Optimal dispatching control system for coordinated operation of energy storage in distribution area and intelligent distribution transformer

Abstract

The application discloses a distribution area energy storage and intelligent distribution transformer collaborative operation optimization scheduling control system and relates to the technical field of power system operation optimization. The application comprises a data acquisition and identification module, which acquires transformer voltage, current and winding temperature in real time and identifies real equivalent heat capacity online; a bidirectional coupling module, which constructs a magnetic flux and temperature bidirectional coupling model containing harmonic correction; a fluctuation decomposition module, which decomposes power grid power fluctuation into a magnetic flux adjustment component and a thermal buffer component, and preferentially utilizes transformer self-regulation capacity; a collaborative decision module, which takes the minimum comprehensive cost of transformer insulation life loss and energy storage cycle life loss as the target and solves the optimal energy storage bearing proportion; and a state recovery module, which adjusts magnetic flux density and temperature to a preset intermediate safety interval after fluctuation suppression. The application realizes economic trade-off of transformer and energy storage cross-device life loss, guarantees distribution area power supply reliability and reduces long-term operation cost of the whole system.

Description

Technical Field

[0001] This invention relates to the field of power system operation optimization technology, and in particular to a distribution area energy storage and smart distribution transformer collaborative operation optimization and dispatch control system. Background Technology

[0002] With the advancement of the construction of new power systems, distribution network areas are facing the dual pressures of load growth and equipment expansion. The traditional method of expanding capacity by replacing large-capacity transformers can reduce distribution pressure. However, this method has problems such as high investment costs, long construction periods, and low equipment utilization rates, making it difficult to meet the needs of modern distribution areas with strong load fluctuations and high penetration of new energy sources. Therefore, most areas are currently installing energy storage systems. By discharging energy storage systems during peak load periods, the capacity gap of transformers can be supplemented, thereby improving the power supply capacity of distribution areas.

[0003] In existing technologies, energy storage systems mostly adopt a fixed threshold triggering mode. When the transformer load rate exceeds a set value, the energy storage system is forced to discharge. When the transformer load rate decreases, the energy storage system is immediately charged. Although this on / off control strategy can suppress transformer overload in the short term, it leads to frequent start-stop and deep charge-discharge of the energy storage system, thereby accelerating the degradation of battery life. Existing solutions lack the ability to perceive and dynamically adjust the battery health status in real time, making it difficult to achieve a balance between ensuring that the transformer does not overload and extending the life of the energy storage system. Summary of the Invention

[0004] The purpose of this invention is to provide an optimized scheduling and control system for the coordinated operation of distribution area energy storage and intelligent distribution transformers, which solves the problems of existing distribution area energy storage systems using fixed threshold triggering, resulting in the transformer's regulation capacity not being fully utilized, frequent deep cycling of energy storage accelerating its lifespan decay, and the lack of a cross-equipment lifespan collaborative decision-making mechanism.

[0005] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0006] This invention is an optimized scheduling and control system for the coordinated operation of distribution area energy storage and smart distribution transformers, comprising:

[0007] Data acquisition and identification module: used to collect transformer voltage, current and winding temperature in real time, calculate the current magnetic flux density of the transformer based on the relationship between voltage and magnetic flux, and identify the current true equivalent heat capacity of the transformer by injecting reactive disturbance and observing the temperature response slope.

[0008] Two-way coupling module: used to construct a two-way coupling model of magnetic flux and temperature including harmonic correction, including magnetothermal constraint and thermomagnetic constraint. The magnetothermal constraint is determined by the sum of iron loss caused by magnetic flux adjustment and additional harmonic loss, and the estimated temperature rise rate is determined by identifying the heat capacity. The thermomagnetic constraint calculates the maximum limit value for upward adjustment of magnetic flux density based on the permeability decay and hysteresis loop distortion at the current transformer temperature.

[0009] Fluctuation decomposition module: When detecting power fluctuations in the power grid, it decomposes the power fluctuations into flux regulation components and thermal buffer components based on a two-way coupling model, and generates a first command to control the output flux regulation component of the flux control device.

