A dynamic power distribution method and device for a hybrid energy storage system

Abstract

The application discloses a kind of dynamic power distribution method and device of hybrid energy storage system, the method includes: the DC bus current reference value and the super capacitor state of charge in hybrid energy storage system are as input, the fuzzy distribution coefficient is calculated by fuzzy control theory, the DC bus current reference value and the duty cycle of super capacitor converter are as input, the total disturbance of power distribution module and reference duty cycle are calculated by superlocal algorithm, according to fuzzy distribution coefficient control lower battery and the duty cycle of super capacitor, the duty cycle of super capacitor is compared with reference duty cycle, total disturbance of power distribution module is introduced, and the duty cycle of hybrid energy storage system bidirectional converter is obtained.By using the application, the charging and discharging characteristics of the battery in the hybrid energy storage system can be considered, the battery distribution current can be controlled, and the service life of the battery can be prolonged.The application can be widely applied in the field of microgrid grid-connected technology as a kind of dynamic power distribution method and device of hybrid energy storage system.

Description

Technical Field

[0001] This invention relates to the field of microgrid grid connection technology, and in particular to a dynamic power distribution method and apparatus for a hybrid energy storage system. Background Technology

[0002] Microgrids, as a novel power generation structure, can aggregate various distributed clean energy sources to supply power to the AC grid. Simultaneously, microgrids can also operate independently in islanded mode. In microgrid grid-connected systems, batteries and supercapacitors are often combined to form high-energy-density, high-power-density hybrid energy storage systems. Under different operating conditions, reasonable power allocation and control methods for hybrid energy storage systems are of significant engineering application importance for fully leveraging the advantages of both and extending their cycle life. Current power allocation strategies for hybrid energy storage systems mainly rely on traditional filter methods. These methods use filters or define rules for input reference values ​​to differentiate the compensation currents of batteries and supercapacitors; that is, batteries compensate for low-frequency changing currents, while supercapacitors compensate for... The remaining high-frequency changing current, but the traditional power distribution method based on filters only considers the characteristics of batteries and supercapacitors, fixing the power distribution ratio. It cannot fully utilize supercapacitors to mitigate the impact of large currents on batteries in actual operating conditions, accelerating the aging process of the batteries themselves, shortening their lifespan, and in severe cases, causing the batteries to explode. During the operation of the microgrid system, the state of charge (SOC) of batteries and supercapacitors in the hybrid energy storage system will also change continuously with the operation of the system. If the actual SOC of the hybrid energy storage system is not considered, charging will still be performed when some components are fully charged, and discharging will still be performed when some components are fully discharged, which will cause overcharging and over-discharging phenomena, seriously affecting the service life of the hybrid energy storage system. Summary of the Invention

[0003] To address the aforementioned technical problems, the present invention aims to provide a dynamic power distribution method and apparatus for a hybrid energy storage system, which can take into account the charging and discharging characteristics of the battery in the hybrid energy storage system, control the battery distribution current, reduce the impact of instantaneous high current on the battery, and extend the battery's service life.

[0004] The first technical solution adopted in this invention is: a dynamic power allocation method for a hybrid energy storage system, comprising the following steps:

[0005] Based on fuzzy control theory, the reference value of DC bus current and the state of charge of hybrid energy storage system are processed by fuzzy calculation to obtain defuzzified allocation coefficients.

[0006] The DC bus current reference value and the bidirectional converter duty cycle are estimated by using a super local algorithm to obtain the total interference and reference duty cycle of the power distribution module.

[0007] According to the preset allocation rules, the hybrid energy storage system is subjected to fuzzy control by defuzzifying the allocation coefficients to obtain the duty cycle of the hybrid energy storage system;

[0008] Based on the total interference of the power distribution module, the duty cycle of the hybrid energy storage system is compared with the reference duty cycle to obtain the duty cycle of the bidirectional converter of the hybrid energy storage system.

[0009] The dynamic power of the hybrid energy storage system is controlled by PWM modulation based on the duty cycle of the bidirectional converter of the hybrid energy storage system.

[0010] Furthermore, the step of performing fuzzy calculations on the DC bus current reference value and the state of charge of the hybrid energy storage system based on fuzzy control theory to obtain the defuzzified allocation coefficients specifically includes:

[0011] Obtain reference values ​​for DC bus current and the state of charge of the hybrid energy storage system;

[0012] Fuzzy control is applied to the DC bus current reference value and the state of charge of the hybrid energy storage system to obtain the corresponding fuzzy language.

[0013] Based on the fuzzy language, corresponding fuzzy inference rules are established and inference is performed on the hybrid energy storage system to obtain the fuzzy allocation coefficients of the hybrid energy storage system under different charging states and different DC bus current reference values.

[0014] The fuzzy allocation coefficients of the hybrid energy storage system are defuzzified using the de-centroid method to obtain the defuzzified allocation coefficients.

[0015] Furthermore, it also includes the classification and management of the state of charge of hybrid energy storage systems, specifically including:

[0016] Based on preset readings, the state of charge of the hybrid energy storage system is divided and managed to obtain danger zones, warning zones, and safe zones.

