Optimization control system and method for battery energy storage system of power grid

Abstract

The present invention belongs to the technical field of battery energy storage control, and discloses an optimization control system and method for a battery energy storage system of a power grid; the method comprises: numbering batteries in a battery energy storage system to obtain battery serial numbers; obtaining health status data of batteries in the battery energy storage system; preprocessing the health status data within a unit time to obtain preprocessed data; dividing the geographical area where the battery energy storage system is located to obtain sub-area serial numbers; obtaining power generation and power transmission distances of different sub-areas within a unit time; establishing a digital model based on battery serial numbers, preprocessed data, sub-area serial numbers, power generation and power transmission distances; setting target conditions based on the digital model, the target conditions comprising a minimum number of idle batteries, a maximum total battery health value and a maximum average power storage rate; and reducing the human resource cost of replacing batteries in a distributed battery energy storage cabinet.

Description

Technical Field

[0001] The present invention relates to the technical field of battery energy storage control, and more specifically, to an optimization control system and method for a battery energy storage system of a power grid. Background Art

[0002] The existing patent application with publication number CN114498698A discloses a distributed energy storage system based on a distribution substation, which is applied to the distribution substation, including a distributed energy storage device connected to the distribution substation, the distributed energy storage device including a control cabinet and a battery cabinet, the control cabinet including an energy storage bidirectional inverter module and an energy controller, one end of the energy storage bidirectional inverter module is connected to the distribution substation, and the other end is connected to the battery cabinet; the energy controller is used to control the operating state of the energy storage bidirectional inverter module according to a preset control strategy to control the working state of the distributed energy storage device; the working state of the distributed energy storage device includes a standby state, a charging state, and a discharging state. This invention greatly reduces the load pressure of the distribution substation, and through energy coordination control, it meets the energy distribution requirements between the coordinated load, the energy storage battery, and the power grid, while improving the power quality of the distribution substation.

[0003] The existing technology still has the following problems:

[0004] When a certain area continuously generates a large amount of electricity, the energy storage batteries in the distributed battery energy storage cabinet corresponding to the area may be in a high-load working state for a long time, resulting in excessive use of the batteries. Other batteries in the same energy storage cabinet may be idle due to insufficient power demand and fail to be fully utilized. This unbalanced workload leads to large differences in the service life of energy storage batteries, which require frequent replacement, thereby increasing the cost of maintenance and replacement. The overall economic efficiency of the energy storage system needs to be further improved.

[0005] In view of this, the present invention proposes a battery energy storage system optimization control system and method for a power grid to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for optimizing and controlling a battery energy storage system of a power grid, comprising:

[0007] Number the batteries in the battery energy storage system and obtain the battery serial number;

[0008] Obtain health status data of batteries in battery energy storage systems;

[0009] Preprocess the health status data within a unit time to obtain preprocessed data;

[0010] Divide the geographical area where the battery energy storage system is located and obtain the sub-area serial number;

[0011] Obtain the power generation and power transmission distance of different sub-areas per unit time;

[0012] A digital model is established based on the battery serial number, preprocessed data, sub-area serial number, power generation and power transmission distance;

[0013] Setting target conditions based on the digital model, the target conditions including a minimum number of idle batteries, a maximum total battery health value, and a maximum average energy storage rate;

[0014] Random Generation Energy storage allocation scheme;

[0015] According to the target conditions, the energy storage allocation plan is optimized using an adaptive allocation algorithm, and the optimized energy storage allocation plan is output to meet the set target conditions.

[0016] Furthermore, the method for optimizing the energy storage allocation scheme by the adaptive allocation algorithm includes:

[0017] Step N1, initialize the particle swarm, each particle swarm represents an energy storage allocation plan, the energy storage allocation plan includes the usage of all batteries in the battery energy storage system in the next unit time; the usage includes use and non-use; use is recorded as 1, and non-use is recorded as 0;

[0018] Step N2, obtaining the number of idle batteries, the total battery health value and the average energy storage rate of each energy storage allocation plan;

[0019] Step N3: Calculate the number of idle batteries, total battery health value and average energy storage rate. Comprehensive fitness of energy storage allocation schemes ;

[0020] Step N4, updating the speed and position of the particles, that is, updating the usage of all batteries in the battery energy storage system in the next unit time, and generating a new energy storage allocation plan;

[0021] Step N5: If the first The total battery health value of particles is greater than the total battery health value corresponding to the historical best position, then let ; If the first The total battery health value of particles is greater than the total battery health value corresponding to the global optimal position, then let ; For the The particle in The position at the iteration; For the The particle finds the best historical position in the corresponding energy storage allocation scheme. The best position in the corresponding energy storage allocation scheme is the one with the maximum total battery health value. The best position among all energy storage allocation schemes found for all particles;

[0022] Step N6, repeat steps N2-N5 until a preset number of repetitions is reached, and then select the energy storage allocation scheme with the highest comprehensive fitness. The energy storage allocation scheme with the highest comprehensive fitness is the output optimized energy storage allocation scheme that meets the set target conditions.

