Distributed hess power coordination control method and system based on fcs-mpc

Abstract

The application relates to the technical field of FCS-MPC, and discloses a distributed HESS power coordination control method and system based on FCS-MPC, the method comprises the following steps: acquiring power demand P ref that needs to be compensated by an energy storage system ref , using wavelet packet transformation in top-layer control to decompose the P into high power density and high energy density, taking the high power density as a power given value of a super capacitor, taking a high energy density part as a power given value of a storage battery after redistribution, and using multi-step FCS-MPC in bottom-layer control to perform online prediction and optimization control on output follow-up given values of the energy storage units; the system comprises a top-layer power distribution module and a bottom-layer power control module. The application can realize the coordination control of the distributed HESS in an islanded direct-current microgrid, has good control dynamic response performance, stable output voltage, and fast and sensitive power follow-up reaction.

Description

Technical Field

[0001] This invention relates to the field of FCS-MPC technology, and in particular to a distributed HESS power coordination control method and system based on FCS-MPC. Background Technology

[0002] Distributed energy storage offers lower costs, higher utilization rates, greater compatibility and reliability, and greater flexibility compared to centralized energy storage. Current energy storage units are mainly divided into two types: energy-type storage, which offers long storage time and low cost but shorter lifespan, primarily using batteries; and power-type storage, which offers fast response and high cycle life but higher cost, primarily using supercapacitors. Combining batteries and supercapacitors into a hybrid energy storage system (HESS) allows their characteristics to complement each other, smoothing out instantaneous power fluctuations in the microgrid, reducing the number of battery charge / discharge cycles to extend battery life, and improving system dynamic response.

[0003] In research on the control of hybrid energy storage systems, some researchers have proposed a distributed cooperative control method based on consistency theory. This method effectively solves the converter output voltage difference caused by droop control by leveraging consistency theory, achieving consistency in the output voltage and output power of each HESS (Hybrid Energy Storage System). Other studies have documented an optimized control strategy for HESS based on Model Predictive Control (MPC), designing constraints such as charging / discharging power and battery state of charge (SoC), and transforming it into a quadratic programming problem for solution, while also considering control strategies when the system is out of constraints. Still other researchers have proposed a finite set model predictive control (FCS-MPC) strategy, which can quickly and dynamically compensate for unbalanced power in the grid. This strategy does not require communication between HESSs, improving the resilience of grid operation.

[0004] However, in these existing technologies, when FCS-MPC is applied to HESS for low-level control, it is usually based on a microgrid containing only a single HESS, without considering the case of multiple HESSs. Furthermore, there is little research on how to specifically allocate power when distributed HESSes meet complex power demands. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a distributed HESS power coordination control method and system based on FCS-MPC, which can realize the coordinated control of distributed HESS in islanded DC microgrids, with good dynamic control response performance, stable output voltage, and fast and sensitive power following response.

[0006] To address the aforementioned technical problems, this invention provides a distributed HESS power coordination control method based on FCS-MPC, comprising:

[0007] Obtain the power demand P that the energy storage system needs to compensate. ref ;

[0008] Top-level control uses wavelet packet transform to transform the P ref It is decomposed into high power density and high energy density. The high power density is used as the power setpoint of the supercapacitor, and the high energy density is redistributed and used as the power setpoint of the battery.

[0009] The underlying control uses multi-step FCS-MPC online predictive optimization control to ensure that the output of the energy storage unit follows the given value.

[0010] In one embodiment of the present invention, the high power density is P ref The waveform obtained after wavelet packet transform decomposition contains the portion with relatively high frequencies, but whose values ​​fluctuate around 0.

[0011] The high energy density is P. ref The waveform obtained after wavelet packet transform decomposition is the part with the lowest frequency and the largest waveform value.

[0012] In one embodiment of the present invention, the redistribution of the high energy density specifically includes:

[0013] Sort all batteries from largest to smallest according to their initial SoC size, numbered 1 to n;

[0014] The adaptive factor y is calculated based on the SoC difference between the batteries. i ;

[0015] According to the adaptive factor y i For the high energy density P ref_bt The power is redistributed, and the i-th battery is assigned a power setpoint P. ref_bti .

