Battery energy storage power station power control method, system, equipment and medium

Abstract

The invention belongs to the technical field of energy storage power station control, and discloses a battery energy storage power station power control method, system and device and a medium. The method comprises the following steps: collecting power grid frequency, power grid voltage, total power demand of a battery energy storage power station and real-time operation state data of each energy storage unit; calculating the total power demand of the peak regulation area and the total power demand of the active support area through a fuzzy adaptive low-pass filter; and calculating a power instruction of the energy storage unit in each area based on the total power demand of the peak regulation area and the total power demand of the active support area, and issuing the power instruction to the corresponding energy storage converter for execution. Power distribution is carried out based on the fuzzy self-adaptive low-pass filter, the cooperative control level of the multi-machine parallel energy storage unit of the battery energy storage power station is improved, the influence of frequent charging and discharging fluctuation of the energy storage unit on the performance of the energy storage system is relieved, the SOC consistency level of the energy storage unit is improved, and the overall charging and discharging capacity of the energy storage power station is improved.

Description

Technical Field

[0001] This invention belongs to the field of energy storage power station control technology, and specifically relates to a power control method, system, equipment and medium for battery energy storage power stations. Background Technology

[0002] Battery energy storage power stations, with their rapid response capabilities, have become crucial resources for actively supporting grid frequency and voltage and enabling flexible regulation. However, peak shaving typically involves continuous and smooth power demand, while active support such as primary frequency regulation and active voltage regulation usually involves power changes on a second-by-second basis. The combination of these two factors leads to significant fluctuations in the total power demand of energy storage power stations, increasing the difficulty of power allocation for multi-unit parallel energy storage power stations. Existing power allocation strategies for energy storage power stations generally adopt average allocation, control mechanisms based on SOC ratio, and control based on output coefficient. These are simple to implement, but the active and reactive power of energy storage units will fluctuate frequently, which will affect the performance of the energy storage system in the long run. Against this backdrop, there is an urgent need for a new allocation strategy that can dynamically sense the system status, accurately decompose power signals, and achieve multi-unit collaborative optimization to reduce the amplitude of power fluctuations in energy storage units, delay the degradation of battery energy storage system lifespan, improve the SOC consistency of energy storage units, and enhance the charging and discharging capabilities of energy storage power stations. Summary of the Invention

[0003] The purpose of this invention is to provide a power control method, system, device and medium for battery energy storage power stations, so as to solve the technical problem of insufficient multi-machine collaborative control in the existing technology when facing multi-objective applications of energy storage power stations.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a power control method for a battery energy storage power station, comprising: The system collects data on grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status of each energy storage unit; the battery energy storage power station includes a peak-shaving zone and an active support zone. By using a fuzzy adaptive low-pass filter, the collected grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit are used to calculate the total power demand in the peak-shaving area and the total power demand in the active support area. Based on the total power demand in the peak-shaving area and the total power demand in the active support area, the power command for the energy storage unit in each area is calculated, and the power command is sent to the corresponding energy storage converter for execution.

[0005] A further improvement of the present invention is that: in the step of collecting grid frequency, grid voltage, total power demand of battery energy storage power station and real-time operating status data of each energy storage unit, the real-time operating status data of each energy storage unit of battery energy storage power station includes the energy storage unit SOC and maximum allowable charge and discharge power.

[0006] A further improvement of this invention is that: in the step of calculating the total power demand in the peak-shaving area and the total power demand in the active support area using the collected grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit through a fuzzy adaptive low-pass filter, the design method of the fuzzy adaptive low-pass filter includes: Frequency deviation and voltage deviation are used as inputs to the fuzzy controller, and the output is the output time constant of the low-pass filter. The fuzzy subsets of frequency deviation are {NB (negative large), NS (negative small), NZ (negative zero), PZ (positive zero), PS (positive small), PB (positive large)}, the fuzzy subsets of voltage deviation are {NB (negative large), NS (negative small), ZE (zero), PS (positive small), PB (positive large)}, and the fuzzy subsets of output time constant are {NB (negative large), NM (negative medium), NS (negative small), NZ (negative zero), PZ (positive zero), PS (positive small), PM (positive medium), PB (positive large)}. The basis for the formulation of fuzzy rules is as follows: When the frequency deviation and voltage deviation are greater than the corresponding first frequency deviation threshold and first voltage deviation threshold, respectively, the time constant of the low-pass filter is reduced; when the frequency deviation and voltage deviation are less than the corresponding second frequency deviation threshold and second voltage deviation threshold, the time constant of the low-pass filter is increased; otherwise, the change in total demand in the peak-shaving area is reduced.

[0007] A further improvement of this invention is that the step of calculating the total power demand in the peak-shaving area and the total power demand in the active support area using the collected grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit through a fuzzy adaptive low-pass filter specifically includes: Based on a fuzzy adaptive low-pass filter controller, the high-frequency and low-frequency components of active power are calculated. The high-frequency component corresponds to the total active power demand of energy storage in the active support zone, and is less than or equal to the maximum allowable charging and discharging active power of the active support zone. The low-frequency component corresponds to the total active power command in the peak-shaving zone, and is less than or equal to the maximum allowable active charging and discharging power of the peak-shaving zone. The total active power demand is equal to the sum of the total active power demand of energy storage in the active support zone and the total active power demand of energy storage in the peak-shaving zone. The total reactive power demand of energy storage in the active support zone is the total reactive power demand of the energy storage power station, and is less than the maximum allowable reactive power output of the active support zone.

