Dynamic charging and discharging power adjusting method based on demand response

Abstract

The invention discloses a dynamic charge and discharge power adjustment method based on demand response, and relates to the technical field of charge and discharge power control, and the method comprises the steps: carrying out the historical data calling of a target power pool, and obtaining the historical discharge data; constructing and obtaining a discharge demand analysis model; performing data acquisition on the power supply area under preset environment acquisition conditions to obtain stable environment data; performing power supply mode identification to obtain a real-time power supply mode; synchronizing the stable environment data and the real-time power supply mode to the discharge demand analysis model to obtain a real-time discharge demand; and real-time discharge capacity is obtained through calculation, and charging power adjustment is carried out. The technical problems of low energy utilization efficiency of the power pool, poor power grid load optimization effect, high operation cost and insufficient reliability and stability of the power grid in the prior art are solved, and the purposes of improving the energy utilization efficiency of the power pool, optimizing the power grid load, reducing the operation cost and improving the reliability and stability of the power grid are achieved. And the reliability and the stability of the power grid are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of charge and discharge power control, and in particular to a method for dynamically adjusting charge and discharge power based on demand response. Background Art

[0002] In today's society, energy demand continues to grow, and higher requirements are placed on the development of sustainable energy. As an important form of energy in modern society, the stability and efficiency of electricity supply are crucial. However, traditional power systems have some limitations in adjusting charging and discharging power. Traditional charging and discharging power adjustment methods usually lack sufficient consideration of demand response, and it is difficult to accurately predict changes in electricity demand, resulting in low energy utilization efficiency and uneven load distribution on the power grid. This not only increases operating costs, but may also cause instability in the power grid and affect the reliability of power supply. In addition, changes in environmental factors also have a significant impact on electricity demand. There are differences in electricity demand in different seasons and time periods, and traditional methods often find it difficult to accurately capture these changes and cannot flexibly adjust charging and discharging power according to actual needs.

[0003] The existing technology has technical problems such as low energy utilization efficiency of power pool, poor grid load optimization effect, high operating cost, and insufficient grid reliability and stability. Summary of the invention

[0004] The present application provides a method for dynamic adjustment of charging and discharging power based on demand response, which is used to solve the technical problems in the prior art such as low energy utilization efficiency of the power pool, poor grid load optimization effect, high operating cost, and insufficient grid reliability and stability.

[0005] In view of the above problems, the present application provides a method for dynamically adjusting charging and discharging power based on demand response, the method comprising:

[0006] Call historical data of the target power pool to obtain historical discharge data, wherein the target power pool integrates multiple energy storage units; perform discharge demand analysis based on the historical discharge data, and construct a discharge demand analysis model; collect data from the power supply area under preset environmental collection conditions to obtain stable environmental data, wherein the stable environmental data has a stable window identifier; identify the power supply mode based on the stable window identifier to obtain the real-time power supply mode; synchronize the stable environmental data and the real-time power supply mode to the discharge demand analysis model to obtain the real-time discharge demand; calculate the real-time discharge amount according to the stable window identifier and the real-time discharge demand, and determine whether the remaining power of the target power pool meets the real-time discharge amount; if the remaining power of the target power pool does not meet the real-time discharge amount, assign tasks to the multiple energy storage units, and adjust the charging power of the target power pool based on the task assignment result.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The target power pool is called for historical data to obtain historical discharge data; a discharge demand analysis is performed based on the historical discharge data to construct a discharge demand analysis model; data is collected in the power supply area under preset environmental collection conditions to obtain stable environmental data; power supply mode recognition is performed to obtain a real-time power supply mode; the stable environmental data and the real-time power supply mode are synchronized to the discharge demand analysis model to obtain real-time discharge demand, calculate the real-time discharge amount, and determine whether the remaining power of the target power pool meets the real-time discharge amount; if the remaining power of the target power pool does not meet the real-time discharge amount, the multiple energy storage units are assigned tasks, and the charging power of the target power pool is adjusted based on the task assignment results. The technical effects of improving the energy utilization efficiency of the power pool, optimizing the grid load, reducing operating costs, and enhancing the reliability and stability of the grid are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A flow chart of a method for dynamically adjusting charging and discharging power based on demand response provided in an embodiment of the present application;

[0011] Figure 2 A flowchart of obtaining a real-time power supply mode in a method for dynamically adjusting charging and discharging power based on demand response provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] The present application provides a method for dynamic adjustment of charging and discharging power based on demand response, which is used to solve the technical problems in the prior art such as low energy utilization efficiency of the power pool, poor grid load optimization effect, high operating cost, and insufficient grid reliability and stability.

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0014] Examples, such as Figure 1 As shown, the present application provides a method for dynamically adjusting charging and discharging power based on demand response, the method comprising:

[0015] Step S100: historical data of a target power pool is called to obtain historical discharge data, wherein the target power pool integrates a plurality of energy storage units.

[0016] Specifically, historical data is called for the target power pool, which is an important facility that integrates multiple energy storage units. By calling historical data, the discharge records of the power pool in the past period of time are obtained. These historical discharge data contain rich information, such as the discharge amount, discharge frequency, and discharge duration in different time periods. These data are crucial for analyzing the performance and behavior patterns of the target power pool. By studying historical discharge data, we can understand the discharge characteristics of the power pool under different environmental conditions and different usage scenarios, thereby providing a strong basis for subsequent analysis and decision-making. For example, the peak and trough of electricity consumption in a specific time period can be determined based on historical data, so as to better plan the charging and discharging strategy of the power pool and improve energy utilization efficiency and system stability.

[0017] Step S200: performing a discharge demand analysis based on the historical discharge data to construct a discharge demand analysis model.

[0018] Specifically, an in-depth discharge demand analysis is performed based on the acquired historical discharge data. First, the historical discharge data is segmented according to the season to obtain multiple seasonal discharge data, so that the impact of different seasons on the discharge demand can be better considered. Then, the seasonal transition period is preset, the discharge data of adjacent seasons are sampled, and the multiple seasonal discharge data are mapped and overlapped based on the sampling results to obtain multiple reliable discharge data, which can more accurately reflect the actual discharge situation. Then, the first reliable discharge data is segmented based on the discharge time to obtain daytime reliable discharge data and nighttime reliable discharge data. After that, the daytime reliable discharge data is disassembled to obtain multiple sample discharge powers and multiple sample environmental data, and random operators are called from the operator library. These sample discharge powers and sample environmental data are used to train and optimize the random operators, so as to obtain the daytime power analysis sub-branch. By analogy, the nighttime power analysis sub-branch is constructed based on the nighttime reliable discharge data. Finally, the daytime power analysis branch and the nighttime power analysis branch are connected in parallel to obtain the first power prediction branch, and the branch is identified by the first season. By analogy, multiple power prediction branches are constructed, and by connecting these multiple power prediction branches in parallel, the construction of the discharge demand analysis model is finally completed. This model can accurately analyze and predict the discharge demand according to different seasons and times, while improving energy utilization efficiency and system stability.