[0016] In one embodiment of the present invention, the adaptive factor y i The calculation method is as follows:

[0017]

[0018] Among them, ΔSoC i =SoC i -SoC i+1 , represents the SoC difference between the i-th and i+1-th batteries; ρ is the difference coefficient, and α represents the base of the difference; a i b represents the maximum and minimum values ​​of the positive difference in consistency. i c represents the consistency coefficient of positive differences. i The coefficient of consistency for negative differences, d i Indicates the maximum and minimum values ​​of negative difference consistency; a i b i c i and d i It is a parameter related to the capacity of the i-th battery, and must satisfy y i ∈(0,1).

[0019] In one embodiment of the present invention, the a i b i c i and d i The condition that the value of satisfies is:

[0020]

[0021] Among them, C bti It is the capacity of the i-th battery, when P ref_bt When ≥0, i∈{1,2,…,n-1}; when P ref_bt When <0, i∈{n,n-1,…,2}.

[0022] In one embodiment of the present invention, according to the adaptive factor y i For the high energy density P ref_bt The power is redistributed, and the i-th battery is assigned a power setpoint P. ref_bti ,include:

[0023] When P ref_bt When positive, the power setpoint for n batteries is:

[0024]

[0025] In the formula, P ref_bti Let y represent the power setpoint of the i-th battery. i This represents the adaptive factor of the i-th battery.

[0026] In one embodiment of the present invention, the multi-step FCS-MPC includes an internal loop determination and an external loop determination. The internal loop determination is used to determine the control signal of the MOS transistor, and the external loop determination is used to perform multi-step prediction.

[0027] In one embodiment of the present invention, the inner loop determines two values, g_optt and x_op. g_optt is used to compare the calculated value of the objective function J with the value under the two states of the control signal and record the minimum value; x_optt is used to record the control signal when the minimum value is reached.

[0028] The external loop has a total of K steps, and after K steps of looping, the control signal for predicting the first step is output.

[0029] In one embodiment of the present invention, the objective function J of the multi-step FCS-MPC is:

[0030] J=λ1(u o -u ref ) 2 +λ2(i L -i Lref ) 2 ,

[0031] Where λ1 and λ2 are weighting coefficients, u o u represents the output voltage of the DC / DC converter. ref Indicate u o Given the value i L Indicates inductor current, i Lref Indicate i L The given value i Lref .

[0032] This invention also provides a distributed HESS power coordination control system based on FCS-MPC, including a top-level power allocation module and a bottom-level power control module.

[0033] The top-level power allocation module obtains the power demand P that the energy storage system needs to compensate. ref Using wavelet packet transform to transform the P ref It is decomposed into high power density and high energy density. The high power density is used as the power setpoint of the supercapacitor, and the high energy density is redistributed and used as the power setpoint of the battery.

[0034] The underlying power control module uses multi-step FCS-MPC online predictive optimization control to ensure that the output of the energy storage unit follows the given value.

[0035] The technical solution of the present invention has the following advantages over the prior art:

[0036] After determining the power demand to be compensated in a distributed HESS, this invention uses wavelet packet transform to convert it into high power density and high energy density. The high power density is directly compensated by capacitors, while the high energy density is redistributed and then compensated by batteries. This allows the energy storage system to meet complex power demands while more quickly and stably achieving the goal of consistent final SoC among multiple batteries with different capacities and initial SoCs. After allocating the power setpoint for each energy storage unit, multi-step FCS-MPC online predictive optimization control is used to accurately follow the setpoint output of the energy storage unit, resulting in good dynamic response performance, stable output voltage, and fast and sensitive power following response. Attached Figure Description

[0037] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein:

[0038] Figure 1 This is a flowchart of the method of the present invention.

[0039] Figure 2 This is a control block diagram of the distributed HESS in an embodiment of the present invention.

[0040] Figure 3 This is a topology diagram of a distributed HESS (Hybrid Energy Storage System) for DC photovoltaic microgrids in an embodiment of the present invention.

[0041] Figure 4 This is a power distribution control block diagram in an embodiment of the present invention.

[0042] Figure 5 This is a circuit topology diagram of a bidirectional DC / DC converter in an embodiment of the present invention.

