User-side Energy Storage Cluster Management System and Method

Abstract

The present application relates to the technical field of energy storage management, and discloses a user-side energy storage cluster management system and method. First, it receives the total charging and discharging demand of the user-side energy storage cluster in the first time period. Then, it collects the state parameters of each energy storage device in the user-side energy storage cluster to obtain a set of energy storage unit state parameters. Next, based on the energy storage unit state parameters of each energy storage device, it performs priority scoring on each energy storage device in the user-side energy storage cluster to obtain a priority queue. Finally, based on the order of the priority queue, it sequentially allocates charging and discharging power to each energy storage device by means of a polling power allocation strategy. In this way, not only the working state of the energy storage device is optimized, but also the response speed and reliability of the system are improved, realizing the intelligent management of the energy storage cluster.

Description

Technical Field

[0001] This application relates to the technical field of energy storage management, and more specifically, to a user-side energy storage cluster management system and method. Background Art

[0002] With the wide application of renewable energy and the development of smart grids, user-side energy storage systems, as key components to enhance the flexibility and reliability of power systems, have received increasing attention. Traditional power systems rely on centralized power plants to supply power to end-users through long-distance transmission lines, which poses many challenges in dealing with load fluctuations, optimizing energy utilization, and reducing carbon emissions. User-side energy storage systems can store excess electrical energy and release it when needed, thus balancing the supply and demand relationship and improving the stability and efficiency of the power grid.

[0003] However, existing user-side energy storage management methods still face several technical and operational challenges in practical applications. First, due to the wide variety of energy storage devices, including battery energy storage systems (BESS), supercapacitors, etc., the technical parameters of each device, such as charge and discharge efficiency, maximum charge and discharge power, state of charge (SOC), and state of health (SOH), vary greatly. How to effectively manage these devices and make them work together has become an urgent problem to be solved. Traditional methods often lack comprehensive consideration of the characteristics of different energy storage devices, resulting in the underperformance of the overall system. Second, when dealing with the charge and discharge requirements of energy storage devices, existing technologies usually adopt fixed allocation strategies or optimization algorithms based on a single index. This method is difficult to adapt to complex working conditions. For example, when multiple energy storage devices participate in regulation simultaneously, some devices may be overused while others are idle, causing resource waste and shortening the device lifespan. In addition, traditional management systems cannot adjust the working states of each device in real time to meet dynamic power demands, which not only affects the response speed of the energy storage system but also limits its emergency response capabilities in case of emergencies.

[0004] Therefore, an optimized user-side energy storage cluster management solution is expected. Summary of the Invention

[0005] To solve the above technical problems, this application is proposed. Embodiments of this application provide a user-side energy storage cluster management system and method, which not only optimize the working states of energy storage devices but also improve the response speed and reliability of the system, realizing the intelligent management of the energy storage cluster.

[0006] According to one aspect of the present application, a method for managing a user-side energy storage cluster is provided, including: receiving the total charging and discharging demand of the user-side energy storage cluster in the first time period; collecting the state parameters of each energy storage device in the user-side energy storage cluster to obtain a set of energy storage unit state parameters; based on the energy storage unit state parameters of each energy storage device, performing a priority score on each energy storage device in the user-side energy storage cluster to obtain a priority queue; based on the order of the priority queue, sequentially allocating charging and discharging power to each energy storage device through a polling power allocation strategy.

[0007] In the above method for managing a user-side energy storage cluster, based on the energy storage unit state parameters of each energy storage device, performing a priority score on each energy storage device in the user-side energy storage cluster to obtain a priority queue includes: performing a priority score according to the following formula, and the formula is expressed as: ; where and represent the SOC balance weight, the SOH balance weight, and the efficiency difference weight, represents the maximum allowable state of charge of the th energy storage device, represents the state of charge of the th energy storage device at time, represents the health state of the th energy storage device, represents the charging and discharging efficiency of the th energy storage device.