[0010] Collaborative decision-making module: Calculates the remaining power fluctuation of the grid based on the magnetic flux regulation component and the thermal buffer component, and obtains the health status of the energy storage system and the transformer insulation life loss rate in real time. Constructs a comprehensive cost function, calculates and decides the proportion of the remaining power fluctuation of the grid to be borne by the energy storage system, generates a second instruction and controls the energy storage system to charge and discharge according to the proportion it bears.

[0011] State recovery module: Used to generate recovery commands after fluctuations are smoothed out, and to restore the transformer's magnetic flux density and temperature to the preset intermediate safe range.

[0012] Furthermore, the data acquisition and identification module includes: a voltage transformer and a current transformer, used to acquire voltage and current signals from the primary and secondary sides of the transformer, respectively; a temperature sensor, pre-installed in the transformer windings, used to acquire winding temperature; and a magnetic flux density calculation unit, which calculates the current operating magnetic flux density of the transformer according to equation (1):

[0013] (1)

[0014] Where U is the acquired voltage signal, f is the grid frequency, and N is the number of transformer turns. The main magnetic flux is used for the main magnetic flux injection unit, which injects a power-free disturbance of preset amplitude and duration into the transformer through the magnetic flux control device; the thermal capacity identification unit is used to record the slope of temperature change during the disturbance, and calculate the current true equivalent thermal capacity of the transformer by combining the change in iron loss caused by the disturbance.

[0015] Furthermore, the operation of the bidirectional coupling module includes the following specific steps:

[0016] A1. Obtain the magnetic permeability and temperature characteristic curves of the transformer core material, as well as the hysteresis loop and temperature distortion data;

[0017] A2. Based on the current winding temperature of the transformer, determine the current permeability attenuation coefficient from the permeability-temperature characteristic curve, and calculate the magnetic flux density saturation critical value at the current temperature in combination with the degree of hysteresis loop distortion, as the maximum limit value.

[0018] A3. Calculate the corresponding iron loss increment based on the magnetic flux density adjustment amount, and calculate the additional harmonic loss based on the hysteresis loop distortion data. Take the sum of the iron loss increment and the additional harmonic loss as the total heat generation increment.

[0019] A4. Divide the total heat increment by the actual equivalent heat capacity to obtain the estimated temperature rise rate;

[0020] A5. When the estimated temperature rise rate is lower than the allowable temperature rise rate threshold corresponding to the current insulation class, the corresponding magnetic flux adjustment is allowed.

[0021] Furthermore, the fluctuation decomposition module operation includes the following specific steps:

[0022] B1. Record the total detected power fluctuations in the power grid as... ;

[0023] B2. Under the conditions of satisfying magnetothermal and thermomagnetic constraints, determine the maximum allowable upward adjustment of magnetic flux density, and calculate the corresponding magnetic flux adjustment component based on the relationship between transformer magnetic flux and power. ;

[0024] B3. Based on the identified true equivalent heat capacity and the temperature rise margin between the current temperature and the maximum allowable temperature, calculate the total heat that the transformer can safely absorb, and divide it by the expected duration of the fluctuation to obtain the thermal buffer component. ;

[0025] B4. Generate the first instruction to control the output power of the magnetic flux control device and simultaneously start temperature monitoring to verify the consistency between the actual temperature rise and the estimated temperature rise rate.

[0026] Furthermore, the collaborative decision-making module operates through the following specific steps:

[0027] C1. Calculate the remaining power fluctuation of the power grid according to formula (2). :

[0028] (2)

[0029] C2. Obtain the internal resistance growth rate, the number of cycles used, and the current available capacity of the energy storage system, and calculate the health status indicators of the energy storage system.

[0030] C3. Calculate the insulation life loss rate of the transformer based on the real-time temperature and temperature rise rate of the transformer, combined with the insulation material aging model.

[0031] C4. Construct the comprehensive cost function (3):

[0032] (3)

[0033] in, The transformer bears the cost of lifespan loss caused by fluctuations in the remaining power grid. The energy storage system bears the cost of cycle life loss caused by fluctuations in the remaining grid power. and These are the weighting coefficients;

[0034] C5. Using the minimum comprehensive cost function as the optimization objective, calculate the proportion k of grid power fluctuations that the energy storage system should bear, and generate a second command to control the energy storage system according to power... Perform charging and discharging operations.

[0035] Furthermore, the state recovery module operation includes the following specific steps:

[0036] D1. After the collaborative decision-making module outputs the second instruction and completes the fluctuation smoothing, determine whether the current grid power has recovered to the steady-state value before the fluctuation.