[0023] Further, the health status data includes voltage value, current value, battery temperature value, internal resistance value and remaining capacity value; the pre-processed data includes voltage average value, current average value, battery temperature average value, internal resistance average value and remaining capacity average value;

[0024] The method for obtaining the number of idle batteries includes:

[0025] Step S1: Arrange the battery numbers in ascending order to create a The battery serial number collection; is the number of batteries in the battery energy storage system; is a positive integer greater than 1; let , ; The first battery serial number in the set Battery serial number, ; It is the idle battery quantity counter;

[0026] Step S2: If the battery serial number set If the average voltage of the battery number is less than the preset open circuit voltage value and the average current is less than the preset static current value, then ;

[0027] Step S3: , repeat step S2 until When , execute step S4;

[0028] Step S4: , record the number of idle batteries at this time .

[0029] Furthermore, the method for obtaining the total battery health value includes:

[0030] ;

[0031] In the formula, is the total battery health value; For the The average voltage value corresponding to each battery number; Preset thresholds for voltage; For the The average current value corresponding to each battery number; Preset threshold value for current; For the The average battery temperature corresponding to each battery number; Preset thresholds for battery temperature; For the The average internal resistance corresponding to each battery number; Preset threshold value for internal resistance; For the The average value of remaining capacity corresponding to each battery serial number; Preset thresholds for remaining capacity; , , , and These are the corresponding preset weights.

[0032] Furthermore, the batteries of the battery energy storage system are distributed in various areas; if there are two batteries in non-adjacent power generation areas, the battery energy storage system optimization control method of the power grid further includes:

[0033] The area with the largest amount of batteries in the energy storage allocation scheme is taken as the main area; it is determined whether the areas where the batteries outside the main area are located are all adjacent to each other. If not, the batteries in the area that is not adjacent to any area are selected, and the corresponding battery information is retrieved; the battery information includes the current power information;

[0034] For the retrieved battery information, a battery whose battery information similarity is higher than a set threshold is selected from an area where an adjacent area exists as a replacement battery, and the selected battery is replaced with the replacement battery, and the energy storage allocation plan is regenerated.

[0035] Furthermore, the selecting a battery whose battery information similarity is higher than a set threshold value as a replacement battery from the area where the adjacent area exists includes:

[0036] Selecting batteries whose battery information similarity is higher than a set threshold from the area where adjacent areas exist;

[0037] If there are multiple batteries, determine the number of areas where batteries are allocated in the adjacent areas of each battery, and select the battery with the largest number of areas as the replacement battery;

[0038] If there are multiple batteries with the largest number of regions, the battery with the highest similarity is selected as the replacement battery.

[0039] Further, the power grid includes a plurality of power generation areas, and the battery is arranged in each power generation area;

[0040] The battery energy storage system optimization control method of the power grid also includes:

[0041] According to the power generation of each power generation area, the power generation area with the largest power generation is selected as the central area, and batteries with similar battery information to the batteries in the non-central area are searched as the batteries to be replaced;

[0042] For each battery, the distances between the corresponding battery to be replaced and the battery and the central area are compared, the battery with the closest distance is selected as the replacement battery to replace the battery, and the energy storage allocation plan is updated.

[0043] Furthermore, the method for obtaining the average storage rate of electric energy includes:

[0044] ;

[0045] In the formula, is the average storage rate of electric energy; is the total power generation of the sub-region; is the total length of the power transmission route; It is the preset power loss coefficient per unit length of the transmission route.

[0046] Furthermore, the comprehensive fitness The calculation methods include:

[0047] ;

[0048] In the formula, , and is the preset weight coefficient.

[0049] Furthermore, the particle velocity update method includes:

[0050] ;

[0051] In the formula, For the The particle in The speed at the iteration; For the The particle in The speed at the iteration; For the The particle in The position at the iteration; is the inertia weight, which controls the continuity of particle velocity; is the individual learning factor; is the group learning factor; and All are random numbers in [0,1];

[0052] Particle position update methods include:

[0053] .

[0054] Furthermore, the electric energy transmission distance is the length of the transmission route between the power generation source corresponding to the sub-area and each battery in the battery energy storage system.

