Hybrid energy storage microgrid model control system and method based on AC / DC power distribution

Abstract

The invention relates to the technical field of new energy storage and power system energy storage control, and discloses a hybrid energy storage microgrid model control system and method based on alternating current and direct current power distribution, and the system comprises a battery conversion module which is used for providing direct current for a common load; the super capacitor converter module is used for providing alternating current to a pulse power load; a load current estimator for estimating an estimated value of a total load current required for supplying a total load composed of the normal load and the pulse power load; the high-pass filter is used for extracting an alternating-current component from the estimated value of the total load current and taking the alternating-current component as the current reference current of the super-capacitor converter module; and the prediction control model is used for predicting the reference current of the next sampling time point according to the current reference current, and adjusting the alternating current of the super capacitor converter module at the next sampling time point into the reference current of the next sampling time point. According to the invention, the robustness of the power system is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy storage and power system energy storage control, and in particular to a hybrid energy storage microgrid model control system and method based on AC / DC power distribution. Background Art

[0002] In a typical shipboard DC microgrid, there are specific loads such as radar, sonar, and electromagnetic weapons, which intermittently draw a large amount of power from the power system. These loads are collectively referred to as pulsed power loads (PPLs). In order to ensure the stable, reliable and efficient operation of the DC microgrid, a bidirectional DC / DC converter structure is usually selected in the shipboard DC microgrid as a direct interface to provide high-power PPLs. The tasks of these converters cover power flow control and voltage / current regulation. However, the existence of PPLs brings potential dangers to the operation of the shipboard DC microgrid. The instantaneous change of load current poses a challenge to the regulation of DC bus voltage and output voltage. The drop and overshoot of the DC bus voltage may cause abnormal operation of the propulsion system, combat weapons and other sensitive loads. Therefore, a pulse power supply with key characteristics such as high reliability, electrical isolation and full controllability is required to ensure stable power supply for PPLs in the shipboard microgrid. In this context, DeDoncker et al. proposed a dual active full bridge (DAB) DC-DC converter. It has attracted extensive attention in shipboard DC microgrids due to its high efficiency, flexibility, high-frequency electrical isolation, high-voltage step-up and step-down capability, and high conversion efficiency. The shipboard power system mainly includes power supply devices, energy storage systems, dual-active full-bridge DC-DC converters with transformers, ordinary loads, and pulse power loads.

[0003] Hybrid Energy Storage System (HESS) is also often used in shipboard DC microgrids to suppress DC bus fluctuations caused by the periodic power demand of PPLs in a short period of time.

[0004] The hybrid energy storage system includes batteries and supercapacitors to smooth intermittent power fluctuations, where batteries are used to smooth low-frequency power fluctuations and supercapacitors are used to smooth high-frequency power fluctuations. The performance of the hybrid energy storage system control strategy directly affects the operating characteristics of the DC microgrid, which makes the hybrid energy storage system control strategy one of the current research focuses and hotspots.

[0005] At present, the DC converter of the energy storage unit in the DC microgrid mainly adopts the PI control method. The energy storage converter based on PI control plays a positive role in the coordinated control of the microgrid, but it also has shortcomings such as long dynamic adjustment time, poor robustness, and difficult parameter design. In addition, the existing hybrid energy storage method for powering pulse loads does not fully utilize the full capacity of the supercapacitor. The output current of the battery converter provides a large periodic component, while the output current of the supercapacitor converter only exists for a short time. This means that the battery still provides peak power, but such a large-capacity battery unit is more expensive than the supercapacitor unit. The droop characteristics of the battery converter will cause periodic fluctuations in the DC bus.

[0006] That is to say, the existing HESS power supply PPLs method does not fully utilize the full capacity of supercapacitors (SC). The battery still provides peak power, but such a large-capacity battery unit is more expensive than the supercapacitor unit. In addition, the droop characteristics of the battery converter will cause periodic fluctuations in the DC bus. Therefore, it is urgent to study a new control system for hybrid energy storage systems that can quickly adapt to pulse load changes to control the AC and DC power distribution of hybrid energy storage systems. Summary of the invention

