Energy storage dynamic power control method, device, equipment, medium and product

Abstract

The application discloses a kind of energy storage dynamic power control method, device, equipment, medium and product, involve energy storage control field.The method includes real-time acquisition photovoltaic output prediction value, load demand prediction value and the battery state parameter of each monomer battery in battery pack;According to the dynamic weight coefficient of each monomer battery determined by battery state parameter, the consistency state score of each monomer battery is further obtained;The difference of real-time acquisition photovoltaic output prediction value and load demand prediction value is determined as real-time total power demand;According to consistency state score and total power demand, the charge-discharge power distribution value of each monomer battery is determined;If the deviation of the actual output power of any monomer battery and charge-discharge power distribution value is greater than preset deviation threshold, the current battery state parameter of each monomer battery is acquired, and power distribution is dynamically adjusted.The application can realize the threefold optimization of battery life extension, photovoltaic efficient consumption and load reliable power supply.

Description

Technical Field

[0001] The present application relates to the field of energy storage control, and in particular to a method, device, equipment, medium and product for dynamic power control of energy storage. Background Art

[0002] As global climate change intensifies and energy security demands escalate, promoting the transition of the energy system toward a low-carbon, clean energy system has become an international consensus. Photovoltaic power generation, due to its widespread resource distribution, advanced technological maturity, and environmental friendliness, has become a core focus of renewable energy development. However, the inherent characteristics of photovoltaic power generation—its output is significantly affected by weather conditions, exhibiting strong volatility and intermittency—pose significant challenges to the stable operation of power systems. For example, when sunlight intensity fluctuates rapidly, photovoltaic output can fluctuate dramatically within a short period of time, leading to grid frequency deviations and voltage overshoots, directly impacting power supply reliability and equipment safety. This volatility is particularly pronounced in large-scale photovoltaic grid-connected scenarios, becoming a key bottleneck restricting the integration of high-proportion renewable energy.

[0003] To mitigate fluctuations in renewable energy demand and enhance grid flexibility, battery energy storage systems are widely considered a key supporting technology for new power systems. Battery energy storage systems have become a mainstream choice due to their fast response, flexible configuration, and high technological maturity. However, in practical applications, battery energy storage systems face two key challenges: First, they face the challenge of dynamic power balancing. There is a significant mismatch between photovoltaic output and load demand on a time scale. For example, peak photovoltaic power generation periods often overlap with low load periods, leading to a surge in demand for "peak shaving and valley filling." Traditional energy storage control strategies often use fixed thresholds or simple rules to manage charge and discharge, making them difficult to adapt to minute- or even second-level photovoltaic fluctuations. This leads to increased curtailment rates and insufficient grid frequency regulation capabilities. Second, there is the issue of battery pack consistency degradation. Over long-term operation, due to manufacturing variations and cyclic aging, battery cells gradually develop inconsistencies in capacity, internal resistance, and state of charge. This inconsistency can trigger a chain reaction, accelerating the aging of some cells due to overcharging or over-discharging, resulting in reduced capacity utilization and shortened lifespan of the entire battery pack, while also increasing system operation and maintenance costs. If not effectively controlled, the rapid degradation of battery pack performance will significantly reduce the economy and reliability of the energy storage system.

[0004] Current techniques for energy storage battery pack power control mainly fall into two categories: (1) power distribution based on photovoltaic output and load demand prediction, which can optimize energy utilization and enhance grid stability, but is highly dependent on prediction accuracy and error sensitivity, and does not consider the consistency state difference of individual cells in the battery pack, which can easily lead to accelerated battery aging; (2) passive balancing control based on the consistency state of the battery pack, which can prolong battery life, reduce overcharging and overdischarging, and reduce the risk of thermal runaway, but does not coordinate with external power demand and has low system energy efficiency. SUMMARY

[0005] The purpose of the present application is to provide a dynamic power control method, device, equipment, medium and product for energy storage, which can achieve the triple optimization of battery life extension, efficient photovoltaic consumption and reliable load power supply.

[0006] To achieve the above purpose, the present application provides the following solutions.

