Performance balancing droop control method for energy storage system of direct-current micro-grid

Abstract

The invention discloses a direct-current micro-grid energy storage system performance balance droop control method, which comprises the following steps of: firstly, evaluating the residual capacity of each energy storage unit battery on line in real time based on an ANN battery capacity evaluation model; secondly, calculating a battery performance index based on the residual capacity of the battery, and calculating a performance index deviation degree of each energy storage unit battery; determining a system working mode according to the maximum deviation degree of the battery performance indexes of all the energy storage units, and starting droop control based on performance balance when the deviation of the battery performance indexes is large; next, each energy storage unit corrects the initial droop coefficient according to the battery performance index deviation degree to obtain an optimized droop coefficient, then a reference value of the battery output voltage of each energy storage unit is obtained through droop control, and finally a PWM switching signal is generated through voltage-current double closed-loop control of an energy storage unit interface DC / DC converter to enable the output to follow the reference value. Through the method, the overall service life of the direct-current micro-grid energy storage system can be effectively prolonged.

Description

Technical Field

[0001] The present invention belongs to the technical field of direct current (DC) microgrid energy storage systems, and in particular relates to a performance balanced droop control method for a DC microgrid energy storage system. Background Art

[0002] In recent years, in response to the intensifying global energy crisis, the use of clean energy sources such as photovoltaics and wind power has become increasingly widespread. However, renewable energy generation is characterized by randomness, volatility, and intermittency, posing significant challenges to the system's source-load balancing and stable operation when integrated into the power system. To address these shortcomings of renewable energy generation, microgrids have emerged as a solution. Energy storage systems are deployed within microgrids. These systems offer bidirectional power regulation, allowing them to charge during low loads and discharge during high loads to offset power imbalances between loads and sources. Integrating this bidirectional power regulation with various energy management strategies, such as multi-energy synergy and load demand response, can effectively smooth power fluctuations in the system, mitigate the adverse impact of large-scale renewable energy integration on power system stability, improve renewable energy absorption efficiency, and enhance the ability to balance power supply and demand.

[0003] Microgrids are primarily categorized into two types: AC microgrids and DC microgrids. Compared to AC microgrids, DC microgrids do not have frequency stability or synchronization stability issues, nor do they require reactive power regulation. Therefore, DC microgrids are gaining increasing attention from academia and industry, and hold broad promise for development in practical applications. A DC microgrid energy storage system is a crucial component of a DC microgrid and consists of an energy storage device and an energy storage control system. Energy storage devices can be composed of components such as batteries, capacitors, and supercapacitors. Battery energy storage, with its high capacity and economical efficiency, occupies a dominant position in energy storage systems, particularly large-capacity energy storage systems. Research on DC microgrids and their energy storage systems is of great practical significance, contributing to the construction of intelligent, efficient, and reliable power systems to meet growing energy demands and environmental protection requirements.

[0004] Due to the limited capacity of individual energy storage units, energy storage systems in DC microgrids typically consist of multiple units connected in parallel on the DC bus. Therefore, current distribution among the units is a fundamental issue in energy management for DC microgrid energy storage devices. Among various current distribution methods, droop control, with its advantages of independence from high-speed communication and simple implementation, has gained widespread application and attention in distributed systems with no or low-rate communication. Traditional droop control methods for DC microgrid energy storage systems are based on battery state of charge (SOC) balancing. This approach prioritizes charging current to cells with lower SOCs during a single charge cycle and discharging current to cells with higher SOCs during a single discharge cycle. This approach achieves SOC balancing among the cells over a period of time, such as several hours, thus preventing overcharging or over-discharging of individual cells.

[0005] However, during the long-term operation of DC microgrid energy storage systems, due to differences in the electrochemical properties, temperature, actual load, and other factors of the energy storage unit batteries, their capacity decay and aging rates vary. After long-term operation, key parameters such as the remaining capacity, internal impedance, and no-load potential of each battery will show significant differences, leading to the problem of gradually increasing performance imbalance among the unit batteries in the energy storage system. When the health of individual energy storage unit batteries is too low, the entire energy storage system can be forced to exit operation for maintenance, reducing the overall service life of the DC microgrid energy storage system. Traditional droop control based on SOC balancing aims to achieve SOC balance within a single charge and discharge cycle of several hours. It assumes that the battery capacity of all energy storage units remains unchanged over the long term. Although the number of charge and discharge cycles of each battery cell can remain the same, it cannot avoid the problem of gradually increasing performance imbalance among the unit batteries due to differences in the aging rate of the energy storage unit batteries. Therefore, it is necessary to consider the long-term goal of energy storage unit battery performance balance and propose new control methods. Summary of the Invention

