Distributed energy access scheduling optimization method, equipment, device and medium based on flexible equipment

Abstract

The present invention discloses a distributed energy access scheduling optimization method, equipment, device and medium based on flexible equipment, which relates to the field of power grid scheduling technology, and comprises the following steps: obtaining distributed energy production prediction data; obtaining the current maximum allowable charge and discharge power and entropy production rate threshold based on a polarization voltage response curve and a wavelet transform modulus maximum mutation detection method, and dynamically optimizing the entropy production rate threshold to obtain constraint conditions; dynamically limiting the charge and discharge depth of an energy storage system according to the distributed energy production prediction data and the constraint conditions; the present application improves the scheduling of distributed energy and the control accuracy of the charge and discharge depth of the energy storage system by dynamically limiting the charge and discharge depth of the energy storage system.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching, and more specifically, to a distributed energy access dispatching optimization method, equipment, device and medium based on flexible equipment. Background Art

[0002] With the acceleration of global energy transformation, distributed energy systems are gaining increasing attention as an important way to achieve sustainability, efficiency and environmental protection in energy production and consumption. Distributed energy, such as solar energy, wind energy and hydropower, has become an important alternative to traditional fossil energy due to its clean and renewable characteristics. However, the access of distributed energy has brought unprecedented challenges to the power grid, including the instability of energy supply, the volatility of power grid load and the impact on the security and stability of the power grid.

[0003] In order to meet these challenges, distributed energy access scheduling optimization methods based on flexible devices have emerged. Flexible devices, such as flexible interconnected devices, intelligent energy storage systems, and advanced power electronic equipment, have become the key to solving the problem of distributed energy access with their flexible, fast, and precise adjustment capabilities. Flexible interconnected devices, relying on voltage source converter technology, can independently adjust voltage and active and reactive power, thereby achieving flexible control of the power grid. This feature enables it to quickly adapt to fluctuations and faults in the power grid, significantly improving the stability and reliability of the power grid. At the same time, flexible interconnected devices also support the access of a variety of distributed energy sources, including wind power, solar energy, and energy storage systems, providing strong support for the large-scale application of new energy.

[0004] For example, the invention patent with announcement number: CN108646552A discloses a multi-objective optimization method for the parameters of natural gas distributed energy units based on genetic algorithms. It establishes a natural gas distributed energy system for the annual energy load demand of buildings; combines the exergy utilization, engineering investment and economic benefits to establish a multi-objective optimization function model for the natural gas distributed energy system; uses a fast non-dominated sorting genetic algorithm with an elite strategy to optimize the calculation of the power generation w, cooling power q1, hot water power q2 and equipment operating time of the cold and hot motor units to guide the design and operation scheduling of the distributed energy system; compares and analyzes the changing laws of the thermal economy and exergy utilization rate of the energy system when various energy prices fluctuate through simulation results, and derives the installation and operation principles of the distributed energy system under different energy prices. After optimizing the installation parameters and operating time of the distributed energy system, the present invention has good economic performance and energy cascade utilization benefits.

[0005] For example, the invention patent with announcement number: CN201698215U discloses a distributed energy network energy efficiency optimization and scheduling system, which is interconnected with the distributed energy network and a remote computer, and includes a host computer for realizing data processing functions, a subordinate computer for realizing process control functions and a communication network switch, wherein the host computer and the subordinate computer are connected via the communication network switch; the remote computer is connected to the host computer and the subordinate computer via the communication network switch to realize remote control and data collection of the distributed energy network; the system can enable the optimization scheduling data of the distributed energy network to smoothly reach the control unit of the energy conversion equipment, thereby achieving the purpose of optimizing energy efficiency, energy saving and environmental protection.