[0019] Step S300: Collect data from the power supply area under preset environmental collection conditions to obtain stable environmental data, wherein the stable environmental data has a stable window identifier.

[0020] Specifically, environmental collection conditions are pre-set to collect data from the power supply area. These environmental collection conditions include specific sensor types and layouts, collection time intervals, data accuracy requirements, etc. Through these preset conditions, it can be ensured that the collected data has a certain degree of accuracy and reliability. When collecting data from the power supply area, stable environmental data will be obtained. These data reflect various environmental factors in the power supply area, such as temperature, humidity, light intensity, etc. These environmental factors will affect the performance and discharge requirements of the target power pool. Stable environmental data has a stable window mark, which indicates that these data are relatively stable within a certain time range. For example, if the change range of environmental data is small within a specific time period, then it can be considered that these data are in a stable state and a corresponding stable window mark is given. Such a mark is helpful for subsequent analysis and processing of data, because stable data can provide a more reliable reference basis to help better understand the environmental conditions of the power supply area and its impact on the target power pool.

[0021] Step S400: performing power supply mode identification based on the stable window identifier to obtain a real-time power supply mode.

[0022] Specifically, the power supply mode is identified based on the stable window identifier to obtain the real-time power supply mode. The stable environmental data represented by the stable window identifier provides an important reference for the identification of the power supply mode. The environmental data corresponding to the stable window identifier is analyzed, including factors such as temperature, humidity, light, and other factors affecting power supply. These data can reflect the basic status of the current power supply area. Then, the power supply mode is judged in combination with the historical power supply data and the current environmental conditions. The power supply mode includes many types, such as peak period power supply, valley period power supply, stable power supply, emergency power supply, etc. The current real-time power supply mode is determined by comprehensive analysis of environmental data and historical modes. For example, if the stable window identifier shows that the current ambient temperature is moderate, the power load is relatively stable, and the historical data is usually a stable power supply mode under similar circumstances, then it can be judged that the current real-time power supply mode is stable power supply. If in some special circumstances, such as sudden weather changes or a sharp increase in power demand, the emergency power supply mode or other special power supply modes will be identified. Obtaining the real-time power supply mode is crucial for reasonably adjusting the charging and discharging strategy of the target power pool. Different power supply modes mean different power supply stability and reliability, which affects the discharge demand and charging opportunity of the target power pool. By accurately identifying the real-time power supply mode, the operation of the target power pool can be better planned, improving the reliability and stability of the power grid.

[0023] Step S500: synchronizing the stable environment data and the real-time power supply mode to the discharge demand analysis model to obtain the real-time discharge demand.

[0024] Specifically, the stable environment data and the real-time power supply mode are synchronously transmitted to the discharge demand analysis model to obtain the real-time discharge demand. The stable environment data contains information about various environmental factors in the power supply area, such as temperature, humidity, light intensity, etc. These data reflect the state of the current power supply environment and will affect the discharge demand of the target power pool. For example, higher temperature will cause the discharge efficiency of the power pool to decrease, thereby increasing the discharge demand to meet the same power load. The real-time power supply mode indicates the current power supply situation, including the stability of the power supply, the voltage level, whether it is in the peak or valley period, etc. Different power supply modes will directly affect the operation strategy and discharge demand of the target power pool. For example, when the power supply is tight during peak hours, it is necessary to more accurately control the discharge amount of the power pool to ensure the power supply of critical loads. These stable environment data and real-time power supply modes are synchronously input into the discharge demand analysis model, which will comprehensively consider these factors and use the pre-established algorithm and model structure to analyze and process the input data. The model will adjust its internal parameters and calculation methods according to different environmental factors and power supply modes to accurately predict the real-time discharge power demand under the current situation. In this way, more accurate real-time discharge power requirements can be obtained, providing a scientific basis for the subsequent charge and discharge management of the target power pool. For example, based on the real-time discharge power requirements, it can be decided whether the power pool needs to be charged to meet the upcoming peak power demand, or the discharge power can be adjusted to adapt to different power supply modes and environmental conditions, thereby improving the energy utilization efficiency of the power pool, optimizing the grid load, reducing operating costs, and enhancing the reliability and stability of the grid.

[0025] Step S600: Calculate the real-time discharge capacity according to the stable window identifier and the real-time discharge demand, and determine whether the remaining power of the target power pool meets the real-time discharge capacity.

[0026] Specifically, firstly, the real-time discharge amount is calculated based on the stable environment data represented by the stable window identifier and the real-time discharge demand determined by the previous steps. The stable window identifier provides information about the current relatively stable environmental conditions, which affects factors such as the efficiency and rate of discharge, while the real-time discharge demand clarifies the power output requirements required under the current situation. By combining the two, the impact of environmental factors on discharge and the actual power demand are comprehensively considered to accurately calculate the real-time discharge amount. Next, it is determined whether the remaining power of the target power pool meets the real-time discharge amount. The current remaining power of the target power pool is compared with the calculated real-time discharge amount. If the remaining power is greater than or equal to the real-time discharge amount, it indicates that the target power pool can meet the power demand under the current situation and no emergency adjustment measures are required. However, if the remaining power is less than the real-time discharge amount, it means that the target power pool cannot continue to meet the current power demand, and corresponding measures need to be taken to adjust the operating state of the power pool, such as task allocation to multiple energy storage units to adjust the charging power, or take other measures to ensure the stability and reliability of power supply. This judgment process is crucial for timely discovery of potential power supply problems and taking effective countermeasures.

[0027] Step S700: If the remaining power of the target power pool does not meet the real-time discharge capacity, tasks are assigned to the multiple energy storage units, and the charging power of the target power pool is adjusted based on the task assignment result.