[0008] A further improvement of this invention is that the step of calculating the power command of the energy storage unit in each region based on the total power demand in the peak-shaving region and the total power demand in the active support region specifically includes: when hour, ; when hour, ; Where a is the SOC range threshold; n is the number of energy storage units in the peak-shaving zone; and m is the number of energy storage units in the active support zone. P ps To meet the active power demand of energy storage in peak-shaving areas, P fv To proactively support the active power demand of energy storage in the region, R soc,ps The SOC range is extremely poor in the peak-shaving region. R soc,fv The active support region has a very poor SOC (State of Charge). Let i be the active power demand of the i-th energy storage unit within the peak-shaving area. To proactively support the active power demand of the i-th energy storage unit within the area; when hour; 1) If the total demand is discharge, arrange the energy storage units in the peak-shaving area and the active support area in descending order of SOC. The discharge power coefficient of each energy storage unit is calculated according to the following formula: When n or m is even:

[0009]

[0010] When n or m is odd:

[0011]

[0012] In the above formula, x ps (i) represents the output coefficient of the i-th energy storage unit in the peak-shaving region, SOC ps (i) represents the SOC of the i-th energy storage unit in the peak-shaving region. max，ps c is the maximum SOC of the peak-shaving energy storage unit. d,ps This is the ratio of active power demand in the peak-shaving region to the maximum permissible discharge active power. fv (i) represents the output coefficient of the i-th energy storage unit in the active support zone, SOC fv (i) represents the SOC of the i-th energy storage unit in the active support zone. max,fv To support the maximum SOC of the energy storage unit in the active support zone, c d,fv It is the ratio of the active power demand in the active support area to the maximum allowable discharge active power; Discharge power of each energy storage unit P ps (i) for:

[0013]

[0014] in, Let be the maximum allowable discharge power of the i-th energy storage unit; 2) If the total demand is charging, arrange the energy storage units in the peak-shaving zone and the active support zone in ascending order of SOC. The charging power coefficient of each energy storage unit is calculated according to the following formula: When n or m is even:

[0015]

[0016] When n or m is odd:

[0017]

[0018] In the above formula, SOC min,ps c is the minimum SOC of the peak-shaving energy storage unit. c,ps State of Charge (SOC) is the ratio of active power demand in the peak-shaving region to the maximum allowable charging active power. min,fv To provide the minimum SOC for the active support zone energy storage unit, c c,fv It is the ratio of the active power demand in the active support area to the maximum allowable charging active power; Charging power of each energy storage unit P ps (i) for:

[0019]

[0020] in, This represents the maximum allowable charging power of the i-th energy storage unit.

[0021] In a second aspect, the present invention provides a power control system for a battery energy storage power station, comprising: The data acquisition unit is used to collect data on grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status of each energy storage unit; the battery energy storage power station includes a peak-shaving area and an active support area. The fuzzy computing unit is used to calculate the total power demand in the peak-shaving area and the total power demand in the active support area by using the collected grid frequency, grid voltage, total power demand of the battery energy storage power station and real-time operating status data of each energy storage unit through a fuzzy adaptive low-pass filter. The control unit is used to calculate the power command of the energy storage unit in each region based on the total power demand in the peak shaving region and the total power demand in the active support region, and to issue the power command to the corresponding energy storage converter for execution.

[0022] A further improvement of the present invention is that, in the step of the acquisition unit acquiring grid frequency, grid voltage, total power demand of the battery energy storage power station and real-time operating status data of each energy storage unit, the real-time operating status data of each energy storage unit of the battery energy storage power station includes the energy storage unit SOC and maximum allowable charge and discharge power.

[0023] A further improvement of the present invention is that the power of the active support zone is greater than or equal to 20% of the rated power of the battery energy storage power station.

[0024] A further improvement of this invention lies in the following: In the step of the fuzzy computing unit calculating the total power demand in the peak-shaving area and the total power demand in the active support area using the collected grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit through the fuzzy adaptive low-pass filter, the design method of the fuzzy adaptive low-pass filter includes: Frequency deviation and voltage deviation are used as inputs to the fuzzy controller, and the output is the output time constant of the low-pass filter. The fuzzy subsets of frequency deviation are {NB (negative large), NS (negative small), NZ (negative zero), PZ (positive zero), PS (positive small), PB (positive large)}, the fuzzy subsets of voltage deviation are {NB (negative large), NS (negative small), ZE (zero), PS (positive small), PB (positive large)}, and the fuzzy subsets of output time constant are {NB (negative large), NM (negative medium), NS (negative small), NZ (negative zero), PZ (positive zero), PS (positive small), PM (positive medium), PB (positive large)}. The basis for the formulation of fuzzy rules is as follows: When the frequency deviation and voltage deviation are greater than the corresponding first frequency deviation threshold and first voltage deviation threshold, respectively, the time constant of the low-pass filter is reduced; when the frequency deviation and voltage deviation are less than the corresponding second frequency deviation threshold and second voltage deviation threshold, the time constant of the low-pass filter is increased; otherwise, the change in total demand in the peak-shaving area is reduced.

[0025] A further improvement of this invention is that the fuzzy computing unit uses a fuzzy adaptive low-pass filter to calculate the total power demand in the peak-shaving area and the total power demand in the active support area based on the collected grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit. Specifically, this includes: Based on a fuzzy adaptive low-pass filter controller, the high-frequency and low-frequency components of active power are calculated. The high-frequency component corresponds to the total active power demand of energy storage in the active support zone, and is less than or equal to the maximum allowable charging and discharging active power of the active support zone. The low-frequency component corresponds to the total active power command in the peak-shaving zone, and is less than or equal to the maximum allowable active charging and discharging power of the peak-shaving zone. The total active power demand is equal to the sum of the total active power demand of energy storage in the active support zone and the total active power demand of energy storage in the peak-shaving zone. The total reactive power demand of energy storage in the active support zone is the total reactive power demand of the energy storage power station, and is less than the maximum allowable reactive power output of the active support zone.

