CN118100242A - Coordinated control method and system for hybrid energy storage system for photovoltaic electric field - Google Patents

Abstract

The invention provides a hybrid energy storage system coordination control method and system for a photovoltaic electric field, and belongs to the technical field of photovoltaic power generation. Comprising the following steps: based on the health state of a single battery, establishing a lithium ion battery life model by a data driving method; calculating an SOC change interval of the lithium battery energy storage array and an SOC change interval of flywheel energy storage according to capacity limit and charge and discharge electric quantity of the energy storage array; filtering and optimizing a high-frequency power signal and a low-frequency power signal which are obtained by primary decomposition of the VMD by using a particle swarm algorithm, and obtaining a high-frequency and low-frequency demarcation point corresponding to a global optimal solution based on an SOC variation interval of flywheel energy storage; redistributing a power instruction of the lithium battery energy storage array and a power instruction of flywheel energy storage according to a high-low frequency demarcation point corresponding to the global optimal solution; and calculating the charge and discharge times of the lithium battery according to the optimized charge and discharge power instruction based on the lithium battery life model.

Description

Coordinated control method and system for hybrid energy storage system for photovoltaic electric field

Technical Field

The present invention relates to the technical field of photovoltaic power generation, and in particular to a method and system for coordinated control of a hybrid energy storage system for a photovoltaic electric field.

Background technique

In recent years, my country has vigorously developed renewable energy generation technology, and solar photovoltaic power generation technology is one of the distributed clean energy sources that has received much attention and promotion. However, photovoltaic power generation is affected by natural conditions such as light and temperature, and its output power has certain volatility and intermittency. The large-scale grid connection of photovoltaic power generation poses severe challenges to the safe and stable operation of the power grid. Therefore, the grid connection power fluctuation of photovoltaic power generation must be limited within a certain range.

Energy storage systems are used to smooth out power fluctuations in photovoltaic power generation grid-connected due to their fast response speed and charge-discharge characteristics. According to the time scale of energy storage to provide energy, energy storage can be divided into energy-type energy storage and power-type energy storage. Energy-type energy storage has a long discharge time and a high energy density, but a short cycle life. Typical energy-type energy storage includes lithium batteries and lead-acid batteries. Power-type energy storage has a short power density and continuous discharge time, and has the characteristics of high power density and long cycle life, such as supercapacitor energy storage and flywheel energy storage. Energy-type energy storage and power-type energy storage have good complementarity in terms of energy density and power density, and the hybrid energy storage system they constitute has high economy.

The traditional hybrid energy storage system allocates low-frequency power signals to energy-type energy storage arrays and high-frequency signals to power-type energy storage arrays based on the response time of the energy storage arrays. However, as the number of times the lithium-ion battery works increases, irreversible physical and chemical processes will occur inside it, forming a solid electrolyte intermediate phase. This phenomenon is called battery degradation. Battery degradation will lead to a series of safety and reliability issues such as reduced battery performance and shortened battery life. Since the flywheel has a long service life and is generally not replaced during use, the cost of replacing lithium-ion batteries becomes the largest expense of hybrid energy storage power stations in addition to the one-time investment. Therefore, the coordinated control of the hybrid energy storage system is of great significance to extending the service life of lithium batteries and reducing system maintenance costs.

For hybrid energy storage systems composed of energy-type and power-type energy storage, domestic and foreign scholars have conducted relevant research on how to formulate effective coordinated control strategies based on their own characteristics while ensuring comprehensive performance. The paper "Unified Energy Control Strategy for Independent Photovoltaic Power Generation System" proposes an energy coordinated control strategy with photovoltaic cell working point optimization function, which uniformly controls the battery, photovoltaic cell and load, and simplifies the control algorithm; the paper "Coordinated Control of Independent DC Microgrid with Automatic Load Power Distribution" proposes a coordinated control strategy with automatic load power distribution, using multiple groups of small-capacity energy storage units to balance distributed power sources and load power to control bus voltage stability. The paper "Model predictive control and improved low-pass filtering strategies based on wind power fluctuation mitigation [J]. Journal of modern power systems and clean energy" adopts model predictive control and uses its advance prediction and priority control characteristics to effectively solve the problem of excessive smoothing in the energy storage compensation process. The paper "Coordinated Control of Hybrid Energy Storage System Based on Variational Mode Decomposition" adopts a hierarchical control strategy to distribute energy storage response instructions and realize the optimization of energy storage system regulation characteristics and long-term stable operation of energy storage units. Different scholars have made achievements in smoothing out the fluctuations of new energy, but no in-depth research has been conducted on how to extend the service life of lithium battery energy storage.

Summary of the invention

In view of this, the present invention provides a coordinated control method and system for a hybrid energy storage system for a photovoltaic field, which can enable the hybrid energy storage system to smooth out the power fluctuations of the photovoltaic output function while extending the service life of the lithium battery and improving the overall economic efficiency of the hybrid energy storage system.