[0028] Specifically, when the remaining power of the target power pool is insufficient to meet the current real-time discharge, the tasks of the energy storage units will be reallocated and the charging power will be adjusted to ensure the stability and reliability of the power supply. The shortage in this case occurs during the external discharge process, and some energy storage units are switching from charging tasks to discharging tasks. In this case, it is necessary to respond quickly and make corresponding adjustments. The task allocation for multiple energy storage units in the target power pool will be determined according to the current state of each energy storage unit. Each energy storage unit is in a different state, some are in the charging process, and some have just completed charging and are about to enter the discharge state. The purpose of task allocation is to find the best match between power demand and the state of the energy storage unit. For example, if some energy storage units have sufficient power and the charging task has just been completed, they will be assigned to the discharge task to quickly replenish the power supply.

[0029] After the task allocation is completed, the charging power of the target power pool is adjusted according to the results of the task allocation. For example, if some energy storage units need to be charged first to prepare for future discharge needs, the charging rate or charging time of these units is adjusted to ensure that they can reach the required power before the next demand comes. At the same time, if some energy storage units are charging and need to switch to discharge tasks, their charging power is reduced or their charging is temporarily interrupted to meet the real-time discharge needs more quickly. By dynamically adjusting task allocation and charging power, it is possible to flexibly respond to changes in power demand, maximize the use of existing energy storage resources, and ensure the stable operation of the power grid.

[0030] In a possible implementation, step S200 further includes:

[0031] Step S210: segmenting the historical discharge data based on seasons to obtain discharge data for multiple seasons.

[0032] Step S220: sampling the discharge data of adjacent seasons during a preset season transition period, and performing mapping and overlapping processing on the discharge data of the plurality of seasons based on the sampling results to obtain a plurality of credible discharge data.

[0033] Step S230: dividing the first credible discharge data based on the discharge time to obtain the daytime credible discharge data and the nighttime credible discharge data, wherein the first credible discharge data is any one of the plurality of credible discharge data.

[0034] Step S240: disassembling the daytime reliable discharge data to obtain a plurality of sample discharge powers and a plurality of sample environmental data.

[0035] Step S250: calling a random operator from an operator library, and using the plurality of sample discharge powers and the plurality of sample environment data to perform training optimization on the obtained random operator, to obtain a daytime power analysis sub-branch.

[0036] Step S260: Similarly, a nighttime power analysis sub-branch is constructed based on the nighttime reliable discharge data.

[0037] Step S270: connecting the daytime power analysis branch and the nighttime power analysis branch in parallel to obtain a first power prediction branch, and using a first season to identify the first power prediction branch.

[0038] Step S280: Similarly, multiple power prediction branches are constructed, and the construction of the discharge demand analysis model is completed by connecting the multiple power prediction branches in parallel.

[0039] Specifically, in order to analyze the historical discharge data more targetedly, it is segmented according to the season. Due to the large differences in climate conditions, electricity demand and other factors in different seasons, this segmentation method helps to gain a deeper understanding of the discharge characteristics of the target power pool in each season. Through detailed combing of historical discharge data, the data is divided into four seasons: spring, summer, autumn and winter, thereby obtaining discharge data for multiple seasons. For example, in summer, due to the high temperature, high-power electrical appliances such as air conditioners are used frequently, resulting in large discharge amounts and discharge patterns that are different from other seasons; and in winter, due to the use of heating equipment, specific discharge patterns are also shown. Such segmentation can provide a basis for the subsequent more accurate construction of a discharge demand analysis model, enabling the model to better adapt to changes in different seasons and improve the accuracy of discharge demand prediction.

[0040] Considering that seasonal alternation will bring about special changes in data, a seasonal transition period is set in advance. During this transition period, the discharge data of adjacent seasons are sampled, and representative sample data are selected from the large amount of discharge data of the two adjacent seasons. For example, data points can be randomly selected according to a certain proportion, or key data can be selected according to specific time nodes. Then, based on the results obtained from these samplings, mapping overlap processing is implemented for multiple seasonal discharge data. Due to the alternation of seasons, there is often a certain overlap in electricity consumption and environmental factors at the end of the previous season and the beginning of the next season. For example, at the end of spring and the beginning of summer, the temperature is in a transitional state, and the electricity demand has both the characteristics of spring and the trend of summer. Through this mapping overlap processing, the data of adjacent seasons during the transition period are fused, so that the discharge data of each season can transition more smoothly at the junction. After such a processing process, multiple credible discharge data are finally obtained. These data have higher reliability and accuracy after considering the impact of seasonal alternation. They can provide a more solid data foundation for subsequent analysis and model construction, ensuring that the discharge demand analysis model can better adapt to seasonal changes and accurately predict the discharge demand in different seasons and during seasonal transitions.

[0041] Select any one of the multiple credible discharge data, which is referred to as the first credible discharge data. Since there are obvious differences in electricity consumption behavior and environmental factors during the day and at night, in order to analyze the discharge situation more deeply, the first credible discharge data is segmented based on the discharge time. According to certain time standards, the first credible discharge data is divided into two parts, namely, daytime credible discharge data and nighttime credible discharge data. For example, the discharge data is divided into two parts, daytime and nighttime, based on the local sunrise and sunset time. During the day, people's production and life activities are usually more frequent, the electricity demand is higher, and environmental factors such as light will also affect the discharge; at night, many production activities stop, the electricity demand will change, and the environment is relatively stable. Through this segmentation method, a more detailed analysis can be carried out for the different characteristics of daytime and nighttime, respectively, to provide more specific data support for the subsequent construction of a more accurate discharge demand analysis model.

[0042] The daytime reliable discharge data is disassembled. Since the daytime reliable discharge data contains rich information, it can be refined into multiple more specific data elements by disassembling, and multiple sample discharge powers and multiple sample environmental data are extracted from the daytime reliable discharge data. The sample discharge power reflects the discharge power of the target power pool at different time points during the day or in different usage scenarios. These different discharge power values ​​can help analyze the changes in electricity demand during the daytime period and the factors that may affect the discharge power. The sample environmental data includes various environmental factors related to discharge during the day, such as temperature, humidity, light intensity, etc. These environmental data are crucial to understanding the daytime discharge situation, because environmental factors often affect the performance and discharge behavior of the target power pool. By disassembling the daytime reliable discharge data to obtain these multiple sample discharge powers and multiple sample environmental data, more detailed and specific basic data are provided for subsequent analysis and model construction, which helps to more accurately grasp the laws and characteristics of daytime discharge, thereby laying the foundation for building a more accurate discharge demand analysis model.