[0007] In a first aspect, the present application provides a dynamic power control method for energy storage, comprising: from each time period entering power distribution, obtaining real-time photovoltaic output prediction value, load demand prediction value and battery state parameter of each individual cell in the battery pack of the battery energy storage system; determining the dynamic weight coefficient of each individual cell according to the battery state parameter of each individual cell; determining the consistency state score of each individual cell according to the dynamic weight coefficient of each individual cell and the battery state parameter of each individual cell; determining the real-time total power demand as the difference between the real-time photovoltaic output prediction value and the load demand prediction value; determining the charging and discharging power distribution value of each individual cell according to the consistency state score of each individual cell and the real-time total power demand; if the deviation of the actual output power of any individual cell from the charging and discharging power distribution value is greater than a preset deviation threshold, obtaining the current battery state parameter of each individual cell, and returning to execute the step of determining the dynamic weight coefficient of each individual cell according to the battery state parameter of each individual cell until the deviation of the actual output power of each individual cell in the battery pack from the charging and discharging power distribution value is less than or equal to the preset deviation threshold, and ending.

[0008] Optionally, the battery state parameter includes state of charge, state of health, temperature and internal resistance.

[0009] Optionally, the dynamic weight coefficient of each individual cell is determined according to the battery state parameter of each individual cell, specifically comprising:

[0010] According to the state of charge of the individual cell, the state of charge deviation of the individual cell is calculated by the formula , wherein is the state of charge of the i-th individual cell at time t, and is the state of charge of the i-th individual cell at time t+1. a state-of-charge deviation of the mth single battery at the time t, is the mth single battery, is the mth single battery at the time t, is the state-of-charge of the mth single battery at the time t,

[0011] a state-of-health deviation of the mth single battery is calculated according to the state of health of the single battery by using the formula ; in the formula, is the mth single battery, is the mth single battery at the time t, is the state of health of the mth single battery at the time t, a temperature deviation of the mth single battery is calculated according to the temperature of the single battery by using the formula ; in the formula, is the mth single battery,

[0012] is the mth single battery at the time t, is the temperature of the mth single battery at the time t, is the optimal working temperature of the battery; a resistance deviation of the mth single battery is calculated according to the internal resistance of the single battery by using the formula ; in the formula, is the mth single battery, is the internal resistance of the mth single battery at the time t, is the threshold upper limit of the internal resistance of the single battery;

[0013] a dynamic weight coefficient of the mth single battery is calculated according to the state-of-charge deviation, the state-of-health deviation, the temperature deviation and the resistance deviation of the single battery by using the formula ; in the formula, is the mth single battery, is the dynamic weight coefficient of the mth battery state parameter of the mth single battery at the time t, is the adjustment factor, is the deviation of the mth battery state parameter of the mth single battery at the time t, is the mth single battery, is the mth single battery at the time t, is the mth battery state parameter of the mth single battery at the time t,

[0014] is the mth single battery, is the dynamic weight coefficient of the mth battery state parameter of the mth single battery at the time t, is the adjustment factor, is the deviation of the mth battery state parameter of the mth single battery at the time t, is the mth single battery, is the mth single battery at the time t, is the mth battery state parameter of the mth single battery at the time t, is the mth single battery, is the mth single battery at the time t, is the mth battery state parameter of the mth single battery at the time t, is the mth single battery, is the mth single battery at the time t, ​​​The degree of deviation.

[0015] Optionally, the consistency status score calculation formula of a single battery is:

[0016] ;

[0017] Where, For the A single battery at a time The consistency status score of For the A single battery at a time The state of charge, For the A single battery at a time health status, For the A single battery at a time temperature, is the optimal operating temperature of the battery, 、 They are the maximum and minimum operating temperatures allowed for the battery within a safe range. For the A single battery at a time The internal resistance, is the upper threshold of the internal resistance of the single cell, 、 、 、 Corresponding to the The charge state, health state, temperature and internal resistance of each single battery at all times The dynamic weight coefficient of .