[0006] In order to solve the problem that the traditional droop control based on SOC balance does not consider the adverse impact of battery performance differences between each energy storage unit in the DC microgrid energy storage system on the overall service life of the entire energy storage system, the battery remaining capacity of the energy storage unit is selected as the evaluation index of its performance status, and this evaluation index is applied to the optimization calculation of the droop coefficient to achieve the purpose of performance balance of each energy storage unit in the DC microgrid energy storage system.

[0007] To achieve the above object, the present invention provides a method for controlling the performance of a DC microgrid energy storage system in a balanced manner, the method comprising:

[0008] Step 1: Establish and train a battery capacity evaluation model based on artificial neural network;

[0009] Step 2: Use the battery capacity evaluation model to perform online real-time evaluation of the remaining capacity of all energy storage unit batteries in the DC microgrid energy storage system to obtain the remaining capacity of each energy storage unit. Based on this, the battery performance evaluation index and battery performance index deviation of each energy storage unit are calculated. The battery performance evaluation index of each energy storage unit is calculated based on the remaining capacity and rated capacity of the battery of the energy storage unit, and the battery performance index deviation of each energy storage unit is calculated based on its own performance evaluation index and the comprehensive evaluation index of the battery performance of all energy storage units.

[0010] Step 3: If the current droop control working mode is S1, and if the absolute value of the battery performance index deviation of a certain energy storage unit is greater than or equal to the preset threshold value t1, the droop control working mode of the DC microgrid energy storage system is switched to S2; if the current working mode is S2, and if the absolute values ​​of the battery performance index deviations of all energy storage units are less than or equal to the preset threshold value t2, the droop control working mode of the DC microgrid energy storage system is switched to S1, where S1 represents droop control based on SOC balance, and the droop coefficient of each energy storage unit is related to the SOC value of the battery of the energy storage unit; S2 represents droop control based on performance balance, and the droop coefficient of each energy storage unit is related to the battery performance index of the energy storage unit, and the initial working mode of the DC microgrid energy storage system is S1; the threshold value t1 is not less than the threshold value t2;

[0011] Step 4: According to the droop control working mode determined in step 3, the correction coefficient of the droop control is updated, and the final droop control coefficient corresponding to the working mode is calculated based on this correction coefficient. The bottom layer of the energy storage unit DC / DC converter usually adopts voltage-current dual closed-loop control. Therefore, the droop control is used as the front-stage loop, and the output of the droop control is used as the output voltage reference value of the energy storage unit DC / DC converter. The PWM switching signal is generated through the voltage-current dual closed-loop control of the bottom layer of the energy storage unit DC / DC converter to realize current or power control of the DC / DC converter of each energy storage unit interface.

[0012] Furthermore, the neurons in the input layer of the battery capacity evaluation model based on the artificial neural network correspond one-to-one to battery-related parameters, and the neurons in the output layer correspond to the battery remaining capacity, wherein the battery-related parameters include battery state of charge, battery output voltage and battery output current.

[0013] Furthermore, the battery performance index is expressed as follows:

[0014] ,

[0015] Among them, D i Indicates the first Battery performance indicators of each energy storage unit, , Indicates the number of all energy storage units in this energy storage system; Indicates the rated capacity of the battery; The first The remaining battery capacity of each energy storage unit.

[0016] Furthermore, the performance index deviation of the i-th energy storage unit battery is expressed as follows:

[0017] ,

[0018] Among them, D avg Indicates the performance indicators D1, D2, ..., D of all energy storage unit batteries in this energy storage system i ,…,D N The comprehensive evaluation index is recorded as the global target value of the battery performance index of this energy storage system; ΔD i Indicates the Battery performance index D of each energy storage unit i The global target value D of the battery performance index of this energy storage system avg If the deviation is positive, it means that the battery performance index of the energy storage unit is better than the target value; if the deviation is negative, it means that the battery performance index of the energy storage unit is worse than the target value.