[0037] D2. When the power grid power returns to steady state, a recovery command is generated. The recovery command includes: controlling the magnetic flux control device to exit the power output mode and adjusting the magnetic flux density to the rated operating point; if the current transformer temperature is higher than the upper limit of the preset intermediate safety range, starting the auxiliary cooling device until the temperature drops back to the intermediate safety range; the preset intermediate safety range is the preset temperature range between the rated temperature and the maximum allowable temperature.

[0038] Furthermore, the collaborative decision-making module also includes a margin constraint unit, which includes:

[0039] The historical fluctuation statistics subunit is used to obtain the power grid power fluctuation amplitude sequence for the same period within a preset statistical period in the past, fit the power grid power fluctuation amplitude sequence with a probability distribution, and take the quantile value corresponding to the preset confidence level as the worst expected fluctuation amount.

[0040] The energy storage margin calculation subunit is used to multiply the worst expected fluctuation amount by the preset fluctuation duration to obtain the minimum energy storage value required to cope with the fluctuation, and the worst expected fluctuation amount is used as the minimum energy storage power value required.

[0041] The constraint application sub-unit is used to simultaneously satisfy preset constraint conditions when the collaborative decision-making module solves for the energy storage system's share of energy consumption, thereby finding the energy storage system's share of energy consumption that minimizes the comprehensive cost function.

[0042] Furthermore, the constraints mentioned in the constraint-applying sub-unit include:

[0043] ,in, Let k represent the available energy of the current energy storage system, and k be the proportion of the remaining grid power fluctuations that the energy storage system will bear, as determined by the collaborative decision-making module. The remaining power grid power fluctuation is represented by t, which is the preset fluctuation duration. This represents the minimum energy storage value.

[0044] ,in, This represents the current available discharge power of the energy storage system. This represents the minimum energy storage capacity.

[0045] The present invention has the following beneficial effects:

[0046] 1. This invention identifies the current true equivalent heat capacity of the transformer by injecting reactive disturbances and observing the temperature response slope, replacing the traditional method that relies on fixed factory parameters. This eliminates model mismatch problems caused by factors such as equipment aging and fouling, making the calculation of the thermal buffer component more accurate and providing a reliable thermal parameter basis for subsequent power fluctuation decomposition.

[0047] 2. This invention constructs a bidirectional coupling model of magnetic flux and temperature that includes harmonic correction. Based on magnetothermal constraints and thermomagnetic constraints, it can accurately quantify the temperature rise cost of the transformer during magnetic flux adjustment and the upper limit of magnetic flux adjustment after temperature rise. While meeting the short-term overload capacity of the transformer body, it ensures that the equipment always operates within the safety boundary.

[0048] 3. This invention decomposes power grid fluctuations into magnetic flux regulation components and thermal buffer components, giving priority to the absorption of some fluctuations by utilizing the transformer's own electromagnetic regulation capability and thermal inertia, and only entrusting the remaining part to the energy storage system for processing. This significantly reduces the frequency of charging and discharging operations and the depth of cycling of the energy storage system, and effectively extends the cycle life of the energy storage battery.

[0049] 4. This invention aims to minimize the sum of the cost of transformer insulation life loss and the cost of energy storage cycle life loss. It constructs a comprehensive cost function and solves for the optimal energy storage bearing ratio, realizing an economic trade-off between equipment life loss. It avoids the one-sidedness of unidirectional protection of transformers or unidirectional protection of energy storage in traditional control strategies, and reduces the long-term operating cost of the entire system while ensuring the reliability of power supply in the distribution area. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0052] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0053] See Figure 1 The distribution area energy storage and smart distribution transformer collaborative operation optimization and scheduling control system includes:

[0054] Data acquisition and identification module: used to collect transformer voltage, current and winding temperature in real time, calculate the current magnetic flux density of the transformer based on the relationship between voltage and magnetic flux, and identify the current true equivalent heat capacity of the transformer by injecting reactive disturbance and observing the temperature response slope.