[0017] Based on the above classification of the state of charge of the hybrid energy storage system, the hybrid energy storage system is divided into four zones when it is in the discharge state: adjustment discharge zone, safe discharge zone, warning discharge zone, and dangerous discharge zone.

[0018] Based on the above classification of the state of charge of the hybrid energy storage system, the hybrid energy storage system is divided into four zones when it is in the charging state: adjustment charging zone, safe charging zone, warning charging zone, and dangerous charging zone.

[0019] Furthermore, the step of using a super local algorithm to perform ESO estimation on the DC bus current reference value and the bidirectional converter duty cycle to obtain the total interference and reference duty cycle of the power distribution module specifically includes:

[0020] Construct equations for a hybrid energy storage system, which include the total disturbance of the hybrid energy storage system and the reference current value of the hybrid energy storage system;

[0021] The total disturbance of the hybrid energy storage system is estimated by using an external extended observer (ESO), and the ESO estimate is obtained.

[0022] The total interference and reference duty cycle of the power distribution module are obtained by calculating the ESO estimate and the reference current value of the hybrid energy storage system using the forward Euler discretization method.

[0023] Furthermore, the preset allocation rules specifically include:

[0024] When the reference current value of the hybrid energy storage system is negative and the hybrid energy storage system is in a charging state, the reference current value of the hybrid energy storage system and the state of charge of the hybrid energy storage system are judged.

[0025] When it is determined that the reference current value of the hybrid energy storage system is greater than the preset current value threshold, the state of charge of the hybrid energy storage system is less than the preset state threshold, and the hybrid energy storage system is in the charging adjustment zone or in the safe zone, the defuzzification allocation coefficient is increased.

[0026] When it is determined that the reference current value of the hybrid energy storage system is greater than the preset current value threshold, or the state of charge of the hybrid energy storage system is greater than the preset state threshold and is in a warning or dangerous charging zone, the power of the hybrid energy storage system is divided equally or the power distribution is adjusted according to the current magnitude.

[0027] If the reference current value of the hybrid energy storage system is less than the preset current value threshold and the state of charge of the hybrid energy storage system is greater than the preset state threshold, the defuzzification allocation coefficient is reduced.

[0028] If the reference current value of the hybrid energy storage system is less than the preset current value threshold, or the state of charge of the hybrid energy storage system is less than the preset state threshold, the defuzzification allocation coefficient is increased.

[0029] When the reference current value of the hybrid energy storage system is positive and the hybrid energy storage system is in a discharging state, the reference current value of the hybrid energy storage system and the state of charge of the hybrid energy storage system are judged.

[0030] When it is determined that the reference current value of the hybrid energy storage system is greater than the preset current value threshold, the state of charge of the hybrid energy storage system is less than the preset state threshold, and the hybrid energy storage system is in the warning or dangerous discharge zone, the defuzzification allocation coefficient is reduced.

[0031] When it is determined that the state of charge of the hybrid energy storage system is in the safe zone or the discharge adjustment zone, the defuzzification allocation coefficient is increased;

[0032] If the reference current value of the hybrid energy storage system is found to be less than the preset current threshold, power allocation is performed according to the state of charge of the hybrid energy storage system.

[0033] When the reference current of the hybrid energy storage system is zero and the hybrid energy storage system is in a state of neither charging nor discharging, the probability allocation strategy of the previous moment is maintained.

[0034] Furthermore, it also includes fuzzy control of the supercapacitor in the hybrid energy storage system based on the defuzzified allocation coefficient to obtain the supercapacitor reference current value, specifically including:

[0035] Obtain the deviation between the DC bus voltage reference value and the DC bus voltage;

[0036] The deviation between the DC bus voltage reference value and the DC bus voltage is adjusted by PI to obtain the DC bus reference current;

[0037] The DC bus reference current is multiplied by the defuzzification allocation coefficient to obtain the product value.

[0038] The DC bus reference current is transmitted to a low-pass filter for filtering to obtain the filtered value.

[0039] The product value and the filtered value are summed to obtain the supercapacitor reference current value.

[0040] Furthermore, it also includes fuzzy control of the battery in the hybrid energy storage system based on the defuzzified allocation coefficient to obtain the battery reference current value, specifically including:

[0041] Obtain the deviation between the DC bus voltage reference value and the DC bus voltage;

[0042] The deviation between the DC bus voltage reference value and the DC bus voltage is adjusted by PI to obtain the DC bus reference current;

[0043] The DC bus reference current is multiplied by the defuzzification allocation coefficient to obtain the product value.

[0044] The difference between the product and the DC bus reference current is calculated to obtain the current difference.

[0045] The current difference is filtered by a low-pass filter to obtain the reference current value of the battery.