[0043] First, a random operator is called from the operator library, which stores various types of operators that can be used for data processing and analysis. The selection of random operators is based on specific algorithm requirements or to introduce a certain degree of randomness and diversity to improve the accuracy and robustness of subsequent analysis. Next, the called random operator is trained and optimized using multiple sample discharge power and multiple sample environmental data. These specific sample data are input into the random operator, and the parameters and structure of the operator are continuously adjusted to enable it to better adapt to the characteristics of daytime discharge data. During the training process, the random operator will learn and adjust according to the input data to minimize the prediction error or maximize a certain performance indicator. After training and optimization, the daytime power analysis sub-branch is obtained. This sub-branch is a tool specifically for power analysis of daytime discharge conditions. It can quickly and accurately analyze the daytime discharge power demand and trend based on the input new daytime discharge data and environmental data. The construction of this sub-branch provides an important component for a more comprehensive analysis of the discharge demand of the entire target power pool in the future, which helps to build a more accurate and efficient discharge demand analysis model.

[0044] A nighttime power analysis sub-branch is constructed using a method similar to that used to construct the daytime power analysis sub-branch. Since the power consumption behavior and environmental factors at night are very different from those during the day, they need to be analyzed separately. First, with the process of constructing the daytime power analysis sub-branch as a reference, a random operator suitable for processing nighttime data is also called from the operator library. This random operator is different from the one used during the daytime in parameter setting or structure to better adapt to the characteristics of nighttime discharge data. Then, multiple sample discharge powers and multiple sample environmental data in the nighttime reliable discharge data are used to train and optimize the called random operator. During the training process, the parameters of the operator are continuously adjusted according to the power consumption pattern and environmental characteristics at night, so that it can accurately analyze the discharge situation at night. After training and optimization, the nighttime power analysis sub-branch is successfully constructed. This sub-branch is specifically used to analyze the discharge power demand and trend at night. It can provide strong support for nighttime power management and planning based on the input nighttime discharge data and environmental data. By constructing the nighttime power analysis sub-branch and cooperating with the daytime power analysis sub-branch, the discharge demand of the target power pool in different time periods can be more comprehensively analyzed, laying the foundation for building a complete discharge demand analysis model.

[0045] The daytime power analysis branch and the nighttime power analysis branch are operated in parallel, which means that two analysis branches specifically for different time periods (daytime and nighttime) are connected together so that they can work together. Through parallel connection, the two branches can receive input data at the same time, analyze them according to the characteristics of daytime and nighttime respectively, and then integrate the analysis results. After parallel connection, the first power prediction branch is obtained. This branch combines the power analysis capabilities of daytime and nighttime. It predicts the power demand of different time periods based on various input data, including time information, environmental data, etc. For example, it is determined whether it is daytime or nighttime according to the current time, and then the corresponding sub-branch is called for analysis, and finally a comprehensive power prediction result is obtained. At the same time, the first season mark is used to mark the first power prediction branch. This is because the electricity demand and environmental factors in different seasons will also be different, and this branch is constructed for a specific season. Through the season mark, the seasonal range to which the branch is applicable can be clarified, which is convenient for distinction and calling in subsequent analysis and application. Such a mark helps to better manage and use the power prediction branches of different seasons, and improve the accuracy and applicability of the entire discharge demand analysis model.

[0046] According to the method similar to that of constructing the first power prediction branch, for different seasons or other specific conditions, the previous steps can be repeated to construct multiple power prediction branches. Each power prediction branch is customized for different seasons, different time period combinations or different power usage scenarios. For example, a power prediction branch is constructed for the daytime and nighttime in spring, another branch is constructed for different situations in summer, and so on. After constructing multiple power prediction branches, the construction of the discharge demand analysis model is completed by connecting these branches in parallel. The parallel process enables each branch to be analyzed independently, while integrating the results together to provide a more comprehensive and accurate power forecast for the entire system. Such a discharge demand analysis model selects the appropriate power prediction branch for analysis according to different input conditions, or integrates the results of multiple branches to obtain the final discharge demand forecast. Through the collaborative work of multiple power prediction branches, the model can better adapt to various complex situations, improve the accuracy and reliability of the target power pool discharge demand analysis, and provide strong decision support for reasonable charge and discharge management.

[0047] In a possible implementation, step S300 further includes:

[0048] Step S310: The environmental collection conditions include collection triggering conditions and collection suppression conditions.

[0049] Specifically, the environmental collection conditions are clearly defined, including collection trigger conditions and collection inhibition conditions. The collection trigger conditions determine when to start collecting environmental data. For example, when a specific time point arrives, the status of a device changes, or other preset conditions are met, the environmental data collection will be triggered. The collection inhibition conditions take effect when the environmental data changes. Once the collection inhibition conditions are met, the collection of environmental data will be stopped. This is to ensure that the collected data has a certain stability and reliability, and avoid unnecessary collection when the environmental data fluctuates frequently. It is worth noting that the collection trigger conditions are actually the inhibition conditions of the last environmental collection. This design makes the environmental data collection process have a certain degree of coherence and logic. When a collection is suppressed, the trigger conditions of the next collection will be set according to the last inhibition situation, thereby ensuring the orderly progress of data collection and the validity of the data. By reasonably setting the collection trigger conditions and collection inhibition conditions, the timing and frequency of environmental data collection can be better controlled, and the efficiency and quality of data collection can be improved.

[0050] In one possible implementation, Figure 2 As shown, step S400 also includes:

[0051] Step S410: Determine whether the stable window identifier falls within the seasonal transition period. If the stable window identifier falls within the seasonal transition period, generate a first transition season and a second transition season.

[0052] Step S420: Determine the discharge time based on the stable window identifier to obtain the real-time power supply type.

[0053] Step S430: Integrate and store the real-time power supply type, the first transition season, and the second transition season to obtain the real-time power supply mode.

[0054] Specifically, the stable window mark is first judged to determine whether it falls into the seasonal transition period. If the stable window mark falls into the seasonal transition period, it means that the current environmental data is at the junction of two seasons. At this time, the first transition season and the second transition season will be generated to clarify which two seasons are in the transition stage. For example, if you are currently in the transition period from spring to summer, then the first transition season may be spring, and the second transition season is summer. Such a division helps to more accurately grasp the special electricity consumption and environmental factors when the seasons change. However, if the stable window mark does not fall into the seasonal transition period, it means that you are currently in a relatively stable season. At this time, you can directly determine the current season without generating a transition season mark.