[0018] Optionally, the charge and discharge power allocation value of each single cell is determined based on the consistency status score of each single cell and the real-time total power demand, specifically including:

[0019] According to the consistency status score of each single battery and the rated capacity of the single battery, the formula , calculate the power allocation weight of each single battery; where, For the A single battery at a time The power allocation weight, For the A single battery at a time The consistency status score of For the A single battery at a time The consistency status score of For the The rated capacity of each single battery, For the The rated capacity of each single battery, is the total number of single cells in the battery pack;

[0020] According to the power distribution weight of each single battery and the real-time total power demand, the formula , calculate the charge and discharge power distribution value of each single battery; where, For the A single battery at a time The charge and discharge power distribution value, For the moment The total power demand, is the charging efficiency coefficient.

[0021] Optionally, the deviation between the actual output power of any single battery and the charge and discharge power distribution value is greater than a preset deviation threshold, specifically including:

[0022] When the actual output power of any single battery meets the charge and discharge power distribution value When the actual output power of any single battery and the charge and discharge power distribution value are determined to be greater than a preset deviation threshold;

[0023] Where, For the A single battery at a time The actual output power, For the A single battery at a time The charge and discharge power distribution value, is the preset deviation threshold.

[0024] In a second aspect, the present application provides a dynamic power control device for energy storage, comprising: a data acquisition module, a dynamic weight determination module, a consistency state score determination module, a total power demand determination module, a power allocation module and a closed-loop feedback module.

[0025] The data acquisition module is used to obtain, in real time, the photovoltaic output forecast, load demand forecast, and battery status parameters of each cell in the battery energy storage system's battery pack, starting from each time period during which power allocation begins. The dynamic weight determination module is used to determine the dynamic weight coefficient for each cell based on the battery status parameters of each cell. The consistency score determination module is used to determine the consistency score for each cell based on the dynamic weight coefficient and battery status parameters of each cell. The total power demand determination module is used to determine the real-time total power demand as the difference between the real-time photovoltaic output forecast and the load demand forecast. The power allocation module is used to determine the charge and discharge power allocation value for each cell based on the consistency score and the real-time total power demand. The closed-loop feedback module is used to obtain the current battery status parameters of each cell if the deviation between the actual output power of any cell and the charge and discharge power allocation value exceeds a preset deviation threshold, and to invoke the dynamic weight determination module until the deviation between the actual output power of each cell in the battery pack and the charge and discharge power allocation value is less than or equal to the preset deviation threshold.

[0026] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the above-described energy storage dynamic power control methods.

[0027] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described energy storage dynamic power control methods.

[0028] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned energy storage dynamic power control methods.

[0029] According to the specific embodiments provided in this application, this application has the following technical effects.

[0030] The present application provides a method, apparatus, device, medium and product for dynamic power control of energy storage, which comprehensively considers the battery state parameters of single cells, determines the dynamic weight coefficient of each single cell, and then obtains the consistency state score of each single cell. The dynamic weight coefficient is used to implement the importance grading of the battery state parameters, overcoming the defect of traditional dynamic power control of energy storage that only adjusts power without considering the consistency state differences of single cells within the battery pack; according to the consistency state score of each single cell and the real-time total power demand, the charge and discharge power distribution value of each single cell is determined; when the deviation between the actual output power and the charge and discharge power distribution value is greater than the preset deviation threshold, the power distribution of the single cell is dynamically adjusted to reduce the risks of extreme battery charge state, low battery health state, excessively high or low battery temperature and high internal resistance battery overload, thereby achieving the triple optimization of extended battery life, efficient photovoltaic absorption and reliable load power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 A flowchart of a method for dynamic power control of energy storage provided in one embodiment of the present application is provided.

[0033] Figure 2 A schematic diagram of the configuration principle of a method for dynamic power control of energy storage provided in one embodiment of the present application.

[0034] Figure 3 A schematic diagram of the functional modules of an energy storage dynamic power control device provided in another embodiment of the present application.

[0035] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0037] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0038] In one exemplary embodiment, as shown in Figure 1 a method for energy storage dynamic power control is provided, comprising steps 101 to 106.

[0039] Step 101: Real-time acquisition of photovoltaic output prediction value, load demand prediction value and battery state parameters of each monomer battery in the battery pack of the battery energy storage system from each time period entering the power distribution.