[0046] Furthermore, the step of comparing the duty cycle of the hybrid energy storage system with a reference duty cycle based on the total interference of the power distribution module to obtain the duty cycle of the bidirectional converter of the hybrid energy storage system specifically includes:

[0047] The duty cycle of the hybrid energy storage system is compared with a reference duty cycle to obtain a comparison value;

[0048] The comparison value is compared with the total interference of the power distribution module;

[0049] If it is determined that the comparison value is greater than a preset threshold or the total interference of the power distribution module is greater than a preset threshold, the reference duty cycle is selected as the duty cycle of the bidirectional converter of the hybrid energy storage system;

[0050] If the comparison value is determined to be less than a preset threshold or the total interference of the power distribution module is less than a preset threshold, the duty cycle of the hybrid energy storage system is selected as the duty cycle of the bidirectional converter of the hybrid energy storage system.

[0051] The second technical solution adopted in this invention is: a dynamic power distribution device for a hybrid energy storage system, comprising a DC bus module, a hybrid energy storage module, a grid-connected inverter module, an AC grid-side module, and a dynamic power management module. The hybrid energy storage module includes a supercapacitor and a battery. The hybrid energy storage module is connected to the DC bus module via a bidirectional converter. The hybrid energy storage module is connected to the input terminal of the grid-connected inverter module. The output terminal of the grid-connected inverter module is connected to the AC grid-side module. The dynamic power management module is connected to the control terminal of the hybrid energy storage module. Wherein:

[0052] The DC bus module is used to provide power to the hybrid energy storage module, grid-connected inverter module, AC grid-side module and dynamic power management module;

[0053] The hybrid energy storage module is used to regulate the output power of the DC bus module;

[0054] The grid-connected inverter module is used to convert the DC power from the DC bus module into three-phase AC power.

[0055] The AC grid-side module is used to transmit three-phase AC power to the user's load equipment;

[0056] The dynamic power management module is used to control the output power of the DC bus module.

[0057] The beneficial effects of the method and apparatus of this invention are as follows: This invention alleviates the impact of large currents on DC microgrids in actual operating conditions by using a hybrid energy storage module, thereby improving the reliability of DC microgrid grid connection; it adopts a novel dynamic power control method that considers the state of charge (SOC) of the hybrid energy storage module to achieve a more reasonable power allocation method for supercapacitors and batteries, realizing dynamic adjustment under various operating conditions and extending the cycle life of the system; based on traditional fuzzy control logic, it calculates the reference duty cycle of the supercapacitor based on a hyperlocal model and compares it with the supercapacitor allocation duty cycle obtained by fuzzy calculation as a reference index, which further improves the reliability of DC microgrid grid connection under unstable conditions compared with traditional fuzzy control. Attached Figure Description

[0058] Figure 1This is a flowchart of the steps of a dynamic power allocation method for a hybrid energy storage system according to the present invention;

[0059] Figure 2 This is a schematic diagram of the topology of the DC microgrid grid-connected system of the present invention;

[0060] Figure 3 This is a schematic diagram of the SOC limit management of the hybrid energy storage system of the present invention;

[0061] Figure 4 This is a flowchart of the fuzzy control process of the present invention;

[0062] Figure 5 This is a novel dynamic power distribution control block diagram of the present invention;

[0063] Figure 6 i is the present invention dcref Membership function graph;

[0064] Figure 7 This is the SOC of the present invention. sc Membership function graph;

[0065] Figure 8 This is the k-membership function graph of the present invention;

[0066] Figure 9 This is the fuzzy rule-based three-dimensional observation diagram of the present invention;

[0067] Reference numerals: 1. Hybrid energy storage module; 2. Supercapacitor; 3. Battery; 4. DC bus module; 5. Photovoltaic absorption array; 6. DC load module; 7. Grid-connected inverter module; 8. AC grid-side module. Detailed Implementation

[0068] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only for ease of explanation and do not limit the order of the steps. The execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.

[0069] The main process of this invention is as follows: The reference value of the DC bus current and the duty cycle of the supercapacitor converter at the previous moment are obtained as inputs to the hyperlocal model. The total disturbance and reference duty cycle of the power distribution module are calculated using a hyperlocal algorithm based on an extended external observer. The fuzzy allocation coefficients are used to calculate the duty cycles of the battery and supercapacitor under fuzzy control according to the specified allocation rules. The calculated supercapacitor duty cycle is compared with the reference duty cycle. The total disturbance of the power distribution module is introduced as a reference to obtain the final duty cycle of the bidirectional converter of the hybrid energy storage system. PWM modulation control is then performed. In other words, based on traditional fuzzy control logic, a hyperlocal model based on an extended external observer is added to estimate and utilize the total disturbance of the system's power distribution module. Compared with traditional fuzzy control, this further improves the reliability of the DC microgrid grid connection under unstable conditions.

[0070] Reference Figure 1 This invention provides a dynamic power allocation method for a hybrid energy storage system. Based on traditional low-pass filter allocation, it considers the state of charge of the hybrid energy storage modules and uses fuzzy theory to achieve a more rational power allocation method for supercapacitors and batteries. The method includes the following steps:

[0071] S1. Obtain the reference value of DC bus current and the state of charge of supercapacitor in the hybrid energy storage system as fuzzy inputs, and calculate the fuzzy allocation coefficients through the specified fuzzy control theory.