[0043] Figure 6 This is a control flowchart of the multi-step FCS-MPC in an embodiment of the present invention. Detailed Implementation

[0044] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0045] Reference Figure 1 As shown, this invention discloses a power coordination control method for a distributed hybrid energy storage system (HESS) based on Finite Control Set Model Predictive Control (FCS-MPC). The control method consists of two layers: the top layer is a power allocation strategy, inputting the power P to be compensated by the distributed HESS.ref Based on the SoC value of each battery in each HESS, the power setpoint for each energy storage unit is output, i.e., the power setpoint value P for each supercapacitor. ref_sci and the power setpoint P for each battery ref_bti The underlying control employs multi-step FCS-MPC, taking the power setpoint as input and directly outputting the control signal f of the MOSFET in the DC / DC converter. sci_1 f sci_2 f bti_1 and f bti_2 , where f sci_1 and f sci_2 It is the control signal for the two MOSFETs in the DC / DC converter of the supercapacitor (i.e. Figure 3 f in sc1_1… f scn_1 f sc1_2… f scn_2 ), f bti_1 and f bti_2 It is the control signal of the two MOSFETs in the DC / DC converter of the battery (i.e. Figure 3 f in bt1_1… f btn_1 f bt1_2… f btn_2 ).

[0046] Includes the following steps:

[0047] S1: Obtain the power demand P that the energy storage system needs to compensate. ref . For example Figure 2 Taking the distributed HESS topology of a DC photovoltaic microgrid as an example, it includes photovoltaic renewable clean energy, DC loads, and multiple HESSes. The distributed HESS and photovoltaic power generation are connected to the DC bus via a DC / DC converter; the DC loads are directly connected to the DC bus. The control block diagram of the distributed HESS is shown below. Figure 3 As shown.

[0048] S2: Top-level control combines wavelet packet transform and a battery SoC-based adaptive factor consistency power allocation method to perform power allocation, and allocates P... ref Assigned to each energy storage unit in the distributed HESS: using wavelet packet transform to convert the P ref The system is decomposed into two parts: high power density and high energy density. The high power density is used as the power setpoint of the supercapacitor. Then, a power allocation method based on battery SoC adaptive factor consistency is designed to redistribute the high energy density. After redistribution, the energy density is used as the power setpoint of the battery.

[0049] This invention employs wavelet packet transform to decompose and reconstruct power requirements. Wavelet packet transform is an improvement upon wavelet transform. While wavelet transform decomposes a signal into high-frequency and low-frequency components, further decomposition only applies to the low-frequency portion, leaving the high-frequency portion unresolved. Wavelet packet transform, however, can further decompose both the low-frequency and high-frequency components, overcoming the limitations of wavelet transform in the high-frequency range. By refining the high-frequency portion through wavelet packet decomposition, supercapacitors directly compensate for the high-frequency components. Since, after decomposition, all signals except the minimum frequency fluctuate around 0 with relatively small values, the minimum frequency signal is handled by the battery. Other signals, based on their number and the number of supercapacitors, are combined into higher-frequency and lower-frequency signals, resulting in a combined signal equal to the number of supercapacitors. Each supercapacitor is then assigned a power setpoint P. ref_sci In this invention, the number of wavelet packet transform layers is determined according to the number of HESS groups.

[0050] The power distribution control block diagram is as follows: Figure 4 As shown. Considering that batteries of different capacities will have different initial SoCs, this invention designs an adaptive factor based on the battery SoC design to redistribute the low-frequency part, ultimately to achieve battery SoC consistency. The known formula for calculating the battery SoC is shown in equation (1):

[0051]

[0052] In the formula, SoC0 is the initial SoC of the battery; C bt It refers to the battery capacity; P I It is the output power of the battery and also the input power of the DC / DC converter; u i It is the battery output voltage, and also the converter input voltage; i L dτ represents the inductor current, and dτ represents the calculus of the time variable. Where P I The relationship is shown in equation (2) below:

[0053]

[0054] In the formula, R is the inductance value; P o This is the converter's output power. R is very small, relative to P. o In this respect, the power consumed can be approximated as negligible. When |P o The larger the value, the greater the change in SoC; when C bt The larger the SoC, the smaller the SoC variation. When allocating power settings for multiple batteries, the goal is to make their SoCs converge. When a battery discharges, the battery with a larger SoC initially needs a larger power setting; the opposite is true when charging. Once the SoCs are consistent, to maintain uniform SoC variation, the power setting allocation is based on the battery's capacity C.bt And so on.

[0055] S2-1: The high power density is P ref After wavelet packet transform decomposition, the waveforms with relatively high frequencies but fluctuating around 0 are analyzed. Based on the number of these signals and the number of supercapacitors, the higher and lower frequency signals are combined. The combined signal is the same as the number of supercapacitors. Then, each supercapacitor is assigned a power setpoint P. ref_sci .