[0026] A further improvement of this invention is that the step of the control unit calculating the power command of the energy storage unit in each region based on the total power demand in the peak-shaving region and the total power demand in the active support region specifically includes: when hour, ; when hour, ; Where a is the SOC range threshold; n is the number of energy storage units in the peak-shaving zone; and m is the number of energy storage units in the active support zone. P ps To meet the active power demand of energy storage in peak-shaving areas, P fv To proactively support the active power demand of energy storage in the region, R soc,ps The SOC range is extremely poor in the peak-shaving region. R soc,fv The active support region has a very poor SOC (State of Charge). Let i be the active power demand of the i-th energy storage unit within the peak-shaving area. To proactively support the active power demand of the i-th energy storage unit within the area; when hour; 1) If the total demand is discharge, arrange the energy storage units in the peak-shaving area and the active support area in descending order of SOC. The discharge power coefficient of each energy storage unit is calculated according to the following formula: When n is even:

[0027]

[0028] When n is odd:

[0029]

[0030] In the above formula, x ps(i) represents the output coefficient of the i-th energy storage unit in the peak-shaving region, SOC ps (i) represents the SOC of the i-th energy storage unit in the peak-shaving region. max，ps c is the maximum SOC of the peak-shaving energy storage unit. d,ps This is the ratio of active power demand in the peak-shaving region to the maximum permissible discharge active power. fv (i) represents the output coefficient of the i-th energy storage unit in the active support zone, SOC fv (i) represents the SOC of the i-th energy storage unit in the active support zone. max,fv To support the maximum SOC of the energy storage unit in the active support zone, c d,fv It is the ratio of the active power demand in the active support area to the maximum allowable discharge active power; Discharge power of each energy storage unit P ps (i) for:

[0031]

[0032] in, Let be the maximum allowable discharge power of the i-th energy storage unit; 2) If the total demand is charging, arrange the energy storage units in the peak-shaving zone and the active support zone in ascending order of SOC. The charging power coefficient of each energy storage unit is calculated according to the following formula: When n or m is even:

[0033]

[0034] When n or m is odd:

[0035]

[0036] In the above formula, SOC min,ps c is the minimum SOC of the peak-shaving energy storage unit. c,ps State of Charge (SOC) is the ratio of active power demand in the peak-shaving region to the maximum allowable charging active power. min,fv To provide the minimum SOC for the active support zone energy storage unit, c c,fv It is the ratio of the active power demand in the active support area to the maximum allowable charging active power; Charging power of each energy storage unit P ps (i) for:

[0037]

[0038] in, is the maximum allowable discharge power of the i-th energy storage unit.

[0039] Thirdly, the present invention provides an electronic device including a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the power control method for the battery energy storage power station.

[0040] Fourthly, the present invention provides a computer-readable storage medium storing at least one instruction, which, when executed by a processor, implements the power control method for the battery energy storage power station.

[0041] Compared with the prior art, the present invention has the following unexpected beneficial effects: This invention provides a power control method for a battery energy storage power station, comprising: collecting grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit; the battery energy storage power station includes a peak-shaving zone and an active support zone; using a fuzzy adaptive low-pass filter, the collected grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit are used to calculate the total power demand of the peak-shaving zone and the total power demand of the active support zone; based on the total power demand of the peak-shaving zone and the total power demand of the active support zone, the power command for each energy storage unit in each zone is calculated, and the power command is sent to the corresponding energy storage converter for execution. This invention uses a fuzzy adaptive low-pass filter for power allocation, which can differentiate the power control between the peak-shaving zone and the active support zone of the energy storage power station by adaptively changing the time constant of the filter, reducing the fluctuation of energy storage power in the peak-shaving zone, mitigating the impact of frequent charge and discharge fluctuations of energy storage units on the performance of the energy storage system, and improving the overall charge and discharge capability of the energy storage power station by improving the SOC consistency level of energy storage units.

[0042] Furthermore, the first layer of this invention uses a fuzzy adaptive low-pass filter to distribute high-frequency signals to the active support zone and low-frequency signals to the peak-shaving zone, thereby smoothing the output of energy storage in the peak-shaving zone; the second layer, based on the tuned charge and discharge coefficient, realizes the secondary power distribution of the total energy storage power demand in each region to the energy storage unit, improves the consistency of the SOC of energy storage in each region, and improves the capacity utilization of the energy storage power station. Attached Figure Description

[0043] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1This is a control block diagram of the joint frequency modulation model according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the power allocation strategy according to an embodiment of the present invention; Figure 3 This is a schematic flowchart of a power control method for a battery energy storage power station according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a battery energy storage power station power control system according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0044] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0045] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0046] Please see Figures 1 to 2 As shown, this embodiment of the invention provides a power control method for a battery energy storage power station, applicable to battery energy storage power stations, particularly battery energy storage power stations with multiple parallel energy storage units, including the following steps: Step S1: Collect grid frequency, grid voltage, total power demand of battery energy storage power station, and real-time operating status data of each energy storage unit; Step S2: Divide the battery energy storage power station into a peak shaving zone and an active support zone. The peak shaving zone is mainly used to respond to the active power demand of the energy storage power station, while the active support zone is used to respond to both active and reactive power demands.

[0047] Step S3: Calculate the total power demand in the peak shaving area and the total power demand in the active support area using a fuzzy adaptive low-pass filter.

[0048] Step S4: Calculate the power command of the energy storage unit in each region and send it to the energy storage converter for execution.