[0040] Step 102: Determining the dynamic weight coefficient of each monomer battery according to the battery state parameters of each monomer battery.

[0041] Step 103: Determining the consistency state score of each monomer battery according to the dynamic weight coefficient of each monomer battery and the battery state parameters of each monomer battery.

[0042] Step 104: Determining the real-time total power demand as the difference between the real-time acquisition of photovoltaic output prediction value and load demand prediction value.

[0043] Step 105: Determining the charge-discharge power distribution value of each monomer battery according to the consistency state score of each monomer battery and the real-time total power demand.

[0044] Step 106: If there is any monomer battery with an actual output power deviating from the charge-discharge power distribution value by more than a preset deviation threshold, acquiring the current battery state parameters of each monomer battery and returning to execute the step of determining the dynamic weight coefficient of each monomer battery according to the battery state parameters of each monomer battery until the actual output power of each monomer battery in the battery pack deviates from the charge-discharge power distribution value by less than or equal to the preset deviation threshold, and ending.

[0045] The steps 101 to 106 described above form a closed-loop feedback mechanism. By fusing real-time data of photovoltaic output prediction, load demand fluctuation and battery pack consistency state, firstly, the battery state parameters are comprehensively considered to determine the consistency state score of each monomer battery, quantifying the available power capacity of each monomer battery; secondly, the total power demand is calculated according to the power difference between photovoltaic and load, and the charge-discharge power of each monomer battery is dynamically distributed in combination with the consistency state score, and high-score monomer batteries are preferentially called; finally, the closed-loop feedback mechanism is triggered by monitoring the actual power output deviation to real-time correct the dynamic weight coefficient and the consistency state score, solving the problems of power distribution and battery state disconnection, response lag and lack of multi-target cooperation in the prior art, realizing the three optimizations of battery life extension, photovoltaic efficient consumption and reliable power supply of load.

[0046] In another exemplary embodiment of the present application, the battery status parameters include: state of charge, state of health (used to reflect the degree of battery capacity attenuation), temperature and internal resistance.

[0047] In another exemplary embodiment of the present application, the dynamic weight coefficient calculation process of the above step 102 can be replaced by the following steps 201 to 205.

[0048] Step 201: Based on the state of charge of the single battery, use the formula , calculate the charge state deviation of the single battery; where, For the A single battery at a time The charge state deviation, For the A single battery at a time state of charge.

[0049] Step 202: Based on the health status of the single battery, use the formula , calculate the health status deviation of the single battery; where, For the A single battery at a time The health status deviation, For the A single battery at a time health status.

[0050] Step 203: Based on the temperature of the single battery, use the formula , calculate the temperature deviation of the single battery; where, For the A single battery at a time The temperature deviation, For the A single battery at a time temperature, is the optimal operating temperature of the battery, Specified by the battery manufacturer.

[0051] Step 204: Based on the internal resistance of the single cell, use the formula , calculate the internal resistance deviation of the single battery; where, For the A single battery at a time The internal resistance deviation, For the A single battery at a time The internal resistance, The upper threshold of the internal resistance of a single cell (a battery exceeding this value is considered to be faulty).

[0052] Step 205: Based on the state of charge deviation, health state deviation, temperature deviation and internal resistance deviation of the single battery, use the formula , calculate the dynamic weight coefficient of the single battery; where, For the The first The battery status parameters at time The dynamic weight coefficient of For the The first The battery status parameters at time The deviation of For the The first The battery status parameters at time The degree of deviation. is the regulating factor, Used to control the concentration of weight distribution. The larger the value, the more the weight tends to the parameter with the smallest deviation (considering multiple situations).

[0053] As can be seen from the above, the weight decreases when the state of charge deviates from the middle value; the lower the health state, the higher the deviation.

[0054] In another exemplary embodiment of the present application, the consistency status score calculation formula of the single battery is:

[0055] ;

[0056] Where, For the A single battery at a time The consistency status score of . 、 They are the maximum and minimum operating temperatures allowed for the battery within a safe range. 、 、 、 Corresponding to the The charge state, health state, temperature and internal resistance of each single battery at all times The dynamic weight coefficient of , and adaptively adjusts according to the operating mode (charging / discharging).