[0072] S11. Define the operating area of ​​the energy storage system's state of charge (SOC);

[0073] Specifically, the danger zone is defined based on the state of charge (SOC) readings of the energy storage system. <SOC low Or SOC > SOC high SOC low =0.2, SOC high =0.8; Warning zone is SOC low <SOC<SOC safe_l or SOC safe_h <SOC<SOC high SOC safe_l =0.4, SOC safe_h =0.6; Safety zone is SIC safe_l <SOC<SIC safe_h ;

[0074] Based on the above-defined state-of-charge operating region, the following operating conditions are defined when the hybrid energy storage system is in the charging state:

[0075] Adjust the charging area to 0 <SOC<SOC safe_l The safe charging zone is for SOC. safe_l<SOC<SOC safe_h , the warning charging zone is SOC safe_h <SOC<SOC high , the dangerous charging zone is SOC high <SOC<1;

[0076] Under the condition that the working regions of the state of charge are defined above, the following working conditions are divided when the hybrid energy storage system is in a discharging state:

[0077] The adjusted discharging zone is SOC safe_h <SOC<1, the safe discharging zone is SOC safe_l <SOC<SOC safe_h , the warning discharging zone is SOC low <SOC<SOC safe_l , the dangerous discharging zone is 0<SOC<SOC low .

[0078] S12. Taking the current reference value i required for suppressing fluctuation on the DC bus side dcref and the state of charge SOC of the supercapacitor sc as inputs of the fuzzy controller, and dynamically adjusting the power distribution coefficient k as the output of the fuzzy controller;

[0079] Specifically, the fuzzy language of i dcref is divided into seven levels, namely NB, NM, NS, ZO, PB, PM, and PS, with the fuzzy subset being {NB, NM, NS, ZO, PB, PM, PS}. When i dcref <0 and the hybrid energy storage system is in a charging state, NB represents that the magnitude of the i dcref charging current is large; NM represents that the magnitude of the i dcref charging current is moderate; NS represents that the magnitude of the i dcref charging current is small. When i dcref >0 and the hybrid energy storage system is in a discharging state, PB represents that the magnitude of the i dcref discharging current is large, NM represents that the magnitude of the i dcref discharging current is moderate, NS represents that the magnitude of the i dcref discharging current is small. When i dcref =0 and the hybrid energy storage system is in a state of neither charging nor discharging, ZO represents that the magnitude of the i dcref current is equal to 0;

[0080] The fuzzy language of SOC sc is divided into three levels, namely L, M, and H, with the fuzzy subset being {L, M, H}. L represents that the SOC sc value is small and the supercapacitor is in a low state of charge, M represents that the SOC sc value is moderate and the supercapacitor is in a safe state of charge, H represents that the SOCsc The value is relatively high, indicating a high-charge state;

[0081] Reference Figure 4 The fuzzy language of k is divided into five categories: LS, SS, M, SB, and LB. The fuzzy subset is {LS, SS, M, SB, LB}. LS represents a very small value of k and a very small amount of power allocated to the supercapacitor; SS represents a relatively small value of k and a relatively small amount of power allocated to the supercapacitor; M represents a power allocation principle where the power is evenly shared between the two; SB represents a relatively large value of k and a relatively large amount of power allocated to the supercapacitor; and LB represents a very large value of k and a very large amount of power allocated to the supercapacitor. Fuzzy control fuzzifies the input to form a specific fuzzy language. Through fuzzy inference rules established by the language, the corresponding output under different conditions is found. Finally, the obtained output is defuzzified and applied to the controlled object. Fuzzy control will dynamically adjust according to different conditions, balance the relationship between multiple inputs, and find the optimal output, which has great flexibility and applicability.

[0082] Reference Figure 6 i dcref Its domain is [-20, 20], i dcref The fuzzy languages ​​are divided into seven categories: NB, NM, NS, ZO, PB, PM, and PS. Their fuzzy subsets are {NB, NM, NS, ZO, PB, PM, PS}. The membership functions for NB and PB are the trapezoidal function from the fuzzy toolbox of Matlab software, while the triangular function is the trimf for the other five languages. By setting the key parameters of the functions, the membership function graphs for each language are obtained.

[0083] Reference Figure 7 SOC sc Its domain is [0,1]. The fuzzy language is divided into three sub-languages: L, M, and H, with fuzzy subsets {L, M, H}. The membership functions for all three languages ​​use the TraPMF trapezoidal function, and the key parameter values ​​are determined by referring to... Figure 3 The state analysis of the hybrid energy storage system yielded membership function graphs for each language.

[0084] Reference Figure 8 The domain of k is [0,1]. The fuzzy languages ​​are divided into five types: LS, SS, M, SB, and LB. The fuzzy subsets are {LS, SS, M, SB, LB}. The membership functions of the five languages ​​are all triangular functions (trimf), and the membership function graphs of each language are obtained.

