Virtual capacity-increasing shared energy storage system for multiple parallel transformers

Abstract

The invention discloses a multi-parallel transformer virtual capacity-increasing shared energy storage system, and relates to the technical field of electric power system engineering.The system comprises a power transformation capacity-increasing analysis module, an energy storage intelligent control module, a power grid capacity-increasing analysis module and a power grid energy management module.The method comprises the steps that firstly, a preset power transformation capacity-increasing model is used for analyzing energy storage battery information of the power transformation capacity-increasing model; the method comprises the following steps: firstly, determining the working state of each transformer, secondly, analyzing the state data of energy storage equipment of each transformer in a power grid through an energy storage control model, setting a corresponding charging and discharging strategy, then, analyzing the load data of each transformer in the power grid by utilizing a power grid load model, obtaining the capacity-increasing demand level of each transformer, and finally, calculating the capacity-increasing demand level of each transformer. And an electric energy configuration optimization scheme is formulated according to the capacity-increasing demand level of each transformer and the power grid energy model, so that the capacity-increasing cost of the transformers is reduced, and the stability of the power system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system engineering, and in particular to a multi-parallel transformer virtual capacity-increasing shared energy storage system. Background Art

[0002] In the field of power system engineering, transformers are a crucial piece of electrical equipment, primarily used for voltage conversion and current transmission, thereby enabling the rational distribution and use of electrical energy. In practice, transformer capacity often needs to be adjusted due to fluctuations in electricity demand and the needs of grid development. Meanwhile, energy storage technology, as an emerging technology, has been widely adopted in power systems, effectively balancing grid loads and improving the stability and reliability of power systems.

[0003] Existing technologies, such as the invention patent application with publication number CN116345701B, disclose a low-voltage reactive compensation intelligent monitoring and control system. This invention analyzes the electrical parameters and environmental parameters of each distribution line and evaluates the reactive compensation influence coefficient of the target low-voltage distribution network, thereby effectively analyzing the reactive compensation effect of the distribution line, ensuring that the compensation effect can be accurately grasped and optimized in the later stage. At the same time, it monitors the operating power data of the low-voltage transformer and evaluates the reactive compensation influence coefficient of the transformer of the target low-voltage distribution network, so that compensation can be carried out in time according to the changes in the transformer power data. This allows for flexible and accurate reactive compensation analysis of the transformer, and then analyzes the required compensation capacity of the target low-voltage distribution network, and performs reactive compensation adjustment processing, thereby improving the accuracy and timeliness of reactive compensation in the low-voltage distribution network, and further improving the distribution operation stability and reliability of the low-voltage distribution network.

[0004] The above scheme has at least the following deficiencies: 1. The above scheme performs reactive power compensation for high and low voltage distribution networks by analyzing a single transformer, and does not virtually increase the capacity of the transformer, resulting in insufficient transformer capacity and being unable to meet the demand for high load capacity increase. When performing reactive power compensation, it only analyzes the required compensation capacity of the low voltage distribution network. When the compensation capacity exceeds the capacity of the capacitor bank, full compensation cannot be performed. The only way is to increase the total compensation capacity by replacing a transformer with a larger capacity, or by using multiple transformers and distributing the load, which increases the cost.

[0005] 2. The above scheme only analyzes the reactive power compensation of the high and low voltage distribution networks and obtains the required compensation capacity of a single transformer. It does not analyze the load data of other transformers to compensate the target transformer. It does not flexibly adjust the storage and release of electric energy according to actual needs, which reduces the flexibility of the system. Summary of the Invention

[0006] In view of the above-mentioned technical deficiencies, the object of the present invention is to provide a multi-parallel transformer virtual capacity expansion shared energy storage system.

[0007] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a multi-parallel transformer virtual capacity expansion shared energy storage system, including the following modules: a substation capacity expansion analysis module, which is used to collect energy storage battery information of each transformer, analyze the energy storage battery information of each transformer based on the substation capacity expansion model, and thus set the status of the transformer energy storage device.

[0008] The energy storage intelligent control module is used to collect the status data of the energy storage equipment of each transformer in the power grid, analyze the status data of the energy storage equipment of each transformer in the power grid based on the energy storage control model, and then set the charging and discharging strategy.