[0055] Furthermore, the method for obtaining the voltage average value includes:

[0056] For the unit time voltage values ​​to establish a voltage value sequence, sum all the voltage values ​​in the voltage value sequence and then divide them by , get the average voltage; is the acquisition frequency, is a positive integer greater than 1;

[0057] The same method as the voltage average value is used to obtain the current average value, the battery temperature average value, the internal resistance average value and the remaining capacity average value.

[0058] A battery energy storage system optimization control system for a power grid, implementing the battery energy storage system optimization control method for the power grid, comprising:

[0059] The first setting module is used to number the batteries in the battery energy storage system and obtain the battery serial number;

[0060] A first acquisition module is used to acquire health status data of batteries in the battery energy storage system;

[0061] A preprocessing module, used to preprocess the health status data within a unit time and obtain preprocessed data;

[0062] The second setting module is used to divide the geographical area where the battery energy storage system is located and obtain the sub-area serial number;

[0063] The second acquisition module is used to obtain the power generation and power transmission distance of different sub-areas in unit time;

[0064] Digital modeling module, which builds a digital model based on battery serial number, preprocessed data, sub-area serial number, power generation and power transmission distance;

[0065] A target setting module, which sets target conditions based on the digital model, wherein the target conditions include a minimum number of idle batteries, a maximum total battery health value, and a maximum average storage rate of electric energy;

[0066] Path generation module, randomly generated Energy storage allocation scheme;

[0067] The path analysis module uses an adaptive allocation algorithm to optimize the energy storage allocation plan according to the target conditions and outputs the optimized energy storage allocation plan to meet the set target conditions.

[0068] Technical effects and advantages of the battery energy storage system optimization control system and method of the power grid of the present invention:

[0069] When a certain area continuously generates a large amount of electric energy, the present invention obtains an energy storage allocation plan through an adaptive allocation algorithm to optimize the three target conditions of the minimum number of idle batteries, the maximum total battery health value and the maximum average electric energy storage rate, outputs the optimized energy storage allocation plan and selects the best plan for execution, so that the battery energy storage system is always in the most balanced battery load distribution when storing electric energy from different areas, avoiding premature degradation of some batteries due to long-term high-load operation, and avoiding performance degradation caused by long-term non-operation of some batteries, extending the overall life of the battery energy storage system of the power grid, such as the distributed battery energy storage cabinet, and at the same time making the average life of each battery in the distributed battery energy storage cabinet as consistent as possible, reducing the human resource cost of replacing batteries in the distributed battery energy storage cabinet.

[0070] By obtaining the power generation and power transmission distance of each sub-region, the system can flexibly adjust the energy storage solution according to the changes in the power generation source (such as wind power, photovoltaic, etc.); for example, in rainy weather, when the power generation of the photovoltaic field decreases, the batteries in the battery energy storage system that store energy for the photovoltaic field may be idle, and when the power generation effect of the wind farm is enhanced, the energy storage system adjusts the energy storage strategy to allocate more electricity to the wind power-related batteries; this dynamic adjustment method can ensure that the energy storage system always maintains efficient operation under different climatic conditions.

[0071] The battery health value is calculated comprehensively by collecting the battery voltage, current, temperature, internal resistance and remaining capacity and performing preprocessing. When the battery health value is low, continuing to store energy may accelerate the battery's degradation. By setting an optimization target for the health value, the energy storage allocation plan can avoid excessive use of batteries in poor health, ensure battery load balance, and extend the service life of the overall system.

[0072] At the same time, as the demand for energy storage increases, the present invention can flexibly add new battery energy storage cabinets and achieve expansion through a modular energy storage allocation solution; the adaptive allocation algorithm within the battery energy storage system optimization control system supports the addition of new sub-areas and batteries at any time without the need for large-scale modifications to the existing architecture; this makes the present invention highly flexible and scalable and able to adapt to future growth needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1This is a schematic diagram of an optimization control system for a battery energy storage system of a power grid according to Embodiment 1 of the present invention;

[0074] Figure 2 This is a flow chart of a method for optimizing and controlling a battery energy storage system of a power grid according to Embodiment 2 of the present invention;

[0075] Figure 3 A schematic diagram of a battery energy storage system and a geographical location of a sub-region according to Embodiment 1 of the present invention;

[0076] Figure 4 This is a flow chart of the adaptive allocation algorithm of embodiment 1 of the present invention. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] Example 1

[0079] See also Figure 1 As shown, the battery energy storage system optimization control system of the power grid described in this embodiment includes: a first setting module, a first acquisition module, a preprocessing module, a second setting module, a second acquisition module, a digital modeling module, a target setting module, a path generation module and a path analysis module, and each module is connected by wire and / or wirelessly.