[0057] The higher the score, the more suitable the battery is for charging and discharging. Indicates that the score decreases when the temperature deviates from the optimal value; internal resistance The greater the internal resistance, the lower the score (excessively high internal resistance may cause thermal runaway).

[0058] In another exemplary embodiment of the present application, the above step 105 can be replaced by the following steps 301 to 302.

[0059] Step 301: Based on the consistency status score of each single cell and the rated capacity of the single cell, use the formula , calculate the power allocation weight of each single battery.

[0060] Where, For the A single battery at a time The power allocation weight, For the A single battery at a time The consistency status score of For the The rated capacity of each single battery (in Ah), For the The rated capacity of each single battery, is the total number of single cells in the battery pack;

[0061] Step 302: Based on the power distribution weight of each single battery and the real-time total power demand, use the formula , calculate the charge and discharge power distribution value of each single battery.

[0062] Where, For the A single battery at a time The charge and discharge power distribution value (unit: kW), For the moment The total power demand, is the charging efficiency coefficient.

[0063] In another exemplary embodiment of the present application, when the actual output power of any single battery and the charge and discharge power distribution value meet When the actual output power of any single battery cell and the charge and discharge power distribution value are determined to have a deviation greater than a preset deviation threshold.

[0064] Where, For the A single battery at a time The actual output power, The actual output power of the single battery is measured by the sensor.

[0065] This application addresses the problems of disconnection between power allocation and battery status, delayed dynamic response, and insufficient multi-objective coordination in existing energy storage power control. It adopts a dynamic weight coefficient, consistency status scoring, and closed-loop feedback mechanism that integrates photovoltaic output, load demand, and battery consistency status. Figure 2 The configuration principle of the present application method shown is as follows: obtain photovoltaic output, load demand, battery parameters (including state of charge, health state, temperature, internal resistance); calculate dynamic weight coefficient and score the consistency state of battery pack; determine power allocation weight according to photovoltaic output and battery rated capacity; calculate charging and discharging power allocation value; obtain actual output power through sensor measurement; determine whether the deviation between actual power and power allocation value is greater than If not, perform power allocation for the next time period. If so, return to step "calculate the dynamic weight coefficient and score the battery pack consistency status."

[0066] The main technical advantages of this application are as follows.

[0067] (1) This application proposes a battery consistency scoring model based on dynamic weights (steps 102 and 103), which comprehensively considers the battery state of charge, health status, temperature and internal resistance, and uses dynamic weight coefficients to implement parameter importance grading, thereby overcoming the single-perspective defect of traditional energy storage dynamic power control, which only adjusts power without considering battery status or only adjusts battery status without considering power.

[0068] (2) This application proposes a power allocation weight and closed-loop feedback mechanism, which dynamically adjusts power allocation to reduce the risks of extreme battery state of charge, low battery health, excessively high or low battery temperature, and high internal resistance battery overload, thereby achieving triple optimization of extended battery life, efficient photovoltaic absorption, and reliable load power supply.

[0069] Based on the same inventive concept, embodiments of the present application also provide an energy storage dynamic power control device for implementing the aforementioned energy storage dynamic power control method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more energy storage dynamic power control device embodiments provided below can be found in the above-mentioned limitations of the energy storage dynamic power control method and will not be further elaborated here.

[0070] In an exemplary embodiment, Figure 3 As shown, a dynamic power control device for energy storage is provided, including: a data acquisition module, a dynamic weight determination module, a consistency state score determination module, a total power demand determination module, a power distribution module and a closed-loop feedback module.