The technical solution adopted by the embodiment of the present invention to solve the technical problem is:

A coordinated control method for a hybrid energy storage system for a photovoltaic electric field, comprising:

Step S1, establishing a lithium-ion battery life model through a data-driven method based on the health status of a single battery;

Step S2, according to the capacity limit and charge and discharge capacity of the energy storage array, simulate and calculate the SOC change range of the lithium battery energy storage array and the SOC change range of the flywheel energy storage;

Step S3, using a particle swarm algorithm to filter and optimize the high-frequency power signal and the low-frequency power signal obtained by the preliminary decomposition of VMD, and obtaining the high-frequency and low-frequency dividing points corresponding to the global optimal solution based on the SOC variation range of the flywheel energy storage;

Step S4, reallocating the power instructions of the lithium battery energy storage array and the flywheel energy storage according to the high and low frequency demarcation points corresponding to the global optimal solution of step S3;

Step S5, based on the lithium battery life model, the number of times the lithium battery is charged and discharged is calculated according to the optimized charge and discharge power instructions.

Preferably, the step S1 comprises:

According to the single battery health status SOH definition formula considering the initial rated capacity and the current rated capacity, the SOH definition formula considering the internal resistance, and the SOH definition formula considering the battery cycle number, a curve relationship diagram between the lithium battery cycle number and the SOH is fitted;

Among them, the definition of the health status SOH of a single battery considering the initial rated capacity and the current rated capacity is:

Where _Ct is the current rated capacity of the energy storage unit; _C0 is the initial rated capacity;

The definition of SOH considering internal resistance is:

Where, R _EOL is the internal resistance value at the end of the battery life, _Ra is the current internal resistance value, and _Rr is the specified internal resistance value of the battery.

The definition of SOH considering the number of battery cycles is:

Where N _rem is the current cycle number, and N _tol is the total cycle number.

Preferably, the step S2 comprises:

Step S21, performing VMD decomposition on the photovoltaic electric field output power to obtain the initial charge and discharge power instructions of the lithium battery energy storage array and the initial charge and discharge power instructions of the flywheel energy storage;

Step S22, calculating the SOC variation range of the lithium battery energy storage array and the SOC variation range of the flywheel energy storage according to each of the initial charge and discharge power instructions, wherein the calculation formula for the charge and discharge power E _H of the energy storage array is:

Wherein, n＝[1,2,…M], M is the number of sampled data, P _H [n] is the charge and discharge power instruction of the energy storage array, η _d is the discharge efficiency, η _c is the charge efficiency, f _s is the sampling frequency, SOC _ref is the initial SOC of the energy storage array, E _rated,h is the configured energy storage array power; SOC _H [n] is the current state of charge of the energy storage array;

Step S23: Correspondingly, the variation range of the SOC of the energy storage array is: [min(SOC _H [n]), max(SOC _H [n])].

Preferably, the step S3 comprises:

Step S31, according to the initial high-low frequency dividing point j, set the initial node X and the number of iterations, let (x ₁ , x ₂ , ... x _N ) represent the particle swarm corresponding to the high-low frequency dividing point, N is the user-defined particle swarm size, and _vi is the direction of particle update:

X＝{x ₁ ,x ₂ ,...,x _N }

V＝{v ₁ ,v ₂ ,...,v _N }

Step S32, calculating the SOC variation range [min(SOC _H [n]), max(SOC _H [n])] corresponding to the particle x _i (i=1, 2, ..., N) according to step S2;

Step S33, setting the SOC interval [0.1, 0.9] of the flywheel energy storage as the limiting boundary condition. If the SOC variation interval corresponding to the current particle is not included in the limiting boundary condition, the current particle is deleted, and the particle speed and position are updated, and finally a new particle group set is obtained;

Step S34, taking the particle √ closest to the restricted boundary condition in the new particle swarm set as the best global optimal solution, and correspondingly, the high-low frequency dividing point j* corresponding to the global optimal solution.

Preferably, the step S4 comprises:

Step S41, reconstructing the high-frequency signal and the low-frequency signal using the high- and low-frequency dividing point j*;

Step S42, using the reconstructed low-frequency signal as the optimized charge and discharge power instruction of the lithium battery energy storage array, and the high-frequency signal as the optimized charge and discharge power instruction of the flywheel energy storage, to achieve optimized distribution of power instructions for each array;

The step S5 comprises:

The new charge and discharge power instruction is input into the lithium battery life model, and the number of charge and discharge cycles is obtained according to the curve relationship diagram of the lithium battery cycle number and SOH.

