阅读说明：本技术 基于动态分组技术的集中式电池储能电站调频控制策略 (Centralized battery energy storage power station frequency modulation control strategy based on dynamic grouping technology ) 是由 余洋 王卜潇 于 2021-09-14 设计创作，主要内容包括：本发明公开了基于动态分组技术的集中式电池储能电站调频控制策略。它包括以下步骤：构建动态分组技术的评价指标体系,并设计自适应天牛须算法的适应度函数；构建自适应天牛须优化算法的自适应步长公式；应用自适应天牛须算法,优化动态分组技术的强制更新阈值；确定动态分组技术的自动更新周期；对电池单元进行分组,应用动态分组技术确定储能电站中三个电池组的调频功率指令,进而确定每个电池单元的调频指令,并使电池单元响应。本发明使用自适应天牛须优化算法搜索强制更新阈值,降低了响应结束后电池单元的荷电状态极差,提升了下一调度时段储能电站的可调度潜力；降低了储能系统的动作次数,提高了其运行的经济性。(The invention discloses a frequency modulation control strategy of a centralized battery energy storage power station based on a dynamic grouping technology. It comprises the following steps: constructing an evaluation index system of a dynamic grouping technology, and designing a fitness function of a self-adaptive longicorn whisker algorithm; constructing a self-adaptive step size formula of a self-adaptive longicorn whisker optimization algorithm; applying a self-adaptive longicorn algorithm to optimize a forced updating threshold value of a dynamic grouping technology; determining an automatic update period of a dynamic grouping technique; grouping the battery units, determining frequency modulation power instructions of three battery packs in the energy storage power station by using a dynamic grouping technology, further determining the frequency modulation instruction of each battery unit, and enabling the battery units to respond. The forced updating threshold is searched by using the self-adaptive longicorn whisker optimization algorithm, so that the extremely poor state of charge of the battery unit after the response is finished is reduced, and the schedulable potential of the energy storage power station in the next scheduling period is improved; the action times of the energy storage system are reduced, and the running economy of the energy storage system is improved.)

1. A centralized battery energy storage power station frequency modulation control strategy based on a dynamic grouping technology is characterized by comprising the following steps:

(1) constructing an evaluation index system of a dynamic grouping technology, and designing a fitness function according to the evaluation index system;

(2) determining the self-adaptive step length of the longicorn stigma search algorithm;

(3) determining a forced updating threshold value w of a dynamic grouping technology by combining a fitness function designed by the invention and applying a self-adaptive longicorn whisker search algorithm;

(4) determining an automatic updating period t of the dynamic grouping technology according to the AGC updating period;

(5) the method comprises the steps of dividing battery units in a centralized battery energy storage power station into 3 battery packs, determining frequency modulation power instructions of the 3 battery packs according to a dynamic grouping technology, further determining the frequency modulation instruction of each battery unit, and enabling the battery units to respond to respective instructions to complete a frequency modulation task.

2. The centralized battery energy storage power station frequency modulation control strategy based on the dynamic grouping technology as claimed in claim 1, wherein the evaluation index system in the step (1) is composed of 4 parts, namely, a standard deviation of a difference value between the output of the energy storage power station and an AGC instruction, an average value of battery unit state of charge extreme differences, a battery unit state of charge extreme difference at the response ending time and forced updating times; the forced updating times are used for representing the increment X of the dynamic grouping times caused by the fact that the state of charge of the battery unit is extremely different from or equal to the set forced updating threshold;

the fitness function designed is:

wherein f is₁Is the standard deviation, N, of the difference between the energy storage system output and the AGC command₁Is the number of AGC commands, s_iEnergy storage system output, y, for each AGC cycle_iExpecting a force value, f, for the AGC command₂Is the very poor average value of the state of charge of the cell, u_iFor extreme cell state of charge, f, at the end of each AGC cycle₃Extreme bad state of charge of the cell at the end of time, f₄The number of times of forced updating is X; alpha is alpha₁、α₂、α₃And alpha₄As weights, the fitness function weights α₁And alpha₂Values of 5 and 1500, respectively, alpha₃And alpha₄The determination method of (2) is as follows:

wherein the content of the first and second substances,in response to the battery unit state of charge being very bad at the end time, X is the forced updating times, and the extreme bad threshold value E at the end time₁And a forced update times threshold value E₂0.05 and 50, respectively.