[0056] S2-2: The high energy density P ref_bt For the P ref After wavelet packet transform decomposition, the lowest frequency waveform with the largest value is selected. Considering that the battery capacity in the distributed HESS differs from the initial SoC, P is then... ref_bt After redistribution using a battery SoC-based adaptive factor consistency power allocation method, each battery is assigned a power setpoint P. ref_bti .

[0057] An adaptive factor-consistent power allocation method based on battery SoC is used for P ref_bt The redistribution is to achieve SoC consistency for the batteries in the distributed HESS. The redistribution of the high energy density specifically includes:

[0058] S2-2-1: Assume that there are n sets of hybrid energy storage in the distributed HESS, P ref After wavelet packet transformation, the power requirement that n batteries need to meet is P. ref_bt Sort all batteries from largest to smallest according to their initial SoC size, numbered 1 to n.

[0059] S2-2-2: Calculate the adaptive factor y based on the SoC difference between batteries. i :

[0060] When the high energy density P ref_bt When the value is positive, all batteries discharge, and the y-values ​​are set sequentially for batteries sorted from 1 to n-1 in ascending order. i When P ref_bt When the value is negative, all batteries are charged, and the y-value is set sequentially for batteries n to 2 in reverse order. i The adaptive factor y i With the battery's SoC and C bt Related, and y i ∈(0,1), y i The calculation method is as follows:

[0061]

[0062] Among them, ΔSoC i =SoC i -SoC i+1 , represents the SoC difference between the i-th and i+1-th batteries; ρ is the difference coefficient, α represents the base of the difference, in this embodiment ρ=1, α>1; a i b represents the maximum and minimum values ​​of the positive difference in consistency. i c represents the consistency coefficient of positive differences. i The coefficient of consistency for negative differences, d i This represents the maximum and minimum values ​​of negative difference consistency; four parameters a i b i c i and d i It is a parameter related to the capacity of the i-th battery, and must satisfy y i ∈(0,1), a i b i c i and d i The condition that the value of satisfies is:

[0063]

[0064] Among them, C bti It is the capacity of the i-th battery, when P ref_bt When ≥0, i∈{1,2,…,n-1}; when P ref_bt When <0, i∈{n,n-1,…,2}.

[0065] S2-2-3: Based on the adaptive factor y i For the high energy density P ref_bt The power is redistributed, and the i-th battery is assigned a power setpoint P. ref_bti .

[0066] When P ref_bt When positive, starting with battery number 1, since its initial SoC is the largest, it should be allocated the largest power setpoint from the beginning, so P... ref_bt Multiplying by y1 directly yields the power setpoint P for battery number 1. ref_bt1 Battery #2 was then taken from the remaining P after allocation. ref_bt -P ref_bt1 Multiplying by y2 yields the power setpoint P for battery number 2. ref_bt2 Battery number 3 is the remaining battery after the first two batteries have been allocated, i.e., P. ref_bt -P ref_bt1 -P ref_bt2 The allocation is carried out in this manner until battery number n does not require setting factor y. nAfter the first n-1 batteries are allocated, the remaining power setpoint P for battery number n is determined. ref_btn .

[0067] Expressed as a formula:

[0068] When P ref_bt When positive, the power setpoint for n batteries is:

[0069]

[0070] In the formula, P ref_bti Let y represent the power setpoint of the i-th battery. i This represents the adaptive factor of the i-th battery. When P ref_bt When the value is negative, the power distribution for battery charging is reversed, starting from battery n and continuing up to battery 1.

[0071] S3: The underlying control uses multi-step FCS-MPC online predictive optimization control to ensure that the output of the energy storage unit follows the given value.

[0072] refer to Figure 5 The matrix-form discretized mathematical model of the converter is shown in equation (6) below:

[0073]

[0074] In the formula, D = I; L represents the inductance value, C represents the load voltage regulator capacitor; R L Indicates the load resistance; x(k) = [i(k)u o (k)] T , represents the input of the model at time k, where i(k) represents the inductor current at time k, u o (k) represents the load voltage at time k; y(k) = x(k) represents the output of the model at time k; T S The sampling period; S1 indicates the state of switch T1; S2 indicates the state of switch T2.

[0075] The model is based on the load voltage u o and inductor current i L The algorithm is established, and the optimization objective is to bring x(k) closer to a given value and follow it. Therefore, we first determine u. o and i L The given value.