[0008] In the above method for managing a user-side energy storage cluster, the energy storage unit state parameters include the state of charge, health state, rated capacity, maximum charging and discharging power, charging and discharging efficiency, minimum allowable state of charge, and maximum allowable state of charge at

[0009] In the above method for managing a user-side energy storage cluster, based on the order of the priority queue, sequentially allocating charging and discharging power to each energy storage device through a polling power allocation strategy includes: Step 41: Select the energy storage device with the highest priority from the priority queue, and perform a SOC constraint check on it to obtain a preliminary set charging and discharging power; Step 42: Perform a power limit check on the preliminary set charging and discharging power to obtain an actual charging and discharging allocation power; Step 43: Subtract the actual charging and discharging allocation power from the total charging and discharging demand in the first time period to obtain the remaining charging and discharging demand to be allocated, and mark the energy storage device with the allocated power as processed; Step 44: Repeat Steps 41 to 43 until a preset condition is met.

[0010] In the above user-side energy storage cluster management method, the preset condition is that the remaining charge and discharge demand to be allocated is zero.

[0011] In the above user-side energy storage cluster management method, the preset condition is that all the energy storage devices have been polled.

[0012] In the above user-side energy storage cluster management method, step 41 includes: estimating whether the state of charge of the energy storage device will be lower than the minimum allowable state of charge after the first time period if it operates at the maximum charge and discharge power; if it will not be lower than the minimum allowable state of charge, then set the preliminary set charge and discharge power of the energy storage device to the maximum charge and discharge power; if it will be lower than the minimum allowable state of charge, then set the preliminary set charge and discharge power of the energy storage device to the charge and discharge power that will just not be lower than the minimum allowable state of charge.

[0013] In the above user-side energy storage cluster management method, step 42 includes: if the preliminary set charge and discharge power exceeds the maximum charge and discharge power, then set the actual charge and discharge allocation power to the maximum charge and discharge power; if the preliminary set charge and discharge power does not exceed the maximum charge and discharge power, then set the actual charge and discharge allocation power to the preliminary set charge and discharge power.

[0014] In the above user-side energy storage cluster management method, it further includes: performing pre-allocation according to the initial priority queue, and updating the health state and charge and discharge efficiency based on the pre-allocation power with the following update strategy, so as to calculate the updated priority score, and iteratively perform steps 41 to 43 through the updated priority queue; wherein, the update strategy includes: first, performing mean regression on the health state score and the charge and discharge efficiency to convert them into score values homogeneous with the power limit ratio, expressed as: ; where represents the maximum value function, represents the health state score value, represents the charge and discharge efficiency score value.

[0015] Then, update the health state score through the degradation attenuation ratio factor of the health state, expressed as: ; where represents the power limit ratio, represents the updated health state score.

[0016] And update the charge and discharge efficiency score through the degradation attenuation ratio factor of the charge and discharge efficiency, expressed as: ; where represents the updated charge and discharge efficiency score.

[0017] Moreover, perform a relevant decay correction on the updated health state score and the updated charge-discharge efficiency score based on as follows: ; where represents the updated health state score after correction, represents the updated charge-discharge efficiency score after correction.

[0018] According to another aspect of the present application, there is also provided a user-side energy storage cluster management system, including: a total charge-discharge demand receiving module, configured to receive the total charge-discharge demand of the user-side energy storage cluster in the first time period; a state parameter acquisition module, configured to acquire the state parameters of each energy storage device in the user-side energy storage cluster to obtain a set of energy storage unit state parameters; an energy storage device priority scoring module, configured to perform priority scoring on each energy storage device in the user-side energy storage cluster based on the energy storage unit state parameters of each energy storage device to obtain a priority queue; and a priority polling power allocation module, configured to sequentially allocate charge-discharge power to each energy storage device based on the order of the priority queue by means of a polling power allocation strategy.

[0019] Compared with the prior art, for the user-side energy storage cluster management system and method provided by the present application, firstly, it receives the total charge-discharge demand of the user-side energy storage cluster in the first time period to ensure that the system understands the current power demand. Then, it acquires the state parameters of each energy storage device, including the state of charge (SOC), state of health (SOH), etc., and constructs a set of state parameters to provide a data basis for subsequent analysis. Based on these state parameters, it performs priority scoring on each energy storage device to form a priority queue to identify the most suitable devices for charge-discharge operations. Finally, according to the order of the priority queue, it sequentially allocates charge-discharge power to each device by means of a polling power allocation strategy to ensure efficient resource utilization and prevent any single device from being overloaded. In this way, not only the working state of the energy storage devices is optimized, but also the response speed and reliability of the system are improved, realizing the intelligent management of the energy storage cluster. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] By describing the embodiments of the present application in more detail in conjunction with the drawings, the above and other objects, features, and advantages of the present application will become more obvious. The drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation to the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0021] Figure 1 is a schematic flowchart of a user-side energy storage cluster management method according to an embodiment of the present application.