[0049] In one specific implementation, in step S3, a fuzzy adaptive low-pass filter controller is designed based on the total active power demand of the energy storage power station and the frequency deviation, and the high-frequency and low-frequency components of the active power are calculated. The high-frequency component corresponds to the total active power demand of the active support zone and is less than or equal to the maximum allowable charging and discharging active power of the active support zone; the low-frequency component corresponds to the total active power command of the peak-shaving zone and is less than or equal to the maximum allowable active charging and discharging power of the peak-shaving zone; the total active power demand is equal to the sum of the total active power demand of the active support zone and the total active power demand of the peak-shaving zone. The total reactive power demand of the active support zone is the total reactive power demand of the energy storage power station and is less than the maximum allowable reactive power output of the active support zone.

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] In the formula, P ps To meet the active power demand of energy storage in peak-shaving areas, P fv To proactively support the active power demand of energy storage in the region, Q fv To proactively support the reactive power demand of energy storage in the region, P all This is the total active power requirement of the energy storage power station. Q all To meet the total reactive power requirements of the energy storage power station, P ps,dismax The maximum allowable discharge power for energy storage in the peak-shaving area. P ps,charmax This represents the maximum allowable charging power for energy storage in the peak-shaving area, where discharging is positive and charging is negative. P fv,dismax To provide the maximum allowable discharge power for the active support zone energy storage, P fv,charmax This refers to the maximum allowable charging power for energy storage in the active support zone.

[0057] In one specific implementation, in step S4, the number n and m of energy storage units in the peak-shaving zone and the active support zone are calculated, and the SOC range is determined. R soc,ps、 R soc,fv。

[0058] when hour, ; when hour, ; In the formula, a is a value between 0 and 0.1, and the specific value can be adjusted according to the actual situation; Let i be the active power demand of the i-th energy storage unit within the peak-shaving area. To proactively support the active power demand of the i-th energy storage unit within the area; R soc,ps The SOC range is extremely poor in the peak-shaving region. R soc,fv The SOC range is extremely poor in the active support region.

[0059] when ; 1) Total demand is discharge, i.e.

[0060] The energy storage units in the peak-shaving and active support zones are arranged in descending order of State of Charge (SOC), and the discharge power coefficient of each energy storage unit is calculated according to the following formula: When n or m is even:

[0061]

[0062] When n or m is odd:

[0063]

[0064] In the above formula, x ps (i) represents the output coefficient of the i-th energy storage unit in the peak-shaving region, SOC ps (i) represents the SOC of the i-th energy storage unit in the peak-shaving region. max，ps c is the maximum SOC of the peak-shaving energy storage unit. d,ps This is the ratio of active power demand in the peak-shaving region to the maximum permissible discharge active power. fv (i) represents the output coefficient of the i-th energy storage unit in the active support zone, SOC fv (i) represents the SOC of the i-th energy storage unit in the active support zone. max,fv To support the maximum SOC of the energy storage unit in the active support zone, c d,fv It is the ratio of the active power demand of the active support area to the maximum allowable discharge active power.

[0065] Discharge power of each energy storage unit P ps(i) :

[0066]

[0067] in, Let be the maximum allowable discharge power of the i-th energy storage unit; 2) The total demand is for charging, i.e.

[0068] The energy storage units in the peak-shaving and active support zones are arranged in ascending order of State of Charge (SOC), and the charging power coefficient of each energy storage unit is calculated according to the following formula: When n or m is even:

[0069]

[0070] When n or m is odd:

[0071]

[0072] In the above formula, SOC min,ps c is the minimum SOC of the peak-shaving energy storage unit. c,ps State of Charge (SOC) is the ratio of active power demand in the peak-shaving region to the maximum permissible charging active power. min,fv To provide the minimum SOC for the active support zone energy storage unit, c c,fv It is the ratio of the active power demand in the active support area to the maximum allowable charging active power; Charging power of each energy storage unit P ps (i) for:

[0073]

[0074] in, This represents the maximum allowable charging power of the i-th energy storage unit.

[0075] This invention provides a power control method for a battery energy storage power station, based on a multi-machine parallel energy storage control strategy using a variable universe of discourse fuzzy adaptive low-pass filter, specifically including the following steps: Step S1: Collect grid frequency, grid voltage, total power demand of battery energy storage power station, and real-time operating status data of each energy storage unit. The real-time operating status data of the energy storage unit includes the energy storage unit's SOC and maximum allowable charging and discharging power.

[0076] Step S2 involves dividing the energy storage power station into a peak-shaving zone and an active support zone. The peak-shaving zone primarily responds to the active power demand of the energy storage power station, while the active support zone responds to both active and reactive power demands. The power output of the active support zone is generally set at a ratio of no less than 20% of the rated power of the energy storage power station.

[0077] Step S3 involves calculating the total power demand in the peak-shaving region and the total power demand in the active support region using a fuzzy adaptive low-pass filter. Specifically, this includes: (1) The design method of fuzzy adaptive low-pass filter is as follows: Frequency deviation and voltage deviation are used as inputs to the fuzzy controller, and the output is the output time constant of the low-pass filter. The fuzzy subsets of frequency deviation are {NB (negative large), NS (negative small), NZ (negative zero), PZ (positive zero), PS (positive small), PB (positive large)}, the fuzzy subsets of voltage deviation are {NB (negative large), NS (negative small), ZE (zero), PS (positive small), PB (positive large)}, and the fuzzy subsets of output time constant are {NB (negative large), NM (negative medium), NS (negative small), NZ (negative zero), PZ (positive zero), PS (positive small), PM (positive medium), PB (positive large)}. The basis for the formulation of fuzzy rules is as follows: When the frequency and voltage deviations are large, the time constant of the low-pass filter should be reduced; when the frequency and voltage deviations are small, the time constant of the low-pass filter should be increased; when the frequency and voltage deviations are moderate, the change in total demand in the peak-shaving area should be reduced.