3. The centralized battery energy storage power station frequency modulation control strategy based on the dynamic grouping technology as claimed in claim 1, wherein the adaptive step size of the adaptive longicorn whisker search algorithm in the step (2) is obtained by the following formula:

μ_n＝-arc tan[a(n+b)]+c (4)

wherein, mu_nFor the adaptive step size, n is the iteration number, and the values of the step size parameters a, b and c are respectively 0.16, 1.5 and 1.361.

4. The centralized battery energy storage power station frequency modulation control strategy based on the dynamic grouping technology as claimed in claim 1, wherein in the step (3), the adaptive longitudina whisker optimization search forced update threshold w is applied in combination with the dynamic grouping technology evaluation index system and the fitness function as claimed in claim 2.

5. The centralized battery energy storage power station frequency modulation control strategy based on the dynamic grouping technology as claimed in claim 1, wherein in the step (5), the dynamic grouping technology based on the adaptive longicorn whisker search algorithm is adopted to group the battery units in the energy storage power station, and in combination with the obtained forced update threshold value w, frequency modulation power instructions of 3 battery packs in the centralized energy storage power station are determined, so that the frequency modulation instruction of each battery unit is determined, and the battery units respond to the respective instructions; the battery cells are immediately regrouped in response satisfying the following equation:

wherein T is the running time of the centralized battery energy storage power station, T is the automatic updating period of the dynamic grouping technology, u_iFor extreme cell state of charge at the end of each AGC cycle, w is the forced update threshold of claim 4.

Technical Field

The invention relates to the field of power systems, in particular to a frequency modulation control strategy for a centralized battery energy storage power station.

Technical Field

By the end of 2020, the accumulated installed scale of the energy storage project which has been put into operation in China is 35.6GW, which accounts for 18.6% of the total scale of the global market, and the increase rate is 9.8% in year and 6.2% in 2019 year. Meanwhile, renewable energy sources such as wind power and photovoltaic are connected to the grid on a large scale, and intermittent and uncertain output characteristics of the renewable energy sources bring great pressure to frequency adjustment of a power system.

The lithium ion battery has the advantages of large capacity, high working voltage, wide allowable working temperature range, long cycle service life and the like, and is widely applied to frequency modulation of a power system. Meanwhile, the centralized energy storage power station is widely applied due to the advantages of centralized layout, strong controllability, good frequency modulation effect and the like. However, if the battery energy storage system does not adopt a reasonable control strategy, the battery unit can move irregularly, and the frequency modulation task is not finished easily. The occurrence of the dynamic grouping technology obviously reduces the action times of the battery units of the centralized battery energy storage power station. In order to reduce the action times of the energy storage unit and improve the schedulable potential of the energy storage unit, a reasonable energy storage control strategy needs to be designed to further improve the working effect of the dynamic grouping technology.

Disclosure of Invention

The invention aims to reduce the action times of the energy storage system and improve the running economy of the energy storage system; meanwhile, the charge state of the battery unit after the response is finished is reduced, and the schedulable potential of the energy storage power station in the next scheduling period is improved. The invention provides a frequency modulation control strategy of a centralized battery energy storage power station, which designs a dynamically grouped evaluation index, obtains a forced updating threshold value by applying a self-adaptive longicorn whisker optimization algorithm on the basis of the evaluation index, and finally verifies the effectiveness of the strategy through simulation.