[0022] Figure 2It is a schematic flowchart of step 4 in the user-side energy storage cluster management method according to an embodiment of the present application.

[0023] Figure 3 It is a schematic flowchart of step 41 in the user-side energy storage cluster management method according to an embodiment of the present application.

[0024] Figure 4 It is a schematic flowchart of step 42 in the user-side energy storage cluster management method according to an embodiment of the present application.

[0025] Figure 5 It is a schematic block diagram of a user-side energy storage cluster management system according to an embodiment of the present application.

[0026] Figure 6 It is a block diagram of an electronic device according to an embodiment of the present application. Detailed implementation manners

[0027] Next, exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described herein.

[0028] Figure 1 It is a schematic flowchart of a user-side energy storage cluster management method according to an embodiment of the present application. As Figure 1 shown, the user-side energy storage cluster management method includes: Step 1: receiving the total charge-discharge demand of the user-side energy storage cluster in a first period; Step 2: collecting the state parameters of each energy storage device in the user-side energy storage cluster to obtain a set of energy storage unit state parameters; Step 3: based on the energy storage unit state parameters of each energy storage device, performing a priority score on each energy storage device in the user-side energy storage cluster to obtain a priority queue; Step 4: based on the order of the priority queue, sequentially allocating charge-discharge power to each energy storage device by means of a polling allocation power strategy.

[0029] Specifically, in step 1, the total charging and discharging demand of the user-side energy storage cluster in the first period is received. It should be understood that by obtaining the total charging and discharging demand in the first period, a clear understanding of future power consumption can be achieved, which not only helps to meet the immediate power consumption needs of users but also ensures the effective utilization of energy storage devices. Considering that the user-side energy storage cluster usually consists of multiple different types of energy storage devices, these devices may be distributed in different geographical locations and serve different power consumption needs. To accurately obtain the total charging and discharging demand in the first period, information from multiple data sources needs to be integrated. In a specific embodiment, the power consumption of users can be monitored in real time through smart meters, and at the same time, combined with weather forecasts, historical power consumption data, and grid dispatching information to predict the power demand in the next period. This method of multi-source data fusion can provide more comprehensive and accurate demand prediction results, thus laying a foundation for the optimized management of the energy storage system.

[0030] Specifically, in this application, considering that the received data often contains a large amount of noise and uncertainty, advanced data cleaning and preprocessing techniques are adopted. For example, filtering algorithms are used to remove outliers, and interpolation methods are used to fill in missing data to ensure that the data input into the management system has high quality and reliability. In addition, machine learning algorithms can also be applied to analyze historical data to identify potential patterns and trends, further improving the accuracy of demand prediction. This method can not only reduce errors caused by human intervention but also effectively cope with complex and changing actual working conditions. At the same time, to ensure the security and efficiency of data transmission, standardized communication protocols such as Modbus and IEC 61850 are usually adopted. Taking Modbus as an example, it is a widely used industrial communication protocol that supports multiple physical layer interfaces (such as RS-485, TCP / IP) and can achieve reliable communication between remote devices. By configuring the corresponding communication module, the energy storage management system can obtain the status parameters and operation data of each energy storage device in real time and upload them to the central control system for unified management and scheduling. This method not only improves the efficiency of data transmission but also enhances the scalability and compatibility of the system.

[0031] Furthermore, considering the diversity and dynamic change characteristics of the user-side energy storage cluster, a single prediction model often fails to meet the actual needs. Therefore, a combined prediction model is adopted, which combines statistical methods (such as time series analysis) with artificial intelligence algorithms (such as neural networks and support vector machines) to improve the prediction accuracy. For example, a time series model based on historical power consumption data can capture long-term trends and seasonal changes, while neural networks are good at dealing with non-linear and complex short-term fluctuations. By integrating the advantages of these two models, more accurate and robust demand prediction results can be generated, providing strong support for subsequent power allocation.

[0032] In a preferred embodiment, a two-way communication channel can also be established between the user-side energy storage cluster and the superior dispatching system or the user management terminal. This channel can adopt an Internet of Things communication architecture based on the MQTT protocol, and use an edge computing gateway to achieve the aggregation and protocol conversion of local data. For example, in the scenario of an industrial and commercial park, the enterprise's electricity management system (EMS) pushes the production plan data for the next day to the energy storage management platform through the OPC UA protocol, and the platform generates the total charge and discharge demand of the energy storage system based on the predicted electricity load curve.