[0085] S2. Obtain the DC bus reference value and the duty cycle of the supercapacitor converter at the previous moment as input to the superlocal model, and calculate the total interference and reference duty cycle of the power distribution module through the superlocal algorithm.

[0086] Specifically, refer to Figure 5The deviation between the DC bus voltage reference value and the DC bus voltage is used to obtain the DC bus reference current through PI tuning. The DC bus reference current is multiplied by the fuzzy output coefficient k, and the result is subtracted from the DC bus reference current to obtain the new input current i of the low-pass filter LPF. dc_d The new input current i of the low-pass filter dc_d Through the transfer function A low-pass filter, where the time constant is Obtain the battery reference current i batref Battery reference current i batref The deviation signal obtained by subtracting the battery current is then applied to the PWM to obtain the bidirectional DC / DC converter control signal after PI tuning. The battery reference current i batref The formula is:

[0087]

[0088] The deviation between the DC bus voltage reference value and the DC bus voltage is used to obtain the DC bus reference current through PI tuning. The DC bus reference current is multiplied by the fuzzy output coefficient k, and the result is multiplied by the current i that does not pass through the low-pass filter LPF. bat_r By performing the above operations, the reference current i of the supercapacitor can be obtained. scref Supercapacitor reference current i scref The deviation signal obtained by subtracting the supercapacitor current from the supercapacitor current is then calibrated by a PI controller to obtain the supercapacitor duty cycle signal. The supercapacitor reference current i scref The formula is:

[0089]

[0090] S3. Calculate the duty cycle of the battery and supercapacitor under fuzzy control by using the fuzzy allocation coefficients according to the specified allocation rules, and compare the calculated supercapacitor duty cycle with the reference duty cycle.

[0091] S31. The rule principle of dynamically adjusting the allocation coefficient k based on fuzzy theory;

[0092] Specifically, as shown in the table below;

[0093] Table 1 Fuzzy Rules

[0094]

[0095] Reference Figure 9 , when i dcref <0, when the hybrid energy storage system is in a charging state, if i dcref Larger, SOC sc If i is relatively small, and the device is in the charging adjustment zone or the safe zone, then k is increased. dcref Larger, SOC scWhen the current is relatively large and the battery is in a warning or dangerous charging zone, power should be evenly distributed or more power should be allocated to the battery according to the current magnitude; if i dcref Smaller, SOC sc If the value is large, decrease k, SOC sc If i is smaller, increase k; when i is smaller, increase k. dcref >0, when the hybrid energy storage system is in a discharge state, if i dcref When the SOCsc is relatively large, and the warning or dangerous discharge zone is in the range, decrease k. If the SOCsc is relatively small, decrease k. sc When in the safe zone or discharge adjustment zone, increase k; if i dcref If it is smaller, then according to SOC sc Assignment is made according to the situation; when i dcref =0, when the hybrid energy storage system is in a state of neither charging nor discharging, no adjustment is needed to the power distribution;

[0096] Finally, based on the fuzzy rules, the fuzzy output under the corresponding fuzzy input can be obtained, and the centroid method is used to transform the fuzzy output k into a precise value.

[0097] S32. Calculate the reference duty cycle of the supercapacitor based on the hyperlocal model, and compare it with the supercapacitor allocation duty cycle obtained by fuzzy calculation as a reference index.

[0098] Specifically, a novel dynamic power allocation method, based on the proposed fuzzy control logic, calculates the reference duty cycle of the supercapacitor using a superlocal model and compares it with the supercapacitor allocation duty cycle obtained by fuzzy calculation as a reference index. This avoids errors caused by large disturbances to the power allocation module and further improves the reliability of the DC microgrid grid connection under unstable conditions compared to using only fuzzy control. This is achieved through the following method.

[0099] The state-space equations for the power electronic converter are constructed as follows:

[0100]

[0101] y(t)=Cx(t)

[0102] In the above formula, Let x(t) represent the derivative form of the system state variables, u(t) represent the converter control quantity, y(t) represent the system output quantity, and A, B, and C represent the system parameter matrices.

[0103] For a single-input, single-output model, its first-order hyperlocal model can be represented as:

[0104]

[0105] In the above formula, The derivative form of the system output is given by F, where F represents the total system disturbance, α represents the scalar parameter, and u represents the actual control quantity of the system.

