A soc equalization method for a multi-energy storage unit power grid

Abstract

This application discloses a State of Charge (SOC) equalization method for a multi-energy storage unit (MSU) power grid, belonging to the field of power grid control. The method includes: acquiring the state of charge (SOC) information of all MSU units within the power grid; acquiring the average SOC of the MSU power grid based on the SOC information; acquiring the SOC deviation of each MSU unit based on the average SOC; acquiring the droop coefficient of each MSU unit based on the SOC deviation; controlling the MSU units based on the droop coefficients and acquiring the bus voltage deviation; and acquiring the parameters of a voltage regulator based on the bus voltage deviation to eliminate the bus voltage deviation. This method solves the problem of difficulty in achieving rapid SOC equalization and current distribution caused by fixed droop coefficient control in current control methods. Therefore, it improves the efficiency and accuracy of SOC equalization adjustment.

Description

Technical Field

[0001] This application belongs to the field of power grid control, specifically, it relates to a SOC equalization method for a multi-energy storage unit power grid. Background Technology

[0002] Developing a new power system based on renewable energy is an important measure for the international community to promote the green transition of energy and facilitate high-quality energy development. With the large-scale application of renewable energy and distributed generation, microgrids have gained widespread attention and development. Compared to AC microgrids, DC microgrids are widely used due to their advantages such as simple structure, low losses, and flexible control, and they do not require consideration of reactive power, harmonics, and frequency issues.

[0003] DC microgrids can operate in grid-connected or stand-alone mode. In stand-alone mode, due to the intermittent and random nature of renewable energy output, DC microgrids typically require multiple distributed energy storage units (DESUs) operating in parallel to maintain system power balance and stabilize the DC bus voltage. When multiple DESUs operate in parallel, improper power distribution among them can lead to some DESUs being deeply discharged or overcharged, prematurely shutting down and severely impacting their lifespan and charging / discharging efficiency. Therefore, researching multi-DESU SOC balancing control strategies is of great significance. Energy storage system control typically uses distributed control, with droop control being a common approach. Droop control offers advantages such as high stability, flexibility, and plug-and-play functionality. However, traditional droop control does not consider the DESU's SOC information; the output current of the energy storage unit is distributed according to a fixed droop coefficient. During the charging and discharging process of the DESU, it is difficult to achieve rapid SOC balancing and reasonable load current distribution, thus affecting the lifespan of the energy storage unit. Furthermore, due to the inherent characteristics of traditional droop control, it is impossible to simultaneously ensure the rational distribution of load power and the stability of bus voltage, thus affecting system stability. Therefore, how to ensure the balance of the State of Charge (SOC) of a multi-energy storage unit grid based on reasonable control is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] To ensure that a multi-energy storage unit power grid maintains SOC balance during operation, this application discloses a SOC balance method for a multi-energy storage unit power grid, specifically:

[0005] A method for SOC equalization in a multi-energy storage unit power grid, the method comprising:

[0006] Obtain the state of charge information of all energy storage units within the power grid;

[0007] Based on the state of charge information, the average state of charge of the multi-energy storage unit power grid is obtained;

[0008] Based on the average state of charge, the state of charge deviation of each energy storage unit is obtained;

[0009] Based on the state of charge deviation, the droop coefficient of the energy storage unit is obtained;

[0010] Based on the droop coefficient of the energy storage unit, the energy storage unit is controlled, and the voltage deviation of the bus is obtained.

[0011] Based on the voltage deviation of the bus, the parameters of the voltage regulator are obtained to eliminate the voltage deviation of the bus.

[0012] Optionally, obtaining the state of charge information of all energy storage units within the power grid includes:

[0013] The operating status of all energy storage units in the power grid is monitored, and the data acquisition time of the energy storage units is obtained;

[0014] Based on the value taking time of the energy storage unit, obtain the state of charge information of all energy storage units;

[0015] Establish the correspondence between the time of energy storage unit's value acquisition and the state of charge information to obtain the state of charge information corresponding to each detection time.

[0016] Optionally, obtaining the average state of charge (SOC) of the multi-energy storage unit grid based on the SOC information includes:

[0017] Obtain the state of charge information of all energy storage units corresponding to each time value;

[0018] Based on the state of charge information of all energy storage units, the average state of charge of the multi-energy storage unit power grid is obtained.

[0019] Also includes:

[0020] Establish the correspondence between the average state of charge and the time of the value of the multi-energy storage unit power grid.

[0021] Optionally, obtaining the state-of-charge deviation of each energy storage unit based on the average state-of-charge includes:

[0022] Based on the data acquisition time of the energy storage unit, obtain the state of charge information of each energy storage unit and the average state of charge of the multi-energy storage unit grid;

[0023] The state of charge (SOC) information of each energy storage unit is obtained, and the difference between the SOC information of each energy storage unit and the mean SOC is obtained, so as to obtain the SOC deviation of each energy storage unit.

[0024] Optionally, obtaining the droop coefficient of the energy storage unit based on the state of charge deviation includes:

[0025] Based on the characteristic parameters of the energy storage unit and the bus voltage parameters, the theoretical droop coefficient is obtained. The equation for determining the theoretical droop coefficient is as follows:

[0026] ;

[0027] Where R0 represents the theoretical droop coefficient; V ref Indicates the bus voltage parameter; V ref-0 I represents the output voltage reference value among the characteristic parameters of the energy storage unit. out0 This refers to the output current, which is a characteristic parameter of the energy storage unit.

[0028] Based on the state of charge deviation, the droop coefficient of the energy storage unit is obtained, and the equation for determining the droop coefficient of the energy storage unit is:

[0029] ;

[0030] Among them, R i The droop factor of the i-th energy storage unit is represented by p; the amplification factor is represented by m; and the SOC is represented by m. i Represents the charge state information of the i-th energy storage unit; SOC avg Indicates the mean of the state of charge; i dc Indicates the current output value of the energy storage unit, i dc When the value is >0, the energy storage is in a discharge state, i dc When ≤0, the energy storage is in a charging state; n represents the power of the state of charge deviation; i represents the index of the number of energy storage units.

[0031] The equation for determining the deviation response coefficient is as follows:

[0032] ;

[0033] Where δ represents the proportional response coefficient related to SOC deviation, which is a constant; ε represents the constant response portion independent of SOC deviation, which is also a constant; ΔSOC i This indicates the deviation in the state of charge.

[0034] Optionally, the method for determining the power value of the state of charge deviation is as follows:

[0035] Based on the range of the power of the state of charge deviation and the number of energy storage units, the possible values ​​of the power of the state of charge deviation are obtained, and the equation for determining the possible values ​​is as follows:

[0036] ;

[0037] Where, n i This represents the possible values ​​of the power of the state-of-charge deviation of the i-th energy storage unit; n min This represents the minimum value within the range of powers of the state of charge deviation; n max The maximum value of the range of the power of the state of charge deviation; N represents the total number of energy storage units;

[0038] Obtain the state of charge deviation of all energy storage units and sort the state of charge deviation of all energy storage units to obtain the state of charge deviation sorting result.