[0033] Specifically, in step 2, the state parameters of each energy storage device in the user-side energy storage cluster are collected to obtain a set of energy storage unit state parameters. It should be understood that accurately grasping the state parameters of each energy storage device is crucial for optimizing the management of the entire energy storage cluster. First, understanding the state of charge and health state of each battery pack helps to formulate reasonable charge and discharge strategies and avoid potential safety hazards caused by overcharging or over-discharging. Second, knowing the maximum charge and discharge power and efficiency of the battery enables the system to maximize energy utilization and reduce operating costs while meeting power demands. In addition, setting the minimum and maximum allowable state of charge not only ensures the safe operation of the battery but also provides a basis for long-term maintenance and prevents potential risks.

[0034] In one embodiment, the energy storage unit state parameters include the state of charge, health state, rated capacity, maximum charge and discharge power, charge and discharge efficiency, minimum allowable state of charge, and maximum allowable state of charge at a certain moment. Among them, the state of charge (SOC): reflects the proportion of the energy currently stored in the battery to its total capacity. The health state (SOH): indicates the degree of degradation of the battery and helps predict its remaining service life. The rated capacity: indicates the maximum storage capacity of the battery. The maximum charge and discharge power: specifies the maximum power level that the battery can withstand without damage. The charge and discharge efficiency: represents the energy conversion efficiency of the battery during the charge and discharge process. The minimum and maximum allowable state of charge: sets the upper and lower limits within the safe operating range of the battery. In this application, each energy storage device in the user-side energy storage cluster is equipped with a series of sensors to monitor its operating state. These sensors can measure the above parameters of the energy storage device in real time and send them to the data center through standardized communication protocols such as Modbus or IEC 61850.

[0035] Specifically, in step 3, based on the state parameters of the energy storage units of each energy storage device, priority scores are given to each energy storage device in the user-side energy storage cluster to obtain a priority queue. It should be understood that the traditional average distribution strategy often leads to overcharging and over-discharging of some devices, shortening the battery life; while the single-factor sorting based solely on SOC or efficiency is difficult to take into account the overall system performance. By constructing a multi-dimensional evaluation model, it can not only meet the flexibility requirements of power grid dispatching, but also extend the service life of energy storage devices, improve the energy conversion efficiency at the same time, and achieve the dual optimization of economy and reliability.

[0036] In one embodiment, based on the state parameters of the energy storage units of each energy storage device, priority scores are given to each energy storage device in the user-side energy storage cluster to obtain a priority queue, including: performing priority scoring according to the following formula, and the formula is expressed as: ; where and represent the SOC balance weight, SOH balance weight and efficiency difference weight, represents the maximum allowable state of charge of the th energy storage device, represents the state of charge of the th energy storage device at moment, represents the health state of the th energy storage device, represents the charge-discharge efficiency of the th energy storage device, represents the priority score of the th energy storage device.

[0037] In the above formula, priority scoring is performed by comprehensively considering three dimensions of the SOC balance, health state and charge-discharge efficiency of the energy storage device. For example, when the of a certain energy storage device is close to , the item approaches zero, resulting in a decrease in the contribution degree of its SOC balance; on the contrary, if the device is in a low state of charge, the score of this item will increase significantly. Similarly, devices with higher SOH values get higher scores in the health state dimension, while devices with charge-discharge efficiency close to the rated value perform better in the efficiency dimension. Specifically, the priority score calculation adopts a multi-dimensional weighted model, where the SOC balance weight reflects the contribution degree of the remaining capacity of the device to the overall schedulability of the system, the SOH balance weight reflects the influence weight of the device health state on the cycle life, and the efficiency difference weight It then measures the energy loss level during the charging and discharging process of the device. Each weight can be determined by comprehensively considering operation objectives, device characteristics, and economic requirements. For example, in a scenario with the main objective of extending battery life, a higher weight value can be assigned to ; while in a scenario pursuing maximum charging and discharging efficiency, the weight coefficient of needs to be increased. In a specific embodiment, the initial weight coefficient is set. It should be noted that the setting of the weight coefficient is not static but needs to be dynamically adjusted according to the device operation status and environmental parameters. When it is detected that the environmental temperature exceeds the set threshold, the weight coefficient of the charging and discharging efficiency can be automatically reduced to avoid the risk of accelerated battery aging caused by high-efficiency operation in a high-temperature environment. This adaptive weight adjustment mechanism is implemented by introducing a fuzzy logic controller, whose input variables include environmental temperature, device aging rate, grid load volatility, etc., and the output result is the dynamically adjusted weight coefficient matrix.