[0009] The power grid capacity expansion analysis module is used to collect the load data of each transformer in the power grid, analyze the load data of each transformer in the power grid based on the power grid load model, and obtain the capacity expansion requirement level of each transformer.

[0010] The grid energy management module is used to collect energy supply data of the grid system, analyze the energy supply data of the grid system based on the capacity increase demand level of each transformer and the grid energy model, and set up an energy configuration optimization plan.

[0011] Preferably, the energy supply data in the power grid system is analyzed, and the specific analysis process is as follows:

[0012] The energy supply data in the power grid system includes the charging change rate, charging rate, discharge rate, load change rate and transmission efficiency evaluation index of each transformer of the target transformer in the power grid system. The charging change rate, charging rate, discharge rate, load change rate and transmission efficiency evaluation index of each transformer of the target transformer are input into the power grid energy model to obtain the output results of the power grid system of each transformer, and the output results have values including 0 and 1.

[0013] If the output result of a transformer is 0, the transformer is not shared with the target transformer. If the output result of a transformer is 1, the transformer is shared with the target transformer and recorded as the shared transformer of the target transformer. In this way, the shared transformers of the target transformer are obtained.

[0014] When the power grid can be charged and the target transformer is not a charging transformer, the shared transformers of the target transformer are connected to charge the charging transformers in the shared transformers of the target transformer. The connection order of the shared transformers is the order of capacity increase demand level from small to large. When the power grid can be discharged and the target transformer is not a discharging transformer, the shared transformers of the target transformer are connected to discharge the discharge transformers in the shared transformers of the target transformer to the target transformer. The connection order of the shared transformers is the order of capacity increase demand level from small to large, so as to obtain a shared optimization scheme of the target transformer. According to the setting process of the shared optimization scheme of the target transformer, the optimization scheme of each transformer is set to obtain a shared optimization scheme of each transformer, so as to obtain an energy configuration optimization scheme.

[0015] The beneficial effects of the present invention are as follows: 1. The present invention first uses a preset transformer capacity expansion model to analyze its energy storage battery information and determine the working status of each transformer. Secondly, through the energy storage control model, the status data of the energy storage equipment of each transformer in the power grid is analyzed and the corresponding charging and discharging strategy is set. Then, using the power grid load model, the load data of each transformer in the power grid is analyzed to obtain the capacity expansion demand level of each transformer. Finally, according to the capacity expansion demand level of each transformer and the power grid energy model, an energy configuration optimization plan is formulated, which reduces the cost of transformer capacity expansion and improves the stability of the power system.

[0016] 2. The present invention increases the virtual capacity of the transformer without replacing the hardware equipment through the virtual capacity expansion shared energy storage system, thereby reducing the transformer energy storage cost. At the same time, through the power configuration optimization scheme, the storage and release of the energy storage equipment are intelligently controlled, the transformer capacity is flexibly adjusted, the grid load is effectively balanced, and the stability and reliability of the power system are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0018] Figure 1 This is a schematic diagram of the system structure connection of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0020] according to Figure 1 As shown, the present invention provides a multi-parallel transformer virtual capacity expansion shared energy storage system, including the following modules: a substation capacity expansion analysis module, an energy storage intelligent control module, a power grid capacity expansion analysis module, a power grid energy management module and a database.

[0021] The energy storage intelligent control module is connected to the power transformation capacity expansion analysis module and the power grid capacity expansion analysis module respectively, and the power grid energy management module is connected to the power grid capacity expansion analysis module and the database respectively.

[0022] The substation capacity expansion analysis module is used to collect the energy storage battery information of each transformer and analyze the energy storage battery information of each transformer based on the substation capacity expansion model to set the status of the transformer energy storage device.

[0023] In a specific embodiment, the energy storage battery information of each transformer is collected, and the specific collection process is as follows: the energy storage battery information of each transformer includes the current peak value, lithium salt concentration change index and transformer load change index of each transformer within a preset time period, and an ion chromatograph is placed at the injection port of the battery pack. The ion chromatograph collects the lithium salt concentrations of each transformer within the preset time period, and obtains the maximum and minimum lithium salt concentrations of each transformer within the preset time period. The sum of the maximum and minimum lithium salt concentrations of each transformer within the preset time period is divided by the difference between the corresponding maximum and minimum lithium salt concentrations to obtain the lithium salt concentration change index of each transformer.