[0007] Based on the fact that the DC converter of the energy storage unit in the current DC microgrid mainly adopts the PI control method, the energy storage converter has the disadvantages of long dynamic adjustment time, poor robustness, and difficult parameter design, and the existing hybrid energy storage power supply PPLs method does not fully utilize the full capacity of the supercapacitor. The battery still provides peak power, but such a large-capacity battery unit is more expensive than the supercapacitor unit. In addition, the droop characteristics of the battery converter will cause periodic fluctuations in the DC bus, affecting the stability of the system. The present invention provides a hybrid energy storage microgrid model control system and method based on AC / DC power distribution. By introducing a load current estimator in the hybrid energy storage DC microgrid model, local measurement and extraction of AC and DC components are realized, and a predictive control model is used to track the AC component of the supercapacitor converter. The battery converter only provides DC power, thereby effectively avoiding large-scale periodic fluctuations in the DC bus.

[0008] The present invention is achieved through the following technical solutions:

[0009] A hybrid energy storage microgrid model control system based on AC / DC power distribution, comprising:

[0010] A battery conversion module is connected to the busbar and is used to provide DC current to ordinary loads;

[0011] A supercapacitor converter module is connected to the bus bar and is used to provide AC current to the pulse power load;

[0012] A load current estimator, used for estimating an estimated value of a total load current required to supply a total load consisting of the ordinary load and the pulse power load based on the battery conversion module, the supercapacitor converter module and the bus voltage information;

[0013] A high-pass filter, used to extract an AC component from the estimated value of the total load current, discretize the current AC component, and use the discretized AC component as a current reference current of the supercapacitor converter module;

[0014] The predictive control model is used to predict the reference current at the next sampling time point according to the current reference current, and adjust the AC current of the supercapacitor converter module at the next sampling time point to the reference current at the next sampling time point.

[0015] As an optimization, the battery conversion module includes a battery module, a first filter inductor L in(BAT) , the first DAB, the first capacitor C in(BAT) , the second capacitor C O(BAT) , wherein the output end of the battery module is connected in series with the first filter inductor L in(BAT) , the first DAB is connected to the bus, the first capacitor C in(BAT) One end of which is connected to the first filter inductor L in(BAT) and the common connection terminal of the first DAB, the first capacitor C in(BAT) The other end of the second capacitor C O(BAT) One end of which is connected to the output end of the first DAB, and the second capacitor C O(BAT) The other end is grounded.

[0016] As an optimization, the supercapacitor converter module includes a supercapacitor SC, a second filter inductor L o(SC) , the second DAB, the third capacitor C in(SC) , the fourth capacitor C O(SC) , wherein the output end of the supercapacitor converter module is connected in series with the second DAB and the second filter inductor L o(SC) Connected to the bus, the third capacitor C in(SC) One end of which is connected to the output end of the supercapacitor converter module, and the third capacitor C in(SC) The other end of the fourth capacitor C O(SC) One end of the second filter inductor L o(SC) and the common connection terminal of the second DAB, the fourth capacitor C O(SC) The other end is grounded.

[0017] As an optimization, the input ends of the common load and the pulse power load are respectively provided with DC / DC modules.

[0018] As an optimization, the predictive control model is an MPC controller.

[0019] As an optimization, the number of the battery conversion module is at least one.

[0020] As an optimization, the estimated value of the total load current is expressed as:

[0021]

[0022] Among them, i load(EST) (s) represents the estimated value of the total load current, V nom is the busbar rated voltage, v bus (s) is the actual bus voltage; C o(BAT) is the size of the second capacitor, ω D represents the second-order differentiator G 2diff (s) cut-off angular frequency, i o(SC) (s) represents the output current of the supercapacitor converter module; R dr represents the droop coefficient of the battery converter, and s represents the complex frequency domain.

[0023] As an optimization, the AC component is expressed as:

[0024]

[0025] Among them, i ref(SC) (s) represents the AC component, G HPF (s) represents a high-pass filter, ω H is the cutoff frequency of the high-pass filter.