[0038] In a preferred embodiment, in terms of the dynamic adjustment of the weight coefficient, a machine learning algorithm can be integrated to optimize the parameter configuration in real time. For example, when it is detected that there has been high-temperature weather for several consecutive days, the adaptive algorithm can increase the weight coefficient of from 0.35 to 0.45 to strengthen the monitoring of the battery health status; when the peak-valley difference rate of the power grid exceeds the preset threshold, the weight of is reduced to 0.15 to give priority to ensuring the power regulation ability. This dynamic adjustment mechanism is implemented through a reinforcement learning method, and its reward function is defined as the maximization of the comprehensive benefit of the system, including multi-dimensional indicators such as battery life loss cost, electricity trading revenue, and grid service compensation. In addition, to prevent the scoring mechanism from failing in extreme cases, a dual verification mechanism is set in this preferred embodiment. The first verification is a physical constraint verification, that is, the allocated power of any device shall not exceed its maximum charging and discharging power limit; the second verification is a safety margin verification to ensure that the remaining power of the device after allocation is not lower than the minimum safety threshold. These two verifications are implemented through a real-time closed-loop feedback mechanism, forming a dynamic regulation closed-loop of "scoring - allocation - verification - correction".

[0039] Specifically, in step 4, based on the order of the priority queue, the charging and discharging power is sequentially allocated to each of the energy storage devices through a polling power allocation strategy. It should be understood that by adopting the polling power allocation strategy, it can ensure that on the premise of meeting the total charging and discharging demand in the first time period, the charging and discharging power is allocated to each energy storage device orderly according to the priority queue. This method helps to balance the load between each energy storage unit, prevent some devices from degrading rapidly due to overuse, and at the same time avoid resource waste caused by other devices being idle.

[0040] In an embodiment, as Figure 2As shown, in step 4, based on the order of the priority queue, charging and discharging power is sequentially allocated to each energy storage device by polling the power allocation policy, including: Step 41: Select the energy storage device with the highest priority from the priority queue, and perform an SOC constraint check on it to obtain a preliminary set charging and discharging power; Step 42: Perform a power limit check on the preliminary set charging and discharging power to obtain the actual charging and discharging allocation power; Step 43: Subtract the actual charging and discharging allocation power from the total charging and discharging demand in the first period to obtain the remaining charging and discharging demand to be allocated, and mark the energy storage device with the allocated power as processed; Step 44: Repeat steps 41 to 43 until a preset condition is met.

[0041] Specifically, after selecting the device with the current highest priority from the priority queue, an SOC constraint check is first performed. In one embodiment, the SOC constraint is performed by directly comparing the current SOC of the device and the maximum allowable SOC. However, considering that the static threshold cannot predict future power flow changes, it is easy to cause safety risks when facing short-term high-power charging and discharging demands.

[0042] Based on this, in a preferred embodiment, as Figure 3 shown, step 41 includes: Step 411: Estimate whether the state of charge of the energy storage device will be lower than the minimum allowable state of charge after the first time period if it operates at the maximum charging and discharging power; Step 412: If it will not be lower than the minimum allowable state of charge, set the preliminary set charging and discharging power of the energy storage device to the maximum charging and discharging power; Step 413: If it will be lower than the minimum allowable state of charge, set the preliminary set charging and discharging power of the energy storage device to the charging and discharging power that will just not be lower than the minimum allowable state of charge. That is, by estimating the charging and discharging behavior of the device within a specified time period, it is judged whether it will break through the safety threshold after power allocation. For example, if the current SOC of a certain device is 90% and the maximum allowable SOC is 95%, the system will estimate the SOC value after operating at the maximum charging power for 30 minutes. If the estimated value exceeds 93% (safety threshold), the preliminary set power will be automatically adjusted to the safety power corresponding to the SOC upper limit. This step expands the static SOC limit to a dynamic safety window by introducing a time series prediction model, effectively avoiding damage to the device due to short-term high-power shocks.