Furthermore, the present invention provides a hybrid energy storage system coordination control system for a photovoltaic electric field, comprising:

Establish a module to build a lithium-ion battery life model based on the health status of individual batteries through a data-driven approach;

The calculation module simulates and calculates the SOC variation range of the lithium battery energy storage array and the SOC variation range of the flywheel energy storage according to the capacity limit and charge and discharge power of the energy storage array;

The particle optimization module uses the particle swarm algorithm to filter and optimize the high-frequency power signal and low-frequency power signal obtained by the preliminary decomposition of VMD, and obtains the high-frequency and low-frequency dividing points corresponding to the global optimal solution based on the SOC change range of the flywheel energy storage;

A distribution module, which redistributes the power instructions of the lithium battery energy storage array and the flywheel energy storage according to the high- and low-frequency demarcation points corresponding to the global optimal solution of step S3;

The calculation module calculates the number of times the lithium battery is charged and discharged based on the lithium battery life model and the optimized charge and discharge power instructions.

Preferably, the establishment module:

According to the single battery health status SOH definition formula considering the initial rated capacity and the current rated capacity, the SOH definition formula considering the internal resistance, and the SOH definition formula considering the battery cycle number, a curve relationship diagram between the lithium battery cycle number and the SOH is fitted;

Among them, the definition of the health status SOH of a single battery considering the initial rated capacity and the current rated capacity is:

Where _Ct is the current rated capacity of the energy storage unit; _C0 is the initial rated capacity;

The definition of SOH considering internal resistance is:

Where, R _EOL is the internal resistance value at the end of the battery life, _Ra is the current internal resistance value, and _Rr is the specified internal resistance value of the battery.

The definition of SOH considering the number of battery cycles is:

Where N _rem is the current cycle number, and N _tol is the total cycle number.

Preferably, the calculation module:

Used to perform VMD decomposition on the output power of the photovoltaic electric field to obtain the initial charge and discharge power instructions of the lithium battery energy storage array and the initial charge and discharge power instructions of the flywheel energy storage;

The SOC variation range of the lithium battery energy storage array and the SOC variation range of the flywheel energy storage are calculated according to each of the initial charge and discharge power instructions, wherein the calculation formula of the energy storage array charge and discharge power E _H is:

Wherein, n = [1, 2, ... M], M is the number of sampled data, P _H [n] is the charge and discharge power instruction of the energy storage array, η _d is the discharge efficiency, η _c is the charge efficiency, f _s is the sampling frequency, SOC _ref is the initial SOC of the energy storage array, E _rated,h is the configured energy storage array power; SOC _H [n] is the current state of charge of the energy storage array;

Correspondingly, the variation range of the SOC of the energy storage array is: [min(SOC _H [n]), max(SOC _H [n])].

Preferably, the particle optimization module:

According to the initial high-low frequency dividing point j, set the initial node X and the number of iterations, let (x ₁ ,x ₂ ,...x _N ) represent the particle swarm corresponding to the high-low frequency dividing point, N is the custom particle swarm size, and vi _is the direction of particle update:

X＝{x ₁ ,x ₂ ,...,x _N }

V＝{v ₁ ,v ₂ ,...,v _N }

Calculate the SOC variation range [min(SOC _H [n]), max(SOC _H [n])] corresponding to the particle x _i (i=1, 2, ..., N) according to the formula of the calculation unit;

The SOC interval [0.1, 0.9] of the flywheel energy storage is set as the limiting boundary condition. If the SOC variation interval corresponding to the current particle is not included in the limiting boundary condition, the current particle is deleted, and the particle speed and position are updated, and finally a new particle swarm set is obtained;

The particle √ closest to the restricted boundary condition in the new particle swarm set is taken as the best global optimal solution, and accordingly, the high-low frequency dividing point j* corresponding to the global optimal solution.

Preferably, the allocation module is used to reconstruct the high-frequency signal and the low-frequency signal with the high- and low-frequency dividing point j*; the reconstructed low-frequency signal is used as the optimized charge and discharge power instruction of the lithium battery energy storage array, and the high-frequency signal is used as the optimized charge and discharge power instruction of the flywheel energy storage, so as to realize the optimized allocation of power instructions of each array;

The calculation module is used to input the new charge and discharge power instruction into the lithium battery life model, and obtain the number of charge and discharge cycles according to the curve relationship diagram of the lithium battery cycle number and SOH.

It can be seen from the above technical scheme that the coordinated control method and system of the hybrid energy storage system for photovoltaic electric fields provided by the embodiment of the present invention establishes a lithium-ion battery life model based on the health status of a single battery. Then, according to the capacity limit and charge and discharge power of the energy storage array, the SOC change range of each energy storage array is calculated. Then, for the high and low frequency power nodes obtained by the preliminary decomposition of VMD, the particle swarm algorithm is used to filter and optimize, and based on the SOC change range of the flywheel array, the minimum output component of the lithium-ion battery energy storage array response is calculated to obtain the optimal value of the flywheel and lithium battery power boundary node. Finally, based on the lithium battery life model, the number of lithium battery charge and discharge times is calculated, and the power instructions of each energy storage array are redistributed after obtaining the global optimal solution. Through the scheme of the present invention, the hybrid energy storage system can smooth the power fluctuations of the photovoltaic output function while extending the service life of the lithium battery and improving the coordinated control strategy of the overall economy of the hybrid energy storage system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a coordinated control method of a hybrid energy storage system for a photovoltaic electric field according to the present invention.