[0039] Based on the sorting results of the state of charge deviation, a correspondence is established between the possible values ​​and the state of charge deviation in the overall sorted data group, so as to determine the power value of the state of charge deviation.

[0040] Optionally, obtaining the state-of-charge (POC) deviation of all energy storage units and sorting the POC deviations of all energy storage units to obtain a POC deviation sorting result includes:

[0041] Step 1: Obtain the state of charge deviation of all energy storage units and randomly decompose it into multiple data groups of equal quantity;

[0042] Step 2: Sort the state of charge deviations within each data group to obtain sorted data groups;

[0043] Step 3: Obtain the mean value of each sorted data group, and sort all sorted data groups based on the mean value.

[0044] Step 4: Obtain the first and second sorted data groups, and obtain the minimum value of the state of charge deviation in the second sorted data group to obtain the minimum value of the second data group.

[0045] Step 5: Obtain the numerical range formed by two adjacent state of charge deviations in the first sorted data group where the minimum value of the second sorted data group is located. Insert the minimum value of the second sorted data group after the smaller value of the numerical range of adjacent state of charge deviations. After the insertion of the minimum value of the second sorted data group, the larger value of the minimum value of the second sorted data group and the numerical range of adjacent state of charge deviations form a new numerical range. Remove the minimum value of the second sorted data group from the second sorted data group to obtain a new second sorted data group.

[0046] Step 6: Obtain the comparison result between the minimum value of the new second sorted data group and the larger value in the new data interval. If the minimum value of the new second sorted data group is less than the larger value in the new data interval, then the minimum value of the new second sorted data group is inserted before the larger value in the new data interval and after the minimum value of the second sorted data group. Otherwise, the minimum value of the new second sorted data group is compared backward from the larger value in the new data interval to obtain the insertion position of the minimum value of the new second sorted data group in the new first sorted data group.

[0047] Step 7: Repeat the methods of Step 5 and Step 6 until all the state of charge deviations in the second data group are inserted into the first data group to obtain a new first sorted data group.

[0048] Step 8: Insert the state of charge deviation in the third sorted data group into the new first sorted data group according to the methods in steps 1 to 7, and so on, until all sorted data groups are sorted into the same data group to obtain the overall sorted data group.

[0049] Optionally, controlling the energy storage unit based on the droop coefficient of the energy storage unit and obtaining the voltage deviation of the bus includes:

[0050] Based on the droop coefficient of the energy storage unit, the energy storage unit is controlled to obtain the controlled energy storage unit.

[0051] The operating parameters of the controlled energy storage unit are obtained, and the voltage deviation of the bus is obtained. The equation for determining the voltage deviation of the bus is:

[0052] ;

[0053] Where ΔU represents the voltage deviation of the bus; V oref Represents the bus input voltage parameters; R represents the load of the energy storage unit; I outi represents the output current of the i-th energy storage unit; j represents the total number of energy storage units.

[0054] Optionally, obtaining the parameters of the voltage regulator based on the voltage deviation of the bus to eliminate the voltage deviation of the bus includes:

[0055] A voltage regulator is installed on the busbar. The voltage regulator is a PI regulator, and the control equation of the PI regulator is:

[0056] ;

[0057] Among them, K p K represents the proportional gain of the PI controller.i U represents the integral coefficient of the PI controller; dc This represents the measured bus voltage;

[0058] Based on the voltage deviation of the bus, the parameters of the voltage regulator are obtained to eliminate the voltage deviation of the bus.

[0059] The beneficial effects of this application include:

[0060] 1. Achieved SOC equalization control of the power grid. In the technical solution of this application, the state of charge information of all energy storage units is determined, and the state of charge deviation is determined. Then, a sinking algorithm is used to remove the deviation, and the parameters in the algorithm are adjusted according to the magnitude of the deviation, thereby constructing a scheme that can adjust the equalization control speed to improve the overall equalization control efficiency of the state of charge.

[0061] 2. Precise SOC control of the power grid is achieved. In the technical solution of this application, the SOC balancing control is applied to all energy storage units, thus enabling individual control of each energy storage unit. In this case, precise SOC control of the power grid can be achieved.

[0062] 3. Achieved reasonable compensation for the grid bus voltage. After controlling the energy storage unit, a possible problem is that the bus voltage does not match the expected voltage demand. In this case, the output voltage or input voltage of the bus does not match the actual operating requirements of the grid. This application addresses this potential problem by implementing PI controller compensation for the bus, thereby ensuring that the bus voltage is the same as the preset target parameters. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the embodiments of this application or the prior art will be briefly introduced below. Obviously, the following description is only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. The drawings are used to provide a further understanding of this disclosure and constitute a part of the specification. They are used together with the following detailed description to explain this disclosure, but do not constitute a limitation of this disclosure. In the drawings:

[0064] Figure 1 A flowchart of a SOC balancing method for a multi-energy storage unit power grid provided in this application embodiment;

[0065] Figure 2 This is a diagram of the power grid system topology in a SOC equalization method for a multi-energy storage unit power grid provided in this application embodiment;

[0066] Figure 3 The diagram shows an equivalent model of a parallel converter with droop control for a SOC equalization method for a multi-energy storage unit power grid provided in this application embodiment.

[0067] Figure 4 An improved droop control block diagram of a SOC equalization method for a multi-energy storage unit power grid provided in this application embodiment;

[0068] Figure 5 Simulation curves of a SOC equalization method for a multi-energy storage unit power grid provided in this application embodiment;

[0069] Figure 6 A diagram showing the arrangement of state-of-charge (SOC) deviation data for a multi-energy storage unit power grid used in an embodiment of this application. Detailed Implementation