[0024] The current intensity of each transformer within the preset time is measured by a current transformer, and the maximum current of each transformer within the preset time is recorded as the current peak value. At the same time, the voltage intensity of each transformer within the preset time is collected by a voltage transformer. The current intensity and voltage intensity of each transformer within the preset time are substituted into the power calculation formula to obtain the apparent power and compensation power of each transformer within the preset time. The apparent power and compensation power of each transformer within the preset time are averaged to obtain the average apparent power and average compensation power of each transformer. The average apparent power and average compensation power of each transformer are substituted into the load change index calculation formula. The load variation index B′a of the ath transformer within the preset time is obtained, where a is the transformer number, a=1,2......m, m>2, e is a natural constant, and P′a and P″ a are the average apparent power and average compensation power of the a-th transformer, P′ and P″ are the preset standard apparent power and standard compensation power, σ1 and σ2 are the weight factors of the preset average apparent power and average compensation power, σ1>0, σ2>0, σ1+σ2=1.

[0025] It should be noted that the standard apparent power P′ is the threshold value of the normal operating power of the power grid. The closer the average apparent power is to the standard apparent power, the closer the current power grid is to the rated power. The value of the standard apparent power is set by the staff. The setting process of the standard parameter P″ is the same as the setting process of the standard parameter P′. For example, P″ is 3.2. The weight factors σ1 and σ2 are both set by the staff, for example, σ1 is 0.3 and σ2 is 0.7.

[0026] In a specific embodiment, the energy storage battery information of each transformer is analyzed, and the specific analysis process is as follows: the current peak value, lithium salt concentration change index and transformer load change index of each transformer within a preset time are input into the substation capacity expansion model to obtain the output results of each transformer, and the output results have values including 0 and 1.

[0027] If the output result of a transformer is 0, it can be charged and is recorded as a charging transformer. If the output result of a transformer is 1, it can be discharged and is recorded as a discharging transformer. In this way, the status of each transformer energy storage device can be obtained.

[0028] It should be noted that the battery state is determined by analyzing the concentration of the electrolyte in the battery, thereby improving the accuracy of the analysis.

[0029] In a specific embodiment, the power conversion capacity increase model expression is:

[0030] Among them, α a is the output result of the a-th transformer, I′ a , A′ a and B′ a are the current peak value, lithium salt concentration variation index and transformer load variation index of the a-th transformer within the preset time length, respectively; I′, A″ and B″ are the standard current peak value, standard lithium salt concentration variation index and standard transformer load variation index preset in the database, respectively; ε1 and ε2 are the weight factors of the lithium salt concentration variation index and the transformer load variation index preset in the database, respectively; ε1>0, ε2>0, ε1+ε2=1, M′ and M″ are the standard charging battery capacity index and standard discharging battery capacity index preset in the database, respectively.

[0031] It should be noted that the setting process of standard parameters I′, A″, B″, M′ and M″ is the same as the setting process of standard parameter P′, for example, I′ is 0.6, A″ is 1.3, B″ is 1.1, M′ is 1.3 and M″ is 1.7, and the setting process of weight factors ε1 and ε2 is the same as the setting process of weight factor σ1, for example, ε1 is 0.58 and ε2 is 0.42.

[0032] The energy storage intelligent control module is used to collect the status data of the energy storage equipment of each transformer in the power grid, analyze the status data of the energy storage equipment of each transformer in the power grid based on the energy storage control model, and then set the charging and discharging strategy.

[0033] In a specific embodiment, the state data of the energy storage device of each transformer in the power grid is collected, and the specific collection process is as follows: the state data of the energy storage device of each transformer in the power grid includes the energy storage power, gate meter power, load rate and working efficiency of each transformer, the energy storage power, gate meter power and real-time load power of each transformer are collected through a power analyzer, the rated capacity of each transformer is obtained from a database, the real-time load power of each transformer is divided by the rated capacity of the transformer to obtain the load rate of each transformer, the electric energy usage of each transformer is collected through an electric meter, the electric energy usage corresponding to each preset working time of each transformer is obtained from a database, the actual working time of each transformer is divided by the preset working time to obtain the working efficiency of each transformer. In a specific embodiment, the state data of the energy storage device of each transformer in the power grid is analyzed, and the specific analysis process is as follows: the energy storage power, gate meter power, load rate and working efficiency of each transformer in the power grid are obtained to obtain the output result of the power grid, and the value of the output result includes 0 and 1.