[0080] The first setting module is used to number the batteries in the battery energy storage system and obtain the battery serial number.

[0081] The battery energy storage system is a battery energy storage cabinet, which includes multiple batteries.

[0082] By obtaining the battery serial number, a unique serial number is assigned to each battery in the battery energy storage system, which will be used in the present invention to identify each battery and the corresponding health status data; it is also used for the specific allocation of different batteries in the subsequent energy storage allocation plan, effectively improving the accuracy of the plan execution.

[0083] The battery serial number is associated with the battery by installing a physical label on the battery, and the corresponding relationship between the battery and the battery serial number is stored in a database.

[0084] A first acquisition module is used to acquire health status data of batteries in the battery energy storage system in real time, wherein the health status data includes voltage value, current value, battery temperature value, internal resistance value and remaining capacity value;

[0085] The voltage and current values ​​of the battery are important parameters that reflect the battery charging status (such as the remaining capacity value) and health status. When the voltage value of the battery is less than the preset open circuit voltage value and the current value is less than the preset static current value within a certain unit time, it can be considered that the battery is in an idle state within the unit time. If the battery is idle for a long time, the battery performance will decline. By obtaining the voltage and current values ​​of the battery, it is helpful to improve the rationality of the subsequent health value, and at the same time help to improve the accuracy of the target conditions in the subsequent adaptive allocation algorithm.

[0086] Battery temperature has an important impact on the battery life and safety. Excessively high temperature may cause the battery to overheat, increasing safety risks. At the same time, the temperature changes of the battery under different load conditions can reflect the working status of the battery. Excessively high temperature may mean that the battery is in an overworking state, which in turn affects the battery life. By obtaining the battery temperature value, it helps to improve the rationality of the health value in the subsequent adaptive allocation algorithm, thereby effectively ensuring that all batteries in the battery energy storage system are used within the appropriate temperature range.

[0087] The internal resistance of a battery is an important indicator of its health status. As the battery is used for a longer time, its internal resistance gradually increases. When the internal resistance is too large, the battery's charging efficiency drops significantly. By obtaining the internal resistance value, it helps to dynamically adjust the battery's energy storage distribution according to the health value in the subsequent adaptive allocation algorithm, thereby increasing the battery's service life.

[0088] The remaining capacity value (SOC) is a key data that reflects the remaining energy storage of the battery. When the remaining capacity value of the battery is high, if the battery continues to be charged, the battery voltage will continue to rise, and may eventually exceed the safety threshold, causing abnormal chemical reactions inside the battery, which may cause gas expansion, thermal runaway and other problems, and in severe cases may cause the battery to catch fire or explode. By obtaining the remaining capacity value, it is helpful to dynamically adjust the battery's energy storage allocation according to the health value in the subsequent adaptive allocation algorithm, thereby improving the safety of the battery energy storage system.

[0089] The voltage value is obtained by a voltage sensor; the current value is obtained by a Hall effect sensor; the battery temperature is obtained by a temperature sensor; the internal resistance value is obtained by a transient response method; and the remaining capacity value is obtained by a coulomb counting method.

[0090] The preprocessing module preprocesses the health status data within a unit time to obtain preprocessed data, wherein the preprocessed data includes an average voltage, an average current, an average battery temperature, an average internal resistance, and an average remaining capacity.

[0091] The method for obtaining the voltage average value includes:

[0092] For the unit time voltage values ​​to establish a voltage value sequence, sum all the voltage values ​​in the voltage value sequence and then divide them by , get the average voltage; is the acquisition frequency, is a positive integer greater than 1.

[0093] The same method as the voltage average value is used to obtain the current average value, the battery temperature average value, the internal resistance average value and the remaining capacity average value.

[0094] The second setting module is used to divide the geographical area where the battery energy storage system is located and obtain sub-area serial numbers.

[0095] Each sub-region has a unique correspondence with the sub-region serial number.

[0096] The method for dividing the sub-region serial numbers includes: each sub-region is divided by the batteries in the battery energy storage system corresponding to the power plant in the sub-region.

[0097] For example, see Figure 3 , the battery energy storage system contains 3 batteries, the battery numbers are 1, 2 and 3; there are 3 electric fields in the geographical area, namely solar electric field, wind electric field and traditional thermal power plant; the solar electric field stores energy through the battery with battery number 1, then the sub-area number corresponding to the solar electric field is ; The wind farm stores energy through the battery with battery number 2, so the sub-area number corresponding to the wind farm is ; The traditional thermal power plant stores energy through the battery with battery number 3, so the sub-area number corresponding to the traditional thermal power plant is .