[0019] Furthermore, in the S2 working mode, the droop coefficient R of each energy storage unit is dpi It can be expressed as:

[0020] ,

[0021] Among them, R 0i represents the initial droop coefficient of the i-th energy storage unit; f dpi Acting on the initial droop coefficient, it represents the correction coefficient of the droop control obtained by the i-th energy storage unit based on the battery performance balance target. When the battery performance of all energy storage units reaches equilibrium, each D i are the same, that is, ΔD i =0 when f dpi = 1, which means that there is no need to adjust the initial droop coefficient.

[0022] In S2 working mode, the droop coefficient of each energy storage unit is expressed as follows:

[0023] ,

[0024] in, Indicates the The final droop coefficient of each energy storage unit is: is a real coefficient greater than 0.

[0025] Furthermore, in the S1 working mode, the droop coefficient R of each energy storage unit is dpi and the SOC value S of the energy storage unit OCi The relationship between them is expressed as follows:

[0026] ,

[0027] Among them, R 0i Still represents the initial droop coefficient of the i-th energy storage unit; f dp (S oci ) is a function with the SOC of the i-th energy storage unit as the independent variable, acting on the initial droop coefficient, and when the SOC of all units is completely balanced, that is, for any two energy storage units i and j, there is S oci = S ocj , at this time there is f dp (S oci ) = f dp (S ocj ) = 1, indicating that there is no need to adjust the initial droop coefficient when the SOC of each energy storage battery reaches complete balance.

[0028] The beneficial effects of the present invention are:

[0029] The present invention evaluates the remaining capacity of the battery online in real time based on the ANN battery capacity evaluation model; secondly, the performance evaluation index of the battery is calculated according to the remaining capacity of the energy storage unit battery, and the system working mode is determined according to the maximum deviation of the performance evaluation index of all energy storage units. When the performance index deviation is large, the droop control based on performance balance is started; finally, each energy storage unit establishes a performance evaluation index based on the remaining capacity to implement performance balance droop control, so that the energy storage unit with poor performance bears a smaller current during the charging and discharging process to reduce its capacity degradation rate, and conversely, the energy storage unit with good performance bears a larger current during the charging and discharging process to increase its capacity degradation rate, thereby ensuring that the performance of each energy storage unit in the DC microgrid energy storage system eventually tends to be balanced, reducing the adverse effects of premature failure of the energy storage unit with poor performance on the overall life of the entire energy storage system, and effectively extending the overall service life of the DC microgrid energy storage system;

[0030] The present invention selects the remaining battery capacity as a benchmark to establish a performance evaluation index for each energy storage unit, and implements droop control based on performance balance based on the performance evaluation index. With the goal of converging the deviation of the battery performance index to 0, droop control is implemented on each energy storage unit in the energy storage system. This can achieve the purpose of balanced performance and uniform degradation of each energy storage unit, thereby improving the overall service life of the energy storage system.

[0031] In the performance-balanced droop control process described in this invention, except for calculating the global target performance value for each energy storage unit, all other calculations are performed by the local controller of each energy storage unit. Communication between the local controller and the central controller is limited to uploading the locally evaluated battery performance index results and receiving the global target performance value and operating mode switching signal. Because the performance index evaluation results of each energy storage battery change slowly, low-speed communication is sufficient, and no additional information exchange or communication is required between the energy storage units.

[0032] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0034] Figure 1 This is a flow chart of a method for controlling the performance balance of a DC microgrid energy storage system according to the present invention;

[0035] Figure 2 This is a performance balanced droop control structure diagram of a DC microgrid energy storage system in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the preferred embodiments are only for illustrating the present invention, and are not intended to limit the scope of protection of the present invention.

[0037] Combine Figure 1 and Figure 2 A method for controlling the performance balance of a DC microgrid energy storage system comprises the following steps:

[0038] Step 1: Establish and train a battery capacity evaluation model based on an artificial neural network (ANN);

[0039] Step 2: Use the battery capacity evaluation model to perform online real-time evaluation of the remaining capacity of the batteries of all energy storage units in the DC microgrid energy storage system. The remaining capacity of each energy storage unit is obtained, and the battery performance evaluation index and battery performance index deviation of each energy storage unit are calculated based on the remaining capacity of the battery and the rated capacity of the battery. The battery performance deviation index of each energy storage unit is calculated based on its own performance evaluation index and the global target value of the battery performance of all energy storage units.