[0106] Based on this, the present invention establishes a first-order hyperlocal model for the dynamic power allocation method of a hybrid energy storage system, using the DC bus reference current and the previous duty cycle signal of the supercapacitor as inputs to the hyperlocal model, and the model is established as follows:

[0107]

[0108] In the above formula, d represents the duty cycle signal of the supercapacitor. When the system is in an unstable state, or the scalar parameter α is chosen to deviate from the accurate value, i.e., α = α ref +e, where e represents the error between the scalar parameter and the exact scalar parameter, the system equation will become:

[0109]

[0110] The system equations can be further written in the following form:

[0111]

[0112] In the above formula, F ′ For the newly calculated disturbance, F ′ =F+ed. It can be seen that when the system is in an unstable state or the scalar parameter selection deviates from the accurate value, the calculation accuracy can be improved by updating the calculation of the total disturbance. Therefore, the calculation of the total system disturbance is very important. For this reason, this invention uses an external extended observer (ESO) to estimate the system state disturbance. The ESO method is established as follows:

[0113]

[0114] In the above equation, z1 represents the extended external observer estimator. z2 represents the observer estimate. The reference duty cycle signal of the supercapacitor based on the hyperlocal model can be calculated using the forward Euler discretization method:

[0115]

[0116] Furthermore, the supercapacitor reference duty cycle signal based on the hyperlocal model is compared with d * Compare with the duty cycle signal d calculated and allocated using the fuzzy algorithm; if |d * -d|>0.15 indicates that the duty cycle calculated by the fuzzy algorithm allocation scheme will have a significant impact on the system compensation current. Therefore, the supercapacitor reference duty cycle signal calculated by the hyperlocal model should be used, along with d. * If the system state disturbance This indicates that the system is in an unstable state with a large system disturbance. In this case, the supercapacitor reference duty cycle signal calculated using the hyperlocal model is also applied, along with d... * In other cases, the duty cycle calculated using a fuzzy algorithm is used for power management.

[0117] S4. Introduce the total interference of the power distribution module as a reference to obtain the final duty cycle of the bidirectional converter of the hybrid energy storage system and perform PWM modulation control.

[0118] Specifically, the working process of this invention is described in detail. The photovoltaic array serves as a power generation unit, and the electrical energy it generates is transmitted to the DC bus side via a DC / DC boost converter. The DC bus then transmits power to the DC load and connects to the AC grid side via a three-phase grid-connected inverter. Due to the uncertainty and instability of photovoltaic array power generation, when its power generation exceeds the current load power, the surplus power is absorbed by the hybrid energy storage system through a bidirectional DC / DC converter; conversely, it is generated through the bidirectional DC / DC converter, thus stabilizing the DC side voltage. The difference between the photovoltaic array's power generation and the load power changes in real time with factors such as light intensity. The battery has high energy density. The characteristics of low power density; supercapacitors have low energy density and high power density. Therefore, for power with large instantaneous changes, supercapacitors can absorb and compensate more quickly. To complement the advantages of both, batteries and supercapacitors are combined into a hybrid energy storage system. High-frequency, rapidly changing instantaneous power is compensated by the supercapacitor, while low-frequency, slowly changing power is compensated by the battery. During the operation of the microgrid system, the state of charge (SOC) of the batteries and supercapacitors in the hybrid energy storage system will also change continuously with the operation of the system. To avoid overcharging and over-discharging affecting the service life of the hybrid energy storage system, a fuzzy model is introduced to define the reference value i of the current required to smooth fluctuations on the DC bus side. dcref State of charge (SOC) of supercapacitors sc The power allocation coefficient k is dynamically adjusted as the input to the fuzzy controller and serves as its output. Through rule definition, the allocation coefficient k for different output conditions is determined, and the allocation coefficient k is... Figure 5 The prescribed rules yield the battery reference current i dvref and supercapacitor reference current i dcrefThe reference current of the battery and supercapacitor is used to obtain the duty cycle signal through PI tuning. When the grid-side system is in an unstable state, the instability of the DC bus voltage will further affect the safety of grid-side operation. Therefore, the duty cycle signal calculated by the fuzzy control algorithm is compared with the reference duty cycle signal calculated based on the established hyperlocal model. If the error between the two is greater than the set threshold, the reference duty cycle signal estimated by the hyperlocal model is used to maintain the stability of the system. Otherwise, the duty cycle signal obtained by the fuzzy control algorithm is used. At the same time, the total system disturbance estimated by the hyperlocal model is used as a reference index. If the total system disturbance changes significantly from the previous time to a preset threshold, it indicates that the system is in an unstable state. The converter should be PWM modulated and controlled according to the reference duty cycle signal calculated by the hyperlocal model.

[0119] Reference Figure 2 A dynamic power distribution device for a hybrid energy storage system comprises a hybrid energy storage module, a DC bus, a grid-connected inverter module, an AC grid-side module, and a dynamic power management module. The hybrid energy storage module is connected to the input terminal of the grid-connected inverter module, the output terminal of the inverter module is connected to the AC grid-side module, and the dynamic power management module is connected to the control terminal of the hybrid energy storage module.

[0120] The hybrid energy storage module includes a supercapacitor and a battery. The battery is connected to the DC bus via a bidirectional converter, and the supercapacitor is also connected to the DC bus via a bidirectional converter. The bidirectional converter consists of two reverse-connected power electronic switching devices and an inductor.

[0121] Furthermore, the DC bus is connected to the input terminals of the hybrid energy storage module, DC load, photovoltaic array, and grid-connected inverter module. The DC load includes power devices that require DC power. The photovoltaic array is a power generation unit, and the electrical energy it generates is transmitted to the DC bus side through a step-up converter. The step-up transformer consists of an inductor and several power electronic switching devices connected together.