[0098] The second acquisition module is used to obtain the power generation and power transmission distance of different sub-areas per unit time; the power transmission distance is the transmission route length between the power generation source corresponding to the sub-area and each battery in the battery energy storage system.

[0099] The power generation is related to the power generation source of the sub-area (such as wind power, photovoltaic power, thermal power, etc.); by obtaining the power generation, the present invention can dynamically adjust the energy storage scheme; for example, in bad weather such as rainy weather, the power generation effect of the solar field is not good, so that the batteries used to store the electricity generated by the solar field in the battery energy storage system are idle; for example, the power generation effect of the wind farm is enhanced, and the batteries used to store the electricity generated by the wind farm in the battery energy storage system are in a high-load state, endangering the battery life; by obtaining the power generation per unit time, the present invention can dynamically allocate electric energy to the batteries in the battery energy storage system, which helps to extend the overall average life of the battery energy storage system.

[0100] The distance of electric energy transmission affects the loss of battery energy storage system during energy storage; the longer the distance of electric energy transmission, the higher the degree of electric energy loss; by obtaining the distance of electric energy transmission, it helps to improve the rationality of subsequent calculation of electric energy storage rate, thereby effectively improving the overall efficiency of subsequent energy storage allocation plan.

[0101] The power generation amount is obtained by monitoring the power generation source through the SCADA system in the sub-area; and the power transmission distance is obtained through the power grid topology model in the power grid management system.

[0102] The digital modeling module establishes a digital model based on the battery serial number, preprocessed data, sub-area serial number, power generation and power transmission distance.

[0103] The target setting module sets target conditions based on the digital model, wherein the target conditions include a minimum number of idle batteries, a maximum total battery health value, and a maximum average energy storage rate.

[0104] The idle battery is a battery whose average voltage per unit time is less than a preset open-circuit voltage value and whose average current is less than a preset static current value; the total battery health value is the sum of the health values ​​of each battery involved in the storage; the health value is the weighted sum of the ratios of the data in the preprocessed data per unit time to the respective preset thresholds; the average electric energy storage rate is the ratio of the total stored electric energy of the battery to the total power generation of the sub-area; the total stored electric energy of the battery is the difference between the total power generation and the loss value during the power transmission process.

[0105] Path generation module, randomly generated Energy storage allocation scheme.

[0106] The path analysis module uses an adaptive allocation algorithm to optimize the energy storage allocation plan according to the target conditions and outputs the optimized energy storage allocation plan to meet the set target conditions.

[0107] See also Figure 4 As shown, the method for optimizing the energy storage allocation scheme by the adaptive allocation algorithm includes:

[0108] Step N1, initialize the particle swarm, each particle swarm represents an energy storage allocation plan, the energy storage allocation plan includes the usage of all batteries in the battery energy storage system in the next unit time; the usage includes use and non-use; use is recorded as 1, and non-use is recorded as 0.

[0109] Exemplarily, there are 3 batteries in the battery energy storage system, and the battery numbers are 1, 2 and 3; among them, the batteries with battery numbers 1 and 3 are used for energy storage, and the battery with battery number 2 is not used for energy storage; then the output energy storage allocation plan is (1,1;2,0;3,1).

[0110] Step N2: Obtain the number of idle batteries, total battery health value and average energy storage rate of each energy storage allocation plan.

[0111] The method for obtaining the number of idle batteries includes:

[0112] Step S1: Arrange the battery numbers in ascending order to create a The battery serial number collection; is the number of batteries in the battery energy storage system; is a positive integer greater than 1; let , ; The first battery serial number in the set Battery serial number, ; It is the idle battery quantity counter;

[0113] Step S2: If the battery set If the average voltage of the battery number is less than the preset open circuit voltage value and the average current is less than the preset static current value, then .

[0114] Step S3: , repeat step S2 until , execute step S4.

[0115] Step S4: , record the number of idle batteries at this time .

[0116] The method for obtaining the total battery health value includes:

[0117] .

[0118] In the formula, is the total battery health value; For the The average voltage value corresponding to each battery number; Preset thresholds for voltage; For the The average current value corresponding to each battery number; Preset threshold value for current; For the The average battery temperature corresponding to each battery number; Preset thresholds for battery temperature; For the The average internal resistance corresponding to each battery number; Preset threshold value for internal resistance; For the The average value of remaining capacity corresponding to each battery serial number; Preset thresholds for remaining capacity; , , , and These are the corresponding preset weights.