[0076] u o This is the output voltage of the DC / DC converter. The distributed HESS is connected in parallel to the DC bus through the converter, so u... o The given value is the bus voltage given value u. ref .

[0077] Each energy storage unit needs to meet its power requirement, which is determined by the converter power relationship (2). L Given a value. Analyzing equation (2) reveals that it is a value about i. L The quadratic equation in one variable can be solved as shown in equation (7):

[0078]

[0079] Among them, i L0 This represents the initial term of a given current.

[0080] Since the converter needs to meet both voltage control and power control requirements, in order to improve the dynamic performance of the control system and reduce steady-state error when the load changes abruptly, in i L0 Add voltage error ε to the basis u and power error ε P The compensation term is shown in equation (8) below:

[0081]

[0082] In the formula, Δu=u ref -u o ; ΔP=P ref -P o ;K Pu K PP K is the compensation ratio coefficient for voltage error and power error. Iu K IP It is the integral coefficient for compensating for voltage error and power error. Therefore, i L The given value i Lref As shown in equation (9):

[0083] i Lref =i L0 +ε u +ε P (9);

[0084] The objective function J for multi-step FCS-MPC is:

[0085] J=λ1(u o -u ref ) 2 +λ2(i L -i Lref ) 2 (10),

[0086] Where λ1 and λ2 are weighting coefficients, and in this embodiment, the values ​​are λ1 = 1 and λ2 = 1, u o u represents the output voltage of the DC / DC converter. refIndicate u o Given the value i L Indicates inductor current, i Lref Indicate i L The given value.

[0087] refer to Figure 6 The multi-step FCS-MPC includes an internal loop determination and an external loop determination. The internal loop determination is used to determine the control signal of the MOS transistor, and the external loop determination is used to perform multi-step prediction.

[0088] The internal i-loop determines and sets two values, g_opt and x_op. g_opt is used to compare the calculated value of the objective function J with the control signal under two states and records the minimum value; x_opt is used to record the control signal at the minimum value. The external j-loop has K steps, and after K steps, it outputs the control signal for the first prediction step. Taking the power setpoint as input, it directly outputs the control signal of the MOSFET in the DC / DC converter, i.e., f. sci_1 and f sci_2 and f bti_1 and f bti_2 .

[0089] This invention also discloses a distributed HESS power coordination control system based on FCS-MPC, including a top-level power allocation module and a bottom-level power control module. The top-level power allocation module obtains the power demand P that the energy storage system needs to compensate. ref Using wavelet packet transform to transform the P ref The system is decomposed into high power density and high energy density. The high power density is used as the power setpoint for the supercapacitor, and the high energy density is redistributed and used as the power setpoint for the battery. The underlying power control module uses multi-step FCS-MPC online predictive optimization control to ensure the energy storage unit's output follows the setpoint.

[0090] This invention, after determining the power demand to be compensated for in a distributed energy storage system (HESS), uses wavelet packet transform to divide it into high power density and high energy density components, which are then compensated by supercapacitors and batteries respectively. A battery-based SoC adaptive factor consistency power allocation method is designed to redistribute the high energy density component. By combining wavelet packet transform with the battery-based SoC adaptive factor consistency power allocation method, the energy storage system can meet complex power demands while more quickly and stably achieving the goal of SoC consistency among multiple batteries with different capacities and initial SoCs. After allocating the power setpoint for each energy storage unit, multi-step FCS-MPC is used at the lower level for online predictive optimization control to ensure the energy storage unit output accurately follows the setpoint, replacing the traditional voltage and current dual closed-loop control. This results in better dynamic response performance, stable output voltage, and rapid and sensitive power tracking.