[0055] The discharge time is judged based on the stable window mark, and the current time information is analyzed to determine whether it is in different time periods such as daytime or nighttime, so as to obtain the real-time power supply type. Different power supply types may correspond to different power demand and power supply characteristics.

[0056] The real-time power supply type, the first transition season, and the second transition season are integrated and stored to obtain the real-time power supply mode. First, the real-time power supply type reflects the specific characteristics of the current power supply, such as peak power supply, valley power supply, or stable power supply. The first transition season and the second transition season clarify whether the current season is in a special period of seasonal alternation and the two specific seasons involved. By integrating and storing this information, the current power supply situation can be fully described. For example, if the real-time power supply type is peak power supply and it is in the transition season from spring to summer, then this integrated information forms a specific real-time power supply mode, which not only considers the time characteristics of power supply, but also combines the impact of seasonal changes. Such a real-time power supply mode is crucial for subsequent analysis and decision-making. It can provide an accurate basis for the management of the target power pool, help determine how to perform charging and discharging operations under the current power supply mode to better meet power demand, and at the same time improve energy utilization efficiency and system stability. By comprehensively considering different power supply situations and seasonal changes, it can more flexibly respond to various complex power supply environments and ensure the reliability and sustainability of power supply.

[0057] In a possible implementation, step S500 further includes:

[0058] Step S510: activating a first real-time prediction branch corresponding to the first transition season and a second real-time prediction branch corresponding to the second transition season in the discharge demand analysis model based on season identification mapping.

[0059] Step S520: activating a first power analysis sub-branch and a second power analysis sub-branch in the first real-time prediction branch and the second real-time prediction branch according to the real-time power supply type.

[0060] Step S530: Synchronize the stable environment data to the first power analysis sub-branch and the second power analysis sub-branch to perform power prediction, and obtain a first real-time prediction result and a second real-time prediction result.

[0061] Step S540: Calculate the mean of the first real-time prediction result and the second real-time prediction result to obtain the real-time discharge demand.

[0062] Specifically, the seasonal identifier is used for mapping operations to activate specific branches in the discharge demand analysis model. When it is determined that the current period is in the seasonal transition period, the identifiers of the first transition season and the second transition season are generated. Based on these seasonal identifiers, the model can accurately identify the first real-time prediction branch and the second real-time prediction branch corresponding to them. By activating these specific real-time prediction branches, the model can more accurately analyze the situation in the seasonal transition period, taking into account the changes in electricity demand in different transition seasons, the influence of environmental factors, etc., to provide a more targeted basis for subsequent power prediction and discharge demand analysis. This can improve the adaptability and accuracy of the model during the seasonal transition period and better meet the power management needs of different seasonal conversion stages.

[0063] According to the real-time power supply type, a mapping operation is further performed in the first real-time prediction branch and the second real-time prediction branch to activate the corresponding first power analysis sub-branch and second power analysis sub-branch. The real-time power supply type reflects the specific status of the current power supply, such as peak power supply, valley power supply or stable power supply. Different power supply types mean different power demand and power supply characteristics. After the real-time power supply type is determined, the corresponding power analysis sub-branches are found in the first real-time prediction branch and the second real-time prediction branch. These sub-branches are specially designed for different power supply types and transition season combinations. For example, the first power analysis sub-branch is applicable to the first transition season and performs power analysis under a specific power supply type. It can take into account the environmental factors of the transition season and the impact of the specific power supply type. Similarly, the second power analysis sub-branch performs power analysis for the second transition season and the corresponding power supply type.

[0064] The stable environment data is synchronously transmitted to the first power analysis sub-branch and the second power analysis sub-branch for power prediction. The stable environment data contains various environmental factors related to the operation of the power system, such as temperature, humidity, light intensity, etc. These stable environment data are simultaneously input into the first power analysis sub-branch and the second power analysis sub-branch. These two sub-branches are respectively for different transition seasons, but both use the input environmental data to analyze and predict power. The first power analysis sub-branch will predict the power in the corresponding transition season based on the received stable environment data and its internal specific algorithm and model structure, thereby obtaining the first real-time prediction result. This result reflects the possible power output of the target power pool under specific environmental conditions and in the first transition season. Similarly, the second power analysis sub-branch will also analyze based on the input stable environment data to obtain a second real-time prediction result, which represents the power prediction value in the second transition season and the current environmental conditions.

[0065] The first real-time prediction result and the second real-time prediction result are averaged. By taking the average value, the prediction results of the two transition seasons and the corresponding power analysis sub-branches can be comprehensively considered to obtain a more accurate real-time discharge demand. This calculation method can balance the impact of different seasons and power supply types during the seasonal transition period, and provide a reliable basis for the charge and discharge management of the target power pool.

[0066] In a possible implementation, step S600 further includes:

[0067] Step S610: If the remaining power of the target power pool meets the real-time discharge capacity, the electricity price is called with the stability window identifier as a constraint to obtain a real-time electricity price sequence.

[0068] Step S620: interactively obtain the charging cycle time of the target power pool, and use 1 / 3 of the charging cycle time to update the window of the stable window identifier, obtain the updated window identifier, and use the updated window identifier as a constraint to call the electricity price and obtain an updated electricity price sequence.

[0069] Step S630: If the updated electricity price sequence is better than the real-time electricity price sequence, a plurality of remaining energy storages of the plurality of energy storage units are interactively obtained, and a charging scheduling sequence is generated according to the plurality of remaining energy storages.

[0070] Step S640: At the starting time identified by the update window, charge management of the multiple energy storage units is performed according to the charging scheduling sequence.

[0071] Specifically, first determine whether the remaining power of the target power pool meets the real-time discharge capacity. If it does, it means that the target power pool has enough power in the current state to meet the current power demand. At this time, the electricity price is called with the stable window identifier as a constraint. The stable window identifier represents a specific time period, specific market conditions, or other constraints related to electricity prices. Through this identifier, it can be determined within which ranges the electricity price query and call is performed. For example, the stable window identifier limits the query to electricity price information within a specific region and a specific time period. After the electricity price is called, a real-time electricity price sequence will be obtained. This sequence contains electricity price data at different time points or under different circumstances within the range determined by the stable window identifier under the conditions met. These electricity price data can help decision makers understand the current electricity price trends and changes.