[0006] The above disclosed technical solutions have at least the following technical problems:

[0007] Traditional distributed energy access scheduling methods often allocate resources based on historical data or fixed rules, which is not accurate enough in scheduling distributed energy and controlling the charge and discharge depth of energy storage systems. In view of the above problems, the present invention proposes a solution. Summary of the invention

[0008] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a distributed energy access scheduling optimization method, equipment, device and medium based on flexible equipment, which dynamically limits the charging and discharging depth of the energy storage system to solve the problem of insufficient precision in the scheduling of distributed energy and the control of the charging and discharging depth of the energy storage system.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A distributed energy access scheduling optimization method based on flexible equipment includes the following steps: obtaining distributed energy production prediction data; obtaining the current maximum allowable charging and discharging power and entropy production rate threshold based on a polarization voltage response curve and a wavelet transform modulus maximum mutation detection method, and dynamically optimizing the entropy production rate threshold to obtain constraint conditions; dynamically limiting the charging and discharging depth of an energy storage system according to the distributed energy production prediction data and the constraint conditions.

[0011] In a preferred embodiment, the obtaining of distributed energy production prediction data is specifically as follows: obtaining first data of distributed energy in the area to be tested, and performing data analysis on the first data to obtain a distributed energy production distribution map, wherein the first data includes meteorological data and distributed energy production; performing discrete processing on the distributed energy production distribution map to draw a distributed energy constraint curve; predicting the meteorological data based on time series analysis, and combining the distributed energy constraint curve to obtain distributed energy production prediction data.

[0012] In a preferred embodiment, the polarization voltage response curve and wavelet transform modulus maximum mutation detection method are used to obtain the current maximum allowable charge and discharge power and entropy production rate threshold, and the entropy production rate threshold is dynamically optimized to obtain constraint conditions, specifically as follows: the polarization voltage response curve at different temperature steps is obtained, and the activation energy value is fitted using the nonlinear least squares method to obtain the activation energy attenuation change curve; the entropy production rate of the energy storage system is obtained, and the current maximum allowable charge and discharge power is obtained by calculating based on the Arrhenius algorithm according to the activation energy, real-time temperature and historical cycle number of the energy storage system; based on the current maximum allowable charge and discharge power constraint on the entropy production rate, the energy loss in the charge and discharge process is monitored in real time, the threshold is set to control the efficiency, and when the entropy production rate exceeds the threshold, the power is automatically reduced.

[0013] In a preferred embodiment, the entropy generation rate threshold acquisition step is as follows: intercepting the entropy generation rate time series data of the cycle before the capacity decays to the initial value N from the full life cycle cycle data of the energy storage system; locating the entropy generation rate mutation point in the entropy generation rate time series data by the wavelet transform modulus maximum detection method; calculating the initial entropy generation rate threshold of the over-limit area of ​​the spatiotemporal distribution of the entropy generation rate mutation point by the quantile method, and dynamically adjusting the local entropy generation rate threshold in combination with the rolling window; obtaining the characteristic data of the entropy generation rate, and classifying the characteristic data to distinguish between abnormal and normal states before the dive, the characteristic data of the entropy generation rate including the first-order difference, autocorrelation coefficient, current density and local volatility; calculating the balance accuracy of the entropy generation rate threshold of the gradient boosting tree model quantization based on the ROC curve, and selecting the critical value that maximizes the balance accuracy as the final entropy generation rate threshold.

[0014] In a preferred embodiment, the method of detecting the modulus maximum of wavelet transform is used to locate the entropy generation rate mutation point in the entropy generation rate time series data, specifically as follows: the entropy generation rate is normalized, and the entropy generation rate signal is decomposed into wavelet coefficients of different scales; the absolute value of each layer of wavelet coefficients is calculated, the candidate position of the mutation point is marked by the local maximum judgment algorithm, and the high-frequency noise is filtered out by the soft threshold method to retain the mutation characteristics; the time-frequency diagram of the wavelet coefficient is drawn, and the original entropy generation rate signal is superimposed to locate the spatiotemporal distribution of the mutation point.

[0015] In a preferred embodiment, the charging and discharging depth of the energy storage system is dynamically limited according to the distributed energy production forecast data and constraints, as follows: the distributed energy production forecast data and load demand are obtained to determine the direction of the net power gap, wherein the net power gap direction includes excess power generation and insufficient power generation; if there is excess power generation, the energy storage system charging operation is performed first, and the charging power is limited to the intersection interval of the real-time maximum allowable charging power and the cycle life constraint parameter through the power dynamic allocation module; if there is insufficient power generation, the maximum allowable discharge power and the entropy generation rate parameter are combined to generate a dynamic discharge depth reference value through a weighted coefficient; the entropy generation rate of the energy storage system is continuously monitored during the scheduling execution cycle, and when the entropy generation rate approaches the entropy generation rate threshold, the gradient load reduction protection mechanism is activated, and the low-loss energy storage unit is switched to first, and the current unit power is reduced.