FIG2 is a fitting curve diagram of the relationship between the number of cycles and SOH of a lithium-ion battery.

Figure 3 is a flow chart of the coordinated control strategy of the hybrid energy storage system.

Figure 4 shows the original output curve and grid-connected power curve of a photovoltaic field for 15 hours.

Figure 5 shows the original power fluctuation curve of a photovoltaic field for 15 hours and the power fluctuation curve after 1 minute of smoothing.

Figure 6 is a comparison of the SOC of the flywheel energy storage array before and after the coordinated control strategy.

Figure 7 is a graph showing the SOC changes of the lithium-ion battery energy storage array before and after the coordinated control strategy.

Detailed ways

The technical scheme and technical effects of the present invention are further elaborated in detail below in conjunction with the accompanying drawings of the present invention.

The present invention is based on the expected grid-connected power of the photovoltaic field and aims to reduce the number of times the lithium battery is charged and discharged. It proposes to establish a lithium-ion battery life model based on the state of health (SOH) of a single battery. Then, based on the high and low frequency power signals obtained by the preliminary decomposition of VMD, the particle swarm filtering algorithm is used to obtain the minimum output component of the lithium-ion battery energy storage array response under the SOC range that ensures the good operation of the flywheel energy storage array, and finally the power of the lithium battery and the flywheel that meet the optimal flywheel SOC range after reconstruction is obtained.

As shown in FIG1 , the present invention provides a method for coordinated control of a hybrid energy storage system for a photovoltaic field, comprising the following steps:

Step S1, establishing a lithium-ion battery life model through a data-driven method based on the health status of a single battery;

Step S2, according to the capacity limit and charge and discharge capacity of the energy storage array, simulate and calculate the SOC change range of the lithium battery energy storage array and the SOC change range of the flywheel energy storage;

Step S3, using a particle swarm algorithm to filter and optimize the high-frequency power signal and the low-frequency power signal obtained by the preliminary decomposition of VMD, and obtaining the high-frequency and low-frequency dividing points corresponding to the global optimal solution based on the SOC variation range of the flywheel energy storage;

Step S4, reallocating the power instructions of the lithium battery energy storage array and the flywheel energy storage according to the high and low frequency demarcation points corresponding to the global optimal solution of step S3;

Step S5, based on the lithium battery life model, the number of times the lithium battery is charged and discharged is calculated according to the optimized charge and discharge power instructions.

In a specific implementation, step S1 is based on the health status of a single battery, and the process of establishing a lithium-ion battery life model through a data-driven method includes:

According to the single battery health status SOH definition formula considering the initial rated capacity and the current rated capacity, the SOH definition formula considering the internal resistance, and the SOH definition formula considering the battery cycle number, the curve relationship between the number of lithium battery cycles and SOH is fitted as shown in Figure 2;

Among them, the definition of the health status SOH of a single battery considering the initial rated capacity and the current rated capacity is:

Where _Ct is the current rated capacity of the energy storage unit; _C0 is the initial rated capacity;

The definition of SOH considering internal resistance is:

Where, R _EOL is the internal resistance value at the end of battery life, which is obtained through simulation test, Ra _is the current internal resistance value, which is obtained through simulation test, and _Rr is the specified internal resistance value of the battery.

The definition of SOH considering the number of battery cycles is:

Where N _rem is the current cycle number, and N _tol is the total cycle number.

Generally speaking, the utilization rate of lithium-ion batteries is divided into four levels: the first level is used in electric devices such as new energy vehicles, requiring a SOH range of 100%-80%; the second level (SOH range of 80%-50%) is often used in power grids and new energy storage devices; the third level (SOH range of 50%-40%) is used for low-end users; the fourth level (SOH below 40%) disassembles and recycles the battery. It can be concluded that the new energy field energy storage battery needs to be replaced when the SOH is 60%, and the battery cycle life is about 3200 times.

In a specific implementation, the step S2 of calculating the SOC variation interval of the lithium battery energy storage array and the SOC variation interval of the flywheel energy storage includes:

Step S21, performing VMD decomposition on the photovoltaic electric field output power (time series data) to obtain the initial charge and discharge power instructions of the lithium battery energy storage array and the initial charge and discharge power instructions of the flywheel energy storage;

Step S22, calculating the SOC variation range of the lithium battery energy storage array and the SOC variation range of the flywheel energy storage according to each initial charge and discharge power instruction, wherein the calculation formula of the energy storage array charge and discharge power E _H is:

Wherein, n = [1, 2, ... M], M is the number of sampled data, P _H [n] is the charge and discharge power instruction of the energy storage array, η _d is the discharge efficiency, η _c is the charge efficiency, f _s is the sampling frequency, SOC _ref is the initial SOC of the energy storage array, E _rated,h is the configured energy storage array power; SOC _H [n] is the current state of charge of the energy storage array;

Step S23: Correspondingly, the variation range of the SOC of the energy storage array is expressed as: [min(SOC _H [n]), max(SOC _H [n])].