[0034] If the output result is 0, it indicates that the grid can be charged and each charging transformer is charged. If the output result is 1, it indicates that the grid needs to be discharged and each discharging transformer is discharged.

[0035] It should be noted that by analyzing the energy storage power, gateway meter power, and load rate, the load on the power grid can be maintained at a stable value. When the grid load is high, the demand for electricity from each load is reduced through discharge. When the grid load is low, each transformer is converted into a load to increase the grid load. Increasing the load only when the grid load is low can increase the stability of the grid.

[0036] In a specific embodiment, the energy storage control model expression is:

[0037] Where β is the output of the power grid, m is the total number of transformers in the power grid, V′ a , C′ a , D′ a and Fa are the energy storage power, gateway power, load rate and working efficiency of the a-th transformer in the power grid, respectively. V′, C′, D′ and F′ are the standard energy storage power, standard gateway power, standard load rate and standard working efficiency preset in the database, respectively. φ1, φ2 and φ3 are the weight factors of energy storage power, gateway power and load rate preset in the database, respectively. φ1>0, φ2>0, φ3>0, φ1+φ2+φ3=1, and W′ is the preset standard power grid load index.

[0038] It should be noted that the setting process of the standard parameters V′, C′, D′, F′ and W′ is the same as the setting process of the standard parameter P′, for example, V′ is 0.68, C′ is 1.24, D′ is 1.16, F′ is 1.24 and W′ is 1.53, and the setting process of the weight factors φ1, φ2 and φ3 is the same as the setting process of the weight factor σ1, for example, φ1 is 0.4, φ2 is 0.2 and φ3 is 0.4.

[0039] The power grid capacity expansion analysis module is used to collect the load data of each transformer in the power grid, analyze the load data of each transformer in the power grid based on the power grid load model, and obtain the capacity expansion requirement level of each transformer.

[0040] In a specific embodiment, the load data of each transformer in the power grid is collected, and the specific collection process is as follows: the load data of each transformer in the power grid includes the load fluctuation rate, maximum load rate, average load rate, average load rate and maximum load rate of each transformer, the power of each power device is collected by the power analyzer of each power device, the power of each power device corresponding to each transformer area is counted, and the total power consumption of each transformer is obtained. If the total power consumption of a transformer collected at a certain time is greater than the preset total power, it is recorded as load collection, and the load collection in each collection period is divided by the total number of collections.

[0041] The load factor is obtained to obtain the load factor of each transformer in each collection period. The average value of the load factor of each transformer in each collection period is calculated to obtain the average load factor of each transformer.

[0042] In a specific embodiment, the load data of each transformer in the power grid is analyzed, and the specific analysis process is as follows: the load change rate, maximum load rate, average load rate, average load rate and maximum load rate of each transformer in the power grid are input into the power grid load model to obtain the output results of each transformer in the power grid. The value s of the output result is the capacity increase requirement level of each transformer, s=1,2......i, i>2, i is the maximum capacity increase requirement level.

[0043] It should be noted that by analyzing the capacity increase demand level of each transformer and then intelligently controlling the storage and release of energy storage equipment, the flexibility of transformer capacity adjustment is increased.

[0044] In a specific embodiment, the grid load model expression is:

[0045] Among them, R a is the output result of the a-th transformer, f′ a 、f″ a , f″′ a , h′ a and h″ a are the load fluctuation rate, average load rate, maximum load rate, average load rate and maximum load rate of the a-th transformer in the power grid, respectively. f′, f″, f″′, h′ and h″ are the standard load fluctuation rate, standard average load rate, standard maximum load rate, standard average load rate and standard maximum load rate preset in the database, respectively. and are the load weight factor and the load weight factor preset in the database,

[0046] N1, N s-1 、N s and N i-1 They are respectively the first demand index, the s-1th demand index, the sth demand index and the i-1th demand index preset in the database.

[0047] It should be noted that the standard parameters f′, f″, f″′, h′, h″, N1, N s-1 、N s and N i-1 The setting process is the same as that of the standard parameter P', for example, f' is 0.5, f" is 1.2, f"' is 1.5, h' is 1.1, h" is 1.6, N1 is 1.21, N s-1 2.61, N s 2.43 and N i-1 The weight factor is 3.1. and The setting process is the same as that of the weight factor σ1, for example 0.32 and It is 0.68.