[0070] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Furthermore, in the embodiments of this application, "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0071] In current power grid control, a large number of energy storage units are added to some systems. These energy storage units need to maintain SOC balance during charging and discharging; otherwise, some units may be overcharged or over-discharged, reducing their lifespan and potentially causing the entire power grid to fail. Research has been conducted on SOC balance in multi-energy storage grids, including: an exponential adaptive adjustment of the droop coefficient method to address the impedance mismatch problem in islanded DC microgrids with multi-energy storage systems, achieving SOC balance by adaptively adjusting "virtual impedance," but this does not consider the overall balancing speed; combining the droop coefficient with a nested arctangent function of SOC and introducing a variable acceleration factor, while using a uniform voltage equalizer to compensate for voltage, can achieve balance, but the variable acceleration factor is prone to step changes, which is detrimental to system stability; all employ a variable adjustment factor SOC. The droop control strategy balances the speed of both by dynamically adjusting the adjustment factor value, but its adjustment factor parameters need further optimization, and its adaptability in complex scenarios needs further verification, as it is prone to power over-limit situations. Considering the differences in line impedance and energy storage capacity, a power function is used to construct the relationship between the droop coefficient and SOC, and an appropriate equalization adjustment coefficient is selected to achieve adaptive control of the droop coefficient. However, the power function parameters need to be selected empirically, which may affect the universality of different capacity scenarios. Based on the adaptive adjustment of the droop coefficient using exponential functions nested with power functions, a power state factor is introduced to achieve power distribution according to capacity ratio, but its robustness under extreme conditions has not been verified. SOC equalization is achieved by constructing a balance factor through the power exponent of line impedance and SOC, and voltage compensation is introduced to stabilize the bus voltage, but continuous line resistance detection increases system complexity. SOC equalization and bus voltage stability are achieved by adjusting the droop coefficient by defining current proportional coefficient and capacity coefficient, but the parameter design is ambiguous and not conducive to engineering applications. Combining a power exponential function to improve SOC The resolution allows the droop coefficient to adaptively adjust the DESU output based on the current SOC information. However, the above method does not consider the voltage deviation caused by droop control. Voltage feedforward compensation is used to design an output voltage compensation outer loop, ensuring the bus voltage converges to the rated value of the common bus voltage. The average output voltage of the energy storage modules represents the bus voltage level, and a PI regulator generates the compensation amount. Hierarchical control is employed, using secondary control to introduce a voltage compensation term to restore the bus voltage. However, the above methods do not fully consider the impact of actual factors such as inconsistent initial SOC values ​​among the DESUs during operation, and therefore cannot fully guarantee the long-term reliable operation of the energy storage system. To address the above control problems, this application proposes a SOC equalization method for a multi-energy storage unit power grid, specifically:

[0072] A method for SOC equalization in a multi-energy storage unit power grid, such as Figure 1The diagram shown is a flowchart of a SOC balancing method for a multi-energy storage unit power grid provided in an embodiment of this application. Specifically:

[0073] S110. Obtain the state of charge information of all energy storage units in the power grid.

[0074] S120. Based on the state of charge information, obtain the average state of charge of the multi-energy storage unit grid.

[0075] S130. Based on the average state of charge, obtain the state of charge deviation of each energy storage unit.

[0076] S140. Based on the state of charge deviation, obtain the droop coefficient of the energy storage unit.

[0077] S150. Based on the droop coefficient of the energy storage unit, control the energy storage unit and obtain the voltage deviation of the bus.

[0078] S160. Based on the voltage deviation of the bus, obtain the parameters of the voltage regulator to eliminate the voltage deviation of the bus.

[0079] The purpose of all the above steps is to achieve SOC balance control of the multi-energy storage unit power grid, and at the same time to perform voltage compensation on the bus to further ensure SOC balance and thus guarantee the control effect.

[0080] The following will explain all the steps above, in detail:

[0081] As described in step S110, the purpose of this step is to monitor the operating status of all energy storage units within the energy storage unit grid. Only in this way can specific operating information be determined, and based on the available results, the current operating information of the energy storage units can be obtained. Subsequent control is then performed based on this information. Specifically:

[0082] The operating status of all energy storage units in the power grid is monitored, and the data acquisition time of the energy storage units is obtained;

[0083] Based on the value taking time of the energy storage unit, obtain the state of charge information of all energy storage units;

[0084] Establish the correspondence between the time of value taking and the state of charge information of the energy storage unit, so as to obtain the state of charge information corresponding to each time value taking.

[0085] For all the energy storage units, a current and / or voltage monitoring device is installed between them and the bus. Of course, other power parameter monitoring devices can also be used to monitor the operating status of the energy storage units in real time.

[0086] In addition, a state of charge monitoring system needs to be set up for each energy storage unit to determine its state of charge.

[0087] In the operation of the monitoring system, it is equipped with timing capabilities. Therefore, it is also necessary to obtain the time corresponding to the acquired power parameters and establish the correspondence between the two parameters.

[0088] The reason for obtaining the time parameter is that in many cases, the charging and discharging of the energy storage unit is set based on the time parameter. In this case, after obtaining the time parameter, the charging and discharging state of the energy storage unit can be determined directly based on the current time point.

[0089] The beneficial effect of the above steps is that, based on the monitoring of the power parameters of the energy storage unit and the determination of the time parameters, the power parameters at the current time node can be determined directly based on the obtained time parameters, thereby improving the rationality of the power parameter values.

[0090] As described in step S120, the purpose of this step is that, given that SOC equalization control is performed for each energy storage unit, it is obviously necessary to obtain a benchmark. Therefore, the mean state of charge is determined here to determine the deviation between the current energy storage unit's state of charge and the overall state of charge. Specifically:

[0091] Obtain the state of charge information of all energy storage units corresponding to each time value;

[0092] Based on the state of charge information of all energy storage units, the average state of charge of the multi-energy storage unit power grid is obtained.

[0093] Also includes:

[0094] Establish the correspondence between the average state of charge and the time of the value of the multi-energy storage unit power grid.

[0095] In this process, real-time state of charge information is acquired for all energy storage units, and a corresponding relationship is established.

[0096] In this process, the average value of the state of charge information obtained within the same time node is calculated, and a correspondence between the average value and the time node is established.

[0097] The beneficial effect of step S120 is that after establishing the correspondence between the state of charge and sampling time of all energy storage units, the sampling time can be used as the anchor point for the correspondence between the state of charge and the mean state of charge of the energy storage unit, so as to achieve the correspondence between the two. At the same time, the data within a time period can be decomposed based on the sampling time, thereby further improving the SOC equalization control accuracy of the energy storage unit grid.

[0098] As described in step S130, the purpose of this step is to determine the deviation for each energy storage unit, based on which the SOC of each energy storage unit can be controlled separately. Specifically:

[0099] Based on the value taking time of the energy storage unit, obtain the state of charge information of each energy storage unit and the average state of charge of the multi-energy storage unit grid;

[0100] The state of charge (SOC) information of each energy storage unit is obtained, and the difference between the SOC information of each energy storage unit and the mean SOC is obtained, so as to obtain the SOC deviation of each energy storage unit.

[0101] Among these, the deviation of the state of charge of the energy storage unit needs to be ensured that the state of charge and the mean value remain at the same time point in the obtained results.

[0102] The beneficial effect of step S130 is that by ensuring that the state of charge and the mean state of charge of the energy storage unit are at the same time point and by calculating the deviation, it is possible to ensure that the SOC of each energy storage unit is independently controlled based on its corresponding state of charge deviation in the obtained deviation calculation results.

[0103] As described in step S140, the purpose of this step is to set a droop coefficient for the obtained state of charge deviation during the control process, and to perform control based on this coefficient. Specifically:

[0104] Based on the characteristic parameters of the energy storage unit and the bus voltage parameters, the theoretical droop coefficient is obtained. The equation for determining the theoretical droop coefficient is as follows:

[0105] ;

[0106] Where R0 represents the theoretical droop coefficient; V ref Indicates the bus voltage parameter; V ref-0 I represents the output voltage reference value among the characteristic parameters of the energy storage unit. out0 This refers to the output current, which is a characteristic parameter of the energy storage unit.