[0122] Furthermore, the grid-connected inverter module includes six power electronic switching devices and an LC filter to convert the DC power from the DC bus into three-phase AC power. The LC filter includes an inductor and a capacitor.

[0123] Furthermore, the AC grid-side module includes a power supply system that needs to be connected to the grid. The power supply system includes a power supply system for transmitting electrical energy to electrical equipment and a power distribution system. The power distribution system includes a voltage transformation system composed of various power distribution equipment and facilities, and a power distribution system for directly distributing electrical energy to end users.

[0124] Furthermore, the dynamic power management module includes a hybrid energy storage system state of charge (SOC) calculation part and a hybrid energy storage system dynamic coefficient power allocation part. The hybrid energy storage system state of charge calculation part transmits data from the hybrid energy storage system voltage and current sensor, and the hybrid energy storage system dynamic coefficient power allocation part transmits data from the hybrid energy storage system state of charge calculation part, the hybrid energy storage system voltage and current sensor, the DC load voltage and current sensor, and the photovoltaic array voltage and current sensor.

[0125] The content of the above method embodiments is applicable to the device embodiments. The specific functions implemented by the device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0126] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A dynamic power allocation method for a hybrid energy storage system, characterized in that, Includes the following steps: Based on fuzzy control theory, the reference value of DC bus current and the state of charge of hybrid energy storage system are processed by fuzzy calculation to obtain defuzzified allocation coefficients. The reference value of DC bus current and the duty cycle of bidirectional converter are estimated by external extended observer ESO through hyperlocal algorithm to obtain the total interference and reference duty cycle of power distribution module. According to the preset allocation rules, the hybrid energy storage system is subjected to fuzzy control by defuzzifying the allocation coefficients to obtain the duty cycle of the hybrid energy storage system; Based on the total interference of the power distribution module, the duty cycle of the hybrid energy storage system is compared with the reference duty cycle to obtain the duty cycle of the bidirectional converter of the hybrid energy storage system. The dynamic power of the hybrid energy storage system is controlled by PWM modulation based on the duty cycle of the bidirectional converter of the hybrid energy storage system.

2. The dynamic power allocation method for a hybrid energy storage system according to claim 1, characterized in that, The step of performing fuzzy calculations on the DC bus current reference value and the state of charge of the hybrid energy storage system based on fuzzy control theory to obtain the defuzzified allocation coefficients specifically includes: Obtain reference values ​​for DC bus current and the state of charge of the hybrid energy storage system; Fuzzy control is applied to the DC bus current reference value and the state of charge of the hybrid energy storage system to obtain the corresponding fuzzy language. Based on the fuzzy language, corresponding fuzzy inference rules are established and inference is performed on the hybrid energy storage system to obtain the fuzzy allocation coefficients of the hybrid energy storage system under different charging states and different DC bus current reference values. The fuzzy allocation coefficients of the hybrid energy storage system are defuzzified using the de-centroid method to obtain the defuzzified allocation coefficients.

3. The dynamic power allocation method for a hybrid energy storage system according to claim 2, characterized in that, It also includes the classification and management of the state of charge of hybrid energy storage systems, specifically including: Based on preset readings, the state of charge of the hybrid energy storage system is divided and managed to obtain danger zones, warning zones, and safe zones. Based on the above classification of the state of charge of the hybrid energy storage system, the hybrid energy storage system is divided into four zones when it is in the discharge state: adjustment discharge zone, safe discharge zone, warning discharge zone, and dangerous discharge zone. Based on the above classification of the state of charge of the hybrid energy storage system, the hybrid energy storage system is divided into four zones when it is in the charging state: adjustment charging zone, safe charging zone, warning charging zone, and dangerous charging zone.

4. The dynamic power allocation method for a hybrid energy storage system according to claim 3, characterized in that, The step of using a super local algorithm to perform ESO estimation on the DC bus current reference value and the bidirectional converter duty cycle to obtain the total interference and reference duty cycle of the power distribution module specifically includes: Construct equations for a hybrid energy storage system, which include the total disturbance of the hybrid energy storage system and the reference current value of the hybrid energy storage system; The total disturbance of the hybrid energy storage system is estimated by using an external extended observer (ESO), and the ESO estimate is obtained. The total interference and reference duty cycle of the power distribution module are obtained by calculating the ESO estimate and the reference current value of the hybrid energy storage system using the forward Euler discretization method.