[0040] Step 3: If the current droop control working mode is S1, and if the battery performance index deviation of a certain energy storage unit is greater than the preset threshold value t1, the droop control working mode of the DC microgrid energy storage system is switched to S2; if the current droop control working mode is S2, and if the battery performance index deviation index of all energy storage units is less than the preset threshold value t2, the droop control working mode of the DC microgrid energy storage system is switched to S1, where S1 represents droop control based on battery SOC balance, and the droop coefficient of each energy storage unit is related to the SOC value of the battery of the energy storage unit; S2 represents droop control based on battery performance balance, and the droop coefficient of each energy storage unit is related to the battery performance index deviation of the energy storage unit; the initial working mode of the DC microgrid energy storage system is S1; the threshold value t1 is not less than the threshold value t2. If t1 is appropriately greater than t2, a hysteresis switching can be formed to avoid repeated and frequent switching of the control mode near the equilibrium point;

[0041] Step 4: According to the working mode determined in step 3, the correction coefficient of the droop control is updated, and the final droop control coefficient (i.e., droop coefficient) corresponding to the working mode is calculated based on the correction coefficient.

[0042] The bottom layer of the energy storage unit DC / DC converter usually adopts voltage-current dual closed-loop control, with droop control as the front-stage loop. Therefore, the output of the droop control serves as the output voltage reference value of the energy storage unit DC / DC converter. The voltage-current dual closed-loop control at the bottom layer of the energy storage unit DC / DC converter generates a PWM (Pulse Width Modulation) switching signal to achieve current or power control of the DC / DC converter of each energy storage unit interface.

[0043] In step 1, the battery capacity evaluation model based on artificial neural network (ANN) may include an input layer, a hidden layer, and an output layer, wherein the neurons in the input layer correspond one-to-one to the battery-related parameters, and the neurons in the output layer correspond to the remaining capacity of the battery. , where the battery related parameters include battery state of charge S OCi , output voltage V bati and output current I bati, that is, input X=[S OCi , V bati , I bati ], output Y=[Q bati ],in, , Indicates the total number of energy storage units.

[0044] The artificial neural network model can be a recurrent neural network (RNN) model, a convolutional neural network (CNN) model, a Transformer model, etc. The battery capacity assessment model is trained by obtaining the battery state of charge, battery output voltage, and battery output current of training samples (i.e., some energy storage units), as well as the remaining battery capacity corresponding to each energy storage unit. This training can be performed offline on a powerful computer using historical data collected from the energy storage system.

[0045] After the model training is completed, the battery capacity evaluation model can be directly used to perform online real-time battery remaining capacity evaluation on the energy storage unit in the DC microgrid system, that is, step 2.

[0046] The battery capacity evaluation model is used to evaluate the remaining capacity of the battery in real time online. The obtained remaining capacity value is used for local calculation of battery performance evaluation indicators. The evaluated battery performance indicators are not only used by the local controller, but also uploaded to the central controller for the global battery performance indicator target value (i.e., the battery performance D1, D2, ..., D of all energy storage units in this energy storage system). i ,…,D N The DC microgrid energy storage system's central controller calculates the performance indicators of all energy storage cells, calculates a global target battery performance indicator, and finds the maximum absolute deviation between each cell's performance indicator and the global target battery performance indicator. This maximum deviation is then compared with a set maximum allowable difference (i.e., a preset threshold t1) to determine whether to switch the system's operating mode. This present invention achieves real-time switching of the system's operating mode by estimating the remaining battery capacity online.

[0047] For the i-th energy storage unit in the DC microgrid energy storage system, the battery state of charge S OCi , output voltage V bati , output current I bati , based on the ANN capacity evaluation model, online real-time evaluation of the remaining capacity Q of the battery bati Based on the remaining capacity Q of the battery bati and battery rated capacity Qrated Calculate battery performance index D i After obtaining the performance indicators of all N energy storage unit batteries in the DC microgrid energy storage system, calculate the global battery performance indicator target value D of all energy storage units in the energy storage system. avg , and then calculate the performance index D of each energy storage unit battery i With the global target value D avg and calculate these differences ΔD i The maximum absolute value (i.e. the maximum unsigned difference) ΔD max , as the judgment condition for switching the system working state: that is, the maximum unsigned difference ΔD max When the maximum unsigned difference ΔD is greater than or equal to the set upper limit of the performance index difference (i.e., threshold t1), the energy storage system switches to the droop control based on performance balance proposed in the present invention (working state S2); max When the value is less than or equal to the lower limit of the allowable difference of the performance indicator (threshold t2), the energy storage system switches back to the traditional droop control based on SOC balance (working state S1), which can be expressed as:

[0048] ,

[0049] Among them, the threshold value t1 is not less than the threshold value t2. If t1 is selected to be appropriately greater than t2, a hysteresis loop can be formed to avoid repeated and frequent switching of the control mode near the equilibrium point. It should be noted that the maximum unsigned difference ΔD max If the deviation of the battery performance index of a certain energy storage unit is greater than or equal to the preset threshold t1, the maximum unsigned difference ΔD max If the deviation is less than or equal to the threshold value t2, it means that the absolute value of the deviation of the battery performance index of all energy storage units is less than or equal to the preset threshold value t2. In the present invention, the droop control method based on SOC balance can be any existing droop control method based on SOC balance.

[0050] For example, when the system operates in S1 mode, the droop coefficient R of each energy storage unit is dpi The relationship between the SOC value of the energy storage unit is expressed as follows:

[0051] ,

[0052] Among them, R 0i Still represents the initial droop coefficient of the i-th energy storage unit; f dp (S oci) is a function with the SOC of the i-th energy storage unit battery as the independent variable, acting on the initial droop coefficient, and realizing the correction result of the droop coefficient by multiplying it with the initial droop coefficient. When the SOC of all energy storage unit batteries reaches complete balance, that is, for any two energy storage units i and j, there is S oci = S ocj , then there should be f dp (S oci ) = f dp (S ocj ) = 1, indicating that there is no need to adjust the initial droop coefficient at this time.

[0053] When the system operates in S1 working mode, it continues to obtain the remaining capacity value online in real time through the ANN-based battery capacity evaluation model, and converts the remaining capacity value into a battery performance evaluation index. Each energy storage unit calculates the battery performance index deviation based on its own battery performance evaluation index to judge whether the droop control switching conditions are met, and has the ability to switch to droop control based on performance balance at any time.

[0054] When the system operates in S2 working mode, the battery performance evaluation index is expressed as follows:

[0055] ,

[0056] Among them, D i Indicates the first Battery performance indicators of each energy storage unit, , Indicates the number of all energy storage units in this energy storage system; Indicates the rated capacity of the battery; The first The remaining battery capacity of each energy storage unit.

[0057] In some embodiments, in order to reduce accidental errors such as voltage and current sampling glitches, D i Abnormal data removal and low-pass filtering are performed.

[0058] The performance index deviation of the i-th energy storage unit battery is expressed as follows:

[0059] ,

[0060] Among them, D avg Indicates the performance of all energy storage unit batteries D1, D2, ..., D i ,…,D N The comprehensive evaluation index is recorded as the target value of the global battery performance index (referred to as the "global target value"); ΔD i Indicates the Battery performance evaluation index D of each energy storage unit i Deviation from the target value of the global battery performance indicator;

[0061] In some embodiments, D avg Can be selected as D1, D2, …, D i ,…, D N The arithmetic mean of

[0062] .

[0063] In some embodiments, D avg You can also choose D1, D2, …, D i ,…, D N Other averages, such as the square mean, can be used to increase the difference between indicators, namely:

[0064] .

[0065] D avg You can also directly select a constant value D as needed c , 0 < D c < 1, so that the performance indicators of all energy storage unit batteries are as close as possible to D c convergence.

[0066] According to the performance evaluation index, the droop coefficient is corrected on the basis of the initial droop coefficient to achieve droop control based on performance balance. Since the most direct factor affecting the capacity degradation of the battery during normal charging and discharging is the size of the charging and discharging current, the larger the charging and discharging current of the battery, the faster its capacity will degrade. Therefore, when there are performance differences between the batteries of the energy storage unit, in order to allow their performance to converge to a consistent level more quickly, the battery with poorer performance, that is, the battery with a negative deviation, should bear a smaller charging and discharging current, and the battery with better performance, that is, the battery with a positive deviation, should bear a larger charging and discharging current. Taking into account that the output voltage of the energy storage unit deviates from the bus voltage by a small amount during normal operation, the size of the charging and discharging power of the energy storage unit is approximately proportional to the size of its charging and discharging current. Therefore, regardless of whether the droop control target quantity of the energy storage unit battery is the usage current or power, the monotonicity of the function of its charging and discharging current and its performance index deviation should be set as an increasing function, that is,

[0067] ,

[0068] Among them, I oi Represents the charge and discharge current of the i-th energy storage unit, ΔD i represents the battery performance deviation of the i-th energy storage unit (or called "battery performance index deviation"), Indicates derivative.