[0107] Based on the state of charge deviation, the droop coefficient of the energy storage unit is obtained, and the equation for determining the droop coefficient of the energy storage unit is:

[0108] ;

[0109] Among them, R i The droop factor of the i-th energy storage unit is represented by p; the amplification factor is represented by m; and the SOC is represented by m. i Represents the charge state information of the i-th energy storage unit; SOC avg Indicates the mean of the state of charge; idc Indicates the current output value of the energy storage unit, i dc When the value is >0, the energy storage is in a discharge state, i dc When ≤0, the energy storage is in a charging state; n represents the power of the state of charge deviation; i represents the index of the number of energy storage units.

[0110] The equation for determining the deviation response coefficient is as follows:

[0111] ;

[0112] Where δ represents the proportional response coefficient related to SOC deviation, which is a constant; ε represents the constant response portion independent of SOC deviation, which is also a constant; ΔSOC i This indicates the deviation in the state of charge.

[0113] Among them, such as Figure 2 The diagram shown illustrates the power grid topology in a multi-energy storage unit grid SOC balancing method provided in this application embodiment. For illustrative purposes, it is assumed that the power grid consists of a photovoltaic power generation system composed of photovoltaic arrays connected to the DC bus via a unidirectional DC / DC converter, while the distributed energy storage system is connected to the DC bus via a bidirectional DC / DC converter to supply power to the load. The distributed energy storage system comprises three sets of batteries of the same capacity, supplementing or absorbing the power deficit or surplus on the DC bus to ensure DC bus voltage stability and system power balance.

[0114] In the operation of the power grid, during the droop control process, the droop coefficient is as follows:

[0115] ;

[0116] Where R0 represents the theoretical droop coefficient; V ref Indicates the bus voltage parameter; V ref-0 I represents the output voltage reference value among the characteristic parameters of the energy storage unit. out0 This represents the output current, which is one of the characteristic parameters of the energy storage unit.

[0117] This method is a traditional control method. To ensure the normal operation of the system, the droop coefficient must be kept within a certain range. If the droop coefficient is too large, it will cause excessive voltage drop at the bus, reducing the stability of the system. If the droop coefficient is too small, the balancing speed will be slow. The selection rule for the droop coefficient is as follows:

[0118] ;

[0119] In the formula It is the maximum value of the DC microgrid bus voltage offset. This is the maximum value of the output current of the energy storage unit converter. A suitable fixed droop coefficient is selected, multiplied by the energy storage converter output current, and then the difference is calculated with the DC bus reference voltage. This difference is then processed through a voltage-current dual closed-loop output PWM signal. However, this can only achieve preliminary current equalization based on the droop coefficient and cannot achieve SOC equalization.

[0120] Among them, such as Figure 3 The diagram shown is an equivalent model of a parallel converter with droop control for a multi-energy storage unit grid SOC equalization method provided in this application embodiment. The ratio of the output currents of the two converters can be obtained as follows:

[0121] ;

[0122] As can be seen from the above formula, when using traditional droop control, the output current of the converter is inversely proportional to the sum of the virtual resistance and the line resistance. If the line resistance is ignored, the output current can be considered to be inversely proportional to the virtual resistance.

[0123] The SOC of each energy storage unit is obtained using the ampere-hour integration method:

[0124] ;

[0125] Where: SOC i State of charge (SOC) 0i The initial state of charge; C i The capacity of the energy storage unit; I i Let be the output current. Taking its derivative, we know that:

[0126] ;

[0127] It is now known that both the magnitude and capacity of the charging and discharging current of the energy storage unit affect the rate of change of the State of Charge (SOC). Therefore:

[0128] ;

[0129] It was then learned that, theoretically, energy storage units of the same capacity could have different actual capacities due to various production and operational reasons, as well as differences in line impedance, resulting in deviations in the SOC of multiple lithium batteries.

[0130] In the technical solution of this application, such as Figure 4 The diagram shown is an improved droop control block diagram of a multi-energy storage unit grid SOC equalization method provided in an embodiment of this application, namely: the scheme used in this application, where SOC is shown in the diagram. i Let i be the SOC value of the i-th unit, and n be the exponent. i V is the output current of the i-th energy storage unit. dcThe bus voltage is used as the reference point. An optimized and improved adaptive droop control strategy dynamically adjusts the droop coefficient based on the SOC deviation of the energy storage unit, achieving SOC equalization as quickly as possible while ensuring that the lithium battery charging and discharging current does not exceed its maximum limit. To compensate for the inherent voltage drop characteristics of traditional droop control, a common voltage deviation is output via a PI regulator using the bus voltage deviation, thereby compensating for the droop curve and automatically restoring the bus voltage.

[0131] In determining the droop coefficient in this application, the droop coefficient of the energy storage unit is obtained based on the state of charge deviation, and the equation for determining the droop coefficient of the energy storage unit is as follows:

[0132] ;

[0133] Among them, R i The droop factor of the i-th energy storage unit is represented by p; the amplification factor is represented by m; and the SOC is represented by m. i Represents the charge state information of the i-th energy storage unit; SOC avg Indicates the mean of the state of charge; i dc Indicates the current output value of the energy storage unit, i dc When the value is >0, the energy storage is in a discharge state, i dc When ≤0, the energy storage is in a charging state; n represents the power of the state of charge deviation; i represents the index of the number of energy storage units.

[0134] The equation for determining the deviation response coefficient is as follows:

[0135] ;

[0136] Where δ represents the proportional response coefficient related to SOC deviation, which is a constant; ε represents the constant response portion independent of SOC deviation, which is also a constant; ΔSOC i This indicates the deviation in the state of charge.

[0137] Among them, i dc When the value is >0, the energy storage is in a discharge state, i dc When δ < 0, the energy storage is in a charging state. In the formula, δ and ε are both constants. This represents the difference between the SOC of the i-th energy storage unit and the average SOC of all energy storage units. The coefficient δ represents the SOC among all DESUs. avg When the difference between the current source energy (SOC) and the current regulation (SOC) is large, the ratio of the droop coefficients of the converter is equivalent to an exponential function containing p (amplification factor) and δ, which slows down the rate of change of the droop coefficient ratio. When ΔSOC is small, a reasonable selection of ε can accelerate the SOC equalization speed of the energy storage unit. In the deviation response coefficient m, the value of ε is required to be ε<1. When it approaches 0, , at this time 1 / m > 1; the value range of δ is required as follows: when the value is relatively large , that is , at this time the influence of ε can be ignored. .

[0138] When the energy storage unit is charging and i dc < 0 and the SOC is higher than the average value, R i > R0, the energy storage unit absorbs less power; when the SOC is lower than the average value, R i < R0, the energy storage unit absorbs more power. When the energy storage unit is discharging and i dc > 0 and the SOC is higher than the average value, R i < R0, the energy storage unit discharges more power; when the SOC is lower than the average value, R i > R0, the energy storage unit discharges more power, and finally the SOC balance is achieved.