[0091] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0092] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0093] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0094] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0095] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A distributed HESS power coordination control method based on FCS-MPC, characterized in that, include: The power demand P that the energy storage system needs to compensate for is obtained ref ​ Top-level control uses wavelet packet transform to transform the P ref It is decomposed into high power density and high energy density. The high power density is used as the power setpoint of the supercapacitor, and the high energy density is redistributed and used as the power setpoint of the battery. The underlying control uses multi-step FCS-MPC online predictive optimization control to ensure the energy storage unit's output follows the given value; The redistribution of the high energy density specifically includes: sorting all batteries from 1 to n according to their initial SoC size from largest to smallest; and calculating the adaptive factor y based on the SoC difference between the batteries. i According to the adaptive factor y i For the high energy density P ref_bt The power is redistributed, and the i-th battery is assigned a power setpoint P. ref_bti ; The adaptive factor y i The calculation method is as follows: ， in, , represents the SoC difference between the i-th and i+1-th batteries; ρ is the difference coefficient, and α represents the base of the difference; a i b represents the maximum and minimum values ​​of the positive difference in consistency. i c represents the consistency coefficient of positive differences. i The negative difference consistency coefficient, d i Indicates the maximum and minimum values ​​of negative difference consistency; a i b i c i and d i It is a parameter related to the capacity of the i-th battery, and must satisfy y i ∈(0,1); The a i b i c i and d i The condition that the value of satisfies is: ， Among them, C bti It is the capacity of the i-th battery, when hour, ;when hour, ; According to the adaptive factor y i For the high energy density P ref_bt The power is redistributed, and the i-th battery is assigned a power setpoint P. ref_bti ,include: When P ref_bt When positive, the power setpoint for n batteries is: ， In the formula, P ref_bti y represents the power setpoint of the i-th battery. i This represents the adaptive factor of the i-th battery.

2. The distributed HESS power coordination control method based on FCS-MPC according to claim 1, characterized in that: The high power density is P ref The waveform obtained after wavelet packet transform decomposition contains the portion with relatively high frequencies, but whose values ​​fluctuate around 0. The high energy density is P ref The waveform obtained after wavelet packet transform decomposition is the part with the lowest frequency and the largest waveform value.

3. The distributed HESS power coordination control method based on FCS-MPC according to claim 1, characterized in that: The multi-step FCS-MPC includes an internal loop determination and an external loop determination. The internal loop determination is used to determine the control signal of the MOS transistor, and the external loop determination is used to perform multi-step prediction.

4. The distributed HESS power coordination control method based on FCS-MPC according to claim 3, characterized in that: The inner loop determines two values, g_optt and x_op. g_optt is used to compare the calculated value of the objective function J with the value under the two control signal states and record the minimum value. x_optt is used to record the control signal when the minimum value is reached. The external loop has a total of K steps, and after K steps of looping, the control signal for predicting the first step is output.

5. The distributed HESS power coordination control method based on FCS-MPC according to any one of claims 1-4, characterized in that: The objective function J of the multi-step FCS-MPC is: ， in, , These are weighting coefficients. This represents the output voltage of the DC / DC converter. Indicate u o The given value, Indicates inductor current. Indicate i L The given value i Lref .

6. A distributed HESS power coordination control system based on FCS-MPC, characterized in that: Includes a top-level power distribution module and a bottom-level power control module. The top-level power allocation module obtains the power demand P that the energy storage system needs to compensate. ref Using wavelet packet transform to transform the P ref It is decomposed into high power density and high energy density. The high power density is used as the power setpoint of the supercapacitor, and the high energy density is redistributed and used as the power setpoint of the battery. The underlying power control module uses multi-step FCS-MPC online predictive optimization control to make the output of the energy storage unit follow the given value; The redistribution of the high energy density specifically includes: sorting all batteries from 1 to n according to their initial SoC size from largest to smallest; and calculating the adaptive factor y based on the SoC difference between the batteries. i According to the adaptive factor y i For the high energy density P ref_bt The power is redistributed, and the i-th battery is assigned a power setpoint P. ref_bti ; The adaptive factor y i The calculation method is as follows: ， in, , represents the SoC difference between the i-th and i+1-th batteries; ρ is the difference coefficient, and α represents the base of the difference; a i b represents the maximum and minimum values ​​of the positive difference in consistency. i c represents the consistency coefficient of positive differences. i The negative difference consistency coefficient, d i Indicates the maximum and minimum values ​​of negative difference consistency; a i b i c i and d i It is a parameter related to the capacity of the i-th battery, and must satisfy y i ∈(0,1); The a i b i c i and d i The condition that the value of satisfies is: ， Among them, C bti It is the capacity of the i-th battery, when hour, ;when hour, ; According to the adaptive factor y i For the high energy density P ref_bt The power is redistributed, and the i-th battery is assigned a power setpoint P. ref_bti ,include: When P ref_bt When positive, the power setpoint for n batteries is: ， In the formula, P ref_bti y represents the power setpoint of the i-th battery. i This represents the adaptive factor of the i-th battery.

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