[0071] The data acquisition module is used to obtain, in real time, the photovoltaic output forecast, load demand forecast, and battery status parameters of each cell in the battery energy storage system's battery pack, starting from each time period during which power allocation begins. The dynamic weight determination module is used to determine the dynamic weight coefficient for each cell based on the battery status parameters of each cell. The consistency score determination module is used to determine the consistency score for each cell based on the dynamic weight coefficient and battery status parameters of each cell. The total power demand determination module is used to determine the real-time total power demand as the difference between the real-time photovoltaic output forecast and the load demand forecast. The power allocation module is used to determine the charge and discharge power allocation value for each cell based on the consistency score and the real-time total power demand. The closed-loop feedback module is used to obtain the current battery status parameters of each cell if the deviation between the actual output power of any cell and the charge and discharge power allocation value exceeds a preset deviation threshold, and to invoke the dynamic weight determination module until the deviation between the actual output power of each cell in the battery pack and the charge and discharge power allocation value is less than or equal to the preset deviation threshold.

[0072] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the charge and discharge power distribution value of each single cell. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a dynamic power control method for energy storage is implemented.

[0073] Those skilled in the art will understand that Figure 4The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the above-mentioned method embodiments are implemented.

[0074] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0075] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0076] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0077] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0078] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0079] The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0080] The principles and implementation manners of the present application are described herein by using specific examples, and the above examples are only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manners and application ranges will have changes. In conclusion, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for controlling dynamic power of energy storage, characterized in that: include: From the moment power allocation begins, the system obtains real-time PV output forecasts, load demand forecasts, and the battery status parameters of each cell in the battery pack of the battery energy storage system. Determine the dynamic weight coefficient of each single cell according to the battery state parameter of each single cell; Determine the consistency status score of each single cell according to the dynamic weight coefficient of each single cell and the battery status parameter of each single cell; The difference between the real-time photovoltaic output forecast value and the load demand forecast value is determined as the real-time total power demand; Determine the charge and discharge power allocation value for each single cell based on the consistency status score of each single cell and the real-time total power demand; If the deviation between the actual output power of any single cell and the charge and discharge power distribution value is greater than the preset deviation threshold, the current battery state parameter of each single cell is obtained, and the process returns to the step of determining the dynamic weight coefficient of each single cell based on the battery state parameter of each single cell until the deviation between the actual output power of each single cell in the battery pack and the charge and discharge power distribution value is less than or equal to the preset deviation threshold, and the process ends; According to the battery status parameters of each single cell, the dynamic weight coefficient of each single cell is determined, including: According to the state of charge of the single battery, use the formula , calculate the charge state deviation of the single battery; where, For the A single battery at a time The charge state deviation, For the A single battery at a time State of charge; According to the health status of the single battery, use the formula , calculate the health status deviation of the single battery; where, For the A single battery at a time The health status deviation, For the A single battery at a time health status; According to the temperature of the single battery, use the formula , calculate the temperature deviation of the single battery; where, For the A single battery at a time The temperature deviation, For the A single battery at a time temperature, is the optimal operating temperature of the battery; According to the internal resistance of the single battery, use the formula , calculate the internal resistance deviation of the single battery; where, For the A single battery at a time The internal resistance deviation, For the A single battery at a time The internal resistance, is the upper threshold of the internal resistance of the single cell; According to the charge state deviation, health state deviation, temperature deviation and internal resistance deviation of the single battery, the formula , calculate the dynamic weight coefficient of the single battery; where, For the The first The battery status parameters at time The dynamic weight coefficient of is the regulating factor, For the The first The battery status parameters at time The deviation of For the The first The battery status parameters at time The degree of deviation; The calculation formula for the consistency status score of a single battery is: ； Where, For the A single battery at a time The consistency status score of For the A single battery at a time The state of charge, For the A single battery at a time health status, For the A single battery at a time temperature, is the optimal operating temperature of the battery, 、 They are the maximum and minimum operating temperatures allowed for the battery within a safe range. For the A single battery at a time The internal resistance, is the upper threshold of the internal resistance of the single cell, 、 、 、 Corresponding to the The charge state, health state, temperature and internal resistance of each single battery at all times The dynamic weight coefficient of .