5. The dynamic power allocation method for a hybrid energy storage system according to claim 4, characterized in that, The preset allocation rules specifically include: When the reference current value of the hybrid energy storage system is negative and the hybrid energy storage system is in a charging state, the reference current value of the hybrid energy storage system and the state of charge of the hybrid energy storage system are judged. When it is determined that the reference current value of the hybrid energy storage system is greater than the preset current value threshold, the state of charge of the hybrid energy storage system is less than the preset state threshold, and the hybrid energy storage system is in the charging adjustment zone or in the safe zone, the defuzzification allocation coefficient is increased. When it is determined that the reference current value of the hybrid energy storage system is greater than the preset current value threshold, or the state of charge of the hybrid energy storage system is greater than the preset state threshold and is in a warning or dangerous charging zone, the power of the hybrid energy storage system is divided equally or the power distribution is adjusted according to the current magnitude. If the reference current value of the hybrid energy storage system is less than the preset current value threshold and the state of charge of the hybrid energy storage system is greater than the preset state threshold, the defuzzification allocation coefficient is reduced. If the reference current value of the hybrid energy storage system is less than the preset current value threshold, or the state of charge of the hybrid energy storage system is less than the preset state threshold, the defuzzification allocation coefficient is increased. When the reference current value of the hybrid energy storage system is positive and the hybrid energy storage system is in a discharging state, the reference current value of the hybrid energy storage system and the state of charge of the hybrid energy storage system are judged. When it is determined that the reference current value of the hybrid energy storage system is greater than the preset current value threshold, the state of charge of the hybrid energy storage system is less than the preset state threshold, and the hybrid energy storage system is in the warning or dangerous discharge zone, the defuzzification allocation coefficient is reduced. When it is determined that the state of charge of the hybrid energy storage system is in the safe zone or the discharge adjustment zone, the defuzzification allocation coefficient is increased; If the reference current value of the hybrid energy storage system is found to be less than the preset current threshold, power allocation is performed according to the state of charge of the hybrid energy storage system. When the reference current of the hybrid energy storage system is zero and the hybrid energy storage system is in a state of neither charging nor discharging, the power allocation strategy of the previous moment is maintained.

6. The dynamic power allocation method for a hybrid energy storage system according to claim 5, characterized in that, It also includes fuzzy control of the supercapacitor in the hybrid energy storage system based on the defuzzified allocation coefficient to obtain the supercapacitor reference current value, specifically including: Obtain the deviation between the DC bus voltage reference value and the DC bus voltage; The deviation between the DC bus voltage reference value and the DC bus voltage is adjusted by PI to obtain the DC bus reference current; The DC bus reference current is multiplied by the defuzzification allocation coefficient to obtain the product value. The DC bus reference current is transmitted to a low-pass filter for filtering to obtain the filtered value. The product value and the filtered value are summed to obtain the supercapacitor reference current value.

7. The dynamic power allocation method for a hybrid energy storage system according to claim 6, characterized in that, It also includes fuzzy control of the batteries in the hybrid energy storage system based on the defuzzification allocation coefficient to obtain the battery reference current value, specifically including: Obtain the deviation between the DC bus voltage reference value and the DC bus voltage; The deviation between the DC bus voltage reference value and the DC bus voltage is adjusted by PI to obtain the DC bus reference current; The DC bus reference current is multiplied by the defuzzification allocation coefficient to obtain the product value. The difference between the product and the DC bus reference current is calculated to obtain the current difference. The current difference is filtered by a low-pass filter to obtain the reference current value of the battery.

8. The dynamic power allocation method for a hybrid energy storage system according to claim 7, characterized in that, The step of comparing the duty cycle of the hybrid energy storage system with a reference duty cycle based on the total interference of the power distribution module to obtain the duty cycle of the bidirectional converter of the hybrid energy storage system specifically includes: The duty cycle of the hybrid energy storage system is compared with a reference duty cycle to obtain a comparison value; The comparison value is compared with the total interference of the power distribution module; If it is determined that the comparison value is greater than a preset threshold or the total interference of the power distribution module is greater than a preset threshold, the reference duty cycle is selected as the duty cycle of the bidirectional converter of the hybrid energy storage system; If the comparison value is determined to be less than a preset threshold or the total interference of the power distribution module is less than a preset threshold, the duty cycle of the hybrid energy storage system is selected as the duty cycle of the bidirectional converter of the hybrid energy storage system.

9. A dynamic power distribution device for a hybrid energy storage system, characterized in that, The dynamic power distribution method for a hybrid energy storage system as described in claim 1 includes a DC bus module, a hybrid energy storage module, a grid-connected inverter module, an AC grid-side module, and a dynamic power management module. The hybrid energy storage module includes a supercapacitor and a battery. The hybrid energy storage module is connected to the DC bus module via a bidirectional converter. The hybrid energy storage module is connected to the input terminal of the grid-connected inverter module. The output terminal of the grid-connected inverter module is connected to the AC grid-side module. The dynamic power management module is connected to the control terminal of the hybrid energy storage module. Wherein: The DC bus module is used to provide power to the hybrid energy storage module, grid-connected inverter module, AC grid-side module and dynamic power management module; The hybrid energy storage module is used to regulate the output power of the DC bus module; The grid-connected inverter module is used to convert the DC power from the DC bus module into three-phase AC power. The AC grid-side module is used to transmit three-phase AC power to the user's load equipment; The dynamic power management module is used to control the output power of the DC bus module.

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