The invention adopts the technical scheme that: a centralized battery energy storage power station frequency modulation control strategy based on a dynamic grouping technology comprises the following steps:

(1) constructing an evaluation index system of a dynamic grouping technology, and designing a fitness function according to the evaluation index system;

(2) determining the self-adaptive step length of the longicorn stigma search algorithm;

(3) determining a forced updating threshold value w of a dynamic grouping technology by combining a fitness function designed by the invention and applying a self-adaptive longicorn whisker search algorithm;

(4) determining an automatic updating period of the dynamic grouping technology according to the AGC updating period;

(5) the method comprises the steps of dividing battery units in a centralized battery energy storage power station into 3 battery packs, determining frequency modulation power instructions of the 3 battery packs according to a dynamic grouping technology, further determining the frequency modulation instruction of each battery unit, and enabling the battery units to respond to respective instructions to complete a frequency modulation task.

In the step (1), the steps of constructing the evaluation index system and the fitness function of the dynamic grouping technology are as follows:

1) the method comprises the following steps of constructing an evaluation index system which comprises 4 parts, namely standard deviation of difference values of output of an energy storage power station and an AGC instruction, average value of extreme differences of the states of charge of battery units, extreme differences of the states of charge of the battery units at the moment of response ending and forced updating times, wherein the forced updating times are used for representing the increase X of the dynamic grouping times caused by the fact that the extreme differences of the states of charge of the battery units are larger than a set forced updating threshold;

2) and determining a fitness function according to the evaluation index system:

wherein f is₁Is the standard deviation, N, of the difference between the energy storage system output and the AGC command₁Is the number of AGC commands, s_tEnergy storage system output, y, for each AGC cycle_tExpecting a force value, f, for the AGC command₂Is the very poor average value of the state of charge of the cell, u_tFor extreme cell state of charge, f, at the end of each AGC cycle₃Extreme bad state of charge of the cell at the end of time, f₄The number of times of forced updating is X; alpha is alpha₁、α₂、α₃And alpha₄As weights, the fitness function weights α₁And alpha₂Values of 5 and 1500, respectively, alpha₃And alpha₄The determination method of (2) is as follows:

wherein the content of the first and second substances,in response to the battery unit state of charge being very bad at the end time, X is the forced updating times, and the extreme bad threshold value E at the end time₁And a forced update times threshold value E₂0.05 and 50, respectively.

In the step (2), the self-adaptive step length determination method of the longicorn whisker search algorithm is as follows:

μ_n＝-arc tan[a(n+b)]+c (4)

wherein, mu_nFor the adaptive step size, n is the iteration number, and the values of the step size parameters a, b and c are respectively 0.16, 1.5 and 1.361.

In the step (3), the process of determining the forced update threshold w of the dynamic grouping technology by the adaptive longicorn whisker search algorithm is as follows:

1) initialization of parameters of a longicorn whisker algorithm:

setting an initial step size (maximum step size), maximum iteration times and step size parameters a, b and c;

2) determining the orientation of the longicorn and normalizing, namely:

in the formula: rands is a random function, and k is a space dimension;

3) initializing the position of the longhorn beetle, and selecting [ -1, 1]The random number between them is used as the initial solution set of the longicorn algorithm and the initial position of the longicorn, and it is stored in X_bestPerforming the following steps;

4) calculating the fitness function value of the longicorn initial position according to the formula (1), and storing the fitness function value in Y_bestPerforming the following steps;

5) updating the left and right positions of the longicorn whiskers according to the following formula (6):

in the formula: x is the number of_lnAnd x_rnRespectively representing the position coordinates, x, of the Tianniu left hair and the Tianniu right hair in the nth iteration_nThe centroid coordinate of the longicorn in the nth iteration is shown, and dis is the distance between two whiskers;

6) updating a solution set of a longicorn whisker algorithm:

according to the positions of the left and right longicorn whiskers, fitness function values f (x) of the left and right longicorn whiskers are respectively obtained by using the formula (1)_ln) And f (x)_rn) Comparing the intensity and updating the longicorn position according to the following formula (7), namely adjusting a forced updating threshold value w of the dynamic grouping technology and calculating a fitness function value at the current position, wherein the fitness function value is superior to Y at the moment_bestThen update X_best、Y_best；

In the formula: sign is a sign function;

7) and judging whether the iteration termination condition is met, if so, outputting the optimal forced updating threshold value of the dynamic grouping technology of the current w, and if not, returning to the step 5).