[0016] The device of the distributed energy access scheduling optimization method based on flexible equipment is characterized in that it includes a data prediction module, an energy storage system coordination module and a charge and discharge depth limitation module, and there is a connection between the modules; the data prediction module is used to obtain distributed energy production prediction data; the energy storage system coordination module is used to obtain the current maximum allowable charge and discharge power and entropy production rate threshold based on the polarization voltage response curve and the wavelet transform modulus maximum mutation detection method, and dynamically optimize the entropy production rate threshold to obtain the constraint condition; the charge and discharge depth limitation module is used to dynamically limit the charge and discharge depth of the energy storage system according to the distributed energy production prediction data and the constraint condition.

[0017] An electronic device, characterized in that the electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a distributed energy access scheduling optimization method based on flexible devices.

[0018] A computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, a distributed energy access scheduling optimization method based on flexible devices is implemented.

[0019] The technical effects and advantages of the distributed energy access scheduling optimization method based on flexible equipment of the present invention are as follows:

[0020] 1. The present invention can dynamically limit the charge and discharge depth of the energy storage system, and can flexibly dispatch according to the real-time output forecast data of distributed energy and the actual needs of the power grid. It significantly improves the grid's acceptance capacity and dispatch flexibility for distributed energy.

[0021] 2. The present invention obtains constraint conditions and dynamically adjusts the charging and discharging strategy of the energy storage system according to these conditions, which helps to avoid energy waste, improve energy utilization efficiency, and achieve sustainable energy utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a structural schematic diagram of the distributed energy access scheduling optimization method based on flexible equipment of the present invention.

[0023] Figure 2 It is a schematic diagram of the system structure of the distributed energy access scheduling optimization method based on flexible equipment of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0025] Embodiment 1, Figure 1 The present invention provides a distributed energy access scheduling optimization method based on flexible equipment, which includes the following steps:

[0026] S1, obtain distributed energy production forecast data.

[0027] In this embodiment, the distributed energy production forecast data is obtained as follows:

[0028] Acquire first data of distributed energy in the area to be tested, and perform data analysis on the first data to obtain a distributed energy production distribution map, wherein the first data includes meteorological data and distributed energy production;

[0029] Discretize the distributed energy production distribution map and draw the distributed energy constraint curve;

[0030] Meteorological data is predicted based on time series analysis, and combined with distributed energy constraint curves to obtain distributed energy production forecast data.

[0031] S2, based on the polarization voltage response curve and the wavelet transform modulus maximum mutation detection method, obtain the current maximum allowable charge and discharge power and entropy production rate threshold, and dynamically optimize the entropy production rate threshold to obtain the constraint conditions.

[0032] In this embodiment, based on the polarization voltage response curve and the wavelet transform modulus maximum mutation detection method, the current maximum allowable charge and discharge power and entropy production rate threshold are obtained, and the entropy production rate threshold is dynamically optimized to obtain the constraint conditions, which are as follows:

[0033] Based on the thin-film temperature sensor array, it is arranged in a 3D grid topology inside the battery module to collect the surface and core temperature of the battery cell, and reconstruct the internal temperature field of the battery in combination with the Kalman filter algorithm;

[0034] The polarization voltage response curves at different temperature steps are obtained, and the activation energy values ​​are fitted using the nonlinear least squares method to obtain the activation energy attenuation curve;

[0035] Obtain the entropy production rate of the energy storage system, calculate based on the Arrhenius algorithm according to the activation energy, real-time temperature and historical cycle number of the energy storage system, and obtain the current maximum allowable charge and discharge power to avoid high temperature overload and protect the energy storage system;

[0036] An entropy generation rate constraint is introduced to monitor the energy loss during the charging and discharging process in real time, and a threshold is set to control the efficiency. When the entropy generation rate exceeds the threshold, the power is automatically reduced to extend the life of the energy storage system.