[0119] The method for obtaining the average electric energy storage rate includes:

[0120] .

[0121] In the formula, is the average storage rate of electric energy; is the total power generation of the sub-region; is the total length of the power transmission route; It is the preset power loss coefficient per unit length of the transmission route.

[0122] Step N3: Calculate the number of idle batteries, total battery health value and average energy storage rate. Comprehensive fitness of energy storage allocation schemes The comprehensive fitness The calculation methods include:

[0123] .

[0124] In the formula, , and is the preset weight coefficient.

[0125] Step N4: Update the speed and position of the particle, that is, update the usage of all batteries in the battery energy storage system in the next unit time, and generate a new energy storage allocation plan; the method for updating the particle speed includes:

[0126] .

[0127] In the formula, For the The particle in The speed at the iteration; For the The particle in The speed at the iteration; For the The particle in The position at the iteration; For the The particle finds the best historical position in the corresponding energy storage allocation scheme. The best position in the corresponding energy storage allocation scheme is the one with the maximum total battery health value. is the best position found globally (i.e., among all energy storage allocation schemes) among all particles; is the inertia weight, which controls the continuity of particle velocity; is the individual learning factor, The larger the value of , the more particles will tend to rely on their own historical experience; is the group learning factor, The larger the value of , the more particles will tend to rely on the common experience of the group; and They are all random numbers in [0,1] to increase the randomness of the search.

[0128] For example, The parameters of each particle are as follows: ; ; ; ; ; ; ; ; .

[0129] Inertia term calculation:

[0130] .

[0131] Individual cognitive item calculation:

[0132] .

[0133] Group cognition item calculation:

[0134] .

[0135] Update speed calculation:

[0136] .

[0137] Particle position update methods include:

[0138] .

[0139] For example, The updated position of a particle is:

[0140] .

[0141] Step N5: If the first The total battery health value of particles is greater than the total battery health value corresponding to the historical best position, then let ; If the first The total battery health value of particles is greater than the total battery health value corresponding to the global optimal position, then let .

[0142] Step N6, repeat steps N2-N5 until a preset number of repetitions is reached, and then select the energy storage allocation scheme with the highest comprehensive fitness. The energy storage allocation scheme with the highest comprehensive fitness is the output optimized energy storage allocation scheme that meets the set target conditions.

[0143] Furthermore, the batteries of the battery energy storage system are distributed in various areas; if there are two batteries in non-adjacent power generation areas, the battery energy storage system optimization control method of the power grid further includes:

[0144] The area with the largest amount of batteries in the energy storage allocation scheme is taken as the main area; it is determined whether the areas where the batteries outside the main area are located are all adjacent to each other. If not, the batteries in the area that is not adjacent to any area are selected, and the corresponding battery information is retrieved; the battery information includes the current power information;

[0145] For the retrieved battery information, a battery whose battery information similarity is higher than a set threshold is selected from an area where an adjacent area exists as a replacement battery, and the selected battery is replaced with the replacement battery, and the energy storage allocation plan is regenerated.

[0146] In this way, by replacing non-adjacent batteries with adjacent batteries, the battery selection areas can be made adjacent, avoiding the problem of inconvenient power scheduling.

[0147] Furthermore, the selecting a battery whose battery information similarity is higher than a set threshold value as a replacement battery from the area where the adjacent area exists includes:

[0148] Selecting batteries whose battery information similarity is higher than a set threshold from the area where adjacent areas exist;

[0149] If there are multiple batteries, determine the number of areas where batteries are allocated in the adjacent areas of each battery, and select the battery with the largest number of areas as the replacement battery;

[0150] If there are multiple batteries with the largest number of regions, the battery with the highest similarity is selected as the replacement battery.

[0151] In this way, since there are the largest number of adjacent areas, the corresponding area is more closely related to the entire scheduling area. Through such a setting, the compactness of battery selection can be improved to avoid excessive power transmission.

[0152] Further, the power grid includes a plurality of power generation areas, and the battery is arranged in each power generation area;

[0153] The battery energy storage system optimization control method of the power grid also includes:

[0154] According to the power generation of each power generation area, the power generation area with the largest power generation is selected as the central area, and batteries with similar battery information to the batteries in the non-central area are searched as the batteries to be replaced;

[0155] For each battery, the distances between the corresponding battery to be replaced and the battery and the central area are compared, the battery with the closest distance is selected as the replacement battery to replace the battery, and the energy storage allocation plan is updated.