In the step (4), an automatic update period of the dynamic grouping technology is determined according to the AGC update period.

In the step (5), the battery units in the centralized battery energy storage power station are divided into 3 battery packs, the frequency modulation power instructions of the 3 battery packs are determined according to a dynamic grouping technology, the frequency modulation instruction of each battery unit is further determined, and the battery units respond to respective instructions, and the steps are as follows:

1) the control center of the centralized battery energy storage power station firstly averagely divides the energy storage units of the centralized battery energy storage power station into three groups, and calculates the average state of charge SOCi of the battery pack i according to the following formula; and three groups were named: priority charging group, standby group and priority discharging group, h_iThe number of battery cells of three battery packs, namely:

meanwhile, when the range of the battery units of the centralized battery energy storage power station is greater than or equal to the forced updating threshold value w, the battery units of the centralized battery energy storage power station are regrouped, namely regrouped when the following formula is met:

wherein T is the running time of the centralized battery energy storage power station, T is the automatic updating period of the dynamic grouping technology, u_tFor the extreme bad state of charge of the battery unit at the end of each AGC period, w is a forced updating threshold value of the dynamic grouping technology;

2) receiving the sent AGC instruction P_cThen, firstly, the working state of the centralized energy storage power station is determined, and the method comprises the following steps:

(1) state of charge (P)_c> 0): make the average state of charge SOC of the battery pack_iPriority action of the lowest pack, i.e. priority charging pack, if each cell P in the pack_i，jIf both run at maximum charging power and are above the AGC command requirement, then only the group is active and the power within the group is evenly distributed, i.e.:

if below the AGC command requirement, each battery cell in the group operates at maximum charging power, namely:

P_i，j＝P_{i，j max} (11)

the average state of charge SOC is then determined_kThe second lowest pack, the reserve pack, also participates in the response, if each cell in the pack is running at maximum charge power above the power differential, then only that pack is active and the power in the pack is evenly distributed, i.e.:

if the power differential requirement is below, then each battery cell in the group is operated at maximum charge power, i.e.:

P_k，j＝P_{k，j max} (13)

repeating the process until the charging power of all action groups meets the AGC instruction requirement, thereby determining the number of the battery packs needing to be acted;

(2) discharge state (P)_c< 0): determining that the number of battery packs needing to act is similar to the charging state in the discharging state;

3) after the number of the battery packs needing to be operated is determined, for the battery pack which operates at the maximum charging and discharging power, each battery unit in the battery pack directly operates at the maximum charging and discharging power, and for the battery pack which operates at the non-maximum charging and discharging power, the charging and discharging power of each battery unit in the battery pack is determined by adopting a power average distribution method;

4) and after the charging and discharging power of each battery unit is determined, the battery units respond to complete the frequency modulation task.

The technical scheme provided by the invention has the beneficial effects that:

the optimal forced updating threshold value of the dynamic grouping technology is determined by using a self-adaptive longicorn whisker search algorithm, on the basis, the battery units of the centralized battery energy storage power station are divided into three groups, the frequency modulation instructions of three battery packs are determined by using the dynamic grouping technology, the frequency modulation instruction of each battery unit is further determined, and the battery units respond to respective instructions. For the power grid, the AGC instruction is effectively tracked by adding the centralized battery energy storage system, and the power quality of the power grid is improved; for the energy storage system, the overcharge and the overdischarge of the battery units of the energy storage system are avoided, and the total action times of the battery units of the energy storage system are reduced, so that the service life loss of the battery is reduced, and the frequency modulation economy of the energy storage system is improved.