[0069] Considering that the output voltage of the energy storage unit deviates from the bus voltage by a small amount during normal operation of the DC microgrid, according to the droop control characteristics, the charge and discharge current of the energy storage unit is approximately inversely proportional to the droop coefficient, that is:

[0070] ,

[0071] Among them, R dpi and R dpj It represents the droop coefficient finally used by the i-th and j-th energy storage units.

[0072] When the system operates in S2 mode, the droop coefficient R of each energy storage unit is dpi It can be expressed as:

[0073] ,

[0074] Then, when the performance of the i-th energy storage unit battery is better than the global performance target value, that is, the performance deviation of the energy storage unit battery ΔD i When it is greater than 0, the droop coefficient of the unit needs to be reduced to allow the unit to bear a larger charge and discharge current. At this time, the correction coefficient f dpi Should be less than 1. On the contrary, when the performance of the i-th energy storage unit battery is worse than the global performance target value, that is, the performance index deviation of the energy storage unit battery ΔD i When it is less than 0, the correction coefficient f dpi Should be greater than 1. Therefore, the correction coefficient f of droop control dpi Deviation ΔD from the energy storage unit battery performance index i The monotonicity of the constructed function should be a decreasing function, that is

[0075] .

[0076] Since in S2 working mode, f dpi is related to ΔD i Related quantities (also related to D i Indirectly related), therefore, f dpi Can be written as f dpi (ΔD i ) or f dpi (D i ), R dpi Can also be written as R dpi (ΔD i ) or R dpi (D i ).

[0077] In some embodiments, f dpi It can be selected as a linear function, that is:

[0078] ,

[0079] in, is a real coefficient greater than 0, The larger the f dpi The smaller the absolute value, the greater the adjustment strength based on the initial droop coefficient. The maximum value needs to be guaranteed within ΔD i There is always > 0.

[0080] In some embodiments, in the S2 operating mode, the relationship between the droop coefficient of the energy storage unit and the battery performance index deviation of the energy storage unit is expressed as follows:

[0081] ,

[0082] By adding f dpi Set as an exponential function to speed up the convergence of battery performance deviation value. 0i represents the initial droop coefficient of the i-th energy storage unit, e represents a natural constant, α and β are real coefficients greater than 0, and α is mainly used to adjust the droop control correction coefficient f when ΔDi is large. dpi The value of ΔD changes the battery performance index deviation ΔDi when it is large. i The convergence speed of β is mainly used to adjust the droop control correction coefficient f when ΔDi is small. dpi The value of the battery performance index changes when the deviation ΔDi is small. i Specifically, when ΔD i When it is larger, that is, ΔD i >>β, f dpi The value of the expression is mainly determined by α, which is close to , therefore, α is mainly used to adjust ΔD i The larger the α is, the stronger the correction strength of the initial droop coefficient R is. 0i The stronger the shrinking effect, the i The larger the value, the more the charge and discharge current distribution value of the energy storage unit increases. The upper limit of the value of α must ensure that the power of the energy storage unit does not exceed the power upper limit after the initial droop coefficient is corrected; ΔD i When it is small, that is, ΔD i <<β, f dpi The value of the expression is close to 1, which means that the i-th energy storage unit is close to the global target value and there is no need to adjust the initial droop coefficient R. 0i Adjustments are made so that β affects the ΔD i When the correction force of the droop coefficient is reduced in the process of converging to zero until it is no longer adjusted, the larger β is, the larger ΔD is.i When it approaches 0, the sooner you start reducing the correction force of the droop coefficient, the more you can set β appropriately to avoid ΔD i The back-and-forth vibration around 0 damages the health of the battery. According to the above formula, if the remaining capacity of the i-th energy storage unit battery is smaller and the performance is worse, the droop coefficient R of the energy storage unit will be dpi The greater the relative increase in the initial droop coefficient, the smaller the charge and discharge current allocated to the energy storage unit through droop control, thereby slowing the performance degradation rate of the unit battery. Therefore, under the proposed droop control method based on performance balance, the battery performance difference of each energy storage unit will converge to 0 after long-term operation, making the performance of each battery tend to be balanced.