[0048] The grid energy management module is used to collect energy supply data of the grid system, analyze the energy supply data of the grid system based on the capacity increase demand level of each transformer and the grid energy model, and set up an energy configuration optimization plan.

[0049] In a specific embodiment, the energy supply data of the power grid system is collected, and the specific collection process is as follows: the energy supply data in the power grid system includes the charging change rate, charging rate, discharge rate, load change rate and transmission efficiency evaluation index of each transformer of the target transformer in the power grid system, the common charging time of each transformer of the target transformer is divided by the charging time of the power grid to obtain the charging rate of each transformer of the target transformer, the common discharge time of each transformer of the target transformer is divided by the discharge time of the power grid to obtain the discharge rate of each transformer of the target transformer, the discharge time of each transformer when the target transformer is not charged is divided by the charging time of the target transformer to obtain the charging change rate of each transformer of the target transformer, and the discharge time of each transformer when the target transformer is not discharged is divided by the discharge time of the target transformer to obtain the load change rate.

[0050] In a specific embodiment, the energy supply data in the power grid system is analyzed, and the specific analysis process is as follows: the charging change rate, charging rate, discharge rate, load change rate and transmission efficiency evaluation index of each transformer of the target transformer are input into the power grid energy model to obtain the output results of the power grid system of each transformer, and the output result values include 0 and 1.

[0051] If the output result of a transformer is 0, the transformer is not shared with the target transformer. If the output result of a transformer is 1, the transformer is shared with the target transformer and recorded as the shared transformer of the target transformer. In this way, the shared transformers of the target transformer are obtained.

[0052] When the power grid can be charged and the target transformer is not a charging transformer, the shared transformers of the target transformer are connected to charge the charging transformers in the shared transformers of the target transformer. The connection order of the shared transformers is the order of capacity increase demand level from small to large. When the power grid can be discharged and the target transformer is not a discharging transformer, the shared transformers of the target transformer are connected to discharge the discharge transformers in the shared transformers of the target transformer to the target transformer. The connection order of the shared transformers is the order of capacity increase demand level from small to large, so as to obtain a shared optimization scheme of the target transformer. According to the setting process of the shared optimization scheme of the target transformer, the optimization scheme of each transformer is set to obtain a shared optimization scheme of each transformer, so as to obtain an energy configuration optimization scheme.

[0053] In a specific embodiment, the grid energy model expression is:

[0054] Among them, γ a is the output result of the a-th transformer, x′ a 、x″ a , y′ a ,y″a and g′ a are the charging rate, charging change rate, discharging rate, load change rate and transmission efficiency evaluation index of the a-th transformer, respectively; x′, x″, y′, y″ and g′ are the standard charging rate, standard charging change rate, standard discharging rate, standard load change rate and standard transmission efficiency evaluation index preset in the database, respectively; λ1, λ2 and λ3 are the charging weight factor, discharging weight factor and transmission efficiency weight factor preset in the database, respectively; λ1>0, λ2>0, λ3>0, λ1+λ2+λ3=1, and Q is the standard shared correlation evaluation index preset in the database.

[0055] It should be noted that the setting process of standard parameters x′, x″, y′, y″, g′ and Q is the same as the setting process of standard parameter P′, for example, x′ is 0.61, x″ is 1.1, y′ is 1.3, y″ is 1.4, Q is 0.8 and Q is 1.8, and the setting process of weight factors λ1, λ2 and λ3 is the same as the setting process of weight factor σ1, for example, λ1 is 0.5, λ2 is 0.2 and λ3 is 0.3.

[0056] A database is used to store standard current peak value, standard lithium salt concentration change index, standard transformer load change index, weight factor of lithium salt concentration change index, weight factor of transformer load change index, standard charging battery capacity index, standard discharging battery capacity index, standard energy storage power, standard gateway meter power, standard load rate, standard working efficiency, weight factor of energy storage power, weight factor of gateway meter power, weight factor of load rate, standard load fluctuation rate, standard average load rate, standard maximum load rate, standard average load rate, standard maximum load rate, load weight factor, load weight factor, first demand index, s-1th demand index, sth demand index and i-1th demand index, standard charging rate, standard charging change rate, standard discharge rate, standard load change rate, standard transmission efficiency evaluation index, charging weight factor, discharging weight factor and transmission efficiency weight factor.