[0037] The attenuation change curve formula of the activation energy is as follows:

[0038]

[0039] The calculation formula of the entropy production rate is as follows:

[0040]

[0041] The calculation formula for the maximum allowable charge and discharge power is as follows:

[0042]

[0043] Where: is the maximum allowable charge and discharge power, is the constant of the energy storage system, is the activation energy, is the initial activation energy, The entity constant is 8.314 J / (mol·K), is the real-time temperature, is the number of historical charge and discharge cycles, is the entropy production ratio, is the entropy production rate, is the entropy production rate threshold, is the Joule loss, is the average temperature inside the battery.

[0044] It should be noted that the maximum charge and discharge power of the energy storage system is corrected through the Arrhenius equation (temperature-dependent rate equation), the entropy production rate ratio and the number of historical cycles are introduced, and the power upper limit is dynamically adjusted to avoid high-temperature overload or excessive attenuation of cycle life.

[0045] The steps for obtaining the entropy production rate threshold are as follows:

[0046] The entropy production rate time series data of 100 cycles before the capacity decays to the initial value N is intercepted from the full life cycle cycle data of the energy storage system. If the number of cycles is insufficient, it is expanded by exponentially weighted moving average.

[0047] The entropy generation rate mutation point in the entropy generation rate time series data is located by using the wavelet transform modulus maximum detection method;

[0048] The initial entropy production rate threshold of the over-limit area of ​​the spatiotemporal distribution of the entropy production rate mutation point is calculated by the quantile method, and the local entropy production rate threshold is dynamically adjusted in combination with the rolling window.

[0049] Acquire characteristic data of entropy production rate, classify the characteristic data based on the gradient boosting tree model, and distinguish between "abnormal before diving" and "normal" states, wherein the characteristic data of entropy production rate includes first-order difference, autocorrelation coefficient, current density and local volatility;

[0050] The balanced accuracy of the quantified entropy production rate threshold of the gradient boosting tree model is calculated based on the ROC curve, and the critical value that maximizes the balanced accuracy is selected as the final entropy production rate threshold.

[0051] The balanced accuracy is calculated based on the ROC curve, and the formula is:

[0052]

[0053] Where: is the balance accuracy, is the true positive rate, which is the proportion of abnormal (before capacity dive) samples that are correctly identified, and measures the sensitivity of the model. is the false positive rate, which is the proportion of normal samples that are mistakenly judged as abnormal.

[0054] The entropy generation rate mutation point in the entropy generation rate time series data is located by wavelet transform modulus maximum detection method, as follows:

[0055] The entropy generation rate is normalized to eliminate the difference in test environment, and the entropy generation rate signal is decomposed into wavelet coefficients of different scales;

[0056] The absolute value of each layer of wavelet coefficients is calculated, the candidate positions of mutation points are marked by the local maximum judgment algorithm, and the high-frequency noise is filtered out by the soft threshold method to retain the significant mutation features;

[0057] Draw the time-frequency diagram of the wavelet coefficients and superimpose the original entropy production rate signal to locate the spatial and temporal distribution of the mutation point.

[0058] It should be noted that if the entropy generation rate continues to be higher than the entropy generation rate threshold, it means that the battery capacity is about to drop significantly, that is, "dive".

[0059] Further, the basis for obtaining the entropy production rate threshold is explained:

[0060] Statistics show that 93% of the samples showed a continuous excess of entropy generation rate before the capacity dropped. The excess threshold is the candidate value of the entropy generation rate threshold. The excess threshold refers to a specific numerical limit used to determine whether the entropy generation rate (the change in battery capacity or the increment of other related parameters) exceeds the normal range.

[0061] S3, dynamically limits the charging and discharging depth of the energy storage system based on the distributed energy production forecast data and constraints.