[0026] As an optimization, the reference current is expressed as:

[0027] i ref(SC) [k]=α4x[k]+α5x[k-1]-α6i ref(SC) [k-1];

[0028]

[0029] v bus_2diff [k] = α0v bus [k]+α1v bus [k-2]

[0030] -α2v bus_2diff [k-1]-α3v bus_2diff [k-2]

[0031] Among them, i ref(SC) [k] is the AC component i ref(SC)(s) is the reference current of the kth switching cycle after discretization, that is, the reference current of the kth switching cycle corresponding to the pulse current required by the pulse power load, v bus [k] represents the bus voltage in the kth switching cycle, i o(SC) [k] represents the output current of the supercapacitor converter module in the kth switching cycle; a2=2(ω D T s -2) / (ω D T s +2), a3=(ω D T s -2) 2 / (ω D T s +2) 2 , a4=2 / (ω D T s +2), a5 = -2 / (ω D T s +2), a6=(ω D T s -2) / (ω D T s +2), T s is the sampling period, v bus_2diff [k] is the intermediate calculation parameter, ω D is the cutoff frequency of the second-order differentiator.

[0032] The present invention also discloses a hybrid energy storage microgrid model control method based on AC / DC power distribution, which distributes the AC / DC power of the hybrid energy storage system through the aforementioned hybrid energy storage microgrid model control system based on AC / DC power distribution, and is characterized by comprising:

[0033] At the current sampling time point, an estimated value of the total load current required to supply the total load is estimated according to the battery conversion module, the supercapacitor converter module and the bus voltage information, wherein the total load includes a normal load and a pulse power load;

[0034] Extracting an AC component from the estimated value of the total load current, and using the AC component at a current sampling time point as a current reference current of the supercapacitor converter module;

[0035] Predicting a reference current at a next sampling time point based on the current reference current, and adjusting the AC current of the supercapacitor converter module at the next sampling time point to the reference current at the next sampling time point;

[0036] At the next sampling time point, the supercapacitor converter module provides AC current to the pulse power load, and the battery conversion module provides DC current to the ordinary load.

[0037] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0038] 1) By extracting the AC component of the load current, the tracking performance and tracking speed of the high-frequency AC current in the hybrid energy storage system are significantly improved;

[0039] 2) The proposed hybrid energy storage system power allocation method effectively avoids large-scale periodic fluctuations in the DC bus voltage and enhances the robustness of the system;

[0040] 3) The method of this patent greatly saves costs and improves the economy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0042] Figure 1 It is a structural schematic diagram of a hybrid energy storage microgrid model control system based on AC / DC power distribution according to the present invention;

[0043] Figure 2 for Figure 1 Specific circuit diagram;

[0044] Figure 3 A price reduction model circuit for a supercapacitor converter module;

[0045] Figure 4 A control system for connecting multiple supercapacitor converter modules in parallel;

[0046] Figure 5 The performance analysis comparison diagram of the MPC control method when the pulse power duty cycle is 50%, where (a) is 10Hz and (b) is 50Hz;

[0047] Figure 6 Performance analysis of the PI control method when the pulse power duty cycle is 50%, where (a) is 10 Hz and (b) is 50 Hz;

[0048] Figure 7 The figure is a comparison chart of the three-phase output current of the supercapacitor converter module under MPC-based power balance and traditional voltage control. DETAILED DESCRIPTION

[0049] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0050] In the present invention, the hybrid energy storage system can compensate for the total power difference between the generator and all loads, so the DC microgrid model can be simplified to a mode in which the hybrid energy storage provides the total load. The supercapacitor converter module is modeled as a current source, and an additional filter inductor L is set at the output port. o(SC) At the same time, a filter inductor L is set at the input end of the battery converter. in(BAT) , to smooth out the effect of current ripple and extend battery life.

[0051] In order to extract the AC component, a load current estimator is first proposed for the system to estimate the total load current of the system. After the AC current is discretized and extracted, it is set as the reference current of the supercapacitor converter module. For the supercapacitor converter module, a pulse power tracking method based on model predictive control is studied to meet the system's requirements for tracking capability and pulse power allocation.

[0052] Next, the control system of the present invention is described in detail.

[0053] This embodiment 1 provides a hybrid energy storage microgrid model control system based on AC / DC power distribution, such as Figure 1 As shown, including:

[0054] A battery conversion module is connected to the busbar and is used to provide DC current to ordinary loads;

[0055] A supercapacitor converter module is connected to the bus bar and is used to provide AC current to the pulse power load;

[0056] A load current estimator, used for estimating an estimated value of a total load current required to supply a total load consisting of the ordinary load and the pulse power load based on the battery conversion module, the supercapacitor converter module and the bus voltage information;

[0057] A high-pass filter, used to extract an AC component from the estimated value of the total load current, discretize the current AC component, and use the discretized AC component as a current reference current of the supercapacitor converter module;

[0058] The predictive control model is used to predict the reference current at the next sampling time point according to the current reference current, and adjust the AC current of the supercapacitor converter module at the next sampling time point to the reference current at the next sampling time point.