[0083] It should be noted that the remaining battery capacity of a single energy storage unit is a physical quantity that changes very slowly. Except for the global battery performance target value and the switching signals of the two droop control working modes, which are calculated by the central controller and sent to the local controllers of each energy storage unit in the DC microgrid energy storage system, all other control steps are implemented in the local controller of each energy storage unit. The communication between the local controller and the central controller only requires low-frequency uploading of locally evaluated battery performance information and receiving global battery performance target values ​​and working mode switching signals. There is no other additional information exchange and communication between the energy storage units, and the communication requirements can be met by relying on a low-speed data bus.

[0084] In summary, the present invention proposes a performance balanced droop control method for a DC microgrid energy storage system, which selects the ratio of the remaining capacity to the rated capacity of the energy storage unit as a performance evaluation indicator, and implements performance balanced droop control on this basis to reduce the adverse effects of gradually increasing performance imbalances between batteries of each energy storage unit under long-term operation, so that energy storage units with poor performance bear smaller currents during the charging and discharging process to reduce their capacity degradation rate, and energy storage units with better performance bear larger currents during the charging and discharging process to accelerate their capacity degradation rate, thereby ensuring that the performance of each energy storage unit in the DC microgrid energy storage system eventually tends to be balanced, reducing the adverse effects of excessively fast attenuation rates of local energy storage units with poor performance on the operation of the energy storage system, and effectively extending the overall service life of the DC microgrid energy storage system.

[0085] Figure 2 The figure shows a performance balanced droop control structure diagram of a DC microgrid energy storage system in an embodiment of the present invention. Figure 2 Only the first Hedi Two energy storage units are used as an example. Taking the i-th energy storage unit as an example, by collecting the relevant operating data of the energy storage unit battery in real time and using the ANN-based battery capacity evaluation model, the remaining battery capacity of the energy storage unit can be determined. Among them, the battery capacity evaluation model 、 and Represents neurons in the input layer, N1, N2, ..., N k ,…,N m represents neurons in the hidden layer, Represents the output layer neurons. The remaining battery capacity of each energy storage unit and battery rated capacity Q rated , can calculate battery performance indicators (such as Figure 2 Middle D i ); According to Battery performance indicators D i and the global target value D of all battery performance indicators in the energy storage system avg The difference between Battery performance deviation ΔD i ; All battery performance deviations constitute the set {ΔD i |1 ≤ i ≤ N}, according to the maximum absolute value ΔD of the elements in the set max Whether it reaches (i.e. is greater than or equal to) the set threshold t1 or the maximum absolute value ΔD max Whether the set threshold value t2 is reached (i.e. less than or equal to) can determine whether the system's operating mode needs to be switched from the traditional SOC balancing-based droop control S1 to the battery performance balancing-based droop control S2 or vice versa. When it is determined that the system needs to switch to or remain in S1 mode, the traditional SOC balancing-based droop control is adopted. At this time, the droop coefficient is related to the SOC value (e.g. Figure 2 R in dpi (S oci )). When it is determined that the system needs to switch to or maintain operation in S2 mode, droop control based on battery performance balance is adopted. At this time, the droop coefficient is related to the battery performance evaluation index (such as Figure 2 R in dpi (D i )). After determining the droop coefficient R dpi After that, the droop control obtains the output voltage reference value V of the energy storage unit interface DC / DC converter according to the IU droop characteristic curve or PU droop characteristic curve determined by the droop coefficient. refi Finally, the energy storage unit interface DC / DC converter implements voltage-current dual closed-loop control to generate the corresponding PWM switching signal d i , thereby adjusting the actual output current or power of each energy storage unit.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for controlling the performance balance of a DC microgrid energy storage system, characterized in that: include: Step 1: Establish and train a battery capacity evaluation model based on artificial neural network; Step 2: Use the battery capacity evaluation model to perform online real-time evaluation of the remaining capacity of all energy storage unit batteries in the DC microgrid energy storage system to obtain the remaining capacity of each energy storage unit. Calculate the battery performance index and battery performance index deviation of each energy storage unit based on the remaining capacity of each energy storage unit. The battery performance index of each energy storage unit is calculated based on the remaining capacity and rated capacity of the battery of the energy storage unit, and the battery performance index deviation of each energy storage unit is calculated based on the battery performance index of the energy storage unit and the comprehensive battery performance evaluation index of all energy storage units. Step 3: If the current droop control working mode is S1, and if the absolute value of the battery performance index deviation of a certain energy storage unit is greater than or equal to the preset threshold value t1, the droop control working mode of the DC microgrid energy storage system is switched to S2; if the current droop control working mode is S2, and if the absolute values ​​of the battery performance index deviations of all energy storage units are less than or equal to the preset threshold value t2, the droop control working mode of the DC microgrid energy storage system is switched to S1, where S1 represents droop control based on SOC balance, and the droop coefficient of each energy storage unit is related to the SOC value of the energy storage unit; S2 represents droop control based on performance balance, and the droop coefficient of each energy storage unit is related to the battery performance index of the energy storage unit. The initial working mode of the DC microgrid energy storage system is S1, and the threshold value t1 is not less than the threshold value t2; Step 4: According to the droop control working mode determined in step 3, the correction coefficient of the droop control is updated, and the final droop coefficient corresponding to the working mode is calculated based on the correction coefficient. The bottom layer of the energy storage unit DC / DC converter adopts voltage-current dual closed-loop control, and the droop control is used as the front-stage loop. Therefore, the droop control outputs the output voltage reference value of the energy storage unit DC / DC converter, and the PWM switching signal is generated through the voltage-current dual closed-loop control of the bottom layer of the energy storage unit DC / DC converter to realize current or power control of each energy storage unit DC / DC converter.