[0139] To verify the above analysis results, taking the discharging process of two parallel equal-capacity DESUs as an example for further explanation. Assume that the output voltages of each converter are not very different, and SOC1 > SOC2, and the capacities of the two energy storages are the same, that is, C1 = C2.

[0140] Let:

[0141] ;

[0142] Then the derivative of the SOC difference between the two energy storage units is:

[0143] ;

[0144] It can be deduced from the above two equations that:

[0145] ;

[0146] It can be seen that the change rate of the SOC difference is proportional to the output current difference, and the current difference determines the equalization direction.

[0147] Furthermore, with the reference voltage being the same and the given voltage being the same, it can be deduced that:

[0148] ;

[0149] Then it can be obtained that:

[0150] ;

[0151] Based on the above equations, it can be deduced that:

[0152] ;

[0153] Based on the Taylor expansion of the exponential function, and using a first-order approximation Then the above formula can be obtained as follows:

[0154] ;

[0155] From the above equation, it can be seen that when SOC1>SOC2, ΔSOC 12 >0 and <0, therefore ΔSOC 12 It gradually approaches 0, meaning that eventually the two sets of SOC can be equal. Vref-i is the output voltage reference value.

[0156] As the above derivation shows, the droop coefficient adaptively adjusts with changes in SOC. During the discharge process of the energy storage unit, the energy storage unit with a larger SOC has a smaller droop coefficient, resulting in a larger output current; conversely, the energy storage unit with a smaller droop coefficient has a smaller output current. Ultimately, this process brings the SOC of each energy storage unit to a balanced state. The SOC balancing process during the charging and discharging of the energy storage unit is similar.

[0157] Based on the above results, simulation analysis was conducted. Assuming the bus voltage setpoint is 750V and the maximum allowable deviation is ±5% of the setpoint (i.e., the maximum voltage variation is 37.5V), the converter's maximum output power is 20kW, and the coefficients δ and ε are 20 and 0.15 respectively, considering the discharge process of a DESUS of the same capacity, R can be obtained. i Regarding SOC i p and n in SOC avg The relationship between R values ​​at 0.4 and 0.6, respectively. i The surface is relatively smooth, and the SOC avg With SOC i The greater the difference between them, the more R i The greater the influence of p and n, the more SOC... i SOC avg At that time, R i R decreases as p and n increase, and vice versa. i It increases with increasing p, but decreases with increasing n, and when SOC i With SOC avg When the difference between p and n is large, p and n are related to R. i The impact of these changes is significant, and the analysis of the DESUS charging process is similar. Therefore, analyzing the relationship between them reveals that the values ​​of p and n cannot be too large. The values ​​of p and n should be reasonably selected based on the actual circuit conditions. This application, through extensive simulation of the technical solution, selects p=80 and n=1.3.

[0158] Among them, in the analysis of the impact of load parameter changes on the balancing effect, such as Figure 5The figure shown is a simulation curve of a SOC equalization method for a multi-energy storage unit power grid provided in an embodiment of this application. It can be seen that under discharge conditions, when the SOC deviation is large and ε is fixed, when the SOC... i <SOC avg That is, when ΔSOC is greater than 0, the smaller δ is, the better R is. i The larger the SOC, the smaller the corresponding output current, and vice versa. i SOC avg That is, the smaller ΔSOC is, the better R is. i The smaller the value of δ, the larger the corresponding output current. However, δ cannot be too small. Taking the balancing of two sets of energy storage as an example, in the discharge state, a smaller δ will cause the droop coefficient to change too drastically, resulting in drastic current changes and excessively rapid current changes, which will affect the balancing speed. Therefore, when the deviation is large, the droop coefficient should be kept to change slowly, and the two should maintain a large current ratio to speed up the balancing speed. When the deviation is small and δ is fixed, a smaller ε and R... i The more pronounced the change, the faster the equilibrium speed. However, a smaller ε value makes the system prone to oscillations and unable to maintain a stable SOC equilibrium state.

[0159] Based on the simulation results above, δ mainly affects the SOC equalization speed in the initial stage, while ε mainly affects the SOC equalization speed and accuracy in the final stage. In practical applications, appropriate δ and ε should be selected to achieve fast and stable SOC equalization. Through extensive analysis and experiments, δ=20 and ε=0.15 were chosen.

[0160] The beneficial effects of step S140 are twofold. First, by using the nth power of the SOC deviation, the SOC deviation range becomes controllable, effectively avoiding the impact of different initial states of charge. The SOC equalization speed is only affected by the deviation between each energy storage unit, increasing the universality of the proposed control strategy. Second, while a fixed amplification factor p can achieve SOC equalization, since the SOC is constantly changing, when the SOC deviation is large, the droop coefficient changes rapidly, the current changes quickly, and the equalization speed slows down accordingly. When the SOC deviation is small, the droop coefficient does not change significantly, resulting in a slower equalization speed and inability to achieve rapid equalization. After increasing the deviation response coefficient, p adaptively adjusts its size according to the SOC deviation, and p / m gradually changes with the SOC deviation. When the SOC deviation is large, the p / m is small, which makes the droop coefficient change slowly, maintains a large droop coefficient ratio, and thus maintains a large current ratio, accelerating the equalization process. When the SOC deviation is small, the p / m is large, which makes the droop coefficient change faster and the droop coefficient ratio change significantly, accelerating the current convergence. This allows the SOC to converge faster in the final equalization stage, ultimately achieving rapid equalization throughout the entire process.

[0161] However, further research revealed that the power value in the aforementioned droop coefficient equation significantly impacts the specific balance control results. Furthermore, the larger the power value, the larger the droop coefficient. An increased droop coefficient also improves the adjustment speed of the deviation. Crucially, the power of this coefficient has a more pronounced effect on the magnitude of the droop coefficient; therefore, this parameter needs to be determined. Specifically:

[0162] Based on the range of the power of the state of charge deviation and the number of energy storage units, the possible values ​​of the power of the state of charge deviation are obtained, and the equation for determining the possible values ​​is as follows:

[0163] ;

[0164] Where, n i This represents the possible values ​​of the power of the state-of-charge deviation of the i-th energy storage unit; n min This represents the minimum value within the range of powers of the state of charge deviation; n max The maximum value of the range of the power of the state of charge deviation; N represents the total number of energy storage units;

[0165] Obtain the state of charge deviation of all energy storage units and sort the state of charge deviation of all energy storage units to obtain the state of charge deviation sorting result.

[0166] Based on the sorting results of the state of charge deviation, a correspondence is established between the possible values ​​and the state of charge deviation in the overall sorted data group, so as to determine the power value of the state of charge deviation.

[0167] In practice, the range of values ​​for the power can be determined. Since energy storage units can be controlled separately, the range of values ​​can be decomposed according to the number of energy storage units.

[0168] Among them, for the droop control method of each energy storage unit, the power number can be directly allocated based on the above equation.

[0169] Among them, the obtained power numbers can be processed using this method to obtain a sequence of related power numbers.