[0078] In one specific implementation, a first frequency deviation threshold, a first voltage deviation threshold, a second frequency deviation threshold, and a second voltage deviation threshold are set. When the frequency deviation and voltage deviation are greater than the corresponding first frequency deviation threshold and first voltage deviation threshold, respectively, the time constant of the low-pass filter is reduced. When the frequency deviation and voltage deviation are less than the corresponding second frequency deviation threshold and second voltage deviation threshold, the time constant of the low-pass filter is increased. Otherwise, the frequency deviation is between the first frequency deviation threshold and the second frequency deviation threshold, and the voltage deviation is between the first voltage deviation threshold and the second voltage deviation threshold, thereby reducing the change in total demand in the peak-shaving area.

[0079] (2) Based on the fuzzy adaptive low-pass filter controller, the high-frequency and low-frequency components of active power are calculated. The high-frequency component corresponds to the total active power demand of the active support zone energy storage and is less than or equal to the maximum allowable charging and discharging active power of the active support zone; the low-frequency component corresponds to the total active power command of the peak-shaving zone and is less than or equal to the maximum allowable active charging and discharging power of the peak-shaving zone; the total active power demand is equal to the sum of the total active power demand of the active support zone energy storage and the total active power demand of the peak-shaving zone energy storage. The total reactive power demand of the active support zone energy storage is the total reactive power demand of the energy storage power station and is less than the maximum allowable reactive power output of the active support zone.

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086] In the formula, P ps To meet the active power demand of energy storage in peak-shaving areas, P fv To proactively support the active power demand of energy storage in the region, Q fv To proactively support the reactive power demand of energy storage in the region, P all This is the total active power requirement of the energy storage power station. Q all To meet the total reactive power requirements of the energy storage power station, P ps,dismax The maximum allowable discharge power for energy storage in the peak-shaving area. P ps,charmax This represents the maximum allowable charging power for energy storage in the peak-shaving area, where discharging is positive and charging is negative. P fv,dismax To provide the maximum allowable discharge power for the active support zone energy storage, P fv,charmax This refers to the maximum allowable charging power for energy storage in the active support zone.

[0087] Step S4: Calculate the power command for each energy storage unit in each region and issue it to the energy storage converter for execution. Specifically, this includes: Calculate the number of energy storage units n and m in the peak-shaving area and the active support area, and the SOC range. R soc,ps、 R soc,fv。

[0088] when hour, ; when hour, ; In the formula, a should be a value between 0 and 0.05. The specific value can be reasonably determined based on factors such as the charge and discharge rate of the energy storage unit and the SOC operating range of the energy storage unit.

[0089] when

[0090] 1) Total demand is discharge, i.e.

[0091] The energy storage units in the peak-shaving and active support zones are arranged in descending order of State of Charge (SOC), and the discharge power coefficient of each energy storage unit is calculated according to the following formula: When n or m is even:

[0092]

[0093] When n or m is odd:

[0094]

[0095] In the above formula, x ps (i) represents the output coefficient of the i-th energy storage unit in the peak-shaving region, SOC ps (i) represents the SOC of the i-th energy storage unit in the peak-shaving region. max，ps c is the maximum SOC of the peak-shaving energy storage unit. d,ps This is the ratio of active power demand in the peak-shaving region to the maximum permissible discharge active power. fv (i) represents the output coefficient of the i-th energy storage unit in the active support zone, SOC fv (i) represents the SOC of the i-th energy storage unit in the active support zone. max,fv To support the maximum SOC of the energy storage unit in the active support zone, c d,fv It is the ratio of the active power demand of the active support area to the maximum allowable discharge active power.

[0096] Discharge power of each energy storage unit P ps (i) :

[0097]

[0098] in, Let be the maximum allowable discharge power of the i-th energy storage unit; 2) The total demand is for charging, i.e.

[0099] The energy storage units in the peak-shaving and active support zones are arranged in ascending order of State of Charge (SOC), and the charging power coefficient of each energy storage unit is calculated according to the following formula: When n or m is even:

[0100]

[0101] When n or m is odd:

[0102]

[0103] In the above formula, SOC min,ps c is the minimum SOC of the peak-shaving energy storage unit. c,ps State of Charge (SOC) is the ratio of active power demand in the peak-shaving region to the maximum permissible charging active power. min,fv To provide the minimum SOC for the active support zone energy storage unit, c c,fv It is the ratio of the active power demand in the active support area to the maximum allowable charging active power; Charging power of each energy storage unit P ps (i) for:

[0104]

[0105] in, This represents the maximum allowable charging power of the i-th energy storage unit.

[0106] Please see Figure 3 As shown, an embodiment of the present invention provides a power control method for a battery energy storage power station, comprising: S100: Collects grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit; the battery energy storage power station includes a peak-shaving area and an active support area. S200: Using a fuzzy adaptive low-pass filter, the collected grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit are used to calculate the total power demand in the peak-shaving area and the total power demand in the active support area. S300 calculates the power command for the energy storage unit in each region based on the total power demand in the peak shaving area and the total power demand in the active support area, and sends the power command to the corresponding energy storage converter for execution.

[0107] In one specific implementation, the step of collecting grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit includes the energy storage unit's SOC and maximum allowable charge / discharge power.

[0108] In one specific embodiment, the power of the active support zone is greater than or equal to 20% of the rated power of the battery energy storage station; preferably, the power of the active support zone is equal to 20%, 30%, 40%, or 50% of the rated power of the battery energy storage station.