[0062] In this embodiment, the charge and discharge depth of the energy storage system is dynamically limited according to the distributed energy production forecast data and constraint conditions, as follows:

[0063] Obtaining distributed energy production forecast data and load demand, and determining the direction of the net power gap, which includes overgeneration and undergeneration;

[0064] If there is excess power generation, the energy storage system is charged, and the charging power is limited to the intersection of the real-time maximum allowable charging power and the cycle life constraint parameter through the power dynamic allocation module;

[0065] If the power generation is insufficient, the maximum allowable discharge power and entropy production rate parameters are combined to generate a dynamic discharge depth reference value through a weighted coefficient;

[0066] The entropy generation rate of the energy storage system is continuously monitored during the scheduling execution cycle. When the entropy generation rate approaches the entropy generation rate threshold, the gradient load reduction protection mechanism is activated, switching to the low-loss energy storage unit and reducing the current unit power.

[0067] Embodiment 2, Figure 2 The invention provides a device for a distributed energy access scheduling optimization method based on flexible equipment, which is characterized by comprising a data prediction module, an energy storage system coordination module and a charge and discharge depth limitation module, and there is a connection between the modules;

[0068] Data prediction module, used to obtain distributed energy production prediction data;

[0069] The energy storage system coordination module is used to obtain the current maximum allowable charge and discharge power and entropy production rate threshold based on the polarization voltage response curve and the wavelet transform modulus maximum mutation detection method, and dynamically optimize the entropy production rate threshold to obtain the constraint conditions;

[0070] The charge and discharge depth limitation module is used to dynamically limit the charge and discharge depth of the energy storage system according to the distributed energy production forecast data and constraints.

[0071] In detail, the modules described in the distributed energy access scheduling optimization device based on flexible equipment in the embodiment of the present invention adopt the same technical means as the distributed energy access scheduling optimization method based on flexible equipment described in the accompanying drawings when used, and can produce the same technical effects, which will not be repeated here.

[0072] This embodiment includes that the electronic device may include a processor, a memory, a communication bus and a communication interface, and may also include a computer program stored in the memory and executable on the processor, such as a distributed energy access scheduling optimization model generation program based on flexible devices.

[0073] Among them, the processor is the control core (Control Unit) of the electronic device, which uses various interfaces and lines to connect the various components of the entire electronic device, and executes various functions of the electronic device and processes data by running or executing programs or modules stored in the memory and calling data stored in the memory.

[0074] The memory includes at least one type of readable storage medium, and in some embodiments, the memory may be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory may be used to store not only application software installed in the electronic device but also various data.

[0075] The communication bus is configured to realize connection and communication between the memory and at least one processor.

[0076] The communication interface is used for communication between the above electronic device and other devices, including a network interface and a user interface.

[0077] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0078] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0079] The distributed energy scheduling optimization model generation program stored in the memory in the electronic device is a combination of multiple instructions. When running in the processor, the steps in the above-mentioned distributed energy access scheduling optimization method based on flexible devices can be implemented.

[0080] Specifically, the specific implementation system of the processor for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0081] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor of an electronic device, it can implement the steps in the above-mentioned distributed energy access scheduling optimization method based on flexible devices.

[0082] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0083] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0084] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0085] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0086] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0087] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A distributed energy access scheduling optimization method based on flexible equipment, characterized in that: The steps include: Obtain distributed energy production forecast data; The polarization voltage response curves at different temperature steps are obtained, and the activation energy values ​​are fitted using the nonlinear least squares method to obtain the activation energy attenuation curve; Obtain the entropy production rate of the energy storage system, and combine the activation energy, real-time temperature and historical charge and discharge cycle number of the energy storage system based on the Arrhenius algorithm to obtain the current maximum allowable charge and discharge power; Based on the current maximum allowable charge and discharge power constraint on the entropy generation rate, the energy loss during the charge and discharge process is monitored in real time to obtain the threshold control efficiency. When the entropy generation rate exceeds the threshold, the power is automatically reduced. The entropy generation rate time series data of the cycle before the capacity decays to the initial value N is intercepted from the full life cycle cycle data of the energy storage system, and the entropy generation rate mutation point in the entropy generation rate time series data is located by wavelet transform modulus maximum mutation detection method; The initial entropy production rate threshold of the over-limit area of ​​the spatiotemporal distribution of the entropy production rate mutation point is calculated by the quantile method, and the local entropy production rate threshold is dynamically adjusted in combination with the rolling window. Based on the ROC curve, the balanced accuracy of the quantified entropy production rate threshold of the gradient boosting tree model is calculated, the critical value of the balanced accuracy is selected as the final entropy production rate threshold, and the constraint conditions are obtained; Dynamically limit the charge and discharge depth of the energy storage system based on distributed energy production forecast data and constraints; The balance accuracy calculation formula is: Where: is the balance accuracy, is the true case rate, is the false positive rate.