[0156] This embodiment further improves the compactness of battery selection by combining power generation areas, thereby providing another method to avoid excessive power transmission. This method uses the largest power generation area as an anchor to maximize the utilization rate of power transmission and optimize the configuration of power transmission.

[0157] Example 2

[0158] See also Figure 2 As shown, this embodiment provides a battery energy storage system optimization control method for a power grid, including:

[0159] Number the batteries in the battery energy storage system and obtain the battery serial number;

[0160] Obtain health status data of batteries in battery energy storage systems;

[0161] Preprocess the health status data within a unit time to obtain preprocessed data;

[0162] Divide the geographical area where the battery energy storage system is located and obtain the sub-area serial number;

[0163] Obtain the power generation and power transmission distance of different sub-areas per unit time;

[0164] A digital model is established based on the battery serial number, preprocessed data, sub-area serial number, power generation and power transmission distance;

[0165] Setting target conditions based on the digital model, the target conditions including a minimum number of idle batteries, a maximum total battery health value, and a maximum average energy storage rate;

[0166] Random Generation Energy storage allocation scheme;

[0167] According to the target conditions, the energy storage allocation plan is optimized using an adaptive allocation algorithm, and the optimized energy storage allocation plan is output to meet the set target conditions.

[0168] Example 3

[0169] The electronic device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in Embodiment 2 of the method for optimizing and controlling a battery energy storage system of a power grid are implemented, for example: Figure 2 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Figure 1 Functionality of the modules shown.

[0170] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the terminal device.

[0171] Those skilled in the art will appreciate that these are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0172] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0173] The memory may be an internal storage unit of the terminal device, such as a hard disk or memory of the terminal device. The memory may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (FlashCard), etc. equipped on the terminal device. Furthermore, the memory may include both an internal storage unit and an external storage device of the terminal device. The memory is used to store the computer program and other programs and data required by the terminal device. The memory may also be used to temporarily store data that has been output or is to be output.

[0174] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0175] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0176] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0177] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiment when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0178] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for optimizing and controlling a battery energy storage system of a power grid, characterized in that: include: Number the batteries in the battery energy storage system and obtain the battery serial number; A battery comes from a battery storage cabinet; Obtain health status data of batteries in battery energy storage systems; The health status data includes voltage value, current value, battery temperature value, internal resistance value and remaining capacity value; Preprocess the health status data within a unit time to obtain preprocessed data; the preprocessed data includes an average voltage, an average current, an average battery temperature, an average internal resistance, and an average remaining capacity; Divide the geographical area where the battery energy storage system is located and obtain the sub-area serial number; Obtain the power generation and power transmission distance of different sub-areas per unit time; A digital model is established based on the battery serial number, preprocessed data, sub-area serial number, power generation and power transmission distance; Setting target conditions based on the digital model, the target conditions including a minimum number of idle batteries, a maximum total battery health value, and a maximum average energy storage rate; Random Generation Energy storage allocation scheme; According to the target conditions, the energy storage allocation scheme is optimized using an adaptive allocation algorithm, and the optimized energy storage allocation scheme is output to meet the set target conditions; the method for optimizing the energy storage allocation scheme using the adaptive allocation algorithm includes: Step N1, initialize the particle swarm, each particle swarm represents an energy storage allocation plan, the energy storage allocation plan includes the usage of all batteries in the battery energy storage system in the next unit time; the usage includes use and non-use; use is recorded as 1, and non-use is recorded as 0; Step N2: Obtain the number of idle batteries, total battery health value and average energy storage rate of each energy storage allocation plan; Step N3: Calculate the number of idle batteries, total battery health value and average energy storage rate. Comprehensive fitness of energy storage allocation schemes ; The method for obtaining the number of idle batteries includes: Step S1: Arrange the battery numbers in ascending order to create a The battery serial number collection; is the number of batteries in the battery energy storage system; is a positive integer greater than 1; let , ; The first battery serial number in the set Battery serial number, ; It is the idle battery quantity counter; Step S2: If the battery serial number set If the average voltage of the battery number is less than the preset open circuit voltage value and the average current is less than the preset static current value, then ; Step S3: , repeat step S2 until When , execute step S4; Step S4: , record the number of idle batteries at this time ; Step N4, updating the speed and position of the particles, that is, updating the usage of all batteries in the battery energy storage system in the next unit time, and generating a new energy storage allocation plan; Step N5: If the first The total battery health value of particles is greater than the total battery health value corresponding to the historical best position, then let ; If the first The total battery health value of particles is greater than the total battery health value corresponding to the global optimal position, then let ; For the The particle in The position at the iteration; For the The particle finds the best historical position in the corresponding energy storage allocation scheme. The best position in the corresponding energy storage allocation scheme is the one with the maximum total battery health value. The best position among all energy storage allocation schemes found for all particles; Step N6, repeat steps N2-N5 until a preset number of repetitions is reached, and then select the energy storage allocation scheme with the highest comprehensive fitness. The energy storage allocation scheme with the highest comprehensive fitness is the output optimized energy storage allocation scheme that meets the set target conditions.