In order to extend the life of lithium batteries, the particle swarm algorithm is used to optimize and screen the high and low frequency power signal decomposition nodes obtained by the initial decomposition of VMD. The particle swarm filtering algorithm is used to obtain the minimum output component of the lithium-ion battery energy storage array response under the SOC range that ensures the good operation of the flywheel energy storage array, thereby reducing the number of lithium-ion battery charge and discharge times and thus extending the life. In the specific implementation, the process of particle optimization in step S3 includes:

Step S31, according to the initial high-low frequency dividing point j, set the initial node X and the number of iterations, let (x ₁ ,x ₂ ,...x _N ) represent the particle swarm corresponding to the high-low frequency dividing point, N is the user-defined particle swarm size, and _vi is the direction of particle update, as shown in equations (6) and (7):

X＝{x ₁ ,x ₂ ,...,x _N } (6)

V＝{v ₁ ,v ₂ ,...,v _N (7)

Step S32, calculating the SOC variation range [min(SOC _H [n]), max(SOC _H [n])] corresponding to the particle x _i (i=1, 2, ..., N) according to the formula of step S2;

Step S33, setting the SOC interval [0.1, 0.9] of the flywheel energy storage as the limiting boundary condition. If the SOC variation interval corresponding to the current particle is not included in the limiting boundary condition, the current particle is deleted, and the particle speed and position are updated, and finally a new particle group set is obtained;

Step S34, taking the particle closest to the restricted boundary condition in the new particle swarm set as the best global optimal solution, obtaining the minimum output component of the lithium-ion battery energy storage array response, and correspondingly, the high-low frequency dividing point j* corresponding to the global optimal solution.

Furthermore, the high-frequency signal and the low-frequency signal are reconstructed using the high- and low-frequency dividing point j*; the reconstructed low-frequency signal is used as the optimized charge and discharge power instruction of the lithium battery energy storage array, and the high-frequency signal is used as the optimized charge and discharge power instruction of the flywheel energy storage, thereby realizing the optimized distribution of power instructions for each array.

Finally, the new charge and discharge power command is input into the lithium battery life model, and the number of charge and discharge cycles is obtained according to the curve relationship between the number of lithium battery cycles and SOH.

Knowing the number of charge and discharge cycles of a lithium battery can give you an intuitive understanding of the usage of the lithium battery and whether it needs to be replaced to ensure better energy storage and charging and discharging effects.

Taking the 15h power output curve of a 15MW photovoltaic field after data preprocessing as an example, the coordinated control strategy is verified. The coordinated control strategy process of the hybrid energy storage system is shown in Figure 3. The lithium-ion battery energy storage system is selected as the energy-type energy storage system, and the flywheel energy storage system is selected as the power-type energy storage system.

The data of 15 hours of a typical day with obvious light intensity in the output power of a photovoltaic electric field with an installed capacity of 15MW in a certain area is selected as the research object. Its original output power is shown in Figure 4, and the power output fluctuation of 1 minute in this time period is shown in the curve in Figure 5. Under normal circumstances, the fluctuation rate should not exceed 1/10 of the installed capacity within 1 minute, and the maximum fluctuation should not exceed the critical line of 1.5MW to meet the standard grid connection requirements of the photovoltaic electric field access to the power system. As shown in Figure 5, the fluctuation exceeds the critical line value by a lot, which is far greater than the grid connection requirement range and does not meet the grid connection requirements. Therefore, the output of the photovoltaic electric field must be smoothed to improve the grid connection quality. The variational modal decomposition of the photovoltaic electric field output power signal is performed to obtain the overall charging and discharging command of the hybrid energy storage system.

According to the charging and discharging response time of different types of energy storage systems, the low-frequency and high-frequency signal decomposition frequencies are selected, the boundary node value j between low-frequency and high-frequency power is determined, and the charging and discharging power instructions of different types of energy storage systems are allocated.

This paper selects 0.01Hz as the cutoff frequency of lithium-ion batteries. Based on the particle swarm algorithm, the power command is secondary distributed. The initial demarcation point j=7 is subjected to particle swarm filtering, and the optimal high-low frequency demarcation point j*=5 is obtained after particle filtering. The SOC of the energy storage array before and after particle filtering is compared, as shown in Figures 6 and 7.

Since the life of the flywheel energy storage system is as long as 20 years, its service life is generally not calculated. As shown in Figure 7, after adopting the proposed control strategy, the SOC change frequency and amplitude of the lithium-ion battery energy storage array are less than before the control strategy. Based on the lithium-ion battery life model, it is calculated that the original number of daily charge and discharge cycles of the lithium-ion battery energy storage array is 5.22 times; after adopting the proposed control strategy, the number of daily charge and discharge cycles is 3.06 times, a daily average reduction of 41.37%.