[0072] First, the charging cycle time required for the target power pool to charge from zero to full power is determined interactively. This charging cycle time is crucial for planning the use of the power pool, and it reflects the charging speed and capacity of the target power pool under different conditions. Considering that charging and discharging need to be carried out synchronously to ensure the sustainability of the power pool, it cannot be turned into a disposable device. Therefore, 1 / 3 of the charging cycle time is used to update the stable window identifier. The stable window identifier originally represents a specific time range or condition. By updating it with a part of the charging cycle time, the window is more in line with the actual charging and discharging rhythm of the power pool. The updated window identifier obtained in this way can better reflect the operating status and electricity price change trend of the power pool in the future. The electricity price is called with the updated window identifier as a constraint. Within the new window range, the corresponding electricity price information is queried and obtained, thereby obtaining an updated electricity price sequence. This sequence contains electricity price data at different times or under different circumstances under the updated time window. By continuously updating the window and obtaining a new electricity price sequence, the change of electricity price can be grasped in real time, providing a more accurate basis for the reasonable arrangement of the charging and discharging operations of the power pool, so as to achieve more efficient energy management and cost control.

[0073] Compare the updated electricity price sequence with the real-time electricity price sequence. If the updated electricity price sequence is better, it means that the electricity price situation in the new time window is more favorable for charging operations. At this time, in order to make full use of this advantage, multiple remaining energy storage conditions of multiple energy storage units are obtained in an interactive manner. These remaining energy storage information reflects how much electricity each energy storage unit can currently store. Then, a charging scheduling sequence is generated based on the specific conditions of these multiple remaining energy storages. This charging scheduling sequence is a charging plan formulated for multiple energy storage units. It takes into account factors such as the remaining energy storage capacity, charging priority, and overall charging efficiency of each energy storage unit. For example, energy storage units with less remaining energy storage but higher charging priority will be given priority for charging, or more charging tasks can be assigned to energy storage units with faster charging speeds.

[0074] When the starting time determined by the update window identifier is reached, the charging management of multiple energy storage units is performed according to the previously generated charging scheduling sequence. This starting time is determined based on the charging cycle time of the target power pool and the update of the stable window identifier, representing a new time node at which more favorable electricity price conditions or other conditions are suitable for charging. The charging scheduling sequence is a charging plan carefully formulated based on the remaining energy storage conditions of multiple energy storage units. It clarifies the specific arrangements such as the charging order, charging amount, and charging speed of each energy storage unit. At the starting time of the update window identifier, the charging operations of multiple energy storage units are managed in sequence according to this charging scheduling sequence. For example, the energy storage units with less remaining energy storage and higher priority are charged first, and the charging current and voltage and other parameters are adjusted according to their charging needs and charging capabilities to ensure a safe and efficient charging process. Then, according to the arrangement in the sequence, the charging of other energy storage units is gradually managed. During the whole process, the charging status of each energy storage unit, such as power changes, temperature changes, etc., is monitored in real time so as to adjust the charging strategy in time to prevent problems such as overcharging and overheating. In this way, charging of multiple energy storage units can be managed in the most reasonable way at the best time point, thus improving energy utilization efficiency, reducing charging costs, and ensuring the stable operation and reliability of the entire energy storage system.

[0075] In a possible implementation, step S700 further includes:

[0076] Step S710: Calculate and obtain the power supply duration of multiple units according to the real-time discharge demand and multiple remaining energy storages.

[0077] Step S720: interactively obtaining multiple cell states of the multiple energy storage units, wherein each cell state in the multiple cell states includes a quantified health, a historical charging frequency, a historical discharging frequency, and a remaining service life.

[0078] Step S730: pre-constructing a demand response quantification function, and obtaining a plurality of demand response indexes by synchronizing the power supply durations of the plurality of units and the plurality of unit states to the demand response quantification function.

[0079] Step S740: Forward serializing the multiple demand response indexes as the charging task ranking of the multiple energy storage units.

[0080] Step S750: interactively obtaining multiple charging power constraints of the multiple energy storage units.

[0081] Step S760: scheduling the multiple energy storage units according to the charging task sequence and dynamically adjusting the charging power of the energy storage units according to the multiple charging power constraints.

[0082] Specifically, the calculation is combined with the real-time discharge demand and the remaining energy storage of each of the multiple energy storage units. The real-time discharge demand represents the current system's demand for electricity. It reflects the amount of electricity that needs to be extracted from the energy storage unit within a specific time period, while the multiple remaining energy storages are the current levels of electricity stored in each energy storage unit. By comparing and calculating the real-time discharge demand with the remaining energy storage of each energy storage unit, it is possible to determine how long each energy storage unit can continue to supply power while meeting the current real-time discharge demand. For example, if the remaining energy storage of an energy storage unit is a certain value, and the real-time discharge demand is a fixed power value, then the power supply duration of the energy storage unit can be obtained by a simple division operation (remaining energy storage divided by the real-time discharge demand power).

[0083] Obtain multiple unit states of multiple energy storage units in an interactive manner. Each energy storage unit has its own specific state information, which is crucial for evaluating the unit's performance, reliability, and planning subsequent use and maintenance. Among them, the quantitative health reflects the current overall health of the energy storage unit. A higher quantitative health means that the unit has good performance and can reliably provide power storage and release functions; a lower quantitative health indicates that the unit has potential problems and requires further inspection or maintenance. The historical charging frequency records the number of times the energy storage unit has been charged in the past. Frequent charging will affect the life and performance of the unit, such as causing the battery capacity to decrease and the internal resistance to increase. By understanding the historical charging frequency, the unit's usage intensity and aging degree can be evaluated. The historical discharge frequency records the number of times the unit has released power in the past. Similar to the charging frequency, a high discharge frequency also has a certain impact on the unit. At the same time, combined with the charging frequency and the discharging frequency, the unit's charge and discharge cycle is analyzed to further understand its performance change trend. The remaining service life indicates the time that the energy storage unit is expected to be used normally in the current state. Understanding the remaining service life, reasonably arranging the replacement plan of the energy storage unit, and ensuring the continuous and stable operation of the system.

[0084] A demand response quantification function is pre-built. The purpose of this function is to take into account multiple different factors to quantify the responsiveness of each energy storage unit to demand. Multiple unit power supply durations and multiple unit states are synchronously input into the demand response quantification function. The unit power supply duration reflects the time continuity of the energy storage unit in meeting demand. A longer power supply duration usually means that the power demand can be met more stably within a certain period of time. Multiple unit states include quantitative health, historical charging frequency, historical discharge frequency, and remaining service life. These state factors jointly affect the performance and reliability of the energy storage unit, and thus affect its responsiveness to demand. These input parameters are processed and calculated through the demand response quantification function, and multiple demand response indexes are finally obtained.