[0109] In one specific embodiment, the step of calculating the total power demand in the peak-shaving area and the total power demand in the active support area using the collected grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit through a fuzzy adaptive low-pass filter includes the following design method: Frequency deviation and voltage deviation are used as inputs to the fuzzy controller, and the output is the output time constant of the low-pass filter. The fuzzy subsets of frequency deviation are {NB (negative large), NS (negative small), NZ (negative zero), PZ (positive zero), PS (positive small), PB (positive large)}, the fuzzy subsets of voltage deviation are {NB (negative large), NS (negative small), ZE (zero), PS (positive small), PB (positive large)}, and the fuzzy subsets of output time constant are {NB (negative large), NM (negative medium), NS (negative small), NZ (negative zero), PZ (positive zero), PS (positive small), PM (positive medium), PB (positive large)}. The basis for the formulation of fuzzy rules is as follows: When the frequency deviation and voltage deviation are greater than the corresponding first frequency deviation threshold and first voltage deviation threshold, respectively, the time constant of the low-pass filter is reduced; when the frequency deviation and voltage deviation are less than the corresponding second frequency deviation threshold and second voltage deviation threshold, the time constant of the low-pass filter is increased; otherwise, the change in total demand in the peak-shaving area is reduced.

[0110] In one specific embodiment, the step of calculating the total power demand in the peak-shaving area and the total power demand in the active support area using a fuzzy adaptive low-pass filter based on the collected grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit specifically includes: Based on a fuzzy adaptive low-pass filter controller, the high-frequency and low-frequency components of active power are calculated. The high-frequency component corresponds to the total active power demand of energy storage in the active support zone, and is less than or equal to the maximum allowable charging and discharging active power of the active support zone. The low-frequency component corresponds to the total active power command in the peak-shaving zone, and is less than or equal to the maximum allowable active charging and discharging power of the peak-shaving zone. The total active power demand is equal to the sum of the total active power demand of energy storage in the active support zone and the total active power demand of energy storage in the peak-shaving zone. The total reactive power demand of energy storage in the active support zone is the total reactive power demand of the energy storage power station, and is less than the maximum allowable reactive power output of the active support zone.

[0111] In one specific implementation, the step of calculating the power command of the energy storage unit in each region based on the total power demand in the peak-shaving region and the total power demand in the active support region specifically includes: when hour, ; when hour, ; Where 'a' is the SOC range threshold, ranging from 0 to 0.45; 'n' is the number of energy storage units in the peak-shaving zone; and 'm' is the number of energy storage units in the active support zone. P ps To meet the active power demand of energy storage in peak-shaving areas, P fv To proactively support the active power demand of energy storage in the region, R soc,ps The SOC range is extremely poor in the peak-shaving region. R soc,fv The active support region has a very poor SOC (State of Charge). Let i be the active power demand of the i-th energy storage unit within the peak-shaving area. To proactively support the active power demand of the i-th energy storage unit within the area; when hour; 1) If the total demand is discharge, arrange the energy storage units in the peak-shaving area and the active support area in descending order of SOC. The discharge power coefficient of each energy storage unit is calculated according to the following formula: When n or m is even:

[0112]

[0113] When n or m is odd:

[0114]

[0115] In the above formula, x ps(i) represents the output coefficient of the i-th energy storage unit in the peak-shaving region, SOC ps (i) represents the SOC of the i-th energy storage unit in the peak-shaving region. max，ps c is the maximum SOC of the peak-shaving energy storage unit. d,ps This is the ratio of active power demand in the peak-shaving region to the maximum permissible discharge active power. fv (i) represents the output coefficient of the i-th energy storage unit in the active support zone, SOC fv (i) represents the SOC of the i-th energy storage unit in the active support zone. max,fv To support the maximum SOC of the energy storage unit in the active support zone, c d,fv It is the ratio of the active power demand in the active support area to the maximum allowable discharge active power; Discharge power of each energy storage unit P ps (i) for:

[0116]

[0117] in, Let be the maximum allowable discharge power of the i-th energy storage unit; 2) If the total demand is charging, arrange the energy storage units in the peak-shaving zone and the active support zone in ascending order of SOC. The charging power coefficient of each energy storage unit is calculated according to the following formula: When n or m is even:

[0118]

[0119] When n or m is odd:

[0120]

[0121] In the above formula, SOC min,ps c is the minimum SOC of the peak-shaving energy storage unit. c,ps State of Charge (SOC) is the ratio of active power demand in the peak-shaving region to the maximum allowable charging active power. min,fv To provide the minimum SOC for the active support zone energy storage unit, c c,fv It is the ratio of the active power demand in the active support area to the maximum allowable charging active power; Charging power of each energy storage unit P ps (i) for:

[0122]

[0123] in, is the maximum discharge power of the i-th energy storage unit.

[0124] Please see Figure 4 As shown, an embodiment of the present invention provides a power control system for a battery energy storage power station, comprising: The data acquisition unit is used to collect data on grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status of each energy storage unit; the battery energy storage power station includes a peak-shaving area and an active support area. The fuzzy computing unit is used to calculate the total power demand in the peak-shaving area and the total power demand in the active support area by using the collected grid frequency, grid voltage, total power demand of the battery energy storage power station and real-time operating status data of each energy storage unit through a fuzzy adaptive low-pass filter. The control unit is used to calculate the power command of the energy storage unit in each region based on the total power demand in the peak shaving region and the total power demand in the active support region, and to issue the power command to the corresponding energy storage converter for execution.

[0125] Please see Figure 5 As shown, this embodiment of the invention provides an electronic device 100 for implementing a power control method for a battery energy storage power station; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0126] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the battery energy storage power station power control method described in the embodiment by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0127] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0128] The memory 101 in the electronic device 100 stores multiple instructions to implement a power control method for a battery energy storage power station, and the processor 102 can execute the multiple instructions to achieve the following: The system collects data on grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status of each energy storage unit; the battery energy storage power station includes a peak-shaving zone and an active support zone. By using a fuzzy adaptive low-pass filter, the collected grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit are used to calculate the total power demand in the peak-shaving area and the total power demand in the active support area. Based on the total power demand in the peak-shaving area and the total power demand in the active support area, the power command for the energy storage unit in each area is calculated, and the power command is sent to the corresponding energy storage converter for execution.