Furthermore, the present invention provides a hybrid energy storage system coordination control system for a photovoltaic electric field, which is used to implement the method shown in FIG1 , and the system includes:

Establish a module to build a lithium-ion battery life model based on the health status of individual batteries through a data-driven approach;

The calculation module simulates and calculates the SOC variation range of the lithium battery energy storage array and the SOC variation range of the flywheel energy storage according to the capacity limit and charge and discharge power of the energy storage array;

The particle optimization module uses the particle swarm algorithm to filter and optimize the high-frequency power signal and low-frequency power signal obtained by the preliminary decomposition of VMD, and obtains the high-frequency and low-frequency dividing points corresponding to the global optimal solution based on the SOC change range of the flywheel energy storage;

The allocation module reallocates the power instructions of the lithium battery energy storage array and the flywheel energy storage according to the high- and low-frequency dividing points corresponding to the global optimal solution of step S3; specifically, the high-frequency signal and the low-frequency signal are reconstructed based on the high- and low-frequency dividing point j*; the reconstructed low-frequency signal is used as the optimized charge and discharge power instruction of the lithium battery energy storage array, and the high-frequency signal is used as the optimized charge and discharge power instruction of the flywheel energy storage, so as to realize the optimized allocation of the power instructions of each array;

The calculation module calculates the number of charge and discharge times of the lithium battery based on the lithium battery life model according to the optimized charge and discharge power instructions; specifically, the new charge and discharge power instructions are input into the lithium battery life model, and the number of charge and discharge cycles is obtained according to the curve relationship diagram of the lithium battery cycle number and SOH.

Build modules for:

According to the single battery health state SOH definition formula considering the initial rated capacity and the current rated capacity, the SOH definition formula considering the internal resistance, and the SOH definition formula considering the battery cycle number, a curve relationship diagram between the number of cycles of the lithium battery and the SOH is fitted; wherein, the single battery health state SOH definition formula considering the initial rated capacity and the current rated capacity is the aforementioned formula (1), the SOH definition formula considering the internal resistance is the aforementioned formula (2), and the SOH definition formula considering the battery cycle number is the aforementioned formula (3);

The computing module is used to:

Perform VMD decomposition on the photovoltaic electric field output power to obtain the initial charge and discharge power instructions of the lithium battery energy storage array and the initial charge and discharge power instructions of the flywheel energy storage;

The SOC variation range of the lithium battery energy storage array and the SOC variation range of the flywheel energy storage are calculated according to each initial charge and discharge power instruction, wherein the calculation formula for the charge and discharge power E _H of the energy storage array refers to the aforementioned formulas (4) and (5); accordingly, the variation range of the SOC of the energy storage array is: [min(SOC _H [n]), max(SOC _H [n])].

The particle optimization module is specifically used for:

According to the initial high-low frequency dividing point j, set the initial node X and the number of iterations, let (x ₁ ,x ₂ ,...x _N ) represent the particle swarm corresponding to the high-low frequency dividing point, N is the custom particle swarm size, and vi _is the direction of particle update:

X＝{x ₁ ,x ₂ ,...,x _N }

V＝{v ₁ ,v ₂ ,...,v _N }

According to the formula of the calculation unit, the SOC variation range [min(SOC _H [n]), max(SOC _H [n])] corresponding to the particle x _i (i=1, 2, ..., N) is calculated;

Set the SOC interval [0.1, 0.9] of the flywheel energy storage as the limiting boundary condition. If the SOC change interval corresponding to the current particle is not included in the limiting boundary condition, delete the current particle, and update the particle speed and position, and finally obtain a new particle swarm set;

The particle √ closest to the restricted boundary condition in the new particle swarm set is taken as the best global optimal solution, and accordingly, the high- and low-frequency dividing point j* corresponding to the global optimal solution.

The workflow of the particle optimization module can refer to the process shown in Figure 3:

Step 1, input the initial high and low frequency dividing point j;

Step 2, calculate whether the flywheel SOC is optimal, if so, jump to step 6; if not, jump to step 3;

Step 3, perform particle swarm filtering optimization;

Step 4, evaluate the function fitness value of the node;

Step 5, update the optimal value of the node and return to step 2;

Step 6, reallocate the flywheel and lithium battery energy storage array instructions to reduce the number of charge and discharge times of the latter;

Step 7, calculating the number of charge and discharge cycles of the lithium battery energy storage array based on the lithium battery life model;

Step 8, end.

The present invention establishes a lithium-ion battery life model based on the health status of a single battery. Then, according to the capacity limit and charge and discharge power of the energy storage array, the SOC variation range of each energy storage array is calculated. Then, for the high and low frequency power nodes obtained by the preliminary decomposition of VMD, the particle swarm algorithm is used to filter and optimize, and based on the SOC variation range of the flywheel array, the minimum output component of the lithium-ion battery energy storage array response is calculated to obtain the optimal value of the flywheel and lithium battery power boundary node. Finally, based on the lithium battery life model, the number of lithium battery charge and discharge times is calculated, and the power instructions of each energy storage array are redistributed after obtaining the global optimal solution. Through the scheme of the present invention, the hybrid energy storage system can smooth the power fluctuations of the photovoltaic output function while extending the service life of the lithium battery and improving the overall economic efficiency of the hybrid energy storage system. Coordinated control strategy.