2. The distributed energy access scheduling optimization method based on flexible equipment according to claim 1 is characterized in that: The obtaining of distributed energy output forecast data is as follows: Acquire first data of distributed energy in the area to be tested, and perform data analysis on the first data to obtain a distributed energy production distribution map, wherein the first data includes meteorological data and distributed energy production; Discretize the distributed energy production distribution map and draw the distributed energy constraint curve; Meteorological data is predicted based on time series analysis, and combined with distributed energy constraint curves to obtain distributed energy production forecast data.

3. The distributed energy access scheduling optimization method based on flexible equipment according to claim 2 is characterized in that: The method of detecting the entropy generation rate mutation point in the entropy generation rate time series data by using the wavelet transform modulus maximum mutation detection method is specifically as follows: The entropy generation rate is normalized and the entropy generation rate signal is decomposed into wavelet coefficients of different scales; The absolute value of each layer of wavelet coefficients is calculated, the candidate positions of mutation points are marked by the local maximum judgment algorithm, and the high-frequency noise is filtered out by the soft threshold method to retain the mutation characteristics; Draw the time-frequency diagram of the wavelet coefficients and superimpose the original entropy production rate signal to locate the spatial and temporal distribution of the mutation point.

4. The distributed energy access scheduling optimization method based on flexible equipment according to claim 3 is characterized in that: The dynamic limitation of the charging and discharging depth of the energy storage system based on the distributed energy production forecast data and constraint conditions is as follows: Obtaining distributed energy production forecast data and load demand, and determining the direction of the net power gap, which includes overgeneration and undergeneration; If there is excess power generation, the energy storage system is charged, and the charging power is limited to the intersection of the real-time maximum allowable charging power and the cycle life constraint parameter through the power dynamic allocation module; If the power generation is insufficient, the maximum allowable discharge power and entropy production rate parameters are combined to generate a dynamic discharge depth reference value through a weighted coefficient; The entropy generation rate of the energy storage system is continuously monitored during the scheduling execution cycle. When the entropy generation rate is equal to the entropy generation rate threshold, the gradient load reduction protection mechanism is activated, switching to the low-loss energy storage unit and reducing the current unit power.

5. The distributed energy access scheduling optimization method based on flexible equipment according to claim 4 is characterized in that: The calculation formula of the maximum allowable charge and discharge power is as follows: Where: is the maximum allowable charge and discharge power, is the constant of the energy storage system, is the activation energy, The entity constant is 8.314, is the real-time temperature, is the number of historical charge and discharge cycles, is the entropy production ratio, is the entropy production rate, is the entropy production rate threshold.

6. A device using the distributed energy access scheduling optimization method based on flexible devices as described in any one of claims 1 to 5, characterized in that: It includes a data prediction module, an energy storage system coordination module, and a charge and discharge depth limit module, and there are connections between the modules; Data prediction module, used to obtain distributed energy production prediction data; The energy storage system coordination module is used to obtain the current maximum allowable charge and discharge power and entropy production rate threshold based on the polarization voltage response curve and the wavelet transform modulus maximum mutation detection method, and dynamically optimize the entropy production rate threshold to obtain the constraint conditions; The charge and discharge depth limitation module is used to dynamically limit the charge and discharge depth of the energy storage system according to the distributed energy production forecast data and constraints.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the distributed energy access scheduling optimization method based on flexible devices as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the distributed energy access scheduling optimization method based on flexible devices as described in any one of claims 1 to 5 is implemented.

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