[0055] The data acquisition and identification module includes: a voltage transformer and a current transformer, used to acquire voltage and current signals from the primary and secondary sides of the transformer, respectively; a temperature sensor, pre-installed in the transformer windings to acquire winding temperature; and a magnetic flux density calculation unit, which calculates the current operating magnetic flux density of the transformer according to equation (1):

[0056] (1)

[0057] Where U is the acquired voltage signal, f is the grid frequency, and N is the number of transformer turns. The main magnetic flux is used for the main magnetic flux injection unit, which injects a power-free disturbance of preset amplitude and duration into the transformer through the magnetic flux control device; the thermal capacity identification unit is used to record the slope of temperature change during the disturbance, and calculate the current true equivalent thermal capacity of the transformer by combining the change in iron loss caused by the disturbance.

[0058] Two-way coupling module: used to construct a two-way coupling model of magnetic flux and temperature including harmonic correction, including magnetothermal constraint and thermomagnetic constraint. The magnetothermal constraint is determined by the sum of iron loss caused by magnetic flux adjustment and additional harmonic loss, and the estimated temperature rise rate is determined by identifying the heat capacity. The thermomagnetic constraint calculates the maximum limit value for upward adjustment of magnetic flux density based on the permeability decay and hysteresis loop distortion at the current transformer temperature.

[0059] The operation of the bidirectional coupling module includes the following specific steps:

[0060] A1. Obtain the magnetic permeability and temperature characteristic curves of the transformer core material, as well as the hysteresis loop and temperature distortion data;

[0061] A2. Based on the current winding temperature of the transformer, determine the current permeability attenuation coefficient from the permeability-temperature characteristic curve, and calculate the magnetic flux density saturation critical value at the current temperature in combination with the degree of hysteresis loop distortion, as the maximum limit value.

[0062] A3. Calculate the corresponding iron loss increment based on the magnetic flux density adjustment amount, and calculate the additional harmonic loss based on the hysteresis loop distortion data. Take the sum of the iron loss increment and the additional harmonic loss as the total heat generation increment.

[0063] A4. Divide the total heat increment by the actual equivalent heat capacity to obtain the estimated temperature rise rate;

[0064] A5. When the estimated temperature rise rate is lower than the allowable temperature rise rate threshold corresponding to the current insulation class, the corresponding magnetic flux adjustment is allowed.

[0065] Fluctuation decomposition module: When detecting power fluctuations in the power grid, it decomposes the power fluctuations into flux regulation components and thermal buffer components based on a two-way coupling model, and generates a first command to control the output flux regulation component of the flux control device.

[0066] The operation of the fluctuation decomposition module includes the following specific steps:

[0067] B1. Record the total detected power fluctuations in the power grid as... ;

[0068] B2. Under the conditions of satisfying magnetothermal and thermomagnetic constraints, determine the maximum allowable upward adjustment of magnetic flux density, and calculate the corresponding magnetic flux adjustment component based on the relationship between transformer magnetic flux and power. ;

[0069] B3. Based on the identified true equivalent heat capacity and the temperature rise margin between the current temperature and the maximum allowable temperature, calculate the total heat that the transformer can safely absorb, and divide it by the expected duration of the fluctuation to obtain the thermal buffer component. ;

[0070] B4. Generate the first instruction to control the output power of the magnetic flux control device and simultaneously start temperature monitoring to verify the consistency between the actual temperature rise and the estimated temperature rise rate.

[0071] Collaborative decision-making module: Calculates the remaining power fluctuation of the grid based on the magnetic flux regulation component and the thermal buffer component, and obtains the health status of the energy storage system and the transformer insulation life loss rate in real time. Constructs a comprehensive cost function, calculates and decides the proportion of the remaining power fluctuation of the grid to be borne by the energy storage system, generates a second instruction and controls the energy storage system to charge and discharge according to the proportion it bears.

[0072] The collaborative decision-making module operates through the following specific steps:

[0073] C1. Calculate the remaining power fluctuation of the power grid according to formula (2). :

[0074] (2)

[0075] C2. Obtain the internal resistance growth rate, the number of cycles used, and the current available capacity of the energy storage system, and calculate the health status indicators of the energy storage system.

[0076] C3. Calculate the insulation life loss rate of the transformer based on the real-time temperature and temperature rise rate of the transformer, combined with the insulation material aging model.