[0170] Among them, the obtained arithmetic sequence of powers is arranged in ascending order, and the state of charge deviation is also arranged in ascending order. Then, according to the order of arrangement, each state of charge deviation is assigned a corresponding power.

[0171] The beneficial effect of this step is that a corresponding power number is set for each energy storage unit, so that the power number parameters of the energy storage unit can be allocated accordingly based on the power number result.

[0172] In the sorting of state-of-charge (SOC) deviations in the scheme disclosed in this application, considering the large number of energy storage units in a large-scale power grid, a significant number of SOC deviations will be generated. To ensure control efficiency, it is necessary to be able to quickly sort these deviations. Specifically:

[0173] Step 1: Obtain the state of charge deviation of all energy storage units and randomly decompose it into multiple data groups of equal quantity;

[0174] Step 2: Sort the state of charge deviations within each data group to obtain sorted data groups;

[0175] Step 3: Obtain the mean value of each sorted data group, and sort all sorted data groups based on the mean value.

[0176] Step 4: Obtain the first and second sorted data groups, and obtain the minimum value of the state of charge deviation in the second sorted data group to obtain the minimum value of the second data group.

[0177] Step 5: Obtain the numerical range formed by two adjacent state of charge deviations in the first sorted data group where the minimum value of the second sorted data group is located. Insert the minimum value of the second sorted data group after the smaller value of the numerical range of adjacent state of charge deviations. After the insertion of the minimum value of the second sorted data group, the larger value of the minimum value of the second sorted data group and the numerical range of adjacent state of charge deviations form a new numerical range. Remove the minimum value of the second sorted data group from the second sorted data group to obtain a new second sorted data group.

[0178] Step 6: Obtain the comparison result between the minimum value of the new second sorted data group and the larger value in the new data interval. If the minimum value of the new second sorted data group is less than the larger value in the new data interval, then the minimum value of the new second sorted data group is inserted before the larger value in the new data interval and after the minimum value of the second sorted data group. Otherwise, the minimum value of the new second sorted data group is compared backward from the larger value in the new data interval to obtain the insertion position of the minimum value of the new second sorted data group in the new first sorted data group.

[0179] Step 7: Repeat the methods of Step 5 and Step 6 until all the state of charge deviations in the second data group are inserted into the first data group to obtain a new first sorted data group.

[0180] Step 8: Insert the state of charge deviation in the third sorted data group into the new first sorted data group according to the methods in steps 1 to 7, and so on, until all sorted data groups are sorted into the same data group to obtain the overall sorted data group.

[0181] Among them, such as Figure 6 The image shown is a data chart illustrating the state-of-charge (SOC) deviation of a multi-energy storage unit power grid, as used in an embodiment of this application. Specifically, as shown... Figure 6 As shown in (a), this is a data graph of all state of charge deviations obtained at the current time point. It can be seen that the data was not sorted during this process.

[0182] For all obtained deviations, they are divided equally according to the number of deviations. The equalization process does not require setting the number of groups; simply obtaining multiple data groups with the same amount of data is sufficient. Figure 6 As shown in (a), it is decomposed into 4 data groups.

[0183] Within each data group, the data is sorted, either in ascending or descending order; here, ascending order is used. Several methods have been developed for data sorting, and none are specified here. It's important to note that the conventional sorting method is not used for all data because it becomes inefficient with large datasets. Figure 6 (b) shows the results after sorting each group.

[0184] After sorting the data in each group, it is necessary to calculate the mean of all data in each group and then sort them according to the mean. This method can ensure that the means of two adjacent data groups are similar, which means that all the data in them are also relatively similar. In this case, after merging the first and second sorted data groups, the more similar data can be merged first, which can reduce the complexity of subsequent merging.

[0185] The process involves first obtaining the first and second groups, and then inserting the minimum value from the second group into the first group. For example, if the data is d, this data is compared with the data in the first group to obtain the numerical range in the first group. The smaller value in the numerical range of adjacent state of charge deviation means that when data d is searched in the first group, the first data in the first group that is less than d is found. This data is the maximum value in the numerical range. The data in the original first group that is less than this data and is adjacent to it is the minimum value in the numerical range. Data d is then inserted into this numerical range.

[0186] In some embodiments, regardless of whether it's an existing sorted data group or a data group that has already been merged, all adjacent data pairs are defined into numerical intervals. During the insertion process between the previous and subsequent sorted data groups, only the data in the previous sorted data group that is greater than the data to be inserted and has the smallest absolute difference between the two values ​​is selected. If this data is the maximum value in the numerical interval, then the data to be inserted is inserted into that numerical interval, falling between the two parameters within that interval. In this method, the number of data intervals formed is less than the number of data points in the data group. Therefore, from an overall perspective, such as when data needs to be inserted before the maximum value of a sorted data group, the number of data comparisons can be reduced.

[0187] In this process, after the minimum value in the second group is successfully inserted into the first group, the original minimum value in the second group is removed, resulting in a new second group. A new minimum value will naturally appear in this new second group. Following the same method, the new minimum value is then inserted into the new first group, and so on, until the entire second group is inserted into the first group. The specific result is as follows: Figure 6 As shown in (c).

[0188] In this method, after a data point from the second group is inserted into the first group, a new data interval is established between the inserted data and its adjacent larger value. For the data following the inserted data in the second group, it is directly compared with the larger value in this new data interval. If it is smaller, the next data point is inserted before the larger value in the new data interval, after the inserted data. If the next data point is larger than the larger value, the comparison continues from that larger value until the first data point in the first group after the larger value that is larger than the next data point is found. The next data point is then inserted before this first data point, forming a new data interval. Other data are inserted using the same method, and finally, the two data groups are merged. This method significantly reduces the number of comparisons between data points, further improving sorting efficiency.

[0189] In this process, after all data from the second group is inserted into the first group, the original third group becomes the second group. The same method is then used to insert the original third group into the new first group, and so on, until all data from all groups has been inserted into the first group, thus obtaining the final result. The specific result is as follows: Figure 6 As shown in (d).

[0190] For steps five and six, to illustrate more accurately, specific data will be used. After decomposing the data groups and sorting the data within each group, four sorted data groups were obtained: First sorted data group: {0.12, 0.15, 0.22, 0.35, 0.36}; Second sorted data group: {0.14, 0.22, 0.42, 0.46, 0.56}; Third sorted data group: {0.16, 0.2, 0.33, 0.46, 0. 52}; The fourth sorted data group: {0.16,0.31,0.56,0.69,0.73}. The overall technical method is to insert the next sorted data group into the previous sorted data group. That is, all the data in the second sorted data group are inserted into the first sorted data group to form a new first sorted data group. Then, the third sorted data group is inserted into the new first sorted data group to obtain a further updated first sorted data group. Finally, the fourth sorted data group is inserted into the further updated first sorted data group.