[0085] Multiple demand response indexes are forward serialized, and the larger the value, the higher the urgency, which means that the higher the demand response index of the energy storage unit, the more urgent it is to charge. The demand response index takes into account multiple factors, such as the unit's power supply time, quantitative health, historical charge and discharge frequency, and remaining service life. These factors together reflect the ability and current status of the energy storage unit in meeting power demand. When the demand response index of a certain energy storage unit is high, it means that it performs relatively poorly in these aspects, and may have less remaining power, lower health, or a higher frequency of use, so it is urgent to charge to restore its good operating state. By forward serializing these demand response indexes, multiple energy storage units are sorted from high to low according to urgency. This sorting result becomes the priority order of the charging task. When performing charging scheduling, the system will give priority to charging for energy storage units with high urgency to ensure that those units that are critical to system operation and urgently need to be charged can be replenished with electricity as soon as possible.

[0086] Multiple charging power constraints of multiple energy storage units are obtained in an interactive manner. Each energy storage unit has specific charging power constraints due to its own characteristics, design specifications, safety requirements and other factors. These charging power constraints include multiple aspects. For example, the maximum charging power constraint limits the maximum power input that the energy storage unit can withstand during the charging process. Exceeding this power will cause damage to the unit or cause safety problems. The minimum charging power constraint is to ensure that under certain circumstances, the energy storage unit can be charged at a certain minimum power to ensure charging efficiency and stability. In addition, there are some dynamic charging power constraints, such as power limits that vary according to factors such as the temperature, remaining power, and health status of the unit. By interactively obtaining these charging power constraints, we can fully understand the restrictions of each energy storage unit during the charging process. This is crucial for reasonably arranging charging plans and ensuring the safety and efficiency of the charging process. When performing charging scheduling and dynamically adjusting the charging power, these constraints must be fully considered to avoid damage to the energy storage unit due to violation of the charging power constraints, while ensuring that the entire energy storage system can operate stably and reliably.

[0087] Multiple energy storage units are scheduled according to the charging task ranking, and the charging power of the energy storage units is dynamically adjusted according to multiple charging power constraints. The charging task ranking is determined according to the demand response index, which clarifies the charging priority of each energy storage unit. High-priority energy storage units are usually those with weak demand response capabilities and urgently need to be charged to improve the overall system performance. When performing charging scheduling, charging is arranged for each energy storage unit in sequence according to the charging task ranking. At the same time, considering that each energy storage unit has specific charging power constraints, including maximum charging power, minimum charging power and other restrictions. During the charging process, it is necessary to monitor the status and charging status of each energy storage unit in real time and make dynamic adjustments based on these charging power constraints. By continuously and dynamically adjusting the charging power, it is possible to meet the charging needs of each energy storage unit while ensuring that the charging process complies with safety regulations, improve the efficiency and reliability of the entire energy storage system, and achieve optimized charging management of multiple energy storage units.

[0088] In a possible implementation, step S730 further includes:

[0089]

[0090] Among them, U is the demand response index, H is the quantitative health, and F is c is the historical charging frequency, F d is the historical discharge frequency, L is the remaining service life, and T is the unit power supply time.

[0091] Specifically, U is the demand response index, which is a quantitative indicator used to measure the energy storage unit's ability to respond to demand. H is the quantitative health, which ranges from 0 to 1, with 1 indicating complete health. The quantitative health reflects the current performance status of the energy storage unit. The higher the health, the stronger the ability to respond to demand. c The historical charging frequency reflects the number of times the energy storage unit has been charged in the past. Frequent charging will affect the life and performance of the unit. d is the historical discharge frequency, which records the number of times the unit has released electricity in the past. A high discharge frequency has a certain impact on the unit. L is the remaining service life, which indicates how long the energy storage unit can still work normally. A longer remaining service life usually means a more stable demand response capability. T is the unit power supply duration, which is directly related to the continuous ability of the energy storage unit to meet power demand. The longer the power supply duration, the better the demand response capability may be.

[0092] Through this function, multiple factors such as quantitative health, historical charging frequency, historical discharging frequency, remaining service life and unit power supply time are comprehensively considered to evaluate the responsiveness of the energy storage unit to demand, providing a scientific basis for charging task sorting and dynamic adjustment of charging power.

[0093] In a possible implementation, step S740 further includes:

[0094] Step S741: negatively serialize the multiple demand response indexes as the discharge task ranking of the multiple energy storage units.

[0095] Step S742: taking the stable window identifier as a constraint, scheduling the multiple energy storage units according to the discharge task sequence to dynamically adjust the power of external discharge according to the real-time discharge demand.

[0096] Specifically, multiple demand response indexes are subjected to negative serialization operations. Negative serialization means arranging the demand response indexes from low to high, and the results of such arrangement are used as the discharge task ranking of multiple energy storage units. The energy storage unit with a lower demand response index has a higher priority in the discharge task ranking. This is because energy storage units with low demand response indexes perform better in some aspects, such as high health and long remaining service life, and are suitable for priority discharge operations. In this way, the discharge order of energy storage units can be arranged more reasonably, improving the operating efficiency and stability of the entire system.

[0097] Taking the stable window identifier as a constraint, multiple energy storage units are scheduled according to the previously obtained discharge task ranking. The stable window identifier limits a specific time range or other related conditions to ensure that the discharge operation is carried out within an appropriate range. According to the discharge task ranking, energy storage units with lower demand response index are given priority for discharge. At the same time, combined with the real-time discharge demand, the current power requirement for external discharge is clarified. According to the real-time discharge demand, the external discharge power of multiple energy storage units is dynamically adjusted. For example, when the real-time discharge demand is large, the discharge power of the energy storage unit will be increased; when the demand is small, the discharge power will be reduced accordingly. The status and discharge of each energy storage unit are monitored in real time, and the discharge power of the energy storage unit is controlled in sequence according to the discharge task ranking to meet the real-time discharge demand, ensure that the discharge process of the energy storage unit is efficient and stable, and can be flexibly adjusted according to actual needs, so as to improve the energy utilization efficiency and reliability of the entire system.