2. The DC microgrid energy storage system performance balanced droop control method according to claim 1, characterized in that: The neurons in the input layer of the battery capacity evaluation model based on artificial neural network correspond one-to-one to battery-related parameters, and the neurons in the output layer correspond to the battery remaining capacity, where the battery-related parameters include battery state of charge, battery output voltage and battery output current.

3. The DC microgrid energy storage system performance balanced droop control method according to claim 1, characterized in that: The battery performance evaluation index is expressed as follows: ， Among them, D i Indicates the first Battery performance indicators of each energy storage unit, , Indicates the number of all energy storage units in the energy storage system; Indicates the rated capacity of the battery; The first The remaining battery capacity of each energy storage unit.

4. The DC microgrid energy storage system performance balanced droop control method according to claim 3, characterized in that: The battery performance index deviation of each energy storage unit is expressed as follows: ， in, Indicates the battery performance indicators of all energy storage units in this energy storage system 、 、…、 、…、 The comprehensive evaluation index is recorded as the target value of the battery performance index of this energy storage system; Indicates the Battery performance indicators of energy storage units and the target value of battery performance index of this energy storage system If the deviation is positive, it means that the battery performance index of the energy storage unit is better than the target value; if the deviation is negative, it means that the battery performance index of the energy storage unit is worse than the target value.

5. The DC microgrid energy storage system performance balanced droop control method according to claim 4, characterized in that: In S2 working mode, the droop coefficient of each energy storage unit is expressed as follows: ， Among them, R dpi Indicates the droop coefficient finally used by the i-th energy storage unit, R 0i represents the initial droop coefficient of the i-th energy storage unit, f dpi Acting on the initial droop coefficient, it represents the correction coefficient of the droop control obtained by the i-th energy storage unit based on the battery performance balance target. It is determined by the performance indicator deviation of the i-th energy storage unit battery itself. e represents a natural constant, and α and β are real coefficients greater than 0.

6. The DC microgrid energy storage system performance balanced droop control method according to claim 4, characterized in that: In S2 working mode, the droop coefficient of each energy storage unit is expressed as follows: ， in, Indicates the The final droop coefficient of each energy storage unit is: Indicates the The initial droop coefficient of each energy storage unit, Acting on the initial droop coefficient, it means The correction coefficient of droop control obtained by each energy storage unit based on the battery performance balancing target is: is a real coefficient greater than 0.

7. The DC microgrid energy storage system performance balanced droop control method according to claim 1, characterized in that: In the S1 operating mode, the relationship between the droop coefficient of each energy storage unit and the SOC value of the energy storage unit is expressed as follows: ， Among them, R dpi Indicates the droop coefficient finally used by the i-th energy storage unit, R 0i represents the initial droop coefficient of the i-th energy storage unit, f dp (S oci ) acts on the initial droop coefficient, which represents the correction coefficient of the droop control obtained by the i-th energy storage unit based on the battery SOC balancing target, which is determined by the SOC value of the battery of the i-th energy storage unit itself.

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