[0057] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the scope of protection of the present invention.

Claims

1. A multi-parallel transformer virtual capacity expansion shared energy storage system, characterized in that: Includes the following modules: The substation capacity expansion analysis module is used to collect the energy storage battery information of each transformer and analyze the energy storage battery information of each transformer based on the substation capacity expansion model to set the status of the transformer energy storage device; The energy storage intelligent control module is used to collect the status data of the energy storage equipment of each transformer in the power grid, analyze the status data of the energy storage equipment of each transformer in the power grid based on the energy storage control model, and then set the charging and discharging strategy; The grid capacity expansion analysis module is used to collect the load data of each transformer in the grid, analyze the load data of each transformer in the grid based on the grid load model, and obtain the capacity expansion requirement level of each transformer; The grid energy management module is used to collect energy supply data of the grid system, analyze the energy supply data of the grid system based on the capacity increase demand level of each transformer and the grid energy model, and set up an energy configuration optimization plan.

2. A multi-parallel transformer virtual capacity expansion shared energy storage system according to claim 1, characterized in that: The energy storage battery information of each transformer is analyzed, and the specific analysis process is as follows: The energy storage battery information of each transformer includes the current peak value, lithium salt concentration change index and transformer load change index of each transformer within a preset time. The current peak value, lithium salt concentration change index and transformer load change index of each transformer within the preset time are input into the power transformation and capacity expansion model to obtain the output result of each transformer. The output result has a value of 0 and 1; If the output result of a transformer is 0, it can be charged and is recorded as a charging transformer. If the output result of a transformer is 1, it can be discharged and is recorded as a discharging transformer. In this way, the status of each transformer energy storage device can be obtained.

3. A multi-parallel transformer virtual capacity expansion shared energy storage system according to claim 2, characterized in that: The power conversion capacity expansion model expression is: Among them, α a is the output result of the ath transformer, a is the transformer number, a=1,2......m, m>2, I′ a , A′ a and B′ a are the current peak value, lithium salt concentration variation index and transformer load variation index of the a-th transformer within a preset time period, respectively; I′, A″ and B″ are the standard current peak value, standard lithium salt concentration variation index and standard transformer load variation index preset in the database, respectively; ε1 and ε2 are the weight factors of the lithium salt concentration variation index and the transformer load variation index preset in the database, respectively; ε1>0, ε2>0, ε1+ε2=1, M′ and M″ are the standard charging battery capacity index and the standard discharging battery capacity index preset in the database, respectively.

4. A multi-parallel transformer virtual capacity expansion shared energy storage system according to claim 2, characterized in that: The state data of the energy storage device of each transformer in the power grid is analyzed, and the specific analysis process is as follows: The status data of the energy storage devices of each transformer in the power grid includes the energy storage power, gate meter power, load rate and working efficiency of each transformer. The energy storage power, gate meter power, load rate and working efficiency of each transformer in the power grid are used to obtain the output results of the power grid. The output results have values of 0 and 1. If the output result is 0, it indicates that the grid can be charged and each charging transformer is charged. If the output result is 1, it indicates that the grid needs to be discharged and each discharging transformer is discharged.

5. A multi-parallel transformer virtual capacity expansion shared energy storage system according to claim 4, characterized in that: The energy storage control model expression is: Where β is the output of the power grid, m is the total number of transformers in the power grid, V′ a , C′ a , D′ a and F a are the energy storage power, gateway power, load rate and working efficiency of the a-th transformer in the power grid, respectively. V′, C′, D′ and F′ are the standard energy storage power, standard gateway power, standard load rate and standard working efficiency preset in the database, respectively. φ1, φ2 and φ3 are the weight factors of energy storage power, gateway power and load rate preset in the database, respectively. φ1>0, φ2>0, φ3>0, φ1+φ2+φ3=1, and W′ is the preset standard power grid load index.

6. The multi-parallel transformer virtual capacity expansion shared energy storage system according to claim 1, characterized in that: The load data of each transformer in the power grid is analyzed, and the specific analysis process is as follows: The load data of each transformer in the power grid includes the load fluctuation rate, maximum load rate, average load rate, average load rate and maximum load rate of each transformer. The load change rate, maximum load rate, average load rate, average load rate and maximum load rate of each transformer in the power grid are input into the power grid load model to obtain the output result of each transformer in the power grid. The value s of the output result is the capacity increase requirement level of each transformer, s=1,2...i, i>2, and i is the maximum capacity increase requirement level.