The above disclosure is only a preferred embodiment of the present invention, which certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiments and equivalent changes made according to the claims of the present invention are still within the scope of the invention.

Claims (10)

1. A method for coordinated control of a hybrid energy storage system for a photovoltaic field, comprising: Step S1, establishing a lithium-ion battery life model through a data-driven method based on the health status of a single battery; Step S2, according to the capacity limit and charge and discharge capacity of the energy storage array, simulate and calculate the SOC change range of the lithium battery energy storage array and the SOC change range of the flywheel energy storage; Step S3, using a particle swarm algorithm to filter and optimize the high-frequency power signal and the low-frequency power signal obtained by the preliminary decomposition of VMD, and obtaining the high-frequency and low-frequency dividing points corresponding to the global optimal solution based on the SOC variation range of the flywheel energy storage; Step S4, reallocating the power instructions of the lithium battery energy storage array and the flywheel energy storage according to the high and low frequency demarcation points corresponding to the global optimal solution of step S3; Step S5, based on the lithium battery life model, the number of times the lithium battery is charged and discharged is calculated according to the optimized charge and discharge power instructions. 2. The method for coordinated control of a hybrid energy storage system for a photovoltaic field according to claim 1, wherein step S1 comprises: According to the single battery health status SOH definition formula considering the initial rated capacity and the current rated capacity, the SOH definition formula considering the internal resistance, and the SOH definition formula considering the battery cycle number, a curve relationship diagram between the lithium battery cycle number and the SOH is fitted; Among them, the definition of the health status SOH of a single battery considering the initial rated capacity and the current rated capacity is: Where _Ct is the current rated capacity of the energy storage unit; _C0 is the initial rated capacity; The definition of SOH considering internal resistance is: Where, R _EOL is the internal resistance value at the end of the battery life, _Ra is the current internal resistance value, and _Rr is the specified internal resistance value of the battery. The definition of SOH considering the number of battery cycles is: Where N _rem is the current cycle number, and N _tol is the total cycle number. 3. The method for coordinated control of a hybrid energy storage system for a photovoltaic field according to claim 2, wherein step S2 comprises: Step S21, performing VMD decomposition on the photovoltaic electric field output power to obtain the initial charge and discharge power instructions of the lithium battery energy storage array and the initial charge and discharge power instructions of the flywheel energy storage; Step S22, calculating the SOC variation range of the lithium battery energy storage array and the SOC variation range of the flywheel energy storage according to each of the initial charge and discharge power instructions, wherein the calculation formula for the charge and discharge power E _H of the energy storage array is: Wherein, n＝[1,2,…M], M is the number of sampled data, P _H [n] is the charge and discharge power instruction of the energy storage array, η _d is the discharge efficiency, η _c is the charge efficiency, f _s is the sampling frequency, SOC _ref is the initial SOC of the energy storage array, E _rated,h is the configured energy storage array power; SOC _H [n] is the current state of charge of the energy storage array; Step S23: Correspondingly, the variation range of the SOC of the energy storage array is: [min(SOC _H [n]), max(SOC _H [n])]. 4. The method for coordinated control of a hybrid energy storage system for a photovoltaic field according to claim 3, wherein step S3 comprises: Step S31, according to the initial high-low frequency dividing point j, set the initial node X and the number of iterations, let (x ₁ , x ₂ , ... x _N ) represent the particle swarm corresponding to the high-low frequency dividing point, N is the user-defined particle swarm size, and _vi is the direction of particle update: X＝{x ₁ ,x ₂ ,...,x _N } V＝{v ₁ ,v ₂ ,...,v _N } Step S32, calculating the SOC variation interval [min(SOC _H [n]), max(SOC _H [n])] corresponding to the particle xi (i=1, 2, ..., N) according to step S2; Step S33, setting the SOC interval [0.1, 0.9] of the flywheel energy storage as the limiting boundary condition. If the SOC variation interval corresponding to the current particle is not included in the limiting boundary condition, the current particle is deleted, and the particle speed and position are updated, and finally a new particle group set is obtained; Step S34, taking the particle √ closest to the restricted boundary condition in the new particle swarm set as the best global optimal solution, and correspondingly, the high-low frequency dividing point j* corresponding to the global optimal solution. 