[0191] In the method of inserting a later sorted data group into a previous sorted data group, if the data in the later sorted data group is arranged in ascending order, then the data in the later sorted data group will also be inserted into the previous sorted data group one by one in ascending order. However, one situation needs to be considered: the position of the minimum value in the later sorted data group within the previous sorted data group is not easily known based on the sorting order. For example, the third data in the later sorted data group may be between the first and second data in the previous sorted data group, or it may be between the second-to-last and last data in the previous sorted data group. In other words, the position of the data in this sorted data group does not reflect the size relationship of the corresponding data in the two sorted data groups. Therefore, a more appropriate method is needed for insertion.

[0192] Taking the first and second sorted data groups as examples, the first data in the second sorted data group is 0.14. Obviously, when it is inserted into the first sorted data group, it is between 0.12 and 0.15. The data (0.12, 0.15) is a numerical range of adjacent state of charge deviation. After 0.14 is inserted into this numerical range, two new numerical ranges are generated, namely (0.12, 0.14) and (0.14, 0.15).

[0193] In this case, after the minimum value in the second sorted data group is inserted into the first sorted data group, the minimum value in the second sorted data group is removed, and the original second sorted data group becomes {0.22,0.42,0.46,0.56}, thus obtaining a new second sorted data group, and the minimum value in the new second sorted data group becomes 0.22.

[0194] Since the minimum value in the new second sorted data group is obviously not less than the minimum value in the original second sorted data group, the insertion position of the minimum value of the new second sorted data group, i.e., 0.22, in the new first sorted data group does not need to consider the newly generated smaller numerical range, i.e., (0.12, 0.14). Therefore, the "two new numerical ranges" mentioned above have actually been further filtered, and the numerical range containing the larger value, i.e. (0.14, 0.15), was finally selected.

[0195] Since the minimum value in the new second sorted data group is not less than the minimum value in the original second sorted data group, it is only necessary to compare the value with the larger value in the new value range. If it is less than the larger value in the new value range, the minimum value in the new second sorted data group is inserted before the larger value in the new value range. Otherwise, further judgment is required.

[0196] The "further judgment" mentioned in the previous paragraph involves comparing the minimum value in the new second sorted data group sequentially from the larger values ​​in the new numerical range until the minimum value in the new second sorted data group first encounters a data value that is not less than that minimum value. That is, in the above example, the minimum value in the new second sorted data group is 0.22, so it starts from 0.15 and compares each data value sequentially. The first data value that is not less than that minimum value is 0.22, so the minimum value in the new second sorted data group is inserted before the first data value that is not less than that minimum value.

[0197] When the new minimum value after the second sort is inserted after the new first sort data group, the new second sort data group becomes {0.42, 0.46, 0.56}, and similarly, the minimum value becomes 0.42.

[0198] In this case, the value range in the new first sorted data group is now (0.15, 0.22). Obviously, 0.42 is greater than 0.22, so we need to continue comparing. We find that the maximum value of the new first sorted data group is 0.36, which is obviously less than 0.42. Therefore, we can insert all the data in the new second sorted data group into the new first sorted data group according to the current sorting.

[0199] After all the data groups after the second sort are inserted into the data groups after the first sort, the original data groups after the second sort become new data groups after the second sort, and a new data group after the first sort is obtained. The new data group after the first sort becomes {0.12, 0.14, 0.15, 0.22, 0.22, 0.35, 0.36, 0.42, 0.46, 0.56}, and the new data group after the second sort becomes {0.16, 0.2, 0.33, 0.46, 0.52}. If the new second sorted data group is inserted into the new first sorted data group, the method is exactly the same as the method mentioned above. After the new second sorted data group is completely inserted into the new first sorted data group, the resulting data group is: {0.12,0.14,0.15,0.16,0.2,0.22,0.22,0.33,0.35,0.36,0.42,0.46,0.46,0.52,0.56}, which becomes the new first sorted data group.

[0200] In this case, the original fourth sorted data group becomes the new second sorted data group, and it is inserted into the new first sorted data group according to the method used above, so as to obtain the final sorting result.

[0201] The beneficial effect of the above steps is that by decomposing all the obtained deviations into segments, sorting each segment separately, and then inserting the next data segment into the first data segment according to the obtained segmented sorting results, the sorting efficiency is greatly improved.

[0202] As shown in step S150, after adjusting the energy storage unit, the bus voltage may deviate from the rated voltage, requiring bus compensation. Specifically:

[0203] Based on the droop coefficient of the energy storage unit, the energy storage unit is controlled to obtain the controlled energy storage unit.

[0204] The operating parameters of the controlled energy storage unit are obtained, and the voltage deviation of the bus is obtained. The equation for determining the voltage deviation of the bus is:

[0205] ;

[0206] Where ΔU represents the voltage deviation of the bus; V oref Represents the bus input voltage parameters; R represents the load of the energy storage unit; I outi represents the output current of the i-th energy storage unit; j represents the total number of energy storage units.

[0207] Among them, traditional droop control always produces a value of RI. outi Voltage deviation can easily lead to a large discrepancy between the actual and rated bus voltage values ​​if the transmission current is too large. To overcome the bus voltage drop caused by traditional droop control and ensure the stability of the bus voltage under rated load operation, recovery control of the bus voltage is added.

[0208] The formula for bus voltage is:

[0209] ;

[0210] Where ΔU represents the voltage deviation of the bus; V oref Represents the bus input voltage parameters; R represents the load of the energy storage unit; I outi represents the output current of the i-th energy storage unit; j represents the total number of energy storage units.

[0211] As described in step S160, the purpose of this step is to compensate for the entire bus voltage after obtaining the bus voltage deviation. Specifically:

[0212] A voltage regulator is installed on the busbar. The voltage regulator is a PI regulator, and the control equation of the PI regulator is:

[0213] ;

[0214] Among them, K p K represents the proportional gain of the PI controller; i U represents the integral coefficient of the PI controller; dc This represents the measured bus voltage;

[0215] Based on the voltage deviation of the bus, the parameters of the voltage regulator are obtained to eliminate the voltage deviation of the bus.

[0216] Once the bus voltage deviation is determined, the irrational coefficient and integral coefficient can be directly determined based on this value, and the corresponding values ​​can be obtained to control the bus voltage deviation.

[0217] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to computer program instructions. The aforementioned computer program can be stored in a non-volatile storage medium, and when executed, it performs the steps of the above method embodiments. Alternatively, if the integrated unit of the present invention is implemented as a software functional module and sold or used as an independent product, it can also be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention.