[0129] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0130] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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.

[0131] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0132] 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.

[0133] 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.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A power control method for a battery energy storage power station, characterized in that, include: The system collects data on grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status of each energy storage unit; the battery energy storage power station includes a peak-shaving zone and an active support zone. By using a fuzzy adaptive low-pass filter, the collected grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit are used to calculate the total power demand in the peak-shaving area and the total power demand in the active support area. Based on the total power demand in the peak-shaving area and the total power demand in the active support area, the power command for the energy storage unit in each area is calculated, and the power command is sent to the corresponding energy storage converter for execution.

2. The power control method for a battery energy storage power station according to claim 1, characterized in that, In the steps of collecting grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit, the real-time operating status data of each energy storage unit of the battery energy storage power station includes the energy storage unit's SOC and maximum allowable charge and discharge power.

3. The power control method for a battery energy storage power station according to claim 1, characterized in that, In the step of calculating the total power demand in the peak-shaving area and the total power demand in the active support area using the collected grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit through a fuzzy adaptive low-pass filter, the design method of the fuzzy adaptive low-pass filter includes: Frequency deviation and voltage deviation are used as inputs to the fuzzy controller, and the output is the output time constant of the low-pass filter. The fuzzy subsets of frequency deviation are {negative large, negative small, negative zero, positive zero, positive small, positive large}, the fuzzy subsets of voltage deviation are {negative large, negative small, zero, positive small, positive large}, and the fuzzy subsets of output time constant are {negative large, negative medium, negative small, negative zero, positive zero, positive small, positive medium, positive large}. The fuzzy rules are based on the following: When the frequency deviation and voltage deviation are greater than the corresponding first frequency deviation threshold and first voltage deviation threshold, respectively, the time constant of the low-pass filter is reduced; when the frequency deviation and voltage deviation are less than the corresponding second frequency deviation threshold and second voltage deviation threshold, the time constant of the low-pass filter is increased; otherwise, the change in total demand in the peak-shaving area is reduced.

4. The power control method for a battery energy storage power station according to claim 1, characterized in that, The steps of calculating the total power demand in the peak-shaving area and the total power demand in the active support area using a fuzzy adaptive low-pass filter based on the collected grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit specifically include: Based on a fuzzy adaptive low-pass filter controller, the high-frequency and low-frequency components of active power are calculated. The high-frequency component corresponds to the total active power demand of energy storage in the active support zone, and is less than or equal to the maximum allowable charging and discharging active power of the active support zone. The low-frequency component corresponds to the total active power command in the peak-shaving zone, and is less than or equal to the maximum allowable active charging and discharging power of the peak-shaving zone. The total active power demand is equal to the sum of the total active power demand of energy storage in the active support zone and the total active power demand of energy storage in the peak-shaving zone. The total reactive power demand of energy storage in the active support zone is the total reactive power demand of the energy storage power station, and is less than the maximum allowable reactive power output of the active support zone.

5. The power control method for a battery energy storage power station according to claim 1, characterized in that, The step of calculating the power command of energy storage units in each region based on the total power demand in the peak-shaving region and the total power demand in the active support region specifically includes: when hour, ; when hour, ; Where a is the SOC range threshold; n is the number of energy storage units in the peak-shaving zone; and m is the number of energy storage units in the active support zone. P ps To meet the active power demand of energy storage in peak-shaving areas, P fv To proactively support the active power demand of energy storage in the region, R soc,ps The SOC range is extremely poor in the peak-shaving region. R soc,fv The active support region has a very poor SOC (State of Charge) range. For the active power demand of the i-th energy storage unit in the peak-shaving area, To proactively support the active power demand of the i-th energy storage unit within the area; when hour; 1) If the total demand is discharge, arrange the energy storage units in the peak-shaving area and the active support area in descending order of SOC. The discharge power coefficient of each energy storage unit is calculated according to the following formula: When n or m is even: When n or m is odd: In the above formula, x ps (i) represents the output coefficient of the i-th energy storage unit in the peak-shaving region, SOC ps (i) represents the SOC of the i-th energy storage unit in the peak-shaving region. max，ps c is the maximum SOC of the peak-shaving energy storage unit. d,ps x is the ratio of active power demand in the peak-shaving region to the maximum allowable discharge active power; fv (i) represents the output coefficient of the i-th energy storage unit in the active support zone, SOC fv (i) represents the SOC of the i-th energy storage unit in the active support zone. max,fv To support the maximum SOC of the energy storage unit in the active support zone, c d,fv It is the ratio of the active power demand in the active support area to the maximum allowable discharge active power; Discharge power of each energy storage unit P ps (i) for: in, Let be the maximum allowable discharge power of the i-th energy storage unit; 2) If the total demand is charging, arrange the energy storage units in the peak-shaving area and the active support area in ascending order of SOC. The charging power coefficient of each energy storage unit is calculated according to the following formula: When n or m is even: When n or m is odd: In the above formula, SOC min,ps c is the minimum SOC of the peak-shaving energy storage unit. c,ps The ratio of active power demand in the peak-shaving region to the maximum allowable charging active power; SOC min,fv To provide the minimum SOC for the active support zone energy storage unit, c c,fv It is the ratio of the active power demand in the active support area to the maximum allowable charging active power; Charging power of each energy storage unit P ps (i) for: in, This represents the maximum allowable charging power of the i-th energy storage unit.