5. The method for coordinated control of a hybrid energy storage system for a photovoltaic electric field according to claim 4, characterized in that: The step S4 comprises: Step S41, reconstructing the high-frequency signal and the low-frequency signal using the high- and low-frequency dividing point j*; Step S42, using the reconstructed low-frequency signal as the optimized charge and discharge power instruction of the lithium battery energy storage array, and the high-frequency signal as the optimized charge and discharge power instruction of the flywheel energy storage, to achieve optimized distribution of power instructions for each array; The step S5 comprises: The new charge and discharge power instruction is input into the lithium battery life model, and the number of charge and discharge cycles is obtained according to the curve relationship diagram of the lithium battery cycle number and SOH. 6. A hybrid energy storage system coordination control system for a photovoltaic field, characterized by comprising: Establish a module to build a lithium-ion battery life model based on the health status of individual batteries through a data-driven approach; The calculation module simulates and calculates the SOC variation range of the lithium battery energy storage array and the SOC variation range of the flywheel energy storage according to the capacity limit and charge and discharge power of the energy storage array; The particle optimization module uses the particle swarm algorithm to filter and optimize the high-frequency power signal and low-frequency power signal obtained by the preliminary decomposition of VMD, and obtains the high-frequency and low-frequency dividing points corresponding to the global optimal solution based on the SOC change range of the flywheel energy storage; A distribution module, which redistributes the power instructions of the lithium battery energy storage array and the flywheel energy storage according to the high- and low-frequency demarcation points corresponding to the global optimal solution of step S3; The calculation module calculates the number of times the lithium battery is charged and discharged based on the lithium battery life model and the optimized charge and discharge power instructions. 7. The hybrid energy storage system coordination control system for a photovoltaic electric field according to claim 6, wherein the establishment module is used to: According to the single battery health status SOH definition formula considering the initial rated capacity and the current rated capacity, the SOH definition formula considering the internal resistance, and the SOH definition formula considering the battery cycle number, a curve relationship diagram between the lithium battery cycle number and the SOH is fitted; Among them, the definition of the health status SOH of a single battery considering the initial rated capacity and the current rated capacity is: Where _Ct is the current rated capacity of the energy storage unit; _C0 is the initial rated capacity; The definition of SOH considering internal resistance is: Where, R _EOL is the internal resistance value at the end of the battery life, _Ra is the current internal resistance value, and _Rr is the specified internal resistance value of the battery. The definition of SOH considering the number of battery cycles is: Where N _rem is the current cycle number, and N _tol is the total cycle number. 8. The hybrid energy storage system coordination control system for a photovoltaic electric field according to claim 7, wherein the calculation module is used for: Perform VMD decomposition on the photovoltaic electric field output power to obtain the initial charge and discharge power instructions of the lithium battery energy storage array and the initial charge and discharge power instructions of the flywheel energy storage; The SOC variation range of the lithium battery energy storage array and the SOC variation range of the flywheel energy storage are calculated according to each of the initial charge and discharge power instructions, wherein the calculation formula of the energy storage array charge and discharge power E _H is: Wherein, n＝[1,2,…M], M is the number of sampled data, P _H [n] is the charge and discharge power instruction of the energy storage array, η _d is the discharge efficiency, η _c is the charge efficiency, f _s is the sampling frequency, SOC _ref is the initial SOC of the energy storage array, E _rated,h is the configured energy storage array power; SOC _H [n] is the current state of charge of the energy storage array; Correspondingly, the variation range of the SOC of the energy storage array is: [min(SOC _H [n]), max(SOC _H [n])]. 9. The hybrid energy storage system coordinated control system for a photovoltaic electric field according to claim 8, characterized in that the particle optimization module is used to: According to the initial high-low frequency dividing point j, set the initial node X and the number of iterations, let (x ₁ ,x ₂ ,...x _N ) represent the particle swarm corresponding to the high-low frequency dividing point, N is the custom particle swarm size, and vi _is the direction of particle update: X＝{x ₁ ,x ₂ ,...,x _N } V＝{v ₁ ,v ₂ ,...,v _N } Calculate the SOC variation range [min(SOC _H [n]), max(SOC _H [n])] corresponding to the particle x _i (i=1, 2, ..., N) according to the formula of the calculation unit; The SOC interval [0.1, 0.9] of the flywheel energy storage is set as the limiting boundary condition. If the SOC variation interval corresponding to the current particle is not included in the limiting boundary condition, the current particle is deleted, and the particle speed and position are updated, and finally a new particle swarm set is obtained; The particle √ closest to the restricted boundary condition in the new particle swarm set is taken as the best global optimal solution, and accordingly, the high-low frequency dividing point j* corresponding to the global optimal solution. 10. The hybrid energy storage system coordination control system for a photovoltaic electric field according to claim 9, characterized in that: The allocation module is used to reconstruct the high-frequency signal and the low-frequency signal with the high- and low-frequency dividing point j*; the reconstructed low-frequency signal is used as the optimized charge and discharge power instruction of the lithium battery energy storage array, and the high-frequency signal is used as the optimized charge and discharge power instruction of the flywheel energy storage, so as to realize the optimized allocation of power instructions of each array; The calculation module is used to input the new charge and discharge power instruction into the lithium battery life model, and obtain the number of charge and discharge cycles according to the curve relationship diagram of the lithium battery cycle number and SOH.