[0043] In a specific embodiment, the time series prediction model is centered around a Transformer-GRU hybrid architecture, capturing long-range dependencies of power fluctuations through the multi-head attention mechanism, and the GRU layer processes short-term time series patterns. The model input includes three types of time series data: historical power data (15-minute-level energy storage charge and discharge power sequences covering the past 72 hours), environmental covariates (12 environmental parameters such as temperature, humidity, grid frequency fluctuations, etc.), and device status sequences (the time series evolution trajectories of SOC, SOH, charge and discharge efficiency). The input window is set to 60 minutes, predicting the SOC evolution trajectory for the next 30 minutes, with the MAPE error controlled within 1.2%.

[0044] After passing the SOC constraint check, the initially set power needs to be double-checked. In a specific embodiment, as Figure 4 shown, step 42 includes: Step 421: If the initially set charge and discharge power exceeds the maximum charge and discharge power, set the actual charge and discharge allocation power to the maximum charge and discharge power; Step 422: If the initially set charge and discharge power does not exceed the maximum charge and discharge power, set the actual charge and discharge allocation power to the initially set charge and discharge power. That is, first compare the rated charge and discharge power limit of the device. If the initial power exceeds the maximum allowable power of the device, limit the actual allocated power to the rated value; if the initial power does not reach the rated value, directly adopt the initial value. For example, if the rated charging power of a device is 500kW, but the initially set power is 600kW, the actual allocated power is limited to 500kW. This step ensures that the device operation does not exceed the physical limit through hard constraints.

[0045] After the allocation is completed, subtract the allocated power from the total demand, and mark the devices that have completed the allocation as "processed". For example, if the total demand is 1000kW, after allocating 300kW to device A, the remaining demand is updated to 700kW. Select the next device from the priority queue and repeat the above process. This iterative process continues until the remaining demand is zero or all devices have completed polling.

[0046] The termination conditions of the polling allocation strategy include two situations: one is that the remaining demand is exactly matched (i.e., the total allocated power is equal to the initial demand); the other is that all devices have completed one round of polling and there is still unsatisfied demand. The former realizes precise power allocation, and the latter triggers a backup strategy (such as calling a backup energy storage or adjusting the demand priority). For example, if the total demand is 800kW, and after one round of allocation, three devices output a total of 750kW, the remaining 50kW demand will recalculate the priority based on the real-time status of the devices and start the second round of allocation. That is, the preset condition is that the remaining charge and discharge demand to be allocated is zero or the preset condition is that all the energy storage devices have been polled.

[0047] Specifically, considering that when charging and discharging an energy storage device with a pre-determined actual charging and discharging allocation power, such as the th energy storage device, under actual working conditions, the actual charging and discharging allocation power will also affect the state of health and charging and discharging efficiency of the corresponding energy storage device. Therefore, when obtaining the actual charging and discharging allocation power, it is preferably to optimize the actual charging and discharging allocation power based on the state of health and charging and discharging efficiency of the selected energy storage device.

[0048] In a preferred embodiment, the user-side energy storage cluster management method further includes: performing pre-allocation according to an initial priority queue, and updating the state of health and charging and discharging efficiency based on the pre-allocated power according to the following update strategy, so as to calculate the updated priority score, and iterating steps 41 to 43 through the updated priority queue.

[0049] Specifically, the update strategy includes: First, assume that for a predetermined energy storage device, the ratio of power limitation for the preliminary set charging and discharging power is , and as described above, the state of health score of the energy storage device is , and the charging and discharging efficiency is . Then, it is necessary to first perform mean regression on the state of health score and the charging and discharging efficiency to convert them into fractional values homogeneous with the power limitation ratio , which is expressed as: ; where represents the maximum value function, represents the state of health fractional value, represents the charging and discharging efficiency fractional value.

[0050] Then, update the state of health score through the degradation attenuation ratio factor of the state of health, which is expressed as: ; where represents the power limitation ratio, represents the updated state of health score.

[0051] And update the charging and discharging efficiency score through the degradation attenuation ratio factor of the charging and discharging efficiency, which is expressed as: ; where represents the updated charging and discharging efficiency score.

[0052] Moreover, perform correlation attenuation correction on the updated state of health score and the updated charging and discharging efficiency score based on , which is expressed as: ; where represents the corrected updated state of health score, represents the corrected updated charging and discharging efficiency score.