2. The method for optimizing and controlling a battery energy storage system of a power grid according to claim 1, characterized in that: The batteries of the battery energy storage system are distributed in various areas; if there are two batteries in non-adjacent power generation areas, the battery energy storage system optimization control method of the power grid further includes: The area with the largest amount of batteries in the energy storage allocation scheme is taken as the main area; it is determined whether the areas where the batteries outside the main area are located are all adjacent to each other. If not, the batteries in the area that is not adjacent to any area are selected, and the corresponding battery information is retrieved; the battery information includes the current power information; For the retrieved battery information, a battery whose battery information similarity is higher than a set threshold is selected from an area where an adjacent area exists as a replacement battery, and the selected battery is replaced with the replacement battery, and the energy storage allocation plan is regenerated.

3. The method for optimizing and controlling a battery energy storage system of a power grid according to claim 2, characterized in that: The step of selecting a battery whose battery information similarity is higher than a set threshold as a replacement battery from an area where adjacent areas exist includes: Selecting batteries whose battery information similarity is higher than a set threshold from the area where adjacent areas exist; If there are multiple batteries, determine the number of areas where batteries are allocated in the adjacent areas of each battery, and select the battery with the largest number of areas as the replacement battery; If there are multiple batteries with the largest number of regions, the battery with the highest similarity is selected as the replacement battery.

4. The method for optimizing and controlling a battery energy storage system of a power grid according to claim 1, characterized in that: The power grid includes a plurality of power generation areas, and the battery is arranged in each power generation area; The battery energy storage system optimization control method of the power grid also includes: According to the power generation of each power generation area, the power generation area with the largest power generation is selected as the central area, and batteries with similar battery information to the batteries in the non-central area are searched as the batteries to be replaced; For each battery, the distances between the corresponding battery to be replaced and the battery and the central area are compared, the battery with the closest distance is selected as the replacement battery to replace the battery, and the energy storage allocation plan is updated.

5. The method for optimizing and controlling a battery energy storage system of a power grid according to claim 1, characterized in that: The particle velocity update methods include: ； In the formula, For the The particle in The speed at the iteration; For the The particle in The speed at the iteration; For the The particle in The position at the iteration; is the inertia weight, which controls the continuity of particle velocity; is the individual learning factor; is the group learning factor; and All are random numbers in [0,1]; Particle position update methods include: 。 6. The method for optimizing and controlling a battery energy storage system of a power grid according to claim 1, characterized in that ,The power transmission distance is the transmission route length between the power generation source corresponding to the sub-area and each battery in the battery energy storage system.

7. The method for optimizing and controlling a battery energy storage system of a power grid according to claim 1, characterized in that , the method for obtaining the voltage average value includes: For the unit time voltage values ​​to establish a voltage value sequence, sum all the voltage values ​​in the voltage value sequence and then divide them by , get the average voltage; is the acquisition frequency, is a positive integer greater than 1; The same method as the voltage average value is used to obtain the current average value, the battery temperature average value, the internal resistance average value and the remaining capacity average value.

8. A battery energy storage system optimization control system for a power grid, implementing the battery energy storage system optimization control method for a power grid as claimed in any one of claims 1 to 7, characterized in that: include: The first setting module is used to number the batteries in the battery energy storage system and obtain the battery serial number; A first acquisition module is used to acquire health status data of batteries in the battery energy storage system; A preprocessing module, used to preprocess the health status data within a unit time and obtain preprocessed data; The second setting module is used to divide the geographical area where the battery energy storage system is located and obtain the sub-area serial number; The second acquisition module is used to obtain the power generation and power transmission distance of different sub-areas in unit time; Digital modeling module, which builds a digital model based on battery serial number, preprocessed data, sub-area serial number, power generation and power transmission distance; A target setting module, which sets target conditions based on the digital model, wherein the target conditions include a minimum number of idle batteries, a maximum total battery health value, and a maximum average storage rate of electric energy; Path generation module, randomly generated Energy storage allocation scheme; The path analysis module uses an adaptive allocation algorithm to optimize the energy storage allocation plan according to the target conditions and outputs the optimized energy storage allocation plan to meet the set target conditions.

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