[0077] C4. Construct the comprehensive cost function (3):

[0078] (3)

[0079] in, The transformer bears the cost of lifespan loss caused by fluctuations in the remaining power grid. The energy storage system bears the cost of cycle life loss caused by fluctuations in the remaining grid power. and These are the weighting coefficients;

[0080] C5. Using the minimum comprehensive cost function as the optimization objective, calculate the proportion k of grid power fluctuations that the energy storage system should bear, and generate a second command to control the energy storage system according to power... Perform charging and discharging operations.

[0081] State recovery module: Used to generate recovery commands after fluctuations are smoothed out, and to restore the transformer's magnetic flux density and temperature to the preset intermediate safe range.

[0082] The state recovery module operation includes the following specific steps:

[0083] D1. After the collaborative decision-making module outputs the second instruction and completes the fluctuation smoothing, determine whether the current grid power has recovered to the steady-state value before the fluctuation.

[0084] D2. When the power grid power returns to steady state, a recovery command is generated. The recovery command includes: controlling the magnetic flux control device to exit the power output mode and adjusting the magnetic flux density to the rated operating point; if the current transformer temperature is higher than the upper limit of the preset intermediate safety range, starting the auxiliary cooling device until the temperature drops back to the intermediate safety range; the preset intermediate safety range is the preset temperature range between the rated temperature and the maximum allowable temperature.

[0085] In this embodiment, the flux control device is connected in parallel to the transformer side and is used to receive the first instruction and adjust the main flux of the transformer according to the first instruction to realize the output of the flux control component. The flux control device can be any one of a flux-controlled transformer, a transformer-type controllable parallel reactor, or a magnetically controlled intelligent distribution transformer. Its basic function is to adjust the magnitude of the main flux by changing the magnetic reluctance or excitation state of the transformer's magnetic circuit. The flux control device is also used to receive the recovery instruction output by the state recovery module, exit the power regulation mode after the fluctuation is smoothed, and restore the flux density to the rated operating point. The auxiliary cooling device is used to receive the cooling start signal output by the state recovery module to accelerate the heat dissipation of the transformer and shorten the time for the temperature to drop back to the intermediate safe range. The auxiliary cooling device can be any one of an air-cooled cooling fan, an oil pump circulating cooler, or a heat sink auxiliary heat dissipation device, and is controlled by the temperature signal to start and stop. This is prior art and will not be described in detail.

[0086] The working principle of this invention is as follows: This system collects transformer voltage, current, and winding temperature in real time, calculates the current magnetic flux density based on the relationship between voltage and magnetic flux, and identifies the true equivalent heat capacity by injecting reactive power disturbance. On this basis, a two-way coupling model of magnetic flux and temperature is constructed to obtain the temperature rise constraint caused by magnetic flux regulation and the constraint of temperature on the upper limit of magnetic flux regulation. When grid power fluctuation is detected, the fluctuation is decomposed into magnetic flux regulation component and thermal buffer component. The transformer's own regulation capability is used to absorb part of the fluctuation first. The remaining fluctuation is calculated by the collaborative decision module based on the energy storage health status and transformer insulation aging rate to construct a comprehensive cost function, solve the optimal energy storage bearing ratio, and perform charging and discharging. After the fluctuation is smoothed out, the system restores the magnetic flux density and temperature to the preset intermediate safety range, reserving a margin for the next regulation.

[0087] In the above embodiments, the dual-objective collaborative decision-making only aims to minimize the overall cost at the current moment to solve for the energy storage share, without considering the weakening effect of the current discharge decision on future response capabilities. When more severe power fluctuations occur later, the energy storage may be unable to respond effectively due to insufficient energy or power margin, causing the transformer to be forced to bear excessive cumulative damage. Therefore, to solve this problem, in this embodiment, the collaborative decision-making module also includes a margin constraint unit, which includes:

[0088] The historical fluctuation statistics subunit is used to obtain the power grid power fluctuation amplitude sequence for the same period within a preset statistical period in the past, fit the power grid power fluctuation amplitude sequence with a probability distribution, and take the quantile value corresponding to the preset confidence level as the worst expected fluctuation amount.

[0089] The energy storage margin calculation subunit is used to multiply the worst expected fluctuation amount by the preset fluctuation duration to obtain the minimum energy storage value required to cope with the fluctuation, and the worst expected fluctuation amount is used as the minimum energy storage power value required.