6. A power control system for a battery energy storage power station, characterized in that, include: The data acquisition unit is used to collect data on grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status of each energy storage unit; the battery energy storage power station includes a peak-shaving area and an active support area. The fuzzy computing unit is used to calculate the total power demand in the peak-shaving area and the total power demand in the active support area by using the collected grid frequency, grid voltage, total power demand of the battery energy storage power station and real-time operating status data of each energy storage unit through a fuzzy adaptive low-pass filter. The control unit is used to calculate the power command of the energy storage unit in each region based on the total power demand in the peak shaving region and the total power demand in the active support region, and to issue the power command to the corresponding energy storage converter for execution.

7. The power control system for a battery energy storage power station according to claim 6, characterized in that, In the steps of the acquisition unit to collect grid frequency, grid voltage, total power demand of the battery energy storage power station and real-time operating status data of each energy storage unit, the real-time operating status data of each energy storage unit of the battery energy storage power station includes the energy storage unit SOC and maximum allowable charge and discharge power.

8. The power control system for a battery energy storage power station according to claim 6, characterized in that, In the process of calculating the total power demand in the peak-shaving zone and the total power demand in the active support zone using the collected grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit through the fuzzy adaptive low-pass filter, the design method of the fuzzy adaptive low-pass filter includes: Frequency deviation and voltage deviation are used as inputs to the fuzzy controller, and the output is the output time constant of the low-pass filter. The fuzzy subsets of frequency deviation are {negative large, negative small, negative zero, positive zero, positive small, positive large}, the fuzzy subsets of voltage deviation are {negative large, negative small, zero, positive small, positive large}, and the fuzzy subsets of output time constant are {negative large, negative medium, negative small, negative zero, positive zero, positive small, positive medium, positive large}. The basis for formulating the fuzzy rules is as follows: When the frequency deviation and voltage deviation are greater than the corresponding first frequency deviation threshold and first voltage deviation threshold, respectively, the time constant of the low-pass filter is reduced; when the frequency deviation and voltage deviation are less than the corresponding second frequency deviation threshold and second voltage deviation threshold, the time constant of the low-pass filter is increased; otherwise, the change in total demand in the peak-shaving area is reduced.

9. The power control system for a battery energy storage power station according to claim 6, characterized in that, The fuzzy computing unit uses a fuzzy adaptive low-pass filter to calculate the total power demand in the peak-shaving area and the total power demand in the active support area using collected grid frequency, grid voltage, total power demand of the battery energy storage power station, and real-time operating status data of each energy storage unit. The specific steps include: Based on a fuzzy adaptive low-pass filter controller, the high-frequency and low-frequency components of active power are calculated. The high-frequency component corresponds to the total active power demand of energy storage in the active support zone, and is less than or equal to the maximum allowable charging and discharging active power of the active support zone. The low-frequency component corresponds to the total active power command in the peak-shaving zone, and is less than or equal to the maximum allowable active charging and discharging power of the peak-shaving zone. The total active power demand is equal to the sum of the total active power demand of energy storage in the active support zone and the total active power demand of energy storage in the peak-shaving zone. The total reactive power demand of energy storage in the active support zone is the total reactive power demand of the energy storage power station, and is less than the maximum allowable reactive power output of the active support zone.

10. The power control system for a battery energy storage power station according to claim 6, characterized in that, The control unit calculates the power command for each energy storage unit in the peak-shaving zone and the active support zone based on the total power demand in both zones. This calculation includes the following steps: when hour, ; when hour, ; Where a is the SOC range threshold; n is the number of energy storage units in the peak-shaving zone; and m is the number of energy storage units in the active support zone. P ps To meet the active power demand of energy storage in peak-shaving areas, P fv To proactively support the active power demand of energy storage in the region, R soc,ps The SOC range is extremely poor in the peak-shaving region. R soc,fv The active support region has a very poor SOC (State of Charge) range. For the active power demand of the i-th energy storage unit in the peak-shaving area, To proactively support the active power demand of the i-th energy storage unit within the area; when hour; 1) If the total demand is discharge, arrange the energy storage units in the peak-shaving area and the active support area in descending order of SOC. The discharge power coefficient of each energy storage unit is calculated according to the following formula: When n or m is even: When n or m is odd: In the above formula, x ps (i) represents the output coefficient of the i-th energy storage unit in the peak-shaving region, SOC ps (i) represents the SOC of the i-th energy storage unit in the peak-shaving region. max，ps c is the maximum SOC of the peak-shaving energy storage unit. d,ps x is the ratio of active power demand in the peak-shaving region to the maximum allowable discharge active power; fv (i) represents the output coefficient of the i-th energy storage unit in the active support zone, SOC fv (i) represents the SOC of the i-th energy storage unit in the active support zone. max,fv To support the maximum SOC of the energy storage unit in the active support zone, c d,fv It is the ratio of the active power demand in the active support area to the maximum allowable discharge active power; Discharge power of each energy storage unit P ps (i) for: in, Let be the maximum allowable discharge power of the i-th energy storage unit; 2) If the total demand is charging, arrange the energy storage units in the peak-shaving area and the active support area in ascending order of SOC. The charging power coefficient of each energy storage unit is calculated according to the following formula: When n or m is even: When n or m is odd: In the above formula, SOC min,ps c is the minimum SOC of the peak-shaving energy storage unit. c,ps The ratio of active power demand in the peak-shaving region to the maximum allowable charging active power; SOC min,fv To provide the minimum SOC for the active support zone energy storage unit, c c,fv It is the ratio of the active power demand in the active support area to the maximum allowable charging active power; Charging power of each energy storage unit P ps (i) for: in, This represents the maximum allowable charging power of the i-th energy storage unit.

11. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the power control method for a battery energy storage power station as described in any one of claims 1 to 5.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the power control method for a battery energy storage power station as described in any one of claims 1 to 5.

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