2. The energy storage dynamic power control method according to claim 1, characterized in that: Based on the consistency status score of each single cell and the real-time total power demand, the charge and discharge power allocation value of each single cell is determined, including: According to the consistency status score of each single battery and the rated capacity of the single battery, the formula , calculate the power allocation weight of each single battery; where, For the A single battery at a time The power allocation weight, For the A single battery at a time The consistency status score of For the A single battery at a time The consistency status score of For the The rated capacity of each single battery, For the The rated capacity of each single battery, is the total number of single cells in the battery pack; According to the power distribution weight of each single battery and the real-time total power demand, the formula , calculate the charge and discharge power distribution value of each single battery; where, For the A single battery at a time The charge and discharge power distribution value, For the moment The total power demand, is the charging efficiency coefficient.

3. The energy storage dynamic power control method according to claim 1, characterized in that: The deviation between the actual output power of any single battery and the charge and discharge power distribution value is greater than the preset deviation threshold, specifically including: When the actual output power of any single battery meets the charge and discharge power distribution value When the actual output power of any single battery and the charge and discharge power distribution value are determined to be greater than a preset deviation threshold; Where, For the A single battery at a time The actual output power, For the A single battery at a time The charge and discharge power distribution value, is the preset deviation threshold.

4. A dynamic power control device for energy storage, characterized in that: include: A data acquisition module is used to obtain the photovoltaic output forecast value, load demand forecast value and battery status parameters of each single cell in the battery pack of the battery energy storage system in real time from each time period entering the power allocation; A dynamic weight determination module is used to determine the dynamic weight coefficient of each single cell according to the battery state parameters of each single cell; A consistency status score determination module is used to determine the consistency status score of each single cell according to the dynamic weight coefficient of each single cell and the battery status parameter of each single cell; A total power demand determination module is used to determine the difference between the real-time photovoltaic output prediction value and the load demand prediction value as the real-time total power demand; The power allocation module is used to determine the charge and discharge power allocation value of each single cell according to the consistency status score of each single cell and the real-time total power demand; A closed-loop feedback module is configured to obtain the current battery state parameters of each single cell if the deviation between the actual output power of any single cell and the charge and discharge power distribution value is greater than a preset deviation threshold, and to call the dynamic weight determination module until the deviation between the actual output power of each single cell in the battery pack and the charge and discharge power distribution value is less than or equal to the preset deviation threshold, and then terminate; According to the battery status parameters of each single cell, the dynamic weight coefficient of each single cell is determined, including: According to the state of charge of the single battery, use the formula , calculate the charge state deviation of the single battery; where, For the A single battery at a time The charge state deviation, For the A single battery at a time State of charge; According to the health status of the single battery, use the formula , calculate the health status deviation of the single battery; where, For the A single battery at a time The health status deviation, For the A single battery at a time health status; According to the temperature of the single battery, use the formula , calculate the temperature deviation of the single battery; where, For the A single battery at a time The temperature deviation, For the A single battery at a time temperature, is the optimal operating temperature of the battery; According to the internal resistance of the single battery, use the formula , calculate the internal resistance deviation of the single battery; where, For the A single battery at a time The internal resistance deviation, For the A single battery at a time The internal resistance, is the upper threshold of the internal resistance of the single cell; According to the charge state deviation, health state deviation, temperature deviation and internal resistance deviation of the single battery, the formula , calculate the dynamic weight coefficient of the single battery; where, For the The first The battery status parameters at time The dynamic weight coefficient of is the regulating factor, For the The first The battery status parameters at time The deviation of For the The first The battery status parameters at time The degree of deviation; The calculation formula for the consistency status score of a single battery is: ； Where, For the A single battery at a time The consistency status score of For the A single battery at a time The state of charge, For the A single battery at a time health status, For the A single battery at a time temperature, is the optimal operating temperature of the battery, 、 They are the maximum and minimum operating temperatures allowed for the battery within a safe range. For the A single battery at a time The internal resistance, is the upper threshold of the internal resistance of the single cell, 、 、 、 Corresponding to the The charge state, health state, temperature and internal resistance of each single battery at all times The dynamic weight coefficient of .

5. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the energy storage dynamic power control method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the energy storage dynamic power control method according to any one of claims 1 to 3 is implemented.

7. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the energy storage dynamic power control method according to any one of claims 1 to 3 is implemented.

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