[0059] In some embodiments, the battery conversion module includes a battery module, a first filter inductor L in(BAT) , the first DAB, the first capacitor C in(BAT) , the second capacitor C O(BAT) , wherein the output end of the battery module is connected in series with the first filter inductor L in(BAT) , the first DAB is connected to the bus, the first capacitor C in(BAT) One end of which is connected to the first filter inductor L in(BAT) and the common connection terminal of the first DAB, the first capacitor C in(BAT) The other end of the second capacitor C O(BAT) One end of which is connected to the output end of the first DAB, and the second capacitor C O(BAT) The other end is grounded.

[0060] In some embodiments, the supercapacitor converter module includes a supercapacitor SC, a second filter inductor L o(SC) , the second DAB, the third capacitor C in(SC) , the fourth capacitor C O(SC) , wherein the output end of the supercapacitor converter module is connected in series with the second DAB and the second filter inductor L o(SC) Connected to the bus, the third capacitor C in(SC) One end of which is connected to the output end of the supercapacitor converter module, and the third capacitor C in(SC) The other end of the fourth capacitor C O(SC) One end of the second filter inductor L o(SC) and the common connection terminal of the second DAB, the fourth capacitor C O(SC) The other end is grounded.

[0061] The input ends of the common load and the pulse power load are respectively provided with DC / DC modules.

[0062] The predictive control model is an MPC controller.

[0063] like Figure 2 As shown in Figure 1, the DC microgrid model is simplified to a mode where the hybrid energy storage provides the total load. The supercapacitor converter module is modeled as a current source, so an additional filter inductor L is set at the output port. o(SC) At the same time, a filter inductor L is set at the input end of the battery converter. in(BAT) , to smooth out the effect of current ripple and extend battery life.

[0064] A load current estimator is proposed for the system to estimate the total load current. The steady-state function of the battery converter with droop control characteristics can be expressed as:

[0065] Vo(BAT) =V nom -R dr I o(BAT) (1)

[0066] Where V o(BAT) and I o(BAT) are the output voltage and current of the battery converter, V nom is the busbar rated voltage, R dr is the droop coefficient. The current of HESS is always equal to the equivalent load current i load , which can be expressed as:

[0067] i load =i o(BAT) +i o(SC) =I d(BAT) -i Co(BAT) +i o(SC) (2)

[0068] Among them, i load 、i o(BAT) 、i o(SC) are the total load current, the battery converter output current, and the supercapacitor converter module output current. d(BAT) is the secondary current of the battery converter, I d(BAT) is its average value, i Co(BAT) is the current through the output capacitor of the battery converter. When the power supply and consumption reach a balance, the bus voltage remains stable, i Co(BAT) Since the battery converter has a droop characteristic, I d(BAT) It is expressed as:

[0069] I d(BAT) =I o(BAT) =(V nom -V bus ) / R dr (3)

[0070] Where V bus is the actual bus voltage, and all capital letters represent the average value of the corresponding voltage. When the load power changes, calculate i Co(BAT) As shown below:

[0071]

[0072] Where C o(BAT) is the output capacitance of the battery converter. According to the above formula, the actual load current load can be derived:

[0073] i load (s)=(V nom -V bus ) / R dr -sCo(BAT) v bus (s)+i o(SC) (s) (5)

[0074] The load current load estimate (estimate of the total load current) can be expressed as:

[0075]

[0076] In the formula, G 2diff (s) is a second-order differentiator, ω D is its cut-off angular frequency. The above formula only requires v bus and i o(SC) Local real-time information, V nom , R dr and C o(BAT) The local parameters are known in advance. A typical differentiator will amplify the noise because its high-frequency gain is close to infinity. The present invention adopts a second-order differentiator, and its frequency domain characteristics are as follows:

[0077]

[0078] When the angular frequency is lower than ω D When G 2diff (s) is a typical differentiator. When the angular frequency is higher than ω D When G 2diff (s) is approximately an integrator with gain, which can attenuate high-frequency noise. In order to extract the AC component of the total load current, a high-pass filter (HPF) is added:

[0079]

[0080] In the formula, ω H is the cutoff frequency of the high-pass filter. Assuming that after the load generates a pulse current, the part passing through the high-pass filter is powered by the supercapacitor conversion module, and the rest is powered by the battery converter module. o(BAT) The ripple can be expressed as:

[0081]

[0082] where t PLL is the pulse duration, D PLL(max) is the maximum pulse power load duty cycle, f PLL(min) is the minimum pulse power load frequency. In the present invention, D PLL(max) is 30%, select ω H ≤0.2πf PLL(min) , so that Δi o(BAT) Less than 20% of the pulse current. A lower ω can be selected HTo obtain a smaller Δi o(BAT) , however, the lower ω H This will cause the high-frequency filter to respond more slowly. The steady-state component cannot pass through the high-frequency filter, so the AC component of the supercapacitor converter module can be expressed as:

[0083]

[0084] By using bilinear transformation, the above formula can be expressed as:

[0085]

[0086] Where Ts is the switching and sampling period, and a0-a6 is expressed as:

[0087]

[0088] The discretized equation is shown in equation (13), where k is the kth switching cycle,

[0089] i ref(SC) [k]=α4x[k]+α5x[k-1]-α6i ref(SC) [k-1] (13)

[0090] in:

[0091]

[0092] v bus_2diff [k] = α0v bus [k]+α1v bus [k-2]

[0093] -α2v bus_2diff [k-1]-α3v bus_2diff [k-2] (14)

[0094] The discretized AC current obtained by formula (13) is set as the reference current of the supercapacitor converter, that is, formula (13) is the reference value of the AC current of the supercapacitor converter in the kth switching cycle (that is, the reference current of the supercapacitor converter module); the supercapacitor converter is modeled. Figure 3 This is the equivalent circuit diagram of the supercapacitor converter using the reduced-order model.

[0095] The second-order state variable function in the figure can be expressed as:

[0096]

[0097] Using the Euler formula to discretize equation (15), the discrete difference equation can be expressed as:

[0098]

[0099] The output current i of the SC converter (supercapacitor converter module) at time k+2 o (correspond Figure 2 i o(SC) ) can be expressed as:

[0100]

[0101] Since the bus voltage will not change dramatically within a sampling period, R Co Set to 1, formula (17) can be simplified to:

[0102]

[0103] That is, the reference current corresponding to the AC current output by the supercapacitor converter module in the k+2th switching cycle is i o [k+2].

[0104] According to the empirical model, I d It can be expressed as:

[0105]

[0106] The SC converter using MPC controller regulates the output current to the reference value i ref , the cost function of the MPC controller is shown in formula (20):

[0107] ct1=α1G1 (20)

[0108] ct1 represents the first cost function and α1 represents the weight coefficient.

[0109] The predictive control model in the present invention does not involve multi-objective control, so the weight coefficient is set to 1. Where G1 can be expressed as:

[0110] G1=(i ref(SC) [k+2]-i o [k+2]) 2 (twenty one)

[0111] The predictive control principle of the predictive control model is as follows: In the control interval of k to k+1, the previous working point D k As the center, the discrete control set D is obtained p , and according to the discrete control set D p Perform current prediction and select the control quantity corresponding to the minimum cost function value as the control quantity of the k+1 cycle. Where u is a coefficient determined according to the number of required prediction elements. The discrete control set Dp can be expressed as:

[0112] D p ={Dk -u△ f …,D k -△ f ,D k ,D k +△ f ,…,D k +u△ f} (twenty two)

[0113] In some embodiments, the number of the battery conversion module is at least one.

[0114] That is, based on the above content, the present invention ductilely proposes a control system for connecting multiple supercapacitor converter models in parallel to achieve transmission power balance of multiple supercapacitor converter models, such as Figure 4 shown.

[0115] The output reference current of each supercapacitor converter meets the requirements:

[0116]

[0117] The cost function of each supercapacitor converter can be expressed as:

[0118]

[0119] In summary, in response to the DC bus voltage fluctuation caused by the short-term periodic power demand of PPLs, this paper studies a pulse power distribution and tracking method for a hybrid energy storage system. This method defines the AC and DC components of the output current, and extracts the load AC current as the output reference current of the SC converter by introducing a current estimator;

[0120] At the same time, an MPC-based pulse power tracking method is studied for the SC converter to improve its tracking performance, and the proposed method is studied to suppress the DC bus voltage fluctuation. Compared with the traditional HESS control strategy, the SC converter based on the MPC controller can track the AC power of the PPLs, while the battery converter only provides DC power, thus effectively avoiding the large-scale periodic fluctuation of the DC bus.