[0053] That is, considering the co-variation with attenuation between the health state and the charge-discharge efficiency, the multi-dimensional attenuation state coupling is performed under respective attenuation models by avoiding simplified independent assumptions, so as to improve the effectiveness of score update.

[0054] Therefore, pre-allocation can be first performed according to the initial priority queue, and the health state and charge-discharge efficiency are updated as described above based on the pre-allocated power to obtain the corrected updated health state score and the corrected updated charge-discharge efficiency score, so as to calculate the updated priority score based on the corrected updated health state score and the corrected updated charge-discharge efficiency score, thereby iterating the above steps 41 to 43 through the updated priority queue to achieve better energy storage cluster management.

[0055] In summary, the user-side energy storage cluster management method provided by this application has been clarified. First, it receives the total charge-discharge demand of the user-side energy storage cluster in the first period to ensure that the system understands the current power demand. Then, it collects the state parameters of each energy storage device, including the state of charge (SOC), health state (SOH), etc., to construct a set of state parameters, providing a data basis for subsequent analysis. Based on these state parameters, priority scores are given to each energy storage device to form a priority queue to identify the devices most suitable for participating in charge-discharge operations. Finally, according to the order of the priority queue, a polling power allocation strategy is used to allocate charge-discharge power to each device in turn to ensure efficient use of resources and no overload of any single device. In this way, not only the working state of the energy storage device is optimized, but also the response speed and reliability of the system are improved, realizing the intelligent management of the energy storage cluster.

[0056] This application also provides a user-side energy storage cluster management system, as Figure 5 shown, the user-side energy storage cluster management system 500 includes: a total charge-discharge demand receiving module 510 for receiving the total charge-discharge demand of the user-side energy storage cluster in the first period; a state parameter acquisition module 520 for acquiring the state parameters of each energy storage device in the user-side energy storage cluster to obtain a set of energy storage unit state parameters; an energy storage device priority scoring module 530 for performing priority scoring on each energy storage device in the user-side energy storage cluster based on the energy storage unit state parameters of each energy storage device to obtain a priority queue; and a priority polling power allocation module 540 for allocating charge-discharge power to each energy storage device in turn based on the order of the priority queue through a polling power allocation strategy.

[0057] This embodiment of the application also provides an electronic device, as Figure 6As shown, the electronic device 10 includes one or more processors 11 and a memory 12. The processor 11 can be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and can control other components in the electronic device 10 to perform desired functions. The memory 12 can include one or more computer program products, and the computer program products can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory can include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory can include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions can be stored on the computer-readable storage media, and the processor 11 can run the program instructions to implement the intelligent leakage protection of the electric water heater and / or other desired functions of the various embodiments of the present application described above. Various contents such as the obtained leakage current signal for a predetermined time period can also be stored in the computer-readable storage media. In one example, the electronic device 10 can further include: an input device 13 and an output device 14, and these components are interconnected through a bus system and / or other forms of connection mechanisms (not shown).

[0058] In addition to the above methods, systems, and electronic devices, an embodiment of the present application can also be a computer program product, which includes computer program instructions that, when run by a processor, cause the processor to perform the corresponding methods provided above.

[0059] The computer program product can be written in any combination of one or more programming languages to write program code for performing the operations of the embodiments of the present application. The programming languages include object-oriented programming languages, such as Java, C++, etc., and also include conventional procedural programming languages, such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as an independent software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0060] In addition, an embodiment of the present application may also be a computer-readable storage medium storing computer program instructions, which, when run by a processor, cause the processor to execute the corresponding methods provided above. The computer-readable storage medium may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0061] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present application. In addition, the above-disclosed specific details are only for illustrative and easy-to-understand purposes and are not limitations. The above details do not limit the present application to necessarily adopt the above specific details for implementation.

[0062] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0063] The above description has been given for purposes of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions, and sub-combinations thereof.