Drawings

The invention will be further described with reference to the accompanying drawings in which:

FIG. 1 is a flow chart of the present invention;

FIG. 2 is a diagram of an adaptive longicorn whisker optimization process;

FIG. 3 is a graph of power allocated for three battery packs;

FIG. 4 shows the result of the distribution of frequency modulation commands to the battery cells;

FIG. 5 shows the response of the battery cell;

FIG. 6 is the power response of three battery packs;

FIG. 7 is a diagram illustrating SOC variations of a battery cell;

FIG. 8 is an average SOC variation of the energy storage system;

FIG. 9 shows a comparison of actual output of the energy storage system with AGC commands;

detailed description of the preferred embodiments

For better understanding of the objects, technical solutions and effects of the present invention, the present invention will be further explained with reference to the accompanying drawings.

The invention provides a frequency modulation control strategy of a centralized battery energy storage power station based on a dynamic grouping technology, and an attached figure 1 is a flow chart of the invention, and the implementation flow comprises the following detailed steps.

Step 1, determining an evaluation index system of a dynamic grouping technology, and determining a fitness function according to the evaluation index system:

1) determining an evaluation index system of the dynamic grouping technology:

the evaluation index system consists of 4 parts, namely a standard deviation of a difference value between the output of the energy storage power station and an AGC instruction, a battery unit charge state range average value, a battery unit charge state range at the response ending moment and forced updating times, wherein the forced updating times are used for representing the increase X of the dynamic grouping times caused by the fact that the battery unit charge state range is greater than a set forced updating threshold value;

2) determining a fitness function as shown in the following formula:

wherein f is₁Is the standard deviation, N, of the difference between the energy storage system output and the AGC command₁Is the number of AGC commands, s_tEnergy storage system output, y, for each AGC cycle_iExpecting a force value, f, for the AGC command₂Is the very poor average value of the state of charge of the cell, u_iFor extreme cell state of charge, f, at the end of each AGC cycle₃Extreme bad state of charge of the cell at the end of time, f₄The number of times of forced updating is X; alpha is alpha₁、α₂、α₃And alpha₄As weights, the fitness function weights α₁And alpha₂Values of 5 and 1500, respectively, alpha₃And alpha₄The determination method of (2) is as follows:

wherein the content of the first and second substances,in response to the battery unit state of charge being very bad at the end time, X is the forced updating times, and the extreme bad threshold value E at the end time₁And a forced update times threshold value E₂0.05 and 50, respectively.

Step 2, determining the self-adaptive step size of the longicorn stigma search algorithm:

the step size factor of the self-adaptive longicorn stigma search algorithm is obtained by the following formula:

μ_n＝-arc tan[a(n+b)]+c (17)

wherein, mu_nFor the adaptive step size, n is the iteration number, and the values of the step size parameters a, b and c are respectively 0.16, 1.5 and 1.361.

Step 3, combining the fitness function provided by the invention, determining a forced update threshold value w of the dynamic grouping technology by applying a self-adaptive longicorn whisker search algorithm:

1) initialization of parameters of a longicorn whisker algorithm:

setting an initial step size (maximum step size), maximum iteration times and step size parameters a, b and c;

2) determining the orientation of the longicorn and normalizing, namely:

in the formula: rands is a random function, and k is a space dimension;

3) step factor calculation:

μ_n＝-arc tan[a(n+b)]+c (19)

wherein n is the iteration number, and the step parameters a, b and c are respectively 0.16, 1.5 and 1.361;

4) initializing the position of the longhorn beetle, and selecting [ -1, 1]The random number between them is used as the initial solution set of the longicorn algorithm and the initial position of the longicorn, and it is stored in X_bestPerforming the following steps;

5) calculating the fitness function value of the longicorn initial position according to the formula (13), and storing the fitness function value in Y_bestPerforming the following steps;

6) updating the left and right positions of the longicorn whiskers according to the following formula (19):

in the formula: x is the number of_lnAnd x_rnRespectively representing the position coordinates, x, of the Tianniu left hair and the Tianniu right hair in the nth iteration_nThe centroid coordinate of the longicorn in the nth iteration is shown, and dis is the distance between two whiskers;

7) updating a solution set of a longicorn whisker algorithm:

according to the positions of the left and right long horns of the longicorn, fitness function values f (x) of the left and right horns are respectively obtained by using the formula (13)_ln) And f (x)_rn) Comparing the intensity and updating the position of the longicorn according to the following formula (20), namely adjusting a forced updating threshold value w of the dynamic grouping technology and calculating a fitness function value at the current position, wherein the fitness function value is superior to Y at the moment_bestThen update X_best、Y_best；

In the formula: sign is a sign function;

8) and judging whether the iteration termination condition is met, if so, outputting the optimal forced updating threshold value of which the current w is the dynamic grouping technology, and if not, returning to the step 6). The optimal forced update threshold obtained by the dynamic grouping technology output by the adaptive longicorn whisker algorithm is 0.0899.