[0121] Matlab / Simulink is used to build a simulation model and verify the proposed solution. The system parameters are shown in Table 1.

[0122] Table 1 DAB converter system parameters

[0123]

[0124] The specific implementation process is as follows: The tracking performance and bus voltage fluctuation of the proposed MPC-controlled SC converter are analyzed through time domain simulation. Figure 5When operating under pulse power load, the SC converter adopts the waveform of MPC control method, where the PPLs duty cycle is 50% and the peak power is about 1kW. Figure 5 It can be obtained that when the PPLs are intermittently stopped, the output current i of the SC converter o(SC) Keeping at -3A, when the PPLs require a peak current of 10A, the SC converter module outputs a peak current of 7A. Therefore, the SC converter provides almost all the AC current of the PPL. In contrast, the output current of the battery converter is BAT The fluctuation is small and the battery converter only provides a small amount of pulse power.

[0125] Figure 6 When operating under pulse power load, the SC converter adopts the waveform of PI controller. Figure 6 (a) and (b) are obtained. The current tracking performance of the SC converter using the PI controller is much slower than that of the SC converter using the MPC controller. Therefore, the battery converter provides a larger pulse power, resulting in more drastic fluctuations in the bus voltage.

[0126] Use Matlab / Simulink to build a simulation model of multiple SC converters in parallel with battery converters, and verify the proposed solution. Figure 7 The simulation waveform of the output current of each SC converter unit in the process from the traditional voltage control method to the MPC control method is given.

[0127] As can be seen from the figure, under traditional voltage control, the transmission power of each SC converter is unbalanced. Since the auxiliary inductance of the SC1 converter is small, its output current is larger than that of the other two SC converters. When the MPC power balancing scheme is adopted, the SC converter can respond to the pulse power load in time and achieve the balance of transmission power.

[0128] Embodiment 2 discloses a hybrid energy storage microgrid model control method based on AC / DC power distribution, which distributes the AC / DC power of the hybrid energy storage system through a hybrid energy storage microgrid model control system based on AC / DC power distribution of Embodiment 1, including:

[0129] At the current sampling time point, an estimated value of the total load current required to supply the total load is estimated according to the battery conversion module, the supercapacitor converter module and the bus voltage information, wherein the total load includes a normal load and a pulse power load;

[0130] Extracting an AC component from the estimated value of the total load current, and using the AC component at a current sampling time point as a current reference current of the supercapacitor converter module;

[0131] Predicting a reference current at a next sampling time point based on the current reference current, and adjusting the AC current of the supercapacitor converter module at the next sampling time point to the reference current at the next sampling time point;

[0132] At the next sampling time point, the supercapacitor converter module provides AC current to the pulse power load, and the battery conversion module provides DC current to the ordinary load.

[0133] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A hybrid energy storage microgrid model control system based on AC / DC power distribution, characterized in that: include: A battery conversion module is connected to the busbar and is used to provide DC current to ordinary loads; A supercapacitor converter module is connected to the bus bar and is used to provide AC current to the pulse power load; A load current estimator, used for estimating an estimated value of a total load current required to supply a total load consisting of the ordinary load and the pulse power load based on the battery conversion module, the supercapacitor converter module and the bus voltage information; A high-pass filter, used to extract an AC component from the estimated value of the total load current, discretize the current AC component, and use the discretized AC component as a current reference current of the supercapacitor converter module; The predictive control model is used to predict the reference current at the next sampling time point according to the current reference current, and adjust the AC current of the supercapacitor converter module at the next sampling time point to the reference current at the next sampling time point.