Claims

1. A user-side energy storage cluster management method, characterized in that: include: Receive the total charging and discharging demand of the user-side energy storage cluster in the first period; Collecting state parameters of each energy storage device in the user-side energy storage cluster to obtain a set of energy storage unit state parameters; Based on the energy storage unit status parameters of each energy storage device, each energy storage device in the user-side energy storage cluster is prioritized to obtain a priority queue; based on the order of the priority queue, charging and discharging power is allocated to each energy storage device in turn through a round-robin power allocation strategy; Wherein, based on the energy storage unit state parameters of each energy storage device, priority scoring is performed on each energy storage device in the user-side energy storage cluster to obtain a priority queue, including: priority scoring is performed using the following formula, and the formula is expressed as: ;in, and Indicates the SOC balance weight, SOH balance weight and efficiency difference weight, Indicates The maximum permissible state of charge of each energy storage device, Indicates Energy storage devices in The state of charge at the moment, Indicates The health status of each energy storage device, Indicates The charging and discharging efficiency of each energy storage device; Among them, based on the order of the priority queue, the charging and discharging power is allocated to each energy storage device in turn through the polling power allocation strategy, including: step 41: selecting the energy storage device with the highest priority from the priority queue, and performing an SOC constraint check on it to obtain a preliminary set charging and discharging power; step 42: performing a power limit check on the preliminary set charging and discharging power to obtain the actual charging and discharging allocated power; step 43: subtracting the actual charging and discharging allocated power from the total charging and discharging demand in the first time period to obtain the remaining charging and discharging demand to be allocated, and marking the energy storage device to which power has been allocated as processed; step 44: repeating steps 41 to 43 until the preset conditions are met.

2. The user-side energy storage cluster management method according to claim 1, characterized in that: The energy storage unit state parameters include The state of charge, health status, rated capacity, maximum charge and discharge power, charge and discharge efficiency, minimum allowable state of charge and maximum allowable state of charge at the moment.

3. The user-side energy storage cluster management method according to claim 1, characterized in that: The preset condition is that the remaining charging and discharging demand to be allocated is zero.

4. The user-side energy storage cluster management method according to claim 1, characterized in that: The preset condition is that all the energy storage devices have been polled.

5. The user-side energy storage cluster management method according to claim 1, characterized in that: The step 41 includes: estimating whether the state of charge of the energy storage device will be lower than the minimum allowable state of charge after operating at the maximum charge and discharge power for a first period of time; if it will not be lower than the minimum allowable state of charge, setting the initial set charge and discharge power of the energy storage device to the maximum charge and discharge power; if it will be lower than the minimum allowable state of charge, setting the initial set charge and discharge power of the energy storage device to a charge and discharge power that is just not lower than the minimum allowable state of charge.

6. The user-side energy storage cluster management method according to claim 5, characterized in that: The step 42 includes: if the initially set charge and discharge power exceeds the maximum charge and discharge power, setting the actual charge and discharge allocation power to the maximum charge and discharge power; if the initially set charge and discharge power does not exceed the maximum charge and discharge power, setting the actual charge and discharge allocation power to the initially set charge and discharge power.

7. The user-side energy storage cluster management method according to claim 6, characterized in that: Also includes: Pre-allocation is performed according to the initial priority queue, and the health status and charge and discharge efficiency are updated according to the following update strategy based on the pre-allocated power, so as to calculate the updated priority score, and iterate steps 41 to 43 through the updated priority queue; wherein the update strategy includes: performing mean regression on the health status score and the charge and discharge efficiency to convert them into score values ​​homogeneous with the power limit ratio, expressed as: ;in, represents the maximum value function, Indicates the health status score value, Represents the charge and discharge efficiency score value; the health state score is updated by the degradation attenuation ratio factor of the health state, expressed as: ;in, represents the power limit ratio, Indicates updating the health status score; the charge and discharge efficiency score is updated by the degradation attenuation ratio factor of the charge and discharge efficiency, which is expressed as: ;in, represents an updated charge and discharge efficiency score; the updated health status score and the updated charge and discharge efficiency score are analyzed based on The relevant attenuation correction is expressed as: ;in, Represents the corrected updated health status score, Indicates the updated charge and discharge efficiency score after correction.

8. A user-side energy storage cluster management system, used to implement the user-side energy storage cluster management method according to any one of claims 1 to 7, characterized in that: include: A total charge and discharge demand receiving module, used to receive the total charge and discharge demand of the user-side energy storage cluster in the first period; A state parameter acquisition module is used to collect the state parameters of each energy storage device in the user-side energy storage cluster to obtain a set of energy storage unit state parameters; an energy storage device priority scoring module is used to perform priority scoring on each energy storage device in the user-side energy storage cluster based on the energy storage unit state parameters of each energy storage device to obtain a priority queue; a priority polling power allocation module is used to allocate charging and discharging power to each energy storage device in turn based on the order of the priority queue and through a polling power allocation strategy.

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