[0218] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for SOC equalization in a multi-energy storage unit power grid, characterized in that, The method includes: Obtain the state of charge information of all energy storage units within the power grid; Based on the state of charge information, the average state of charge of the multi-energy storage unit power grid is obtained; Based on the average state of charge, the state of charge deviation of each energy storage unit is obtained; Based on the state of charge deviation, the droop coefficient of the energy storage unit is obtained, including: Based on the characteristic parameters of the energy storage unit and the bus voltage parameters, the theoretical droop coefficient is obtained. The equation for determining the theoretical droop coefficient is as follows: ； Where R0 represents the theoretical droop coefficient; V ref Indicates the bus voltage parameter; V ref-0 I represents the output voltage reference value among the characteristic parameters of the energy storage unit. out0 This refers to the output current, which is a characteristic parameter of the energy storage unit. Based on the state of charge deviation, the droop coefficient of the energy storage unit is obtained, and the equation for determining the droop coefficient of the energy storage unit is: ； Among them, R i The droop factor of the i-th energy storage unit is represented by p; the amplification factor is represented by m; and the SOC is represented by m. i Represents the charge state information of the i-th energy storage unit; SOC avg Indicates the mean of the state of charge; i dc Indicates the current output value of the energy storage unit, i dc When the value is >0, the energy storage is in a discharge state, i dc When ≤0, the energy storage is in a charging state; n represents the power of the state of charge deviation; i represents the index of the number of energy storage units. The equation for determining the deviation response coefficient is as follows: ； Where δ represents the proportional response coefficient related to SOC deviation, which is a constant; ε represents the constant response portion independent of SOC deviation, which is also a constant; ΔSOC i This indicates the deviation in the state of charge. Based on the droop coefficient of the energy storage unit, the energy storage unit is controlled, and the voltage deviation of the bus is obtained. Based on the voltage deviation of the bus, the parameters of the voltage regulator are obtained to eliminate the voltage deviation of the bus.

2. The SOC equalization method for a multi-energy storage unit power grid according to claim 1, characterized in that, The acquisition of the state of charge information of all energy storage units in the power grid includes: The operating status of all energy storage units in the power grid is monitored, and the data acquisition time of the energy storage units is obtained; Based on the value taking time of the energy storage unit, obtain the state of charge information of all energy storage units; Establish the correspondence between the time of value taking and the state of charge information of the energy storage unit, so as to obtain the state of charge information corresponding to each time value taking.

3. The SOC equalization method for a multi-energy storage unit power grid according to claim 1, characterized in that, The step of obtaining the average state of charge (SOC) of the multi-energy storage unit grid based on the SOC information includes: Obtain the state of charge information of all energy storage units corresponding to each time value; Based on the state of charge information of all energy storage units, the average state of charge of the multi-energy storage unit power grid is obtained. Also includes: Establish the correspondence between the average state of charge and the time of the value of the multi-energy storage unit power grid.

4. The SOC equalization method for a multi-energy storage unit power grid according to claim 1, characterized in that, The step of obtaining the state-of-charge deviation of each energy storage unit based on the average state-of-charge value includes: Based on the value taking time of the energy storage unit, obtain the state of charge information of each energy storage unit and the average state of charge of the multi-energy storage unit grid; The state of charge (SOC) information of each energy storage unit is obtained, and the difference between the SOC information of each energy storage unit and the mean SOC is obtained, so as to obtain the SOC deviation of each energy storage unit.

5. The SOC equalization method for a multi-energy storage unit power grid according to claim 1, characterized in that, The method for determining the power value of the state of charge deviation is as follows: Based on the range of the power of the state of charge deviation and the number of energy storage units, the possible values ​​of the power of the state of charge deviation are obtained, and the equation for determining the possible values ​​is as follows: ； Where, n i This represents the possible values ​​of the power of the state-of-charge deviation of the i-th energy storage unit; n min This represents the minimum value within the range of powers of the state of charge deviation; n max The maximum value of the range of the power of the state of charge deviation; N represents the total number of energy storage units; Obtain the state of charge deviation of all energy storage units and sort the state of charge deviation of all energy storage units to obtain the state of charge deviation sorting result. Based on the sorting results of the state of charge deviation, a correspondence is established between the possible values ​​and the state of charge deviation in the overall sorted data group, so as to determine the power value of the state of charge deviation.

6. The SOC equalization method for a multi-energy storage unit power grid according to claim 5, characterized in that, The step of obtaining the state of charge deviation (SOP) of all energy storage units and sorting the SOPs of all energy storage units to obtain the SOP sorting result includes: Step 1: Obtain the state of charge deviation of all energy storage units and randomly decompose it into multiple data groups of equal quantity; Step 2: Sort the state of charge deviations within each data group to obtain sorted data groups; Step 3: Obtain the mean value of each sorted data group, and sort all sorted data groups based on the mean value. Step 4: Obtain the first and second sorted data groups, and obtain the minimum value of the state of charge deviation in the second sorted data group to obtain the minimum value of the second data group. Step 5: Obtain the numerical range formed by two adjacent state of charge deviations in the first sorted data group where the minimum value of the second sorted data group is located. Insert the minimum value of the second sorted data group after the smaller value of the numerical range of adjacent state of charge deviations. After the insertion of the minimum value of the second sorted data group, the larger value of the minimum value of the second sorted data group and the numerical range of adjacent state of charge deviations form a new numerical range. Remove the minimum value of the second sorted data group from the second sorted data group to obtain a new second sorted data group. Step 6: Obtain the comparison result between the minimum value of the new second sorted data group and the larger value in the new data interval. If the minimum value of the new second sorted data group is less than the larger value in the new data interval, then the minimum value of the new second sorted data group is inserted before the larger value in the new data interval and after the minimum value of the second sorted data group. Otherwise, the minimum value of the new second sorted data group is compared backward from the larger value in the new data interval to obtain the insertion position of the minimum value of the new second sorted data group in the new first sorted data group. Step 7: Repeat the methods of Step 5 and Step 6 until all the state of charge deviations in the second data group are inserted into the first data group to obtain a new first sorted data group. Step 8: Insert the state of charge deviation in the third sorted data group into the new first sorted data group according to the methods in steps 1 to 7, and so on, until all sorted data groups are sorted into the same data group to obtain the overall sorted data group.

7. The SOC equalization method for a multi-energy storage unit power grid according to claim 1, characterized in that, The control of the energy storage unit based on the droop coefficient of the energy storage unit and the acquisition of the bus voltage deviation include: Based on the droop coefficient of the energy storage unit, the energy storage unit is controlled to obtain the controlled energy storage unit. The operating parameters of the controlled energy storage unit are obtained, and the voltage deviation of the bus is obtained. The equation for determining the voltage deviation of the bus is: ； Where ΔU represents the voltage deviation of the bus; V oref Represents the bus input voltage parameters; R represents the load of the energy storage unit; I outi represents the output current of the i-th energy storage unit; j represents the total number of energy storage units.

8. The SOC equalization method for a multi-energy storage unit power grid according to claim 1, characterized in that, The step of obtaining parameters of the voltage regulator based on the voltage deviation of the bus to eliminate the voltage deviation of the bus includes: A voltage regulator is installed on the busbar. The voltage regulator is a PI regulator, and the control equation of the PI regulator is: ； Among them, K p K represents the proportional gain of the PI controller; i U represents the integral coefficient of the PI controller; dc This represents the measured bus voltage; Based on the voltage deviation of the bus, the parameters of the voltage regulator are obtained to eliminate the voltage deviation of the bus.

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