[0098] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0099] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0100] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for dynamically adjusting charging and discharging power based on demand response, characterized in that: The method comprises: Calling historical data of a target power pool to obtain historical discharge data, wherein the target power pool integrates multiple energy storage units; Performing a discharge demand analysis based on the historical discharge data to construct a discharge demand analysis model; Collect data from the power supply area under preset environmental collection conditions to obtain stable environmental data, wherein the stable environmental data has a stable window identifier; Perform power supply mode identification based on the stable window identifier to obtain a real-time power supply mode; Synchronizing the stable environment data and the real-time power supply mode to the discharge demand analysis model to obtain the real-time discharge demand; Calculating a real-time discharge amount according to the stable window identifier and the real-time discharge demand, and determining whether the remaining power of the target power pool meets the real-time discharge amount; If the remaining power of the target power pool does not meet the real-time discharge capacity, tasks are assigned to the multiple energy storage units, and the charging power of the target power pool is adjusted based on the task assignment result.

2. A method for dynamically adjusting charging and discharging power based on demand response as claimed in claim 1, characterized in that: Performing a discharge demand analysis based on the historical discharge data to construct a discharge demand analysis model, the method further includes: Segmenting the historical discharge data based on seasons to obtain discharge data for multiple seasons; Sampling the discharge data of adjacent seasons during a preset season transition period, and performing mapping and overlapping processing on the discharge data of the multiple seasons based on the sampling results to obtain multiple credible discharge data; Based on the discharge time, the first credible discharge data is divided to obtain the daytime credible discharge data and the nighttime credible discharge data, wherein the first credible discharge data is any one of the plurality of credible discharge data; Disassembling the reliable daytime discharge data to obtain a plurality of sample discharge powers and a plurality of sample environmental data; Calling a random operator from an operator library, and using the plurality of sample discharge powers and the plurality of sample environment data to perform training optimization on the obtained random operator, to obtain a daytime power analysis sub-branch; By analogy, a nighttime power analysis sub-branch is constructed based on the nighttime reliable discharge data; Connecting the daytime power analysis branch and the nighttime power analysis branch in parallel to obtain a first power prediction branch, and using a first season to identify the first power prediction branch; By analogy, multiple power prediction branches are constructed, and the construction of the discharge demand analysis model is completed by connecting the multiple power prediction branches in parallel.

3. A method for dynamically adjusting charging and discharging power based on demand response as claimed in claim 2, characterized in that: The environmental collection conditions include collection triggering conditions and collection inhibiting conditions.

4. A method for dynamically adjusting charging and discharging power based on demand response as claimed in claim 3, characterized in that: The power supply mode is identified based on the stable window identifier to obtain a real-time power supply mode, and the method further includes: Determine whether the stable window identifier falls within the seasonal transition period, and if the stable window identifier falls within the seasonal transition period, generate a first transition season and a second transition season; Based on the stable window identifier, the discharge time is judged to obtain the real-time power supply type; The real-time power supply type, the first transition season and the second transition season are integrated and stored to obtain the real-time power supply mode.

5. A method for dynamically adjusting charging and discharging power based on demand response as claimed in claim 4, characterized in that: The stable environment data and the real-time power supply mode are synchronized to the discharge demand analysis model to obtain the real-time discharge demand. The method further includes: activating a first real-time prediction branch corresponding to the first transition season and a second real-time prediction branch corresponding to the second transition season in the discharge demand analysis model based on season identification mapping; According to the real-time power supply type, the first power analysis sub-branch and the second power analysis sub-branch are mapped and activated in the first real-time prediction branch and the second real-time prediction branch; Synchronizing the stable environment data to the first power analysis sub-branch and the second power analysis sub-branch to perform power prediction, and obtaining a first real-time prediction result and a second real-time prediction result; The first real-time prediction result and the second real-time prediction result are averaged to obtain the real-time discharge demand.

6. A method for dynamically adjusting charging and discharging power based on demand response as claimed in claim 1, characterized in that: The method further comprises: If the remaining power of the target power pool meets the real-time discharge capacity, the electricity price is called with the stability window identifier as a constraint to obtain a real-time electricity price sequence; Interactively obtain the charging cycle time of the target power pool, and use 1 / 3 of the charging cycle time to perform window update of the stable window identifier to obtain an updated window identifier, and use the updated window identifier as a constraint to perform electricity price call to obtain an updated electricity price sequence; If the updated electricity price sequence is better than the real-time electricity price sequence, interactively obtaining multiple remaining energy storages of the multiple energy storage units, and generating a charging scheduling sequence according to the multiple remaining energy storages; At the starting time identified by the update window, charging management of the multiple energy storage units is performed according to the charging scheduling sequence.

7. A method for dynamically adjusting charging and discharging power based on demand response as claimed in claim 6, characterized in that: If the remaining power of the target power pool does not meet the real-time discharge capacity, tasks are assigned to the multiple energy storage units, and the charging power of the target power pool is adjusted based on the task assignment results. The method further includes: Calculate and obtain the power supply duration of multiple units according to the real-time discharge demand and the multiple remaining energy storages; Interactively obtaining multiple unit states of the multiple energy storage units, wherein each unit state of the multiple unit states includes a quantified health, a historical charging frequency, a historical discharging frequency, and a remaining service life; Pre-constructing a demand response quantification function, and obtaining a plurality of demand response indexes by synchronizing the power supply durations of the plurality of units and the plurality of unit states to the demand response quantification function; Forward serializing the plurality of demand response indexes as a charging task ranking of the plurality of energy storage units; interactively obtaining a plurality of charging power constraints of the plurality of energy storage units; The multiple energy storage units are scheduled according to the charging task sorting, and the charging power of the energy storage units is dynamically adjusted according to the multiple charging power constraints.

8. A method for dynamically adjusting charging and discharging power based on demand response as claimed in claim 7, characterized in that: The demand response quantification function is as follows: Among them, U is the demand response index, H is the quantitative health, and F is c is the historical charging frequency, F d is the historical discharge frequency, L is the remaining service life, and T is the unit power supply time.

9. A method for dynamically adjusting charging and discharging power based on demand response as claimed in claim 7, characterized in that: The method further comprises: Negatively serializing the plurality of demand response indexes as the discharge task ranking of the plurality of energy storage units; Taking the stable window identifier as a constraint, the multiple energy storage units are scheduled according to the discharge task sequence to dynamically adjust the power of external discharge according to the real-time discharge demand.

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