[0090] The constraint application sub-unit is used to simultaneously satisfy preset constraint conditions when the collaborative decision-making module solves for the energy storage system's share of energy consumption, thereby finding the energy storage system's share of energy consumption that minimizes the comprehensive cost function.

[0091] The constraints mentioned in the constraint-applying sub-unit include:

[0092] ,in, Let k represent the available energy of the current energy storage system, and k be the proportion of the remaining grid power fluctuations that the energy storage system will bear, as determined by the collaborative decision-making module. The remaining power grid power fluctuation is represented by t, which is the preset fluctuation duration. This represents the minimum energy storage value.

[0093] ,in, This represents the current available discharge power of the energy storage system. This represents the minimum energy storage capacity.

[0094] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A distribution area energy storage and intelligent distribution transformer collaborative operation optimization and scheduling control system, characterized in that, include: Data acquisition and identification module: used to collect the voltage, current and winding temperature of the transformer in real time, calculate the current magnetic flux density of the transformer based on the voltage and the magnetic flux relationship, and identify the current true equivalent heat capacity of the transformer by injecting reactive disturbance and observing the temperature response slope. Two-way coupling module: used to construct a two-way coupling model of magnetic flux and temperature including harmonic correction, including magnetothermal constraint and thermomagnetic constraint. The magnetothermal constraint is the estimated temperature rise rate determined by the sum of iron loss and additional harmonic loss caused by magnetic flux adjustment combined with the true equivalent heat capacity. The thermomagnetic constraint calculates the maximum limit value for upward adjustment of magnetic flux density based on the permeability decay and hysteresis loop distortion at the current transformer winding temperature. Fluctuation decomposition module: When detecting power fluctuations in the power grid, it decomposes the power fluctuations into flux regulation components and thermal buffer components based on a two-way coupling model, and generates a first command to control the output flux regulation component of the flux control device. Collaborative decision-making module: Calculates the remaining power fluctuation of the grid based on the magnetic flux regulation component and the thermal buffer component, and obtains the health status of the energy storage system and the transformer insulation life loss rate in real time. Constructs a comprehensive cost function, calculates and decides the proportion of the remaining power fluctuation of the grid to be borne by the energy storage system, generates a second instruction and controls the energy storage system to charge and discharge according to the proportion it bears. State recovery module: Used to generate recovery commands after fluctuations are smoothed out, and to restore the transformer flux density and winding temperature to the preset intermediate safe range.

2. The distribution area energy storage and intelligent distribution transformer collaborative operation optimization scheduling control system according to claim 1, characterized in that, The data acquisition and identification module includes: a voltage transformer and a current transformer, used to acquire voltage and current signals from the primary and secondary sides of the transformer, respectively; a temperature sensor, pre-installed in the transformer windings, used to acquire winding temperature; and a magnetic flux density calculation unit, used to calculate the current operating magnetic flux density of the transformer according to equation (1): （1） Where U is the acquired voltage signal, f is the grid frequency, and N is the number of transformer turns. The main magnetic flux is used for the main magnetic flux injection unit, which injects a power-free disturbance of preset amplitude and duration into the transformer through the magnetic flux control device; the thermal capacity identification unit is used to record the slope of temperature change during the disturbance, and calculate the current true equivalent thermal capacity of the transformer by combining the change in iron loss caused by the disturbance.

3. The distribution area energy storage and intelligent distribution transformer collaborative operation optimization scheduling control system according to claim 1, characterized in that, The operation of the bidirectional coupling module includes the following specific steps: A1. Obtain the magnetic permeability and temperature characteristic curves of the transformer core material, as well as the hysteresis loop and temperature distortion data; A2. Based on the current winding temperature of the transformer, determine the current permeability attenuation coefficient from the permeability-temperature characteristic curve, and calculate the magnetic flux density saturation critical value at the current temperature in combination with the degree of hysteresis loop distortion, as the maximum limit value. A3. Calculate the corresponding iron loss increment based on the magnetic flux density adjustment amount, and calculate the additional harmonic loss based on the hysteresis loop distortion data. Take the sum of the iron loss increment and the additional harmonic loss as the total heat generation increment. A4. Divide the total heat increment by the actual equivalent heat capacity to obtain the estimated temperature rise rate; A5. When the estimated temperature rise rate is lower than the allowable temperature rise rate threshold corresponding to the current insulation class, the corresponding magnetic flux adjustment is allowed.