Typical 1-hour AGC instruction data in a certain place and a year are taken as research objects, the period of the AGC instruction is 4s, the installed capacity of the centralized energy storage power station is 100MW, a forced updating threshold value w of a dynamic grouping technology is determined by adopting the self-adaptive longicorn stigma, and the searching process is shown in the attached figure 2.

Step 4, the automatic updating period of the dynamic grouping technology is 13 min.

Step 5, dividing the battery units in the centralized battery energy storage power station into 3 battery packs, determining frequency modulation power instructions of the 3 battery packs according to a dynamic grouping technology, further determining the frequency modulation instruction of each battery unit, enabling the battery units to respond to respective instructions, and completing a frequency modulation task:

1) the state of charge SOC of the battery cell v is calculated as follows_v，t；

SOC characterizes the remaining charge of the battery, and accurate SOC estimation is very important for describing the battery state. The SOC estimation method mainly comprises an open-circuit voltage method, an ampere-hour integration method, a neural network, Kalman filtering and the like. Because the ampere-hour integral method is simple in principle and easy to calculate, the ampere-hour integral method is adopted to estimate the SOC of the energy storage system:

wherein: SOC_v，tThe SOC of the vth battery cell at time t; p_bv，tThe charging and discharging power of the v-th battery unit at the time t is a positive value during charging and a negative value during discharging; c_vThe maximum energy storage capacity of the v-th battery unit;

when the battery unit participates in frequency modulation operation, a high-rate charging and discharging mode is not generally adopted, so the output of the battery unit can be expressed as:

in the formula: p_r·bvDistributing the frequency modulation power for the v-th battery unit lower layer; p_{b max v}And P_{b min v}Maximum charging power and maximum discharging power of the vth battery cell, respectively;

the service life of the battery is prevented from being damaged due to overcharge and overdischarge during the operation of the battery unit, the capacity limit of the operation of the energy storage system is represented by SOC, and the corresponding constraint conditions are as follows:

wherein: SOC_{max v}And SOC_{min v}Respectively represent the upper and lower limits of the vth battery cell SOC.

1) Calculating the average state of charge SOC of the battery pack i according to the following formula_i(ii) a The energy storage units are divided into three groups according to the size relation of the charge states, and the three groups are named as: a priority charging group, a standby group, and a priority discharging group, namely:

meanwhile, when the range of the battery units of the centralized battery energy storage power station is greater than or equal to the forced updating threshold value w, the battery units of the centralized battery energy storage power station are regrouped, namely regrouped when the following formula is met:

wherein T is the running time of the centralized battery energy storage power station, T is the automatic updating period of the dynamic grouping technology,u_tfor the extreme bad state of charge of the battery unit at the end of each AGC period, w is a forced updating threshold value of the dynamic grouping technology;

2) receiving the sent AGC instruction P_cThen, firstly, the working state of the centralized energy storage power station is determined, and the method comprises the following steps:

(1) state of charge (P)_c> 0): make the average state of charge SOC of the battery pack_iPriority action of the lowest pack, i.e. priority charging pack, if each cell P in the pack_i，jIf both run at maximum charging power and are above the AGC command requirement, then only the group is active and the power within the group is evenly distributed, i.e.:

if below the AGC command requirement, each battery cell in the group operates at maximum charging power, namely:

P_i，j＝P_{i，j max} (28)

the average state of charge SOC is then determined_kThe second lowest pack, the reserve pack, also participates in the response, if each cell in the pack is running at maximum charge power above the power differential, then only that pack is active and the power in the pack is evenly distributed, i.e.:

if the power differential requirement is below, then each battery cell in the group is operated at maximum charge power, i.e.:

P_k，j＝P_{k，j max} (30)

repeating the process until the charging power of all action groups meets the AGC instruction requirement, and further determining the number of the battery packs needing to be acted;

(2) discharge state (P)_c< 0): determining that the number of battery packs needing to act is similar to the charging state in the discharging state;

3) after the number of the battery packs needing to act is determined, for the battery pack which operates at the maximum charging and discharging power, each battery unit in the battery pack directly operates at the maximum charging and discharging power, and for the battery pack which operates at the non-maximum charging and discharging power, the charging and discharging power of each battery unit in the battery pack is determined by adopting a power average distribution method.

Since the SOC difference of the battery cells in the battery pack may be large, when participating in the frequency modulation, it is desirable that the battery cells with lower SOC are charged and discharged more and less, and the battery cells with higher SOC are discharged more and less, so as to achieve the relative balance of SOC of each battery cell in the battery pack. For this purpose, a Sigmoid function is used to describe the charging and discharging process of the battery cell so as to represent the charging and discharging capacity of the battery cell.

The charge function of the battery cell is:

the discharge function of the cell is:

wherein: SOC_v，t-1The state of charge of the v-th cell in the battery pack at the time t-1.

The power allocation scheme for SOC equalization is as follows:

when the battery pack is charged:

when the battery pack discharges:

in the formula: p_r·bj，tAnd distributing power for the battery pack j at the moment t.

In the frequency modulation process, the specifically distributed power of the three groups is shown in fig. 3, the frequency modulation power command distributed by each battery unit is shown in fig. 4, and it can be known from fig. 4 that the situation exceeding the maximum charging and discharging power of the battery unit occurs due to the fact that the AGC command is too large; however, due to the limitation of the charging unit itself, the actual power output by the battery unit should be less than the maximum charge-discharge power of the battery unit.

4) And after the charging and discharging power of each battery unit is determined, the battery units respond to complete the frequency modulation task.

FIG. 5 shows the actual response power of the battery cells, comparing with FIG. 4, the response power of ten battery cells is maintained below the maximum charging/discharging power; fig. 6 shows the actual response power of the three battery packs, and comparing fig. 6 with fig. 3, the actual output power of the three battery packs is slightly smaller than the distributed power at a part of time because the power distributed to the battery cells in individual time periods is slightly larger than the charging and discharging power of the battery cells. Compared with the initial state of charge range of 0.1, the range average value of ten battery units in the running process is 0.05, so that the range of the battery units in the running process is well reduced; fig. 7 shows the state of charge change of ten battery cells, where the difference of the state of charge at the end time is 0.026, which is relatively significantly improved compared with the difference of the initial state of charge of 0.1, thereby improving the schedulable potential of the centralized energy storage power station; fig. 9 shows a relationship between an actual output of the energy storage power station and an AGC command, where a standard deviation of a difference between the output of the energy storage system and the AGC command is 2.07, an output tracking AGC command of the energy storage power station is better, and a frequency modulation effect is relatively better.

The frequency modulation control strategy of the centralized battery energy storage power station based on the dynamic grouping technology in the research is compared with the action times of the battery units of the energy storage system in the traditional power direct distribution method, and the result is shown in table 1. Since the traditional method does not consider energy storage system grouping, the action times of the energy storage battery unit are as high as 9000 times. The action times of the energy storage battery unit under the research scheme are only 5896 times, the action times of the battery unit are effectively reduced through the strategy, and the service life loss of energy storage resources is slowed down.

TABLE 1 number of energy storage cell events under two methods

In conclusion, the control strategy reduces the grouping times of the dynamic grouping technology, reduces the extremely poor state of charge of the battery unit after the response is finished, and improves the schedulable potential of the energy storage power station in the next scheduling period; meanwhile, the action times of the energy storage system are reduced, and the running economy of the energy storage power station is improved.