7. The multi-parallel transformer virtual capacity expansion shared energy storage system according to claim 6, characterized in that: The grid load model expression is: Among them, R a is the output result of the ath transformer, a is the transformer number, a=1,2......m,m>2,f″ a 、f″ a , f″′ a , h′ a and h″ a are the load fluctuation rate, average load rate, maximum load rate, average load rate and maximum load rate of the a-th transformer in the power grid, respectively. f′, f″, f″′, h′ and h″ are the standard load fluctuation rate, standard average load rate, standard maximum load rate, standard average load rate and standard maximum load rate preset in the database, respectively. and are the load weight factor and the load weight factor preset in the database, N1, N s-1 、N s and N i-1 They are respectively the first demand index, the s-1th demand index, the sth demand index and the i-1th demand index preset in the database.

8. The multi-parallel transformer virtual capacity expansion shared energy storage system according to claim 1, characterized in that: The energy supply data in the power grid system is analyzed, and the specific analysis process is as follows: The energy supply data in the power grid system includes the charging change rate, charging rate, discharge rate, load change rate, and transmission efficiency evaluation index of each target transformer in the power grid system. The charging change rate, charging rate, discharge rate, load change rate, and transmission efficiency evaluation index of each target transformer are input into the power grid energy model to obtain the output results of the power grid system of each transformer. The output results have values including 0 and 1. If the output result of a transformer is 0, then the transformer is not shared with the target transformer. If the output result of a transformer is 1, then the transformer is shared with the target transformer and recorded as the shared transformer of the target transformer. In this way, the shared transformers of the target transformer are obtained. When the power grid can be charged and the target transformer is not a charging transformer, the shared transformers of the target transformer are connected to charge the charging transformers in the shared transformers of the target transformer. The connection order of the shared transformers is the order of capacity increase demand level from small to large. When the power grid can be discharged and the target transformer is not a discharging transformer, the shared transformers of the target transformer are connected to discharge the discharge transformers in the shared transformers of the target transformer to the target transformer. The connection order of the shared transformers is the order of capacity increase demand level from small to large, so as to obtain a shared optimization scheme of the target transformer. According to the setting process of the shared optimization scheme of the target transformer, the optimization scheme of each transformer is set to obtain a shared optimization scheme of each transformer, so as to obtain an energy configuration optimization scheme.

9. The multi-parallel transformer virtual capacity expansion shared energy storage system according to claim 8, characterized in that: The grid energy model expression is: Among them, γ a is the output result of the ath transformer, a is the transformer number, a=1,2......m, m>2, x′ a 、x″ a , y′ a ,y″ a and g′ a are the charging rate, charging change rate, discharging rate, load change rate and transmission efficiency evaluation index of the a-th transformer, respectively; x′, x″, y′, y″ and g′ are the standard charging rate, standard charging change rate, standard discharging rate, standard load change rate and standard transmission efficiency evaluation index preset in the database, respectively; λ1, λ2 and λ3 are the charging weight factor, discharging weight factor and transmission efficiency weight factor preset in the database, respectively; λ1>0, λ2>0, λ3>0, λ1+λ2+λ3=1, and Q is the standard shared correlation evaluation index preset in the database.

10. The multi-parallel transformer virtual capacity expansion shared energy storage system according to claim 1, characterized in that: It also includes a database for storing standard current peak, standard lithium salt concentration change index, standard transformer load change index, weight factor of lithium salt concentration change index, weight factor of transformer load change index, standard charging battery capacity index, standard discharging battery capacity index, standard energy storage power, standard gateway meter power, standard load rate, standard working efficiency, weight factor of energy storage power, weight factor of gateway meter power, weight factor of load rate, standard load fluctuation rate, standard average load rate, standard maximum load rate, standard average load rate, standard maximum load rate, load weight factor, load weight factor, first demand index, s-1th demand index, sth demand index and i-1th demand index, standard charging rate, standard charging change rate, standard discharge rate, standard load change rate, standard transmission efficiency evaluation index, charging weight factor, discharge weight factor and transmission efficiency weight factor.

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