4. The distribution area energy storage and intelligent distribution transformer collaborative operation optimization scheduling control system according to claim 1, characterized in that, The operation of the fluctuation decomposition module includes the following specific steps: B1. Record the total detected power fluctuations in the power grid as... ; B2. Under the conditions of satisfying magnetothermal and thermomagnetic constraints, determine the maximum allowable upward adjustment of magnetic flux density, and calculate the corresponding magnetic flux adjustment component based on the relationship between transformer magnetic flux and power. ; B3. Based on the identified true equivalent heat capacity and the temperature rise margin between the current temperature and the maximum allowable temperature, calculate the total heat that the transformer can safely absorb, and divide it by the expected duration of the fluctuation to obtain the thermal buffer component. ; B4. Generate the first instruction to control the output power of the magnetic flux control device and simultaneously start temperature monitoring to verify the consistency between the actual temperature rise and the estimated temperature rise rate.

5. The distribution area energy storage and intelligent distribution transformer collaborative operation optimization scheduling control system according to claim 1, characterized in that, The collaborative decision-making module operates through the following specific steps: C1. Calculate the remaining power fluctuation of the power grid according to formula (2). : （2） C2. Obtain the internal resistance growth rate, the number of cycles used, and the current available capacity of the energy storage system, and calculate the health status indicators of the energy storage system. C3. Calculate the insulation life loss rate of the transformer based on the real-time temperature and temperature rise rate of the transformer, combined with the insulation material aging model. C4. Construct the comprehensive cost function (3): （3） in, The transformer bears the cost of lifespan loss caused by fluctuations in the remaining power grid. The energy storage system bears the cost of cycle life loss caused by fluctuations in the remaining grid power. and These are the weighting coefficients; C5. Using the minimum comprehensive cost function as the optimization objective, calculate the proportion k of grid power fluctuations that the energy storage system should bear, and generate a second command to control the energy storage system according to power... Perform charging and discharging operations.

6. The distribution area energy storage and intelligent distribution transformer collaborative operation optimization scheduling control system according to claim 1, characterized in that, The operation of the state recovery module includes the following specific steps: D1. After the collaborative decision-making module outputs the second instruction and completes the fluctuation smoothing, determine whether the current grid power has recovered to the steady-state value before the fluctuation. D2. When the power grid power returns to steady state, a recovery command is generated. The recovery command includes: controlling the magnetic flux control device to exit the power output mode and adjusting the magnetic flux density to the rated operating point; if the current transformer temperature is higher than the upper limit of the preset intermediate safety range, starting the auxiliary cooling device until the temperature drops back to the intermediate safety range; the preset intermediate safety range is the preset temperature range between the rated temperature and the maximum allowable temperature.

7. The distribution area energy storage and intelligent distribution transformer collaborative operation optimization scheduling control system according to claim 5, characterized in that, The collaborative decision-making module further includes a margin constraint unit, which includes: The historical fluctuation statistics subunit is used to obtain the power grid power fluctuation amplitude sequence for the same period within a preset statistical period in the past, perform probability distribution fitting on the power grid power fluctuation amplitude sequence, and take the quantile value corresponding to the preset confidence level as the worst expected fluctuation amount. The energy storage margin calculation subunit is used to multiply the worst expected fluctuation amount by the preset fluctuation duration to obtain the minimum energy storage value required to cope with the fluctuation, and to use the worst expected fluctuation amount as the minimum energy storage power value required. The constraint application sub-unit is used to simultaneously satisfy preset constraint conditions when the collaborative decision-making module solves for the energy storage system's share of energy consumption, thereby finding the energy storage system's share of energy consumption that minimizes the comprehensive cost function.

8. The distribution area energy storage and intelligent distribution transformer collaborative operation optimization scheduling control system according to claim 7, characterized in that, The constraint conditions mentioned in the constraint application subunit include: ,in, Let k represent the available energy of the current energy storage system, and k be the proportion of the remaining grid power fluctuations that the energy storage system will bear, as determined by the collaborative decision-making module. The remaining power grid power fluctuation is represented by t, which is the preset fluctuation duration. This represents the minimum energy storage value. ,in, This represents the current available discharge power of the energy storage system. This represents the minimum energy storage capacity.

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