influence value curves are sorted according to the priority of each energy storage adjustment value to obtain a first sequence; the influence value curves in the first sequence are sorted and adjusted and evaluated in sequence to determine a target adjustment method, and energy storage is adjusted according to the target adjustment method.

9. A multi-scenario networking control method applicable to a modular energy storage system according to claim 8, characterized in that: The method of sequentially adjusting and evaluating according to the order of each impact value curve in the first sequence includes: Identify energy storage adjustment periods based on the impact value curve; When there is no energy storage adjustment period, the energy storage adjustment value corresponding to the corresponding impact value curve is marked as the target adjustment value, and the target adjustment method is determined according to the target adjustment value; When there is an energy storage adjustment period, an energy storage adjustment simulation is performed, and after the simulation adjustment, the prediction information graph is updated based on the load prediction model and the condition prediction model to obtain the corresponding prediction information graph under each energy storage adjustment method, which is marked as a prediction simulation information graph; the prediction conditions and prediction loads corresponding to each time in the prediction simulation information graph are analyzed by the energy storage analysis model to obtain the corresponding impact values; whether the energy storage adjustment method is qualified is judged according to each impact value; when there is no qualified energy storage adjustment method, the next impact value curve is evaluated; when there is a qualified energy storage adjustment method, the target adjustment method is determined according to the qualified energy storage adjustment method and the energy storage adjustment value.

10. A multi-scenario networking control device suitable for a modular energy storage system, characterized in that: include: Memory for storing computer programs; A processor, used to implement a multi-scenario networking control method applicable to a modular energy storage system as described in any one of claims 1 to 9 when executing the computer program.

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