2. A hybrid energy storage microgrid model control system based on AC / DC power distribution according to claim 1, characterized in that: The battery conversion module includes a battery module, a first filter inductor L in(BAT) , the first DAB, the first capacitor C in(BAT) , the second capacitor C O(BAT) , wherein the output end of the battery module is connected in series with the first filter inductor L in(BAT) , the first DAB is connected to the bus, the first capacitor C in(BAT) One end of which is connected to the first filter inductor L in(BAT) and the common connection terminal of the first DAB, the first capacitor C in(BAT) The other end of the second capacitor C O(BAT) One end of which is connected to the output end of the first DAB, and the second capacitor C O(BAT) The other end is grounded.

3. A hybrid energy storage microgrid model control system based on AC / DC power distribution according to claim 1, characterized in that: The supercapacitor converter module includes a supercapacitor SC, a second filter inductor L o(SC) , the second DAB, the third capacitor C in(SC) , the fourth capacitor C O(SC) , wherein the output end of the supercapacitor converter module is connected in series with the second DAB and the second filter inductor L o(SC) Connected to the bus, the third capacitor C in(SC) One end of which is connected to the output end of the supercapacitor converter module, and the third capacitor C in(SC) The other end of the fourth capacitor C O(SC) One end of the second filter inductor L o(SC) and the common connection terminal of the second DAB, the fourth capacitor C O(SC) The other end is grounded.

4. A hybrid energy storage microgrid model control system based on AC / DC power distribution according to claim 1, characterized in that: The input ends of the common load and the pulse power load are respectively provided with DC / DC modules.

5. A hybrid energy storage microgrid model control system based on AC / DC power distribution according to claim 1, characterized in that: The predictive control model is an MPC controller.

6. A hybrid energy storage microgrid model control system based on AC / DC power distribution according to claim 1, characterized in that: The number of the battery conversion module is at least one.

7. A hybrid energy storage microgrid model control system based on AC / DC power distribution according to claim 1, characterized in that: The estimated value of the total load current is expressed as: Among them, i load(EST) (s) represents the estimated value of the total load current, V nom is the busbar rated voltage, v bus (s) is the actual bus voltage; C o(BAT) is the size of the second capacitor, ω D represents the second-order differentiator G 2diff (s) cut-off angular frequency, i o(SC) (s) represents the output current of the supercapacitor converter module; R dr Represents the droop factor of the battery converter.

8. A hybrid energy storage microgrid model control system based on AC / DC power distribution according to claim 7, characterized in that: The AC component is expressed as: Among them, i ref(SC) (s) represents the AC component, G HPF (s) represents a high-pass filter, ω H is the cutoff frequency of the high-pass filter.

9. A hybrid energy storage microgrid model control system based on AC / DC power distribution according to claim 8, characterized in that: The reference current is expressed as: I ref(SC) [k]=α4x[k]+α5x[k-1]-α6i ref(SC) [k-1]; v bus_2diff [k]=α0v bus [k]+α1v bus [k-2] -α2v bus_2diff [k-1]-α3v bus_2diff [k-2] Among them, i ref(SC) [k] is the AC component i ref(SC) (s) The reference current of the kth switching cycle after discretization, v bus [k] represents the bus voltage in the kth switching cycle, i o(SC) [k] represents the output current of the supercapacitor converter module in the kth switching cycle; a2=2(ω D T s -2) / (ω D T s +2), a3=(ω D T s -2) 2 / (ω D T s +2) 2 , a4=2 / (ω D T s +2), a5 = -2 / (ω D T s +2), a6=(ω D T s -2) / (ω D T s +2), T s is the sampling period, v bus_2diff [k] is the intermediate calculation parameter, ω D is the cutoff frequency of the second-order differentiator.

10. A hybrid energy storage microgrid model control method based on AC / DC power distribution, which distributes the AC / DC power of a hybrid energy storage system through a hybrid energy storage microgrid model control system based on AC / DC power distribution according to any one of claims 1 to 9, characterized in that: include: At the current sampling time point, an estimated value of the total load current required to supply the total load is estimated according to the battery conversion module, the supercapacitor converter module and the bus voltage information, wherein the total load includes a normal load and a pulse power load; Extracting an AC component from the estimated value of the total load current, and using the AC component at a current sampling time point as a current reference current of the supercapacitor converter module; Predicting a reference current at a next sampling time point based on the current reference current, and adjusting the AC current of the supercapacitor converter module at the next sampling time point to the reference current at the next sampling time point; At the next sampling time point, the supercapacitor converter module provides AC current to the pulse power load, and the battery conversion module provides DC current to the ordinary load.

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